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The genomic landscape of response to EGFR blockade in colorectal cancer

Abstract

Colorectal cancer is the third most common cancer worldwide, with 1.2 million patients diagnosed annually. In late-stage colorectal cancer, the most commonly used targeted therapies are the monoclonal antibodies cetuximab and panitumumab, which prevent epidermal growth factor receptor (EGFR) activation1. Recent studies have identified alterations in KRAS2,3,4 and other genes5,6,7,8,9,10,11,12,13 as likely mechanisms of primary and secondary resistance to anti-EGFR antibody therapy. Despite these efforts, additional mechanisms of resistance to EGFR blockade are thought to be present in colorectal cancer and little is known about determinants of sensitivity to this therapy. To examine the effect of somatic genetic changes in colorectal cancer on response to anti-EGFR antibody therapy, here we perform complete exome sequence and copy number analyses of 129 patient-derived tumour grafts and targeted genomic analyses of 55 patient tumours, all of which were KRAS wild-type. We analysed the response of tumours to anti-EGFR antibody blockade in tumour graft models and in clinical settings and functionally linked therapeutic responses to mutational data. In addition to previously identified genes, we detected mutations in ERBB2, EGFR, FGFR1, PDGFRA, and MAP2K1 as potential mechanisms of primary resistance to this therapy. Novel alterations in the ectodomain of EGFR were identified in patients with acquired resistance to EGFR blockade. Amplifications and sequence changes in the tyrosine kinase receptor adaptor gene IRS2 were identified in tumours with increased sensitivity to anti-EGFR therapy. Therapeutic resistance to EGFR blockade could be overcome in tumour graft models through combinatorial therapies targeting actionable genes. These analyses provide a systematic approach to evaluating response to targeted therapies in human cancer, highlight new mechanisms of responsiveness to anti-EGFR therapies, and delineate new avenues for intervention in managing colorectal cancer.

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Figure 1: Schematic diagram of integrated genomic and therapeutic analyses.
Figure 2: Effect of cetuximab treatment on growth of colorectal tumours with different somatic alterations.
Figure 3: Genetic alterations involved in secondary resistance to anti-EGFR therapy.
Figure 4: Therapeutic intervention in preclinical trials to overcome resistance to anti-EGFR antibody blockade.

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Primary accessions

EMBL/GenBank/DDBJ

Data deposits

Sequence data have been deposited at the European Genome-phenome Archive, which is hosted at the European Bioinformatics Institute, under study accession EGAS00001001305.

References

  1. Van Cutsem, E., Cervantes, A., Nordlinger, B. & Arnold, D. on behalf of the ESMO Guidelines Working Group. Metastatic colorectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 25 (Suppl. 3), iii1–iii9 (2014)

    Article  Google Scholar 

  2. Diaz, L. A., Jr et al. The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers. Nature 486, 537–540 (2012)

    ADS  CAS  Article  Google Scholar 

  3. Misale, S. et al. Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer. Nature 486, 532–536 (2012)

    ADS  CAS  Article  Google Scholar 

  4. Amado, R. G. et al. Wild-type KRAS is required for panitumumab efficacy in patients with metastatic colorectal cancer. J. Clin. Oncol. 26, 1626–1634 (2008)

    CAS  Article  Google Scholar 

  5. De Roock, W. et al. Effects of KRAS, BRAF, NRAS, and PIK3CA mutations on the efficacy of cetuximab plus chemotherapy in chemotherapy-refractory metastatic colorectal cancer: a retrospective consortium analysis. Lancet Oncol. 11, 753–762 (2010)

    CAS  Article  Google Scholar 

  6. Tol, J. et al. Markers for EGFR pathway activation as predictor of outcome in metastatic colorectal cancer patients treated with or without cetuximab. Eur. J. Cancer 46, 1997–2009 (2010)

    CAS  Article  Google Scholar 

  7. Sartore-Bianchi, A. et al. PIK3CA mutations in colorectal cancer are associated with clinical resistance to EGFR-targeted monoclonal antibodies. Cancer Res. 69, 1851–1857 (2009)

    CAS  Article  Google Scholar 

  8. Bardelli, A. et al. Amplification of the MET receptor drives resistance to anti-EGFR therapies in colorectal cancer. Cancer Discov. 3, 658–673 (2013)

    CAS  Article  Google Scholar 

  9. Bertotti, A. et al. A molecularly annotated platform of patient-derived xenografts (“xenopatients”) identifies HER2 as an effective therapeutic target in cetuximab-resistant colorectal cancer. Cancer Discov. 1, 508–523 (2011)

    CAS  Article  Google Scholar 

  10. Yonesaka, K. et al. Activation of ERBB2 signaling causes resistance to the EGFR-directed therapeutic antibody cetuximab. Sci. Transl. Med. 3, 99ra86 (2011)

    Article  Google Scholar 

  11. Montagut, C. et al. Identification of a mutation in the extracellular domain of the epidermal growth factor receptor conferring cetuximab resistance in colorectal cancer. Nature Med. 18, 221–223 (2012)

    CAS  Article  Google Scholar 

  12. Bettegowda, C. et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci. Transl. Med. 6, 224ra224 (2014)

    Article  Google Scholar 

  13. Diaz, L. A., Jr, Sausen, M., Fisher, G. A. & Velculescu, V. E. Insights into therapeutic resistance from whole-genome analyses of circulating tumor DNA. Oncotarget 4, 1856–1857 (2013)

    Article  Google Scholar 

  14. Leary, R. J. et al. Integrated analysis of homozygous deletions, focal amplifications, and sequence alterations in breast and colorectal cancers. Proc. Natl Acad. Sci. USA 105, 16224–16229 (2008)

    ADS  CAS  Article  Google Scholar 

  15. Barber, T. D., Vogelstein, B., Kinzler, K. W. & Velculescu, V. E. Somatic mutations of EGFR in colorectal cancers and glioblastomas. N. Engl. J. Med. 351, 2883 (2004)

    CAS  Article  Google Scholar 

  16. Moroni, M. et al. Somatic mutation of EGFR catalytic domain and treatment with gefitinib in colorectal cancer. Ann. Oncol. 16, 1848–1849 (2005)

    CAS  Article  Google Scholar 

  17. Wesche, J., Haglund, K. & Haugsten, E. M. Fibroblast growth factors and their receptors in cancer. Biochem. J. 437, 199–213 (2011)

    CAS  Article  Google Scholar 

  18. Heinrich, M. C. et al. PDGFRA activating mutations in gastrointestinal stromal tumors. Science 299, 708–710 (2003)

    ADS  CAS  Article  Google Scholar 

  19. Dibb, N. J., Dilworth, S. M. & Mol, C. D. Switching on kinases: oncogenic activation of BRAF and the PDGFR family. Nature Rev. Cancer 4, 718–727 (2004)

    CAS  Article  Google Scholar 

  20. Marks, J. L. et al. Novel MEK1 mutation identified by mutational analysis of epidermal growth factor receptor signaling pathway genes in lung adenocarcinoma. Cancer Res. 68, 5524–5528 (2008)

    CAS  Article  Google Scholar 

  21. Algars, A., Lintunen, M., Carpen, O., Ristamaki, R. & Sundstrom, J. EGFR gene copy number assessment from areas with highest EGFR expression predicts response to anti-EGFR therapy in colorectal cancer. Br. J. Cancer 105, 255–262 (2011)

    CAS  Article  Google Scholar 

  22. Moroni, M. et al. Gene copy number for epidermal growth factor receptor (EGFR) and clinical response to antiEGFR treatment in colorectal cancer: a cohort study. Lancet Oncol. 6, 279–286 (2005)

    CAS  Article  Google Scholar 

  23. Parsons, D. W. et al. Colorectal cancer: mutations in a signalling pathway. Nature 436, 792 (2005)

    ADS  CAS  Article  Google Scholar 

  24. Misale, S. et al. Blockade of EGFR and MEK intercepts heterogeneous mechanisms of acquired resistance to anti-EGFR therapies in colorectal cancer. Sci. Transl. Med. 6, 224ra226 (2014)

    Article  Google Scholar 

  25. Zanella, E. R. et al. IGF2 is an actionable target that identifies a distinct subpopulation of colorectal cancer patients with marginal response to anti-EGFR therapies. Sci. Transl. Med. 7, 272ra212 (2015)

    Article  Google Scholar 

  26. Kavuri, S. M. et al. HER2 activating mutations are targets for colorectal cancer treatment. Cancer Discov. 5, 832–841 (2015)

    CAS  Article  Google Scholar 

  27. Voigt, M. et al. Functional dissection of the epidermal growth factor receptor epitopes targeted by panitumumab and cetuximab. Neoplasia 14, 1023–1031 (2012)

    CAS  Article  Google Scholar 

  28. Koefoed, K. et al. Rational identification of an optimal antibody mixture for targeting the epidermal growth factor receptor. MAbs 3, 584–595 (2011)

    Article  Google Scholar 

  29. Pavlicek, A. et al. Molecular predictors of sensitivity to the insulin-like growth factor 1 receptor inhibitor Figitumumab (CP-751,871). Mol. Cancer Ther. 12, 2929–2939 (2013)

    CAS  Article  Google Scholar 

  30. Jones, S. et al. Comparative lesion sequencing provides insights into tumor evolution. Proc. Natl Acad. Sci. USA 105, 4283–4288 (2008)

    ADS  CAS  Article  Google Scholar 

  31. Siena, S. et al. Phase II open-label study to assess efficacy and safety of lenalidomide in combination with cetuximab in KRAS-mutant metastatic colorectal cancer. PLoS One 8, e62264 (2013)

    ADS  CAS  Article  Google Scholar 

  32. Galimi, F. et al. Genetic and expression analysis of MET, MACC1, and HGF in metastatic colorectal cancer: response to met inhibition in patient xenografts and pathologic correlations. Clin. Cancer Res. 17, 3146–3156 (2011)

    CAS  Article  Google Scholar 

  33. Baralis, E., Bertotti, A., Fiori, A. & Grand, A. LAS: a software platform to support oncological data management. J. Med. Syst. 36 (Suppl. 1), S81–S90 (2012)

    Article  Google Scholar 

  34. Jones, S. et al. Personalized genomic analyses for cancer mutation discovery and interpretation. Sci. Transl. Med. 7, 283ra253 (2015)

    Article  Google Scholar 

  35. Needleman, S. B. & Wunsch, C. D. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48, 443–453 (1970)

    CAS  Article  Google Scholar 

  36. Leary, R. J., Cummins, J., Wang, T. L. & Velculescu, V. E. Digital karyotyping. Nature Protocols 2, 1973–1986 (2007)

    CAS  Article  Google Scholar 

  37. Jiao, Y. et al. Exome sequencing identifies frequent inactivating mutations in BAP1, ARID1A and PBRM1 in intrahepatic cholangiocarcinomas. Nature Genet. 45, 1470–1473 (2013)

    CAS  Article  Google Scholar 

  38. Sjoblom, T. et al. The consensus coding sequences of human breast and colorectal cancers. Science 314, 268–274 (2006)

    ADS  Article  Google Scholar 

  39. Kan, Z. et al. Diverse somatic mutation patterns and pathway alterations in human cancers. Nature 466, 869–873 (2010)

    ADS  CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank S. Angiuoli, D. Riley, L. Kann, M. Shukla, and C. L. McCord for their assistance with next-generation sequencing analyses, and F. Galimi and S. M. Leto for their help with Sanger sequencing analyses and functional studies. This work was supported by the John G. Ballenger Trust, FasterCures Research Acceleration Award, the European Community’s Seventh Framework Programme, the AIRC Italian Association for Cancer Research (Special Program Molecular Clinical Oncology 5×1000, project 9970, and Investigator Grants projects 14205 and 15571), American Association for Cancer Research (AACR) – Fight Colorectal Cancer Career Development Award in memory of Lisa Dubow (project 12-20-16-BERT), the Commonwealth Foundation, Swim Across America, US National Institutes of Health grant CA121113, Fondazione Piemontese per la Ricerca sul Cancro-ONLUS (5×1000 Italian Ministry of Health 2011), Oncologia Ca’ Granda ONLUS, and the SU2C-DCS International Translational Cancer Research Dream Team Grant (SU2C-AACR-DT1415). We acknowledge Merck for a gift of cetuximab. Stand Up To Cancer is a program of the Entertainment Industry Foundation administered by the American Association for Cancer Research. A.B. and L.T. are members of the EurOPDX Consortium.

Author information

Authors and Affiliations

Authors

Contributions

A.B. and E.P. conceived the project, designed and performed experiments, interpreted results and co-wrote the manuscript. S.J., V.A., V.A., B.L., M.S., J.P., C.A.H., M.N., K.L., F.S., F.C., G.M., E.R.Z., D.R., N.R., A.M., A.M., G.P., M.S., S.M., and A.C. performed experiments, analysed data, prepared tables, or participated in discussion of the results. M.K. and J.L. contributed reagents. Q.K.L. undertook all pathological evaluations. C.T., N.N., R.K., and R.S. performed statistical analyses. A.S.-B., S.S., and L.A.D. provided clinically annotated samples and supervised experimental designs. L.T. and V.E.V. conceived the project, supervised experimental designs, interpreted results, and co-wrote the manuscript.

Corresponding authors

Correspondence to Andrea Bertotti, Livio Trusolino or Victor E. Velculescu.

Ethics declarations

Competing interests

L.A.D. and V.E.V. are co-founders of Personal Genome Diagnostics and are members of its Board of Directors. V.E.V. and L.A.D. own Personal Genome Diagnostics stock, which is subject to certain restrictions under Johns Hopkins University policy. The authors are entitled to a share of the royalties received by the University on sales of products related to genes described in this manuscript. The terms of these arrangements are managed by the Johns Hopkins University in accordance with its conflict-of-interest policies.

Extended data figures and tables

Extended Data Figure 1 EGFR signalling pathway genes involved in cetuximab resistance or sensitivity.

Altered cell-surface receptors or members of RAS or PI3K pathways identified in this study are indicated. Somatic alterations related to resistance or sensitivity are highlighted in red or green boxes, respectively. The percentages indicate the fraction of KRAS wild-type tumours containing the somatic alterations in the specified genes. For the following genes a subset of alterations are indicated: PDGFRA kinase domain mutations; EGFR ecto- and kinase domain mutations and amplifications.

Extended Data Figure 2 Pan-HER monoclonal antibody mixture binds epitopes different from those recognized by cetuximab.

a, The H383 (green) and the S484/G485 (light blue) residues in EGFR domain III are critical for the binding of Pan-HER anti-EGFR antibodies 1277 and 1565, respectively28. Antibodies 1277 and 1565 (ref. 28) bind to an epitope distinct from that of cetuximab, which may contribute to the superior tumour growth inhibition in the presence of mutations at residue 465. Mutations identified in this study affecting G465 (red) and the S492 amino acid (yellow) previously reported to confer cetuximab resistance11 are shown for reference. Similarly to mutations affecting S492, the alterations at 465 that we identified in this study (G465R and G465E) involve changes from a non-polar uncharged side chain to large electrically charged arginine or glutamic acid residues, respectively, and predict resistance to cetuximab. b, Critical EGFR amino acids selectively recognized by both cetuximab and panitumumab as determined by phage screening are shown in blue and include P373, K467, P411, K489, D379, F376 (ref. 27). Residue G465 is in close proximity to K467 and other residues that have been shown to influence the binding of both cetuximab and panitumumab27.

Extended Data Figure 3 Expression of IRS2 according to response categories in tumour graft models.

Results were obtained using Illumina-based oligonucleotide microarrays in 100 tumour grafts that had no mutations in the KRAS, NRAS, BRAF, or PIK3CA genes. Response categories are defined in the main text. OR, objective response; SD, stable disease; PD, progressive disease. P < 0.001 for OR compared with PD and SD compared with PD by one-way ANOVA and Bonferroni’s multiple comparison test. IRS2 expression values are shown in Supplementary Table 10.

Extended Data Figure 4 Functional studies of genetic alterations associated with cetuximab response.

a, b, Ectopic expression of mutations that correlated with resistance to EGFR blockade prevented responsiveness to cetuximab. NCI-H508 cells expressing EGFR G465E (a, left) or DDK-tagged MAP2K1 K57N (b, left) were refractory to cetuximab in dose-dependent viability assays after 6 days of treatment. Results are the means ± s.d. of two independent experiments performed in biological triplicates (n = 6) for EGFR G465E and three independent experiments performed in biological triplicates (n = 9) for MAP2K1 K57N compared with mock vector controls. Biochemical responses of NCI-H508 EGFR G465E (a, right) and NCI-H508 MAP2K1 K57N (b, right) treated with cetuximab for 24 h were documented by western blot analyses. c, Genetic silencing of IRS2 (IRS2 shRNA) in NCI-H508 cells reduced sensitivity to cetuximab in dose-dependent viability assays (left). Results are the means ± s.d. of two independent experiments performed in biological triplicates (n = 6). In biochemical studies using western blot analyses (right), IRS2 knockdown attenuated EGF-dependent activation of AKT (P-AKT) and ERK (P-ERK). Cells were treated for 10 min with the indicated concentrations of EGF. Tubulin was used as a loading control. Western blots for total EGFR, ERK, and AKT proteins were run with the same lysates as those used for anti-phosphoprotein detection but on different gels. All western blots are representative of two independent experiments.

Extended Data Figure 5 Signalling consequences of FGFR inhibition in FGFR1-amplified CRC477.

Immunohistochemistry with the indicated antibodies and morphometric quantitations of representative tumours at the end of treatment. Results are the means ± s.d. of five fields (×40) from two tumours for each experimental point (n = 10). Scale bar, 300 μm. P-ERK, phospho-ERK; P-S6, phospho-S6. NT, not treated (vehicle); CET, cetuximab; BGJ, BGJ398. *P < 0.05; **P < 0.01 by two-tailed Student’s t-test.

Extended Data Figure 6 Signalling consequences of EGFR inhibition in EGFR mutant (V843I) CRC334.

Immunohistochemistry with the indicated antibodies and morphometric quantitations of representative tumours at the end of treatment. Results are the means ± s.d. of five fields (×40) from two tumours for each experimental point (n = 10). Scale bar, 300 μm. AFA, afatinib. **P < 0.01; ***P < 0.001 by two-tailed Student’s t-test.

Extended Data Figure 7 Signalling consequences of PDGFR inhibition in PDGFRA mutant (R981H) CRC525.

Immunohistochemistry with the indicated antibodies and morphometric quantitations of representative tumours after acute treatment (4 h after imatinib and 24 h after cetuximab administration). Results are the means ± s.d. of five fields (×40) from two tumours for each experimental point (n = 10). Scale bar, 300 μm. **P < 0.01 by two-tailed Student’s t-test.

Extended Data Figure 8 Signalling consequences of MEK1 inhibition in MAP2K1 mutant (K57KN) CRC343.

Immunohistochemistry with the indicated antibodies and morphometric quantitations of representative tumours at the end of treatment. Results are the means ± s.d. of five fields (×40) from two tumours for each experimental point (n = 10). Scale bar, 300 μm. AZD, AZD6244; SCH, SCH772984. ***P < 0.001 by two-tailed Student’s t-test.

Extended Data Figure 9 Signalling consequences of EGFR inhibition in EGFR mutant (G465E) CRC104.

Immunohistochemistry with the indicated antibodies and morphometric quantitations of representative tumours at the end of treatment. Results are the means ± s.d. of five fields (×40) from two tumours for each experimental point (n = 10). Scale bar, 300 μm. PAN, panitumumab. NS, not significant; **P <0.01 by two-tailed Student’s t-test.

Extended Data Figure 10 Signalling consequences of EGFR inhibition in EGFR mutant (G465R) CRC177.

Immunohistochemistry with the indicated antibodies and morphometric quantitations of representative tumours at the end of treatment. Results are the means ± s.d. of five fields (×40) from two tumours for each experimental point (n = 10). Scale bar, 300 μm. *P < 0.05; ***P < 0.001 by two-tailed Student’s t-test.

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Bertotti, A., Papp, E., Jones, S. et al. The genomic landscape of response to EGFR blockade in colorectal cancer. Nature 526, 263–267 (2015). https://doi.org/10.1038/nature14969

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