Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

21 Breast Cancer Genome Affymetrix SNP6 Array Data

ABSTRACT: Cancer evolves dynamically, as clonal expansions supersede or overlap one another, driven by shifting selective pressures, mutational processes and disrupted cancer genes. These processes mark the genome, such that a cancerÕs life history is encrypted in the timing, ploidy, clonality and patterns of somatic mutation. We developed bioinformatic algorithms to decipher this narrative, and applied them to 21 breast cancer genomes. We find that mutational processes evolve across the lifespan of a breast tumor, with cancer-specific signatures of point mutations and chromosomal instability often emerging late but contributing extensive genetic variation. Subclonal diversification is prominent, providing insight into the dynamics of clonal expansion in breast cancer. Most point mutations are found in just a fraction of tumor cells, together with frequent variegation in chromosomal copy number. Every tumor studied here has a dominant subclonal lineage, representing more than 50% of tumor cells. Minimal expansion of these subclones occurs until many hundreds to thousands of mutations have accumulated, implying the existence of long-lived, quiescent lineages of cells that are capable of substantial proliferation upon acquisition of enabling genomic changes. Expansion of the dominant subclone to an appreciable mass may therefore represent the final rate-limiting step in a breast cancer's development, triggering diagnosis.

ORGANISM(S): Homo sapiens

DISEASE(S): ductal breast cancer

SUBMITTER: Stuart McLaren

PROVIDER: E-MTAB-1087 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Mutational processes molding the genomes of 21 breast cancers.

Nik-Zainal Serena S Alexandrov Ludmil B LB Wedge David C DC Van Loo Peter P Greenman Christopher D CD Raine Keiran K Jones David D Hinton Jonathan J Marshall John J Stebbings Lucy A LA Menzies Andrew A Martin Sancha S Leung Kenric K Chen Lina L Leroy Catherine C Ramakrishna Manasa M Rance Richard R Lau King Wai KW Mudie Laura J LJ Varela Ignacio I McBride David J DJ Bignell Graham R GR Cooke Susanna L SL Shlien Adam A Gamble John J Whitmore Ian I Maddison Mark M Tarpey Patrick S PS Davies Helen R HR Papaemmanuil Elli E Stephens Philip J PJ McLaren Stuart S Butler Adam P AP Teague Jon W JW Jönsson Göran G Garber Judy E JE Silver Daniel D Miron Penelope P Fatima Aquila A Boyault Sandrine S Langerød Anita A Tutt Andrew A Martens John W M JW Aparicio Samuel A J R SA Borg Åke Å Salomon Anne Vincent AV Thomas Gilles G Børresen-Dale Anne-Lise AL Richardson Andrea L AL Neuberger Michael S MS Futreal P Andrew PA Campbell Peter J PJ Stratton Michael R MR

Cell 20120517 5

All cancers carry somatic mutations. The patterns of mutation in cancer genomes reflect the DNA damage and repair processes to which cancer cells and their precursors have been exposed. To explore these mechanisms further, we generated catalogs of somatic mutation from 21 breast cancers and applied mathematical methods to extract mutational signatures of the underlying processes. Multiple distinct single- and double-nucleotide substitution signatures were discernible. Cancers with BRCA1 or BRCA2 ...[more]

PMID: 22608084

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Project description:Intratumoral heterogeneity (ITH) arises from distinct subclonal expansion following genetic or epigenetic alterations, and profoundly influences how tumors response to their immune microenvironment. Tumor progression trees based on single-cell mutational profiles have made it possible to trace subclonal evolution; however, conventional tree-building methods can incorporate only a limited number of cells and mutations, restricting their application to larger single-cell data. To investigate the effect of ITH on the therapeutic response of melanoma, we have developed Trisicell (https://trisicell.rtfd.io), a computational toolkit for scalable inference of mutational ITH through assessment of single-cell genomic variant data. By applying Trisicell to genetically matched mouse melanoma datasets, we found that expressed mutations are sufficient to effectively drive subclonal evolution. On single-cell, full-length RNA sequencing data of mouse melanoma from preclinical immune checkpoint blockade (ICB) studies, the analysis showed that the subtree-seeding mutations in the trees identified distinct subclones associated with a specific developmental state and neural crest lineage markers. Using the tree to trace cell lineages, we found that neoantigens depleted by ICB were predominantly expressed in minor subclones, suggesting that post-treatment recurrence is driven by immunoediting. Moreover, these neoantigens were enriched with those derived from frameshift mutations and mutated nuclear genes. Importantly, recurrently mutated genes in ICB-responding human melanoma exhibited the same features. We next used Trisicell to analyze single-cell, full-length RNA data of brain metastases (BM) from melanoma patients treated with ICB, and discovered that relapsing BM from ICB-responding patients exhibited subclones that also expressed a higher fraction of frameshift mutations and were associated with elevated levels of infiltrated T cells. Notably, they also exhibited more mutated HLA genes and expressed high level of the novel immune checkpoint HLA-G, a putative local immunosuppressive mechanism. In summary, applying Trisicell to single-cell transcriptomic analyses allowed us to identify novel features of ICB-responding melanoma neoantigens and distinct mechanisms for adapting to immunotherapy at different sites. These results have important implications for both melanoma evolution and target identification for immunotherapies, including ICB and cancer vaccines. This is the first study to trace subclonal evolution of neoantigens at single-cell resolution, demonstrating that Trisicell and our datasets represent important resources for the field.

Dataset Information

21 Breast Cancer Genome Affymetrix SNP6 Array Data

Publications

Mutational processes molding the genomes of 21 breast cancers.

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