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Molecular definition of a metastatic lung cancer state reveals a targetable CD109–Janus kinase–Stat axis

Nature Medicine volume 23, pages 291–300 (2017)Cite this article

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Abstract

Lung cancer is the leading cause of cancer deaths worldwide, with the majority of mortality resulting from metastatic spread. However, the molecular mechanism by which cancer cells acquire the ability to disseminate from primary tumors, seed distant organs, and grow into tissue-destructive metastases remains incompletely understood. We combined tumor barcoding in a mouse model of human lung adenocarcinoma with unbiased genomic approaches to identify a transcriptional program that confers metastatic ability and predicts patient survival. Small-scale in vivo screening identified several genes, including Cd109, that encode novel pro-metastatic factors. We uncovered signaling mediated by Janus kinases (Jaks) and the transcription factor Stat3 as a critical, pharmacologically targetable effector of CD109-driven lung cancer metastasis. In summary, by coupling the systematic genomic analysis of purified cancer cells in distinct malignant states from mouse models with extensive human validation, we uncovered several key regulators of metastatic ability, including an actionable pro-metastatic CD109–Jak–Stat3 axis.

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Figure 1: Design, barcode analysis, and isolation of samples for gene expression profiling from different stages of metastatic progression.
Figure 2: Lung tumors undergo stepwise changes in gene expression programs during metastatic progression.
Figure 3: In vivo functional screening identifies CD109 as a driver of metastatic ability.
Figure 4: CD109 regulates Stat3 activity to drive malignant cellular phenotypes and metastatic ability.
Figure 5: Jak–Stat3 signaling is a critical pro-metastatic effector of Cd109.
Figure 6: Pharmacological inhibition of Jak–Stat signaling inhibits metastatic ability of lung adenocarcinoma cells.

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Acknowledgements

We thank P. Chu and S. Cheemalavagu for technical assistance, the Stanford Shared FACS Facility and Protein and Nucleic Acid Facility for expert assistance, J. Pollack and X. Gong for reagents, S. Dolan and A. Orantes for administrative support, and D. Feldser, C. Murray, and members of the Winslow lab for helpful comments. This work was supported by an American Lung Association Fellowship (C.-H.C.), the US National Institutes of Health (NIH) (grant no. F32-CA189659 (J.J.B.), T32-CA009302 (A.L.S.), R01-GM102484 (J.B.L.), R01-CA157510 (E.A.S.-C.), R01-CA175336 (M.M.W.), and R01-CA204620 (M.M.W.)), the Stanford Biomedical Informatics Training Grant from the National Library of Medicine (LM-07033; P.G.G.), a Bio-X Stanford Interdisciplinary Graduate Fellowship (P.G.G.), Stanford Graduate Fellowships (Z.N.R. and G.R.), National Science Foundation Graduate Research Fellowships (D.R.C. and Z.N.R.), the Spider Internship Funds (A.F.W.), an Alfred Sloan Fellowship (A.K.), a V Foundation for Cancer Research Martin D. Abeloff, M.D. V Scholar Award (M.M.W.), and, in part, by a Stanford Cancer Institute support grant (NIH P30-CA124435).

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Authors and Affiliations

  1. Department of Genetics, Stanford University School of Medicine, Stanford, California, USA

    Chen-Hua Chuang, Zoë N Rogers, Jennifer J Brady, Rosanna K Ma, Shin-Heng Chiou, Aidan F Winters, Barbara M Grüner, Gokul Ramaswami, Jin Billy Li, Anshul Kundaje & Monte M Winslow

  2. Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, California, USA

    Peyton G Greenside

  3. Cancer Biology Program, Stanford University School of Medicine, Stanford, California, USA

    Dian Yang, Deborah R Caswell, Andrew L Spencley, E Alejandro Sweet-Cordero & Monte M Winslow

  4. Department of Surgery, Stanford University School of Medicine, Stanford, California, USA

    Kimberly E Kopecky

  5. Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA

    Leanne C Sayles & E Alejandro Sweet-Cordero

  6. Department of Computer Science, Stanford University, Stanford, California, USA

    Anshul Kundaje

  7. Department of Pathology, Stanford University School of Medicine, Stanford, California, USA

    Monte M Winslow

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  1. Chen-Hua Chuang
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Contributions

C.-H.C. and M.M.W. conceived the study; C.-H.C. designed, conducted, and analyzed the majority of experiments; P.G.G. conducted the majority of bioinformatics analyses; Z.N.R., J.J.B., D.Y., R.K.M., D.R.C., S.-H.C., A.F.W., B.M.G., A.L.S., and K.E.K. conducted experiments, analyzed data and contributed to the Discussion; G.R. and J.B.L. (SNPiR), L.C.S. and E.A.S.-C. (pLKO library and human lung cell lines), and A.K. (bioinformatics) provided crucial reagents and discussion; and M.M.W. and C.-H.C. wrote the manuscript with comments from all authors.

Corresponding author

Correspondence to Monte M Winslow.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Figures

Supplementary Figures 1–22 (PDF 15113 kb)

Supplementary Table 1

Gene expression profiles for all ex vivo samples. (XLS 27399 kb)

Supplementary Tables 2–8

Supplementary Table 2: Gene sets that are higher in late stage primary tumours(TnonMet and TMet) relative to KPT-Early/KT samples. Supplementary Table 3: Gene sets that are lower in late stage primary tumours(TnonMet and TMet) relative to KPT-Early/KT samples. Supplementary Table 4: Gene sets that are higher in metastases (Met) relative to nonmetastatic primary tumours (TnonMet). Supplementary Table 5: Gene sets that are lower in metastases (Met) relative to nonmetastatic primary tumours (TnonMet). Supplementary Table 6: Gene expression changes elicited by Cd109 knockdown. Supplementary Table 7: Gene sets that are higher in cells with Cd109 knocked down relative to control cells. Supplementary Table 8: Gene sets that are lower in cells with Cd109 knocked down relative to control cells. (XLS 3803 kb)

Supplementary Table 9

List of pLKO plasmids (XLSX 30 kb)

Supplementary Table10

List of qPCR Primers (XLSX 25 kb)

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Chuang, CH., Greenside, P., Rogers, Z. et al. Molecular definition of a metastatic lung cancer state reveals a targetable CD109–Janus kinase–Stat axis. Nat Med 23, 291–300 (2017). https://doi.org/10.1038/nm.4285

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