Targeted sequencing in DLBCL, molecular subtypes, and outcomes: a Haematological Malignancy Research Network report

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Abstract

Based on the profile of genetic alterations occurring in tumor samples from selected diffuse large B-cell lymphoma (DLBCL) patients, 2 recent whole-exome sequencing studies proposed partially overlapping classification systems. Using clustering techniques applied to targeted sequencing data derived from a large unselected population-based patient cohort with full clinical follow-up (n = 928), we investigated whether molecular subtypes can be robustly identified using methods potentially applicable in routine clinical practice. DNA extracted from DLBCL tumors diagnosed in patients residing in a catchment population of ∼4 million (14 centers) were sequenced with a targeted 293-gene hematological-malignancy panel. Bernoulli mixture-model clustering was applied and the resulting subtypes analyzed in relation to their clinical characteristics and outcomes. Five molecular subtypes were resolved, termed MYD88, BCL2, SOCS1/SGK1, TET2/SGK1, and NOTCH2, along with an unclassified group. The subtypes characterized by genetic alterations of BCL2, NOTCH2, and MYD88 recapitulated recent studies showing good, intermediate, and poor prognosis, respectively. The SOCS1/SGK1 subtype showed biological overlap with primary mediastinal B-cell lymphoma and conferred excellent prognosis. Although not identified as a distinct cluster, NOTCH1 mutation was associated with poor prognosis. The impact of TP53 mutation varied with genomic subtypes, conferring no effect in the NOTCH2 subtype and poor prognosis in the MYD88 subtype. Our findings confirm the existence of molecular subtypes of DLBCL, providing evidence that genomic tests have prognostic significance in non-selected DLBCL patients. The identification of both good and poor risk subtypes in patients treated with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) clearly show the clinical value of the approach, confirming the need for a consensus classification.

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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

A. Subramanian, P. Tamayo, V. K. Mootha … (2005)

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

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PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes.

Vamsi K Mootha, Cecilia M. Lindgren, Karl-Fredrik Eriksson … (2003)

DNA microarrays can be used to identify gene expression changes characteristic of human disease. This is challenging, however, when relevant differences are subtle at the level of individual genes. We introduce an analytical strategy, Gene Set Enrichment Analysis, designed to detect modest but coordinate changes in the expression of groups of functionally related genes. Using this approach, we identify a set of genes involved in oxidative phosphorylation whose expression is coordinately decreased in human diabetic muscle. Expression of these genes is high at sites of insulin-mediated glucose disposal, activated by PGC-1alpha and correlated with total-body aerobic capacity. Our results associate this gene set with clinically important variation in human metabolism and illustrate the value of pathway relationships in the analysis of genomic profiling experiments.

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Genetics and Pathogenesis of Diffuse Large B-Cell Lymphoma

Bin Zhou, Randy Gascoyne, Weihong Xu … (2018)

BACKGROUND Diffuse large B-cell lymphomas (DLBCLs) are phenotypically and genetically heterogeneous. Gene-expression profiling has identified subgroups of DLBCL (activated B-cell–like [ABC], germinal-center B-cell–like [GCB], and unclassified) according to cell of origin that are associated with a differential response to chemotherapy and targeted agents. We sought to extend these findings by identifying genetic subtypes of DLBCL based on shared genomic abnormalities and to uncover therapeutic vulnerabilities based on tumor genetics. METHODS We studied 574 DLBCL biopsy samples using exome and transcriptome sequencing, array-based DNA copy-number analysis, and targeted amplicon resequencing of 372 genes to identify genes with recurrent aberrations. We developed and implemented an algorithm to discover genetic subtypes based on the co-occurrence of genetic alterations. RESULTS We identified four prominent genetic subtypes in DLBCL, termed MCD (based on the co-occurrence of MYD88 L265P and CD79B mutations), BN2 (based on BCL6 fusions and NOTCH2 mutations), N1 (based on NOTCH1 mutations), and EZB (based on EZH2 mutations and BCL2 translocations). Genetic aberrations in multiple genes distinguished each genetic subtype from other DLBCLs. These subtypes differed phenotypically, as judged by differences in gene-expression signatures and responses to immunochemotherapy, with favorable survival in the BN2 and EZB subtypes and inferior outcomes in the MCD and N1 subtypes. Analysis of genetic pathways suggested that MCD and BN2 DLBCLs rely on “chronic active” B-cell receptor signaling that is amenable to therapeutic inhibition. CONCLUSIONS We uncovered genetic subtypes of DLBCL with distinct genotypic, epigenetic, and clinical characteristics, providing a potential nosology for precision-medicine strategies in DLBCL. (Funded by the Intramural Research Program of the National Institutes of Health and others.)