Sex differences have been observed in multiple facets of cancer epidemiology, treatment and biology, and in most cancers outside the sex organs. Efforts to link these clinical differences to specific molecular features have focused on somatic mutations within the coding regions of the genome. Here we report a pan-cancer analysis of sex differences in whole genomes of 1983 tumours of 28 subtypes as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We both confirm the results of exome studies, and also uncover previously undescribed sex differences. These include sex-biases in coding and non-coding cancer drivers, mutation prevalence and strikingly, in mutational signatures related to underlying mutational processes. These results underline the pervasiveness of molecular sex differences and strengthen the call for increased consideration of sex in molecular cancer research.
Aim: We examined methylation changes in cell-free DNA (cfDNA) in metastatic castration-resistant prostate cancer (mCRPC) during treatment. Patients & methods: Genome-wide methylation analysis of sequentially collected cfDNA samples derived from mCRPC patients undergoing androgen-targeting therapy was performed. Results: Alterations in methylation states of genes previously implicated in prostate cancer progression were observed and patients that maintained methylation changes throughout therapy tended to have a longer time to clinical progression. Importantly, we also report that markers associated with a highly aggressive form of the disease, neuroendocrine-CRPC, were associated with a faster time to clinical progression. Conclusion: Our findings highlight the potential of monitoring the cfDNA methylome during therapy in mCRPC, which may serve as predictive markers of response to androgen-targeting agents.
Despite the rapid advance in single-cell RNA sequencing (scRNA-seq) technologies within the last decade, single-cell transcriptome analysis workflows have primarily used gene expression data while isoform sequence analysis at the single-cell level still remains fairly limited. Detection and discovery of isoforms in single cells is difficult because of the inherent technical shortcomings of scRNA-seq data, and existing transcriptome assembly methods are mainly designed for bulk RNA samples. To address this challenge, we developed RNA-Bloom, an assembly algorithm that leverages the rich information content aggregated from multiple single-cell transcriptomes to reconstruct cell-specific isoforms. Assembly with RNA-Bloom can be either reference-guided or reference-free, thus enabling unbiased discovery of novel isoforms or foreign transcripts. We compared both assembly strategies of RNA-Bloom against five state-of-the-art reference-free and reference-based transcriptome assembly methods. In our benchmarks on a simulated 384-cell data set, reference-free RNA-Bloom reconstructed 37.9%-38.3% more isoforms than the best reference-free assembler, whereas reference-guided RNA-Bloom reconstructed 4.1%-11.6% more isoforms than reference-based assemblers. When applied to a real 3840-cell data set consisting of more than 4 billion reads, RNA-Bloom reconstructed 9.7%-25.0% more isoforms than the best competing reference-based and reference-free approaches evaluated. We expect RNA-Bloom to boost the utility of scRNA-seq data beyond gene expression analysis, expanding what is informatically accessible now.
Inherited genetic variation has important implications for cancer screening, early diagnosis, and disease prognosis. A role for germline variation has also been described in shaping the molecular landscape, immune response, microenvironment, and treatment response of individual tumors. However, there is a lack of consensus on the handling and analysis of germline information that extends beyond known or suspected cancer susceptibility in large-scale cancer genomics initiatives. As part of the Personalized OncoGenomics program in British Columbia, we performed whole-genome and transcriptome sequencing in paired tumor and normal tissues from advanced cancer patients to characterize the molecular tumor landscape and identify putative targets for therapy. Overall, our experience supports a multidisciplinary and integrative approach to germline data management. This includes a need for broader definitions and standardized recommendations regarding primary and secondary germline findings in precision oncology. Here, we propose a framework for identifying, evaluating, and returning germline variants of potential clinical significance that may have indications for health management beyond cancer risk reduction or prevention in patients and their families.
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Cervical cancer is the most common cancer affecting sub-Saharan African women and is prevalent among HIV-positive (HIV+) individuals. No comprehensive profiling of cancer genomes, transcriptomes or epigenomes has been performed in this population thus far. We characterized 118 tumors from Ugandan patients, of whom 72 were HIV+, and performed extended mutation analysis on an additional 89 tumors. We detected human papillomavirus (HPV)-clade-specific differences in tumor DNA methylation, promoter- and enhancer-associated histone marks, gene expression and pathway dysregulation. Changes in histone modification at HPV integration events were correlated with upregulation of nearby genes and endogenous retroviruses.
Cell-free DNA (cfDNA) has become a comprehensive biomarker in the fields of non-invasive cancer detection and monitoring, organ transplantation, prenatal genetic testing and pathogen detection. While cfDNA samples can be obtained using a broad variety of approaches, there is an urgent need to standardize analytical tools aimed at assessing its basic properties. Typical methods to determine the yield and fragment size distribution of cfDNA samples are usually either blind to genomic DNA contamination or the presence of enzymatic inhibitors, which can confound and undermine downstream analyses. Here, we present a novel droplet digital PCR assay to identify suboptimal samples and aberrant cfDNA size distributions, the latter typically associated with high levels of circulating tumour DNA (ctDNA). Our assay was designed to promiscuously cross-amplify members of the human olfactory receptor (OR) gene family and includes a customizable diploid locus for the determination of absolute cfDNA concentrations. We demonstrate here the utility of our assay to estimate the yield and quality of cfDNA extracts and deduce fragment size distributions that correlate well with those inferred by capillary electrophoresis and high throughput sequencing. The assay described herein is a powerful tool to establish quality controls and stratify cfDNA samples based on presumed ctDNA levels, then facilitating the implementation of robust, cost-effective and standardized analytical workflows into clinical practice.
Motivation: Networks are used to relate topological structure to system dynamics and function, particularly in ecology and systems biology. Network analysis is often guided or complemented by data-driven visualization. Hive plots, one of many network visualizations, distinguish themselves as providing a general, consistent, and coherent rule-based representation to motivate hypothesis development and testing.
Results: Here, we present HyPE, Hive Panel Explorer, a software application that creates a panel of interactive hive plots. HyPE enables network exploration based on user-driven layout rules and parameter combinations for simultaneous rendering of multiple network views. We demonstrate HyPE's features by exploring a microbial co-occurrence network constructed from forest soil microbiomes.
Availability: HyPE is available under the GNU license: https://github.com/hallamlab/HivePanelExplorer. A wiki, including a tutorial, is available at https://github.com/hallamlab/HivePanelExplorer/wiki.
Supplementary information: Supplementary data are available at Bioinformatics online.
Background: Tattoos may cause a variety of adverse reactions in the body, including immune reactions and infections. However, it is unknown whether tattoos may increase the risk of lymphatic cancers such as non-Hodgkin Lymphoma (NHL) and multiple myeloma (MM).
Methods: Participants from two population-based case-control studies were including in logistic regression models to examine the association between tattoos and risk of NHL and MM.
Results: A total of 1518 participants from the NHL study (737 cases) and 742 participants from the MM study (373 cases) were included in the analyses. No statistically significant associations were found between tattoos and risk of NHL or MM after adjusting for age, sex, ethnicity, education, BMI, and family history.
Conclusions: We did not identify any significant associations between tattoos and risk of MM, NHL, or NHL subtypes in these studies.
Impact: Though biologically plausible, tattoos were not associated with increased risk of NHL or MM in this study. Future studies with greater detail regarding tattoo exposure may provide further insights.