Whole genome sequencing (WGS) of prospectively collected tissue biopsies of 442 metastatic breast cancer (mBC) patients reveals that, compared to primary BC, tumour mutational burden (TMB) doubled, relative contributions of mutational signatures shifted, and mutation frequency of six known driver genes increased in mBC. Significant associations with pre-treatment were observed as well. The contribution of mutational signature 17 was significantly enriched in patients pre-treated with 5-FU, taxanes, platinum and/or eribulin, whereas the here identified de novo mutational signature I was significantly associated with pre-treatment containing platinum-based chemotherapy. Clinically relevant subgroups of tumours were identified exhibiting either homologous recombination deficiency (13%), high TMB (11%) or specific alterations (24%) linked to sensitivity to FDA-approved drugs. This study provides important novel insight into the biology of mBC and identifies clinically useful genomic features for future improvement of patient management.

Metastatic castration-resistant prostate cancer (mCRPC) has a highly complex genomic landscape. With the recent development of novel treatments, accurate stratification strategies are needed. Here we present the whole-genome sequencing (WGS) analysis of fresh-frozen metastatic biopsies from 197 mCRPC patients. Using unsupervised clustering based on genomic features, we define eight distinct genomic clusters. We observe potentially clinically relevant genotypes, including microsatellite instability (MSI), homologous recombination deficiency (HRD) enriched with genomic deletions and BRCA2 aberrations, a tandem duplication genotype associated with CDK12−/− and a chromothripsis-enriched subgroup. Our data suggests that stratification on WGS characteristics may improve identification of MSI, CDK12−/− and HRD patients. From WGS and ChIP-seq data, we show the potential relevance of recurrent alterations in non-coding regions identified with WGS and highlight the central role of AR signaling in tumor progression. These data underline the potential value of using WGS to accurately stratify mCRPC patients into clinically actionable subgroups.

BackgroundCurrent normalization methods for RNA-sequencing data allow either for intersample comparison to identify differentially expressed (DE) genes or for intrasample comparison for the discovery and validation of gene signatures. Most studies on optimization of normalization methods typically use simulated data to validate methodologies. We describe a new method, GeTMM, which allows for both inter- and intrasample analyses with the same normalized data set. We used actual (i.e. not simulated) RNA-seq data from 263 colon cancers (no biological replicates) and used the same read count data to compare GeTMM with the most commonly used normalization methods (i.e. TMM (used by edgeR), RLE (used by DESeq2) and TPM) with respect to distributions, effect of RNA quality, subtype-classification, recurrence score, recall of DE genes and correlation to RT-qPCR data.ResultsWe observed a clear benefit for GeTMM and TPM with regard to intrasample comparison while GeTMM performed similar to TMM and RLE normalized data in intersample comparisons. Regarding DE genes, recall was found comparable among the normalization methods, while GeTMM showed the lowest number of false-positive DE genes. Remarkably, we observed limited detrimental effects in samples with low RNA quality.ConclusionsWe show that GeTMM outperforms established methods with regard to intrasample comparison while performing equivalent with regard to intersample normalization using the same normalized data. These combined properties enhance the general usefulness of RNA-seq but also the comparability to the many array-based gene expression data in the public domain.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2246-7) contains supplementary material, which is available to authorized users.

Tumors of germline mutated carriers show homologous recombination (HR) deficiency (HRD), resulting in impaired DNA double-strand break (DSB) repair and high sensitivity to PARP inhibitors. Although this therapy is expected to be effective beyond germline mutated carriers, a robust validated test to detect HRD tumors is lacking. In this study, we therefore evaluated a functional HR assay exploiting the formation of RAD51 foci in proliferating cells after irradiation of fresh breast cancer tissue: the recombination REpair CAPacity (RECAP) test. Fresh samples of 170 primary breast cancer were analyzed using the RECAP test. The molecular explanation for the HRD phenotype was investigated by exploring deficiencies, mutational signatures, tumor-infiltrating lymphocytes (TIL), and microsatellite instability (MSI). RECAP was completed successfully in 148 of 170 samples (87%). Twenty-four tumors showed HRD (16%), whereas six tumors were HR intermediate (HRi; 4%). HRD was explained by deficiencies (mutations, promoter hypermethylation, deletions) in 16 cases, whereas seven HRD tumors were non-BRCA related. HRD tumors showed an increased incidence of high TIL counts ( = 0.023) compared with HR proficient (HRP) tumors and MSI was more frequently observed in the HRD group (2/20, 10%) than expected in breast cancer (1%; = 0.017). RECAP is a robust functional HR assay detecting both -deficient and-proficient HRD tumors. Functional assessment of HR in a pseudo-diagnostic setting is achievable and produces robust and interpretable results.