Okosun, Jessica

Abstract Secondary central nervous system (CNS) large B-cell lymphoma (SCNSL) occurs in the de novo setting, as a CNS-isolated relapse, or synchronous (concomitant CNS and systemic) relapse. SCNSL is a devastating event without therapeutic consensus. Thus, we aimed to evaluate treatment outcomes in an international cohort. Progression-free survival (PFS), overall survival (OS), and cumulative incidence of relapse (CIR, estimated using competing-risk models) were reported. Prognostic factors were identified in a 6-month landmark multivariate analysis. Outcomes after thiotepa autologous stem cell transplant (ASCT) and chimeric antigen receptor (CAR) T-cell therapy (CAR-T) delivered at relapse were compared after propensity score matching (PSM). A total of 1139 patients were included in the analysis (de novo: 537; relapsed SCNSL: 602). Two-year PFS estimates were 40.4%, 43.9%, and 16.2% for de novo SCNSL, CNS-isolated relapse, and synchronous relapse, respectively. Patients with CNS-isolated relapse demonstrated low rates of systemic recurrence (24-month CIR, 6%). Thiotepa-ASCT correlated with longer survival in de novo SCNSL (PFS: hazard ratio [HR], 0.57; P = .005; and OS: HR, 0.62; P = .023) and CNS-isolated relapses (PFS: HR, 0.55; P = .002; and OS: HR, 0.39; P< .0001). ASCT (thiotepa or no thiotepa) also associated with improved survival in synchronous relapses (PFS: HR, 0.57; P = .023; and OS: HR, 0.48; P = .019). Higher survival with thiotepa-ASCT than CAR-T was observed after PSM (PFS: HR, 0.45; P = .005 and OS: HR, 0.41; P = .014). These data support thiotepa-ASCT in eligible patients, particularly de novo disease and CNS-isolated relapses. CNS-isolated relapse was infrequently associated with systemic recurrence, supporting treatment regimens adopted from primary CNS lymphoma.

Introduction: Scant data exist on prognostic factors, survival and treatment (Tx)-related outcomes in SCNSL. We conducted a multicenter international analysis to identify prognostic factors and outcomes in SCNSL at diagnosis (de novo), or after frontline Tx (1L) either with (concomitant) or without (isolated) systemic (syst) relapse. Methods: This study collected data from patients (pts) treated between 2001-2023 at 35 centers. Overall (OS) and progression-free survival (PFS) were calculated from SCNSL diagnosis to first event (death for OS, relapse/death for PFS) or last follow-up. Uni- (UVA) and multivariable (MVA) Cox analyses were performed to assess prognostic factors; Kaplan-Meier method was used to estimate survival. Results: 1173 (US: 863, UK: 273, Canada: 37) pts with SCNSL were included. Baseline characteristics were median age: 62 years (interquartile range 52.5-69), male sex: 58.7%, elevated LDH: 71.1%, stage III/IV: 88.6%, HIV: 3%, transformed LBCL (tLBCL): 13.8%, non-germinal center B-cell like (non-GCB) subtype: 52.3%, double expressor (DE): 40%, MYC rearrangement (MYC-R): 28.3%, MYC/BCL2 double hit: 16.6%, MYC/BCL2/BCL6 triple hit (TH): 6.1%. Among 522 pts with de novo SCNSL, 84.2% had involvement of a single CNS compartment (44.7% brain parenchyma; 40.4% leptomeningeal [lepto]). 1L regimens included 36.2% R-CHOP with high-dose methotrexate (HD-MTX), 12.5% MARIETTA-type, and 9.8% R-CODOX-M/IVAC; 54.6% received intrathecal chemotherapy. Consolidation Thiotepa-based autologous stem cell transplant (TT-ASCT) occurred in 15.3%. The majority of pts who underwent TT-ASCT had previously achieved CR or PR in syst (92.5%) and CNS (72.5%) compartments. At median follow-up 25.7 months (mos), subsequent relapse was observed in 27.5% TT-ASCT recipients: 36.3% had isolated CNS, 27.4% syst, and 36.3% concomitant. 588 pts presented with SCNS after 1L (66.2% isolated, 33.8% concomitant). Brain parenchyma involvement was observed in 56.8%, lepto in 22.6%. Tx included 31.6% HD-MTX combination (eg, R-MPV, MTR); 18.4% HD-MTX +/- rituximab; 16.2% MARIETTA-type; and 9.4% platinum-based regimen. Consolidation TT-ASCT occurred in 22.4% (n=132), and 13.8% (n=81) received CAR-T cell therapy (excluding subsequent relapses in de novo group). Syst (65.1%) & CNS (69.1%) responses (CR/PR) were higher before TT-ASCT compared to CAR-T cell (49.5% & 54.5%, respectively). After TT-ASCT, 52 pts (39.3%) relapsed, mainly in the CNS (n=30, 57.7%), with only 4 (7.7%) concomitant. After CAR-T, 48 pts (59.3%) relapsed: 15 (31.2%) isolated in CNS and 15 (31.2%) concomitant. 12-month cumulative incidence of relapse (with death as competing risk) was 21.1% after TT-ASCT and 55.8% after CAR-T. In 63 pts (5.4%), the first CNS event occurred >2 lines of Tx. With median follow-up of the entire cohort of 24.6 mos, median PFS and OS were 11 and 21 mos, respectively. mPFS and mOS were significantly better in pts with de novo SCNSL (13.4 & 47.4 mos) compared to SCNSL after 1L (10.4 & 14.2 mos) and >2 lines of Tx (6.5 & 9.7 mos); P<0.0001. Factors associated with significantly shorter OS in UVA across all presentations were age >60, male sex, GCB subtype, MYC-R, tLBCL, DE, TH, elevated LDH, ECOG PS ≥2, SCNSL after 1L (vs. de novo), and failure to achieve syst/CNS response to 1L. Of these, age >60, male sex, MYC-R, ECOG PS ≥2, and failure to achieve response to 1L remained significant in MVA. In de novo cases, age >60, DE, ECOG PS≥2, and failure to achieve syst/CNS response to 1L were associated with poorer OS; for pts with SCNSL after 1L, age >60, GCB subtype, and ECOG PS ≥2 were significant. Pts treated with HD-MTX-based regimens after 1L experienced longer OS than those treated with platinum regimens or high-dose cytarabine. In de novo cases, TT-ASCT was significantly associated with longer OS (HR=0.58; P=0.025) in MVA after adjusting for significant factors, 1L type, and response. For SCNSL at relapse, TT-ASCT (HR=0.34; P<.0001) and isolated SCNSL presentation (vs. concomitant HR=0.70, P=0.007) were associated with longer OS, adjusting for significant factors, type of second-line Tx, & site of CNS relapse. Conclusions: This study represents the largest analysis of SCNSL inclusive of all modes of presentation. Concomitant SCNSL at relapse was associated with poor outcomes, while TT-ASCT consolidation was associated with improved OS in de novo & relapsed disease. Despite advances in LBCL SCNSL remains an unmet therapeutic need.

SummaryDespite the clinical and molecular heterogeneity of follicular lymphoma (FL), there remains a lack of biomarker‐directed therapeutic approaches in routine clinical practice, with the notable exception of the EZH2 inhibitor tazemetostat in EZH2‐mutant FL. Here we examined whether gene mutation status predicts response to clinical mTOR inhibitors (mTORi) in FL, by performing targeted mutational profiling of biopsies from 21 relapsed/refractory FL patients treated with mTORi everolimus or temsirolimus within clinical trials. We observed an enrichment of mutations within the catalytic histone acetyltransferase (HAT) domain of CREBBP in mTORi‐responders, and describe distinct transcriptional characteristics and co‐occurring mutations of FL harbouring these mutations; reinforcing the growing appreciation of CREBBPHAT mutation as a key biological determinant and its promise as a therapeutic biomarker in FL.

Abstract Follicular lymphoma is the most common indolent lymphoma accounting for approximately 20%–25% of all new non‐Hodgkin lymphoma diagnoses in western countries. Whilst outcomes are mostly favorable, the spectrum of clinical phenotypes includes high‐risk groups with significantly inferior outcomes. This review discusses recent updates in risk stratification and treatment approaches from upfront treatment for limited and advanced stage follicular lymphoma to the growing options for relapsed, refractory disease with perspectives on how to approach this from a personalized lens. Notable gaps remain on how one can precisely and prospectively select optimal treatment for patients based on varying risks, with an anticipation that an increased understanding of the biology of these different phenotypes and increasing refinement of imaging‐ and biomarker‐based tools will, in time, allow these gaps to be closed.

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.

Background:With an expanding array of novel treatment options in follicular lymphoma (FL), we largely lack effective biomarkers to predict response in this molecularly heterogeneous disease. Activating mutations focused on components of the nutrient‐sensing arm of the mTORC1 pathway (RRAGC, ATP6V1B2, ATP6AP1), are particularly enriched in FL, occurring in ∼30% of cases. Meanwhile, clinical mTOR inhibitors (mTORi) have shown some promise, with response rates of 50–60% in relapsed/refractory FL.Aims:We therefore sought to determine whether gene mutation status ‐ particularly in genes regulating the mTOR pathway – may represent biomarkers of mTORi‐response in FL.Methods:We retrospectively analyzed pre‐treatment tumor samples from relapsed/refractory FL patients treated on two phase II clinical trials with single agent mTOR inhibitors, everolimus (NCT00436618) or temsirolimus (NCT00290472), with well characterized clinical outcomes. In total, 21 patients had available biopsies, consisting of 15 everolimus‐ and 6 temsirolimus‐treated cases.Tumor DNA extracted from formalin fixed paraffin embedded (FFPE) tissue was subjected to targeted sequencing using a 22 gene panel comprising genes recurrently mutated in FL, and PI3K/AKT/mTOR pathway genes mutated in germinal center lymphomas. Libraries were prepared using capture‐based target enrichment (Nonacus), and sequenced on the NextSeq550 (Illumina) with a mean coverage depth of x700. A stringent bioinformatic pipeline was adopted with variants called with VarScan2, and a 10% variant allele frequency (VAF) threshold was employed to filter out sequencing artefacts due to the FFPE material. 100% (15/15) of a selection of variants were validated by Sanger sequencing. Staining for pS6 was undertaken on tissue slides to examine downstream mTOR activation by immunohistochemistry.Results:Overall response rate (CR and PR) was 48% (10/21). Mutational analysis showed no significant association between clinical response and mTORC1‐pathway mutations (mut) taken individually or collectively; although, absolute numbers of mutations in these genes were low, including RRAGC (responder: 0/10 mut vs non‐responder: 1/11 mut), ATP6AP1 (responder: 1/10 vs non‐responder: 1/11), and ATP6V1B2 (responder: 1/10 vs non‐responder 3/11). Notably, no difference in pS6 staining was seen between responders and non‐responders.However, we found mutations in the epigenetic regulator CREBBP to be enriched in the responder group (100% (10/10) responders vs 54.5% (6/11) non‐responders, p = 0.04). This effect was more pronounced when considering only mutations within the catalytic histone acetyltransferase (HAT) domain of CREBBP, with HAT mutations seen in 100% (10/10) responders, and 27.3% (3/11) non‐responders (p = 0.001).Summary/Conclusion:Mutations in the mTOR pathway did not predict for response to mTOR inhibition in our study. The enrichment of CREBBP mutations in responders, and particularly those affecting the catalytic HAT domain, points to CREBBP as a potential biomarker of response which warrants validation in trials of drugs modulating the PI3K/AKT/mTOR axis. More broadly our data may lend support to a distinct clinical and biological behavior of CREBBP HAT domain‐mutant disease that also warrants further evaluation. Taken together, our findings highlight the potential opportunities afforded by undertaking correlative studies, and underlines the need to incorporate prospective biomarker discovery into clinical trials in order to help focus future therapeutic strategies more rationally.