Background: The genetic alterations associated with survival in patients with diffuse large B cell lymphomas (DLBCL) treated with combined rituximab-CHOP (R-CHOP) chemo-immunotherapy are not well understood. Methods: 92 patients with DLBCL treated with R-CHOP who had a biopsy available at the time of diagnosis were included in the study. 31 patients were classified as treatment failures defined as progression < 6 months of completing R-CHOP and 61 patients were classified as treatment successes defined as a maintained remission >2 years after diagnosis. We used genome-wide BAC array comparative genomic hybridization (aCGH) to determine the genomic copy number imbalances. The presence of genetic gains and losses were determined using the intersection between visual annotations and a Hidden Markov model algorithm. DLBCL cell of origin (COO) subtype distinctions (GCB vs ABC) were determined using a Bayesian predictor model on gene expression derived from custom Affymetrix arrays (Dave, N Engl J Med, 2006;354:2431). A permutation test was used to identify genetic regions that were significantly different between treatment failures and treatment successes. Functional pathway analysis was performed using Ingenuity software. A novel model based clustering algorithm was applied to the normalized data to determine if any association with outcome correlated with the observed genetic alterations. Results: Lymphoma progressed in 31/92 (34%) patients < 6 months after R-CHOP (median follow-up = 4 y). All 92 patients had successful aCGH and 81 had COO available for this analysis. The International Prognostic Index (IPI) and COO were predictive of outcome (p=0.04, p=0.02, respectively). 451 regions containing 338 genes were associated with treatment failure with a p-value of <10−6. Gains in 9q33.3 were found in 13 patients (14%) and were significantly associated with treatment failure p<10−8. This region contains genes such as HSPA5, a negative regulator of apoptosis and PPP6C, a positive regulator of the cell cycle by targeting IKBe thereby removing inhibition of the NFkB pathway. Deletions in 17p12 were detected in 24 (26%) and were the most statistically significantly associated with treatment failure p<10−9. This region contains tumor necrosis factor (TNF) receptor superfamily member (TNFRSF13B or TACI) which, when deleted or mutated, has been previously shown to lead to activation of the noncanonical NFkB pathway and B cell proliferation. 21 of these 24 patients also had deletion of 17p13 at the TP53 locus (p<10−6). Neither 9p33.3 nor 17p12 deletion was associated with COO distinctions. Using Ingenuity, pathways involving apoptosis and cellular proliferation, specifically those involving P53, MYC and HSPA5 genes, were over-represented in treatment failures (p=2.04 × 10−4). Unsupervised clustering of the aCGH data demonstrated that 60% of cases could be stratified into 4 genetic sub-groups based on the presence of 1q+, 6q−, +7 and the concurrent presence of +3 and +18. Supervised analysis demonstrated that the +3/+18 group and the 6q− group were associated with ABC subtype of DLBCL whereas the +7 and 1q+ groups were associated with the GCB subtype. However, these genetic groups did not correlate with treatment outcome. Conclusions: Some genetic alterations cluster together and can distinguish COO subtypes of DLBCL. Gains on 9q33.3 and deletions of 17p12 are common alterations detected by high resolution aCGH in DLBCL. Most importantly, these alterations involve genes known to be critical in B cell proliferation and apoptosis and alterations at these sites are strongly associated with treatment failure (p values <10−8) in patients treated with R-CHOP.

Background: Identifying patients with high-risk of progression or death is important in developing novel treatment strategies in diffuse large B-cell lymphoma (DLBCL). The International Prognostic Index (IPI) is a commonly used score to classify the prognostic risk of previously untreated (1L) DLBCL patients. Although perceived as important in understanding the health status of patients, patient-reported health-related quality-of-life (HRQoL) measures have not been studied extensively as prognostic factors in DLBCL. In this study, we explored the prognostic value of pretreatment HRQoL in progression free survival (PFS) and overall survival (OS) in 1L DLBCL patients, using data from the phase III GOYA study (NCT01287741, Obinutuzumab-CHOP vs Rituximab-CHOP). Method: Four preselected pretreatment HRQoL subscale scores (lymphoma specific [LYMS], physical functioning [PF2], role functioning [RF2], and fatigue [FA]) were derived from two HRQoL questionnaires (EORTC-QLQ C30 and FACT-Lym) in the GOYA study. Each subscale was dichotomized to indicate low or high HRQoL based on their respective median scores (table). Sensitivity analyses were similarly evaluated. The prognostic value of each HRQoL subscale was evaluated using Cox proportional hazard models, adjusted for the five components of IPI. Table.Summary of prognostic value for the four pre-treatment HRQoL subscalesSummary3-year OS estimate2 (95% CI)Cox proportional hazard modelSubscale(n1)QuestionnaireMedian (min-max)Low HRQoLHigh HRQoLOS HR3 (95% CI)PFS HR3(95% CI)Lymphoma specific(n = 1246)FACT-LYM47 (7-60)0.78 (0.74, 0.81)0.85 (0.82, 0.87)0.7 (0.51, 0.95)0.81(0.63, 1.03)Physical functioning(n = 1254)EORTC-QLQ C3087(0-100)0.77 (0.74, 0.80)0.86 (0.83, 0.89)0.6 (0.43, 0.85)0.72(0.56, 0.93)Role functioning(n = 1256)EORTC-QLQ C3083(0-100)0.78 (0.75, 0.81)0.84 (0.81, 0.88)0.72(0.52, 0.99)0.84(0.65, 1.07)Fatigue(n = 1256)EORTC-QLQ C3033(0-100)0.74(0.70, 0.78)0.84 (0.82, 0.87)0.68 (0.5, 0.92)0.94(0.73, 1.2)1Only patients with valid respective pretreatment HRQoL scores were included in each analysis2OS estimated using the Kaplan-Meier method3HR = hazard ratio representing high HRQoL vs. low HRQoL, adjusted for the five components of IPI: age(≤60 vs >60), ECOG PS(0-1 vs 2-3), lactate dehydrogenase level (LDH)(≤1 normal vs >1 normal), Ann Arbor stage (Stage I or II vs III or IV disease), and extranodal sites(≤1 vs >1 extranodal site) Results: All four HRQoL subscales contributed independent prognostic value to patient outcome (table). Results show that high HRQoL is associated with better survival outcome (higher 3-year OS estimate and lower risk [HR]) compared with low HRQoL. Among the four subscales, PF2 had the highest estimated contribution to prognosis (OS: HR = 0.59, 95% CI: [0.42, 0.84], PFS: HR = 0.71, 95% CI [0.55, 0.93]). Conclusion: Our findings demonstrate the potential of patient-reported HRQoL measures in providing prognostic value in addition to IPI, which may contribute to improved risk stratification and inform treatment decisions for DLBCL patients. Citation Format: Huang Huang, Asim Dayte, Ming Fan, Andrea Knapp, Rama Balakrishnan, Sandhya Balasubramanian, Julia Chae, Emma Roth, Tina Nielsen, Joseph N. Paulson, Peter Trask. Listen to the patients: Assessing the prognostic value of pre-treatment health-related quality of life in 1L DLBCL patients [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2035.

No abstract