Loss of BRCA1 or BRCA2 markedly increases the rate of base substitution mutagenesis and has distinct effects on genomic deletions

Loss-of-function mutations in the BRCA1 and BRCA2 genes increase the risk of cancer. Owing to their function in homologous recombination repair, much research has focused on the unstable genomic phenotype of BRCA1/2 mutant cells manifest mainly as large-scale rearrangements. We used whole-genome sequencing of multiple isogenic chicken DT40 cell clones to precisely determine the consequences of BRCA1/2 loss on all types of genomic mutagenesis. Spontaneous base substitution mutation rates increased sevenfold upon the disruption of either BRCA1 or BRCA2, and the arising mutation spectra showed strong and specific correlation with a mutation signature associated with BRCA1/2 mutant tumours. To model endogenous alkylating damage, we determined the mutation spectrum caused by methyl methanesulfonate (MMS), and showed that MMS also induces more base substitution mutations in BRCA1/2-deficient cells. Spontaneously arising and MMS-induced insertion/deletion mutations and large rearrangements were also more common in BRCA1/2 mutant cells compared with the wild-type control. A difference in the short deletion phenotypes of BRCA1 and BRCA2 suggested distinct roles for the two proteins in the processing of DNA lesions, as BRCA2 mutants contained more short deletions, with a wider size distribution, which frequently showed microhomology near the breakpoints resembling repair by non-homologous end joining. An increased and prolonged gamma-H2AX signal in MMS-treated BRCA1/2 cells suggested an aberrant processing of stalled replication forks as the cause of increased mutagenesis. The high rate of base substitution mutagenesis demonstrated by our experiments is likely to significantly contribute to the oncogenic effect of the inactivation of BRCA1 or BRCA2

Fast and accurate mutation detection in whole genome sequences of multiple isogenic samples with IsoMut

Background: Detection of somatic mutations is one of the main goals of next generation DNA sequencing. A wide range of experimental systems are available for the study of spontaneous or environmentally induced mutagenic processes. However, most of the routinely used mutation calling algorithms are not optimised for the simultaneous analysis of multiple samples, or for non-human experimental model systems with no reliable databases of common genetic variations. Most standard tools either require numerous in-house post filtering steps with scarce documentation or take an unpractically long time to run. To overcome these problems, we designed the streamlined IsoMut tool which can be readily adapted to experimental scenarios where the goal is the identification of experimentally induced mutations in multiple isogenic samples. Methods: Using 30 isogenic samples, reliable cohorts of validated mutations were created for testing purposes. Optimal values of the filtering parameters of IsoMut were determined in a thorough and strict optimization procedure based on these test sets. Results: We show that IsoMut, when tuned correctly, decreases the false positive rate compared to conventional tools in a 30 sample experimental setup; and detects not only single nucleotide variations, but short insertions and deletions as well. IsoMut can also be run more than a hundred times faster than the most precise state of art tool, due its straightforward and easily understandable filtering algorithm. Conclusions: IsoMut has already been successfully applied in multiple recent studies to find unique, treatment induced mutations in sets of isogenic samples with very low false positive rates. These types of studies provide an important contribution to determining the mutagenic effect of environmental agents or genetic defects, and IsoMut turned out to be an invaluable tool in the analysis of such data. © 2017 The Author(s)

Frequent CHD1 Deletions in Prostate Cancers of African American Men Is Associated With Rapid Disease Progression

We analyzed genomic data from the prostate cancer of African- and European American men to identify differences contributing to racial disparity of outcome. We also performed FISH-based studies of Chromodomain helicase DNA-binding protein 1 (CHD1) loss on prostate cancer tissue microarrays. We created CHD1-deficient prostate cancer cell lines for genomic, drug sensitivity and functional homologous recombination (HR) activity analysis. Subclonal deletion of CHD1 was nearly three times as frequent in prostate tumors of African American than in European American men and it associates with rapid disease progression. CHD1 deletion was not associated with HR deficiency associated mutational signatures or HR deficiency as detected by RAD51 foci formation. This was consistent with the moderate increase of olaparib and talazoparib sensitivity with several CHD1 deficient cell lines showing talazoparib sensitivity in the clinically relevant concentration range. CHD1 loss may contribute to worse disease outcome in African American men

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

Importance: Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective: To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants: In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements: Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results: Overall, 144 231 screening mammograms from 85 580 US women (952 cancer positive ≤12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance: While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation

Deterministic Evolutionary Trajectories Influence Primary Tumor Growth: TRACERx Renal.

The evolutionary features of clear-cell renal cell carcinoma (ccRCC) have not been systematically studied to date. We analyzed 1,206 primary tumor regions from 101 patients recruited into the multi-center prospective study, TRACERx Renal. We observe up to 30 driver events per tumor and show that subclonal diversification is associated with known prognostic parameters. By resolving the patterns of driver event ordering, co-occurrence, and mutual exclusivity at clone level, we show the deterministic nature of clonal evolution. ccRCC can be grouped into seven evolutionary subtypes, ranging from tumors characterized by early fixation of multiple mutational and copy number drivers and rapid metastases to highly branched tumors with >10 subclonal drivers and extensive parallel evolution associated with attenuated progression. We identify genetic diversity and chromosomal complexity as determinants of patient outcome. Our insights reconcile the variable clinical behavior of ccRCC and suggest evolutionary potential as a biomarker for both intervention and surveillance

Uncertainty Measurements for the Reliable Classification of Mammograms

International audienc