Converging and evolving immuno-genomic routes toward immune escape in breast cancer.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
21 Feb 2024
21 Feb 2024
Historique:
received:
21
08
2023
accepted:
19
01
2024
medline:
22
2
2024
pubmed:
22
2
2024
entrez:
21
2
2024
Statut:
epublish
Résumé
The interactions between tumor and immune cells along the course of breast cancer progression remain largely unknown. Here, we extensively characterize multiple sequential and parallel multiregion tumor and blood specimens of an index patient and a cohort of metastatic triple-negative breast cancers. We demonstrate that a continuous increase in tumor genomic heterogeneity and distinct molecular clocks correlated with resistance to treatment, eventually allowing tumors to escape from immune control. TCR repertoire loses diversity over time, leading to convergent evolution as breast cancer progresses. Although mixed populations of effector memory and cytotoxic single T cells coexist in the peripheral blood, defects in the antigen presentation machinery coupled with subdued T cell recruitment into metastases are observed, indicating a potent immune avoidance microenvironment not compatible with an effective antitumor response in lethal metastatic disease. Our results demonstrate that the immune responses against cancer are not static, but rather follow dynamic processes that match cancer genomic progression, illustrating the complex nature of tumor and immune cell interactions.
Identifiants
pubmed: 38383522
doi: 10.1038/s41467-024-45292-1
pii: 10.1038/s41467-024-45292-1
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1302Informations de copyright
© 2024. The Author(s).
Références
Gruosso, T. et al. Spatially distinct tumor immune microenvironments stratify triple-negative breast cancers. J. Clin. Invest. 129, 1785–1800 (2019).
pubmed: 30753167
pmcid: 6436884
doi: 10.1172/JCI96313
Zhang, X. et al. Breast cancer neoantigens can induce CD8+ T-cell responses and antitumor immunity. Cancer Immunol. Res. 5, 516–523 (2017).
pubmed: 28619968
pmcid: 5647648
doi: 10.1158/2326-6066.CIR-16-0264
Narang, P., Chen, M., Sharma, A. A., Anderson, K. S. & Wilson, M. A. The neoepitope landscape of breast cancer: Implications for immunotherapy. BMC Cancer https://doi.org/10.1186/s12885-019-5402-1 (2019).
Denkert, C. et al. Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: a pooled analysis of 3771 patients treated with neoadjuvant therapy. Lancet Oncol. 19, 40–50 (2018).
pubmed: 29233559
doi: 10.1016/S1470-2045(17)30904-X
Luen, S. J., Savas, P., Fox, S. B., Salgado, R. & Loi, S. Tumour-infiltrating lymphocytes and the emerging role of immunotherapy in breast cancer. Pathology 49, 141–155 (2017).
pubmed: 28049579
doi: 10.1016/j.pathol.2016.10.010
Beatty, G. L. & Gladney, W. L. Immune escape mechanisms as a guide for cancer immunotherapy. Clin. Cancer Res. https://doi.org/10.1158/1078-0432.CCR-14-1860 (2015).
Ling, S. et al. Extremely high genetic diversity in a single tumor points to prevalence of non-Darwinian cell evolution. Proc. Natl Acad. Sci. USA 112, E6496–E6505 (2015).
pubmed: 26561581
pmcid: 4664355
doi: 10.1073/pnas.1519556112
Angelova, M. et al. Evolution of metastases in space and time under immune selection. Cell 175, 751–765.e16 (2018).
pubmed: 30318143
doi: 10.1016/j.cell.2018.09.018
Gerlinger, M. et al. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat. Genet. https://doi.org/10.1038/ng.2891 (2014).
Siegel, M. B. et al. Integrated RNA and DNA sequencing reveals early drivers of metastatic breast cancer. J. Clin. Invest. 128, 1371–1383 (2018).
pubmed: 29480819
pmcid: 5873890
doi: 10.1172/JCI96153
De Mattos-Arruda, L. et al. The genomic and immune landscapes of lethal metastatic breast cancer. Cell Rep. https://doi.org/10.1016/j.celrep.2019.04.098 (2019).
St. Paul, M. & Ohashi, P. S. The roles of CD8+ T cell subsets in antitumor immunity. Trends Cell Biol. 30, 695–704 (2020).
doi: 10.1016/j.tcb.2020.06.003
Morisita, M. Measuring of the dispersion and analysis of distribution patterns. Mem Fac. Sci. Kyushu Univ. Ser. E Biol. 2, 215–235 (1959).
Cesano, A. & Warren, S. Bringing the next generation of immuno-oncology biomarkers to the clinic. Biomedicines 6, 14 (2018).
pubmed: 29393888
pmcid: 5874671
doi: 10.3390/biomedicines6010014
Ayers, M. et al. IFN-γ–related mRNA profile predicts clinical response to PD-1 blockade. J. Clin. Invest. 127, 2930–2940 (2017).
pubmed: 28650338
pmcid: 5531419
doi: 10.1172/JCI91190
McGranahan, N. et al. Allele-specific HLA loss and immune escape in lung cancer evolution. Cell 171, 1259–1271.e11 (2017).
pubmed: 29107330
pmcid: 5720478
doi: 10.1016/j.cell.2017.10.001
Kawaguchi, S., Higasa, K., Shimizu, M., Yamada, R. & Matsuda, F. HLA‐HD: an accurate HLA typing algorithm for next‐generation sequencing data. Hum. Mutat. 38, 788–797 (2017).
pubmed: 28419628
doi: 10.1002/humu.23230
Lee, C. J. et al. Stat2 stability regulation: an intersection between immunity and carcinogenesis. Exp. Mol. Med. https://doi.org/10.1038/s12276-020-00506-6 (2020).
Kalaora, S. et al. Immunoproteasome expression is associated with better prognosis and response to checkpoint therapies in melanoma. Nat. Commun. 11, 896 (2020).
pubmed: 32060274
pmcid: 7021791
doi: 10.1038/s41467-020-14639-9
Yates, L. R. et al. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat. Med. 21, 751–759 (2015).
pubmed: 26099045
pmcid: 4500826
doi: 10.1038/nm.3886
Gerstung, M. et al. The evolutionary history of 2,658 cancers. Nature 578, 122–128 (2020).
pubmed: 32025013
pmcid: 7054212
doi: 10.1038/s41586-019-1907-7
Rosenthal, R. et al. Neoantigen-directed immune escape in lung cancer evolution. Nature https://doi.org/10.1038/s41586-019-1032-7 (2019).
Zhang, Z. et al. T cell dysfunction and exhaustion in cancer. Front. Cell Dev. Biol. 8, 17 (2020).
pubmed: 32117960
pmcid: 7027373
doi: 10.3389/fcell.2020.00017
De Mattos-Arruda, L. et al. Neoantigen prediction and computational perspectives towards clinical benefit: recommendations from the ESMO Precision Medicine Working Group. Ann. Oncol. 31, 978–990 (2020).
pubmed: 32610166
doi: 10.1016/j.annonc.2020.05.008
McGranahan, N. et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351, 1463–1469 (2016).
pubmed: 26940869
pmcid: 4984254
doi: 10.1126/science.aaf1490
ImmunoMind Team. immunarch: An R Package for Painless Bioinformatics Analysis of T-Cell and B-Cell Immune Repertoires. Zenodo. https://doi.org/10.5281/zenodo.3367200 (2019).
Shugay, M. et al. VDJtools: unifying post-analysis of T cell receptor repertoires. PLoS Comput. Biol. 11, e1004503 (2015).
pubmed: 26606115
pmcid: 4659587
doi: 10.1371/journal.pcbi.1004503
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
pubmed: 29608179
pmcid: 6700744
doi: 10.1038/nbt.4096
Zappia, L. & Oshlack, A. Clustering trees: a visualization for evaluating clusterings at multiple resolutions. Gigascience 7, giy083 (2018).
pubmed: 30010766
pmcid: 6057528
doi: 10.1093/gigascience/giy083
Rempala, G. A. & Seweryn, M. Methods for diversity and overlap analysis in T-cell receptor populations. J. Math. Biol. 67, 1339–1368 (2013).
pubmed: 23007599
doi: 10.1007/s00285-012-0589-7
Finotello, F. et al. Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. Genome Med. 11, 34 (2019).
pubmed: 31126321
pmcid: 6534875
doi: 10.1186/s13073-019-0638-6
Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G. & Hacohen, N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160, 48–61 (2015).
pubmed: 25594174
pmcid: 4856474
doi: 10.1016/j.cell.2014.12.033
Pareja, F. et al. The genomic landscape of mucinous breast cancer. J. Natl Cancer Inst. 111, 737–741 (2019).
pubmed: 30649385
pmcid: 6624163
doi: 10.1093/jnci/djy216
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
pubmed: 19451168
pmcid: 2705234
doi: 10.1093/bioinformatics/btp324
Auwera, G. A. et al. From FastQ data to high‐confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinform. 43, 11.10.1–11.10.33 (2013).
Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. https://doi.org/10.1038/nbt.2514 (2013).
McLaren, W. et al. The Ensembl variant effect predictor. Genome Biol. https://doi.org/10.1186/s13059-016-0974-4 (2016).
Thorvaldsdóttir, H., Robinson, J. T. & Mesirov, J. P. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief. Bioinform. https://doi.org/10.1093/bib/bbs017 (2013).
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
pubmed: 20644199
pmcid: 2928508
doi: 10.1101/gr.107524.110
Saunders, C. T. et al. Strelka: accurate somatic small-variant calling from sequenced tumor–normal sample pairs. Bioinformatics 28, 1811–1817 (2012).
pubmed: 22581179
doi: 10.1093/bioinformatics/bts271
Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).
pubmed: 22300766
pmcid: 3290792
doi: 10.1101/gr.129684.111
Narzisi, G. et al. Accurate detection of de novo and transmitted INDELs within exome-capture data using micro-assembly. Nat. Methods 11, 1033–1036 (2013).
doi: 10.1038/nmeth.3069
Narzisi, G. et al. Genome-wide somatic variant calling using localized colored de Bruijn graphs. Commun. Biol. 1, 1–9 (2018).
doi: 10.1038/s42003-018-0023-9
Ashley, C. W. et al. Analysis of mutational signatures in primary and metastatic endometrial cancer reveals distinct patterns of DNA repair defects and shifts during tumor progression. Gynecol. Oncol. 152, 11–19 (2019).
pubmed: 30415991
doi: 10.1016/j.ygyno.2018.10.032
Carter, S. L. et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 30, 413–421 (2012).
pubmed: 22544022
pmcid: 4383288
doi: 10.1038/nbt.2203
Geyer, F. C. et al. Recurrent hotspot mutations in HRAS Q61 and PI3K-AKT pathway genes as drivers of breast adenomyoepitheliomas. Nat. Commun. 9, 1816 (2018).
pubmed: 29739933
pmcid: 5940840
doi: 10.1038/s41467-018-04128-5
Weigelt, B. et al. The landscape of somatic genetic alterations in breast cancers from atm germline mutation carriers. J. Natl Cancer Inst. 110, 1030–1034 (2018).
pubmed: 29506079
pmcid: 6136925
doi: 10.1093/jnci/djy028
Landau, D. A. et al. Evolution and impact of subclonal mutations in chronic lymphocytic leukemia. Cell 152, 714–726 (2013).
pubmed: 23415222
pmcid: 3575604
doi: 10.1016/j.cell.2013.01.019
Ng, C. K. Y. et al. The landscape of somatic genetic alterations in metaplastic breast carcinomas. Clin. Cancer Res. 23, 3859–3870 (2017).
pubmed: 28153863
pmcid: 5511565
doi: 10.1158/1078-0432.CCR-16-2857
Shen, R. & Seshan, V. E. FACETS: Allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing. Nucleic Acids Res. 44, 1–9 (2016).
doi: 10.1093/nar/gkw520
Cheng, D. T. et al. Memorial sloan kettering-integrated mutation profiling of actionable cancer targets (MSK-IMPACT): A hybridization capture-based next-generation sequencing clinical assay for solid tumor molecular oncology. J. Mol. Diagn. 17, 251–264 (2015).
pubmed: 25801821
pmcid: 5808190
doi: 10.1016/j.jmoldx.2014.12.006
Pareja, F. et al. Loss-of-function mutations in ATP6AP1 and ATP6AP2 in granular cell tumors. Nat. Commun. 9, 3533 (2018).
pubmed: 30166553
pmcid: 6117336
doi: 10.1038/s41467-018-05886-y
Ellrott, K. et al. Scalable open science approach for mutation calling of tumor exomes using multiple genomic pipelines. Cell Syst. 6, 271–281.e7 (2018).
pubmed: 29596782
pmcid: 6075717
doi: 10.1016/j.cels.2018.03.002
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
pubmed: 24695404
pmcid: 4103590
doi: 10.1093/bioinformatics/btu170
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
pubmed: 27043002
doi: 10.1038/nbt.3519
Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinforma. 12, 323 (2011).
doi: 10.1186/1471-2105-12-323
Jurtz, V. et al. NetMHCpan-4.0: improved peptide–MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data. J. Immunol. 199, 3360–3368 (2017).
pubmed: 28978689
doi: 10.4049/jimmunol.1700893
Schenck, R. O., Lakatos, E., Gatenbee, C., Graham, T. A. & Anderson, A. R. A. NeoPredPipe: high-throughput neoantigen prediction and recognition potential pipeline. BMC Bioinforma. 20, 264 (2019).
doi: 10.1186/s12859-019-2876-4
Kaartinen, T. et al. Low interleukin-2 concentration favors generation of early memory T cells over effector phenotypes during chimeric antigen receptor T-cell expansion. Cytotherapy 19, 689–702 (2017).
pubmed: 28411126
doi: 10.1016/j.jcyt.2017.03.067
Frahm, N. et al. Consistent cytotoxic-T-lymphocyte targeting of immunodominant regions in human immunodeficiency virus across multiple ethnicities. J. Virol. 78, 2187–2200 (2004).
pubmed: 14963115
pmcid: 369231
doi: 10.1128/JVI.78.5.2187-2200.2004
Slota, M., Lim, J.-B., Dang, Y. & Disis, M. L. ELISpot for measuring human immune responses to vaccines. Expert Rev. Vaccines 10, 299–306 (2011).
pubmed: 21434798
pmcid: 3360522
doi: 10.1586/erv.10.169
Waskom, M. Seaborn: statistical data visualization. J. Open Source Softw. 6, 3021 (2021).
doi: 10.21105/joss.03021
Favero, F. et al. Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data. Ann. Oncol. 26, 64–70 (2015).
pubmed: 25319062
doi: 10.1093/annonc/mdu479
Gillis, R., Gillis, S. & Roth, A. PyClone-VI: scalable inference of clonal population structures using whole genome data. bioRxiv 21, 571 (2020).
Dang, H. X. et al. ClonEvol: clonal ordering and visualization in cancer sequencing. Ann. Oncol. 28, 3076–3082 (2017).
pubmed: 28950321
pmcid: 5834020
doi: 10.1093/annonc/mdx517
Salgado, R. et al. The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014. Ann. Oncol. 26, 259–271 (2015).
pubmed: 25214542
doi: 10.1093/annonc/mdu450
Nederlof, I. et al. Comprehensive evaluation of methods to assess overall and cell-specific immune infiltrates in breast cancer. Breast Cancer Res. 21, 151 (2019).
pubmed: 31878981
pmcid: 6933637
doi: 10.1186/s13058-019-1239-4
Rathore, A. S. et al. CD3+, CD4+ & CD8+ tumour infiltrating lymphocytes (TILs) are predictors of favourable survival outcome in infiltrating ductal carcinoma of breast. Indian J. Med. Res. 140, 361–369 (2014)
Nielsen, T. O. et al. Assessment of Ki67 in breast cancer: updated recommendations from the International Ki67 in Breast Cancer Working Group. J. Natl Cancer Inst. 113, 808–819 (2021).
pubmed: 33369635
doi: 10.1093/jnci/djaa201
Lundberg, M., Eriksson, A., Tran, B., Assarsson, E. & Fredriksson, S. Homogeneous antibody-based proximity extension assays provide sensitive and specific detection of low-abundant proteins in human blood. Nucleic Acids Res. 39, e102 (2011).
pubmed: 21646338
pmcid: 3159481
doi: 10.1093/nar/gkr424
Assarsson, E. et al. Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLoS ONE 9, e95192 (2014).
pubmed: 24755770
pmcid: 3995906
doi: 10.1371/journal.pone.0095192
Blanco-Heredia, J. jblancoheredia/genomic_immune_tnbc_2024: 11DEC23 (v0.0). Zenodo. https://doi.org/10.5281/zenodo.10359740 (2023).
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