Habermann, Thomas M.

Abstract Over the past decade, classification systems using high-throughput genetic data have been developed to identify molecular subtypes of DLBCL. Although not a primary goal, these classifiers were also proposed as prognostic factors in their respective publications. To date, however, no study has directly compared these classifiers' ability to predict overall (OS) and event-free survival (EFS) in the same cohort. Herein, we sought to systematically compare predictive performance, benchmarked against the IPI, of several current established molecular classifiers and evaluate if any add significant independent prognostic information beyond the IPI. WES (including paired germline filter) and RNAseq data from newly diagnosed DLBCL patients (N=432; 2018 WHO criteria) in the Mayo Clinic/University of Iowa Lymphoma Molecular Epidemiology Resource (MER) were available for analysis (WES n=112, RNAseq n=40, or both n=280). The number of cases able to be classified (including as unclassified) for each classifier was as follows: IPI (432, 100%), cell-of-origin (COO, by Hans algorithm; 346, 80.1%), refined COO (by gene expression with Dark Zone Signature; 320, 100%), Haematological Malignancy Research Network (HMRN; 349, 89.0%), B-cell state (320, 100%), Lymphoma Ecotype (320, 100%), Lymphoma Microenvironment (LME; 320, 100%), Tumor microenvironment (TME26; 320, 100%), SubLymE (312, 97.5%), LymphGen (358, 91.3%), and DLBclass (223, 58.2%). OS was defined as time from diagnosis to all-cause death while EFS was defined as time from diagnosis to disease progression, initiation of second-line treatment, or all-cause death. We calculated Harrell's c-statistics for each Cox proportional hazards model evaluating the discriminative performance of each classifier in predicting OS/EFS. Using bootstrap validation, we obtained optimism-corrected c-statistics, which we report in the results. We performed likelihood ratio tests (LRT) comparing a univariable Cox model with IPI alone and models including both IPI and a given classifier to assess if a classifier provided independent prognostic information beyond the IPI. All patients were treated with immunochemotherapy (IC) with curative intent, 247 (57%) were male, median age at diagnosis was 65 years (IQR 55-72 years), 390 (96%) were White, 209 (60%) had GCB subtype by Hans, 22 (5.1%) had MYC double-hit by FISH (MYC and BCL2 or BCL6), and 162 (37.9%) had an IPI ≥ 3. The median follow-up was 7.0 years, 167 (39%) experienced an event, and 137 (32%) died. In a univariable Cox model, the c-statistic for IPI predicting OS was 0.66 (95% confidence interval [CI] 0.61-0.70). All other classifiers had c-statistics of ≤0.56. The c-statistic for IPI predicting EFS was 0.64 (95% CI 0.60-0.69), and all other classifiers had c-statistics of ≤0.58. The univariate DLBclass results for OS and EFS were not significantly impacted when restricted to cases with confidence >0.7 (n=136). In Cox models of IPI alone versus IPI plus an individual classifier, the only classifier that provided statistically significant, independent information for OS was TME26 (LRT p=0.02). Adjusting for IPI, patients who were TME26-negative had significantly inferior OS compared to TME26-positive cases (HR 1.63, 95% CI 1.07-2.47; p=0.02). For EFS, DLBclass (restricted to confidence >0.7) added significant prognostic information beyond IPI (LRT p=0.02). This finding was largely driven by C5 subtype; relative to C1 subtype and adjusting for IPI, C5 subtype had significantly inferior EFS (HR 5.53, 95% CI 1.81-17.0; p=0.003). In summary, we compared prognostic performance of multiple DLBCL molecular classifiers with respect to OS and EFS in a single real-world, prospective cohort of newly diagnosed, IC-treated DLBCL. Our data supports two key findings: all classifiers had modest performance for prognosis, and none individually outperformed the IPI in predicting OS nor EFS. Second, only TME26 and DLBclass (confidence >0.7) provided additional prognostic information beyond that captured by the IPI for OS and EFS, respectively. This finding suggests that the tumor immune microenvironment and characteristics of C5 DLBCLs (ABC-subtype, CD79B mutations) can potentially offer significant independent information for predicting survival and response to front-line immunochemotherapy. If replicated, these findings could inform risk-adapted treatment approaches and aid in identifying high-risk patients for enrollment in genomically-informed clinical trials.

Abstract Background: Despite advances in LBCL treatment, 35–45% of patients relapse or have refractory disease highlighting an unmet clinical need. Clinical trials are essential to advancing lymphoma treatment, yet participation remains low – especially among certain populations. We hypothesized that participation in clinical trials as part of first-line therapy is associated with improved survival outcomes and conducted this retrospective study in a multi-center cohort. Methods: The LEO cohort prospectively enrolled newly diagnosed patients with LBCL between 2015 and 2020 at 8 academic medical centers in the United States. LBCL histology's included T-cell/histiocyte rich LBCL, Primary mediastinal LBCL, high grade B-cell lymphoma and DLBCL NOS. Pathology was reviewed and classified by a LEO pathologist. Clinical and treatment data (including trial participation) were abstracted from medical records. The primary outcome endpoint is overall survival (OS) defines as the time from diagnosis to death from any cause or last follow up. Univariable and multivariable Cox proportional hazards models were used to evaluate the association between OS and therapeutic trial participation. Univariable models assessed each baseline covariate, with and without interaction with trial participation. The multivariable model adjusted for age, sex, race/ethnicity, diagnosis to treatment interval [DTI], and IPI, including an interaction between IPI and trial participation which was justified per minimal Akaike information criteria. All models clustered on institution. Results: Among the 2636 patients with LBCL, the median age was 63 (range 18 – 99), 57% were male, 75% non-Hispanic white (NHW), 12% Hispanic (H) and 7.3% African American (AA), 62% had Stage III/IV disease, and 214 (8.0%) participated in a clinical trial as part of first line therapy. Overall, between ages 18 – 80, clinical trial participation increased with age (55 % in ages 18-30 vs 11% in ages 71-80) but dropped significantly for patients > 80 (3.4%). There were no significant differences in the rate of trial participation by sex (male 8.6 % vs female 7.5%; p=0.35), race/ethnicity (NHW 8.9% vs H 5.5% vs AA 6.3%; p=0.061), body mass index (underweight [<18.5] 4.5% vs normal [18.5-24.9] 7.7% vs overweight [25-29] 8.3% vs obese (>30) 8.5; p = 0.75) or Rural-Urban Continuum Codes (Metro[1-3] 8.5% vs non-metro Urban [4-6] 7.6% vs non-metro Rural [7-9] 5.4 %; p=0.49). Patients with advanced disease more commonly participated in a trial. Most patients received systemic treatment (98%), with 94% of patients receiving an anti-CD20 antibody and 91% of patients receiving anthracycline-based therapy. Importantly, there was no difference in anthracycline use in trial participants vs nonparticipants (92 vs 91%; p=0.53). Patients with high IPI scores [3-5] were more likely to participate on trials compared to those with low scores [1-2] (8% vs 5.9% respectively, p= 0.012). Patients who participated in clinical trials had significantly longer median DTI (26 days vs 21 days; p <0.0001) and had a significantly lower number of comorbidities compared to non-participants (mean 0.55 vs. 0.69; p=0.032). Among patients with low IPI (0–2), 5-year OS was 89% trial participants (CTP) compared to 82% for non-participants (CTNP). Among patients with high IPI (3–5), 5-year OS was 78% for CTP vs 60%for CTNP. The multivariable Cox model demonstrated a significant interaction between trial participation and IPI, with trial participation with low IPI having a non-significant hazard ratio (HR) of 0.87 with 95% CI (0.57,1.32), while trial participation with high IPI had a significant HR of 0.57 (0.44, 0.74). Additionally, increased age, male sex, black race, and lower DTI were associated with increased hazard. Conclusion: Patients at high risk of poor outcomes such as those with advanced age (80+), higher comorbidities burden, and those needing more urgent therapy (shorter DTI) were less likely to participate in clinical trials. In this multi-institutional cohort of patients with LBCL treated at academic centers, frontline trial participants had outcomes that were equivalent or better than those receiving standard care. These finding underscore the value of clinical trial participation even in the frontline setting and support trial designs that expand inclusion- allowing participation of patients who are older, medically complex or in need urgent therapy.

Abstract Introduction: THRLBCL is a rare disease accounting for less than 10% of all diffuse large B-cell lymphoma (DLBCL) subtypes. THRLBCL was associated with poor outcomes in the pre-rituximab era, with limited contemporaneous data. Optimal frontline treatment remains unclear with some studies advocating for intensive regimens. The goal of this international study is to analyze the survival and treatment-related outcomes in patients with newly diagnosed THRLBCL treated with rituximab-containing chemotherapy and to compare with a DLBCL, not otherwise specified (NOS) cohort. Methods: THRLBCL and DLBCL patients prospectively enrolled in the Lymphoma Epidemiology of Outcomes (LEO) (NCT02736357) (7/2015 to 5/2020), Molecular Epidemiology Resource (1/2010 to 6/2015), and Czech Lymphoma Study Group project (NiHiL) (NCT03199066) (2/2010 to 11/2023) were analyzed. Diagnosis was made in dedicated hematopathology centers in both registries. We excluded patients with prior nodular lymphocyte-predominant Hodgkin lymphoma, central nervous system involvement, and not treated with rituximab-containing therapy. Event-free survival (EFS) and overall survival (OS) were estimated by Kaplan-Meier.Univariate associations were derived via Cox model and multivariate models were constructed by forward selection of significant variables with P≤0.05. Propensity score matching (1:5, caliper 0.2) between THRLBCL and DLBCL NOS patients was performed using IPI score components and treatment as covariates. Results: We identified 158 patients (LEO: 67 and NiHiL: 91) with THRLBCL. Among them, 140 patients received rituximab-containing regimens and were included in this analysis. Median age was 58 years (range, 19-89) with 57% aged ≤60 years. Most patients were male (n= 88; 63%) and had ECOG performance status (PS) of 0-1 (n= 111; 79%), stage III-IV (n= 111; 79%), elevated LDH (n= 92; 66%), and B symptoms (n= 84; 60%). Fifty (36%) patients had ≥2 extranodal (EN) sites, mostly liver (n= 39; 28%) and lung (n= 16; 11%), bone marrow (BM) involvement was observed in 26% (n= 37). Bulky disease (≥7.5cm) was present in 50 (36%) and IPI score ≥3 in 68 (48%) patients. The median diagnosis-to-treatment interval was 23 days (IQ range, 13-38.5). Frequent regimens were R-CHOP (n= 106; 75.7%), followed by intensive regimens such as R-MegaCHOP/ESHAP, R-HyperCVAD/MA, and R-CODOX-M/IVAC (n= 15; 10.7%), and R-CHOEP/EPOCH-R (n= 10; 7.14%). With a median follow-up of 3.92 years, the 4-year EFS & OS were 71% and 80%, respectively. Lymphoma was the main cause of death (n= 15; 11%) among 30 events. No difference in 4-year EFS (70% vs. 76%; P= 0.55) & OS (80% vs 82%; P= 0.96) were observed between R-CHOP and intensive regimens. Factors associated with shorter EFS and OS in univariate analysis for THRLBCL were ECOG PS 2-4: (HR= 3.3; P<.001 & HR= 5.97, P<.001, respectively); ≥2 EN sites (HR= 1.85, P= 0.043 & HR= 2.54, P= 0.014, respectively); B symptoms (HR= 2.35, P= 0.014 & HR= 2.84, P= 0.023) and IPI score (HR= 1.5, P= 0.002 & HR= 2.16, P<.001). Stage III-IV and BM involvement were associated with shorter OS only (HR= 7.36, P= 0.05 & HR= 2.18, P= 0.036, respectively). Among them, only ECOG PS 2-4 (OS: HR= 3.6, P= 0.003) and B symptoms (EFS: HR= 2.3, P= 0.046) were independent predictors of survival in multivariate analysis. Finally, we compared baseline characteristics and survival between THRLBCL (n= 140) and DLBCL NOS (n= 6099; LEO/MER: 2069 & NiHiL: 4030) treated with rituximab-containing regimens. Patients with THRLBCL were younger (≤60 years: 57% vs. 38%; P<.001), more frequently had ECOG PS 0-1 (79% vs 76%; P= 0.012), stage III-IV (79% vs 63%; P<.001), B symptoms (60% vs 38%; P<.001), and liver involvement (28% vs 7%; P<.001), compared to DLBCL NOS. Subtype was not associated with survival (OS HR= 0.79, P= 0.262 & EFS HR= 0.93, P= 0.662) when adjusted for IPI score. With a median follow-up of 4.89 years in the DLBCL NOS cohort, we observed similar 4-year EFS (71% vs 67%; P= 0.197) & OS between histologies (80% vs 77%; P= 0.125). These results were confirmed in propensity matching for EFS (HR= 0.98, P= 0.897) and OS (HR= 1.07, P= 0.732). Conclusions: In this large international analysis of patients with newly diagnosed THRLBCL contemporaneously treated, we observed similar survival rates compared to DLBCL NOS treated with R-CHOP and intensive regimens. ECOG PS and the presence of B symptoms were independent factors associated with survival.

Abstract Background Previous studies in aggressive B-cell lymphoma demonstrate that patients with shorter diagnosis-to-treatment intervals (DTI) represent those with higher risk disease. This suggests that patients enrolled in clinical trials may carry a selection bias as they often have longer DTI than patients in the general population. This has important implications for clinical research design and the generalizability of clinical trials in aggressive lymphomas. However, the prognostic implications of DTI have not been evaluated in peripheral T-cell lymphomas (PTCL). Therefore, we examined DTI and its association with clinical factors and outcomes in a prospective observational cohort of patients with PTCL from the United States within the LEO (Lymphoma Epidemiology and Outcomes) and MER (University of Iowa and Mayo Clinic Specialized Programs of Research Excellence Molecular Epidemiology Resource). Patients and Methods We evaluated patients enrolled to the MER (2002-2015) and LEO (2015-2020) cohorts with the following forms of PTCL: PTCL-not otherwise specified (PTCL-NOS, n=217), nodal T-follicular helper (TFH) cell lymphoma (nTFHL, n=153), anaplastic large cell lymphoma (ALCL), ALK positive (ALCL, ALK+, n=74), ALCL, ALK negative (ALCL, ALK-, n=95), enteropathy associated T-cell lymphoma (n=18), and monomorphic epitheliotropic intestinal T-cell lymphoma (n=2). Associations of DTI with clinical factors and outcomes were examined using Kaplan Meier and Cox models. Event free survival (EFS) was defined as the time from start of treatment to relapse/progression, start of 2nd line therapy, or death from any cause. Overall survival (OS) was defined as the time from start of treatment until death from any cause. DTI was defined as the number of days between the first diagnostic biopsy and the start of therapy. Patients who were initially observed or had DTI > 100 days were excluded. Results In 559 patients (229 MER and 330 LEO), median age was 60, 34% had IPI>2 and 71% were stage III/IV. 472 (84%) patients initially received anthracycline-based chemotherapy. Median DTI was 23 days (interquartile range (IQR): 12-39) and was similar in MER (21 days, IQR: 11-33) and LEO (25 days, IQR: 14-42). 168 (30%) patients had DTI 0-14 days, 178 (32%) 15-28 days, and 213 (38%) 29-100 days. Overall in the cohort, shorter DTI was associated with a worse 5-year OS (DTI 0-14 days: 43%; DTI 15-28 days: 53%; DTI 29-100 days: 58%, p<0.001) and 5-year EFS (DTI 0-14 days: 31%; DTI 15-28 days: 39%; DTI 29-100 days: 45%, p<0.001). In multivariable analyses, the association of DTI with outcomes remained consistent after adjusting for age and International Prognostic Index (IPI). Shorter DTI was strongly associated with adverse clinical factors, including elevated lactate dehydrogenase levels, poor performance status, B-symptoms, and higher IPI in both cohorts (all P < .003). DTI was not associated with age, sex, or distance from the LEO/MER center (all p>0.3). 6% patients with DTI of 0-14 days received 1L treatment on a clinical trial, compared to 11% with DTI 15-28 days and 13% with DTI 29-100 days, underscoring that patients with lower DTI may be underrepresented in clinical trials. When examining within PTCL subtypes, the association between short DTI (0-14 days) and inferior outcomes was most prominent in PTCL, NOS (OS HR=2.13, 95% CI: 1.52-2.94; EFS HR =1.49, 95% CI: 1.19-1.85). Associations between short DTI and outcomes were weaker in other subtypes: nTFHL (OS HR=1.28, 95% CI: 0.83-2.00; EFS HR =1.22, 95% CI: 0.82-1.81), ALCL, ALK-, (OS HR=1.30, 95% CI: 0.59-2.86; EFS HR =1.32, 95% CI: 0.68-2.56), ALCL, ALK+, (OS HR=1.05, 95% CI: 0.26-4.17; EFS HR =2.12, 95% CI: 0.84-5.26). Conclusion Shorter DTI is associated adverse prognostic clinical factors (LDH, stage, high IPI) and inferior EFS and OS in newly diagnosed PTCL. Patients with DTI ≤ 14 days, which represents approximately 1/3 patients with PTCL, may be underrepresented in clinical trials. Understanding of this selection bias should inform clinical trial design and interpretation. Future studies should consider strategies to minimize barriers to enrollment for patients with low DTI.

Abstract Introduction: Outcomes for FL have markedly improved, and the 10-year (yr) overall survival (OS) for patients (pts) diagnosed in the rituximab era is about 80%. Historically, FL is the leading cause of death in the first decade after diagnosis (dx); however, modern outcomes beyond 10 yrs after dx remain unknown. Here we report long-term outcomes of FL from a large prospective cohort of pts with FL from the rituximab era. Methods: Pts with biopsy-proven FL 1-3A at dx were prospectively enrolled within 9 months of dx from 2002-2015 in the MER. Outcomes of interest were OS, event-free survival (EFS), histologic transformation (HT), and cause of death. HT was defined as subsequent biopsy-proven FL 3B, diffuse large B cell lymphoma, or high-grade B cell lymphoma. Analyses were performed using Cox models and KM curves; cause of death was evaluated using a competing risk approach. A subset analysis was performed in younger (18-59 yrs) vs older (60+ yrs) pts. A landmark analysis was conducted in pts alive at 10 yrs. Results: 1,155 pts with FL 1-3A were enrolled in the MER from 2002-2015. Median age at dx was 60 yrs (IQR 51-69). Clinical characteristics were: 67% stage III/IV, 12% grade 3A, 4% ECOG PS 2-4, and 19% elevated LDH. Initial management was 35% observation (obs), 12% R-monotherapy (R-mono), 39% immunochemotherapy (IC; 43% R-CHOP, 28% R-CVP, 27% BR), and 14% other. FLIPI scores were 40% low, 33% intermediate (int), and 27% high; FLIPI24 risk categories were 49% low, 25% int, and 26% high. At a median follow-up of 14 yrs (IQR 11-18 yrs), 751 events, 148 HTs, and 402 deaths were observed. EFS estimates (95% CI) were: 10-yr: 40% (37-43), 15-yr: 31% (28-35), and 18-yr: 27% (24-31). 15 yr & 18 yr EFS by initial txs were 22% (18-27) & 18% (14-25) for obs, 30% (23-40) & 18% (11-30) for R-mono, and 40% (35-46) & 38% (33-45) for IC. The incidence (95% CI) of HT was 12% (10-14) at 10 yrs, 13% (11-16) at 15 yrs, and 14% (12-17) at 18 yrs. OS (95% CI) for the entire cohort was 77% (75-80%) at 10 yrs, 63% (60-66) at 15 yrs, and 55% (51-59) at 18 yrs. The incidence of non-lymphoma-related death (non-LRD) surpassed lymphoma-related death (LRD) after 12 yrs in pts with known cause of death. Estimates (95% CI) for LRD were 11% (10-13) at 10 yrs, 14% (12-17) at 15 yrs, and 16% (14-19) at 18 yrs, while estimates for non-LRD were at 8% (7-10) at 10 yrs, 17% (15-20) at 15 yrs, and 22% (19-26) at 18 yrs. Among pts diagnosed before age 60, LRD remained the predominant cause of mortality into the second decade following dx, with incidences (95% CI) of LRD vs. non-LRD of 7% (5-9) vs 3% (2-4) at 10 yrs, 10% (8-13) vs 5% (3-8) at 15 yrs, and 13% (9-17) vs 6% (4-10) at 18 yrs, respectively. In patients age >60 non-LRD was the predominant cause of mortality in the second decade. Incidence of LRD vs. non-LRD in those aged ≥60 at dx were 15% (13-19) vs 13% (10-16) at 10 yrs, 18% (15-22) vs 28% (24-32) at 15 yrs, and 20% (16-24) vs 35% (31-41) at 18 yrs, respectively. In the landmark analysis of pts alive at 10 yrs from dx (n=720), median age was 69 yrs (IQR 60-77). 63 pts (9%) had HT prior to landmark. The median number of prior treatments was 1 (IQR 1-2, range 1-8). Risk of death (95% CI) was 19% (15-22) in the next 5 yrs (i.e. 15 yrs from dx) and 29% (24-33) in the next 8 yrs (18 yrs from dx). The risk of LRD vs non-LRD was 4% (3-6) vs 12% (9-15) at 5 yrs and 7% (3-9) vs 18% (10-23) at 8 yrs.Conclusions: Long-term outcomes for pts with FL remain excellent into the second decade from dx. HT risk decreases significantly after 10 yrs, though late HTs still occur. At 15 yrs from dx, 40% of pts remained alive and event-free after 1L IC. Nearly a quarter of pts observed at dx remained treatment-free. The most common cause of death after 10 yrs from dx was from non-lymphoma causes, notably in older pts aging into their 70s and 80s. While younger pts have a lower overall risk of LRD compared to older pts, FL represents the leading cause of death in this population during the second decade following dx. These results provide critical insights into the natural history of FL diagnosed in the modern era.

Abstract Background: Waldenström Macroglobulinemia (WM) is an indolent B-cell lymphoplasmacytic lymphoma characterized by heterogeneous clinical behavior and variable progression patterns. While MYD88L265Pand CXCR4 mutations are frequently encountered, the broader genomic landscape underpinning disease evolution from smoldering WM (SWM) to active treatment naïve WM (TNWM), post-treatment remission WM (PTWM) and relapsed/refractory WM (RRWM) remains incompletely understood. To address this gap, we studied the mutational profile across the WM disease spectrum. Methods We performed whole exome sequencing (WES) on sorted (CD19 and/or CD138) bone marrow aspirates of patients across the spectrum of WM. Paired germline sequencing was performed using peripheral blood mononuclear cells (PBMCs). Unique molecular identifier-tagged reads were deduplicated using fgbio. WES data were processed through the Sarek pipeline. Any valid mutation from either caller, present on the index or in COSMIC was considered. Copy number variations (CNVs) were inferred via CNVkit and summarized at the gene and arm level (≥50% directional change). Mean tumor and germline coverages were 174X and 74X, respectively. Fisher's exact test was used for enrichment and hypergeometric test for overlap analyses. Results A total of 78 high-quality WM samples [15 SWM, 44 TNWM, 13 RRWM, 6 PTWM in remission (all with paired TNWM samples)] were sequenced. The mutational burden varied across disease states (median SWM=1.48 m/Mb, TNWM= 1.12 m/Mb, RRWM = 1.59 m/Mb; p=0.04). Density plots revealed bimodal VAF distributions, consistent with clonal and subclonal architectures in WM. Overall, 77% harbored a MYD88L265P mutation by WES. Among MYD88^WT^ patients, AS-PCR identified MYD88L265P in 4 (25%) not detected by WES, raising the overall mutation rate to 83%, plus one with a non-L265P variant. Low VAF (<10%) MYD88L265P mutations were seen in 6% of matched PBMCs. MYD88 copy number gain was seen in 2 (17%) MYD88WT TNWM patients. CXCR4 mutations were detected in 38% by WES (18% each frameshift and nonsense). Two (4%) CXCR4WT samples were positive by PCR, giving an overall rate of 40%. CXCR4 mutations were more frequent in RRWM (54%) vs TNWM (41%) and SWM (27%), though not statistically significant. Del(6q) and Del(17p) were noted in 16% (n=12) and 7% (n=5) of patient samples. All del(17p) events were noted in TNWM samples and in one paired post-treatment sample. Overall rate of del(17p) and TP53 mutation was 14% (n=11). RRWM was enriched in ARID1A alterations, a gene implicated in chromatin remodeling and plasma cell identity formation [38% (n=5) in RRWM vs. 11% (n=6) in TNWM and SWM], compared to TNWM and SWM p=0.03]. Alterations in NF-kB signaling gene MAP3K14 were more frequent in RRWM (31%, n=4) compared to the SWM/TNWM cohort [(7%), n=4; p=0.036). We noted an enrichment in the TP53 mutation rate in RRMW [n=3, 23%, OR 7.7] compared to TNWM and SWM cohorts [n=2, 4%), p=0.04]. Mutations in FUBP1 (p=0.03), an upstream regulator of MYC, and CARD11 (p=.043), a B-cell signaling scaffold protein were also more frequent in RRWM. Notably, none of the SWM patients harbored an ARID1A alteration (p=0.057), suggesting acquisition during progression. CNV analysis revealed that MAP3K14 amplifications were frequent in RRWM (n=3, 23%) compared to SWM/TNWM [n=2 (4%), p=0.043]. RRWM samples showed increased CD79B mutations (15%) and significant overlap with the C5/ABC diffuse large B-cell lymphoma (DLBCL) genomic cluster (Chapuy et al., Nat. Med. 2018; p=0.007). This overlap remained significant after excluding MYD88L265P (p=0.019), driven by CARD11, CD79B, and IGLL5, suggesting that a subset of RRWM may acquire an ABC DLBCL-like mutational profile. These findings warrant further investigation using transcriptional profiling. Conclusions This study provides a genomic map of WM evolution from precursor to relapse. Increasing somatic mutations and CNVs support stepwise progression. Enrichment of genes involved in chromatin remodeling and plasma cell identity formation (ARID1A), NF-kB or BCR signaling (CARD11, MAP3K14), and DNA damage repair (TP53) were noted at relapse, all of which have been shown to drive aggressive disease biology. Emergence of an ABC-like genotype in a proportion of RRWM indicates convergent evolution with aggressive lymphomas. Transcriptional studies are warranted to explore this further.

Abstract Introduction Although chemo-free regimens are increasingly used in elderly patients with large B-cell lymphoma (LBCL), most still receive conventional chemoimmunotherapy. Patients up to 75–80 years old are usually treated with full-dose R-CHOP, while 80+ patients receive the attenuated R-miniCHOP regimen as standard of care. Prior studies have shown that in patients aged 65+ years, R-CHOP was associated with improved survival based on propensity score matching (PSM) analysis (Al-Sarayfi, Am J Hematol, 2023), though this did not include comorbidity data. Furthermore, no formal studies have focused specifically on 80+ patients. We aimed to perform a PSM comparison of R-CHOP versus R-miniCHOP to better inform treatment decisions in this population. Methods We harmonized and pooled data from 237 LBCL patients receiving chemotherapy with rituximab (out of all 263 80+ patients) from the prospective LEO cohort (USA, n=147; 2015–2020) and NiHiL registry (Czech Republic, single center, n=116; 2010–2023). Of them, 205 (86%) receiving anthracycline(A)-based regimens were included. Baseline characteristics, comorbidities, treatments, and outcomes were collected. PSM (1:1, caliper 0.2) was performed to compare R-CHOP and R-miniCHOP using age, ECOG performance status (PS), clinical stage, lactate dehydrogenase (LDH), and major comorbidities (heart disease, history of stroke, and other recent cancer) as matching variables. The endpoints were overall survival (OS), event-free survival (EFS), and lymphoma-specific survival (LSS, defined as death attributed to lymphoma progression). Results Among 205 A-treated patients, median age was 83 years (range 80–99); 30% had ECOG PS 2–4, 67% clinical stage III–IV, 34% ≥2 involved extranodal sites, 59% elevated LDH, and 58% had International Prognostic Index (IPI) scores 3–5. Median follow-up was 5.5 years; median OS and EFS were 4.1 and 3.4 years, resp. A-based regimens comprised full-dose R-CHOP-like (n=79; 39%; R-CHOP n=74, DA-EPOCH-R n=5) and R-miniCHOP (n=126; 61%). Patients receiving R-CHOP-like regimens had more favorable baseline characteristics (median age 82 vs 83 years, P=0.04; ECOG PS 2–4: 16% vs 39%, P<0.01; IPI 3–5: 46% vs 66%, P=0.03) and a lower, though not statistically significant, comorbidity burden (mean 1.14 vs 1.39; heart disease 35% vs 45%, P=0.22; history of stroke 4% vs 6%, P=0.64; other cancer 6% vs 5%, P=0.87). R-CHOP-like regimens vs R-miniCHOP had superior OS (median 6.3 vs 3.4 years; HR=0.61, 95% CI 0.42–0.90; P=0.01) and EFS (median 5.3 vs 2.1 years; HR=0.58, 95% CI 0.40–0.83; P<0.01), with no significant difference in LSS (2-year 89% vs 82%, HR=0.77, 95% CI 0.39–1.54, P=0.46). To mitigate selection bias, we performed PSM on patients with available baseline characteristics treated with R-CHOP (n=69) and R-miniCHOP (n=121), resulting in 63 matched pairs. Matched cohorts were well-balanced on age (median 82 years in both cohorts; P=0.68), ECOG PS 2–4 (17% vs 19%; P=0.82), IPI 3–5 (52% vs. 59%; P=0.41), and comorbidity burden (mean 1.10 vs. 1.29; P=0.30). The presence of heart disease (40% in both cohorts), history of stroke (5% vs 6%), and cancer (5% vs 6%) were similar. Survival modestly favored R-CHOP, but differences were not significant: median OS 5.3 vs 3.5 years (HR=0.73; 95% CI 0.46–1.15; P=0.17), median EFS 3.8 vs 2.8 years (HR=0.76; 95% CI 0.49–1.18; P=0.23), and 2-year LSS 88% vs 84% (HR=1.09; 95% CI 0.45–2.63; P=0.85). Treatment-related mortality was similar in both cohorts (16%). Among R-miniCHOP patients, survival outcomes did not differ between matched (n=63) and unmatched (n=63) groups (OS P=0.72; EFS P=0.23; LSS P=0.27). Unmatched (n=16) R-CHOP patients had a trend toward improved survival compared to matched (n=63) individuals (OS P=0.10; EFS P=0.049; LSS P=0.24). Conclusion This is the first PSM-based analysis comparing R-CHOP and R-miniCHOP in LBCL patients 80+ years old. Despite inherent limitations of a non-randomized design and limited number of matched patients, our findings – derived from two prospective cohorts – suggest that R-miniCHOP may offer outcomes comparable to R-CHOP, particularly with respect to LSS. While these results support R-miniCHOP as an appropriate frontline option, treatment decisions remain highly individualized. These data may help inform clinical decision-making and provide a basis for designing future trials targeting this population, where R-miniCHOP could serve as a comparator arm. Funding: NU21-03-00411, U01 CA195568.

Abstract Despite a characteristic indolent course, a substantial subset of follicular lymphoma (FL) patients has an early relapse with a poor outcome. Thus far, efforts to identify factors that predict survival have been unsuccessful. However, we and others have demonstrated the prognostic relevance of CD4+ T cells in the tumor microenvironment (TME) of FL and developed a bio-clinical risk model (called BioFLIPI) that offers improved risk stratification (Mondello et al, BCJ 2021). However, the mechanisms defining different BioFLIPI scores remain to be fully understood. To identify the programs associated with the BioFLIPI scores, we first interrogated the RNA-seq of purified B cells from 67 newly diagnosed FL1-3A (BioFLIPI 1 n=21; BioFLIPI 2-3 n=36; BioFLIPI 4 n=10). We found 1,156 upregulated genes and downregulation of 930 genes in BioFLIPI 4 vs 1, including well-known immune molecules such as CD70, IL10RA, CD69, and CCR6 (FDR<0.05). Gene set enrichment analysis (GSEA) using the MSigDB confirmed a negative enrichment in multiple immune response and interferon gamma signatures and revealed striking positive enrichment for cell proliferation and antigen presentation pathways in BioFLIPI 4 compared to BioFLIPI 1 tumors. The same pathways were dysregulated in BioFLIPI 2-3 but a lower magnitude. To investigate the somatic mutations associated with different BioFLIPI scores, we performed WES of the same 67 FLs and further expanded this cohort for a total of 90 patients (BioFLIPI 1 n=26; BioFLIPI 2-3 n=51; BioFLIPI 4 n=13). We found enrichment for mutations in several regulators of the germinal center in BioFLIPI 4 and 2-3, such as MEF2B (p=0.029), FOXO1 (p=0.038) and IRF8 (p=0.035), suggesting the cooperation of multiple oncogenic events promoting lymphomagenesis. In contrast, BioFLIPI 1 tumors harbored a higher rate of mutations in TNFRSF14 gene, known to enhance anti-lymphoma immune response. To investigate whether BioFLIPI modulates immune signatures of the microenvironment, we performed TME deconvolution by applying CIBERSORTx to bulk RNA-seq of these 67 FLs. We found that FLs with BioFLIPI 4 and 2-3 were enriched for CD4+ Tcm exh (p=0.02), Tem exh (p=0.04), Tfhexh (p=0.07) and NK cells (p=0.01) compared to BioFLIPI 1. In line with this data, CyTOF performed on a subset of these patients (BioFLIPI 1 n=12; BioFLIPI 2-3 n=25; BioFLIPI 4 n=6) confirmed progressive increase of CD4+ Tcm exh (p=0.006), Tem exh (p=0.009) and Tfh exh (p=0.025) cells from BioFLIPI 1 to BioFLIPI 4. Single cell RNA/BCR-seq of 21 FLs (BioFLIPI 1 n=6; BioFLIPI 2-3 n=12; BioFLIPI 4 n=3) corroborated a higher abundance of Tem exh, cytotoxic (CTL) Tem exh, Tfhexh and NK cells in BioFLIPI 4 and 2-3. Conversely, CD8+ naïve T cells were prevalent in BioFLIPI 1 tumors. Differential gene expression in T cell clusters from BioFLIPI 4 vs BioFLIPI 1 FL revealed a marked upregulation of multiple immune genes involved in antigen presentation (e.g., HLA-DRB, HLA-DQA, HLA-DPA/B) and immune response (e.g., ETS1, TNFSF8, IL7R). GSEA showed a positive enrichment for antigen presentation signatures and negative enrichment for TNFα signaling and Oxphos metabolism. Similar findings were observed in BioFLIPI 2-3 FLs. Using CellChat analysis, we found a stronger interaction between malignant B cells and Tem exh and CTL Tem exh cells in BioFLIPI 4 and 2-3 vs BioFLIPI 1 tumors. This was linked to an increased strenght of MHCI-CD8 interaction and concordant decrease of MHCII-CD4. Notably, the suppressive BTLA-TNFRSF14 signaling axis was present in BioFLIPI 4 and 2-3 but not in BioFLIPI 1 FLs. This was accompanied by a decreased signaling through TNF/LTA-TNFRSF1B, CD80/CD86-CD28/CTLA4. Linking the BioFLIPI-specific immunogenicity to lymphomagenesis, we found that FL patients with BioFLIPI 4 had two-three folds higher risk of transformation to an aggressive lymphoma (12% vs 7%) or death (21% vs 3%) than those with BioFLIPI 1, suggesting that this immune dysfunction favors the expansion of a dominant tumor clone and promotes a more aggressive form of FL. Collectively, our findings demonstrate that BioFLIPI scores in FL reflect distinct patterns of tumor immunogenicity that shape the TME. Notably, high BioFLIPI scores are paradoxically associated with enhanced antigen presentation alongside a suppressed immune response. This suggests a sustained antigen-specific T cell response that may drive immune exhaustion, promote tumor progression, and ultimately lead to histologic transformation

Abstract Background: Follicular lymphoma (FL) is a germinal center–derived B cell malignancy characterized by the clonal expansion of a dominant malignant B cell population. While prior studies have identified transcriptional subtypes across patients, most analyses have treated B cells within individual tumors as a transcriptionally uniform population. However, this assumption has not been systematically tested using high-resolution, clonotype-resolved transcriptomic data. The broader landscape of intratumoral B cells—including those not belonging to the dominant clone—remains poorly defined, and their roles in disease biology are not well understood. These unexpanded, intratumoral B (ITB) cells may include residual non-malignant populations, early-stage malignant cells, or immune-modulating cells with distinct roles in the tumor microenvironment. It is also unknown whether their presence varies across patients or correlates with clinical features or immune microenvironmental interactions. Methods: We performed integrated single-cell 5′ RNA sequencing and B cell receptor (BCR) repertoire sequencing on 7 newly diagnosed FL 1-3A fresh-frozen lymph node biopsies (Dasari et al., Blood Cancer Discov 2025). Using a high-confidence approach that incorporates heavy and light chain pairing, V and J gene usage, and CDR3 nucleotide junction identity, we classified B cells into two groups: (1) cells belonging to the dominant expanded clone (FL B cells) and (2) all other B cells with detectable and non-expanded BCRs (ITB). These clonal identities were then integrated with single-cell transcriptomic profiles using Seurat and Immcantation pipelines. Differential expression analysis was conducted using edgeR to compare FL B cells to ITB cells across all FL biopsies. Results: After quality filtering, a total of 44,637 single cells were retained across all 7 samples (minimum per sample: 3,847 cells), including malignant and non-malignant B cells as well as diverse tumor microenvironment populations. The proportion of B cells assigned to the dominant FL clone varied substantially across tumors, ranging from 22% to 89%, indicating that some tumors contain a substantial population of ITB cells. In one tumor, we identified two expanded malignant B cell clones comprising 40.6% and 34.4% of all cells. These clones exhibited non-overlapping V and J gene usage (IGHV3-11/IGHJ5 vs. IGHV3-72/IGHJ3), distinct CDR3 junctions, and different immunoglobulin isotypes (IGHG1 vs. IGM). BCL2 was expressed in most cells within both clones (Clone 1: 87.8%, Clone 2: 67.0%), with mean log-normalized expression levels of 1.79 and 0.95, respectively. Both clones formed distinct UMAP clusters and showed transcriptional divergence from ITBs, with 453 differentially expressed genes (DEGs) identified for Clone 1 and 98 for Clone 2 (adjusted p<0.01, |log₂FC|>1, min.pct≥0.25). Additionally, 748 DEGs were identified between the two clones. This evidence of clonal divergence within a single FL tumor reinforces the need to reconsider intratumoral homogeneity in FL and suggests that multiple malignant clones may sometimes coexist. When comparing FL clones to ITB cells across tumors we found that FL clones exhibited a transcriptional program enriched for proliferation and cell cycle regulators, including MKI67 (p<0.005), E2F1 (p<0.0001), CDKN2A (p<0.0001), and CDKN2B (p<0.01), as well as elevated expression of AICDA (p<0.01), EZH2 (p<0.0001), and PD-L1 (p<0.05). As expected, the malignant cells also had higher expression of BCL2 (p<0.01) and CD10/MME (p<0.0001). In contrast, ITB cells demonstrated relatively higher expression of SELL (p<0.0001) and TNFRSF14 (p<0.05), which were significantly downregulated in the FL clone. Conclusions: Together, these findings challenge the prevailing notion that FL tumors are defined by a single homogeneous malignant B cell population. Instead, our data support a model in which multiple transcriptionally and functionally distinct B cell populations coexist within tumors, including dominant clones and a diverse population of ITB cells. This is, to our knowledge, the first study to comprehensively characterize these populations using integrated, lineage-aware single-cell multiomic profiling across FL patients. These insights may ultimately inform new strategies for understanding tumor evolution, immune evasion, and therapeutic targeting in follicular lymphoma.

Abstract Background: Prognostication for patients with non-transformed relapsed/refractory (R/R) follicular lymphoma (FL) is not well defined. Progression of disease within 24 months (POD24) and number of lines of prior therapy are commonly used to benchmark outcomes and for trial eligibility in this space. However, a comprehensive model for risk prediction in R/R FL is lacking. Here we utilize patients from the LEO Consortium for Real World Evidence (CReWE) and Iowa/Mayo Clinic Lymphoma SPORE MER to develop a clinical prediction model for R/R FL. Methods: Patients with grade 1-3A FL were prospectively enrolled at diagnosis in the MER and/or initiated second line or later therapy (2L+) for non-transformed FL at one of the 8 LEO Centers from a previous LEO CReWE study (Casulo et al, Lancet Haem 2022). All available lines of therapy were abstracted; lines of therapy after histologic transformation were not used in the analyses. A management plan of observation was considered as a type of therapy in the 1L setting but not in the 2L+ setting. Clinical variables and outcomes were abstracted at each line of therapy. Variables considered for modeling included demographics, clinical labs, treatment history, and pertinent clinical variables (including FLIPI and FLIPI24 components). Multiple imputation was used to address missing data in modeling; beta-2 microglobulin (B2M) was unable to be imputed due to the amount of missing data. Index therapy was defined as the date of initiation of a line of therapy (2L, 3L or 4L) for non-transformed FL. The primary endpoint for model development was the time from index therapy until the histologic transformation or lymphoma-related death (HT/LD). Cox proportional hazards models were used for model development. Modeling was first performed separately for each line of therapy and then evaluated across lines. Bootstrapping on imputed datasets was used to assess optimism in model performance. Results: N=1001 patients initiated 2L+ therapy and were utilized for modeling. Of these patients, N=654 initiated 3L and N=344 initiated 4L therapy. Median age at index therapy was (60, 61, and 62) for lines 2-4, respectively. Median follow-up by line of therapy was 8.2, 6.1, and 6,1 years and number of HT/LD events for modeling was 272, 154, and 88, respectively. Observed outcomes decreased modestly as the line therapy increased: median event-free (1.6, 1.5, and 1.2 years) and overall survival (17.6 years, 14.5 years, and 12.3 years), for lines 2-4, respectively. 5-year estimates of HT/LD by line of therapy were 21.6% (95% CI: 18.9-24.3), 23.7% (95% CI: 19.9-27.4) and 29.3% (95% CI: 23.3-34.7), respectively. A clinical prediction model was independently developed for each line of therapy; the final model for each line consisted of the same variables: increasing age, male sex, higher LDH, lower hemoglobin, higher white blood cell count, prior immunochemotherapy exposure, and shorter time since initial diagnosis. The parameter estimates for these variables were broadly similar across the different lines of therapy. The model output is an individual patient's risk of HT/LD. Patients with high-risk FLP-R, as defined using the upper quartile of FLP-R risk scores for each line, had observed 5 year HT/LD rates of 43%, 44% and 51% in lines 2-4, respectively. In contrast, patients with the lower quartile of FLP-R risk scores had observed 5 year HT/LD rates of 10%, 6% and 8% in lines 2-4, respectively. The optimism-corrected c-statistic for the FLP-R model across all lines of therapy was 0.685. This added significant information beyond FLIPI diagnosis (c=0.533) FLIPI at index line (c=0.563), the line of therapy (c=0.525), POD24 status (c=0.578), or all 4 variables combined (c=0.614). Conclusions: The FLP-R model identifies a group of patients with non-transformed FL in the R/R setting who are at very high risk of transformation and/or lymphoma-related death across lines of therapy. FLP-R features 4 of the 5 components of the 1L FLIPI24 prediction model (B2M was unavailable for modeling) with the addition of sex, treatment history and time since initial diagnosis. The number of prior therapies is not sufficient to identify high-risk populations for clinical trial enrichment in the R/R FL setting and model discrimination for FLP-R was greatly improved over FLIPI and/or POD24 across lines of therapy. A Shiny app will be provided for model implementation; external validation is planned.