Impact of Cell of Origin (COO) on Long Term Outcomes Post Autologous Hematopoietic Cell Transplant in Patients with Relapsed/ Refractory Chemotherapy Sensitive De-Novo Diffuse Large B-Cell Lymphoma (DLBCL)

Introduction The molecular basis for the clinical heterogeneity observed in DLBCL was initially elucidated with gene expression profiling, identifying three distinct subgroups with prognostic relevance based on COO namely activated B-cell like (ABC), germinal center B-cell like (GCB) and a type 3 subtype. Shortly afterwards the Han's algorithm was developed allowing for widespread applicability and correlation of gene expression with protein expression. GCB profile at diagnosis is generally associated with superior long term outcomes compared to non-GCB in patients treated with conventional chemo-immunotherapy. Patients with relapsed/refractory disease (R/R) after front-line therapy undergo salvage treatment and, upon demonstration of objective response, proceed with high dose therapy and autologous transplantation (auto-HCT). Whether COO by Han's algorithm retains its prognostic significance post auto-HCT in patients with R/R DLBCL is not well established. Methods A retrospective analysis was carried out using the Mayo database across all three sites. It included all patients between 18-75 years of age who were diagnosed with de-novo DLBCL and underwent first auto-HCT for R/R disease. Patients must have received salvage treatment and shown chemotherapy sensitive disease. Patients with transformed DLBCL or other variants and those who underwent auto-HCT in first complete remission for high risk disease were excluded. Patients who had less than a partial response at time of transplantation and still underwent auto-HCT were also excluded. Primary endpoints included overall survival (OS); relapse and relapse-free survival (RFS). Continuous variables were summarized as mean (standard deviation) and median (range). Categorical variables were reported as frequency (percentage). Continuous baseline variables were compared between GCB and non-GCB using Wilcoxon rank sum test and categorical baseline variables were compared with Chi-squared test. Kaplan-Meier method was used to estimate 5 and 10-year rates of freedom from long-term events and draw corresponding survival curves. Multivariate Cox regression models were used to evaluate the impact of cell of origin on long-term survival after adjusting for baseline factors. All tests were two-sided with alpha level set at 0.05 for statistical significance. Results A total of 357 patients underwent auto-HCT between 2005 and 2018. Cell of origin was determined for 284 patients and these were included in the analysis. Median age was 61 (range: 19-78) years, and 64% patients were male. GCB and non-GCB patients did not differ significantly with regards to baseline factors (Table 1). During a median follow up of 1.7 years, 139 patients died, 151 had relapse and 175 either died or had relapse. Five year OS for GCB patients versus (vs) non-GCB was at 44% vs 64% (p=0.004) (Figure 1); relapse was at 67% vs 49% (p=0.012) and RFS was at 28% vs 50% (p=0.003). The difference between GCB and non-GCB groups remained statistically significant in multivariate analysis, with GCB patients 60% more likely to die and 50% more likely to experience relapse compared to non-GCB patients. The only other factor that retained significance in multivariate analysis for both endpoints was response at the time of transplant (Table 2A-C). Conclusions In this analysis, GCB DLBCL results in worse OS and higher relapse vs. non-GCB DLBCL following an auto-HCT. It is unclear if relapse/progression of GCB subtype after front-line therapy alters its natural history into a more aggressive disease. Download : Download high-res image (494KB) Download : Download full-size image Disclosures Ansell: Trillium: Research Funding; ADC Therapeutics: Research Funding; Affimed: Research Funding; Bristol Myers Squibb: Research Funding; AI Therapeutics: Research Funding; Takeda: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding. Foran: Xencor: Research Funding; Trillium: Research Funding; Takeda: Research Funding; Kura Oncology: Research Funding; Aptose: Research Funding; Aprea: Research Funding; Actinium: Research Funding; Boehringer Ingelheim: Research Funding; Abbvie: Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Servier: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Revolution Medicine: Consultancy; H3Biosciences: Research Funding; Agios: Honoraria, Research Funding. Tun: Celgene: Research Funding; Mundipharma: Research Funding; Curis: Research Funding; TG Therapeutics: Research Funding; Acrotech: Research Funding; DTRM Biopharma: Research Funding; Bristol-Myers Squibb: Research Funding. Kharfan-Dabaja: Daiichi Sankyo: Consultancy; Pharmacyclics: Consultancy.