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Mutational signatures impact the evolution of anti-EGFR antibody resistance in colorectal cancer

Nature Ecology & Evolution volume 5, pages 1024–1032 (2021)Cite this article

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Abstract

Anti-EGFR antibodies such as cetuximab are active against KRAS/NRAS wild-type colorectal cancers (CRCs), but acquired resistance invariably evolves. It is unknown which mutational mechanisms enable resistance evolution and whether adaptive mutagenesis (a transient cetuximab-induced increase in mutation generation) contributes in patients. Here, we investigate these questions in exome sequencing data from 42 baseline and progression biopsies from cetuximab-treated CRCs. Mutation loads did not increase from baseline to progression, and evidence for a contribution of adaptive mutagenesis was limited. However, the chemotherapy-induced mutational signature SBS17b was the main contributor of specific KRAS/NRAS and EGFR driver mutations that are enriched at acquired resistance. Detectable SBS17b activity before treatment predicted shorter progression-free survival and the evolution of these specific mutations during subsequent cetuximab treatment. This result suggests that chemotherapy mutagenesis can accelerate resistance evolution. Mutational signatures may be a new class of cancer evolution predictor.

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Fig. 1: Cetuximab resistance models and analysis of mutation loads in 21 tumours treated with single-agent cetuximab.
Fig. 2: Mutational signatures in tumours treated with cetuximab.
Fig. 3: Relationship of mutational signatures to specific KRAS/NRAS and EGFR mutations.
Fig. 4: Association of detected SBS17b at BL with specific KRAS/NRAS and EGFR mutation evolution at the time of acquired resistance and with PFS.

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Data availability

All analyses were performed on previously published datasets3,5,20,26,27,28. The datasets can be accessed as described in the primary publications. The DNA sequencing data from the Prospect-C trial are deposited in the European Genome-phenome Archive with the accession code EGAS00001003367. As they include exome sequencing data that could permit the re-identification of trial participants, a data sharing agreement is required as stated in the primary publication3.

Code availability

The custom code to reproduce the mutational signature modelling is freely available on Github (https://github.com/AWoolston/Evolution-of-anti-EGFR-antibody-resistance).

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Acknowledgements

D.C. received funding from the NIHR Biomedical Research Centre for Cancer at the Institute of Cancer Research and the Royal Marsden Hospital. M.G., A.W. and L.J.B. received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 820137). The paper is dedicated to the memory of Tim Morgan, who supported this work with a generous donation.

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Authors and Affiliations

  1. Translational Oncogenomics Laboratory, The Institute of Cancer Research, London, UK

    Andrew Woolston, Louise J. Barber, Beatrice Griffiths & Marco Gerlinger

  2. Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain

    Oriol Pich & Nuria Lopez-Bigas

  3. Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain

    Nuria Lopez-Bigas

  4. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain

    Nuria Lopez-Bigas

  5. Tumour Profiling Unit, The Institute of Cancer Research, London, UK

    Nik Matthews

  6. Gastrointestinal Cancer Unit, The Royal Marsden Hospital, London, UK

    Sheela Rao, David Watkins, Ian Chau, Naureen Starling, David Cunningham & Marco Gerlinger

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Contributions

M.G. conceived, funded and supervised the molecular analysis. D.C. is the chief investigator of the Prospect-C trial and funded the trial. N.S., I.C., S.R. and D.W. recruited the trial patients. B.G. prepared the trial samples, and N.M. supervised the sequencing. L.J.B. performed the ctDNA sequencing and analysis. A.W. performed the bioinformatics analysis. O.P. and N.L.-B. provided the analysis of metastatic CRC samples from the Hartwig Medical Foundation. A.W. and M.G. performed the statistical analysis. A.W. and M.G. wrote the manuscript. L.J.B., O.P. and N.L.-B. provided feedback. All authors approved the final manuscript.

Corresponding author

Correspondence to Marco Gerlinger.

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Competing interests

I.C. has consultant/advisory roles with Eli-Lilly, BMS, MSD, Merck KG, Roche, Bayer and Five Prime Therapeutics. D.C. receives research funding from Amgen, Sanofi, Merrimack, Astra Zeneca, Celegene, MedImmune, Bayer, 4SC, Clovis, Eli-Lilly, Janssen and Merck KG. M.G. and N.S. receive research funding from Merck KG and BMS. The other authors declare no competing interests.

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Peer review information Nature Ecology & Evolution thanks Christos Karapetis, Peter Campbell and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Plots of cancer cell content, sequencing depth and mutation load for the paired BL/PD biopsies from 21 patients in the Prospect-C trial.

a, Estimated cancer cell contents of paired BL and PD samples. A 1:1 ratio line has been added for reference. b, Mutation load vs. mean sequencing depth for all BL and PD samples. p-value from Spearman’s test. A linear regression line has been added for reference. c, Mutation load vs. cancer cell content for all BL and PD samples. p-value from Spearman’s test. A linear regression line has been added for reference.

Extended Data Fig. 2 Clonal mutation trees for 21 tumors from the Prospect-C trial.

Grouped into cases with prolonged benefit and primary progression. The numbers next to the trunk or the branches indicate clonal somatic mutations.

Extended Data Fig. 3 Number of unique mutations detected for each of 21 paired biopsies from the Prospect-C trial vs. time lapse between BL and PD biopsies.

p-value from Spearman’s test. A linear regression line has been added for reference.

Extended Data Fig. 4 Proportion of SBS mutations attributed to each mutational signature.

Signatures were selected using the ‘ColoRect-AdenoCa’ samples from the SigProfiler TCGA whole exome cohort (n = 496) (syn11801497.7). All signatures in the cohort with a non-zero mutation attribution were considered along with all MMR-deficiency signatures and platinum treatment signatures. Plots show the cohort wide signature attribution among (a) all 21 Prospect-C samples and (b) only in the PD tumors of the 12 patients with prolonged benefit. The red horizontal dashed line illustrates the 3% threshold used to define signatures as ‘active’ and the red box shows the signatures retained for subsequent analysis. SBS17a and SBS17b are described as ‘connected’ signatures15. SBS17a was retained due to the inclusion of SBS17b despite not reaching the threshold.

Extended Data Fig. 5 Signature attributions based on 21 paired BL/PD biopsies from the Prospect-C trial using MutationalPatterns and deconstructSigs.

a, Mutation signature attribution using independent decomposition methods (deconstructSigs and MutationalPatterns). b, Fig. 2 repeated with the ‘fit_to_sigs’ function in MutationalPatterns to assess the variability of estimates between methods.

Extended Data Fig. 6 Mutation frequency profiles of treatment naïve CRCs from the TCGA Pan-Cancer study vs. the KRAS hotspot mutations identified in ref. 28.

The TCGA profile has been adjusted to only consider KRAS mutations that were assessed in the CORRECT trial.

Extended Data Fig. 7 Modelling the impact of mutational signatures on the likelihood of acquired hotspot mutations.

a, Modelled mutational profile of a BL tumor with prolonged benefit. Exome normalised reference signatures have been scaled by the observed signature exposures of the 12 BL tumors with prolonged benefit to represent a mutation probability at each trinucleotide mutation context. b, Observed mutation frequencies of KRAS/NRAS Q61H vs. all other KRAS/NRAS hotspot mutations identified in CRCs with acquired EGFR-AB resistance3,5,26. c, Modelled mutation accumulation of the permanent signatures. A varying acceleration parameter of x1, x5, x10 is applied to the tumor growth period. d, Impact of SBS17b and SBS35 on the likelihood of generating KRAS/NRAS Q61H mutations vs. all other detected KRAS/NRAS hotspot mutations.

Extended Data Fig. 8 Analysis of an independent cohort of 239 patients with metastatic colorectal cancer and a KRAS/NRAS G12/G13 or Q61H mutation.

a, Total mutations attributed to SBS17b. Statistical significance was assessed with the Fisher’s exact test. b, Proportion of tumors with a detectable SBS17b signature activity. Statistical significance was assessed with the Mann-Whitney U test.

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Woolston, A., Barber, L.J., Griffiths, B. et al. Mutational signatures impact the evolution of anti-EGFR antibody resistance in colorectal cancer. Nat Ecol Evol 5, 1024–1032 (2021). https://doi.org/10.1038/s41559-021-01470-8

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