Clonal evolution and resistance to EGFR blockade in the blood of colorectal cancer patients

Journal name:
Nature Medicine
Year published:
Published online
Corrected online

Colorectal cancers (CRCs) evolve by a reiterative process of genetic diversification and clonal evolution. The molecular profile of CRC is routinely assessed in surgical or bioptic samples1. Genotyping of CRC tissue has inherent limitations; a tissue sample represents a single snapshot in time, and it is subjected to spatial selection bias owing to tumor heterogeneity. Repeated tissue samples are difficult to obtain and cannot be used for dynamic monitoring of disease progression and response to therapy. We exploited circulating tumor DNA (ctDNA) to genotype colorectal tumors and track clonal evolution during treatment with the epidermal growth factor receptor (EGFR)-specific antibodies cetuximab or panitumumab. We identified alterations in ctDNA of patients with primary or acquired resistance to EGFR blockade in the following genes: KRAS, NRAS, MET, ERBB2, FLT3, EGFR and MAP2K1. Mutated KRAS clones, which emerge in blood during EGFR blockade, decline upon withdrawal of EGFR-specific antibodies, indicating that clonal evolution continues beyond clinical progression. Pharmacogenomic analysis of CRC cells that had acquired resistance to cetuximab reveals that upon antibody withdrawal KRAS clones decay, whereas the population regains drug sensitivity. ctDNA profiles of individuals who benefit from multiple challenges with anti-EGFR antibodies exhibit pulsatile levels of mutant KRAS. These results indicate that the CRC genome adapts dynamically to intermittent drug schedules and provide a molecular explanation for the efficacy of rechallenge therapies based on EGFR blockade.

At a glance


  1. Mutated KRAS alleles emerge in circulating DNA during anti-EGFR therapy and decline when treatment is suspended.
    Figure 1: Mutated KRAS alleles emerge in circulating DNA during anti-EGFR therapy and decline when treatment is suspended.

    (af) Detection of KRAS mutations in mCRC patients AOUP-CRC04 (a), AOUP-CRC01 (b), AOUP-CRC06 (c), AOUP-CRC03 (d), ONCG-CRC71 (e) and MET amplification in patient ONCG-CRC72 (f) in circulating DNA of patients who developed acquired resistance to first-line chemotherapy plus anti-EGFR treatment and then received other lines of treatment. Gray bars represent the variation of tumor load, compared to baseline, during systemic treatments specified in arrows below the graphs. Tumor load is calculated as follows: measurable disease at the initiation of treatment (baseline) is assumed as 100%; responses or progression are calculated as the percentage of tumor load compared to baseline, as per Response Evaluation Criteria in Solid Tumors (RECIST) criteria. Relevant clinical events are indicated in gray boxes below the graphs. Black lines indicate the frequency of KRAS mutation (percentage of alleles) or MET copy number alteration, detected in circulating DNA at the time points indicated below the graphs. Dotted blue line indicates CEA (carcinoembryonic antigen) values. PD, progressive disease; Cetux, cetuximab; Panit, panitumumab; Bev, bevacizumab; Irino, irinotecan; Folfoxiri, folinic acid, 5-fluorouracil, oxaliplatin and irinotecan; Folfiri, folinic acid, 5-fluorouracil and irinotecan; Folfox, folinic acid, fluorouracil and oxaliplatin.

  2. Re-challenge with EGFR-specific antibodies in CRC cells and patients.
    Figure 2: Re-challenge with EGFR-specific antibodies in CRC cells and patients.

    (a) Two CRC cell populations (DiFi A and DiFi B) that developed KRAS amplification as a resistance mechanism to cetuximab were allowed to replicate in the absence of the antibody for 160 d. Top, KRAS amplification assessed by qPCR in the indicated cell models (parental/sensitive, resistant derivatives and resistant cells after 160 d of antibody withdrawal). Gray bars indicate KRAS gene copy number alterations. Statistical differences were calculated by Student's t-test. Data are expressed as means ± s.d. of three independent experiments. ***P ≤ 0.001; **P ≤ 0.01. Bottom, cetuximab sensitivity assay. Data points represent means ± s.d. of three independent experiments. (b) Clinical synopsis of mCRC patient HMAR-CRC07 treated with irinotecan plus cetuximab achieving stable disease (SD) for approximately 6 months. At progression, the patient received capecitabine plus oxaliplatin (Xelox) with further progression of the disease after 3 months. The patient was subsequently re-treated with irinotecan plus cetuximab, achieving a partial response (PR). Gray area represents tumor load (percentage of baseline, calculated as described in Fig. 1 legend); dotted blue line indicates CEA (carcinoembryonic antigen) values. (c) Clinical synopsis of mCRC patient ONCG-CRC74, who was treated with cetuximab as a third-line therapy, achieving a partial response that lasted 13 months; the patient then refused further therapy because of skin toxicity. At disease progression, the subject underwent radiotherapy and treatment with 5-fluorouracil (5-FU) with PR until progression occurred after 6 months. The patient was re-challenged with anti-EGFR treatment, achieving long-lasting stable disease (7 months). Gray area represents tumor load (percentage of baseline, calculated as described in Fig 1 legend); dotted blue line indicates CEA values. Cetux, cetuximab.

  3. Mutated KRAS mutant clones dynamically evolve in response to pulsatile EGFR-specific antibody therapy.
    Figure 3: Mutated KRAS mutant clones dynamically evolve in response to pulsatile EGFR-specific antibody therapy.

    Dynamics of KRAS mutant clones in plasma samples of mCRC patients HMAR-CRC08 (a), ONCG-CRC69 (b) and AOUP-CRC05 (c), with each receiving the indicated therapies. Gray bars represent variation of tumor load, compared to baseline, during treatments as specified below the graphs. Tumor load was calculated as described in Figure 1 legend. Relevant clinical events are indicated in gray boxes below the individual graphs. Black and red lines indicate the frequency of KRAS mutation (percentage of alleles) detected in circulating DNA at the indicated time points. Black stars represent analyzed tissue samples. Dotted blue line indicates CEA values. Cetux, cetuximab; Panit, panitumumab; Rego, regorafenib. Folfoxiri, folinic acid, 5-fluorouracil, oxaliplatin and irinotecan; Folfiri, folinic acid, 5-fluorouracil and irinotecan; Folfox, folinic acid, fluorouracil and oxaliplatin; Tomox, raltitrexed plus oxaliplatin; TAS-102, tipiracil hydrochloride.

Change history

Corrected online 26 June 2015

In the version of this article initially published online, Alberto Bardelli´s e-mail address was incorrect. The correct address is The error has been corrected in the print, PDF and HTML versions of this article.


  1. Wong, N.A. et al. RAS testing of colorectal carcinoma—a guidance document from the Association of Clinical Pathologists Molecular Pathology and Diagnostics Group. J. Clin. Pathol. 67, 751757 (2014).
  2. Douillard, J.Y. et al. Panitumumab-FOLFOX4 treatment and RAS mutations in colorectal cancer. N. Engl. J. Med. 369, 10231034 (2013).
  3. Di Nicolantonio, F. et al. Wild-type BRAF is required for response to panitumumab or cetuximab in metastatic colorectal cancer. J. Clin. Oncol. 26, 57055712 (2008).
  4. Bardelli, A. et al. Amplification of the MET receptor drives resistance to anti-EGFR therapies in colorectal cancer. Cancer Discov. 3, 658673 (2013).
  5. 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, 508523 (2011).
  6. Yonesaka, K. et al. Activation of ERBB2 signaling causes resistance to the EGFR-directed therapeutic antibody cetuximab. Sci. Transl. Med. 3, 99ra86 (2011).
  7. Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883892 (2012).
  8. Vogelstein, B. et al. Cancer genome landscapes. Science 339, 15461558 (2013).
  9. De Mattos-Arruda, L. et al. Capturing intra-tumor genetic heterogeneity by de novo mutation profiling of circulating cell-free tumor DNA: a proof-of-principle. Ann. Oncol. 25, 17291735 (2014).
  10. Overman, M.J. et al. Use of research biopsies in clinical trials: are risks and benefits adequately discussed? J. Clin. Oncol. 31, 1722 (2013).
  11. Diaz, L.A. & Bardelli, A. Liquid biopsies: genotyping circulating tumor DNA. J. Clin. Oncol. 32, 579586 (2014).
  12. Siravegna, G. & Bardelli, A. Genotyping cell-free tumor DNA in the blood to detect residual disease and drug resistance. Genome Biol. 15, 449 (2014).
  13. Fleischhacker, M. & Schmidt, B. Circulating nucleic acids (CNAs) and cancer–a survey. Biochim Biophys Acta. 1775, 181232 (2007).
  14. Schwarzenbach, H., Hoon, D.S. & Pantel, K. Cell-free nucleic acids as biomarkers in cancer patients. Nat. Rev. Cancer 11, 426437 (2011).
  15. Misale, S. et al. Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer. Nature 486, 532536 (2012).
  16. Diaz, L.A. et al. The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers. Nature 486, 537540 (2012).
  17. Reinert, T. et al. Analysis of circulating tumour DNA to monitor disease burden following colorectal cancer surgery. Gut. (2015).
  18. Sanmamed, M.F. et al. Quantitative cell-free circulating BRAFV600E mutation analysis by use of droplet digital PCR in the follow-up of patients with melanoma being treated with BRAF inhibitors. Clin. Chem. 61, 297304 (2015).
  19. Diehl, F. et al. BEAMing: single-molecule PCR on microparticles in water-in-oil emulsions. Nat. Methods 3, 551559 (2006).
  20. Hindson, B.J. et al. High-throughput droplet digital PCR system for absolute quantitation of DNA copy number. Anal. Chem. 83, 86048610 (2011).
  21. Bardelli, A. & Siena, S. Molecular mechanisms of resistance to cetuximab and panitumumab in colorectal cancer. J. Clin. Oncol. 28, 12541261 (2010).
  22. Misale, S., Di Nicolantonio, F., Sartore-Bianchi, A., Siena, S. & Bardelli, A. Resistance to anti-EGFR therapy in colorectal cancer: from heterogeneity to convergent evolution. Cancer Discov. 4, 12691280 (2014).
  23. Palacio-Rúa, K.A., Isaza-Jiménez, L.F., Ahumada-Rodríguez, E. & Muñetón-Peña, C.M. Genetic analysis in APC, KRAS, and TP53 in patients with stomach and colon cancer. Rev. Gastroenterol Mex. 79, 7989 (2014).
  24. Network, C.G.A. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330337 (2012).
  25. Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401404 (2012).
  26. Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal 6, pl1 (2013).
  27. Beaver, J.A. et al. Detection of cancer DNA in plasma of early stage breast cancer patients. Clin. Cancer Res. (2014).
  28. Montagut, C. et al. Evolution of heterogeneous mechanisms of acquired resistance to cetuximab-based therapy in colorectal cancer. in ASCO Vol. abstr 3526 (2014).
  29. Arena, S. et al. Emergence of multiple EGFR extracellular mutations during cetuximab treatment in colorectal cancer. Clin. Cancer Res. (2015).
  30. Mohan, S. et al. Changes in colorectal carcinoma genomes under anti-EGFR therapy identified by whole-genome plasma DNA sequencing. PLoS Genet. 10, e1004271 (2014).
  31. Valtorta, E. et al. KRAS gene amplification in colorectal cancer and impact on response to EGFR-targeted therapy. Int. J. Cancer 133, 12591265 (2013).
  32. Hata, A. et al. Panitumumab rechallenge in chemorefractory patients with metastatic colorectal cancer. J. Gastrointest. Cancer 44, 456459 (2013).
  33. 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).
  34. Morelli, M.P. et al. Characterizing the patterns of clonal selection in circulating tumor DNA from patients with colorectal cancer refractory to anti-EGFR treatment. Ann. Oncol. (2015).
  35. Bettegowda, C. et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci. Transl. Med. 6, 224ra224 (2014).
  36. Das Thakur, M. et al. Modelling vemurafenib resistance in melanoma reveals a strategy to forestall drug resistance. Nature 494, 251255 (2013).
  37. Seghers, A.C., Wilgenhof, S., Lebbé, C. & Neyns, B. Successful rechallenge in two patients with BRAF-V600-mutant melanoma who experienced previous progression during treatment with a selective BRAF inhibitor. Melanoma Res. 22, 466472 (2012).
  38. Hata, A., Katakami, N., Kaji, R., Fujita, S. & Imai, Y. Does T790M disappear? Successful gefitinib rechallenge after T790M disappearance in a patient with EGFR-mutant non-small-cell lung cancer. J. Thorac. Oncol. 8, e2729 (2013).
  39. Nakamura, T. et al. Application of a highly sensitive detection system for epidermal growth factor receptor mutations in plasma DNA. J. Thorac. Oncol. 7, 13691381 (2012).
  40. Eisenhauer, E.A. et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer 45, 228247 (2009).
  41. Ma, E.S., Wong, C.L., Law, F.B., Chan, W.K. & Siu, D. Detection of KRAS mutations in colorectal cancer by high-resolution melting analysis. J. Clin. Pathol. 62, 886891 (2009).
  42. Tiacci, E. et al. Simple genetic diagnosis of hairy cell leukemia by sensitive detection of the BRAF-V600E mutation. Blood 119, 192195 (2012).
  43. Gonzalez de Castro, D. et al. A comparison of three methods for detecting KRAS mutations in formalin-fixed colorectal cancer specimens. Br. J. Cancer 107, 345351 (2012).
  44. Sundström, M. et al. KRAS analysis in colorectal carcinoma: analytical aspects of Pyrosequencing and allele-specific PCR in clinical practice. BMC Cancer 10, 660 (2010).
  45. Hayden, R.T. et al. Comparison of droplet digital PCR to real-time PCR for quantitative detection of cytomegalovirus. J. Clin. Microbiol. 51, 540546 (2013).
  46. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589595 (2010).
  47. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 20782079 (2009).
  48. Forbes, S.A. et al. COSMIC: exploring the world′s knowledge of somatic mutations in human cancer. Nucleic. Acids. Res. (2014).
  49. Ye, K., Schulz, M.H., Long, Q., Apweiler, R. & Ning, Z. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics 25, 28652871 (2009).

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Author information

  1. Present address: Cancer Research UK London Research Institute, London, UK.

    • Sebastijan Hobor


  1. University of Torino, Department of Oncology, Torino, Italy.

    • Giulia Siravegna,
    • Enzo Medico,
    • Federica Di Nicolantonio &
    • Alberto Bardelli
  2. Candiolo Cancer Institute – Fondazione Piemontese per l'Oncologia (FPO), Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Candiolo, Torino, Italy.

    • Giulia Siravegna,
    • Benedetta Mussolin,
    • Michela Buscarino,
    • Giorgio Corti,
    • Giovanni Crisafulli,
    • Simona Lamba,
    • Sebastijan Hobor,
    • Giuseppe Rospo,
    • Enzo Medico,
    • Silvia Marsoni,
    • Federica Di Nicolantonio &
    • Alberto Bardelli
  3. Fondazione Italiana per la Ricerca sul Cancro (FIRC) Institute of Molecular Oncology (IFOM), Milano, Italy.

    • Giulia Siravegna
  4. Niguarda Cancer Center, Ospedale Niguarda Ca' Granda, Milano, Italy.

    • Andrea Cassingena,
    • Alessio Amatu,
    • Calogero Lauricella,
    • Emanuele Valtorta,
    • Valentina Motta,
    • Silvio Veronese,
    • Salvatore Siena &
    • Andrea Sartore-Bianchi
  5. Colorectal Cancer Unit, Medical Oncology Division 1, AOU Città della Salute e della Scienza, San Giovanni Battista Hospital, Turin, Italy.

    • Agostino Ponzetti &
    • Patrizia Racca
  6. Azienda Ospedaliero-Universitaria Pisana and Università di Pisa, Pisa.

    • Chiara Cremolini,
    • Carlotta Antoniotti,
    • Alfredo Falcone &
    • Fotios Loupakis
  7. Istituto Nazionale Tumori Fondazione G. Pascale - IRCCS, Naples, Italy.

    • Antonio Avallone,
    • Fabiana Tatangelo &
    • Alfredo Budillon
  8. Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Barcelona, Spain.

    • Beatriz Bellosillo &
    • Clara Montagut
  9. Massachusetts General Hospital Cancer Center, Boston, Massachusetts.

    • Ryan B Corcoran


A. Bardelli and G.S. designed the study. G.S., B.M., M.B., C.L., S.L., S.H., E.V., V.M., F.T. and B.B. performed the experiments. G.S., B.M., G. Corti, G. Crisafulli, E.M., G.R., S.V., F.D.N. and S.M. analyzed data. A.C., A.P., C.C., A. Amatu, A. Avallone, C.A., A. Budillon, C.M., P.R., A.F., R.B.C., F.L., S.S. and A.S.-B. treated patients and provided clinical samples. A. Bardelli and G.S. wrote the manuscript. A. Bardelli and A.S.-B. supervised the study.

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The authors declare no competing financial interests.

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