Enhancing knowledge discovery from cancer genomics data with Galaxy

Audit of B-cell cancer genes

ABSTRACT

Comprehensive genetic analysis of tumors with exome or whole-genome sequencing has enabled the identification of the genes that are recurrently mutated in cancer. This has stimulated a series of exciting advances over the past 15 years, guiding us to new molecular biomarkers and therapeutic targets among the common mature B-cell neoplasms. In particular, diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), and Burkitt lymphoma (BL) have each been the subject of considerable attention in this field. Currently, >850 genes have been reported as targets of protein-coding mutations in at least 1 of these entities. To reduce this to a manageable size, we describe a systematic approach to prioritize and categorize these genes, based on the quality and type of supporting data. For each entity, we provide a list of candidate driver genes categorized into Tier 1 (high-confidence genes), Tier 2 (candidate driver genes), or Tier 3 (lowest-confidence genes). Collectively, this reduces the number of high-confidence genes for these 3 lymphomas to a mere 144. This further affirms the substantial overlap between the genes relevant in DLBCL and each of FL and BL. These highly curated and annotated gene lists will continue to be maintained as a resource to the community. These results emphasize the extent of the knowledge gap regarding the role of each of these genes in lymphomagenesis. We offer our perspective on how to accelerate the experimental confirmation of drivers using a variety of model systems, using these lists as a guide for prioritizing genes.

Molecular characteristics and markers of advanced clear cell renal cell carcinoma: Pitfalls due to intratumoral heterogeneity and identification of genetic alterations associated with metastasis

OBJECTIVES

To identify clear cell renal cell carcinoma-related gene mutations potentially associated with aggressive disease, sarcomatoid differentiation or poor prognosis.

METHODS

We carried out genomic analysis of 217 tumor foci from 25 patients with conventional clear cell renal cell carcinoma (14 patients), clear cell renal cell carcinoma with sarcomatoid differentiation (six patients) and non-clear cell renal cell carcinoma (five patients). Each tumor nodule on the tissue block that corresponded to the same focus on the slide was separated from the normal parenchyma and other histologically distinct areas of tumor. The isolated tumor foci were used for subsequent analyses and sequencing. Deoxyribonucleic acid from the formalin-fixed paraffin-embedded tissues was extracted. Multiplex bar-coded polymerase chain reaction amplification was carried out using next-generation sequencing libraries.

RESULTS

Overall, 67 protein alterations, including amino acid alterations, frame shifts and splice site mutations in seven genes were identified in the cohort of renal cell carcinoma tumors included in this study. Fewer patients with clear cell renal cell carcinoma with sarcomatoid differentiation had clear cell renal cell carcinoma-related mutations in comparison with patients with conventional clear cell renal cell carcinoma. Additionally, the average number of unique clear cell renal cell carcinoma-related protein alterations per patient was significantly lower in clear cell renal cell carcinoma with sarcomatoid differentiation than in conventional clear cell renal cell carcinoma. Mutations in PBRM1 were identified in a higher proportion of patients with high-grade tumors (World Health Organization/International Society of Urological Pathology grade 4) and in the primary tumors of six of 10 (60%) patients with metastatic disease.

CONCLUSIONS

Although there are pitfalls due to intratumoral heterogeneity and sampling bias, mutations in PBRM1 may be associated with metastasis and aggressive disease in clear cell renal cell carcinoma.

Using Informatics Tools to Identify Opportunities for Precision Medicine in Diffuse Large B-cell Lymphoma

INTRODUCTION

Diffuse large B-cell lymphoma (DLBCL) is genetically and clinically heterogeneous. Despite advances in genomic subtyping, standard frontline chemoimmunotherapy has remained unchanged for years. As high-throughput analysis becomes more accessible, characterizing drug-gene interactions in DLBCL could support patient-specific treatment strategies.

MATERIALS AND METHODS

From our systematic literature review, we compiled a comprehensive list of somatic mutations implicated in DLBCL. We extracted reported and primary sequencing data for these mutations and assessed their association with signaling pathways, cell-of-origin subtypes, and clinical outcomes.

RESULTS

Twenty-two targetable mutations present in ≥ 5% of patients with DLBCL were associated with unfavorable outcomes, yielding a predicted population of 31.7% of DLBCL cases with poor-risk disease and candidacy for targeted therapy. A second review identified 256 studies that had characterized the drug-gene interactions for these mutations via in vitro studies, mouse models, and/or clinical trials.

CONCLUSIONS

Our novel approach linking the data from our systematic reviews with informatics tools identified high-risk DLBCL subgroups, DLBCL-specific drug-gene interactions, and potential populations for precision medicine trials.

Visual Analytics of Genomic and Cancer Data: A Systematic Review

Visual analytics and visualisation can leverage the human perceptual system to interpret and uncover hidden patterns in big data. The advent of next-generation sequencing technologies has allowed the rapid production of massive amounts of genomic data and created a corresponding need for new tools and methods for visualising and interpreting these data. Visualising genomic data requires not only simply plotting of data but should also offer a decision or a choice about what the message should be conveyed in the particular plot; which methodologies should be used to represent the results must provide an easy, clear, and accurate way to the clinicians, experts, or researchers to interact with the data. Genomic data visual analytics is rapidly evolving in parallel with advances in high-throughput technologies such as artificial intelligence (AI) and virtual reality (VR). Personalised medicine requires new genomic visualisation tools, which can efficiently extract knowledge from the genomic data and speed up expert decisions about the best treatment of individual patient’s needs. However, meaningful visual analytics of such large genomic data remains a serious challenge. This article provides a comprehensive systematic review and discussion on the tools, methods, and trends for visual analytics of cancer-related genomic data. We reviewed methods for genomic data visualisation including traditional approaches such as scatter plots, heatmaps, coordinates, and networks, as well as emerging technologies using AI and VR. We also demonstrate the development of genomic data visualisation tools over time and analyse the evolution of visualising genomic data.

Genome-wide discovery of somatic regulatory variants in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is an aggressive cancer originating from mature B-cells. Prognosis is strongly associated with molecular subgroup, although the driver mutations that distinguish the two main subgroups remain poorly defined. Through an integrative analysis of whole genomes, exomes, and transcriptomes, we have uncovered genes and non-coding loci that are commonly mutated in DLBCL. Our analysis has identified novel cis-regulatory sites, and implicates recurrent mutations in the 3′ UTR of NFKBIZ as a novel mechanism of oncogene deregulation and NF-κB pathway activation in the activated B-cell (ABC) subgroup. Small amplifications associated with over-expression of FCGR2B (the Fcγ receptor protein IIB), primarily in the germinal centre B-cell (GCB) subgroup, correlate with poor patient outcomes suggestive of a novel oncogene. These results expand the list of subgroup driver mutations that may facilitate implementation of improved diagnostic assays and could offer new avenues for the development of targeted therapeutics.

The driver mutations for the two main molecular subgroups of diffuse large B-cell lymphoma (DLBCL) are poorly defined. Here, an integrative genomics analysis identifies 3′ UTR NFKBIZ mutations within the activated B-cell DLBCL subgroup and small FCGR2B amplifications in the germinal centre B-cell DLBCL subgroup.

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.