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Background Precision oncology involves analysis of individual cancer samples to understand the genes and pathways involved in the development and progression of a cancer. To improve patient care, knowledge of diagnostic, prognostic, predisposing, and drug response markers is essential. Several knowledgebases have been created by different groups to collate evidence for these associations. These include the open-access Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase. These databases rely on time-consuming manual curation from skilled experts who read and interpret the relevant biomedical literature. Methods To aid in this curation and provide the greatest coverage for these databases, particularly CIViC, we propose the use of text mining approaches to extract these clinically relevant biomarkers from all available published literature. To this end, a group of cancer genomics experts annotated sentences that discussed biomarkers with their clinical associations and achieved good inter-annotator agreement. We then used a supervised learning approach to construct the CIViCmine knowledgebase. Results We extracted 121,589 relevant sentences from PubMed abstracts and PubMed Central Open Access full-text papers. CIViCmine contains over 87,412 biomarkers associated with 8035 genes, 337 drugs, and 572 cancer types, representing 25,818 abstracts and 39,795 full-text publications. Conclusions Through integration with CIVIC, we provide a prioritized list of curatable clinically relevant cancer biomarkers as well as a resource that is valuable to other knowledgebases and precision cancer analysts in general. All data is publically available and distributed with a Creative Commons Zero license. The CIViCmine knowledgebase is available at http://bionlp.bcgsc.ca/civicmine/.

In this meeting overview, we summarise the scientific program and organisation of the 16th International Society for Computational Biology Student Council Symposium in 2020 (ISCB SCS2020). This symposium was the first virtual edition in an uninterrupted series of symposia that has been going on for 15 years, aiming to unite computational biology students and early career researchers across the globe.

Understanding a mutation in cancer requires knowledge of the different roles that genes play in cancer as drivers, oncogenes and tumor suppressors. We present CancerMine, a high-quality text-mined knowledgebase that catalogues over 856 genes as drivers, 2,421 as oncogenes and 2,037 as tumor suppressors in 426 cancer types. We compile 3,485 genes that are not in the IntOGen resource of drivers and complement the Cancer Gene Census with 3,136 new genes identified as oncogenes and tumor suppressors. CancerMine provides a method for gene-centric clustering of cancer types illustrating genetic similarities between cancer types of different organs and was validated against data from the Cancer Genome Atlas (TCGA) project. Finally with 178 novel cancer gene mentions in publications each month, this resource will be updated monthly, pre-empting the need to manually curate the ever-increasing number of novel cancer associated genes. CancerMine is viewable through a web portal (http://bionlp.bcgsc.ca/cancermine/) and available for download (https://github.com/jakelever/cancermine).

Metastatic adenoid cystic carcinomas (ACCs) can cause significant morbidity and mortality. Because of their slow growth and relative rarity, there is limited evidence for systemic therapy regimens. Recently, molecular profiling studies have begun to reveal the genetic landscape of these poorly understood cancers, and new treatment possibilities are beginning to emerge. The objective is to use whole-genome and transcriptome sequencing and analysis to better understand the genetic alterations underlying the pathology of metastatic and rare ACCs and determine potentially actionable therapeutic targets. We report five cases of metastatic ACC, not originating in the salivary glands, in patients enrolled in the Personalized Oncogenomics (POG) Program at the BC Cancer Agency. Genomic workup included whole-genome and transcriptome sequencing, detailed analysis of tumor alterations, and integration with existing knowledge of drug–target combinations to identify potential therapeutic targets. Analysis reveals low mutational burden in these five ACC cases, and mutation signatures that are commonly observed in multiple cancer types. Notably, the only recurrent structural aberration identified was the well-described MYB-NFIB fusion that was present in four of five cases, and one case exhibited a closely related MYBL1-NFIB fusion. Recurrent mutations were also identified in BAP1 and BCOR, with additional mutations in individual samples affecting NOTCH1 and the epigenetic regulators ARID2, SMARCA2, and SMARCB1. Copy changes were rare, and they included amplification of MYC and homozygous loss of CDKN2A in individual samples. Genomic analysis revealed therapeutic targets in all five cases and served to inform a therapeutic choice in three of the cases to date.

We present a collection of Galaxy tools representing many popular algorithms for detecting somatic genetic alterations from cancer genome and exome data. We implemented methods for parallelization of these tools within Galaxy to accelerate runtime and have demonstrated their usability on cloud-based infrastructure and commodity hardware. Some tools represents extensions or refinement of existing toolkits to yield visualizations suited to cohort-wide cancer genomic analysis. For example, we present Oncocircos and Oncoprintplus, which generate data-rich summaries of exome-derived somatic mutation. Workflows that integrate several of these to perform some standard data integration and visualization tasks are demonstrated on a cohort of 96 diffuse large B-cell lymphomas, enabling the discovery of multiple candidate lymphoma-related genes that have not been reported previously.