Germline breast cancer susceptibility genes, tumor characteristics, and survival.

BACKGROUND: Mutations in certain genes are known to increase breast cancer risk. We study the relevance of rare protein-truncating variants (PTVs) that may result in loss-of-function in breast cancer susceptibility genes on tumor characteristics and survival in 8852 breast cancer patients of Asian descent. METHODS: Gene panel sequencing was performed for 34 known or suspected breast cancer predisposition genes, of which nine genes (ATM, BRCA1, BRCA2, CHEK2, PALB2, BARD1, RAD51C, RAD51D, and TP53) were associated with breast cancer risk. Associations between PTV carriership in one or more genes and tumor characteristics were examined using multinomial logistic regression. Ten-year overall survival was estimated using Cox regression models in 6477 breast cancer patients after excluding older patients (≥75years) and stage 0 and IV disease. RESULTS: PTV9genes carriership (n = 690) was significantly associated (p < 0.001) with more aggressive tumor characteristics including high grade (poorly vs well-differentiated, odds ratio [95% confidence interval] 3.48 [2.35-5.17], moderately vs well-differentiated 2.33 [1.56-3.49]), as well as luminal B [HER-] and triple-negative subtypes (vs luminal A 2.15 [1.58-2.92] and 2.85 [2.17-3.73], respectively), adjusted for age at diagnosis, study, and ethnicity. Associations with grade and luminal B [HER2-] subtype remained significant after excluding BRCA1/2 carriers. PTV25genes carriership (n = 289, excluding carriers of the nine genes associated with breast cancer) was not associated with tumor characteristics. However, PTV25genes carriership, but not PTV9genes carriership, was suggested to be associated with worse 10-year overall survival (hazard ratio [CI] 1.63 [1.16-2.28]). CONCLUSIONS: PTV9genes carriership is associated with more aggressive tumors. Variants in other genes might be associated with the survival of breast cancer patients. The finding that PTV carriership is not just associated with higher breast cancer risk, but also more severe and fatal forms of the disease, suggests that genetic testing has the potential to provide additional health information and help healthy individuals make screening decisions

Characterisation of protein-truncating and missense variants in PALB2 in 15 768 women from Malaysia and Singapore

Funder: National University Cancer Institute, Singapore; FundRef: http://dx.doi.org/10.13039/501100011105Background: Rare protein-truncating variants (PTVs) in partner and localiser of BRCA2 (PALB2) confer increased risk to breast cancer, but relatively few studies have reported the prevalence in South-East Asian populations. Here, we describe the prevalence of rare variants in PALB2 in a population-based study of 7840 breast cancer cases and 7928 healthy Chinese, Malay and Indian women from Malaysia and Singapore, and describe the functional impact of germline missense variants identified in this population. Methods: Mutation testing was performed on germline DNA (n=15 768) using targeted sequencing panels. The functional impact of missense variants was tested in mouse embryonic stem cell based functional assays. Results: PTVs in PALB2 were found in 0.73% of breast cancer patients and 0.14% of healthy individuals (OR=5.44; 95% CI 2.85 to 10.39, p<0.0001). In contrast, rare missense variants in PALB2 were not associated with increased risk of breast cancer. Whereas PTVs were associated with later stage of presentation and higher-grade tumours, no significant association was observed with missense variants in PALB2. However, two novel rare missense variants (p.L1027R and p.G1043V) produced unstable proteins and resulted in a decrease in homologous recombination-mediated repair of DNA double-strand breaks. Conclusion: Despite genetic and lifestyle differences between Asian and other populations, the population prevalence of PALB2 PTVs and associated relative risk of breast cancer, are similar to those reported in European populations

Polygenic risk scores for prediction of breast cancer risk in Asian populations.

PURPOSE: Non-European populations are under-represented in genetics studies, hindering clinical implementation of breast cancer polygenic risk scores (PRSs). We aimed to develop PRSs using the largest available studies of Asian ancestry and to assess the transferability of PRS across ethnic subgroups. METHODS: The development data set comprised 138,309 women from 17 case-control studies. PRSs were generated using a clumping and thresholding method, lasso penalized regression, an Empirical Bayes approach, a Bayesian polygenic prediction approach, or linear combinations of multiple PRSs. These PRSs were evaluated in 89,898 women from 3 prospective studies (1592 incident cases). RESULTS: The best performing PRS (genome-wide set of single-nucleotide variations [formerly single-nucleotide polymorphism]) had a hazard ratio per unit SD of 1.62 (95% CI = 1.46-1.80) and an area under the receiver operating curve of 0.635 (95% CI = 0.622-0.649). Combined Asian and European PRSs (333 single-nucleotide variations) had a hazard ratio per SD of 1.53 (95% CI = 1.37-1.71) and an area under the receiver operating curve of 0.621 (95% CI = 0.608-0.635). The distribution of the latter PRS was different across ethnic subgroups, confirming the importance of population-specific calibration for valid estimation of breast cancer risk. CONCLUSION: PRSs developed in this study, from association data from multiple ancestries, can enhance risk stratification for women of Asian ancestry

The data, they are a-changin’

The cost of deriving actionable knowledge from large datasets has been decreasing thanks to a convergence of positive factors: low cost data generation, inexpensively scalable storage and processing infrastructure (cloud), software frameworks and tools for massively distributed data processing, and parallelisable data analytics algorithms. One observation that is often overlooked, however, is that each of these elements is not immutable, rather they all evolve over time. As those datasets change over time, the value of their derivative knowledge may decay, unless it is preserved by reacting to those changes. Our broad research goal is to develop models, methods, and tools for selectively reacting to changes by balancing costs and benefits, i.e. through complete or partial re-computation of some of the underlying processes. In this paper we present an initial model for reasoning about change and re-computations, and show how analysis of detailed provenance of derived knowledge informs re-computation decisions. We illustrate the main ideas through a real-world case study in genomics, namely on the interpretation of human variants in support of genetic diagnosis.</p

The data, they are a-changin’

The cost of deriving actionable knowledge from large datasets has been decreasing thanks to a convergence of positive factors: low cost data generation, inexpensively scalable storage and processing infrastructure (cloud), software frameworks and tools for massively distributed data processing, and parallelisable data analytics algorithms. One observation that is often overlooked, however, is that each of these elements is not immutable, rather they all evolve over time. As those datasets change over time, the value of their derivative knowledge may decay, unless it is preserved by reacting to those changes. Our broad research goal is to develop models, methods, and tools for selectively reacting to changes by balancing costs and benefits, i.e. through complete or partial re-computation of some of the underlying processes. In this paper we present an initial model for reasoning about change and re-computations, and show how analysis of detailed provenance of derived knowledge informs re-computation decisions. We illustrate the main ideas through a real-world case study in genomics, namely on the interpretation of human variants in support of genetic diagnosis.</p

SVI:A simple single-nucleotide human variant interpretation tool for clinical use

The rapid evolution of Next Generation Sequencing technology will soon make it possible to test patients for genetic disorders at population scale. However, clinical interpretation of human variants extracted from raw NGS data in the clinical setting is likely to become a bottleneck, as long as it requires expert human judgement. While several attempts are under way to try and automate the diagnostic process, most still assume a specialist’s understanding of the variants’ significance. In this paper we present our early experiments with a simple process and prototype clinical tool for single-nucleotide variant filtering, called SVI, which automates much of the interpretation process by integrating disease-gene and disease-variant mapping resources. As the content and quality of these resources improve over time, it is important to identify past patients’ cases which may benefit from re-analysis. By persistently recording the entire diagnostic process, SVI can selectively trigger case re-analysis on the basis of updates in the external knowledge sources.</p

From Scripted HPC-Based NGS Pipelines to Workflows on the Cloud

In this paper we describe our initial experiences in the Cloud-e-Genome project with moving the whole exome sequencing pipeline from the scripted HPC-based solution to a workflow enactment system running in the cloud. We discuss shortcomings of the existing approach based on scripts and list benefits that a workflow-based solution can provide. Despite the effort it involved to wrap all required tools in the form of workflow blocks and the restrictions of the dataflow model used to represent workflows we expect the migration to significantly improve the current status of the pipeline. Our target is to enable flexibility, traceability and reproducibility of the solution, so that it can better fit the evolution of tools, data and pipeline itself and allow us to run it at national scale. This work will become foundation for the more complete system that includes variant filtering and interpretation for the diagnostic purposes

SVI:A simple single-nucleotide human variant interpretation tool for clinical use

The rapid evolution of Next Generation Sequencing technology will soon make it possible to test patients for genetic disorders at population scale. However, clinical interpretation of human variants extracted from raw NGS data in the clinical setting is likely to become a bottleneck, as long as it requires expert human judgement. While several attempts are under way to try and automate the diagnostic process, most still assume a specialist’s understanding of the variants’ significance. In this paper we present our early experiments with a simple process and prototype clinical tool for single-nucleotide variant filtering, called SVI, which automates much of the interpretation process by integrating disease-gene and disease-variant mapping resources. As the content and quality of these resources improve over time, it is important to identify past patients’ cases which may benefit from re-analysis. By persistently recording the entire diagnostic process, SVI can selectively trigger case re-analysis on the basis of updates in the external knowledge sources.</p

From scripted HPC-based NGS pipelines to workflows on the cloud

In this paper we describe our initial experiences in the Cloud-e-Genome project with moving the whole exome sequencing pipeline from the scripted HPC-based solution to a workflow enactment system running in the cloud. We discuss shortcomings of the existing approach based on scripts and list benefits that a workflow-based solution can provide. Despite the effort it involved to wrap all required tools in the form of workflow blocks and the restrictions of the dataflow model used to represent workflows we expect the migration to significantly improve the current status of the pipeline. Our target is to enable flexibility, traceability and reproducibility of the solution, so that it can better fit the evolution of tools, data and pipeline itself and allow us to run it at national scale. This work will become foundation for the more complete system that includes variant filtering and interpretation for the diagnostic purposes.</p

From scripted HPC-based NGS pipelines to workflows on the cloud

In this paper we describe our initial experiences in the Cloud-e-Genome project with moving the whole exome sequencing pipeline from the scripted HPC-based solution to a workflow enactment system running in the cloud. We discuss shortcomings of the existing approach based on scripts and list benefits that a workflow-based solution can provide. Despite the effort it involved to wrap all required tools in the form of workflow blocks and the restrictions of the dataflow model used to represent workflows we expect the migration to significantly improve the current status of the pipeline. Our target is to enable flexibility, traceability and reproducibility of the solution, so that it can better fit the evolution of tools, data and pipeline itself and allow us to run it at national scale. This work will become foundation for the more complete system that includes variant filtering and interpretation for the diagnostic purposes.</p