Phase 2 study of panobinostat with or without rituximab in relapsed diffuse large B-cell lymphoma.

The majority of diffuse large B-cell lymphoma (DLBCL) tumors contain mutations in histone-modifying enzymes (HMEs), indicating a potential therapeutic benefit of histone deacetylase inhibitors (HDIs), and preclinical data suggest that HDIs augment the effect of rituximab. In this randomized phase 2 study, we evaluated the response rate and toxicity of panobinostat, a pan-HDI administered 30 mg orally 3 times weekly, with or without rituximab, in 40 patients with relapsed or refractory de novo (n = 27) or transformed (n = 13) DLBCL. Candidate genes and whole exomes were sequenced in relapse tumor biopsies to search for molecular correlates, and these data were used to quantify circulating tumor DNA (ctDNA) in serial plasma samples. Eleven of 40 patients (28%) responded to panobinostat (95% confidence interval [CI] 14.6-43.9) and rituximab did not increase responses. The median duration of response was 14.5 months (95% CI 9.4 to "not reached"). At time of data censoring, 6 of 11 patients had not progressed. Of the genes tested for mutations, only those in MEF2B were significantly associated with response. We detected ctDNA in at least 1 plasma sample from 96% of tested patients. A significant increase in ctDNA at day 15 relative to baseline was strongly associated with lack of response (sensitivity 71.4%, specificity 100%). We conclude that panobinostat induces very durable responses in some patients with relapsed DLBCL, and early responses can be predicted by mutations in MEF2B or a significant change in ctDNA level at 15 days after treatment initiation. This clinical trial was registered at www.ClinicalTrials.gov (#NCT01238692).

Genetic Landscapes of Relapsed and Refractory Diffuse Large B-Cell Lymphomas.

PURPOSE: Relapsed or refractory diffuse large B-cell lymphoma (rrDLBCL) is fatal in 90% of patients, and yet little is known about its biology. EXPERIMENTAL DESIGN: Using exome sequencing, we characterized the mutation profiles of 38 rrDLBCL biopsies obtained at the time of progression after immunochemotherapy. To identify genes that may be associated with relapse, we compared the mutation frequency in samples obtained at relapse to an unrelated cohort of 138 diagnostic DLBCLs and separately amplified specific mutations in their matched diagnostic samples to identify clonal expansions. RESULTS: On the basis of a higher frequency at relapse and evidence for clonal selection, TP53, FOXO1, MLL3 (KMT2C), CCND3, NFKBIZ, and STAT6 emerged as top candidate genes implicated in therapeutic resistance. We observed individual examples of clonal expansions affecting genes whose mutations had not been previously associated with DLBCL including two regulators of NF-κB: NFKBIE and NFKBIZ We detected mutations that may be affect sensitivity to novel therapeutics, such as MYD88 and CD79B mutations, in 31% and 23% of patients with activated B-cell-type of rrDLBCL, respectively. We also identified recurrent STAT6 mutations affecting D419 in 36% of patients with the germinal center B (GCB) cell rrDLBCL. These were associated with activated JAK/STAT signaling, increased phospho-STAT6 protein expression and increased expression of STAT6 target genes. CONCLUSIONS: This work improves our understanding of therapeutic resistance in rrDLBCL and has identified novel therapeutic opportunities especially for the high-risk patients with GCB-type rrDLBCL. Clin Cancer Res; 22(9); 2290-300. ©2015 AACR.

Methods for sample acquisition and processing of serial blood and tumor biopsies for multicenter diffuse large B-cell lymphoma clinical trials.

Increasingly, targeted therapies are being developed to treat malignancies. To define targets, determine mechanisms of response and resistance, and develop biomarkers for the successful investigation of novel therapeutics, high-quality tumor biospecimens are critical. We have developed standard operating procedures (SOPs) to acquire and process serial blood and tumor biopsies from patients with diffuse large B-cell lymphoma enrolled in multicenter clinical trials. These SOPs allow for collection and processing of materials suitable for multiple downstream applications, including immunohistochemistry, cDNA microarrays, exome sequencing, and metabolomics. By standardizing these methods, we control preanalytic variables that ensure high reproducibility of results and facilitate the integration of datasets from such trials. This will facilitate translational research, better treatment selection, and more rapid and efficient development of new drugs. See all the articles in this CEBP Focus section, "Biomarkers, Biospecimens, and New Technologies in Molecular Epidemiology."

Analysis of genomic abnormalities in tumors: a review of available methods for Illumina two-color SNP genotyping and evaluation of performance.

Several methods have recently been proposed for identifying copy number alterations (CNAs) in genomic DNA from tumors, using the signals arising from two-color genotyping technologies. Although copy number estimation in normal tissue has been well studied, methods developed for normal tissue tend to perform poorly when applied to tumors, due to normal cell contamination, varying levels of ploidy, and genetic heterogeneity within the tumor. Here we compare the performance of seven methods (DNA-Chip Analyzer software (dCHIP), GenoCNA software, allele-specific copy number analysis of tumors (ASCAT), OncoSNP software, genome alteration print (GAP) visualization, CNVpartition software plug-in for the Genome Studio software, and Partek Genomics Suite software) that have been established for two-color CNA analysis on the Illumina platform, using two ovarian cancer cell lines where spectral karyotyping analysis has also been performed, and two tissue samples, one from a highly malignant ovarian cancer and one from a benign ovarian tumor, all of which harbor significantly different genomic abnormalities. ASCAT shows very stable estimates of CNAs, as does OncoSNP when jointly analyzing paired normal DNA. We found the best performance, in general to be from ASCAT.

The genomic landscape of TP53 and p53 annotated high grade ovarian serous carcinomas from a defined founder population associated with patient outcome.

High-grade ovarian serous carcinomas (HGSC) are characterized by TP53 mutations and non-random patterns of chromosomal anomalies, where the nature of the TP53 mutation may correlate with clinical outcome. However, the frequency of common somatic genomic events occurring in HGSCs from demographically defined populations has not been explored. Whole genome SNP array, and TP53 mutation, gene and protein expression analyses were assessed in 87 confirmed HGSC samples with clinical correlates from French Canadians, a population exhibiting strong founder effects, and results were compared with independent reports describing similar analyses from unselected populations. TP53 mutations were identified in 91% of HGSCs. Anomalies observed in more than 50% of TP53 mutation-positive HGSCs involved gains of 3q, 8q and 20q, and losses of 4q, 5q, 6q, 8p, 13q, 16q, 17p, 17q, 22q and Xp. Nearly 400 regions of non-overlapping amplification or deletion were identified, where 178 amplifications and 98 deletions involved known genes. The subgroup expressing mutant p53 protein exhibited significantly prolonged overall and disease-free survival as compared with the p53 protein null subgroup. Interestingly, a comparative analysis of genomic landscapes revealed a significant enrichment of gains involving 1q, 8q, and 12p intervals in the subgroup expressing mutant p53 protein as compared with the p53 protein null subgroup. Although the findings show that the frequency of TP53 mutations and the genomic landscapes observed in French Canadian samples were similar to those reported for samples from unselected populations, there were differences in the magnitude of global gains/losses of specific chromosomal arms and in the spectrum of amplifications and deletions involving focal regions in individual samples. The findings from our comparative genomic analyses also support the notion that there may be biological differences between HGSCs that could be related to the nature of the TP53 mutation.