Breast cancer is a complex molecular disease comprising several biological subtypes. However, daily routine diagnosis is still based on a small set of well-characterized clinico-pathological variables. Here, we try to link the two worlds of surgical pathology and multilayered molecular profiling by analyzing the relationships between clinico-pathological phenotypes and mutational loads of breast cancer. We evaluated the number of mutated genes with somatic non-silent mutations in different subgroups of breast cancer based on clinico-pathological, including immunohistochemical and tumour characteristics. The analysis was performed for a cohort of 687 primary breast cancer patients with mutational profiling, gene expression and clinico-pathological data available from The Cancer Genome Atlas (TCGA) project. The number of mutated genes was strongly positively associated with higher tumour grade (p = 1.4e-14) and with the different immunohistochemical and PAM50 molecular subtypes of breast cancer (p = 1.4e-10 and p = 4.3e-10, respectively). We observed significant associations (|R| > 0.4) between the abundance of mutated genes and expression levels of genes related to proliferation in the overall cohort and hormone receptor positive cohort, including the Recurrence Score gene signature (e.g., MYBL2 and BIRC5). Specific mutated genes (TP53, NCOR1, NF1, PTPRD and RB1) were highly significantly associated with high loads of mutated genes. Multivariate analysis for overall survival (OS) revealed a worse survival for patients with high numbers of mutated genes (hazard ratio = 4.6, 95% CI: 1.0 - 20.0, p = 0.044). Here, we report a strong association of the number of mutated genes with immunohistochemical and PAM50 subtypes and tumour grade in breast cancer. We provide evidence that specific levels of the mutational load underlie different morphological and biological phenotypes, which collectively constitute the current basis of pathological diagnosis. Our study is a step towards genomics-informed breast pathology and will provide a basis for future studies in this field bridging the gap between morphology, tumour biology and medical oncology.
Keywords: breast cancer; clinical parameters; genetics; mutations; pathology; prognosis; staging; tumour grade; tumour size.