Liquid versus tissue biopsy for detecting acquired resistance and tumor heterogeneity in gastrointestinal cancers

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Abstract

During cancer therapy, tumor heterogeneity can drive the evolution of multiple tumor subclones harboring unique resistance mechanisms in an individual patient1,2,3. Previous case reports and small case series have suggested that liquid biopsy (specifically, cell-free DNA (cfDNA)) may better capture the heterogeneity of acquired resistance4,5,6,7,8. However, the effectiveness of cfDNA versus standard single-lesion tumor biopsies has not been directly compared in larger-scale prospective cohorts of patients following progression on targeted therapy. Here, in a prospective cohort of 42 patients with molecularly defined gastrointestinal cancers and acquired resistance to targeted therapy, direct comparison of postprogression cfDNA versus tumor biopsy revealed that cfDNA more frequently identified clinically relevant resistance alterations and multiple resistance mechanisms, detecting resistance alterations not found in the matched tumor biopsy in 78% of cases. Whole-exome sequencing of serial cfDNA, tumor biopsies and rapid autopsy specimens elucidated substantial geographic and evolutionary differences across lesions. Our data suggest that acquired resistance is frequently characterized by profound tumor heterogeneity, and that the emergence of multiple resistance alterations in an individual patient may represent the ‘rule’ rather than the ‘exception’. These findings have profound therapeutic implications and highlight the potential advantages of cfDNA over tissue biopsy in the setting of acquired resistance.

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Fig. 1: Identification of acquired resistance mechanisms in liquid versus tumor biopsy.
Fig. 2: Comparison of multiple tumor biopsies versus liquid biopsy in a BRAF-mutant colorectal cancer patient.
Fig. 3: Serial liquid biopsy and autopsy in a patient with FGFR2 fusion-positive gastric cancer (TPS177).

Data availability

Sequencing data from WES and RNA-Seq are available in the database of Genotypes and Phenotypes with the accession number phs001853.v1.p1.

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Acknowledgements

Grant Support: This work is partially supported by the NIH/NCI Gastrointestinal Cancer Specialized Program of Research Excellence P50 (CA127003, R01CA208437, K08CA166510 and U54CA224068), a Damon Runyon Clinical Investigator Award and a Stand Up To Cancer Colorectal Dream Team Translational Research Grant (grant number SU2C-AACR-DT22-17 to R.B.C.). Research grants are administered by the American Association of Cancer Research, the scientific partners of Stand Up To Cancer. The work is partially supported by the Broad/IBM Cancer Resistance Research Project (G.G. and L.P.) and the Susan Eid Tumor Heterogeneity Initiative (D.J.). The research leading to these results has received funding from FONDAZIONE AIRC under 5 per Mille 2018 ID. 21091 program – P. I. Bardelli Alberto. The research leading to these results has received funding from FONDAZIONE AIRC: AIRC Investigator Grant 2015 – ID. 16788; AIRC Investigator Grant 2018 – ID. 21923; AIRC–CRUK–FC AECC Accelerator Award – contract 22795. This work was also supported by the European Community’s Seventh Framework Programme (grant agreement number 602901 MErCuRIC), H2020 (grant agreement number 635342-2 MoTriColor), IMI (contract number 115749 CANCER-ID), Ministero della Salute (project NET-2011-02352137) and Fondazione Piemontese per la Ricerca sul Cancro–ONLUS 5 per Mille 2014 and 2015 Ministero della Salute. G.S. was funded by a Roche per la Ricerca grant (2017) and AIRC three-year fellowship (2017).

Author information

A.R.P., I.L., L.G., D.J., G.G. and R.B.C. conceived of the study; A.R.P., I.L., L.E., L.G., C.L., G.S., D.L., K.R., E.E.M., E.E.V.S., F.U., F.F.d.l.C., I.J.F., B.N., H.A.S., F.A., I.D.-G., M.H.-R., D.D.-S., D.T.T., A.J.I., A.B., L.P., D.J., G.G. and R.B.C. performed data analysis and interpretation; I.L., L.E., C.L., D.L., K.R., E.E.M., F.U., L.P. and G.G. performed analysis of whole exome sequencing and subclonal reconstruction; A.R.P., I.L., L.G., G.S., D.D.-S., D.T.T., A.J.I., A.B., L.P., D.J., G.G. and R.B.C. supervised the data analysis; A.R.P., I.L., L.E., C.L., K.R., E.E.V.S., F.U., K.S., B.P.D., L.P., G.G. and R.B.C. participated in writing of the manuscript and generated figures; M.H., B.P.D., T.R.G., and V.A.A. suggested manuscript edits; A.R.P., L.G., E.E.V.S., C.J.P., J.N.A., L.S.B., J.W.C., B.G., J.E.M., R.D.N., E.R., D.P.R., C.D.W., E.L.K., J.E.F., D.D.-S., D.T.T., A.X.Z., J.Y.W., T.S.H., A.J.I., D.J., R.B.C., M.H., A.W., V.A. performed sample and data collection; funding was obtained by T.R.G. and G.G.; and A.R.P., I.L., E.E.V.S., C.J.P., M.H., K.S., A.W., B.P.D., and G.G. and R.B.C. participated in project administration.

Correspondence to Gad Getz or Ryan B. Corcoran.

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Competing interests

A.R.P. is a consultant/advisory board member for PureTech, Driver, Foundation Medicine and Eisai, and has institutional research funding from Array, Plexxikon, Guardant, BMS, MacroGenics, Genentech, Novartis, OncoMed and Tolero. L.G. is a consultant/advisory board member for Debiopharm, H3 Biomedicine and Pieris Pharmaceuticals, and a steering committee member for Agios Pharmaceuticals, Taiho Pharmaceuticals and Debiopharm. E.R. is an advisory board member/consultant for Helsinn, Heron, BASF, American Imaging Management, Napo, Immuneering and Vector Oncology. D.P.R. serves on advisory boards for MPM Capital, Gritstone Oncology, Oncorus and TCR2, has equity in MPM Capital and Acworth Pharmaceuticals, and serves as an author for Johns Hopkins University Press, UpToDate and McGraw Hill. C.D.W. is a consultant for Celgene. J.E.F. is an employee at Novartis, has served as an advisory board member/consultant for Merrimack and N-of-One, and received research funding from Novartis, Roche/Genentech, Agios, Takeda, Sanofi, Celgene and Exelixis, as well as travel support from Roche/Genentech. E.L.K. is an employee of Novartis. M.H.-R. is an employee of Forma Therapeutics. D.T.T. is a consultant/advisory board member for Merrimack, Roche Ventana and EMD Millipore–Sigma, is a founder and has equity in PanTher Therapeutics, and receives research funding from ACD-Biotechne. A.X.Z. is a consultant/advisor for AstraZeneca, Bayer, Bristol-Myers Squibb, Eisai, Eli Lilly, Exelixis, Merck, Novartis and Roche/Genentech, and received research funding from Bayer, Bristol-Myers Squibb, Eli Lilly, Merck and Novartis. T.S.H. is a consultant/advisory board member for Merck and EMD Serono, and received research support from Taiho, AstraZeneca, Bristol-Myers Squibb, Mobetron and Ipsen. A.J.I. is a consultant for DebioPharm, Chugai and Roche, received research support from Sanofi and has equity in ArcherDX. T.R.G. is or has been a consultant/advisor for Foundation Medicine, GlaxoSmithKline, Sherlock Biosciences and Forma Therapeutics, and holds equity in Sherlock Biosciences and Forma Therapeutics. R.B.C. is a consultant/advisory board member for Amgen, Array Biopharma, Astex Pharmaceuticals, Avidity Biosciences, BMS, C4 Therapeutics, Chugai, Elicio, Fog Pharma, Fount Therapeutics, Genentech, LOXO, Merrimack, N-of-One, Novartis, nRichDX, Revolution Medicines, Roche, Roivant, Shionogi, Shire, Spectrum Pharmaceuticals, Symphogen, Taiho and Warp Drive Bio, holds equity in Avidity Biosciences, C4 Therapeutics, Fount Therapeutics, nRichDX and Revolution Medicines, and has received research funding from Asana, AstraZeneca and Sanofi. D.J. is an advisor/consultant for Novartis, Genentech, Eisai, Ipsen and EMD Serono, and receives research support from Novartis, Genentech, Eisai, EMD Serono, Takeda, Celgene and Placon. G.G. receives research funds from IBM and Pharmacyclics, and is an inventor on patent applications related to MuTect, ABSOLUTE, and POLYSOLVER. K.R., F.U., C.L. and L.P. are listed as co-inventors on a patent application currently pending review at the USPTO. I.L. L.E., D.L., E.E.V.S., E.E.M., M.H., K.S., C.J.P., A.W., B.P.D., F.F.d.l.C., I.J.F., B.N., H.A.S., J.N.A., L.S.B., J.W.C., B.G., J.E.M., R.D.N., F.A., I.D.-G., D.D.-S. and V.A.A. declare no competing interests.

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Extended Data

Extended Data Fig. 1 Phylogenetic trees for paired tumor biopsies.

Phylogenetic trees for patients with WES for pre- and post-treatment samples. The numbers of somatic alterations assigned to each cluster and detected events in known cancer genes appear on the branches. Two-dimensional plots show the CCF distributions of subclones in the pre- and post-treatment samples. Events in known cancers are shown next to their subclonal cluster.

Extended Data Fig. 2 Patients with multiple postprogression tumor biopsies.

Three of five patients with multiple postprogression tumor biopsies are shown. The other two patients (TPS037 and TPS177) are shown in Figs. 2 and 3, respectively. For each patient, the location of each tumor biopsy, as well as the resistance alterations (blue) and truncal alterations (orange) detected in cfDNA and in each tumor specimen, are shown. TPS001 is a patient with RAS wild type colorectal cancer who developed resistance to an anti-EGFR antibody8. Biopsy of one liver lesion revealed an activating MEK1 (MAP2K1) mutation, and biopsy of a second liver lesion identified a KRAS mutation. Both resistance alterations were detected in postprogression cfDNA. TPS007 is an FGFR2 fusion-positive cholangiocarcinoma patient who developed resistance to an FGFR inhibitor5. Five FGFR2 resistance mutations were identified in postprogression cfDNA, and three of these alterations were identified in distinct liver lesions harvested through a rapid autopsy program, with one lesion harboring two FGFR2 alterations. TPS011 is a patient with RAS wild type colorectal cancer who developed resistance to an anti-EGFR antibody. A recurrent colon tumor harbored a KRAS mutation, and an EGFR extracellular domain mutation known to interfere with antibody binding was identified in an ovarian metastasis, whereas both alterations were detected in cfDNA. Importantly, in all patients, individual resistance mechanisms emerging in distinct metastatic lesions were detectable in cfDNA.

Extended Data Fig. 3 Biclusters of patients based on similar changes (δ) in somatic alteration.

Biclustering of four δ matrices, reflecting changes in CCF of mutations or copy number in known cancer genes or genesets, yielded significant biclusters (all empirical P values < 0.0001; Methods). The biclusters from all four δ matrices included at least one bicluster with patient TPS130 (a patient with an unknown mechanism of resistance). Patient TPS130 consistently biclustered together with TPS021 and TPS037 (patients with known mechanisms of resistance) across all matrices, highlighting the possibility that additional genomic alterations contribute to resistance beyond the identified resistance alterations. a, The change in somatic alterations, δ, was calculated based on WES data of the samples closest to the start and end of therapy. We biclustered four δ matrices (\(\delta _{{\rm{cancer}}\;{\rm{gene}}}^{{\rm{copy}}\,{\rm{number}}}\), \(\delta _{{\rm{geneset}}}^{{\rm{mutation}}}\), \(\delta _{{\rm{geneset}}}^{{\rm{copy}}\,{\rm{number}}}\) and \(\delta _{{\rm{geneset}}}^{{\rm{copy}}\,{\rm{number}}\;{\rm{and}}\;{\rm{mutation}}}\)) and assessed their significance by comparing the size of biclusters against n = 10,000 permuted matrices with a two-sided t-test (Methods). be, Illustration of the biclustering results (using BiMax) of the four δ matrices (biclusters are listed in Supplementary Table 6). Outlined in red are biclusters containing TPS130 observed in all four δ matrices.

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