Smith, Alexandra

Abstract Overall Survival estimates from diagnosis are valuable for guiding treatment, but do not consider the years already survived. Conditional survival (CS) provides dynamic survival predictions over time. This study was conducted to estimate CS at 1–8 years from diagnosis and the impact of baseline prognostic factors on CS in multiple myeloma (MM) patients. This is a retrospective study including 2556 MM patients diagnosed between 2004–2019. CS (t|s) was defined as the probability of surviving t years given survival of s years. Median age was 64 years. Median follow-up was 6.2 years and median overall survival from diagnosis was 7.5 years. The 5-year CS estimates at s = 0, 1, 2, 3, and 5 years were 0.64, 0.61, 0.61, 0.61, and 0.58, respectively. On multivariate analysis, age ≥ 65 and proteasome inhibitor + immunomodulatory-based induction were associated with decreased survival and increased survival, respectively, retained at 5 years. The adverse impact of 1q gain/amplification, high-risk IgH translocation, and ISS-3 was significant at 1 and 3 years but not 5 years. Chromosome 17 abnormality was associated with decreased survival only at 1 year. Among MM patients, 5-year CS was stable at 1–5 years from diagnosis. The prognostic impact of high-risk cytogenetic factors decreased with additional years survived.

Abstract Despite the effectiveness of immuno-chemotherapy, 40% of patients with diffuse large B-cell lymphoma (DLBCL) experience relapse or refractory disease. Longitudinal studies have previously focused on the mutational landscape of relapse but fell short of providing a consistent relapse-specific genetic signature. In our study, we have focused attention on the changes in GEP accompanying DLBCL relapse using archival paired diagnostic/relapse specimens from 38 de novo patients with DLBCL. COO remained stable from diagnosis to relapse in 80% of patients, with only a single patient showing COO switching from activated B-cell–like (ABC) to germinal center B-cell–like (GCB). Analysis of the transcriptomic changes that occur following relapse suggest ABC and GCB relapses are mediated via different mechanisms. We developed a 30-gene discriminator for ABC–DLBCLs derived from relapse-associated genes that defined clinically distinct high- and low-risk subgroups in ABC–DLBCLs at diagnosis in datasets comprising both population-based and clinical trial cohorts. This signature also identified a population of <60-year–old patients with superior PFS and OS treated with ibrutinib–R-CHOP as part of the PHOENIX trial. Altogether this new signature adds to the existing toolkit of putative genetic predictors now available in DLBCL that can be readily assessed as part of prospective clinical trials.

Background: Although diffuse large B cell lymphoma (DLBCL) can be cured using immuno-chemotherapy, 40% of patients experience relapse or refractory disease. Large-scale profiling studies have mainly focused on DLBCL at diagnosis, resolving different outcome groups based on gene expression (e.g. cell-of-origin (COO) or molecular high grade), MYC/BCL2 translocations (double-hit lymphoma) or gene mutations and copy number aberrations (Schmitz et al, NEJM 2018; Chapuy et al, NatureMedicine 2018). In comparison, longitudinal studies have been hindered by the limited availability of sequential biopsy samples. To date, the relapse-specific gene mutations identified are limited and inconsistent across studies. In our study, we have focussed attention on the changes in gene expression profile (GEP) accompanying DLBCL relapse. Methods: We retrospectively collected archival paired diagnostic/relapse formalin fixed paraffin embedded tumor biopsies from 38 de novo DLBCL patients collected from multiple UK sites treated with rituximab-based immuno-chemotherapy, where partial or complete remission was reported following treatment. COO classification was performed by the Lymph2Cx assay on NanoString to distinguish activated B-cell-like (ABC) and germinal center B-cell-like (GCB) subtypes. The Ion AmpliSeq™ Transcriptome Human Gene Expression Kit was used to measure the expression levels of > 20,000 genes on the paired samples. Results: COO remained stable from diagnosis to relapse in 17 ABC-ABC pairs, 11 GCB-GCB pairs and 4 unclassified (UNC)-UNC pairs. Frank COO switching was observed in 6 cases (1 ABC-GCB, 2 ABC-UNC, 2 GCB-UNC, 1 UNC-ABC). Pairs with stable COO were taken forward for further analysis. Gene expression analysis using the limma R package identified 163 and 136 genes as differentially expressed (DE) (p <= 0.01 and absolute log2FC > 1) between the diagnostic and relapse biopsies in ABC and GCB tumors respectively, with only a one gene overlap. Gene Set Enrichment Analysis further suggested that ABC and GCB relapses are mediated via different mechanisms, with tumor growth and proliferation signatures enriched in ABC relapses, whilst adaptive immunity-related signatures accompanied GCB relapses. Next, we aimed to utilise our relapse-specific genes to identify outcome predictors at diagnosis using publicly available GEP datasets. In order to increase our discovery power and accuracy, a larger set of DE genes from the paired differential analysis (796 genes in ABC pairs and 387 from GCB pairs) were selected (p <= 0.05) and subsequently used in a training cohort (GEP from Reddy et al, Cell 2017). The Prediction Analysis for Microarrays R (PAMR) algorithm identified a 30-gene signature within DE genes from ABC pairs (Fig1.A), capable of separating the 249 ABC cases into 136 low and 113 high-risk cases with significantly inferior overall survival (Hazard Ratio (HR)=1.89, log-rank p=0.0017, measure of goodness-of-fit C-index=0.71; Fig1.B). No equivalent signature was found in the GCB cases using this approach. The prognostic significance of this 30-gene discriminator was successfully validated using a linear predictor in two independent GEP datasets: 1) a population-based cohort (Lenz et al, NEJM 2008) with 93 R-CHOP-treated ABC cases identifying 47 low and 46 high-risk cases (HR=1.92, p=0.046, C-index=0.77; Fig1.C) and 2) a clinical trial dataset (REMoDL-B, Davies et al, Lancet Oncol 2019) with 255 ABC cases identifying 110 low and 145 high-risk ABC cases (HR=1.95, p=0.0051, C-index=0.70; Fig1.D). Conclusions: Here we describe a 30-gene discriminator in ABC-DLBCL, derived from genes differentially expressed between diagnosis and relapse, that allowed the definition of clinically distinct high and low risk subgroups in ABC-DLBCLs at diagnosis. The clinical translation of such a tool may be useful to guide therapy for this unfavourable subgroup of ABC-DLBCLs. Validation of this signature is currently underway in additional datasets and further study is required to understand the contribution of these genes in DLBCL pathology. Disclosures Korfi: Roche: Consultancy. Burton:Celgene: Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees. Rule:TG Therapeutics: Consultancy, Honoraria; Napp: Consultancy; Kite: Consultancy; Pharmacyclics: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Sunesis: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria, Research Funding; Astra-Zeneca: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Crosbie:Janssen: Honoraria. Scott:Celgene: Consultancy; Janssen: Consultancy, Research Funding; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoSting [Institution], Research Funding; Roche/Genentech: Research Funding. Rimsza:NanoSting: Patents & Royalties: Named inventor on a patent licensed to NanoSting [Institution]. Davies:Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Research Funding; Bayer: Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Honoraria, Research Funding; Karyopharma: Membership on an entity's Board of Directors or advisory committees, Research Funding; GSK: Research Funding; Acerta Pharma: Honoraria, Research Funding; ADCT Therapeutics: Honoraria, Research Funding; BioInvent: Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; MorphoSys AG: Honoraria, Membership on an entity's Board of Directors or advisory committees. Gribben:Abbvie: Consultancy, Honoraria, Research Funding; Acerta/Astra Zeneca: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding. Okosun:Gilead Sciences: Honoraria, Research Funding. Johnson:Epizyme: Honoraria, Research Funding; Novartis: Honoraria; Kite: Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria; Boehringer Ingelheim: Honoraria; Takeda: Honoraria; Genmab: Honoraria; Celgene: Honoraria; Incyte: Honoraria. Fitzgibbon:Epizyme: Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Speakers Bureau.

Objective Determinants of the increased risk of diffuse large B-cell lymphoma (DLBCL) in SLE are unclear. Using data from a recent lymphoma genome-wide association study (GWAS), we assessed whether certain lupus-related single nucleotide polymorphisms (SNPs) were also associated with DLBCL. Methods GWAS data on European Caucasians from the International Lymphoma Epidemiology Consortium (InterLymph) provided a total of 3857 DLBCL cases and 7666 general-population controls. Data were pooled in a random-effects meta-analysis. Results Among the 28 SLE-related SNPs investigated, the two most convincingly associated with risk of DLBCL included the CD40 SLE risk allele rs4810485 on chromosome 20q13 (OR per risk allele=1.09, 95% CI 1.02 to 1.16, p=0.0134), and the HLA SLE risk allele rs1270942 on chromosome 6p21.33 (OR per risk allele=1.17, 95% CI 1.01 to 1.36, p=0.0362). Of additional possible interest were rs2205960 and rs12537284. The rs2205960 SNP, related to a cytokine of the tumour necrosis factor superfamily TNFSF4, was associated with an OR per risk allele of 1.07, 95% CI 1.00 to 1.16, p=0.0549. The OR for the rs12537284 (chromosome 7q32, IRF5 gene) risk allele was 1.08, 95% CI 0.99 to 1.18, p=0.0765. Conclusions These data suggest several plausible genetic links between DLBCL and SLE.

Abstract Background: Excess adiposity has been associated with lymphomagenesis, possibly mediated by increased cytokine production causing a chronic inflammatory state. The relationship between obesity, cytokine polymorphisms, and selected mature B-cell neoplasms is reported. Method: Data on 4,979 cases and 4,752 controls from nine American/European studies from the InterLymph consortium (1988–2008) were pooled. For diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), and chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL), joint associations of body mass index (from self-reported height and weight) and 12 polymorphisms in cytokines IL1A (rs1800587), IL1B (rs16944, rs1143627), IL1RN (rs454078), IL2 (rs2069762), IL6 (rs1800795, rs1800797), IL10 (rs1800890, rs1800896), TNF (rs1800629), LTA (rs909253), and CARD15 (rs2066847) were investigated using unconditional logistic regression. BMI-polymorphism interaction effects were estimated using the relative excess risk due to interaction (RERI). Results: Obesity (BMI ≥ 30 kg/m2) was associated with DLBCL risk [OR = 1.33; 95% confidence interval (CI), 1.02–1.73], as was TNF-308GA+AA (OR = 1.24; 95% CI, 1.07–1.44). Together, being obese and TNF-308GA+AA increased DLBCL risk almost 2-fold relative to those of normal weight and TNF-308GG (OR = 1.93; 95% CI, 1.27–2.94), with a RERI of 0.41 (95% CI, −0.05–0.84; Pinteraction = 0.13). For FL and CLL/SLL, no associations with obesity or TNF-308GA+AA, either singly or jointly, were observed. No evidence of interactions between obesity and the other polymorphisms were detected. Conclusions: Our results suggest that cytokine polymorphisms do not generally interact with BMI to increase lymphoma risk but obesity and TNF-308GA+AA may interact to increase DLBCL risk. Impact: Studies using better measures of adiposity are needed to further investigate the interactions between obesity and TNF-308G&gt;A in the pathogenesis of lymphoma. Cancer Epidemiol Biomarkers Prev; 24(7); 1061–70. ©2015 AACR.