An integrated tumor, immune and microbiome atlas of colon cancer.
Journal
Nature medicine
ISSN: 1546-170X
Titre abrégé: Nat Med
Pays: United States
ID NLM: 9502015
Informations de publication
Date de publication:
05 2023
05 2023
Historique:
received:
29
12
2021
accepted:
28
03
2023
medline:
24
5
2023
pubmed:
19
5
2023
entrez:
18
5
2023
Statut:
ppublish
Résumé
The lack of multi-omics cancer datasets with extensive follow-up information hinders the identification of accurate biomarkers of clinical outcome. In this cohort study, we performed comprehensive genomic analyses on fresh-frozen samples from 348 patients affected by primary colon cancer, encompassing RNA, whole-exome, deep T cell receptor and 16S bacterial rRNA gene sequencing on tumor and matched healthy colon tissue, complemented with tumor whole-genome sequencing for further microbiome characterization. A type 1 helper T cell, cytotoxic, gene expression signature, called Immunologic Constant of Rejection, captured the presence of clonally expanded, tumor-enriched T cell clones and outperformed conventional prognostic molecular biomarkers, such as the consensus molecular subtype and the microsatellite instability classifications. Quantification of genetic immunoediting, defined as a lower number of neoantigens than expected, further refined its prognostic value. We identified a microbiome signature, driven by Ruminococcus bromii, associated with a favorable outcome. By combining microbiome signature and Immunologic Constant of Rejection, we developed and validated a composite score (mICRoScore), which identifies a group of patients with excellent survival probability. The publicly available multi-omics dataset provides a resource for better understanding colon cancer biology that could facilitate the discovery of personalized therapeutic approaches.
Identifiants
pubmed: 37202560
doi: 10.1038/s41591-023-02324-5
pii: 10.1038/s41591-023-02324-5
pmc: PMC10202816
doi:
Substances chimiques
Biomarkers, Tumor
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1273-1286Informations de copyright
© 2023. The Author(s).
Références
National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology https://www.nccn.org/guidelines/category_1 (2023).
Argilés, G. et al. Localised colon cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 31, 1291–1305 (2020).
pubmed: 32702383
doi: 10.1016/j.annonc.2020.06.022
Sayaman, R. W. et al. Germline genetic contribution to the immune landscape of cancer. Immunity 54, 367–386 (2021).
pubmed: 33567262
pmcid: 8414660
doi: 10.1016/j.immuni.2021.01.011
Pagès, F. et al. International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet 391, 2128–2139 (2018).
pubmed: 29754777
doi: 10.1016/S0140-6736(18)30789-X
Mlecnik, B. et al. Integrative analyses of colorectal cancer show immunoscore is a stronger predictor of patient survival than microsatellite instability. Immunity 44, 698–711 (2016).
pubmed: 26982367
doi: 10.1016/j.immuni.2016.02.025
Bruni, D., Angell, H. K. & Galon, J. The immune contexture and Immunoscore in cancer prognosis and therapeutic efficacy. Nat. Rev. Cancer 20, 662–680 (2020).
pubmed: 32753728
doi: 10.1038/s41568-020-0285-7
Foersch, S. et al. Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer. Nat. Med. https://doi.org/10.1038/s41591-022-02134-1 (2023).
Iglesia, M. D. et al. Genomic analysis of immune cell infiltrates across 11 tumor types. J. Natl Cancer Inst. 108, djw144 (2016).
pubmed: 27335052
pmcid: 5241901
doi: 10.1093/jnci/djw144
Danaher, P. et al. Pan-cancer adaptive immune resistance as defined by the Tumor Inflammation Signature (TIS): results from The Cancer Genome Atlas (TCGA). J. Immunother. Cancer 6, 63 (2018).
pubmed: 29929551
pmcid: 6013904
doi: 10.1186/s40425-018-0367-1
Roelands, J. et al. Oncogenic states dictate the prognostic and predictive connotations of intratumoral immune response. J. Immunother. Cancer 8, e000617 (2020).
pubmed: 32376723
pmcid: 7223637
doi: 10.1136/jitc-2020-000617
Liu, J. et al. An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell 173, 400–416 (2018).
pubmed: 29625055
pmcid: 6066282
doi: 10.1016/j.cell.2018.02.052
Poore, G. D. et al. Microbiome analyses of blood and tissues suggest cancer diagnostic approach. Nature 579, 567–574 (2020).
pubmed: 32214244
pmcid: 7500457
doi: 10.1038/s41586-020-2095-1
Dohlman, A. B. et al. The cancer microbiome atlas: a pan-cancer comparative analysis to distinguish tissue-resident microbiota from contaminants. Cell Host Microbe 29, 281–298 (2021).
pubmed: 33382980
pmcid: 7878430
doi: 10.1016/j.chom.2020.12.001
Aran, D., Sirota, M. & Butte, A. J. Systematic pan-cancer analysis of tumour purity. Nat. Commun. 6, 8971 (2015).
pubmed: 26634437
doi: 10.1038/ncomms9971
Bindea, G. et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 39, 782–795 (2013).
pubmed: 24138885
doi: 10.1016/j.immuni.2013.10.003
Guinney, J. et al. The Consensus Molecular Subtypes of colorectal cancer. Nat. Med. 21, 1350–1356 (2015).
pubmed: 26457759
pmcid: 4636487
doi: 10.1038/nm.3967
Mima, K. et al. Fusobacterium nucleatum in colorectal carcinoma tissue and patient prognosis. Gut 65, 1973–1980 (2016).
pubmed: 26311717
doi: 10.1136/gutjnl-2015-310101
Wang, E., Worschech, A. & Marincola, F. M. The Immunologic Constant of Rejection. Trends Immunol. 29, 256–262 (2008).
pubmed: 18457994
doi: 10.1016/j.it.2008.03.002
Galon, J., Angell, H. K., Bedognetti, D. & Marincola, F. M. The continuum of cancer immunosurveillance: prognostic, predictive, and mechanistic signatures. Immunity 39, 11–26 (2013).
pubmed: 23890060
doi: 10.1016/j.immuni.2013.07.008
Bertucci, F. et al. The Immunologic Constant of Rejection classification refines the prognostic value of conventional prognostic signatures in breast cancer. Br. J. Cancer 119, 1383–1391 (2018).
pubmed: 30353048
pmcid: 6265245
doi: 10.1038/s41416-018-0309-1
Hendrickx, W. et al. Identification of genetic determinants of breast cancer immune phenotypes by integrative genome-scale analysis. Oncoimmunology 6, e1253654 (2017).
pubmed: 28344865
pmcid: 5353940
doi: 10.1080/2162402X.2016.1253654
Sherif, S. et al. The immune landscape of solid pediatric tumors. J. Exp. Clin. Cancer Res. 41, 199 (2022).
pubmed: 35690832
pmcid: 9188257
doi: 10.1186/s13046-022-02397-z
Bertucci, F. et al. Immunologic Constant of Rejection signature is prognostic in soft-tissue sarcoma and refines the CINSARC signature. J. Immunother. Cancer 10, e003687 (2022).
pubmed: 35017155
pmcid: 8753443
doi: 10.1136/jitc-2021-003687
Rozenblit, M. et al. Transcriptomic profiles conducive to immune-mediated tumor rejection in human breast cancer skin metastases treated with Imiquimod. Sci. Rep. 9, 8572 (2019).
pubmed: 31189943
pmcid: 6561945
doi: 10.1038/s41598-019-42784-9
Mason, M. et al. A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer. Preprint at bioRxiv https://doi.org/10.1101/2022.12.05.518667 (2022).
Roelands, J. et al. Immunogenomic classification of colorectal cancer and therapeutic implications. Int. J. Mol. Sci. 18, 2229 (2017).
pubmed: 29064420
pmcid: 5666908
doi: 10.3390/ijms18102229
Schumacher, T. N. & Scheper, W. A liquid biopsy for cancer immunotherapy. Nat. Med 22, 340–341 (2016).
pubmed: 27050586
doi: 10.1038/nm.4074
Simoni, Y. Bystander CD8
pubmed: 29769722
doi: 10.1038/s41586-018-0130-2
Scheper, W. Low and variable tumor reactivity of the intratumoral TCR repertoire in human cancers. Nat. Med. 25, 89–94 (2019).
pubmed: 30510250
doi: 10.1038/s41591-018-0266-5
Bolotin, D. A. et al. MiXCR: software for comprehensive adaptive immunity profiling. Nat. Methods 12, 380–381 (2015).
pubmed: 25924071
doi: 10.1038/nmeth.3364
van der Leun, A. M., Thommen, D. S. & Schumacher, T. N. CD8
pubmed: 32024970
pmcid: 7115982
doi: 10.1038/s41568-019-0235-4
Bailey, M. H. et al. Comprehensive characterization of cancer driver genes and mutations. Cell 173, 371–385 (2018).
pubmed: 29625053
pmcid: 6029450
doi: 10.1016/j.cell.2018.02.060
Zhang, J. et al. Germline mutations in predisposition genes in pediatric cancer. N. Engl. J. Med. 373, 2336–2346 (2015).
pubmed: 26580448
pmcid: 4734119
doi: 10.1056/NEJMoa1508054
Gröbner, S. N. et al. The landscape of genomic alterations across childhood cancers. Nature 555, 321–327 (2018).
pubmed: 29489754
doi: 10.1038/nature25480
Saad, M. et al. Genetic predisposition to cancer across people of different ancestries in Qatar: a population-based, cohort study. Lancet Oncol. 23, 341–352 (2022).
pubmed: 35150601
doi: 10.1016/S1470-2045(21)00752-X
Ellrott, K. et al. Scalable open science approach for mutation calling of tumor exomes using multiple genomic pipelines. Cell Syst. 6, 271–281 (2018).
pubmed: 29596782
pmcid: 6075717
doi: 10.1016/j.cels.2018.03.002
Giannakis, M. et al. Genomic correlates of immune-cell infiltrates in colorectal carcinoma. Cell Rep. 15, 857–865 (2016).
pubmed: 27149842
pmcid: 4850357
doi: 10.1016/j.celrep.2016.03.075
Colaprico, A. et al. Interpreting pathways to discover cancer driver genes with Moonlight. Nat. Commun. 11, 69 (2020).
pubmed: 31900418
pmcid: 6941958
doi: 10.1038/s41467-019-13803-0
Harpaz, N. et al. Mucinous histology, BRCA1/2 mutations, and elevated tumor mutational burden in colorectal cancer. J. Oncol. 2020, e6421205 (2020).
Muzny, D. M. et al. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012).
doi: 10.1038/nature11252
Angelova, M. et al. Evolution of metastases in space and time under immune selection. Cell 175, 751–765 (2018).
pubmed: 30318143
doi: 10.1016/j.cell.2018.09.018
Kostic, A. D. et al. Genomic analysis identifies association of Fusobacterium with colorectal carcinoma. Genome Res. 22, 292–298 (2012).
pubmed: 22009990
pmcid: 3266036
doi: 10.1101/gr.126573.111
Wei, Z. et al. Could gut microbiota serve as prognostic biomarker associated with colorectal cancer patients’ survival? A pilot study on relevant mechanism. Oncotarget 7, 46158–46172 (2016).
pubmed: 27323816
pmcid: 5216788
doi: 10.18632/oncotarget.10064
Mima, K. et al. Fusobacterium nucleatum and T cells in colorectal carcinoma. JAMA Oncol. 1, 653–661 (2015).
pubmed: 26181352
pmcid: 4537376
doi: 10.1001/jamaoncol.2015.1377
Gur, C. et al. Binding of the Fap2 protein of Fusobacterium nucleatum to human inhibitory receptor TIGIT protects tumors from immune cell attack. Immunity 42, 344–355 (2015).
pubmed: 25680274
pmcid: 4361732
doi: 10.1016/j.immuni.2015.01.010
Gur, C. et al. Fusobacterium nucleatum supresses anti-tumor immunity by activating CEACAM1. Oncoimmunology 8, e1581531 (2019).
pubmed: 31069151
pmcid: 6492956
doi: 10.1080/2162402X.2019.1581531
Udayasuryan, B. et al. Fusobacterium nucleatum induces proliferation and migration in pancreatic cancer cells through host autocrine and paracrine signaling. Sci. Signal. 15, eabn4948 (2022).
pubmed: 36256708
pmcid: 9732933
doi: 10.1126/scisignal.abn4948
Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).
pubmed: 23028285
pmcid: 3447976
doi: 10.1371/journal.pcbi.1002687
Broz, M. L. et al. Dissecting the tumor myeloid compartment reveals rare activating antigen-presenting cells critical for T cell immunity. Cancer Cell 26, 638–652 (2014).
pubmed: 25446897
pmcid: 4254577
doi: 10.1016/j.ccell.2014.09.007
Helmink, B. A., Khan, M. A. W., Hermann, A., Gopalakrishnan, V. & Wargo, J. A. The microbiome, cancer, and cancer therapy. Nat. Med. 25, 377–388 (2019).
pubmed: 30842679
doi: 10.1038/s41591-019-0377-7
Nejman, D. et al. The human tumor microbiome is composed of tumor type-specific intracellular bacteria. Science 368, 973–980 (2020).
pubmed: 32467386
pmcid: 7757858
doi: 10.1126/science.aay9189
Smith, M. et al. Gut microbiome correlates of response and toxicity following anti-CD19 CAR T cell therapy. Nat. Med. 28, 713–723 (2022).
pubmed: 35288695
pmcid: 9434490
doi: 10.1038/s41591-022-01702-9
Gopalakrishnan, V. et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359, 97–103 (2017).
pubmed: 29097493
pmcid: 5827966
doi: 10.1126/science.aan4236
Liang, H. et al. Predicting cancer immunotherapy response from gut microbiomes using machine learning models. Oncotarget 13, 876–889 (2022).
pubmed: 35875611
pmcid: 9295706
doi: 10.18632/oncotarget.28252
Routy, B. et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359, 91–97 (2018).
pubmed: 29097494
doi: 10.1126/science.aan3706
Spencer, C. N. et al. Dietary fiber and probiotics influence the gut microbiome and melanoma immunotherapy response. Science 374, 1632–1640 (2021).
pubmed: 34941392
pmcid: 8970537
doi: 10.1126/science.aaz7015
Simpson, R. C. et al. Diet-driven microbial ecology underpins associations between cancer immunotherapy outcomes and the gut microbiome. Nat. Med 28, 2344–2352 (2022).
pubmed: 36138151
doi: 10.1038/s41591-022-01965-2
Chalabi, M. et al. Neoadjuvant immunotherapy leads to pathological responses in MMR-proficient and MMR-deficient early-stage colon cancers. Nat. Med. 26, 566–576 (2020).
pubmed: 32251400
doi: 10.1038/s41591-020-0805-8
Messaoudene, M. et al. A natural polyphenol exerts antitumor activity and circumvents anti-PD-1 resistance through effects on the gut microbiota. Cancer Discov. 12, 1070–1087 (2022).
pubmed: 35031549
pmcid: 9394387
doi: 10.1158/2159-8290.CD-21-0808
Liu, L. et al. Breast cancer stem cells characterized by CD70 expression preferentially metastasize to the lungs. Breast Cancer 25, 706–716 (2018).
pubmed: 29948958
doi: 10.1007/s12282-018-0880-6
Galeano Niño, J. L. et al. Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer. Nature 611, 810–817 (2022).
pubmed: 36385528
pmcid: 9684076
doi: 10.1038/s41586-022-05435-0
Noviello, T. M. R. et al. Guadecitabine plus ipilimumab in unresectable melanoma: five-year follow-up and correlation with integrated, multiomic analysis in the NIBIT-M4 trial. Preprint at medRxiv https://doi.org/10.1101/2023.02.09.23285227 (2023).
Łuksza, M. et al. Neoantigen quality predicts immunoediting in survivors of pancreatic cancer. Nature 606, 389–395 (2022).
pubmed: 35589842
pmcid: 9177421
doi: 10.1038/s41586-022-04735-9
Zapata, L. et al. Immune selection determines tumor antigenicity and influences response to checkpoint inhibitors. Nat. Genet. 55, 451–460 (2023).
pubmed: 36894710
pmcid: 10011129
doi: 10.1038/s41588-023-01313-1
Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).
pubmed: 20080505
pmcid: 2828108
doi: 10.1093/bioinformatics/btp698
Huang, K. et al. Pathogenic germline variants in 10,389 adult cancers. Cell 173, 355–370 (2018).
pubmed: 29625052
pmcid: 5949147
doi: 10.1016/j.cell.2018.03.039
Vogelstein, B. et al. Cancer genome landscapes. Science 339, 1546–1558 (2013).
pubmed: 23539594
pmcid: 3749880
doi: 10.1126/science.1235122
Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol 1, 1–16 (2017).
Wilkerson, M. D. & Hayes, D. N. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 26, 1572–1573 (2010).
pubmed: 20427518
pmcid: 2881355
doi: 10.1093/bioinformatics/btq170
Jiménez-Sánchez, A., Cast, O. & Miller, M. L. Comprehensive benchmarking and integration of tumor microenvironment cell estimation. Methods Cancer Res. 79, 6238–6246 (2019).
pubmed: 31641033
doi: 10.1158/0008-5472.CAN-18-3560
Barbie, D. A. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462, 108–112 (2009).
pubmed: 19847166
pmcid: 2783335
doi: 10.1038/nature08460
Thorsson, V. et al. The immune landscape of cancer. Immunity 48, 812–830 (2018).
pubmed: 29628290
pmcid: 5982584
doi: 10.1016/j.immuni.2018.03.023
Sayaman, R. W. et al. Analytic pipelines to assess the relationship between immune response and germline genetics in human tumors. STAR Protoc. 3, 101809 (2022).
pubmed: 36595917
pmcid: 9772839
doi: 10.1016/j.xpro.2022.101809
Benci, J. L. et al. Opposing functions of interferon coordinate adaptive and innate immune responses to cancer immune checkpoint blockade. Cell 178, 933–948 (2019).
pubmed: 31398344
pmcid: 6830508
doi: 10.1016/j.cell.2019.07.019
Beausang, J. F. et al. T cell receptor sequencing of early-stage breast cancer tumors identifies altered clonal structure of the T cell repertoire. Proc. Natl Acad. Sci. USA 114, E10409–E10417 (2017).
pubmed: 29138313
pmcid: 5715779
doi: 10.1073/pnas.1713863114
D’Angelo, F. et al. The molecular landscape of glioma in patients with neurofibromatosis 1. Nat. Med. 25, 176–187 (2019).
pubmed: 30531922
doi: 10.1038/s41591-018-0263-8
Bonneville, R. et al. Landscape of microsatellite instability across 39 cancer types. JCO Precis. Oncol. 1, 1–15 (2017).
Szolek, A. et al. OptiType: precision HLA typing from next-generation sequencing data. Bioinformatics 30, 3310–3316 (2014).
pubmed: 25143287
pmcid: 4441069
doi: 10.1093/bioinformatics/btu548
Zhang, J. et al. The combination of neoantigen quality and T lymphocyte infiltrates identifies glioblastomas with the longest survival. Commun. Biol. 2, 1–10 (2019).
doi: 10.1038/s42003-019-0369-7
Truong, D. T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods 12, 902–903 (2015).
pubmed: 26418763
doi: 10.1038/nmeth.3589
Wang, R.-F., Cao, W.-W. & Cerniglia, C. E. PCR detection of Ruminococcus spp. in human and animal faecal samples. Mol. Cell. Probes 11, 259–265 (1997).
pubmed: 9281411
doi: 10.1006/mcpr.1997.0111
Salter, S. J. et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87 (2014).
pubmed: 25387460
pmcid: 4228153
doi: 10.1186/s12915-014-0087-z
Weiss, S. et al. Correlation detection strategies in microbial data sets vary widely in sensitivity and precision. ISME J. 10, 1669–1681 (2016).
pubmed: 26905627
pmcid: 4918442
doi: 10.1038/ismej.2015.235
Peschel, S., Müller, C. L., von Mutius, E., Boulesteix, A.-L. & Depner, M. NetCoMi: network construction and comparison for microbiome data in R. Brief. Bioinform. 22, bbaa290 (2021).
pubmed: 33264391
doi: 10.1093/bib/bbaa290
Henderson, G. et al. Improved taxonomic assignment of rumen bacterial 16S rRNA sequences using a revised SILVA taxonomic framework. PeerJ 7, e6496 (2019).
pubmed: 30863673
pmcid: 6407505
doi: 10.7717/peerj.6496
Spratt, D. E. et al. Racial/ethnic disparities in genomic sequencing. JAMA Oncol. 2, 1070–1074 (2016).
pubmed: 27366979
pmcid: 5123755
doi: 10.1001/jamaoncol.2016.1854
Roelands, J. et al. Supplementary Data AC-ICAM. Figshare https://doi.org/10.6084/m9.figshare.16944775.v1 (2023).