Highlights from the 16th International Society for Computational Biology Student Council Symposium 2020.

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. [Abstract copyright: Copyright: © 2021 Cuypers WL et al.

Text-mining clinically relevant cancer biomarkers for curation into the CIViC database

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/

Utility of machine learning approaches for cancer diagnosis and analysis from RNA sequencing

The highest number of cancer-associated deaths are attributable to metastasis. These include rare cancer types that lack established treatment guidelines, or cancers that become resistant to established lines of therapy. Precision oncology projects aim to develop treatment options for these patients by obtaining a detailed molecular view of the cancer. Scientists use sequencing data like whole-genome sequencing and RNA-sequencing to understand the biology of the cancer. A significant challenge in this process is diagnosing the cancer type of the sample since the observed measurements are best understood with this context. Routine histopathology relies on tissue morphology and can fail to provide a determinative diagnosis when the cancer metastasizes, presents biology attributable to multiple different cancer types, or presents as a rare cancer type. Molecular data has revealed differences in the genetic makeup of cancers that appear morphologically similar, motivating the use of molecular diagnostics. Nevertheless, no existing tools utilize the output from these sequencing modalities in its entirety (that is, without feature selection). There is also limited work evaluating the utility of pan-cancer molecular diagnostics in a precision oncology trial. In this work we review an ongoing precision oncology trial and identify the impact of sequencing-based approaches on cancer diagnosis. We develop SCOPE, a machine-learning method that uses RNA-Seq profiles of tumours for automated cancer diagnosis. We show that this method, which uses over 17,688 gene measurements as input, has better classification accuracy than when using statistically prioritized marker genes, can deconvolve cancer-types with mixed histology, and has high performance in metastatic cancers and cancers of unknown origin. In precision oncology, manual analysis of the tumour's genomic profile is used to understand tumour biology and driver pathways. We find that by assessing the classifier's dependence on gene subsets, we can automatically calculate the importance of various biological programs in individual tumours. Pathways prioritized through this tool - called PIE - show a high overlap with manual integrative analysis performed by expert bioinformaticians to identify clinically important genomic changes. Lastly, we demonstrate that PIE facilitates cohort-wide cancer analysis and discovery of novel sub-groups in advanced cancers.Science, Faculty ofGraduat

Text-mining clinically relevant cancer biomarkers for curation into the CIViC database

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/.Other UBCNon UBCReviewedFacult

Enhancing knowledge discovery from cancer genomics data with Galaxy.

The field of cancer genomics has demonstrated the power of massively parallel sequencing techniques to inform on the genes and specific alterations that drive tumor onset and progression. Although large comprehensive sequence data sets continue to be made increasingly available, data analysis remains an ongoing challenge, particularly for laboratories lacking dedicated resources and bioinformatics expertise. To address this, we have produced a collection of Galaxy tools that represent many popular algorithms for detecting somatic genetic alterations from cancer genome and exome data. We developed new methods for parallelization of these tools within Galaxy to accelerate runtime and have demonstrated their usability and summarized their runtimes on multiple cloud service providers. Some tools represent 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 these to achieve data integration and visualizations are demonstrated on a cohort of 96 diffuse large B-cell lymphomas and enabled the discovery of multiple candidate lymphoma-related genes. Our toolkit is available from our GitHub repository as Galaxy tool and dependency definitions and has been deployed using virtualization on multiple platforms including Docker

Detection and genomic characterization of a mammary-like adenocarcinoma

Whole-genome and transcriptome sequencing were performed to identify potential therapeutic strategies in the absence of viable treatment options for a patient initially diagnosed with vulvar adenocarcinoma. Genomic events were prioritized by comparison against variant distributions in the TCGA pan-cancer data set and complemented with detailed transcriptome sequencing and copy-number analysis. These findings were considered against published scientific literature in order to evaluate the functional effects of potentially relevant genomic events. Analysis of the transcriptome against a background of 27 TCGA cancer types led to reclassification of the tumor as a primary HER2+ mammary-like adenocarcinoma of the vulva. This revised diagnosis was subsequently confirmed by follow-up immunohistochemistry for a mammary-like adenocarcinoma. The patient was treated with chemotherapy and targeted therapies for HER2+ breast cancer. The detailed pathology and genomic findings of this case are presented herein

The impact of whole genome and transcriptome analysis (WGTA) on predictive biomarker discovery and diagnostic accuracy of advanced malignancies

In this study, we evaluate the impact of whole genome and transcriptome analysis (WGTA) on predictive molecular profiling and histologic diagnosis in a cohort of advanced malignancies. WGTA was used to generate reports including molecular alterations and site/tissue of origin prediction. Two reviewers analyzed genomic reports, clinical history, and tumor pathology. We used National Comprehensive Cancer Network (NCCN) consensus guidelines, Food and Drug Administration (FDA) approvals, and provincially reimbursed treatments to define genomic biomarkers associated with approved targeted therapeutic options (TTOs). Tumor tissue/site of origin was reassessed for most cases using genomic analysis, including a machine learning algorithm (Supervised Cancer Origin Prediction Using Expression [SCOPE]) trained on The Cancer Genome Atlas data. WGTA was performed on 652 cases, including a range of primary tumor types/tumor sites and 15 malignant tumors of uncertain histogenesis (MTUH). At the time WGTA was performed, alterations associated with an approved TTO were identified in 39 (6%) cases; 3 of these were not identified through routine pathology workup. In seven (1%) cases, the pathology workup either failed, was not performed, or gave a different result from the WGTA. Approved TTOs identified by WGTA increased to 103 (16%) when applying 2021 guidelines. The histopathologic diagnosis was reviewed in 389 cases and agreed with the diagnostic consensus after WGTA in 94% of non-MTUH cases (n = 374). The remainder included situations where the morphologic diagnosis was changed based on WGTA and clinical data (0.5%), or where the WGTA was non-contributory (5%). The 15 MTUH were all diagnosed as specific tumor types by WGTA. Tumor board reviews including WGTA agreed with almost all initial predictive molecular profile and histopathologic diagnoses. WGTA was a powerful tool to assign site/tissue of origin in MTUH. Current efforts focus on improving therapeutic predictive power and decreasing cost to enhance use of WGTA data as a routine clinical test