The genomic landscape of response to EGFR blockade in colorectal cancer

Journal name:
Nature
Volume:
526,
Pages:
263–267
Date published:
DOI:
doi:10.1038/nature14969
Received
Accepted
Published online

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.

At a glance

Figures

  1. Schematic diagram of integrated genomic and therapeutic analyses.
    Figure 1: Schematic diagram of integrated genomic and therapeutic analyses.

    To examine the effect of genomic alterations on sensitivity to anti-EGFR blockade, we performed whole-exome and copy-number analyses of 129 early-passage tumour grafts and targeted analyses of 55 tumours from patients, all of which were KRAS wild-type (top). Twenty-two of the tumour grafts were from patients who had been previously treated with anti-EGFR therapy. One hundred and sixteen of these tumour grafts were evaluated for response to cetuximab in preclinical therapeutic trials (bottom left). Integration of genomic and therapeutic information was used to identify candidate resistance and response genes, and to design preclinical trials using novel compounds to overcome resistance to EGFR blockade (bottom right).

  2. Effect of cetuximab treatment on growth of colorectal tumours with different somatic alterations.
    Figure 2: Effect of cetuximab treatment on growth of colorectal tumours with different somatic alterations.

    Waterfall plot of tumour volume changes after cetuximab treatment, compared with baseline, in 116 KRAS wild-type tumour grafts. Alterations related to therapeutic resistance or sensitivity are shown in the indicated colours (complete lists of alterations are in Supplementary Tables 3, 4 and 6). For the following genes, a subset of alterations is indicated: MET amplification; FGFR1 amplification; PDGFRA kinase domain mutations; BRAF V600 hotspot mutations; PTEN homozygous deletion or truncating mutations; PIK3CA exon 20 mutations; EGFR ecto- and kinase domain mutations and amplifications. The maximum threshold for tumour growth was set at 200%.

  3. Genetic alterations involved in secondary resistance to anti-EGFR therapy.
    Figure 3: Genetic alterations involved in secondary resistance to anti-EGFR therapy.

    a, The locations of mutations in EGFR ectodomain are shown including G465 (red) and the S492 residue known to confer cetuximab resistance11 (yellow). b, Evolution of EGFR mutations in two CRCs with acquired resistance to cetuximab. Cetuximab-naive samples were sequenced to investigate the presence of EGFR G465 mutations (red) before treatment. For each sample, the fraction of mutant tags is indicated. Met, metastases. c, As a control for tumour cellularity, for each lesion the fraction of TP53 mutant reads (vertical axis) was plotted against the fraction of reads with EGFR ectodomain mutations (horizontal axis).

  4. Therapeutic intervention in preclinical trials to overcome resistance to anti-EGFR antibody blockade.
    Figure 4: Therapeutic intervention in preclinical trials to overcome resistance to anti-EGFR antibody blockade.

    a–f, Tumour growth curves in tumour graft cohorts from individual patients with FGFR1 amplification (CRC477) (a), EGFR kinase mutation (CRC334) (b), PDGFRA R981H mutation (CRC525) (c), MAP2K1 K57N mutation (CRC343) (d), and EGFR ectodomain mutations (e, CRC104; f, CRC177) treated with placebo or targeted treatments. Mean tumour volumes ± s.e.m. are shown (n = 5 mice per group for CRC525 and CRC177; n = 6 mice per group for all other models). a, b, Combination versus cetuximab, P < 0.01; c, combination versus cetuximab, not significant; d, SCH772984 + AZD6244 versus either monotherapy, P < 0.01; e, f, afatinib, Pan-HER or panitumumab + afatinib versus panitumumab, P < 0.01. Statistical analysis was performed by two-way analysis of variance (ANOVA).

  5. EGFR signalling pathway genes involved in cetuximab resistance or sensitivity.
    Extended Data Fig. 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.

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

  7. Expression of IRS2 according to response categories in tumour graft models.
    Extended Data Fig. 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.

  8. Functional studies of genetic alterations associated with cetuximab response.
    Extended Data Fig. 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.

  9. Signalling consequences of FGFR inhibition in FGFR1-amplified CRC477.
    Extended Data Fig. 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.

  10. Signalling consequences of EGFR inhibition in EGFR mutant (V843I) CRC334.
    Extended Data Fig. 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.

  11. Signalling consequences of PDGFR inhibition in PDGFRA mutant (R981H) CRC525.
    Extended Data Fig. 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.

  12. Signalling consequences of MEK1 inhibition in MAP2K1 mutant (K57KN) CRC343.
    Extended Data Fig. 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.

  13. Signalling consequences of EGFR inhibition in EGFR mutant (G465E) CRC104.
    Extended Data Fig. 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.

  14. Signalling consequences of EGFR inhibition in EGFR mutant (G465R) CRC177.
    Extended Data Fig. 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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References

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Author information

  1. Present address: European Institute of Oncology (IEO), 20141 Milan, Italy

    • Dario Ribero
  2. These authors contributed equally to this work.

    • Andrea Bertotti &
    • Eniko Papp
  3. These authors jointly supervised this work.

    • Livio Trusolino &
    • Victor E. Velculescu

Affiliations

  1. Department of Oncology, University of Turin Medical School, 10060 Candiolo, Turin, Italy

    • Andrea Bertotti,
    • Barbara Lupo,
    • Giorgia Migliardi,
    • Eugenia R. Zanella &
    • Livio Trusolino
  2. Translational Cancer Medicine, Surgical Oncology, and Clinical Trials Coordination, Candiolo Cancer Institute – Fondazione del Piemonte per l’Oncologia IRCCS, 10060 Candiolo, Turin, Italy

    • Andrea Bertotti,
    • Barbara Lupo,
    • Francesco Sassi,
    • Francesca Cottino,
    • Giorgia Migliardi,
    • Eugenia R. Zanella,
    • Alfredo Mellano,
    • Andrea Muratore,
    • Silvia Marsoni &
    • Livio Trusolino
  3. National Institute of Biostructures and Biosystems (INBB), 00136 Rome, Italy

    • Andrea Bertotti
  4. Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA

    • Eniko Papp,
    • Vilmos Adleff,
    • Valsamo Anagnostou,
    • Jillian Phallen,
    • Carolyn A. Hruban,
    • Qing Kay Li,
    • Rachel Karchin,
    • Robert Scharpf,
    • Luis A. Diaz Jr &
    • Victor E. Velculescu
  5. Personal Genome Diagnostics, Baltimore, Maryland 21224, USA

    • Siân Jones,
    • Mark Sausen,
    • Monica Nesselbush &
    • Karli Lytle
  6. Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21204, USA

    • Collin Tokheim,
    • Noushin Niknafs &
    • Rachel Karchin
  7. Department of Surgery, Mauriziano Umberto I Hospital, 10128 Turin, Italy

    • Dario Ribero &
    • Nadia Russolillo
  8. Liver Transplantation Center, San Giovanni Battista Hospital, 10126 Turin, Italy

    • Gianluca Paraluppi &
    • Mauro Salizzoni
  9. Department of Surgical Sciences, University of Turin Medical School, 10126 Turin, Italy

    • Mauro Salizzoni
  10. Symphogen A/S, 2750 Ballerup, Denmark

    • Michael Kragh &
    • Johan Lantto
  11. Niguarda Cancer Center, Ospedale Niguarda Ca’ Granda, 20162 Milan, Italy

    • Andrea Cassingena,
    • Andrea Sartore-Bianchi &
    • Salvatore Siena
  12. University of Milan Medical School, 20162 Milan, Italy

    • Salvatore Siena
  13. Swim Across America Laboratory, The Ludwig Center for Cancer Genetics and Therapeutics at Johns Hopkins, Baltimore, Maryland 21287, USA

    • Luis A. Diaz Jr

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.

Competing financial 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.

Corresponding authors

Correspondence to:

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

Author details

Extended data figures and tables

Extended Data Figures

  1. Extended Data Figure 1: EGFR signalling pathway genes involved in cetuximab resistance or sensitivity. (426 KB)

    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.

  2. Extended Data Figure 2: Pan-HER monoclonal antibody mixture binds epitopes different from those recognized by cetuximab. (347 KB)

    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.

  3. Extended Data Figure 3: Expression of IRS2 according to response categories in tumour graft models. (68 KB)

    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.

  4. Extended Data Figure 4: Functional studies of genetic alterations associated with cetuximab response. (303 KB)

    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.

  5. Extended Data Figure 5: Signalling consequences of FGFR inhibition in FGFR1-amplified CRC477. (1,363 KB)

    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.

  6. Extended Data Figure 6: Signalling consequences of EGFR inhibition in EGFR mutant (V843I) CRC334. (1,398 KB)

    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.

  7. Extended Data Figure 7: Signalling consequences of PDGFR inhibition in PDGFRA mutant (R981H) CRC525. (1,334 KB)

    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.

  8. Extended Data Figure 8: Signalling consequences of MEK1 inhibition in MAP2K1 mutant (K57KN) CRC343. (1,650 KB)

    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.

  9. Extended Data Figure 9: Signalling consequences of EGFR inhibition in EGFR mutant (G465E) CRC104. (1,653 KB)

    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.

  10. Extended Data Figure 10: Signalling consequences of EGFR inhibition in EGFR mutant (G465R) CRC177. (1,510 KB)

    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.

Supplementary information

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  1. Supplementary Information (6.4 MB)

    This file contains Supplementary Tables 1-10.

Additional data