Burack, Richard

Abstract Germinal center (GC)-derived lymphomas arise within a specialized immune ecosystem, yet whether malignant cells direct its remodeling remains unclear. Integrating genomic, transcriptomic, spatial, and functional analyses across follicular lymphoma and diffuse large B-cell lymphoma, we define a developmental framework linking malignant B-cell differentiation state to tumor microenvironment (TME) organization. Rather than segregating by histology, lymphomas align along a shared differentiation continuum in which proliferative dark zone (DZ) states associate with immune-depleted TMEs, whereas post-GC/memory B-cell (MBC) states drive inflamed but immunosuppressed TMEs. Spatial profiling reveals collapse of GC architecture and emergence of suppressive myeloid niches along this trajectory. Single-cell lineage reconstruction uncovers a DZ-to-MBC developmental axis underlying lymphoma evolution. Genetic reprogramming in vivo demonstrates that malignant differentiation state is sufficient to instruct TME remodeling. Collectively, these findings establish malignant differentiation state as a causal determinant of immune architecture across GC-derived lymphomas and identify lineage-directed immune reprogramming as a potential therapeutic strategy.

Abstract Recent single-cell transcriptomic studies of follicular lymphoma (FL) have revealed heterogeneity among malignant B (MB) cells and selected components of the tumor microenvironment (TME), but have been constrained by relatively small sample sizes and a lack of spatial context. We performed comprehensive cellular profiling and spatial dissection of over five million cells by integrating single-nucleus RNA sequencing (snRNA-seq) from 182 samples (FL, n = 167; control, n = 15) with single-cell spatial transcriptomics (ST) from 386 samples (FL, n = 365; control, n = 21). After stringent quality control, 1,289,073 and 3,968,427 cells from snRNA-seq and ST data, respectively, were analyzed. Unsupervised clustering of snRNA-seq data identified 58 TME cell subtypes. To extend this classification to the ST dataset, we integrated snRNA-seq and ST data by co-embedding them into a shared principal component space, enabling robust batch correction and cell-type label transfer across modalities. Using this supervised annotation framework, ST data recapitulated 54 TME cell subtypes. MB, proliferating MB (MBprolif), and healthy B (HB) cells were classified using sample-level unsupervised clustering in ST. Macrophages exhibited previously uncharacterized heterogeneity in transcriptomic profiles and spatial distribution. We identified CXCL13-expressing macrophage subtypes with high intra- and peri-follicular abundance, termed follicular macrophages (FMs). Non-negative matrix factorization of intrafollicular cell fractions identified five TME archetypes: FM, follicular stroma, two follicular T, and HB archetypes. The FM archetype was enriched for intrafollicular macrophages, including FMs, and showed a significantly higher proportion of grade 3A/3B cases (53.4%) compared to other archetypes (≤16.5%). It was also associated with a higher frequency of high tumor burden requiring treatment initiation rather than a watch-and-wait (WW) approach. Among patients managed by WW, those with the FM archetype showed significantly shorter event-free survival (EFS) compared to those with non-FM archetypes, whereas this association was not observed among treated patients. The percentage of intrafollicular macrophages (IFMP) showed excellent predictive performance for the FM archetype (area under the curve: 0.90). IFMP calculated in a published multiplex spatial protein imaging dataset from an independent cohort of 242 FL patients validated the associations between high IFMP and both pathology grade 3A/3B and shorter EFS in WW patients. Consistently, spatial neighborhood and cell–cell communication analyses revealed co-localization of FMs and MBprolif cells, forming a distinct niche with activated signaling mediated by FM-derived CXCL13, APRIL, and BAFF. Our integrative multi-modal analysis defines previously unrecognized cellular heterogeneity and spatial architecture in FL and highlights macrophage-enriched follicular ecosystems associated with aggressive clinicopathological features, providing a framework for improved biological understanding and patient management. Citation Format: Yoshiaki Abe, Bijal Thakkar, Atish Kizhakeyil, Ashley Wilson, Andrew L. Feldman, Jared Henderson, Amy Ayers, Sara Borgschatz, R. Andrew Harkins, Priya Lakra, Daisuke Kaji, Jonathon B. Cohen, David Russler-Germain, Eric Mou, Francisco Vega, Jennifer Chapman, Giorgio Inghirami, Carla Casulo, Izidore S. Lossos, Jean L. Koff, Chijioke Nze, Peter Martin, Sergei Syrbu, Kiran Vij, David L. Jaye, James R. Cerhan, Christopher R. Flowers, Anne J. Novak, Richard Burack, Dai Chihara, Mamiko Sakata-Yanagimoto, Joshua Tobin, Xubin Li, Michael R. Green. Spatially resolved microenvironment profiling reveals distinct follicular lymphoma archetypes [abstract]. In: Proceedings of the Fifth AACR International Meeting on Advances in Malignant Lymphoma: From Discovery to Clinical Impact; 2026 Jun 24-27; Philadelphia, PA. Philadelphia (PA): AACR; Blood Cancer Discov 2026;7(3_Suppl):Abstract nr A049.

Abstract A short diagnosis-to-treatment interval independently predicts inferior outcomes in follicular lymphoma. Using international discovery and validation cohorts, we demonstrate its prognostic value beyond established prognostic indices, with implications for risk stratification, clinical trial design, and interpretation.

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.

Abstract Introduction Short diagnosis-to-treatment interval (DTI) is associated with aggressive disease biology and inferior outcomes in diffuse large B-cell lymphoma (DLBCL), and is recognized as a potential source of selection bias in clinical trials if not properly addressed. However, its prognostic significance in follicular lymphoma (FL) remains poorly characterized. We aimed to evaluate the prognostic significance of DTI in newly diagnosed FL patients who received frontline (1L) immunochemotherapy (IC), using a large international dataset and an independent external validation cohort. Methods For the discovery dataset, we evaluated patients diagnosed between 2002 and 2018 from ten observational cohorts as part of the FLIPI24 Consortium. External validation was conducted using data from patients enrolled in the Lymphoma Epidemiology of Outcomes (LEO) Cohort between 2010 and 2015. All patients had FL grade 1–3A and received 1L IC within 100 days of diagnosis. Patients with a concurrent DLBCL were excluded. Missing baseline laboratory values were addressed via multiple imputation. The primary endpoint was event-free survival at 24 months (EFS24); secondary endpoint was 5-year overall survival (OS), both calculated from treatment initiation. DTI was analyzed as continuous (via splines) and dichotomized variable (≤14 vs >14 days; as defined in DLBCL). Results In the discovery cohort (n=3,907), median age was 61 years (IQR 53–69), and 50% were male; 49% of patients were high-risk according to FLIPI, 26% according to PRIMA-PI, and 38% according to FLIPI24. Included patients received R-CHOP (54%), R-CVP (26%), or B-R (20%) as 1L treatment, followed by anti-CD20 maintenance in 60%. The median DTI was 30 days (IQR 17–48). Using the 14-day cut-off, 851 patients (22%) had short DTI. These patients had more adverse baseline characteristics, including B symptoms (42% vs 32%, P<0.01), ECOG performance status ≥2 (15% vs 8%, P<0.01), elevated LDH (51% vs 40%, P<0.01), hemoglobin <12 g/dL (30% vs 17%, P<0.01), and elevated β2-microglobulin (56% vs 44%, P<0.01). Prognostic indices (PIs) were also more frequently high-risk in the short-DTI group: FLIPI (56% vs 47%), PRIMA-PI (36% vs 24%), and FLIPI24 (51% vs 34%, all P<0.01). Spline modeling showed a decreasing EFS24 rate with increasing DTI, reaching a plateau at 30–40 days. In logistic regression model, each additional week of DTI improved EFS24 (OR 0.92, P<0.01). DTI attenuated but remained prognostic after adjusting for PIs (OR 0.93 for FLIPI, 0.95 for PRIMA-PI, 0.96 for FLIPI24, all P<0.01). When analyzed as a dichotomized variable, short DTI was associated with inferior EFS24: OR 1.74 adjusted for FLIPI, 1.63 for PRIMA-PI, and 1.53 for FLIPI24 (all P<0.01). The 2-year EFS of short vs long DTI groups was 71% vs 82% (HR 1.50, 95% CI 1.34–1.67, P<0.01), 5-year OS was 80% vs 86% (HR 1.38, 95% CI 1.21–1.56, P<0.01), and the 5-year cumulative risk of histologic transformation was 7.7% vs. 5.4% (P<0.01). In the validation cohort (n=516), median age was 61 years (IQR 52–69 years), 55% were male; 36% of patients were high-risk according to FLIPI; 29% according to PRIMA-PI, and 33% according to FLIPI24. Patients received B-R (59%) or R-CHOP (41%) as 1L treatment. The median DTI was 31 days (IQR 19–48), 84 patients (16%) had DTI ≤14 days. Similar patterns were observed; patients with short DTI had adverse baseline characteristics, PIs, and shorter 2-year EFS (72% vs 84%, HR 1.69, 95% CI 1.13–2.55, P=0.01). DTI remained associated with EFS24 when analyzed as a continuous variable (per week: OR 0.88 unadjusted, 0.90 with FLIPI, 0.89 with PRIMA-PI, and 0.89 with FLIPI24), and dichotomized (≤14 vs >14 days: OR 1.83 adjusted for FLIPI, 1.88 for PRIMA-PI, and 1.73 for FLIPI24). Conclusion Short DTI, defined as ≤14 days (observed in 22% of FL patients vs 46% in DLBCL), is a strong and independent predictor of inferior outcomes in newly diagnosed FL patients treated with IC. While DTI correlates with adverse baseline features and established PIs, it retains independent prognostic value. Although a 14-day threshold is reasonable for risk modeling, DTI contains greater informational value when modeled as a continuous variable. Among PIs, FLIPI24 most strongly attenuated the DTI effect, underscoring its superior discriminative power over FLIPI and PRIMA-PI. Clinical trials designs should mitigate any potential barriers to enrollment of FL patients requiring urgent therapy.

PURPOSE Although most patients with follicular lymphoma (FL) can expect an indolent course, progressive lymphoma remains the primary cause of death during the first decade after diagnosis. Progression of disease within 24 months (POD24) of starting first-line (1L) immunochemotherapy defines a high-risk population with poor survival, but better risk stratification at diagnosis is needed. METHODS The FLIPI24 model was developed and internally validated to predict 24-month event rates using individual data from 4,485 patients treated with 1L immunochemotherapy from 10 observational cohorts of FL. Overall and cause-specific survival was further evaluated in FLIPI24 risk groups. External validation in the 1L immunochemotherapy setting was performed using the prospective observational Lymphoma Epidemiology of Outcomes (LEO) cohort (N = 565) and three randomized phase III trials (N = 3,192); extension to all patients with FL (any 1L therapy) was performed in the LEO cohort (N = 1,445) and its Molecular Epidemiology Resource subcohort (N = 1,074). RESULTS The FLIPI24 model uses age and four blood-based variables (hemoglobin, lactate dehydrogenase, beta-2 microglobulin, and WBC count). FLIPI24 showed consistent performance across validation and extension data sets, which was superior to existing prognostic tools. Across the four external immunochemotherapy validation data sets, patients with high-risk FLIPI24 (23%-32% of patients) had significantly higher 24-month event rates (22%-35%) and inferior 5-year overall survival (77%-83%) compared with patients with low-risk FLIPI24 (29%-31% of patients, 24-month event rates: 10%-12%; 5-year OS: 96%-97%). Results were consistent when evaluating lymphoma-related death and when extended to all patients with FL. CONCLUSION The FLIPI24 model robustly stratifies, at diagnosis, patients with FL at increased risk of lymphoma-related death versus patients with very low lymphoma-related mortality during the first decade after diagnosis. FLIPI24 can be used to enrich future clinical trial designs in newly diagnosed FL.

Background: Positron emission tomography (PET) scan is the imaging modality of choice for initial staging and response assessment of follicular lymphoma (FL). Quantitative parameters such as standardized uptake value (SUV) have been investigated as putative prognosticators in FL, with high baseline SUVmax predicting aggressive disease. To date, there is no data to guide treatment choices in patients (pts) with high SUVmax but with no documented evidence of histologic transformation (HT). Specifically, it remains unclear whether anthracycline-containing regimens, such as R-CHOP, are more beneficial than R-Bendamustine (BR) in these pts. Therefore, we aimed to compare the efficacy of frontline R-CHOP and BR in newly diagnosed FL pts based on the SUVmax at baseline PET (bPET). Patients and Methods: Pts with newly diagnosed, biopsy-proven FL1-2 and FL3A who had a bPET scan and who received either first-line R-CHOP or BR within 6 months of diagnosis between 2002 and 2022 were evaluated. Analysis was restricted to pts with age < 70 years to reduce confounding of age with treatment selection. Cohorts included were the FOLL12 (n=562, Luminari et al JCO 2022) and the R-chemotherapy arm of the RELEVANCE (n=216, Morschhauser et al NEJM 2018) clinical trials, the prospectively enrolled Molecular Epidemiology Resource (MER) and the Lymphoma Epidemiology of Outcomes (LEO) cohorts (n=288) and a retrospective cohort from 13 Australian centers (n=219). Choice of chemotherapy regimen was per treating physician. Maintenance was per protocol (FOLL12/RELEVANCE) or per treating physician (MER/LEO/Australia). Event-free survival (EFS) and overall survival (OS) were defined as time from treatment initiation to progression, relapse, retreatment or death; both were censored at 72 months due to varying follow-up across cohorts. SUVmax was grouped by tertile within each cohort to account for potential differences in SUVmax distributions and patient populations across cohorts. Kaplan Meier curves and multivariable Cox models cohort-stratified and adjusted for clinical factors (FLIPI, ECOG PS, age, grade 3A) were used to evaluate outcomes. Results: The median age at diagnosis was 57 years (IQR 49-64). 651 (51%) pts were treated with R-CHOP and 634 (49%) with BR. Within the study cohorts, there was a prevalence of cases receiving BR compared to R-CHOP in the Australian (90% vs 10%) and MER/LEO (68% vs 32%) cohorts, while the opposite was observed in the RELEVANCE (16% vs 84%) and FOLL12 (37% vs 63; p<0.001) trials. Pts receiving R-CHOP were slightly younger (median 56 vs 58 years, p=0.005), more frequently had grade 3A (17% vs 7%, p<0.001), elevated LDH (27% vs 22%, p=0.029), bulky disease > 7 cm (44% vs 37%, p=0.019), lower ECOG PS (PS >1 7% vs 13%, p<0.001), and higher rate of high risk FLIPI (37% vs 30%, p=0.005). Baseline SUVmax was slightly lower in R-CHOP (median 10.7, IQR 7.9-14.9) vs BR (median 11.0, IQR 8.2-14.2) treated pts (p=0.0045). At a median follow-up of 56 months (range, 1-72 months), 415 pts (32%) had an event and 103 (8%) died. Higher SUVmax was not associated with EFS (middle tertile HR=0.96, 95% CI 0.76-1.22; upper tertile HR=0.86, 95% CI 0.68-1.09; p=0.45) or OS (middle tertile HR=1.28, 95% CI 0.80-2.05; upper tertile HR=0.97, 95% CI 0.59-1.59; p=0.44) in multivariable models stratified on cohort and adjusted for treatment and clinical factors. We then compared outcomes by treatment within tertiles of SUVmax. In multivariable models, R-CHOP had modestly inferior EFS to BR in low (HR=1.27, 95% CI 0.84-1.93) and middle tertiles (HR=1.49, 95% CI 0.99-2.25), and modestly superior OS to BR in low (HR=0.59, 95% CI 0.23-1.47) and middle (HR=0.79, 95% CI 0.37-1.71) tertiles. Both EFS (HR=1.06, 95% CI 0.71-1.58) and OS (HR=1.02, 95% CI 0.45-2.30) for R-CHOP were similar to BR in the highest tertile. No comparisons identified a statistically significant benefit for either therapy in multivariable models when evaluating SUVmax tertile subsets. Conclusions: In this large multicenter study of newly diagnosed FL pts with bPET who received frontline immunochemotherapy, higher baseline SUVmax was not associated with inferior outcomes after adjusting for treatment and clinical factors. Within the caveats of retrospective studies and confounding of treatment selection, these data provide no clear insights for SUVmax-based therapy selection in first-line follicular lymphoma pts treated with immunochemotherapy.

Background: FL3B is the least common among FL subtypes, accounting for 5-10% of cases. Almost half of FL3B tumors have concurrent diffuse large B cell lymphoma (DLBCL) in the diagnostic tissue biopsy, while FL3B rarely coexists with FL1-2 or FL3A. Several lines of evidence suggest that FL3B displays distinct biological and clinical characteristics resembling aggressive DLBCL rather than the indolent FL1-3A. Accordingly, DLBCL anthracycline-containing regimens, such as R-CHOP, are the mainstay of therapy for FL3B with or without coexisting DLBCL. However, while some studies show that R-CHOP therapy in FL3B was associated with 5-year overall survival (OS) rate of 54%, others report outcomes comparable to FL3A with 79-90% survival. Additional controversy involves the clinical implication of pure FL3B (FL3Bp) vs FL3B with concurrent DLBCL (FL3Bc). Thus, it remains unclear if FL3B clinically resembles DLBCL or lower-grade FL and whether FL3Bp has a different prognosis from FL3Bc. To address these questions, we performed a pooled study of two large prospective multicenter study cohorts to compare the clinical characteristics and outcomes of newly diagnosed FL3B with FL1-2, FL3A and DLBCL patients, all of whom were uniformly treated with frontline R-CHOP. Patients and Methods: Weevaluated 469 newly diagnosed patients with FL1-2 (n=216), FL3A (n=170) and FL3B (n=83) who received R-CHOP within 6 months of diagnosis and 739 de novo DLBCL (GCB n=455 and non-GCB n=284) who were prospectively enrolled into the Molecular Epidemiology Resource (MER) and the Lymphoma Epidemiology of Outcomes (LEO) cohorts between 2002 and 2021. FL3B cases were designated as FL3Bc and FL3Bp based on the presence or absence of coexisting DLBCL, respectively. Histologic diagnosis was determined by an expert pathologist at each participating center and centrally reviewed. Event free survival (EFS) and OS were defined as time from diagnosis to progression, relapse, retreatment or death, and early progression defined as failing to achieve EFS at 24 months (EFS24). Associations between clinical features and EFS or OS were evaluated using Kaplan Meier curves and Cox proportional hazards models unadjusted and adjusted for age and FLIPI (comparisons with FL1-2 and FL3A) or IPI (comparisons with DLBCL). Results: The median age was 61 years (range, 18-94 years). Among FL3B patients, 21 (25%) had FL3Bc and 62 (75%) FL3Bp. Baseline characteristics and outcomes were broadly similar between the two FL3B subtypes. We then compared all FL3B patients to those with lower-grade FL. Patients with FL3B showed similar clinical features to those with FL1-2 and FL3A, with exception for a lower tumor burden. After R-CHOP, FL1-2 patients had an inferior EFS (HR 2.13 95%CI 1.31-3.46) than those with FL3B, whereas there was no difference compared to FL3A. OS was similar across all FL grades. Although FL1-2 patients failed to achieve EFS24 more frequently than FL3B and FL3A (34% vs 18% and 15%, p<0.001), FL3B patients who did fail EFS24 had almost three folds higher risk of subsequent mortality (HR 2.84 95%CI 1.35-6.01) than other FLs. At 5-year follow-up, FL3B patients had twice the risk of relapse with an aggressive subtype (i.e., DLBCL, high-grade B-cell lymphoma) than those with FL1-2 and FL3A, who instead often recurred with indolent disease (i.e., FL1-2, FL3A; p<0.001). We further compared FL3B patients to those with DLBCL classified based on cell-of-origin into GCB and non-GCB subtypes. At diagnosis, patients with FL3B had more favorable clinical features than those with GCB and non-GCB DLBCL. Compared to FL3B, patients with non-GCB DLBCL had an inferior EFS (HR 1.73 95%CI 1.07-2.79) and OS (HR 2.04 95%CI 1.11-3.75), while those with GCB DLBCL did not show a significant difference in EFS (HR 1.41 95%CI 0.88-2.25) or OS (HR 1.69, 95%CI 0.93-3.06) likely due to insufficient statistical power. Patients with FL3B failed EFS24 less frequently than patients with GCB or non-GCB DLBCL (17% vs 25% and 32%; p=0.01). Among patients who failed EFS24, those with FL3B had a similar subsequent OS as both GCB (HR 1.41 95%CI 0.73-2.72) and non-GCB DLBCL (HR 1.27 95%CI 0.65-2.47). Conclusions: We report that FL3Bp and FL3Bc appear clinically similar. While most FL3B patients have a prognosis comparable to FL1-2 and FL3A, 18% exhibit aggressive behavior with poor outcome reminiscent of DLBCL. Future studies with a larger population will be needed to confirm our findings.