Abstract

Abstract 295

Introduction:

Diffuse large B-cell lymphoma (DLBCL) is the most common type of aggressive non-Hodgkin lymphoma (NHL), accounting for approximately 30–40% of all new lymphoma cases. While standard therapy using rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) has significantly increased the survival of DLBCL patients, approximately one third of DLBCL patients still remain unresponsive to or relapse after standard treatment. Further investigation into the genomic architecture of DLBCL will contribute to elucidating the causes of the poor outcomes in this subgroup of patients. While the copy number and the gene expression profiles of DLBCL specimens have been well described as separate analyses, a large-scale high resolution integration of both orthologous measurements has yet to be reported. The integration of these two data types in a clinically well-annotated cohort of DLBCL is crucial as it can potentially distinguish driver from passenger genomic aberrations and reveal associations with clinical outcome.

Methods and Patients:

Affymetrix SNP 6.0 microarrays were used to ascertain the copy number profiles in 151 pretreatment biopsies of DLBCL that were representative of the population of DLBCL patients treated at the British Columbia Cancer Agency. Clinical outcome data were available for all 151 patients with 142 patients receiving R-CHOP or R-CHOP-like treatment. Matching RNA-seq libraries were used to quantitate the gene expression levels in 91 samples. The SNP 6.0 pre-processing method cRMAv2 was used to generate raw probe intensities that were then normalized to 1258 HapMap3 SNP 6.0 arrays. Copy number state calls were predicted using HMM-Dosage. RNA-seq data were aligned using the split-read aware aligner GSNAP and gene expression values were generated using the metric reads per kilobase of transcript per million mapped reads. DriverNet analyses were utilized to predict functionally relevant driver genes and outcome correlations in R-CHOP treated patients were performed using Cox regression and the log-rank test.

Results:

The copy number landscape derived from the SNP 6.0 microarrays revealed previously reported large scale chromosomal deletions in chromosome 6p and amplifications in chromosomes 3, 7 and 18. By integrating the gene expression with copy number data, we found that gene copy number was correlated with its own gene expression (classified as being cis-correlated) in 23.5% of genes. In addition, we investigated copy number aberrations which were highly correlated with gene expression across the genome (classified as trans-correlated). This analysis revealed aberration hotspots in genomic locations 3q26-q28 (TBL1XR1, BCL6, TP63), 17p12 (NCOR1, MAP2K4), 18q11.1-q11.2 (RBBP8) and 22q11.21 (BID, IL17RA) suggesting that these hotspots regulate important pathways that may contribute to the pathogenesis of DLBCL. We identified previously reported focal amplifications (e.g. REL) and deletions (e.g. B2M, CDKN2A). Moreover, we identified novel focal deletions, including homozygous deletions, in chromatin modifying genes: LCOR (7.9%), RCOR1 (9.9%), and NCOR1 (17.9%), all of which were cis-correlated and were validated using fluorescence in situ hybridization. DriverNet analyses identified RCOR1 deletions as one of the main driver alterations. RCOR1 deletions were also found to be associated with progression-free survival (5-year progression-free survival: deleted 40% vs. non-deleted 75%, p=0.0188).

Discussion:

Our systematic integration of SNP 6.0 and RNA-seq data confirmed findings of previous studies and also revealed novel genomic aberration hotspots and highly focal and frequent deletions in chromatin modifying genes. Results derived from our large-scale high resolution data set indicate the feasibility and efficacy of integrative genomic analyses in revealing novel and pathogenetically relevant genomic aberrations in lymphoid cancers. The discovery of the association of RCOR1 deletions with progression-free survival suggests that RCOR1 deletions could be used as a prognostic marker and might indicate a molecular phenotype that can be targeted by novel therapeutic agents in DLBCL.

Disclosures:

No relevant conflicts of interest to declare.

Author notes

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Asterisk with author names denotes non-ASH members.