metastasis research

Understanding Metastasis

Introduction.

Our current understanding of metastasis is in large part based on landmark discoveries from the past two decades.  A brief description of cancer metastasis is provided below. A YouTube video is available that visualizes the process.

What is metastatic cancer?

Tumor metastasis (from the Greek “change of place”) is the movement of tumor cells from the site where the cancer began, to grow in other sites of the body. It is a complex process that is only partially understood today at the biochemical and molecular levels. When a cancer patient wakes from surgery and asks “Has it spread?”, they are asking if the tumor has metastasized. For many cancers, surgery and radiation therapy remove or destroy the the primary tumor. It is the spread of cancer cells to other (secondary) sites and their growth in these sites that can contribute to some cancer patients’ sickness (morbidity) and deaths (mortality).

The tumor metastatic process has been compared to a marathon. Tumor cells have to invade the solid tissues around the primary tumor site. The tissue in which the tumor arose is complex, containing other cells such as fibroblasts, a protein filled matrix that provides a solid support and immune cells and lymphatic drainage. Tumors have to invade past these barriers.  To do so they develop the ability to move. Tumor cells do not float out of a tissue, they crawl. Basically, tumor cells react to factors in their environment, they put out a “finger” of the cell toward the attractant and ratchet the cell forward. To move, tumor cells must alter their adhesion to other cells and to the protein matrix in a very dynamic fashion. They may also have to create a pathway amongst the tissue, by degrading the protein matrix using enzymes (proteases).

Is it likely that a cancer will become metastatic?

Fortunately for us, the metastatic process is very inefficient. Researchers using experimental models estimate that 0.01% of the tumor cells that enter the bloodstream eventually form a metastasis. However, it remains difficult to determine when-or-if a cancer will become metastatic. Consequently, every cancer is evaluated for its risk of becoming metastatic and patients are treated accordingly. An advanced stage cancer is more likely to metastasize and is treated more aggressively then an early stage benign cancer.

How does metastatic cancer spread?

Tumor cells can spread around the body using one of two major “highways”. All tissues are served by blood vessels (which provide oxygen and nutrients) and also lymphatic vessels which drain excess fluid to nearby lymph glands. For many cancer cells, their first opportunity to escape is to use the lymphatic drainage system. This is why for many cancers lymph nodes are biopsied or removed at surgery to see if the cancer has spread, and oncologists us the information to determine the “stage” of the cancer. Cancer cells can enter the bloodstream either indirectly via the lymphatics, or directly from a vessel in the primary tumor. The bloodstream is a very harsh environment with a high velocity of flow and full of immune cells. Moreover, cancer cells are used to being attached to the proteinaceous matrix, many tumor cells die when detached from their support and have to swim ( detachment mediated death is called anoikis, another Greek word describing the death of leaves from as they detach from trees in the Fall). The majority of tumor cells get stuck (arrest) in the first capillary bed that they float into. This is why colon cancer tends to metastasize to the liver, etc. This is not always the case, however, and some tumor cells end up in distant organs. How do tumor cells get out of the bloodstream? In essence, they attach to the endothelial cells lining the blood vessels and the endothelial cells retract, they move apart, to permit the tumor cells to enter the tissue. This may be a normal reaction of endothelial cells to immune cells, cells of our immune systems migrate in and out of the bloodstream all the time to maintain surveillance. In fact, tumor cells can disguise themselves as lymphocytes by expressing similar molecules on their surface that fool the endothelial cells. These molecules may also determine their apparent ability to “home” to specific organs preferentially, as they may respond to gradients of chemicals differentially expressed there.

What happens after the cancer spreads to other organs?

Upon arriving in a distant organ, a metastatic tumor can grow and form, what is called, a secondary tumor or metastatic lesion. We know less about this process than the previous steps. While a tumor, by definition, can grow almost indefinitely, the growth in the primary tumor site is not always identical to growth elsewhere. In the primary tumor site, growth may have been aided by specific factors in the matrix or by interactions with specific neighboring cell types. When a tumor arrives in a new organ, it has to establish new interactions with the local tissue.

About 100 years ago, a British pathologist named Dr. Stephen Paget described metastasis by a “Seed and Soil” hypothesis. He proposed that flowers send seeds everywhere (just as tumor cells disseminate everywhere), but that seeds only grow in receptive soil. Thus metastatic tumor cells need to have an environment which supports their growth. A successful metastatic cells establishes a unique relationship with surrounding tissue called a “tumor-host interaction. Tumor cells release chemical signals that elicit a positive and supportive response from the surrounding tissue.  Tumor cells must establish a blood supply (angiogenesis) to continue to grow. The best described tumor-host interaction is found in the metastasis of breast and prostate cancer metastasis to bone. Once in the bone, the tumor cells initiate a cycle where the tumor cells release agents that promote degradation of the bone and this, in turn, promote tumor growth. This ultimately results in growth of the metastatic tumor and degradation of the bone.

Not all metastatic tumor cells that arrive in a distant organ go on to form a metastatic lesion. In fact, most of them don’t. The metastatic colonization process can be halted or retarded by a poorly understood process called tumor dormancy. Essentially, tumor cells can stay alive, but stop dividing; alternatively they can die (apoptosis) at rates equal to their proliferation, so that the small tumor fails to increase in size. There are probably many reasons for tumor dormancy. For instance, a lack of angiogenesis may cause dormancy, where a lack of an adequate blood supply may fail to provide sufficient oxygen and nutrients for growth. The environment of the metastatic site (matrix and other cells) may also enforce dormancy, or localized host defenses and immune responses may also contribute. We have a very incomplete understanding of the conditions that promote dormancy or bring a tumor cell out of this state. The breaking of dormancy is why some cancers recur 20-30 years after they were initially treated.

What are my treatment options?

It remains extremely challenging to treat metastatic cancer and the anti-metastasis therapies are drastically different for each individual cancers. Therefore, all options must be reviewed with informed medical professions. While tumor cells in a metastatic location were originally derived from the primary tumor of the originating organ, their relative response to treatment can be dramatically different. In fact, many tumor cells in the metastatic site do not respond to the treatment that is effective for the primary tumor. Currently the only means to prevent metastasis is the removal of the original (primary cancer). The treatment of existing metastases is subject to numerous variables and should be discusses with an informed medial professional.

Where do I find help?

Individuals that are concerned about their health status should seek help with their physician. Cancer is a complex disease and its metastatic dissemination requires rapid and complex intervention. Since cancer is a progressive disease, it is important to act timely and seek help now rather than wait. Besides consulting a physician, it is important to become informed on the specific disease, because each cancer behaves and is treated differently. The internet can be an excellent source of information but such information should be discussed with supporting medical council whenever possible.

For many patients, cancer is a chronic, life-long disease. Fortunately, treatments are improving and many cancer patients are living with their disease rather than dying of it. Nevertheless, the diagnosis, treatment, and subsequent life changes can be challenging. Many patients find great benefit in developing a support network that includes their family, friends, and patient-support advocates from a variety of agencies that have a unique understanding of the challenges faced by a cancer survivor. Patient-support advocates can be found in all major medical facilities that provide treatment for cancer. In addition there are several cancer-specific organizations that can either provide support or help identify those options.

KIS, a target of SOX4, regulates the ID1-mediated enhancement of β-catenin to facilitate lung adenocarcinoma cell proliferation and metastasis

• Open access
• Published: 25 July 2024
• Volume 150 , article number  366 , ( 2024 )

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• Jing-Xia Chang 1 ,
• Meng Zhang 1 ,
• Li-Li Lou 1 ,
• He-Ying Chu 1 &
• Hua-Qi Wang 1  

Kinase interacting with stathmin (KIS) is a serine/threonine kinase involved in RNA processing and protein phosphorylation. Increasing evidence has suggested its involvement in cancer progression. The aim of this study was to investigate the role of KIS in the development of lung adenocarcinoma (LUAD). Dual luciferase assay was used to explore the relationship between KIS and SOX4, and its effect on ID1/β-catenin pathway.

Real-time qPCR and western blot were used to assess the levels of KIS and other factors. Cell proliferation, migration, and invasion were monitored, and xenograft animal model were established to investigate the biological functions of KIS in vitro and in vivo.

In the present study, KIS was found to be highly expressed in LUAD tissues and cell lines. KIS accelerated the proliferative, migratory and invasive abilities of LUAD cells in vitro, and promoted the growth of LUAD in a mouse tumor xenograft model in vivo. Mechanistically, KIS activated the β-catenin signaling pathway by modulating the inhibitor of DNA binding 1 (ID1) and was transcriptionally regulated by SOX4 in LUAD cells.

KIS, a target of SOX4, regulates the ID1-mediated enhancement of β-catenin to facilitate LUAD cell invasion and metastasis.

Avoid common mistakes on your manuscript.

Introduction

Lung cancer (LC) is one of the cancers with a high incidence and mortality rate. It is estimated that ~ 2.20 million new cases and 1.79 million deaths occur each year due to LC (Thai et al. 2021 ). LC is classified into non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) according to cytological types, with NSCLC accounting for ~ 85% of the diagnosed cases (Denisenko et al. 2018 ; Hutchinson et al. 2019 ). Lung adenocarcinoma (LUAD) is the most common subtype of NSCLC (Duma et al. 2019 ). Patients with LUAD usually have no obvious respiratory symptoms, and thus they are not diagnosed until the disease is at an advanced stage; these patients thus face a high mortality rate (Li et al. 2018 ; Spella and Stathopoulos 2021 ). Currently, the main treatment methods for LUAD include chemotherapy, targeted therapies and the use of immune checkpoint inhibitors (Tartour and Zitvogel 2013 ). With the development of molecular pathology, key genes associated with cancer progression are regarded as targets for targeted therapy (Jin et al. 2020 ). Although some have achieved good efficacy, the numbers of patients who achieve clinical benefits from this treatment remain limited (Jin et al. 2020 ; Song et al. 2021 ; Zuo et al. 2020 ). It is therefore imperative to identify more effective indicators for the molecular pathology of LUAD.

Kinase interacting with stathmin (KIS) is a serine (Ser, S)/threonine (Thr, T) kinase whose C-terminal domain contains a U2AF homology motif (UHM)(Francone et al. 2010 ). It can interact with splicing factors, such as SF1 and SF3b155 through the UHM motif (Manceau et al. 2008 ). Recently, the role of KIS in cancer progression has been reported. An abnormally elevated activity of KIS may be associated with some aspects of tumor development in different types of human cancer, such as pancreatic and ovarian cancer (Grant et al. 2018 ; Katchman et al. 2017 ; Wang et al. 2018 ). Previous studies have suggested roles of KIS in proliferation, migration and invasion of tumor cells (Gao et al. 2022 ; Xu et al. 2021 ). KIS knockdown strongly suppresses gastric cancer cell proliferation and invasion (Feng et al. 2020 ). However, the expression, function and mechanisms of action of KIS in LUAD remain unclear. Based on data from the UALCAN database (Ualcan.path.uab.edu/analysis), it has been found that KIS expression in LUAD is significantly upregulated, suggesting that it plays a role as a tumor promoter.

The inhibitor of DNA binding 1 (ID1) is one of the ID proteins. It is located on human chromosome 20q11 (Mathew et al. 1995 ). ID1 has been widely studied, and is mainly related to cell senescence, proliferation, survival and tumorigenesis(Suh et al. 2008 ; Tang et al. 2002 ). Studies have shown that ID1 promotes the proliferation, migration and invasion of NSCLC cells (Bhattacharya et al. 2010 ; Li et al. 2017a ). It has been shown that the downregulation of ID1 suppresses the malignant phenotype of hepatocellular carcinoma by inhibiting the β-catenin pathway (Chen et al. 2021 ; Yin et al. 2017 ). Through the GSE121733 dataset from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ), it has been observed that ID1 expression is significantly downregulated in KIS siRNA-transfected liver cancer cells, indicating ID1 as a potential downstream effector molecule of KIS in liver cancer; however, whether ID1 is regulated by KIS in LUAD remains to be determined.

Sex-determining region Y (Sry)-box-containing (SOX) transcription factors play key developmental roles in the majority of tissues and organs (Sarkar and Hochedlinger 2013 ). The SOX family includes total of 20 members, and the family is divided into nine subgroups that range from SOXA to SOXH (She and Yang 2015 ) SOX4 is one of the members widely studied in relation to cancer (Tiwari et al. 2013 ; Vervoort et al. 2018a ). The SOX4 gene is often amplified and overexpressed in > 20 types of malignancies, including cancers of the lung (Friedman et al. 2004 ), prostate (Liu et al. 2006 ), bladder (Aaboe et al. 2006 ) and breast (Cancer Genome Atlas, 2012 ). A number of studies have demonstrated that SOX4 knockdown reduces the tumorigenesis and metastasis of cancer cells (Bilir et al. 2013 ; Vervoort et al. 2018b ; Zhang et al. 2012a ). In lung cancer, SOX4 also exhibits functional oncogenic properties (Medina et al. 2009 ). Various miRNAs in NSCLC cells can target SOX4 and inhibit migration and invasion, as well as epithelial-to-mesenchymal transition in cancer cells (Li et al. 2015 ; Tang et al. 2017 ), further demonstrating that SOX4 is a complete regulator of malignancies. By predicting that there may be a binding between SOX4 and KIS promoter regions, it was hypothesized that SOX4 may transcriptionally influence KIS expression.

To the best of our knowledge, the SOX4/KIS/ID1 axis has not been previously reported in lung adenocarcinoma, lung squamous cell carcinoma or other tumors. Based on the aforementioned findings, in the present study, it was hypothesized that KIS may play an oncogenic role in LUAD progression by modulating ID1, and KIS is a potential target of SOX4. The results of the present study provide new insight into the role of KIS in LUAD, which may aid future research.

Materials and methods

Bioinformatics analysis.

To explore differentially expressed genes (DEGs) between LUAD and normal tissues, gene expression data were obtained from the UALCAN website based on The Cancer Genome Atlas (TCGA) database ( http://ualcan.path.uab.edu/analysis.html ) and the Gene Expression Omnibus (GEO) database (GSE32863 and GSE40419) ( https://www.ncbi.nlm.nih.gov/gds/ ). The GSE32863 was a dataset of gene expression beadchip for 58 lung adenocarcinoma and 58 adjacent non-tumor lung fresh frozen tissues and the GSE40419 was a dataset of RNA-Seq for 87 lung adenocarcinomas and 77 adjacent normal tissues. DEG analysis of tumor vs. normal tissue from TCGA database and the GEO database was performed using the R package edgeR and the GEO2R online analysis tool, respectively. Genes that met the criteria log 2 |fold change| >0.5 and P  < 0.01, were considered to be significant DEGs. The results of the intersection of the up- and downregulated genes between TCGA and the GEO datasets are presented in a Venn diagram through using the R package Venn Diagram. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of overlapping DEGs was conducted using the R software ‘cluster Profiler’ package. In addition, Kaplan-Meier Plotter database ( https://kmplot.com ) was used to analyze the effect of KIS expression on overall survival of LUAD patients.

Tumor specimens

A total of 30 pairs of fresh tissue samples and paracancerous samples from patients with LUAD who had not received chemoradiotherapy were collected. All patients provided written informed consent to participate in the research. The study methodologies conformed to the standards set by the Declaration of Helsinki and were approved by the Ethical Review Board of the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

Cell lines and cell culture

A total of six LUAD cell lines (cat. no. iCell-h011, A549; cat. no. iCell-h155, NCI-H1650; cat. no. iCell-h156, NCI-H1975; cat. no. iCell-h068, HCC-827; cat. no. iCell-h159, NCI-H358 and cat. no. iCell-h153, NCI-H1299) and an immortalized normal human bronchial epithelial cell line (cat. no. iCell-h023, BEAS-2B) were purchased from iCell Bioscience Inc. and STR profiling was used for authentication. The 293T cells were purchased from Shanghai Zhong Qiao Xin Zhou Biotechnology Co., Ltd. The A549 cells were maintained in F-12 K medium (Wuhan Servicebio Technology Co., Ltd.) containing 10% fetal bovine serum (FBS; Tianhang Bio), while the remaining five LUAD cell lines were grown in Roswell Park Memorial Institute1640 medium (RPMI-1640; Beijing Solarbio Science & Technology Co., Ltd.) containing 10% FBS. The BEAS-2B and 293T cells were maintained in Dulbecco’s modified Eagle’s medium (DMEM; Wuhan Servicebio Technology Co., Ltd.) containing 10% FBS. All cells were maintained in a 5% CO 2 incubator at 37˚C.

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR)

The relative RNA levels of genes were assessed using RT-qPCR. Total RNA was isolated using TRIzol reagent (BioTeke Corporation). The concentration of RNA was determined using a UV spectrophotometer NANO 2000 (Thermo Fisher Scientific, Inc.). The obtained RNA was reverse transcribed using BeyoRT II M-MLV reverse transcriptase (Beyotime Institute of Biotechnology). qPCR was performed on an ExicyclerTM 96 (Bioneer) using 2X Taq PCR MasterMix and SYBR-Green (Beijing Solarbio Science & Technology Co., Ltd.) under the following thermocycling conditions: Initial denaturation at 94˚C for 5 min, followed by 40 cycles at 94˚C for 20 s, 60˚C for 30 s and 72˚C for 40 s for denaturation, annealing and elongation. Information regarding primers is presented in Table  1 . The relative RNA levels of genes were quantified using the 2 −ΔΔCq method(Livak and Schmittgen 2001 ).

Western blot analysis

Total protein was extracted from the cells and isolated using RIPA lysis buffer (Beyotime Institute of Biotechnology). Protein quantification was performed using the BCA protein concentration determination kit (Beyotime Institute of Biotechnology). A total of 15–30 µg protein samples were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS‐PAGE) (Beyotime Institute of Biotechnology) on 8%, 10% and 15% gels and then transferred to polyvinylidene fluoride (PVDF) membranes (Thermo Fisher Scientific, Inc.). Subsequently, the membranes were incubated with primary antibodies at 4˚C overnight and secondary antibodies at 37˚C for 40 min, respectively. The antibodies used were as follows: anti-KIS (cat. no. 11624-1-AP; 1:1,000; Proteintech Group, Inc.), anti-PCNA (cat. no. A12427; 1:1,000), anti-CDK4 (cat. no. A11136; 1:1,000), anti-matrix metalloproteinase (MMP)-9 (cat. no. A11147; 1:1,000), anti-MMP-2 (cat. no. A6247; 1:1,000) (all from ABclonal Biotech Co., Ltd.), anti-ID1 (cat. no. ab283650; 1:1,000; Abcam), anti-active β-catenin (cat. no. #33,893; 1:500), anti-c-Myc (cat. no. #18,583; 1:1,000), anti-axis inhibition protein 2 (Axin2; cat. no. #2151; 1:1,000) (all from Cell Signaling Technology, Inc.), anti-SOX4 (cat. no. A10717; 1:500; ABclonal Biotech Co., Ltd.), anti-β-actin (cat. no. 60008-1-Ig; 1:2,000), goat anti-rabbit IgG-HRP (cat. no. SA00001-2; 1:10,000) and goat anti-mouse IgG-HRP (cat. no. SA00001-1; 1:10,000) (all from Proteintech Group, Inc.). The signals were detected using enhanced chemiluminescence (ECL; Shanghai 7 sea biotech Co., Ltd.) and analyzed using a gel imaging system (Gel-Pro-Analyzer software) (WD-9413B, Beijing Liuyi Biotechnology Co., Ltd.).

Lentivirus production and stable cell line selection

Two LUAD cell lines with a moderate KIS expression (NCI-H1650 and HCC-827) were selected for analyses. The lentivirus particles (Lv-KIS, Lv-Vector Lv-shRNA1-KIS, Lv-shRNA2-KIS and Lv-shNC) were obtained from Wanleibio Co., Ltd. The sequences of shRNAs were as follows: shRNA1-KIS: 5’-ccgGGATGTCAGTGTTTCGGAATTttcaagagaAATTCCGAAACACTGACATCCttttt-3’; shRNA2-KIS: 5’-ccgAGATGTTGTAGAAGATGTAAAttcaagagaTTTACATCTTCTACAACATCTttttt-3’; shNC: 5’-ccgTTCTCCGAACGTGTCACGTttcaagagaACGTGACACGTTCGGAGAAttttt-3’. Briefly, 2.5 µg plasmid or 75 pmol shRNA fragments were cotransfected with lipofectamine 3000 (Thermo Fisher Scientific, Inc.) into 293T cells. After transfection for 48 h at 37˚C, lentivirus particles were harvested. Two LUAD cell lines were infected with Lv-KIS to overexpress KIS and with Lv-shRNA1-KIS/Lv-shRNA2-KIS to knockdown KIS. Stable cell lines were obtained followed by puromycin selection. The efficiency of infection was examined using RT-qPCR and western blot analysis.

Cell proliferation assay

In order to examine cell proliferation, cell suspensions (5 × 10 3 cells/well) were seeded in a 96-well plate and cultured in an incubator with 5% CO 2 at 37˚C. After the cells were cultured for 0, 24, 48 and 72 h, 10 µl Cell Counting Kit-8 (CCK-8) solution (Beijing Solarbio Science & Technology Co., Ltd.) were added to each well. The optical density value of the cells was determined using a microplate reader (BioTek Instruments, Inc.) at 450 nm.

Colony formation assay

The cells were seeded in petri dishes at a density of 400 cells per dish and cultured in an incubator with 5% CO 2 at 37˚C. Following incubation at 37˚C for 2 weeks, the cells formed visible cell colonies. The colonies were then fixed with 4% paraformaldehyde (Shanghai Aladdin Biochemical Technology Co., Ltd.) at room temperature for 15 min and stained with Giemsa (Beijing Leagene Biotechnology Co., Ltd.) (Leagene, China) at room temperature for 5 min. The rate of colony formation was calculated.

Transwell assay

To evaluate cell migration, the LUAD cells were blown off the culture plate and dispersed as single cells in serum-free culture medium. A total of 200 µL cell suspension was then added to the upper chamber of the Transwell (Corning, Inc.). The lower chamber was filled with 800 µL culture medium containing 10% FBS. After culturing in an incubator with 5% CO 2 and saturated humidity at 37˚C for 24 h, the cells that migrated to the lower chamber were fixed with 4% paraformaldehyde (Shanghai Aladdin Biochemical Technology Co., Ltd.) for 15 min at room temperature. The cells were then washed twice with PBS, stained with crystal violet (Amresco LLC) at room temperature for 2 min and the residual crystal violet solution was washed away with distilled water. Finally, the migrated cells from five random fields were counted under an inverted microscope (Olympus Corporation). To evaluate cell invasion, the upper chamber was precoated with diluted Matrigel (Corning, Inc.). The remaining procedures were the same as those aforementioned for the Transwell migration assay.

Xenograft animal model

Male BALB/c nude mice (6 weeks old, 16 ± 1 g) were obtained from Jiangsu Huachuang Xinnuo Pharmaceutical Technology Co., Ltd. Mice were provided with free access to food and water in an environment with a 12-h light/dark cycle, a temperature of 22 ± 1˚C and 45–55% humidity. Following 1 week of adaptive feeding, the mice were randomly divided into 4 groups: shNC, shKIS, vector and KIS group. Stable KIS-overexpressing, low-expressing NCI-H1650 cells or control cells (1 × 10 6 cells in 0.1 mL serum-free medium) were subcutaneously injected into the right axilla of the mice. Animal health and behavior were monitored daily and the tumor volume was monitored every 5 days over a 25-day period. The tumor volume was calculated as follows: ½ (L × W 2 ), where L is the length and W is the tumor’s width. Animals were sacrificed by inhalation of CO 2 at a chamber displacement rate of 30% volume/min. Then the tumors were separated and weighed. The experiments were approved by the Institutional Animal Care and Use Committee of the First Affiliated Hospital of Zhengzhou University.

Immunohistochemistry

Tumor tissues were fixed with 4% paraformaldehyde at room temperature, dehydrated, embedded in paraffin and sectioned at a thickness of 5 μm. The sections were immunohistochemically stained using anti-Ki67 (cat. no. AF0198; 1:100; Affinity Biosciences) and anti-KIS (cat. no. 11624-1-AP; 1:100; Proteintech Group, Inc.) antibodies. As the secondary antibody, goat anti-rabbit IgG-HRP (cat. no. # 31,460; 1:500; Thermo Fisher Scientific, Inc.) was used. Staining was performed at room temperature using 3,3-diaminobenzidine (DAB; Fuzhou Maixin Biotech Co., Ltd.) for 10 s and counterstained with hematoxylin (Beijing Solarbio Science & Technology Co., Ltd.) for 3 min.

Luciferase assay

TopFlash or FopFlash (Beyotime Institute of Biotechnology) and pGMLR-TK luciferase reporter vectors (Beyotime Institute of Biotechnology) were co-transfected into the cells. The cells were cultured in an incubator with 5% CO 2 at 37˚C for 48 h. The cell suspension was centrifuged at 1000 rpm for 5 min at 4˚C and washed twice with PBS, and then 250 µL cell lysis buffer was added. Following the instructions provided with the luciferase test kit (Nanjing KeyGen Biotech Co., Ltd.), the luciferase activities of cell samples were examined.

Through prediction using the JASPAR database ( https://jaspar.genereg.net/ ), the binding of the SOX4 and KIS promoter was found. In order to detect the binding of the SOX4 and KIS promoter, the KIS promoter fragment was constructed into Firefly luciferase reporter vector and then Firefly luciferase reporter vector and SOX4 overexpression plasmid (1 µg) were co-transfected with lipofectamine 3000 (Thermo Fisher Scientific, Inc.) into 293T cells. After transfection for 48 h, luciferase activity was determined as described above, compared with Renilla luciferase activity.

Cell transfection

Upon reaching 70% confluency, the cells were transfected with 75 pmol siNC or ID1-siRNA, 2.5 µg SOX4 overexpression plasmid (Ov-SOX4) or control vector using lipofectamine 3000 (Thermo Fisher Scientific, Inc.). The target sequences of siRNAs were as follows: ID1-siRNA: 5’-CAAUGAUCACCGACUGAAATT-3’; siNC: 5’-UUCUCCGAACGUGUCACGUTT-3’.

Oligonucleotide pull-down assay

The binding of the KIS promoter to SOX4 protein in the NCI-H1650 and HCC-827 cells was verified using the DNA pulldown lit (Guangzhou BersinBio Biological Co., Ltd.) according the manufacturer’s protocol.

Statistical analyses

All experiments in the present study were performed in triplicate. Data are expressed as the mean ± standard deviation (SD). GraphPad Prism 8.0.2 software was used to analyze the data and draw graphs. Differences between two groups were analyzed using the paired or unpaired Student’s t-test. Differences between multiple groups was analyzed using one-way ANOVA, followed by Tukey’s multiple comparisons test. A P-value < 0.05 was considered to indicate a statistically significant difference.

Identification and functional enrichment analysis of DEGs

To identify DEGs between tumor and normal tissues, the present study used TCGA and the GEO databases. A Venn diagram was obtained by the intersection of all up- or downregulated genes in TCGA and the GEO databases. Among these DEGs that passed the screening criteria, 1,023 were significantly downregulated (Fig.  1 A) and 1,072 were upregulated (Fig.  1 B). KIS was one of the upregulated DEGs and we found that high KIS expression was associated with lower overall survival in LUAD patients through Kaplan-Meier Plotter database (Fig. S1 )(Nagy et al. 2021 ). Besides, we were very interested in the role of KIS in LUAD based on its reports in common cancer diseases including gastric(Feng et al. 2020 ), liver (Wei et al. 2019 ) and colorectal (Xu et al. 2021 ) cancer. Therefore, KIS was selected for further analysis. GO functional and KEGG pathway enrichment analyses of the overlapping DEGs were performed to investigate the role of DEGs in LUAD at a functional level (Fig. S4 ). The results revealed that the enriched GO terms, including cell substrate adhesion, tissue migration, epithelial cell proliferation and the regulation of epithelial cell proliferation (Fig.  1 C), and the enriched KEGG terms, including cytokine-cytokine receptor interaction and cell adhesion molecule (Fig.  1 D) were found in the downregulated overlapping DEGs. For the upregulated overlapping DEGs, the enriched GO terms, including nuclear division, mitotic cell cycle phase transition and nuclear chromosome segregation (Fig.  1 E), and the enriched KEGG terms, including cell cycle, DNA replication (Fig.  1 F) were found.

Bioinformatics analysis of DEGs in different gene microarrays of LUAD. (A and B) A Venn diagram of the (A) downregulated and (B) upregulated DEGs overlapping between the TCGA LUAD dataset (LUAD) and GEO datasets (GSE32863 and GSE40419). (C and D) The cycle graph of downregulated DEGs enriched in (C) GO functional and (D) KEGG pathways. (E and F) The cycle graph of upregulated DEGs enriched in (E) GO functional and (F) KEGG pathways. The different colored lines indicate different GO terms and KEGG pathways. DEGs, differentially expressed genes; LUAD, lung adenocarcinoma; FC, fold change; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes

KIS is highly expressed in LUAD tissues and cell lines

The mRNA expression of KIS in LUAD and normal tissues was retrieved using the UALCAN website based on TCGA database (Fig.  2 A). As demonstrated by the findings, the mRNA expression of KIS was significantly upregulated in LUAD tissues compared with normal tissues, according to the database. To validate this discovery, 30 pairs of fresh tissue samples and paracancerous samples were collected from patients with LUAD who had not received chemoradiotherapy, and the mRNA levels of KIS were detected using RT-qPCR. The results revealed that the mRNA levels of KIS were enhanced in LUAD tissues in comparison with paracancerous tissues (Fig.  2 B). As was expected, the mRNA levels of ID1 and SOX4 were also increased (Fig.  2 C and D). In addition, similar findings were obtained for the protein levels of KIS in LUAD tissues in comparison with paracancerous tissues (Fig.  2 E). Subsequently, the expression of KIS was analyzed in several human LUAD cell lines and a bronchus epithelium cell line using western blot analysis (Fig. S2 A and B). As illustrated in figure, the expression of KIS was significantly higher in cancer cells than in normal cells.

KIS is highly expressed in LUAD tissues and LUAD cell lines. (A) KIS mRNA expression in LUAD tissues and normal tissues was analyzed using the UALCAN website based on TCGA database. (B-D) The mRNA levels of KIS, ID1 and SOX4 were detected in LUAD tissues from patients who did not receive chemoradiotherapy and paracancerous samples using reverse transcription-quantitative PCR. (E) The protein levels of KIS were detected LUAD tissues from patients who did not receive chemoradiotherapy and paracancerous samples using western blot analysis (part of the data). ** P  < 0.01, compared with normal tissues or paracancerous samples. KIS, Kinase interacting with stathmin; LUAD, lung adenocarcinoma; TCGA, The Cancer Genome Atlas; ID1, inhibitor of DNA binding 1; SOX4, (Sry)-box-containing 4

KIS facilitates the proliferation of LUAD cells

KIS was successfully knocked down or overexpressed by lentiviral infection in both NCI-H1650 (Fig. S3 , left panel) and HCC-827 (Fig. S3 , right panel) cells. The results of CCK-8 assay revealed that both the KIS knockdown groups (sh1-KIS, sh2-KIS) exhibited a significantly lower proliferation rate compared with the control group (sh-NC) (Fig.  3 A). By contrast, the overexpression group (KIS) exhibited a significantly increased LUAD cell proliferation rate in (Fig.  3 B). Consistently, the cell proliferation rate decreased or increased with the knockdown or overexpression of KIS, respectively in both types of cells, as demonstrated using colony formation assay (Fig.  3 C and D). Furthermore, western blot analysis revealed that the expression of proliferation-associated proteins, including PCNA and CDK4 decreased/increased significantly with KIS knockdown/overexpression in these two types of cells (Fig.  3 E). All the aforementioned results demonstrated that KIS promoted the proliferation of LUAD cells.

KIS facilitates the proliferation of lung adenocarcinoma cells. (A and B) The Cell Counting Kit-8 was used to monitor cell proliferation at 72 h following KIS knockdown or overexpression. (C and D) Colony formation assay of cellular proliferation was conducted following KIS knockdown or overexpression. (E) The protein levels of PCNA and CDK4 were detected using western blot analysis in NCI-H1650 (left panel) and HCC-827 (right panel) cells following KIS knockdown or overexpression. * P  < 0.05 and ** P  < 0.01, compared with shNC group or vector group. KIS, kinase interacting with stathmin; PCNA, proliferating cell nuclear antigen; CDK4, cyclin-dependent kinase 4

KIS accelerates the migration and invasion of LUAD cells

The effects of KIS on LUAD cell migration and invasion were investigated using Transwell assays. The results revealed that the knockdown of KIS significantly inhibited (Fig.  4 A and B), whereas the overexpression of KIS accelerated the migratory and invasive abilities of LUAD cells (Fig.  4 C and D). The protein expression of MMP in LUAD cells was detected using western blot analysis to explore the mechanisms involved in the regulatory effects of KIS on the migration and invasion of LUAD cells (Fig.  4 E). The findings suggested that KIS knockdown negatively regulated MMP-2 and MMP-9 expression in LUAD cells, whereas KIS overexpression exerted an opposite regulatory effect. On the whole, these results confirmed that KIS promoted the migration and invasion of LUAD cells.

KIS facilitates the migration and invasion of lung adenocarcinoma cells. (A and B) Images of migratory (upper panel) and invasive (lower panel) cells following KIS knockdown were captured at 24 h. Cell migratoin and invastoin were examined using Transwell assay (scale bar, 100 μm). The graph on the right panel indicates the quantified numbers of migratory and invasive cells. (C and D) The migratory (upper panel) and invasive (lower panel) capacities were evaluated at 24 h following KIS overexpression (scale bar, 100 μm). (E) The protein levels of MMP-9 and MMP-2 were detected using western blot analysis in NCI-H1650 and HCC-827 cells following KIS knockdown or overexpression. ** P  < 0.01, compared with shNC group or vector group. KIS, kinase interacting with stathmin; MMP, matrix metalloproteinase

KIS promotes tumor growth of LUAD in a nude mouse tumor xenograft model

To investigate the tumor-promoting effects of KIS on LUAD cells in vivo, the LUAD cells in which KIS was knocked down/overexpressed and the corresponding control cells were injected into nude mice. The results revealed that there was no significant difference in mouse weight compared to the control group, and tumor progression was significantly inhibited in the mice inoculated with cells in which KIS was knocked down, whereas it was promoted in the mice inoculated with KIS-overexpressing cells as compared with the controls (Fig.  5 A-D). Immunohistochemical analysis further demonstrated that the expression of the cell proliferation markers, Ki67 and KIS, was higher/lower in the mice inoculated with cells subjected to KIS overexpression/knockdown as compared with the controls (Fig.  5 E).

Role of KIS in the tumor growth of NCI-H1650 cells. (A) The tumor growth curve of NCI-H1650 cells following KIS knockdown (left panel) or overexpression (right panel) in tumor xenografts from nude mice. (B) Weight of nude mice after tumor formation. (C) Tumors from mice injected with NCI-H1650 cells subjected to KIS knockdown or overexpression and the corresponding control. (D) Weight of tumors derived from NCI-H1650 cells subjected to KIS knockdown or overexpression. (E)  Immunohistochemistry of Ki67 and KIS in tumor tissues of mice. ** P  < 0.01, compared with shNC group or vector group. KIS, kinase interacting with stathmin

KIS activates the ID1-mediated β-catenin signaling pathway

As ID1 was identified as a potential downstream effector molecule of KIS via analysis of KIS siRNA-transfected liver cancer cells in the GSE121733 dataset, the ID1 and its related β-catenin pathway was further investigated in the present study. The levels of ID1 and the pathway-related proteins, active β-catenin, c-Myc and Axin2, were examined in LUAD cells in which KIS was knocked down or overexpressed (Fig.  6 A and B). The results revealed that the protein levels of ID1, active β-catenin, c-Myc and Axin2 were significantly decreased in the KIS knockdown groups (Fig.  6 A), whereas they were markedly increased in the overexpression group (Fig.  6 B). Simultaneously, the results of RT-qPCR demonstrated that the silencing of KIS led to the downregulation of the mRNA levels of c-Myc and Axin2, two downstream factors of β-catenin (Fig.  6 C and E); however, the overexpression of KIS led to the upregulation of the levels of these two factors (Fig.  6 D and F). Furthermore, results of dual luciferase assay revealed that β-catenin activity was significantly lower in the cells in which KIS was knocked down (Fig.  6 G), whereas it was higher in the cells overexpressing KIS (Fig.  6 H). Collectively, results suggested that KIS the activated ID1-mediated β-catenin signaling pathway in LUAD.

KIS facilitates the activation of the ID1-mediated β-catenin signaling pathway. NCI-H1650 and HCC-827 cells were used to perform the experiments. (A and B) The protein levels of ID1, active β-catenin, c-Myc and Axin2 were detected following KIS knockdown or overexpression. (C-F) The mRNA levels of c-Myc and Axin2 were measured following KIS knockdown or overexpression. (G and H) TOP/FOP flash assay revealed the luciferase activity of β-catenin following KIS knockdown or overexpression. * P  < 0.05 and ** P  < 0.01, compared with shNC group or vector group. KIS, kinase interacting with stathmin; ID1, inhibitor of DNA binding 1; Axin2, axis inhibition protein 2

KIS promotes LUAD development by modulating ID1

To further evaluate whether ID1 is responsible for the promoting effects of KIS on LUAD progression, the NCI-H1650 cells stably overexpressing KIS were transfected with ID1 siRNA. First, ID1 siRNA (si-ID1) was transfected into blank cells for the detection of the transfection efficiency, and the expression of ID1 was confirmed using RT-qPCR and western blot analysis (Fig.  7 A). Subsequently, it was determined whether ID1 knockdown exerted a suppressive effect on the ability of KIS to promote the proliferation, migration and invasion of LUAD cells. Similar to previous results, KIS overexpression promoted the proliferation, migration and invasion of cancer cells. However, KIS-overexpressing cells transfected with ID1 siRNA (KIS + si-ID1) exhibited a significantly lower proliferation, migration and invasion rate compared with the control siRNA cells (KIS + si-NC) in vitro (Fig.  7 B-D). Furthermore, the results of western blot analysis of the expression of PCNA, CDK4, MMP-9 and MMP-2 revealed that ID1 knockdown suppressed the proliferation, migration and invasion of KIS-overexpressing cells (Fig.  7 E). In addition, KIS overexpression enhanced the luciferase activity of β-catenin, and elevated the mRNA expression of c-Myc and Axin2; however, opposite results were obtained in KIS-overexpressing cells transfected with ID1 siRNA (Fig.  7 F and G). These findings thus suggested that the function of KIS in LUAD was partially mediated by ID1.

KIS facilitates LUAD development by modulating ID1. (A) The transfection efficiency of ID1 shRNA in NCI-H1650 cells was verified at the mRNA and protein level. KIS-overexpressing NCI-H1650 cells were transfected with ID1 siRNA to silence ID1, and these cells were subjected to analyses following 24 h of transfection. (B) Cell Counting Kit-8 assay of cellular proliferation. (C) The migratory (upper panel) and invasive (lower panel) capacities of cells were evaluated at 24 h following transfection using Transwell assay (scale bar, 100 μm). (D) The numbers of migratory and invasive cells were quantified. (E) The protein levels of PCNA, CDK4, MMP-9 and MMP-2 were detected using western blot analysis. (F) TOP/FOP flash assay revealed the luciferase activity of β-catenin. (G) The mRNA levels of c-Myc and Axin2 were measured. * P  < 0.05 and ** P  < 0.01, compared with shNC group, vector group or KIS + si-NC group. KIS, kinase interacting with stathmin; ID1, inhibitor of DNA binding 1; Axin2, axis inhibition protein 2; PCNA, proliferating cell nuclear antigen; CDK4, cyclin-dependent kinase 4; MMP, matrix metalloproteinase

KIS is transcriptionally regulated by SOX4 in LUAD cells

Through prediction using the JASPAR database ( https://jaspar.genereg.net/ ), binding sites were identified between the KIS promoter region and the transcription factor, SOX4, suggesting that KIS may be a target of SOX4. To validate this finding, NCI-H1650 and HCC-827 cells were transfected with SOX4 overexpression plasmid. As shown in Fig.  8 A, the overexpression of SOX4 was confirmed to be successful by the detection of mRNA and protein expression. Notably, the KIS mRNA and protein levels increased after SOX4 was overexpressed (Fig.  8 B). The combination of SOX4 and KIS promoter was then detected using luciferase assay. The results demonstrated that the luciferase activity of SOX4 on the KIS -driven promoter was significantly promoted (Fig.  8 C). DNA pull-down assay and western blot analysis revealed that the KIS promoter bound to SOX4 (Fig.  8 D). These results thus suggested that KIS was regulated by SOX4 in LUAD cells.

KIS is regulated by SOX4 in lung adenocarcinoma cells. (A) The transfection efficiency of SOX4 overexpression was verified in NCI-H1650 and HCC-827 cells at the mRNA and protein level. (B) KIS mRNA and protein levels were increased following SOX4 overexpression. (C) The effect of SOX4 on KIS-driven promoter activity was explored using a luciferase assay. (D) DNA pull-down assay was conducted with KIS biotinylated binding sites #1 and #2 as probes. Western blot analysis revealed that KIS bound to SOX4. ** P  < 0.01, compared with vector group or Promoter (-1344-+5) + Vector group; ns, not significant, compared with PGL3-basic group. KIS, kinase interacting with stathmin; SOX4, (Sry)-box-containing 4

In the present study, with the use of bioinformatics analysis of LUAD DEGs in TCGA database and two datasets from the GEO database, it was found that KIS was an upregulated DEG overlapping in these datasets. GO and KEGG pathway enrichment analyses revealed that these overlapping DEGs were also associated with the division and proliferation of cells, and the cell cycle, which revealed that cell growth, invasion and migration were critical physiological processes leading to the development of LUAD. The oncogenic role of KIS in the progression of several types of cancer has been reported, such as gastric cancer (Feng et al. 2020 ), hepatocellular carcinoma (Wei et al. 2019 ) and ovarian cancer(Katchman et al. 2017 ); however, its expression pattern and functional role in LUAD remain unclear. Similar reports aimed at defining the role of KIS in pancreatic ductal adenocarcinoma (PDAC) have demonstrated that KIS expression is low in non-malignant pancreatic cells and tissues, whereas its expression is significantly increased in several PDAC cell lines and tissues (Luo et al. 2022a ). Similar results were obtained in the present study, in that KIS was highly expressed in LUAD tissues and cell lines, suggesting that KIS plays a role in the progression of LUAD. However, a limitation of the present study was that we were unable to obtain sufficient clinical samples to perform an analysis of the association between KIS expression and clinical parameters in patients with LUAD. We mainly explored the mechanism of KIS in LUAD from the cellular perspective.

LUAD cells are characterized by insidious, high infiltration and destructive growth (Yu et al. 2021 ). In the early stages, tumors often invade the body through lymph or blood vessel (Zhang et al. 2018 ). Proliferation, migration and invasion are key properties to describe cells. A previous study reported that the the oncogene yes-associated protein (YAP)-dependent induction of KIS supports the proliferation, but not the migration of liver cancer cells. Additionally, KIS induces cell proliferation and cell cycle progression through the phosphorylation of p27 kip1 in leukemia cells (Nakamura et al. 2008 ). In the present study, KIS overexpression promoted the proliferation of LUAD cells, whereas KIS knockdown exerted the opposite effects. However, as opposed to its role in liver cancer cells, KIS promoted the migration and invasion of LUAD cells. A possible explanation for this may be that the targets of KIS differ among diverse types of cancer cells.

ID proteins are a subgroup of the basic helix-loop-helix (bHLH) proteins which lack the basic DNA-binding region (Meng et al. 2020 ). ID proteins function as dominant negative regulators of bHLH transcription factors by heterodimerization with other bHLH factors, and inhibiting their binding to DNA (Zhao et al. 2020 ). ID1 has been reported to play a carcinogenic role in a variety of tumors. In tumor cells, increased levels of ID1 are associated with a poorly differentiated and aggressive phenotype (Schindl et al. 2003 ; Schoppmann et al. 2003 ), and ID1 affects cell survival and metastasis by regulating multiple pathways. For example, the activation of mitogen-activated protein kinase (MAPK) signaling has been demonstrated as a mechanism of ID1-induced cancer cell proliferation (Ling et al. 2002 ). Shin et al. found that ID1 was able to regulate the expression of Wingless and INT-1 (Wnt)/β-catenin signal transduction regulators(Shin et al. 2011 ). Another study demonstrated that the activation of Wnt-β-catenin signaling led to the accumulation of β-catenin in the nucleus, which occurs in > 80% of colorectal cancer cases (Wanitsuwan et al. 2008 ). In more than half of cancer cases, including breast, colorectal, melanoma and leukemia, β-catenin accumulates in the nucleus or cytoplasm (Damsky et al. 2011 ; Gekas et al. 2016 ; Khramtsov et al. 2010 ; Kobayashi et al. 2000 ). In addition, β-catenin promotes tumor progression by inhibiting T-cell responses (Hong et al. 2015 ). β-catenin activity is influenced by binding factors that affect its stability, cellular localization and transcriptional activity (Shang et al. 2017 ). Axin2 and proto-oncogene c-Myc are two representative target genes of the β-catenin pathway (Rennoll et al. 2014 ). As previously reported, ID1 induces c-Myc activation through the Wnt/β-catenin pathway, thereby promoting G6PD transcription, and then activating the pentose phosphate pathway, resulting in chemoresistance to oxaliplatin in hepatocellular carcinoma (Yin et al. 2017 ). The Wnt/β-catenin signaling pathway is a complex pathway that regulates the expression of key developmental genes by regulating the level of β-catenin (Cheng et al. 2019 ). β-catenin is a signal converter in cells, whose abnormal regulation can lead to early carcinogenesis (Zhang and Wang 2020 ). However, to the best of our knowledge, to date, there is no available evidence to reveal the effects of KIS on the ID1/β-catenin pathway in LUAD cells. Therefore, to demonstrate this effect, the present study measured the mRNA and protein expression levels of ID1, c-Myc and Axin2, and the activity of active β-catenin. In the present study, KIS overexpression increased both the levels of three factors and the activity of active β-catenin, while KIS knockdown exerted the opposite effect. These results indicated that KIS promoted the ID1-mediated β-catenin signaling pathway. The present study then investigated whether the effects of KIS on LUAD progression are mediated via ID1. The results demonstrated that KIS-overexpressing cells transfected with ID1 siRNA reversed the promoting effects of KIS overexpression on the proliferation, migration and invasion of LUAD cells. Moreover, KIS-overexpressing cells transfected with ID1 siRNA exhibited a suppressed activity of β-catenin, as well as decreased mRNA levels of c-Myc and Axin2. Therefore, it was confirmed that KIS facilitated LUAD progression by modulating ID1.

SOX4, a member of the SOX transcription factor family, is also upregulated in a number of human malignancies(Dai et al. 2017 ). Our previous research has suggested that miR-363-3p targets NEDD9 and SOX4 to the inhibit migration, invasion and epithelial-mesenchymal transition of NSCLC cells (Chang et al. 2020 ). In another study, shRNA was used to specifically knock down SOX4 in the Xuanwei female lung cancer cell line (XWLC-05), and experiments using nude mice revealed that this led to increased apoptosis, and decreased cell proliferation and metastasis (Zhou et al. 2015 ). These findings suggest that SOX4 is a factor involved in LC. Notably, the results reported in the study by Zhang et al. demonstrated that SOX4 promoted breast cancer growth and metastasis, and upregulated the transcriptional level of one of the cytokines (CXCR7) by binding to the CXCR7 promoter (Zhang et al. 2020 ). Moreover, binding may exist between SOX4 and KIS promoter regions through predictions from the JASPAR database. The results of the present study confirmed this hypothesis and demonstrated that KIS expression was upregulated by SOX4 in LUAD cells. In other words, KIS is a downstream gene of SOX4.

In clinical trials, high levels of SOX4 protein were positively correlated with status of differentiated degree, clinical stage, T classification, N classification, M classification in NSCLC, and the higher level of SOX4 expression was markedly correlated with poor overall survival in NSCLC patients (Wang et al., 2015 ). Similar clinical analyses about SOX4 in acute myeloid leukemia (Lu et al. 2017 ), breast cancer (Zhang et al., 2012b ) and bladder carcinoma (Aaboe et al. 2006 ) have also been reported. In addition, the clinical significance and biological role of ID1 in lung cancer was emphasized (Castañón et al., 2017 ; Li et al. 2017b ; Román et al. 2019 ). In view of KIS, it was considered to be a novel marker for personalized prediction of pancreatic cancer prognosis (Luo et al., 2022b) and was associated with a mechanism of non-genetic resistance to targeted therapy in melanoma (Smith et al., 2022 ). According to the above results in this study, the potential role of KIS inhibition is expected to be a new target for LUAD treatment.

In conclusion, the findings of the present study demonstrated that KIS promoted the proliferation, migration and invasion of LUAD cells through the ID1-mediated β-catenin signaling pathway, and was regulated by SOX4 in LUAD cells (Fig.  9 ). The SOX4/KIS/ID1/β-catenin axis has the potential to function as a therapeutic target in LUAD. KIS inhibitors will be the focus of our future research.

Potential role of KIS in the development of LUAD. In the cancerogenesis of LUAD, KIS is transcriptionally regulated by SOX4 and activates ID1-mediated β-catenin signaling pathway, which in turn promotes the proliferation and metastasis of LUAD cells and accordingly impacts the progression of LUAD. KIS, kinase interacting with stathmin; LUAD, lung adenocarcinoma

Data availability

The datasets generated and analyzed during the present study are available from the corresponding author upon reasonable request.

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Chang, JX., Zhang, M., Lou, LL. et al. KIS, a target of SOX4, regulates the ID1-mediated enhancement of β-catenin to facilitate lung adenocarcinoma cell proliferation and metastasis. J Cancer Res Clin Oncol 150 , 366 (2024). https://doi.org/10.1007/s00432-024-05853-9

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Taking a closer look at eye cancer: Research offers new insight into high rate of metastasis

by Edith Cowan University

New research from Edith Cowan University (ECU) is one step closer to understanding why uveal melanoma, the deadliest form of eye cancer, has such a high rate of metastasis.

Uveal melanoma is a rare cancer type with an incidence of 7.6 per million adults in Australia and represents around 5% of all melanomas. Patients presenting with uveal melanoma have a 50% chance of the disease metastasizing or spreading from the eye, commonly to the liver, even after successful treatment of the tumors within the eye.

Metastases of uveal melanoma could develop up to 20 years after the primary tumor treatment, and the median survival in patients after a diagnosis of metastases, is between 5 to 18 months.

ECU Vice Chancellor's Research Fellow, Dr. Vivian Chua noted that after diagnosis of the disease in the liver, patient survival is often short due to the lack of effective treatment options.

"The metastatic tumors respond poorly to many treatment options that had been shown to be effective in other cancer types including skin melanoma. It is unclear how and why uveal melanoma spread or metastasize to the liver.

"Identifying the mechanisms that drive uveal melanoma metastasis will likely uncover strategies to prevent uveal melanoma spreading or the development of metastatic uveal melanoma, which is the cause of death of patients."

Dr. Chua's most recent research focused on alterations in the BRCA1-associated protein 1 (BAP1) gene. The BAP1 gene is functionally involved in modulating the characteristics of cancer cells, particularly uveal melanoma. The work is published in the journal Science Signaling .

Alterations in the BAP1 gene lead to loss of the BAP1 protein function and expression and are associated with an increased risk of metastasis of uveal melanoma and poorer patient survival. BAP1 alterations have also been reported in other cancer types such as mesothelioma and cholangiocarcinoma.

"However, the roles of BAP1 loss or deficiency in uveal melanoma remains unclear," Dr. Chua said.

Dr. Chua, who recently returned to Australia following eight years at Thomas Jefferson University, in Philadelphia, engineered human uveal melanoma cell cultures that are BAP1-deficient to re-exhibit BAP1, to allow for a comparison between the BAP1-deficient and BAP1-proficient uveal melanoma cells.

"We found that BAP1-deficient cells are slow-growing, and this was associated with the cells exhibiting low activity of the S6 protein. This is consistent with the known function of the S6 protein to regulate cancer cell growth. These characteristics were also associated with the BAP1-deficient cells surviving better under conditions deprived of amino acids .

"Overall, we have uncovered a role of BAP1 deficiency in uveal melanoma," said Dr. Chua.

Cancer cells require lots of nutrients to survive and grow but during metastasis or spreading, the surrounding environment, such as in the bloodstream, can often be deprived of nutrients. Results identified by this ECU researcher suggest that BAP1-deficient uveal melanoma cells can survive or thrive under conditions that are deprived of nutrients, particularly, amino acids, thereby allowing them to spread successfully.

"My research is now aimed at investigating what mechanisms support BAP1-deficient cell survival under amino acid deprivation and identifying co-players of S6. I expect findings from these studies to uncover strategies to effectively treat uveal melanoma or prevent the development of metastatic disease," Dr. Chua said.

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Metastatic Cancer: When Cancer Spreads

What is metastatic cancer.

The Challenges of Living with Metastatic Cancer

Survivors describe “scanxiety,” financial concerns, and other issues.

Cancer that spreads from where it started to a distant part of the body is called metastatic cancer. For many types of cancer, it is also called stage IV (4) cancer. The process by which cancer cells spread to other parts of the body is called metastasis .

When observed under a  microscope and tested in other ways, metastatic cancer cells have features like that of the primary cancer and not like the cells in the place where the metastatic cancer is found. This is how doctors can tell that it is cancer that has spread from another part of the body.

Metastatic cancer has the same name as the primary cancer. For example, breast cancer that spreads to the lung is called metastatic breast cancer, not lung cancer. It is treated as stage IV breast cancer, not as lung cancer.

Sometimes when people are diagnosed with metastatic cancer, doctors cannot tell where it started. This type of cancer is called cancer of unknown primary origin, or CUP . See the Carcinoma of Unknown Primary page for more information.

How Cancer Spreads

Metastasis: How Cancer Spreads

During metastasis, cancer cells spread from the place in the body where they first formed to other parts of the body.

Cancer cells spread through the body in a series of steps. These steps include:

• growing into, or invading, nearby normal tissue
• moving through the walls of nearby lymph nodes or blood vessels
• traveling through the lymphatic system and bloodstream to other parts of the body
• stopping in small blood vessels at a distant location, invading the blood vessel walls, and moving into the surrounding tissue
• growing in this tissue until a tiny tumor forms
• causing new blood vessels to grow, which creates a blood supply that allows the metastatic tumor to continue growing

Most of the time, spreading cancer cells die at some point in this process. But, as long as conditions are favorable for the cancer cells at every step, some of them are able to form new tumors in other parts of the body. Metastatic cancer cells can also remain inactive at a distant site for many years before they begin to grow again, if at all. ( Audio descriptive and interactive transcript version of video available .)

Where Cancer Spreads

In metastasis, cancer cells break away from where they first formed and form new tumors in other parts of the body. 

Cancer can spread to almost any part of the body, although different types of cancer are more likely to spread to certain areas than others. The most common sites where cancer spreads are bone, liver, and lung. The following list shows the most common sites of metastasis, not including the lymph nodes, for some common cancers:

Symptoms of Metastatic Cancer

Metastatic cancer does not always cause symptoms. When symptoms do occur, what they are like and how often you have them will depend on the size and location of the metastatic tumors. Some common signs of metastatic cancer include:

• pain and fractures, when cancer has spread to the bone
• headache, seizures , or dizziness, when cancer has spread to the brain
• shortness of breath, when cancer has spread to the lung
• jaundice or swelling in the belly, when cancer has spread to the liver

Treatment for Metastatic Cancer

There are treatments for most types of metastatic cancer. Often, the goal of treating metastatic cancer is to control it by stopping or slowing its growth. Some people can live for years with metastatic cancer that is well controlled. Other treatments may improve the quality of life by relieving symptoms. This type of care is called palliative care . It can be given at any point during treatment for cancer.

The treatment that you may have depends on your type of primary cancer, where it has spread, treatments you’ve had in the past, and your general health. To learn about treatment options, including clinical trials , find your type of cancer among the  PDQ® Cancer Information Summaries for Adult Treatment and Pediatric Treatment .

When Metastatic Cancer Can No Longer Be Controlled

If you have been told your cancer can no longer be controlled, you and your loved ones may want to discuss end-of-life care. Whether or not you choose to continue treatment to shrink the cancer or control its growth, you can always receive palliative care to control the symptoms of cancer and the side effects of treatment. Information on coping with and planning for end-of-life care is available in the Advanced Cancer section of this site.

Ongoing Research

Researchers are studying new ways to kill or stop the growth of primary and metastatic cancer cells. These ways include:

• helping your immune system fight cancer
• disrupting the steps in the process that allow the cancer cells to spread
• targeting specific genetic changes in tumors

Visit the Metastatic Cancer Research  page on this site to stay informed of ongoing research funded by NCI .

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Preventing cancer cells from colonising the liver

Researchers at ETH Zurich have uncovered how colorectal cancer cells colonise the liver. Their findings could open up new ways to suppress this process in the future.

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• ETH Zurich researchers have discovered proteins on the surface of colorectal cancer cells and liver cells that bind together and that play a major role in the formation of new metastases.
• The binding of the proteins triggers fundamental changes in colorectal cancer cells that allow them to take root in the liver.
• These new findings will help to develop future treatments that may hinder the formation of often fatal metastases.

In cases where cancer is fatal, nine out of ten times the culprit is metastasis. This is when the primary tumour has sent out cells, like seeds, and invaded other organs of the body. While medicine has made great progress in treating primary tumours, it is still largely helpless in the face of metastasis. Currently, there are no medications that prevent this process.

A team of researchers led by Andreas Moor in the Department of Biosystems Science and Engineering at ETH Zurich in Basel have now published results in the journal external page Nature call_made showing how colorectal cancer cells colonise the liver. Their findings will help to develop treatments with which it may be possible to hinder the metastatic process.

Molecular docking mechanism unlocked

Cancer is said to metastasise when cells from the primary tumour break off and travel via the circulatory system to other parts of the body. “Colorectal cancer metastasises to the liver because of how our blood flows,” Moor says. Blood is first enriched with nutrients in the intestines before it goes to the liver, which metabolises the nutrients. For colorectal cancer cells, the liver is the last stop. “They get caught in the liver’s capillary network,” Moor says.

Costanza Borrelli, a doctoral student, and other members of Moor’s team have now shown that the liver cells also play a large role in whether or not the cancer cells lodged there can colonise their new location. Science has known for over a century that, much like plant seeds in soil, cancer cells are dependent on their environment, yet it was previously unknown which molecular mechanisms play a role here.

Using sophisticated tests on genetically modified mice, Moor and his team have discovered that the secret lies in certain proteins on the cell surface. When liver cells have a protein called Plexin-B2 and the colorectal cancer cells possess certain proteins from the semaphorin family, the colorectal cancer cells can attach themselves to the liver cells.

Signposts in the nervous system

Cancer cells that have semaphorins on their surface are especially dangerous, as attested by clinical studies cited by Moor’s researchers in their paper. The study data shows that colorectal cancer metastasises earlier and more frequently to the liver if the tumour has large amounts of semaphorin.

Expression pattern and correlations with clinicopathologic parameters and survival of PHLDA family members in clear cell renal cell carcinoma (ccRCC). A Expression level of PHLDA2 between tumor and normal tissues in pan-cancer in the TCGA database. * p  < 0.05, ** p  < 0.01, *** p  < 0.001. **** p  < 0.0001. B Expression level of PHLDA1, PHLDA2, PHLDA3 between tumor ( n  = 101) and normal tissues ( n  = 101) in ccRCC in GSE40435. C Expression level of PHLDA1, PHLDA2, PHLDA3 between tumor ( n  = 72) and normal tissues ( n  = 72) in ccRCC in GSE53757. D – F Expression level of PHLDA1, PHLDA2, PHLDA3 between tumor ( n  = 117) and normal tissues ( n  = 79) in ccRCC in West China Hospital cohort. G – L Correlations between PHLDA2 expression level and clinicopathologic parameters of ccRCC, including age, gender, pT stage, pN stage, metastatic status, ISUP grade. (M) Expression level of PHLDA1, PHLDA2, PHLDA3 between ccA and ccB subtype of ccRCC. N – P Associations between PHLDA2 expression and OS, DSS, PFI of ccRCC patients by Kaplan–Meier survival analysis

We then focused on ccRCC, and obtained microarray data from two GEO data sets to validate the expression profile of PHLDA family members. Unsurprisingly, a notable pattern of elevated PHLDA1, PHLDA2 and PHLDA3 expression in tumor tissues was observed in both GSE40435 and GSE53757 (all p  < 0.05) (Fig.  1 B, C ). Furthermore, we performed RNA-seq on 117 treatment-naïve ccRCC primary tumors and 79 adjacent renal tissues collected from West China Hospital, Sichuan University and compared the expression level of PHLDA family members between tumor and normal tissues, yielding consistent results (all p  < 0.05) (Fig.  1 D–F).

The expression of PHLDA2 but not PHLDA1 or PHLDA3 could be served as an independent prognostic factor in ccRCC

To explore the clinical significance of PHLDA family expression in ccRCC, we collected clinicopathologic parameters, including age, gender, pT stage, pN stage, metastatic status, and ISUP grade, and analyzed the association of PHLDA family expression with these features. Regarding PHLDA1, negative correlations were observed between PHLDA1 expression level and metastatic status, ISUP grade (both p  < 0.05) (Supplementary Fig. 1C–H). On the opposite, significant positive correlations were found between PHLDA2 expression level and pT stage, pN stage, metastatic status, ISUP grade (all p  < 0.05) (Fig.  1 G–L). Similarly, for PHLDA3, positive correlations were observed between PHLDA3 expression level and pN stage, metastatic status and ISUP grade (all p  < 0.05) (Supplementary Fig. 1I–N). Besides, the expression level of PHLDA3 differed between genders, with an elevation observed in male patients with ccRCC ( p  < 0.05). In addition, we classified ccRCC samples from the TCGA database into good risk ccA and poor risk ccB subtypes using a well-established 34-gene signature. To our interest, PHLDA1 expression was higher in ccA subtype, while conversely, we found higher expression of both PHLDA2 and PHLDA3 in ccB subtype (Fig.  1 M).

Further analysis was conduct to comprehend how PHLDA family influences patient prognosis in ccRCC. KM survival analysis in the TCGA ccRCC cohort revealed that higher PHLDA1 expression was associated with better OS ( p  < 0.05), DSS ( p  < 0.05) and PFI ( p  = 0.054) (Supplementary Fig. 1O–Q). On the contrary, higher expression of PHLDA2 was associated with poorer OS, DSS and PFI (all p  < 0.05) (Fig.  1 N–P). For PHLDA3, no correlations were observed between expression level and OS, DSS (Supplementary Fig. 1R, S). However, higher PHLDA3 expression could still predict a worse PFI ( p  < 0.05) (Supplementary Fig. 1T).

To investigate whether expression of PHLDA family could serve as an independent prognostic factor in ccRCC, univariate and multivariate Cox regression analysis was then performed. For OS, besides age, pTNM stage and ISUP grade, only PHLDA2 expression, but not PHLDA1 or PHLDA3 expression, was validated as an independent prognostic factor, with lower expression level predicting a favorable outcome (HR 1.293, 95% CI 1.067–1.567, p  < 0.01) (Table  1 ). In addition, the role of only PHLDA2 expression as an independent predictor for DSS (HR 1.585, 95% CI 1.238–2.029, p  < 0.001) (Table  2 ) and PFI (HR 1.304, 95% CI 1.059–1.604, p  < 0.05) (Table  3 ) in ccRCC was confirmed.

PHLDA2 overexpression was associated with DNA hypomethylation

Since only PHLDA2 could be served as an independent prognostic factor in ccRCC, we then focused on PHLDA2 to explore its role in ccRCC combining multi-omics data. We initially detected the mutation and copy number variation status to find out the mechanism underlying the up-regulation of PHLDA2 in ccRCC. However, no mutation of PHLDA2 was found in TCGA ccRCC cohort. Besides, no correlation was found between copy number and expression level of PHLDA2 (Fig.  2 A). Taken together, the results suggested that genetic alteration could not explain the dysregulation of PHLDA2 in ccRCC.

Up-regulation of PHLDA2 was associated with aberrant DNA hypomethylation in ccRCC. A Expression level of PHLDA2 among tumors with different status of copy number variation, including single copy deletion, diploid, low-level amplification. B Heatmap showed DNA methylation level of all probes in PHLDA2 between tumor and normal tissues in ccRCC. C – I DNA methylation level of cg05167973, cg04720330, cg21259253, cg16057921, cg07482372, cg15658784, cg01691090 between tumor and normal tissues in ccRCC. *** p  < 0.001. J – N DNA methylation level of cg05167973, cg04720330, cg21259253, cg07482372, cg15658784 among ccRCC with different pTNM stages. * p  < 0.05, ** p  < 0.01. O Heatmap illustrated correlations of PHLDA2 expression with DNA methylation level of all probes in PHLDA2 in ccRCC. P – R Associations between DNA methylation level of cg04720330, cg21259253, cg26799802 and OS of ccRCC patients by Kaplan–Meier survival analysis

Next, the methylation status of PHLDA2 was examined. Compared to normal tissues, multiple probes demonstrated decreased methylation level in cccRCC, including cg05167973, cg04720330, cg21259253, cg16057921, cg07482372, cg15658784, cg01691090 ( p  < 0.05) (Fig.  2 B–I). Besides, the methylation levels of cg05167973, cg04720330, cg21259253, cg07482372, cg15658784 ( p  < 0.05) were positively correlated with pTNM stage in ccRCC (Fig.  2 J–N). In addition, we investigated the correlations between methylation levels of probes and PHLDA2 expression. It turned out that lower level of methylation level in cg05167973 ( r  = − 0.14, p  < 0.05), cg04720330 ( r  = − 0.24, p  < 0.05), cg21259253 ( r  = − 0.13, p  < 0.05), cg07482372 ( r  = − 0.13, p  < 0.05) and cg26799802 ( r  = − 0.13, p  < 0.05) was associated with increased PHLDA2 expression, indicating that elevated expression of PHLDA2 in ccRCC could be attributed to methylation modification (Fig.  2 O). Moreover, hypomethylation of cg04720330 ( p  = 0.133), cg21259253 ( p  = 0.081) and cg26799802 ( p  = 0.254) indicated poorer OS in ccRCC, although without statistical significance (Fig.  2 P–R).

Eventually, we divided ccRCC samples into PHLDA2-high (PHLDA2-H) and PHLDA2-low (PHLDA2-L) subgroups based on stratification by median expression of PHLDA2, and compared the mutational landscape between the two subgroups. The results illustrated that in the PHLDA2-H subgroup, the most frequently mutated genes were VHL (43%), followed by PBRM1 (39%), TTN (21%), SETD2 (15%), BAP1 (14%) (Fig.  3 A). In the PHLDA2-L subgroup, the top five most frequently mutated genes were VHL (55%), PBRM1 (44%), TTN (14%), SETD2 (9%) and MTOR (8%) (Fig.  3 A). We further compared the mutation rate of 4 genes (VHL, PBRM1, SETD2, BAP1) which play essential roles in the development of ccRCC between PHLDA2-H and PHLDA2-L subgroups (Fig.  3 B–E). Interestingly, VHL mutations were more enriched in PHLDA2-L subgroup compared to PHLDA2-H subgroup (55% vs. 43%, p  < 0.05). On the contrary, PHLDA2-L subgroup had lower SETD2 (9% vs. 14%, p  = 0.099) and BAP1 (8% vs. 14%, p  < 0.05) mutation rate compared to PHLDA2-H subgroup. Consistently, higher expression of PHLDA2 was observed in BAP1-mutated patients ( p  < 0.05) and SETD2-mutated patients ( p  = 0.06), compared to patients with wild type BAP1 or SETD2, respectively (Supplementary Fig. 2A–D). In addition, PHLDA2 expression was positively correlated with TMB score ( r  = 0.168, p  = 0.001) but was not associated with MSI score ( r  = 0.086. p  > 0.05) (Fig.  3 F, G ).

Distinct mutational landscapes between PHLDA2-H and PHLDA2-L subgroups. A Top 10 mutated genes in PHLDA2-H and PHLDA2-L subgroups in ccRCC. B – E Comparisons of VHL, PBRM1, SETD2, BAP1 mutation rate between PHLDA2-H and PHLDA2-L subgroups in ccRCC. F Correlation between PHLDA2 expression and tumor mutational burden in ccRCC. G Correlation between PHLDA2 expression and microsatellite instability in ccRCC

ECM, cell cycle, and immune-related pathways were enriched in PHLDA2-H subgroup

The difference of transcriptomic characteristics between PHLDA2-H and PHLDA2-L subgroups was further explored. In TCGA ccRCC cohort, compared with PHLDA2-L subgroup, we identified a total of 3212 differential expressed genes (DEGs) in PHLDA2-H subgroup, of which 712 were up-regulated, and 2500 were down-regulated (Supplementary Table 1). KEGG pathway and GO term enrichment analyses were performed to illustrate the function roles of these genes. The results revealed that DEGs up-regulated in PHLDA2-H subgroup were significantly enriched in ECM-related pathways, including ECM organization, extracellular structure organization, collagen metabolic process, collagen catabolic process (Fig.  4 A). Besides, EMT-related pathways were enriched. DEGs down-regulated in PHLDA2-H subgroup were remarkably enriched in regulation of PH, monovalent inorganic cation homeostasis, etc. (Fig.  4 B). Next, GSEA was conducted, and the results revealed that multiple pathways related to ECM remodeling were up-regulated in PHLDA2-H subgroup, which was consistent with the results above (Fig.  4 C). Cell cycle-related pathways, including E2F targets, G2M checkpoint, mitotic spindle, were also enriched in PHLDA2-H subgroup (Fig.  4 D–F). Moreover, various immune-related pathways, in which both positive and negative regulations of the immune system were observed, were up-regulated as well, reflecting the complicated nature of the TME in PHLDA2-H subgroup of ccRCC (Fig.  4 G).

ECM, cell cycle and immune-related pathways were enriched in PHLDA2-H subgroup. A KEGG pathway and GO term enrichment analysis of genes up-regulated in PHLDA2-H subgroup compared with PHLDA2-L subgroup in ccRCC. B KEGG pathway and GO term enrichment analysis of genes down-regulated in PHLDA2-H subgroup compared with PHLDA2-L subgroup in ccRCC. D – F Enrichment of cell cycle-related pathways, including E2F targets, G2M checkpoint, mitotic spindle in PHLDA2-H subgroup in ccRCC by GSEA. G Enrichment of immune-related pathways in PHLDA2-H subgroup in ccRCC by GSEA. H Enrichment of cell cycle-related pathways, including E2F targets, G2M checkpoint, mitotic spindle in PHLDA2-H subgroup in ccRCC by ssGSEA in the CheckMate025 cohort. I Enrichment of immune-related pathways, including TNF-α, complement, inflammatory response, allograft rejection in PHLDA2-H subgroup in ccRCC by ssGSEA in the CheckMate025 cohort. * p  < 0.05, ** p  < 0.01, *** p  < 0.001

Next, we utilized hallmark gene set scores calculated by performing ssGSEA on transcriptomic data from CheckMate025, a randomized trial of nivolumab versus everolimus in patients with metastatic ccRCC. Similarly, the results demonstrated that the ssGSEA scores of E2F targets, G2M checkpoint, mitotic spindle, along with immune-related hallmarks, were enriched in PHLDA2-H subgroup (Fig.  4 H, I ).

Immunosuppressive microenvironment in PHLDA2-H subgroup might impair the efficacy of immunotherapy

Afterwards, we focused on the characteristics of TME between the two subgroups in the TCGA ccRCC cohort. The infiltration level of various immune cells was first estimated based on CIBERSORT algorithm (Fig.  5 A). The results reflected a negative correlation of PHLDA2 expression with infiltration of T cells CD4 memory resting ( r  = − 0.221, p  < 0.001), eosinophils ( r  = − 0.182, p  < 0.001), mast cells resting ( r  = − 0.104, p  < 0.05). Among the immune cells the infiltration levels of which were positively correlated with PHLDA2 expression, Tregs showed the strongest positive correlation ( r  = 0.251, p  < 0.001). Regarding that Tregs play a crucial role in immunosuppression, we then gave emphasis to this subpopulation. Exactly, elevated expression of classical Treg markers, including FOXP3 and IL2RA, were detected in PHLDA2-H subgroup ( p  < 0.001) (Fig.  5 B, C ). Moreover, the enhanced recruitment of Tregs in PHLDA2-H subgroup was confirmed using data from CheckMate025 and IMmotion151 (Fig.  5 D, E ). Actually, Tregs trigger T cell exhaustion, thereby mediate the suppression of anti-tumor immunity. Based on this point, we then evaluated the degree of T cell exhaustion between the two subgroups. Strong positive correlations were observed between the expression level of PHLDA2 and multiple terminal exhaustion markers, including CSF1 ( r  = 0.504, p  < 0.001), TOX2 ( r  = 0.347, p  < 0.001), GEM ( r  = 0.302, p  < 0.001)), LAYN ( r  = 0.259, p  < 0.001), MYO1E ( r  = 0.228, p  < 0.001), and early exhaustion markers, including JUNB ( r  = 0.429, p  < 0.001), HSPA1A ( r  = 0.307, p  < 0.001) (Fig.  5 F). Given the results that ECM-related pathways were up-regulated in PHLDA2-H subgroup, in which cancer associated fibroblast (CAF) plays a vital role, we then investigated the association between PHLDA2 and CAF, and observed positive correlations between the expression level of PHLDA2 and all CAF markers, including ACTA2 ( r  = 0.337, p  < 0.001), MYH11 ( r  = 0.231, p  < 0.001), COL1A1 ( r  = 0.433, p  < 0.001), COL1A2 ( r  = 0.405, p  < 0.001), TAGLN ( r  = 0.385, p  < 0.001), PDGFRB ( r  = 0.359, p  < 0.001) (Fig.  5 G–L). In addition, the expression level of several immune checkpoints was explored, and we found higher expression level of PDCD1, PDCD1LG2, LAG3, TIGIT, CTLA4 in PHLDA2-H subgroup ( p  < 0.05) (Fig.  5 M).

Immunosuppressive microenvironment in PHLDA2-H subgroup might impair the efficacy of immunotherapy. A Correlations between PHLDA2 expression and infiltration level of different immune cells in ccRCC by Cibersort algorithm. * p  < 0.05, ** p  < 0.01, *** p  < 0.001. B , C Expression level of Treg markers, including FOXP3 and IL2RA, between PHLDA2-H and PHLDA2-L subgroups in ccRCC. D Infiltration level of Tregs between PHLDA2-H and PHLDA2-L subgroups in ccRCC by xCell algorithm in the CheckMate025 cohort. E Infiltration level of Tregs between PHLDA2-H and PHLDA2-L subgroups in ccRCC by xCell algorithm in the IMmotion151 cohort. F Correlations between PHLDA2 expression and expression of terminal exhaustion markers, including CSF1, TOX2, GEM, LAYN, MYO1E, and early exhaustion markers, including JUNB, HSPA1A, in ccRCC. G – L Correlations between PHLDA2 expression and expression of CAF markers in ccRCC. M Comparisons of expression of immune checkpoints between PHLDA2-H and PHLDA2-L subgroups in ccRCC. N Comparisons of T cell dysfunction score, T cell exclusion score and TIDE score between PHLDA2-H and PHLDA2-L subgroups in ccRCC

Ultimately, we calculated and compared the TIDE scores, which were used to evaluate the immunotherapy predictive efficacy, of PHLDA2-H and PHLDA2-L subgroup. In contrast to the PHLDA2-L subgroup, both the dysfunction score and the exclusion score, and the integrated TIDE score of PHLDA2-H subgroup were much higher (all p  < 0.001), indicating the poor response of PHLDA2-H subgroup to ICI-based treatment (Fig.  5 N).

We further explored our transcriptomic data of 117 primary ccRCC tumors to validate the immunosuppressive features of TME in PHLDA2-H subgroup. Samples were divided into PHLDA2-H and PHLDA2-L subgroups by the median value of PHLDA2 expression and the DEGs between the two subgroups were identified (Fig.  6 A). GSEA showed enrichment of immune-related pathways, including allograft rejection, IFN-γ response, complement, IFN-α, inflammatory response, in PHLDA2-H subgroup (Fig.  6 B). Cibersort algorithm was applied to calculate the infiltration level of immune cells, and positive correlation was observed between PHLDA2 expression and infiltration level of Tregs ( r  = 0.232) and markers of Tregs, IL2RA ( r  = 0.34), FOXP3 ( r  = 0.237) (all p  < 0.05) (Fig.  6 C–E). The association of PHLDA2 expression with immune checkpoints was further investigated, and positive correlation was observed, especially LAG3 ( r  = 0.35, p  < 0.05) (Fig.  6 F). In general, results derived from our transcriptomic data was highly consistent with those derived from the TCGA database, further confirming an immunosuppressive TME in the PHLDA2-H subgroup of ccRCC.

Validation of an immunosuppressive microenvironment in PHLDA2-H subgroup in ccRCC in West China Hospital cohort. A Volcano plot showed differential expressed genes between PHLDA2-H and PHLDA2-L subgroups in ccRCC in West China Hospital cohort. B Enrichment of immune-related pathways, including allograft rejection, IFN-γ, complement, IFN-α, inflammatory response, in PHLDA2-H subgroup in ccRCC in West China Hospital cohort. C Correlations between PHLDA2 expression and infiltration level of different immune cells in ccRCC by Cibersort algorithm in West China Hospital cohort. D , E Correlations between PHLDA2 expression and Treg markers, including IL2RA and FOXP3, in ccRCC in West China Hospital cohort. F Correlations between PHLDA2 expression and expression of immune checkpoints in ccRCC in West China Hospital cohort

Elevated PHLDA2 expression could predict the therapeutic effects of ICI plus TKI combination therapy in ccRCC

To verify whether the differences in immune phenotypes of TME between PHLDA2-H and PHLDA2-L subgroups could accurately predict the efficacy of immunotherapy, we employed data from three RCT, namely CheckMate025, IMmotion151 and JAVELIN101, to explore the predictive role of PHLDA2.

In CheckMate025 cohort, patients with advanced ccRCC for which they had received previous treatment with one or two regimens of anti-angiogenic treatment were enrolled, and those receiving nivolumab were selected for further analyzed. We grouped the patients into PHLDA2-H and PHLDA2-L subgroup according to the median expression of PHLDA2. Inevitably, both the OS ( p  = 0.144) and the PFS ( p  = 0.001) of PHLDA2-H subgroup treated with nivolumab were worse (Fig.  7 A, B ). Furthermore, patients in PHLDA2-L subgroup had better objective response rate (ORR) ( p  = 0.175) and disease control rate (DCR) ( p  < 0.05) than those in PHLDA2-H subgroup (Supplementary Fig. 3A, B). The expression level of PHLDA2 was higher in patients who did not reach a DCR ( p  < 0.05) (Supplementary Fig. 3C).

Elevated PHLDA2 expression could predict the therapeutic effects of ICI plus TKI combination therapy in ccRCC. A Associations between PHLDA2 expression and OS of metastatic ccRCC patients in the nivolumab arm in the CheckMate025 cohort. B Associations between PHLDA2 expression and PFS of metastatic ccRCC patients in the nivolumab arm in the CheckMate025 cohort. C Associations between PHLDA2 expression and PFS of metastatic ccRCC patients in the atezolizumab plus bevacizumab arm in the IMmotion151 cohort. D Associations between PHLDA2 expression and PFS of metastatic ccRCC patients in the avelumab plus axitinib arm in the JAVELIN101 cohort. E Associations between PHLDA2 expression and PFS of metastatic ccRCC patients in the sunitinib arm in the IMmotion151 cohort. F Associations between PHLDA2 expression and PFS of metastatic ccRCC patients in the sunitinib arm in the JAVELIN101 cohor

Although combinations of immunotherapy with anti-angiogenic agents as first-line therapy significantly improve outcomes of metastatic RCC patients, there still exists patients who cannot benefit from this regimen. We then further explored whether PHLDA2 expression could predict the therapeutic efficacy of combination therapy. In IMmotion151 cohort, we selected patients with metastatic ccRCC receiving first-line atezolizumab plus bevacizumab and grouped them into PHLDA2-H and PHLDA2-L subgroups. As expected, PHLDA2-H subgroup achieved poorer PFS than PHLDA2-L subgroup ( p  = 0.012) (Fig.  7 C). In PHLDA2-L subgroup, higher proportion of patients with disease control was observed ( p  = 0.103) (Supplementary Fig. 3D). In JAVELIN101 cohort, patients with advanced ccRCC receiving avelumab plus axitinib as first-line treatment were grouped into PHLDA2-H and PHLDA2-L subgroup, and we observed poorer PFS in PHLDA2-H subgroup, which was consistent with the results from IMmotion151 cohort ( p  = 0.003) (Fig.  7 D). However, no difference in PFS was found between PHLDA2-H and PHLDA2-L subgroups among patients receiving first-line sunitinib in both IMmotion151 and JAVELIN101 cohorts (Fig.  7 E, F ). The role of PHLDA1 and PHLDA3 expression in predicting therapeutic efficacy was also explored across the three cohort, without correlation findings (Supplementary Fig. 3E–P).

Taken together, PHLDA2 expression can be served specially as a robust predictive biomarker for immunotherapy plus anti-angiogenic agent combination in ccRCC.

Over the past decade, the therapeutic landscape of metastatic ccRCC has undergone rapid evolution. The application of ICIs plus anti-angiogenic agents represents a new standard of care for the first-line treatment of ccRCC, and has prolonged the survival of patients with metastatic ccRCC. Nevertheless, due to the heterogeneity of ccRCC, about 6–20% of patients experience primary resistance, and durable effects are only observed in a limited subset of patients. Hence, identifying robust biomarkers to predict the optimal candidates for ICI plus anti-angiogenic agent combination therapy in ccRCC is urgently needed.

PHLDA2, one crucial member of the PHLDA family encoding proteins containing PH domains which are highly conserved throughout eukaryotes, is located in a cluster of imprinted genes on chromosome 11p15.5, with preferential expression from the maternal allele in placenta. Previous studies demonstrated that elevated expression of PHLDA2 has been reported to be associated with fetal growth restriction [ 28 ]. Besides, PHLDA2 induced apoptosis, inhibited proliferation and led to inadequate invasion of trophoblast cells, suggesting an important role of PHLDA2 in the occurrence and progression of pregnancy-associated complications [ 29 , 30 ]. In addition, over-expression of PHLDA2 has been reported to promote tumor development in multiple cancers. Knockdown of PHLDA2 activated apoptosis and autophagy, eventually causing inhibition of tumor growth through AKT/mTOR signaling in both colorectal cancer and glioma [ 18 , 19 ]. Wang et al. revealed that in liver cancer, HSPA8 bound to the promoter of PHLDA2 to up-regulate its transcription through the coactivating transcription factor ETV4, and promoted the growth of tumor cells [ 17 ]. However, in certain cancers, PHLDA2 could also act as a tumor suppressor. In osteosarcoma, one type of aggressive bone tumor, PHLDA2 displayed decreased expression level, suggested a favorable prognosis for patients, and overexpressed PHLDA2 could impair tumorigenesis and metastasis both in vitro and in vivo [ 31 , 32 ]. Thus, whether PHLDA2 functions as an oncogene or a tumor suppressor depends on the type of cancer. In this study, we observed for the first time that the expression level of PHLDA2 was up-regulated, consistent with the majority of cancers, including liver cancer, colorectal cancer, glioma, lung cancer [ 17 , 18 , 19 , 33 ]. Furthermore, elevated PHLDA2 expression was associated with adverse clinicopathologic features and poorer survival in ccRCC. Thus, PHLDA2 could be served as a promising biomarker to predict the prognosis of ccRCC patients.

Epigenetic modifications, especially alterations in DNA methylation have been found in all types of cancers, including ccRCC [ 34 , 35 ]. Hypomethylation of various tumor promoter genes has been identified in ccRCC. IL8, a chemokine that stimulates tumor cell proliferation and increases angiogenesis, was maximally hypomethylated in tumor tissue compared to normal tissue [ 36 ]. Cho et al. examined the methylation status of G250, which aids cancer progression by neutralizing the surrounding acidic pH, in ccRCC cell lines and normal kidney tissue samples, and demonstrated hypomethylation in the 5’ region [ 37 ]. In this study, the methylation status of PHLDA2 was investigated, and we found that, compared to normal tissues, several probes, including cg05167973, cg04720330, cg21259253, cg16057921, cg07482372, cg15658784, cg01691090, was hypomethylated in ccRCC. Besides, the expression of PHLDA2 was negatively correlated with methylation level of these probes, suggesting the potential role of DNA hypomethylation in aberrant up-regulation of PHLDA2 in ccRCC. Furthermore, hypomethylation of probes, for instance, cg04720330 and cg21259253, indicated higher pTNM stage and poorer OS in ccRCC. To our knowledge, no studies have investigated the role of DNA methylation alterations in PHLDA2 dysregulation in cancers, except for one study conducted by Fu and colleagues, which revealed that DNA methylation status of PHLDA2 in peripheral blood was associated with breast cancer susceptibility [ 38 ]. Our results reinforced the significance of DNA methylation alterations in ccRCC pathogenesis, pointing to the potential use of epigenetic modulators in the treatment of this malignancy.

The tumor microenvironment represents an intricate ecosystem which comprises tumor cells and a multitude of non-cancerous cells, including immune cells and stromal cells, and heavily affects disease biology and responses to systemic therapy [ 39 , 40 , 41 ]. ccRCC has been identified as an immunogenic tumor, characterized by rich infiltrates of T cells [ 42 , 43 ]. Nonetheless, the majority of tumor-infiltrating T cells exhibited an immunosuppressive phenotype, characterized by high expression of multiple immune checkpoints, thus cannot mount anti-tumor responses effectively [ 42 ]. In addition, Gigante et al. revealed that CD8+ T cells from ccRCC patients expressed reduced levels of anti-apoptotic and proliferation-associated gene products when compared with normal donor T cells due to miR-29b and miR-198 overexpression, thus leading to immune dysfunction [ 44 ]. Multiple cell populations contribute to the immune evasion of tumor cells and suppression of T cell activation, among which Tregs play fundamental roles. Increased infiltration level of Tregs has been reported to predict worse prognosis across many cancers, and restrain therapeutic efficacy of ICI [ 45 , 46 , 47 , 48 , 49 ]. In this study, we also illustrated that PHLDA2 expression was positively correlated with Treg infiltrates in ccRCC in TCGA cohort, CheckMate025 cohort and IMmotion151 cohort. In addition, we observed pathways involved in ECM remodeling mainly enriched in PHLDA2-H subgroup. ECM components can establish an immunosuppressive microenvironment to stimulate tumor growth, and finally results in poor clinical response to immunotherapy [ 50 ]. To our interest, CAFs play vital role in ECM remodeling. Therefore, the association of PHLDA2 expression with CAFs was then explored and strong positive correlations were observed between the expression level of PHLDA2 and CAF markers, indicating higher infiltrates of CAFs in PHLDA2-H subgroup. It’s worth noting that ccRCC is characterized by metabolic dysregulation, for instance, hypoxia-inducible factor pathway and kynurenine pathway, which not only promote angiogenesis but also facilitate tumor growth and immune evasion [ 51 , 52 ]. Previous studies have reported that PHLDA family members play vital roles in cancer metabolism, suggesting that in ccRCC, PHLDA2 may also shape an immunosuppressive microenvironment by regulating metabolism [ 53 ]. In sum, the results showed that elevated PHLDA2 expression was associated with a more immunosuppressive microenvironment in ccRCC, which might impair the efficacy of immunotherapy. Afterwards, our hypothesis was validated by three RCTs in ccRCC, namely CheckMate025, IMmotion151 and JAVELIN101. Regarding PHLDA2-H subgroup, we observed over-expression of several immune checkpoints, including LAG3, TIGIT, suggesting the promising role of combination of ICIs targeting different immune checkpoints in this group of patients. Given that previous studies have demonstrated that PHLDA family members could mediate resistance to TKI targeted therapy, we investigated the association of expression of PHLDA family members and prognosis of ccRCC patients receiving sunitinib at the same time, whereas no significant difference was observed between subgroups, suggesting different functions in specific types of cancer [ 16 ]. Thus, elevated PHLDA2 expression could be served exclusively as a robust biomarker predicting unfavorable outcomes of ICI plus anti-angiogenic agent combination therapy in ccRCC. Further research should be conducted to investigate the predictive role of PHLDA2 for therapeutic efficacy of ICI monotherapy or ICI plus anti-angiogenic agent combination therapy among other cancer types.

Although our study is innovative in investigating the association between PHLDA2 expression and prognosis and treatment responses of ccRCC, it has certain limitations. This study is mainly based on bioinformatics analysis, which needs profound studies to investigate the underlying mechanism of PHLDA2 in the development of ccRCC through in vivo or in vitro experiments. Furthermore, it is still unclear why PHLDA2 expression affects the therapeutic effect of ICI plus anti-angiogenic agent in ccRCC, and further studies are required to explore potential treatment strategies for ccRCC patients with higher PHLDA2 expression.

Our comprehensive analysis illustrated the expression profile of PHLDA family members in pan-cancer, including ccRCC, for the first time. Further investigation demonstrated that up-regulation of PHLDA2 was associated with adverse clinicopathologic parameters and could be served as an independent high-risk prognostic factor in ccRCC. Elevated PHLDA2 expression was contributed by DNA hypomethylation, and mediated an immunosuppressive microenvironment featured by high infiltrates of Tregs and CAFs. Eventually, using data derived from three RCTs, we confirmed that elevated PHLDA2 expression could robustly predict worse therapeutic efficacy of ICI plus anti-angiogenic agent combination therapy in ccRCC. Our results expand the dimension of precision medicine, and further studies should be conducted to explore the potential role of PHLDA2 as a predictor biomarker for response of ICI or ICI-based combination therapy among other types of cancer.

Availability of data and materials

RNA-seq data of treatment-naïve ccRCC primary tumors and adjacent renal tissues collected from West China Hospital, Sichuan University are available from the corresponding author upon reasonable request.

Abbreviations

Adrenocortical carcinoma

Bladder urothelial carcinoma

Breast invasive carcinoma

Cervical squamous carcinoma and endocervical adenocarcinoma

Cholangio carcinoma

Colon adenocarcinoma

Lymphoid neoplasm diffuse large B-cell lymphoma

Esophageal carcinoma

Glioblastoma multiforme

Head and neck squamous cell carcinoma

Kidney chromophobe

Kidney renal clear cell carcinoma

Kidney renal papillary cell carcinoma

Acute myeloid leukemia

Brain lower grade glioma

Liver hepatocellular carcinoma

Lung adenocarcinoma

Lung squamous cell carcinoma

Mesothelioma

Ovarian serous cystadenocarcinoma

Pancreatic adenocarcinoma

Pheochromocytoma and paraganglioma

Prostate adenocarcinoma

Rectum adenocarcinoma

Skin cutaneous melanoma

Stomach adenocarcinoma

Testicular germ cell tumors

Thyroid carcinoma

Uterine corpus endometrial carcinoma

Uterine carcinosarcoma

Uveal melanoma

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Acknowledgements

Not applicable.

This work was supported by the Natural Science Foundation of China (NSFC 82202901, 82101719, 82373422, 82203280), China Postdoctoral Science Foundation (2021M692281, 2021M702344), the Natural Science Foundation of Sichuan Province (2023NSFSC1856), Sichuan Province Science and Technology Support Program (2022YFS0305).

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Junjie Zhao, Xiuyi Pan and Zilin Wang contributed equally.

Zhenhua Liu, Hao Zeng and Jiayu Liang jointly supervised this work.

Authors and Affiliations

Department of Urology, Institute of Urology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Wuhou District, Chengdu, Sichuan, China

Junjie Zhao, Zilin Wang, Dingbang Liu, Yu Shen, Xinyuan Wei, Chenhao Xu, Xingming Zhang, Xu Hu, Junru Chen, Jinge Zhao, Bo Tang, Guangxi Sun, Pengfei Shen, Zhenhua Liu, Hao Zeng & Jiayu Liang

Department of Pathology, West China Hospital, Sichuan University, Chengdu, China

Department of Radiology, West China Hospital, Sichuan University, Chengdu, China

Yuntian Chen

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Conception and design: JZ, XP, ZW. Development of methodology: JZ, XP, ZW. Analysis and interpretation of data: JZ, XP, ZW, YC, DL, YS, XW, CX. Writing, review, and/or revision of the manuscript: All authors.

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Correspondence to Zhenhua Liu , Hao Zeng or Jiayu Liang .

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Supplementary Information

40001_2024_1939_moesm1_esm.tif.

Supplementary Material 1. Supplementary Fig. 1. Expression pattern and correlations with clinicopathologic parameters and survival of PHLDA1 and PHLDA3 in ccRCC. (A) Expression level of PHLDA1 between tumor and normal tissues in pan-cancer in the TCGA database. * p  < 0.05, ** p  < 0.01, *** p  < 0.001. **** p  < 0.0001. (B) Expression level of PHLDA3 between tumor and normal tissues in pan-cancer in the TCGA database. (C–H) Correlations between PHLDA1 expression level and clinicopathologic parameters of ccRCC, including age, gender, pT stage, pN stage, metastatic status, ISUP grade. (I–N) Correlations between PHLDA3 expression level and clinicopathologic parameters of ccRCC, including age, gender, pT stage, pN stage, metastatic status, ISUP grade. (O–Q) Associations between PHLDA1 expression and OS, DSS, PFI of ccRCC patients by Kaplan–Meier survival analysis. (R–T) Associations between PHLDA3 expression and OS, DSS, PFI of ccRCC patients by Kaplan–Meier survival analysis.

40001_2024_1939_MOESM2_ESM.tif

Supplementary Material 2. Supplementary Fig. 2. Correlations between PHLDA2 expression and mutation status in ccRCC. (A) Expression of PHLDA2 between VHL-wt (wild type) and VHL-mt (mutated type) subgroups in ccRCC. (B) Expression of PHLDA2 between PBRM1-wt and PBRM1-mt subgroups in ccRCC. (C) Expression of PHLDA2 between SETD2-wt and SETD2-mt subgroups in ccRCC. (D) Expression of PHLDA2 between BAP1-wt and BAP1-mt subgroups in ccRCC.

40001_2024_1939_MOESM3_ESM.tif

Supplementary Material 3. Supplementary Fig. 3. Correlations between PHLDA2 expression and tumor response of metastatic ccRCC patients receiving immunotherapy, and associations between PHLDA1 and PHLDA3 expression and therapeutic efficacy of systemic treatment in metastatic ccRCC patients. (A) Comparisons of proportion of patients who achieved an objective response between PHLDA2-H and PHLDA2-L subgroups (left), and comparisons of proportion of patients in PHLDA2-L subgroup between noORR (patients who did not achieve an objective response) and ORR (patients who achieved an objective response) subgroups (right) in the nivolumab arm in the CheckMate025 cohort. (B) Comparisons of proportion of patients who achieved disease control between PHLDA2-H and PHLDA2-L subgroups (left), and comparisons of proportion of patients in PHLDA2-L subgroup between noDCR (patients who did not achieve disease control) and DCR (patients who achieved disease control) subgroups (right) in the nivolumab arm in the CheckMate025 cohort. (C) Expression of PHLDA2 between noDCR and DCR subgroups in the nivolumab arm in the CheckMate025 cohort. (D) Comparisons of proportion of patients who achieved disease control between PHLDA2-H and PHLDA2-L subgroups (left), and comparisons of proportion of patients in PHLDA2-L subgroup between noDCR (patients who did not achieve disease control) and DCR (patients who achieved disease control) subgroups (right) in the atezolizumab plus bevacizumab arm in the IMmotion151 cohort. (E, F) Associations between PHLDA1 expression and PFS, OS of metastatic ccRCC patients in the nivolumab arm in the CheckMate025 cohort. (G) Associations between PHLDA1 expression and PFS of metastatic ccRCC patients in the atezolizumab plus bevacizumab arm in the IMmotion151 cohort. (H) Associations between PHLDA1 expression and PFS of metastatic ccRCC patients in the avelumab plus axitinib arm in the JAVELIN101 cohort. (I, J) Associations between PHLDA3 expression and PFS, OS of metastatic ccRCC patients in the nivolumab arm in the CheckMate025 cohort. (K) Associations between PHLDA3 expression and PFS of metastatic ccRCC patients in the atezolizumab plus bevacizumab arm in the IMmotion151 cohort. (L) Associations between PHLDA3 expression and PFS of metastatic ccRCC patients in the avelumab plus axitinib arm in the JAVELIN101 cohort. (M–P) Associations between PHLDA1 and PHLDA3 expression and PFS of metastatic ccRCC patients in the sunitinib arm in the IMmotion151 and JAVELIN101 cohort.

Supplementary Material 4.

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Zhao, J., Pan, X., Wang, Z. et al. Epigenetic modification of PHLDA2 is associated with tumor microenvironment and unfavorable outcome of immune checkpoint inhibitor-based therapies in clear cell renal cell carcinoma. Eur J Med Res 29 , 378 (2024). https://doi.org/10.1186/s40001-024-01939-9

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DOI : https://doi.org/10.1186/s40001-024-01939-9

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Systems Biology of Cancer Metastasis

Yasir suhail.

1. Department of Biomedical Engineering, University of Connecticut Health Center, Farmington, CT

2. Cancer Systems Biology @ Yale (CaSB@Yale), Yale University, West Haven, CT

Margo P. Cain

3. Department of Cancer Biology, MD Anderson Cancer Center, Houston, TX

Kiran Vanaja Gireesan

Paul a. kurywchak, andre levchenko, raghu kalluri.

Cancer metastasis is no longer viewed as a linear cascade of events, but rather as a series of concurrent, partially overlapping processes, as successfully metastasizing cells assume new phenotypes while jettisoning older behaviors. Lack of a systemic understanding of this complex phenomenon has limited progress in developing treatments for metastatic disease. Because metastasis has traditionally been investigated in distinct physiological compartments, the integration of these complex and interlinked aspects remains a challenge for both systems-level experimental and computational modeling of metastasis. Here, we present some of the current perspectives on the complexity of cancer metastasis, the multi-scale nature of its progression, and a systems-level view of the processes underlying the invasive spread of cancer cells. We also highlight the gaps in our current understanding of cancer metastasis as well as insights emerging from interdisciplinary systems biology approaches to understand this complex phenomenon.

eTOC blurb:

Cancer metastasis is a complex disease, arising from a growing tumor from which cells escape to other parts of the body. For long, cancer metastasis was considered as a combination of steps, which were studied separately, limiting our understanding of this complex disease. Here we present the new developments, and our perspective on how the new systems biology approach is changing our view of cancer metastasis as an integrated multiscale phenomenon comprising of interlinked parts that allow tumors to metastasize.

Introduction

Cancer metastasis, the processes involving dissemination of cancer cells from a primary lesion to distal organs, is the principal cause of cancer lethality. Dissemination of cells from a primary tumor involves a variety of cellular mechanisms. These include invading through, or colluding with, stroma, escaping immune surveillance by inhibiting or co-opting their anti-tumorigenic processes, evading and modulating the tissue microenvironment, and evolving resistance to therapeutic intervention( Fischer et al., 2015 ; Kalluri, 2016 ; Li et al., 2016b ; Massague and Obenauf, 2016 ). Recent reports provide strong evidence that metastasis is non-linear, and involves multiple parallel overlapping routes( Harper et al., 2016 ; Lambert et al., 2017 ; Te Boekhorst and Friedl, 2016 ). The reductive disease models that were necessary to establish the field of metastasis research and provide foundational concepts are limited in completely characterizing metastasis owing to the integrated and complex nature of its constituent processes. The multi-parametric and multi-scale nature of cancer metastasis warrants a renewed focus on comprehensive experimental and computational approaches that provide systems-level insight, versus the investigation of isolated steps in a complex network of events. Systems biology approaches that result in predictive and testable models of complex phenotypes through integration of expertise from diverse fields including cancer biology, oncology, genetics, mathematics, bioinformatics, imaging, physics, and computer science could provide a more holistic understanding of the complete metastatic process. In this Review, we present insights into the complexity of cancer metastasis, the multi-scale nature of its progression, and a systems-level view of investigating the processes involved in invasive spread of cancer cells. We highlight the gaps in our understanding of steps involved in tumor metastasis and insights emerging from interdisciplinary systems biology approaches to this important cancer process.

Cancer Metastasis: A Dynamic Selection Process

Cancer cells exist in a continuum of phenotypic states that facilitate transition between residence in the primary lesion to local invasion, systemic circulation, and eventual seeding and colonization of distal secondary sites ( Figure 1 ). A metastatic lesion is the result of one or several cells acquiring the capacity to circumvent a series of molecular and biophysical hurdles, which would present unsurmountable obstacles to their non-metastasized counterparts ( Aceto et al., 2014 ; Lambert et al., 2017 ; Wang et al., 2017 ). Notably, many of these capabilities may rely on cell behaviors that are mutually incompatible or incongruent with those needed to establish the primary tumor. For example, while cells in the initial tumor mass may be proliferative, many reports indicate that disseminating cells or cells undergoing an epithelial-to-mesenchymal transition (EMT) largely suspend proliferation ( Rojas-Puentes et al., 2016 ; Vega et al., 2004 ; Zheng et al., 2015 ). Metastatic cancer cells exhibit an anti-correlation between the so-called “grow” and “go” phenotypic states; however, once a metastatic cell is arrested in a secondary site, it could resume proliferation, even after remaining dormant for prolonged periods( Giese et al., 1996 ; Hatzikirou et al., 2012 ). A comprehensive patient cohort study of metastasis revealed that proliferative cancers were associated with increased metabolism and stress response, while the ones with more EMT like phenotype were more inflamed( Robinson et al., 2017 ). Current and future experimental and computational modeling of metastasis must accommodate this plasticity while integrating the many steps in the metastatic process. These steps include the initiation of metastasis and local invasion, travel to and colonization of distant metastatic sites, and evasion of the immune system, often manifested by a state of dormancy ( Figure 1 ).

Metastasis is a complex, multiscale process which involves multiple sub-processes occurring in parallel through partially overlapping routes. Emerging evidence suggests that pre-malignant lesions are capable of giving rise to distant, latent metastasis and are thus not only associated with late stage primary tumors. However, it is still commonly thought that metastasis occurs mainly through dissemination from malignant lesions when microenvironmental stressors induce cellular reprogramming events that facilitate cellular migration and invasion towards more nutrient-rich niches. These stressors, associated with metabolic reprogramming, can trigger phenotypic changes in cancer cells to adopt more mesenchymal-like states that are not binary, but plastic, with the cells capable of sampling these dynamic states throughout the metastatic process. While this may advance a cell’s ability to metastasize, it is now appreciated that it is likely not the only mechanism by which metastasis occurs. Indeed, there are multiple parallel mechanism co-opted by cancer cells. Lymphatics and blood vasculature are the primary route of cell seeding into the common metastatic organs across cancer types (lymph nodes, liver, lung, bone marrow, and brain), though the tropism cancer exhibit for specific organs is still poorly understood. The combination of genetic and epigenetic changes, and interactions with the diverse milieu of cells in the host microenvironment, determines cancer cell survival and outgrowth.

Initiation of Metastasis

The notion that metastatic lesions are formed from cancer cells that have disseminated from advanced primary tumors has been substantially revised following the identification of disseminated tumor cells in the bone marrow of patients with early stage disease( Schardt et al., 2005 ),( Klein et al., 1999 ). This phenomenon has also been demonstrated in spontaneously metastasizing, autochthonous animal models of breast cancer, where cells shed from premalignant lesions survived in distal organs and later gave rise to micro-metastases( Harper et al., 2016 ). Similarly, early dissemination outside of the preneoplastic lesion has been illustrated in pancreatic cancer models( Muzumdar et al., 2016 ; Rhim et al., 2012 ). Presentation of micro-metastases in early stages of disease adds complexity to diagnosis, detection and ultimately, to patient treatment ( Figure 1 ).

The introduction of technologies such as intravital live cell imaging and carefully engineered in vitro systems have revealed that parallel mechanisms can co-exist for cancer cells to escape from a primary lesion. For example, it is now appreciated that invasive programs are more diverse than initially understood, with evidence suggesting that single or clusters of cells could disseminate from the primary tumor mass( Aceto et al., 2014 ; Massague and Obenauf, 2016 ; Ye et al., 2015 ; Yu et al., 2013 ), and that these programs are both cell intrinsic( Alexander et al., 2008 ), and triggered in response to the extracellular microenvironment( Hofschroer et al., 2017 ; Wolf et al., 2007 ). Cancer cells are capable of morphologically adapting to the physical constraints of the microenvironment, including deformation of the rigid nucleus, which, in turn, can lead to chromosomal instability, altered gene expression, and metastasis( Bakhoum et al., 2018 ). Such invasion-induced chromosomal instability suggests a potential mechanism for increased genomic heterogeneity at sites of metastasis. The notion that epithelial cells must first adopt an exclusively mesenchymal phenotype to promote metastasis has also been recently disputed. Work using organoid cultures of primary breast cancer epithelial cells has shown that during collective epithelial cell migration, the leading cell remains positive for the basal epithelial marker cytokeratin 14 (K14)( Cheung et al., 2013 ). Similarly, intravital imaging has captured E-cadherin retained by clusters of cancer cells exiting the tumor( Friedl and Gilmour, 2009 ). Studies in animal models of pancreatic and breast cancer further suggest that suppression of EMT through genetic depletion of transcription factors implicated in EMT has no effect on the rate of metastatic disease( Zheng et al., 2015 ). In contrast, recent reports indicate that the EMT transcription factor, Zeb1 is required for pancreatic tumor metastasis( Krebs et al., 2017 ), and that post-EMT mesenchymal-like cells cooperate with epithelial cells to eventually become metastatic by paracrine signaling( Neelakantan et al., 2017 ).

The initiation of metastasis is not simply a cell-autonomous event but is heavily influenced by complex tissue microenvironments. It has long been recognized that interactions between cancer cells, stromal fibroblasts, endothelial cells, immune cells as well as alterations in tissue oxygen tension and the architecture of the adjacent extracellular matrix profoundly impact tumor progression. Stabilization of the hypoxia-inducible factor (HIF), a transcription factor, can trigger a switch from collective migration to amoeboid migration as the oxygen tension fluctuates in the tumor, stimulating reciprocal signaling between mesenchymal stem cells and cancer cells promoting metastatic phenotype( Lehmann et al., 2017 ),( Chaturvedi et al., 2013 ). Tumor associated macrophages (TAMs) can be stimulated by cancer cell-secreted lactate to promote angiogenesis( Colegio et al., 2014 ), a requirement for distant metastasis, and more recent findings suggest that TAMs induce early dissemination of Her2+ breast cancer cells( Linde et al., 2018 ). Generally, the influence of the microenvironment on cancer cell invasion and migration are both cell autonomous and tissue context-dependent, adding a further layer of complexity to this process( Spill et al., 2016 ). Understanding the complex interactions between cancer cells and the tumor microenvironment that lead to metastasis will require integration of extensive molecular characterization data collected from in vitro and in vivo experimental models. An example is the use of data-driven modeling of protein phosphorylation in pancreatic cancer, which facilitated understanding of reciprocal molecular interactions between cancer cells and stroma, allowing system-wide delineation of the role of heterotypic cell-cell interactions in tumor growth( Tape, 2016 ; Tape et al., 2016 ).

Metastatic Colonization and Cancer Outgrowth

Colonization of distant tissues by disseminated tumor cells is an extremely inefficient process. While relatively numerous circulating tumor cells (CTCs) are detected in the blood of cancer patients, with reports indicating > 1000 CTCs/ml of blood plasma, disproportionally few metastases are clinically detectable( Nagrath et al., 2007 ). Following arrest in the vascular bed, a successfully metastasized cell has to extravasate and survive in a new tissue microenvironment that may or may not be conducive to survival. Overt metastatic lesions are primarily detected in select organ sites (liver, lung, bone, brain) but rarely in others (kidney, heart, stomach) ( Figure 1 )( Fidler, 2003 ). Therefore, metastatic colonization is not merely an outgrowth of rogue cancer cells from the primary organ, but arises via complex interplay between disseminated cancer cells and tissue microenvironments across the organism.

While little is known about the preference of cancer subtypes for distinct tissues, or about the receptiveness of a tissue as a metastatic site, various efforts are being pursued to further our understanding of such tissue tropism. A parabiosis mouse model approach has uncovered a strong preference for ovarian cancer hematogenous metastasis to the omentum regulated by specific ligand-receptor interaction between the two compartments( Pradeep et al., 2014 ). Meanwhile, downregulation of the metastasis suppressor RARRES3 facilitates breast cancer tropism to the lung by increasing cellular adhesion to lung parenchyma( Morales et al., 2014 ). Circulating cytokines and growth factors, as well as microRNA loaded exosomes are hypothesized to contribute to conditioning of pre-metastatic niches ( Figure 1 ). Animal studies have uncovered pro-metastatic roles for exosomes via their effects on increasing vascular permeability and their priming of the resident cells of the metastatic site to create a pro-inflammatory and metabolically active niche( Becker et al., 2016 ; Costa-Silva et al., 2015 ; Schillaci et al., 2017 ). Recent reports have also suggested role of exosomes in tissue tropism, via specific integrin receptors on the exosome( Hoshino et al., 2015 ). However, our understanding of the systemic role of exosomes, their interactions with recipient cells, the duration of the effect, and whether they specifically target distinct cells or organs remains incomplete. Future systems biology models of metastasis, both experimental and computational, will benefit from explicit incorporation of intercellular communication which could provide a way to integrate dynamic multi-scale characteristics of the metastatic cascade. This approach may lend deep insight into the role of the primary tumor in determining tumor tropism, as well as metastatic niche formation.

Cancer Dormancy

What endures as one of the most confounding clinical phenomenon is that patients may undergo tumor resection and then remain apparently disease-free for months, years, and even decades only to relapse and be diagnosed with late stage metastatic disease( van Maaren et al., 2016 ). This may be a result of cell seeding from minimum residual disease after resection of the primary tumor or preexisting clinically undetectable micrometastases, but may also arise from early-disseminated cells that have remained dormant and resistant to therapy until suddenly reawakened to initiate proliferation into clinically detectable macrometastases. Emerging experimental observations note that dormant metastatic cells that later develop into overt lung metastases disseminated during the early stages of primary tumor development( Harper et al., 2016 ). Hypoxia within the primary tumor microenvironment may also predispose a sub-population of to-be-disseminated cells to dormancy( Fluegen et al., 2017 ). Where dormant cells reside and how they maintain their cell cycle arrested state is being explored through interdisciplinary approaches that combine live-cell imaging, laser capture microdissection and single cell transcriptomics. Some studies focus on the cell-intrinsic state of the dormant cancer cell, such as their epigenetic state, while others provide evidence for elements of the microenvironment, such as the resident quiescent vascular cells, for maintaining adjacent disseminated tumor cells in a dormant, growth arrested state 53,54 ,( Ghajar et al., 2013 ). Cellular latency can also emerge via the stochastic switch to self-imposed quiescence of a fraction of the cells, for example, by inhibiting WNT pathways, as the proliferative population is eliminated by immune surveillance( Malladi et al., 2016 ). Immune surveillance induced mass dormancy, therefore, can give rise to cellular dormancy, with macrometastatic growth occurring due to either the removal of immune pressure or acquiring other evasive traits.

Questions remain about how the mechanisms controlling dormancy are disrupted to allow for re-emergence and metastatic outgrowth years or decades later. Whether cell-intrinsic, microenvironmental and/or systemic changes related to normal physiological processes such as aging or diet impact dormancy is actively being investigated. Astrocyte-derived exosomes suppress PTEN in dormant metastatic cells, allowing for the outgrowth of lesions in the brain( Zhang et al., 2015 ), thus demonstrating a role for the microenvironment in guiding the reactivation of dormant cancer cells. Other studies have provided evidence that the primary origin of dormant cells may also play a role. For example, cancer cells from organs of endodermal lineage that cycle more slowly (i.e. liver, pancreas, lung) may behave differently than cancer cells originating from sites with persistent cellular turnover such as the colon or gut( Furukawa et al., 2015 ; Taylor et al., 2013 ; Wells et al., 2013 ). In addition, the unique characteristics of the metastatic site will likely impact reactivation of disseminated tumor cells arriving in that organ. Cells assuming latency by avoiding immune surveillance by WNT signaling induced quiescence can stochastically proliferate if immune surveillance is removed( Malladi et al., 2016 ). Indeed, it is a common observation that after resection of primary tumors, which typically serves as the chief source of paracrine signaling creating pre-metastatic niches, already metastasized cells remain in dormancy to be activated many years later. Mechanisms causing emergence from latency are not yet well understood, but in the absence of pre-metastatic niches, a regrowing tumor may also signal to create new niches. A recent study showed that inflammatory signals like IL-6 could make liver a more favorable pro-metastatic environment for pancreatic cancer( Lee et al., 2019 ). Considering that the latent cell has already gone through the process of metastasis and is now also proliferating, it is much more primed to create secondary node than a primary tumor cell would be, and therefore a reoccurring disease is probably more aggressive. Resolving the question of when cancer cells could take advantage of the pre-metastatic niche versus entering a dormant state will involve detailed mapping of the intercellular signaling of primary, quiescent, and metastatic cells with the stroma and the cell state trajectories during this communication.

It is tempting to consider that tumor cell dormancy follows an evolutionary conserved mechanism adapted from other organisms and is launched in response to stress( Carcereri de Prati et al., 2017 ; Seidel and Kimble, 2011 ; Senft and Ronai, 2016 ; Sosa et al., 2013 ),( Herrick et al., 2017 ; McGillivray et al., 2015 ; Pal et al., 2016 ; Schubert et al., 2015 ). Genetics, tissue microenvironment, and timing are almost certainly some of the factors that impact a cell’s capacity to enter and exit from dormancy. The development of therapeutic strategies targeting mechanisms that facilitate dormant cell reactivation could prevent expansion of disseminated tumor cells or minimally residual disease. However, prior to drug development efforts, a multi-parameter approach is necessary to better understand how inhibiting the exit from dormancy may impact normal physiological processes-.

Systems Approaches to Studying Metastasis

Understanding how the complex molecular-level behavior of cancer cells and their interactions with the tumor microenvironment lead to metastasis will require integration of physiological metastasis models and extensive phenotypic and molecular characterization. Sophisticated informatics and computational approaches will be necessary to make sense of these dynamic and multivariate relationships and to generate testable hypotheses that eventually lead to better patient treatment options ( Table 1 ). Also shown are both experimental and computational systems approaches to study cancer metastasis at different stages of progression ( Table 2 and Figure 2 ). Implementation of such approaches will involve transforming the concept of individual events in metastasis to an integrative, multi-scale process characterized by system abstraction as shown in Figure 3 .

Tumor outgrowth from the primary node to a more metastatic phenotype entails different physiological environs which pose different experimental and analytical constraints for data acquisition, visualization, sample acquisition, requiring tailored approaches to explore metastasis. Similarly, analytical techniques to mechanistically understand cancer progression at different physiological stages also differ.

To understand and describe cancer metastasis at the multiple scales it exists in as a disease require integration of its characteristics at multiple scales. Technological developments in sequencing, imaging, immunological assays etc. have enabled integrated collection of data at multiple scales in which cancer metastasis manifests (molecular, cellular, tissue-level, organ-level, epidemiological, and clinical), as well as along the steps involved in the metastatic cascade. Our ability to describe events on those scales will need to be integrated to develop a more holistic and systems understanding of cancer metastasis.

A consolidated list of experimental and computational techniques utilized in Systems Biology of Cancer Metastasis.

Physiological stages of metastasis present unique questions, as well as experimental avenues to explore the progression of metastasis.

Computational Systems Biology Approaches to Metastasis Research

The biological mechanisms underlying cancer metastasis occur at multiple biological scales. The ability to describe events across scales is needed to understand tumor cell dissemination in an integrated manner. For a metastatic tumor cell, these scales include genetic and epigenetic alterations, protein-protein interactions, and metabolic requirements, which together control various molecular signaling networks responsible for autonomous progression to metastasis. At the tissue or organ level, homotypic and heterotypic interactions between cells, and with the microenvironment further increase the complexity of the disease phenotype.

The multi-scale perspective of cancer metastasis requires an appreciation that the phenotypes at one scale are likely informed by regulatory events at other scales. Here we provide a perspective on some of the computational approaches used to model cancer metastasis, including emerging data analysis techniques that are becoming tools in the arsenal of many systems biologists.

Mechanistic Models of Metastasis – towards linking mechanisms at different scales

Computational biology provides mathematical frameworks that represent fundamental biological processes for the purposes of generating testable, and often non-intuitive predictions about disease mechanisms. An optimal computational model of metastasis will derive observations not accessible by current experimental technologies, inspire clinically important testable hypotheses, and facilitate deeper understanding of the mechanisms of metastasis. Metastasis operates across multiple spatial and temporal scales. Therefore, individual models constructed at one scale (for example, a static picture of gene regulatory networks operating within a cell, the dynamic simulation of a signaling pathway promoting cell motility, or the spatial representation of invasion of cancer cells into the stroma) will need to be integrated into a multiscale framework representative of a systems understanding of metastasis.

Mechanistic modeling based on ordinary or partial differential equations has been used to study various aspects of the metastatic processes, such as using the genetic, transcriptional and metabolic networks to build the signaling pathways leading to cell migration and invasion. The combination of experimental observations and differential equation based modeling of signal transduction has revealed mechanisms by which extracellular ligands promote tumor growth and invasion( Kirouac et al., 2013 ; Kochanczyk et al., 2017 ; Korkut et al., 2015 ; Park et al., 2017 ; Ryu et al., 2015 ). A generalized mathematical model of protein kinase signaling, crucial in regulating various steps of cancer metastasis, revealed that phosphatases have more pronounced effect than kinases on the rate of duration of signaling( Heinrich et al., 2002 ). On these lines, individual subsystems and processes have been successfully modeled in considerable detail. For example, a recent mechanistic model was able to predict the stability of a partial epithelial-mesenchymal transition (EMT) in vivo based upon the in silico prediction that additional molecular participants in the core EMT machinery must exist to promote hybrid EMT phenotypic behaviors( Jia et al., 2017 ; Jolly et al., 2016 ). A comprehensive model of the phosphoinositide pathway could contribute to the understanding of cell polarity and the initiation of chemotaxis and invasion( Olivenca et al., 2018 ). In this model, all species of the pathways are modeled, along with their spatial localization within the cell, suggesting strategies to control the activities of various molecular species within the pathway. Similarly, a dynamic model of hypoxia inducible factor-1 alpha (HIF-1a) based signaling has revealed key features of HIF-1a stabilization, and its effect on transcriptional activity of the cell( Nguyen et al., 2013 ).

Mechanistic models have provided detailed understanding of many signaling pathways, augmented by parallel developments in biosensors, activity reporters, microscopy, microfluidics etc. However, complex phenomena like metastasis involve interactions of many more molecular species than can be observed and modeled directly, and therefore more systems approaches are necessary which do not rely on detailed observations of all the molecular species involved in the described process. Large scale cell signaling networks with quantitative reaction constants could be used for the predictive modeling of metastasis. However, the development of such genome wide networks is limited both by the ability to simultaneously observe the activity of large numbers of signaling molecules, and the computational power required to infer and simulate these networks across cells and for extended periods of time. At the gene and transcript levels, most computational approaches are informed by large-scale ‘omics studies. Modeling of larger signaling networks may involve abstraction at a higher level, using methods such as Petri nets( Pennisi et al., 2016 ), Boolean networks( Lu et al., 2015 ; Steinway et al., 2014 ), Bayesian networks( Friedman et al., 2000 ) or systemic perturbation-effect networks( Korkut et al., 2015 ).

Reconstructed gene regulatory networks were used to identify the regulatory program inducing metastasis in breast cancer( Ahmad et al., 2012 ; Walsh et al., 2017 ), and the role of BACH1 in bone metastasis( Liang et al., 2012 ). Similarly, metabolomic networks can be modeled at the whole cell level using flux balance and constraint-based reconstruction and analysis (COBRA) methods( Becker et al., 2007 ), predicting the genetic bottlenecks within the energy utilization program of a cell. Such analyses have been used to infer the mechanism of the oncogenic Warburg effect for the pro-metastatic microRNA-122( Tsai et al., 2009 ), and identify the reprogramming of adaptive metabolic and transcriptomic profiles in metastatic cancer stem cells( Aguilar et al., 2016 ; Wu et al., 2017 ).

A crucial feature of cancer metastasis, and many other biological processes, is the existence of biological hierarchies, or scales. To accommodate hierarchies inherent in cancer metastasis, more discrete models have been employed, including cellular automaton and other agent-based modeling approaches. Cellular automata models typically involve two- or multi-dimensional lattice of entities with a finite number of states, wherein latticed entities can interact with each other through an iterative process defined by a fixed set of rules. The automaton also lends itself to integrating with ODE based models within each entity, referred to as Hybrid Cellular Automaton Models. Similarly, agent based computational modeling has been used to model biological systems wherein each biological entity is referred to as an agent, with a set of attributes, which interact in a rule framework. Agent-based computational modeling of the tumor microenvironment that incorporates spatial diffusion of oxygen, nutrients and cytokines offers a platform for high-throughput hypothesis testing in silico and can help to explain intra-tumoral heterogeneity, non-uniform drug transport, and disparate therapeutic response under hypoxic and low pH conditions( Waclaw et al., 2015 ),( Ibrahim-Hashim et al., 2017 ; Robertson-Tessi et al., 2015 ). Agent based models allow easy compartmentalization, quintessential in biological systems at all scales, and incorporate the complexity of association and dissociation between biological entities( Blinov et al., 2017 ). As an example, an agent based simulator to model 3D multicellular systems has been created incorporating biophysical principles( Ghaffarizadeh et al., 2018 ). Another model, combined with spatial data generated from microfluidic metabolic secretion detection chambers showed that spatial organization of cells in a tumor creates spatial metabolic signatures, which drive macrophage specification and angiogenesis( Carmona-Fontaine et al., 2017 ). Agent based models can also be utilized to test hypotheses which are otherwise difficult to access experimentally. For example, competing hypotheses of self-seeding versus primary metastatic seeding were tested using agent based models: where circulating tumor cells repopulate the primary tumor, or secondary seeding: where metastasized cells from primary tumor form secondary nodes which return back to the primary tumor via vasculature( Scott et al., 2013 ). Because they replicate real biological cellular entities, hybrid automatons or agent based models can be used to model complex cell-microenvironmental interactions in a detailed mechanistic fashion in the context of metastasis( Pennisi et al., 2009 ; Wang et al., 2015 ). In addition to the biochemical signaling pathways, metastasis also involves interactions between cells in both the tissue of tumor origin, or in distal metastatic nodes. While a detailed systems understanding of the multiple steps involved in this process is lacking, there have been attempts at stochastic modeling of some of these steps at various levels of detail( In et al., 2017 ; Newton et al., 2012 ; Speer et al., 1984 ). While stochastic modeling may not completely describe the mechanism, it does provide an approach to generate hypotheses for metastatic site selection guided by clinical observations. Mechanistic models have been successful in describing biological phenomena where parameters are known, or can be optimally searched in a constrained manner, e.g. to model signaling motifs at subcellular levels, or using cellular automata showing interactions between a few cells. The objective is to integrate these sub-systems models to create a systems understanding of metastasis. However, such integration of sub-system models may not capture all the emergent behaviors at larger scales.

The field of systems biology could benefit from the introduction of model formalisms, the absence of which has constrained multi-scale integration of the various sub-system models. There have been attempts to formalize and describe biological interactions at different scales. Systems Biology Markup Language is a complex multi-paradigm XML-based standard for model description( Hucka et al., 2003 ). Multiple software packages use SBML or a subset of its capabilities for simulation, visualization, or editing systems biology models( Konig et al., 2012 ; Lopez et al., 2013 ). The Biomodels database has made more than 8000 SBML databases available for use by other researchers( Le Novere et al., 2006 ). While SBML is versatile and capable of representing models developed in many systems biology studies, emerging experimental knowledge is not necessarily used to continuously refine a complete and unified model covering all aspects of cancer invasion and growth. Integrative frameworks and platforms have recently been developed and adopted, including PySB( Lopez et al., 2013 ), and natural language processing based formalism to create automated assembly of signaling formalism from word descriptions( Gyori et al., 2017 ). The goal of a systems integration of the metastatic cascade would be served well if cancer biologists and systems biologists arrive towards a common formalism to describe interactions of molecules, or cells in the context of their localization and states.

The multi-scale perspective of cancer metastasis requires an appreciation that the phenotypes that manifest at one scale are likely informed by regulatory events at other scales. As an example, relevant to clinical applications, the genetic landscape of cancer determines epidemiology, while cellular and tissue level scales involving interaction of cancer cells with matrix and other cell types inform the histological outcomes. For example, technologies like direct tissue proteomics allow high resolution proteomics from paraffin-embedded microdissection biopsies. Using laser-ablated microbiopsies, this approach identified more than 400 prostate specific genes as well as distinct metabolic pathways which may contribute to progression of prostate cancer at different stages( Hwang et al., 2007 ). Moreover, laser capture micro-ablation, combined with single cell transcriptomics, or even flow cytometry, are able to connect a tumor’s architecture with its molecular scale( Joseph and Gnanapragasam, 2011 ). In another example of bridging across scales, mathematical modeling was used to present an eco-evolutionary perspective of metastasis, explaining how mutations could facilitate evolution of cancer cells to break tissue homeostasis, and in turn transform the tissue environment itself( Basanta and Anderson, 2017 ).

Statistical Network Models – going beyond single gene associations in cancer metastasis

Systems biology is often referred to as networks of networks, integrating the knowledge of various interacting species across different biological scales. The era of big data collection has moved beyond sequencing of the genome, or the transcriptome, and now extends to identification of large number of metabolites, proteins with post-translational modifications and high content information about their subcellular localization. Measurement of these multi-omics has prompted the use of more sophisticated bioinformatics techniques that go beyond estimating the importance of individual genes to understanding how networks of molecules cooperate to promote cancer metastasis. Network information provides a convenient format for researchers to incorporate previous biological knowledge to generate hypothesis. These networks can be genetic, signaling, or metabolic, and operate within a cell or between cells. Whole genome sequencing has facilitated attempts to identify causal genetics of metastasis, but its integration with gene regulatory networks can further elicit specific gene interactions mediating metastatic phenotypes( Leiserson et al., 2015 ),( Paull et al., 2013 ). The large number of passenger mutations in metastatic tumors, and relatively limited number of patient samples poses a challenge, but heuristics applied to interactome network can allow prioritization of genes for further investigation. Network approaches have been applied to protein interaction, regulatory, gene co-expression networks, as well as on mutation co-occurrence to provide systems molecular interaction snapshots of metastasis( Shin et al., 2017 ). In addition, systems network analysis can be used to generate falsifiable hypotheses linking disease to holistic systems properties, as has been shown for metastasis as a progression towards network entropy ( Teschendorff and Severini, 2010 ; West et al., 2012 ) .

Another level of complexity emerges from the multiple interdependent molecular snapshots of a metastatic cell: the transcriptomic state informs proteomic changes, which would then inform the metabolomics states of cells. Although techniques have emerged to get single cell snapshots in all these states, these snapshots are only partially predictive of phenotypes. As an example, highly resolved multiple drug treatment regimen and high density observations in cancer cells combined with computational modeling revealed that sequential administration of drugs re-wires signaling cascades leading to enhanced apoptosis( Lee et al., 2012 ; Miller et al., 2016 ). As the quality of interaction data improves and more sophisticated methods are devised, it should be possible to combine multiple types of interactions. For example, understanding how the signaling networks within migrating cells in metastasis integrate with the host cells using ligand receptor interactions can shed light on the functional impact of some driver mutations. Understanding the network of post-translational modifications in signaling in conjunction with the transcription factor-target regulatory interactions can help delineate the differences between the effect of mutations in protein coding regions, cis-regulatory regions, and changes in gene expression causing metastasis.

Different approaches in machine learning, e.g. learning data representations, or deep learning have also begun to derive useful inferences from experimental observations, which could serve to parametrize and scale current models of metastasis. Although deep learning has been used mostly in the fields of vision, architectures like recursive neural networks, or their variants, can be used for knowledge extraction since they are more attuned for sequential information often present in omics data( Min et al., 2017 ). Machine learning approaches are also well suited to glean insight from heterogeneous cell populations present in cancer microenvironment, which necessitate analysis of multiple modalities of experimental observations in high volume. For example, deep learning has been used to increase accuracy in the diagnosis of metastatic lesions from histological slides( Litjens et al., 2016 ; Wang et al., 2016 ) and fMRI images( Liu et al., 2017b ). Similarly, algorithms are being developed to reclassify cancer subtypes based on more detailed genotypic data which were previously based on histological analysis, presenting opportunities for more targeted therapy( Cancer Genome Atlas Research, 2014 ; Han et al., 2014 ). Although generally viewed as “black box” approaches unable to inform about disease mechanisms, other approaches provide avenues for interpretable neural network models( Michael et al., 2018 ; Samek et al., 2017 ). Other recent developments in deep learning methods have been applied in the analysis of complex systems of interacting components seen in various physical systems( Broecker et al., 2017 ; Schindler et al., 2017 ), and similar methods may be ripe for the analysis of the dynamics of cell populations in metastasis, beyond what can be modeled using biochemical pathways within cells. Recent theoretical development of deep learning models that operate on graph structured data( Defferrard et al., 2016 ; Kipf and Welling, 2016 ) has the potential to integrate protein and gene interaction network information to relate cancer related phenotypes with genotypes and expression data( Ma and Zhang, 2018 ; Rhee et al., 2017 ).

In the future, network analysis and machine learning approaches may be used to directly inform a mechanistic understanding of metastasis. Dcell uses deep neural networks to model the growth phenotype of gene deletion, while explaining the prediction in terms of a chain of causal changes in different modules such as subcellular structures and pathways( Ma et al., 2018 ). Such methods can be augmented to include systems of multiple cell types to address phenotypes relevant to metastasis. Innovative applications of network inference and machine learning could bridge the gap between large amounts of biological data and incomplete mechanistic models using concepts such as equation discovery( Dzeroski and Todorovski, 2008 ) and hybrid mechanistic-machine learning models( Hua et al., 2006 ). At a minimum, they will inform, and more likely prioritize, the molecular and biochemical components that are included in more traditional systems biology-based mechanistic models.

Experimental Models and Technologies to Facilitate Systems Study of Metastasis

Systems biology is often approached through a circular iterative process, where data collection informs computational modeling to generate testable hypotheses, and in turn, the outcome of the subsequent hypothesis testing refines the computational model. Required for these iterations are quantitative multivariate experimental approaches that adequately capture the complexity (or simplicity) of the system, and, in many cases, provide longitudinal data ( Table 1 , Figure 2 ). In the study of cancer metastasis, the most often utilized experimental systems range from single-cell measurement techniques that provide a snapshot of the molecular characteristics of metastatic cells to genetically modified mouse models (GEMMs) that facilitate the study of the entire metastatic process. Further, investigating the progression of cancer metastasis may require tailored approaches to understand the mechanistic underpinnings driving those processes ( Table 2 ). Some of the more recent developments regarding experimental systems to inform or test systems biology models are reviewed below along with a wish list for technology developers interested in contributing to this emerging area.

Single-cell approaches to studying cancer metastasis

The recent decreases in the cost and concomitant increases in the portability and flexibility of single-cell technology will facilitate a deeper understanding of tumor cell heterogeneity in metastasis( Bush et al., 2017 ; Gierahn et al., 2017 ). Particularly, single-cell DNA and RNA sequencing techniques are employed to investigate primary tumors, metastatic lesions, circulating tumor cells, and disseminated tumor cell singlets and clusters( Leung et al., 2017 ; Ting et al., 2014 ; Tirosh et al., 2016 ). A better understanding of the numerous molecular characteristics that give rise to metastatic cells will lead to better predictive models for patient treatment and outcome. However, when employing these approaches to parameterize or test the hypotheses derived from computational systems biology models, it is important to note that single-cell genomic and transcriptomic data pose analytical hurdles, including a low signal to noise ratio due to low read coverage and non-trivial normalization that currently makes it challenging to compare multiple datasets across patients or experimental conditions. Additionally, interrogation of small cell populations, such as dormant metastatic cells, may limit statistically meaningful insights if desired population comparisons are not carefully considered during experimental design. Considerable theoretical work has produced numerous useful data analysis techniques for single-cell genomics, transcriptomics, and proteomics, and the subsequent incorporation of these data into predictive computational models is now beginning( Liu et al., 2017a ; Marcotte et al., 2016 ; Setty et al., 2016 ; Tanay and Regev, 2017 ; Villani et al., 2017 ).

One challenge in interpreting tumor heterogeneity at the single-cell level is the biological role of “rare” cancer cells captured at a single time point. Such cells could be viewed either as an important minor subpopulation, forming the tail of a distribution representing a cancer cell signature, or as traversing through a dynamic cellular state. In the absence of a longitudinal sampling strategy, coupling information at the genomic or transcriptomic level with experimental measurements of cell phenotype may provide guidance to the level of granularity required when interpreting single time point data. Techniques that can accurately quantify, at the single cell level, a physical characteristic, such as a change in cell mass or modulation of cell motility due to drug treatment or an environmental perturbation, could provide the additional data required to differentiate between a stable and transient cell state when coupled with single-cell molecular data( Rojas-Puentes et al., 2016 ). Even when longitudinal data is available, the integration of multiple data types, such as bulk and single-cell whole-genome sequencing and transcriptomic profiling may be required to predict an outcome with accuracy( Brady et al., 2017 ). Finally, single-cell sequencing of metastatic lesions coupled with computational methods and lineage tracing of metastases in animal models amenable to imaging, such as C. elegans , or zebrafish, holds the potential to address outstanding questions about the origins and clonality of metastasis, as well as the emergence of new phenotypes such as drug resistance( Brown et al., 2017 ; Heilmann et al., 2015 ; Kyriakakis et al., 2015 ).

Whole-organism experimental systems for metastasis research

Genetically modified animal models of metastasis have been developed and successfully utilized to study all known steps within the metastatic cascade. The benefits and drawbacks of the most frequently utilized models have been thoroughly reviewed elsewhere( Gomez-Cuadrado et al., 2017 ; van Marion et al., 2016 ). Developments in gene editing have facilitated large-scale in vivo studies of the genetic alterations that drive cancer tumor growth and metastasis( Chen et al., 2015 ; Kalhor et al., 2018 ). Computational integration of these data with information regarding modulation of epigenetic state and alteration of the transcriptome could provide insight into disease mechanism.

The development of lineage tracing tools( Sikandar et al., 2017 ) could allow for straightforward parameterization or testing of systems biology models through visualization of the kinetics of metastasis or by quantitatively reporting on cell populations in distant metastatic sites of interest. High temporal resolution is on the wish list of many systems biologists who study metastasis, but is difficult to obtain in mammalian models except through the incorporation of imaging windows that facilitate multiple rounds of high resolution intravital imaging( Entenberg et al., 2017 ; Suijkerbuijk and van Rheenen, 2017 ). Rapid advancement in functional imaging of metabolic or signaling state of tissues in vivo could be used to contextualize metastatic steps with the tissue microenvironment ( Herzog et al., 2013 ; Knox et al., 2018 ; Miloushev et al., 2018 ; Shangguan et al., 2018 ). Zebrafish have emerged as an alternative system to study aspects of cancer metastasis at high temporal and spatial resolution ( Heilmann et al., 2015 ), allowing for quantification of cell-cell or cell-stroma interactions and relatively easy genetic manipulation. However, in the instances when it is desirable to directly study human cells, zebrafish offer a suboptimal immune microenvironment, similar to the many xenograft-derived in vivo mouse models currently in-use. Finally, it is worth noting that a popular drug screening tool, the patient derived xenograft (PDX) mouse model, has not yet found high utility within the cancer metastasis field. There are limited PDX systems that recapitulate metastases observed in human patients ( Jiang et al., 2015 ; Whittle et al., 2015 ). However, humanized mouse models combined with PDX provide avenues to study cancer metastasis in the context of the correct host immune system ( Jespersen et al., 2017 ; Rongvaux et al., 2017 ; Zheng et al., 2018 ).

Bioengineered in vitro models for studying metastasis

In vitro and ex vivo tumor models offer simplified systems that can be perturbed with relative ease, while also being able to incorporate some of the complexity of tumor microenvironments such as variations in mechanical properties, matrix chemistry, paracrine signals, and cell-cell interactions( Katt et al., 2016 ; Singh et al., 2018 ; Zanoni et al., 2016 ). Andrew Ewald’s group, for example, has utilized human breast cancer organoids grown in three dimensional culture, complemented by engineered mouse models, to show that extracellular matrix characteristics inform invasive behavior and demonstrated that Twist1 regulates Protein Kinase D1 (PKD1) expression to drive metastatic dissemination ( Shamir et al., 2016 ). Jan Lammerding has studied the role of mechanical stress during transendothelial migration of cancer cells using microfluidic cancer transmigration models incorporating tissue mechanics, biochemical properties, and cell-cell interactions ( Cao et al., 2016 ; Singh et al., 2018 ). A liver microphysiological system, in which all the major cell types in the liver microenvironment are cultured together, including liver-specific immune cells, is being employed by Alan Wells and colleagues to study hepatotoxicity of cancer drugs as well as metastasis and dormancy of breast cancer cells in human liver tissue( Wheeler et al., 2014 ).

Systemic Perturbation Approaches

With the increasing adoption of CRISPR/Cas9 technology to target the human genome, image-guided tissue isolation, sequencing, and informatics, related approaches have been developed to systematically identify genetic basis for the various steps involved in the metastatic cascade. These screens typically involve creating a library of starting cellular cohort with single, or multiple genetic manipulation in each cell, and selectively sorting the cells that successfully pass through a phenotypic filter, identifying the genes that are enriched in the selected cohort. Enrichment hits could be revalidated using the same filter, or in a more complex system. Experimental models have also been developed that combine library screens with a phenotypic filter such as cell motility or combine a phenotypic functionality, such as dissemination combined with subsequent bioinformatics analysis( Konen et al., 2017 ). CRISPR/Cas9 based screens are increasingly being utilized to create autochthonous models that facilitate investigation of the combinatorial genetic mutations that drive cancer metastasis ( Chow et al., 2017 ; Shen et al., 2017 ). Similarly, platforms investigating the role of extracellular matrix at different stages of metastases have been developed to explore non-cell autonomous processes. As an example, Claudia Fischbach’s group observed that the link between cancer malignancy and obesity could be partially attributed to increased collagen deposition by adipocyte stem cells in response to conditioned medium from tumor cells. These findings correlate with observations that adipose tissue in obese patients is stiffer, more collagenous, and may abet cancer metastasis by selective enrichment of cancer stem cell subpopulations( Chandler et al., 2012 ; Seo et al., 2015 ).

Precision medicine: deeper omics understanding in the clinical setting

In addition to informing both drug development and a causal understanding of metastasis, systems biology has also been instrumental in the development of patient specific causal analysis and treatment modality( Jia and Zhao, 2014 ). Techniques like patient specific xenografts allow the identification of targetable tumor specific antigens and the optimization of drug regimens( Karamboulas et al., 2018 ; Lange et al., 2018 ; Malaney et al., 2014 ). Patient and sample specific analysis is even more pertinent for cancer metastasis because the range of possible genetic, epigenetic, or transcriptomic variant combinations that may be causal for overcoming each obstacle from cancer initiation to proliferation, invasion, etc. may be more than the number of patient samples. Therefore, the classical approach of finding statistically significant differences between pathological and control samples is inadequate, with the number of parameters growing much larger than the number of samples. Single sample (N of 1) analysis methods seek to solve these problems in several ways. Classically, N of 1 studies relied on the collection of paired control-intervention data from the same individual by applying some temporal sequence of treatment and placebo/control. For cancer and metastasis, the comparison of matched samples of some combination of normal, primary tumor, and metastatic tumor has been used to find causal mutations and/or suitable interventions( Al-Ahmadie et al., 2014 ; Brannon and Sawyers, 2013 ; Kadakia et al., 2015 ). Califano et al. have developed a network analysis method to differentiate between causal and passenger mutations for individual patients based on the plausible set of causal genes that may be affecting the master regulators through a regulatory network( Chen et al., 2014 ). Identification of the pathologically important regulators allows the targeted drug screens on the patient derived xenografts to arrive at a treatment plan. We believe similar approaches that integrate the existing knowledge of cancer biology (via the construction of regulatory network), patient specific genotyping, and experimental techniques are an ideal application of systems biology in the service of patient care.

The development of less invasive tests, such as liquid biopsies for cancer biomarkers( Bettegowda et al., 2014 ) has opened an avenue for the wider application of precision medicine for pre-emptive surveillance for cancer initiation, metastatic growth( Li et al., 2010 ), chances of metastasis after treatment on the primary site, or for the selection of appropriate therapy. These biomarkers could be the expression of certain genes( Zheng et al., 2007 ), methylation patterns( Barault et al., 2018 ; Garrigou et al., 2016 ) or mutations( Board et al., 2010 ; Diehl et al., 2008 ) in circulating tumor DNA, the types of circulating tumor and immune cells( Ilie et al., 2012 ), and detected from blood or other material such as sputum, urine, or stool. The discovery and correct interpretation of these biomarkers is again dependent on the statistical issues regarding small sample size and larger number of parameters.

Integrating Models of Metastasis

Cancer metastasis is a multidimensional process, unfolding at many biological scales, including molecular signaling networks, protein-protein interactions, metabolism, cell-cell and cell-ECM interactions, organ-level control, disease manifestation and epidemiology. Our ability to describe events in each of those scales will need to be augmented to also inform events in other scales. Integrating the knowledge of all these snapshots at a single cell level would allow determination of consistently active motifs driving metastatic phenotypes. These biological scales can be practically useful as nodes of data integration, and positioning our understanding of the systemic nature of metastasis ( Figure 3 ). Integration of results across all scales may be very challenging, and so a piecemeal approach is warranted. Multi-scale modeling across scales defined by informatics, e.g. genomic, epigenomic, transcriptomic scales is now attempted by specialized next generation sequencing (NGS) techniques, including ChIP Seq (Chromatin Immunoprecipitation Sequencing), ATAC Seq (to access chromatin accessibility genome wide), PRO Seq (Precision nuclear run-on sequencing)( Meyer and Liu, 2014 ), and many other technologies. The objective of these technologies is essentially to systematically explore the mechanism of gene regulation resulting in a given transcriptomic output. Several attempts are now made to correlatively analyze the transcriptomic, proteomic, and even metabolomics scales systematically in a physiological system( Bahado-Singh et al., 2017 ; Garcia et al., 2012 ; Rabinowitz et al., 2017 ; Zhao et al., 2018 ). Such integrative approaches will be crucial in mechanistically understanding cancer metastasis as a multiscale disease ( Figure 3 ).

Integration of data across scales, both not characterized by “sequence information” is fraught with challenges that are more profound. Well characterized phenotypes with richly-defined feature sets could be mapped to transcriptomic, and genomic scales; e.g. characterizing tumor organoid shape and other characteristics for precision medicine, as well as RNA Sequencing ( Clark et al., 2018 ; Ewald, 2017 ). Tissue level details can be integrated with transcriptomics by spatial scRNA sequencing, for which more and more methods are being developed ( Moncada et al., 2019 ; Stahl et al., 2016 ). Similarly, signaling state of cells can be correlatively assessed with the transcriptomic state( Zhang et al., 2019 ), which is likely to be benefitted by single cell RNA sequencing correlated with parallel multiplexed imaging or cytometry. Intercellular communication between metastatic cancer cells and stromal cells, including the immune subpopulations, could be integratively explored by scRNA sequencing, simplistically by creating ligand-receptor interaction maps, and in a more detailed manner by combining parallel means of data collection, e.g. secretome analysis. These integrative methods aim towards validating and confirming the information flow from one physiological scales to another, however, more mechanistic integrative efforts are being made combining computational modeling of signals between cells constrained by defined boundaries in the context of a tissue( Ghaffarizadeh et al., 2018 ; Letort et al., 2019 ; Zangooei and Habibi, 2017 ).

Experimental in vitro or ex vivo models that faithfully recapitulate the entire process of metastasis are coming online with the advent of tissue-on-a-chip and organ-on-a-chip technologies ( Table 1 ). These systems are likely to offer a controlled environment to build and test computational models that describe the various sub-systems of the metastatic cascade. These types of multi-scale systems biology approaches will offer a way to test large numbers of hypotheses regarding development and treatment of metastatic disease before advancing to the infinitely complex in vivo environments. It is only through such systems-level understanding of cancer metastasis that we can hope to decrease the lethality of the disease and continue to develop detection and treatment strategies. A global and mechanistic model of metastasis with a predictive flow of information across larger scales is still a work in progress, and requires methods to formalize data collection across scales, techniques to validate derived changes at a scale informed by another scale, as well as systems approaches to understand progression of metastasis combining several spatio-temporal scales ( Figure 3 ).

Cancer metastasis has continued to confound researchers owing to its complex, and not completely determinable, pathological trajectory. The complexity in disease progression arises because essentially cancer is a disease of abnormal cell proliferation, and metastasis involves successfully encountering physiological hurdles, both aspects resulting in a strong evolutionary thrust defining the metastatic cascade. Cancer metastasis, is therefore an evolving disease and is a combined outcome of cells that metastasize, as well as a series of microenvironmental factors they interact with, collude, or surmount. Although, each instance of metastasis could be unique, the quest is to find commonalities which could be therapeutically targeted. The complexity of metastasis through its chronological progression, and its manifestation in various biological scales calls for a systems approach to mechanistically understand metastasis. This approach will involve integrating information gained across different scales, and mechanistically modeling disease progression as a functional outcome of the complex interaction between cancer and the ecology it interacts with. Important technical advancements in recent years to gain knowledge at a systems level at various biological scales have raised a hope that cancer metastasis may itself be modeled using a multiscale and systems approach. The need is to develop methods to integrate these approaches across scales, and stage of metastasis towards creating a systems mechanistic model of cancer metastasis.

Acknowledgements:

We would like to extend our sincere thanks to Dr. Shannon K. Hughes, National Cancer Institutes for providing crucial stewardship in envisaging, organizing, and executing the writing of this manuscript. This manuscript could not have been written without her support. We would also like to extend our gratitude to Dr. Daniel Gallahan, and Dr. Nancy Boudreau from the National Cancer Institutes for providing constructive criticisms in support in writing the manuscript. Funding for this work was provided by National Cancer Institute U54 Center Grant on Cancer Systems Biology 1U54CA209992.

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• Published: 22 July 2024

The impact of sex on metastasis

• Yingsheng Zhang 1 &
• Xue Li   ORCID: orcid.org/0000-0002-3072-0573 1  

Nature Reviews Cancer ( 2024 ) Cite this article

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Sex matters in metastasis, but it has received little attention in research. Here, we highlight the emerging and important roles of biological sex in metastasis and advocate for mechanistic and quantitative studies for the future development of sex-tailored therapies.

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Acknowledgements

This work is supported by NIH/NCI, USA (P01CA278732-01 and R01CA267108-01A1).

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Departments of Medicine and Biomedical Sciences, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA

Yingsheng Zhang & Xue Li

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Zhang, Y., Li, X. The impact of sex on metastasis. Nat Rev Cancer (2024). https://doi.org/10.1038/s41568-024-00725-3

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Published : 22 July 2024

DOI : https://doi.org/10.1038/s41568-024-00725-3

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