The somatic mutation profiles of 2,433 breast cancers refine their genomic and transcriptomic landscapes

Abstract

The genomic landscape of breast cancer is complex, and inter- and intra-tumour heterogeneity are important challenges in treating the disease. In this study, we sequence 173 genes in 2,433 primary breast tumours that have copy number aberration (CNA), gene expression and long-term clinical follow-up data. We identify 40 mutation-driver (Mut-driver) genes, and determine associations between mutations, driver CNA profiles, clinical-pathological parameters and survival. We assess the clonal states of Mut-driver mutations, and estimate levels of intra-tumour heterogeneity using mutant-allele fractions. Associations between PIK3CA mutations and reduced survival are identified in three subgroups of ER-positive cancer (defined by amplification of 17q23, 11q13–14 or 8q24). High levels of intra-tumour heterogeneity are in general associated with a worse outcome, but highly aggressive tumours with 11q13–14 amplification have low levels of intra-tumour heterogeneity. These results emphasize the importance of genome-based stratification of breast cancer, and have important implications for designing therapeutic strategies.

Tumor mutational burden is a determinant of immune-mediated survival in breast cancer

“…Mutation counts and updated clinical data were obtained from Pereira and colleagues. 52 GISTIC-processed copy number data were obtained from cBioPortal (http://www.cbioportal.org/). 53,54 In aggregate, 1,895 evaluable cases had corresponding gene expression, mutation count and overall survival data.…”

“…A 173-gene exome sequencing panel was used to identify somatic gene mutations and generate measures of mutational burden (gene count). 52 …”

“…52 While the TCGA approach (mutation rate) accounts for sample-to-sample variation in tumor sequencing depth, the METABRIC approach (mutation count) does not. We therefore sought to determine if estimates of TMB derived from METABRIC differed significantly to that of TCGA.…”

“…al. 52 (for METABRIC data) utilized different algorithms and mutation detection thresholds. Furthermore, while the TCGA approach utilized matched normal tissue for all somatic mutation calls, the METABRIC approach utilized a “pooled” normal strategy (ie, only a minor fraction of patient normal tissue specimens were sequenced), and thus, was reliant on a greater degree of inference for mutation calling.…”

Impact of chemotactic factors and receptors on the cancer immune infiltrate: a bioinformatics study revealing homogeneity and heterogeneity among patient cohorts

“…46 We also included the METABRIC dataset 81 in this analysis, because we had access to the information whether patients were alive or deceased, a status that is related to disease prognosis and treatment response. In four out of these five datasets (all except the Korde cohort, probably due to its small size), expression of chemotactic factors/receptors that positively correlated with leukocyte infiltration across distinct cancer types (as defined in Figure 4) was positively associated with treatment response (Bonnefoi, Hatzis and Tabchy datasets) or overall survival (METABRIC dataset) (Table 2).…”