Genetic subdivisions of follicular lymphoma defined by distinct coding and noncoding mutation patterns.

Follicular lymphoma (FL) accounts for ∼20% of all new lymphoma cases. Increases in cytological grade are a feature of the clinical progression of this malignancy, and eventual histologic transformation (HT) to the aggressive diffuse large B-cell lymphoma (DLBCL) occurs in up to 15% of patients. Clinical or genetic features to predict the risk and timing of HT have not been described comprehensively. In this study, we analyzed whole-genome sequencing data from 423 patients to compare the protein coding and noncoding mutation landscapes of untransformed FL, transformed FL, and de novo DLBCL. This revealed 2 genetically distinct subgroups of FL, which we have named DLBCL-like (dFL) and constrained FL (cFL). Each subgroup has distinguishing mutational patterns, aberrant somatic hypermutation rates, and biological and clinical characteristics. We implemented a machine learning-derived classification approach to stratify patients with FL into cFL and dFL subgroups based on their genomic features. Using separate validation cohorts, we demonstrate that cFL status, whether assigned with this full classifier or a single-gene approximation, is associated with a reduced rate of HT. This implies distinct biological features of cFL that constrain its evolution, and we highlight the potential for this classification to predict HT from genetic features present at diagnosis.

Coding and noncoding drivers of mantle cell lymphoma identified through exome and genome sequencing.

Mantle cell lymphoma (MCL) is an uncommon B-cell non-Hodgkin lymphoma (NHL) that is incurable with standard therapies. The genetic drivers of this cancer have not been firmly established, and the features that contribute to differences in clinical course remain limited. To extend our understanding of the biological pathways involved in this malignancy, we performed a large-scale genomic analysis of MCL using data from 51 exomes and 34 genomes alongside previously published exome cohorts. To confirm our findings, we resequenced the genes identified in the exome cohort in 191 MCL tumors, each having clinical follow-up data. We confirmed the prognostic association of TP53 and NOTCH1 mutations. Our sequencing revealed novel recurrent noncoding mutations surrounding a single exon of the HNRNPH1gene. In RNA-seq data from 103 of these cases, MCL tumors with these mutations had a distinct imbalance of HNRNPH1 isoforms. This altered splicing of HNRNPH1 was associated with inferior outcomes in MCL and showed a significant increase in protein expression by immunohistochemistry. We describe a functional role for these recurrent noncoding mutations in disrupting an autoregulatory feedback mechanism, thereby deregulating HNRNPH1 protein expression. Taken together, these data strongly imply a role for aberrant regulation of messenger RNA processing in MCL pathobiology.

Molecular determinants of outcomes in relapsed or refractory mantle cell lymphoma treated with ibrutinib or temsirolimus in the MCL3001 (RAY) trial.

Mantle cell lymphoma (MCL) is a rare, incurable lymphoma subtype characterized by heterogeneous outcomes. To better understand the clinical behavior and response to treatment, predictive biomarkers are needed. Using residual archived material from patients enrolled in the MCL3001 (RAY) study, we performed detailed analyses of gene expression and targeted genetic sequencing. This phase III clinical trial randomized patients with relapsed or refractory MCL to treatment with either ibrutinib or temsirolimus. We confirmed the prognostic capability of the gene expression proliferation assay MCL35 in this cohort treated with novel agents; it outperformed the simplified MCL International Prognostic Index in discriminating patients with different outcomes. Regardless of treatment arm, our data demonstrated that this assay captures the risk conferred by known biological factors, including increased MYC expression, blastoid morphology, aberrations of TP53, and truncated CCND1 3' untranslated region. We showed the negative impact of BIRC3 mutations/deletions on outcomes in this cohort and identified that deletion of chromosome 8p23.3 also negatively impacts survival. Restricted to patients with deletions/alterations in TP53, ibrutinib appeared to abrogate the deleterious impact on outcome. These data illustrate the potential to perform a molecular analysis of predictive biomarkers on routine patient samples that can meaningfully inform clinical practice.

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.

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.