Citations

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 Introduction: Targeted therapy yields deep and durable response in histiocytic neoplasms.However, relapse rate following interruption is high at 75-77%, with a median time to relapse 6-9 months in previous studies. Outcomes after discontinuation in those who have already achieved a durable response are not well understood and are the focus of this study. Methods: We performed a multicenter retrospective study, including 4 US centers, including patients with histiocytic disorders treated with BRAF inhibitor (BRAFi), MEK inhibitor (MEKi), or combination BRAF/MEKi, and whose therapy was interrupted after ≥12 months of therapy following a best response of partial (PR) or complete response (CR). Progression-free survival (PFS) and overall survival (OS) were analyzed with Kaplan-Meier method. Cox regression was performed to identify clinical and molecular factors that are associated with PFS. Results: 26 patients were included (median age 55 [range 12-72]; male 53.8%; Caucasian 69.2%). Most predominant histology was Erdheim-Chester Disease (ECD) (n=15, 57.5%), followed by mixed histiocytosis (n=5, 19.2%). Rosai-Dorfman Disease (RDD) (n=4, 15.4%), and Langerhans Cell Histiocytosis (LCH) (n=2, 7.7%). All had BRAF V600E analysis and 10 (38.5%) were positive. 19 patients underwent additional molecular testing; alternative mutations identified included MAP2K1/2 (n=6, 23.1%), NRAS (n=1, 3.8%), RAF1 (n=1, 3.8%), ASXL1 (n=1, 3.8%), and SRSF2 (n=1, 3.8%). Targeted therapy was given as frontline therapy (n=8, 30.8%) or in the relapsed refractory setting (n=18, 69.2%) with median 1 prior line of therapy (range 0-7). Targeted therapies include BRAFi (n=5, 19.2%; 2 dabrafenib, 3 vemurafenib), MEKi (n=17, 65.4%; 14 cobimetinib, 3 trametinib), and BRAF/MEKi (n=4, 15.4%; 3 dabrafenib/trametinib, 1 vemurafenib/cobimetinib). PR and CR occurred in 8 (30.8%) and 18 (69.2%) patients, respectively. The median duration of treatment was 32 months (range 13-60) (12-24 months: 9 [34.6%]; 24-36 months: 7 [26.9%]; >36 months: 10 [38.5%]). Median time from initiation of targeted therapy to best response was 6 months (<6 months: 14 [53.8%]; 6-12 months: 5 [19.2%]; >12 months: 7 [26.9%]). Median duration from best response to drug discontinuation was 19 months (12-24 months: 14 [53.8%]; 24-36 months: 7 [26.9%]; >36 months: 5[19.2%]). Reasons for drug discontinuation include toxicity (n=8, 30.8%), patient preference (n=13, 50.0%), cost prohibition (n=1, 3.8%), and other critical medical conditions (n= 4,15.4%). Eleven patients (42.3%) experienced disease progression after discontinuation of targeted therapy, 5 patients progressed within 6 months. Systemic and CNS progression were captured by PET/CT and MRI brain, respectively. At median follow-up of 33.0 months (95%CI 24.3-47.1), median PFS, defined by time from targeted therapy discontinuation to progression or death, was 29 months (95% confidence interval [CI] not estimable). Median OS was not reached. In Cox regression model, BRAF V600E mutation (hazard ratio [HR] 4.16, 95% CI 1.10-15.70, p 0.036) and CNS involvement (HR 4.64, 95% CI 1.21-17.67, p 0.025) were associated with inferior PFS. MEKi containing regimen trended toward improved PFS (HR 0.35, 95%CI 0.10-1.21, p 0.097). There was no difference in PFS when assessing demographics, histology, prior therapies, best response to targeted therapy, treatment duration, or time from best response to discontinuation. Of the 11 patients with progression, 9 were re-challenged with targeted therapy (7 switched class), 2 did not receive next line therapy (1 transitioned to hospice and passed from progression, 1 has pending evaluation for next line therapy). Of 6 with response assessments, all recaptured response (1 CR, 5 PR), and none had progressed by data cutoff. Conclusions: In patients with histiocytic neoplasm and prior durable response to targeted therapy, ~40% experienced disease progression after treatment discontinuation. BRAF V600E mutation and CNS disease are associated with inferior PFS, suggesting these subgroups may not be appropriate for limited duration of treatment. Novel approaches are needed in these high-risk patients.

Abstract Background: Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma (NHL), accounting for approximately 30% of all NHL cases. DLBCL diagnosis relies on histologic examination, immunophenotyping, molecular and fluorescence in situ hybridization (FISH) analysis to differentiate it from similar NHL subtypes with overlapping morphological features, including more high-grade lymphomas like double-hit lymphoma. Because FISH is performed in a panel of single- or dual-target test format and analyzed through microscopy and manual image analysis, assessment of multiple target biomarkers can be cumbersome and expensive. As additional biomarkers for diagnosis, subtyping, prognosis, and therapeutic decision-making become available, target panels will become larger, posing additional challenges to clinical laboratories. Patients and Methods In this study, Hi-C sequencing of FFPE samples was performed to identify all genomic rearrangements by a single sequencing analysis for an unselected DLBCL case series of 159 patients, and the results were compared to FISH for the detection of clinically important variants. Results Hi-C sequencing showed superior performance compared to FISH: standard FISH for DLBCL includes testing for MYC, BCL2, and BCL6. Between FISH and Hi-C, these biomarkers were detected 105 times, of which, FISH detected 80/105 (76%) and Hi-C detected 102/105 (97%). The cohort included 4 double-hit lymphomas by FISH but Hi-C sequencing detected five. Besides DLBCL diagnostic biomarkers (MYC, BCL2, BCL6), many additional biomarkers were detected by Hi-C sequencing including variants not tested by FISH because the clinical suspicion was ambiguous and the relevant FISH probes were not used, and those not tested because the biomarkers were not thought to be common and, hence, left out of the FISH panel. In total, Hi-C sequencing detected additional biomarkers in 42 (26%) cases, including (1) variants targeted by FISH (i.e. MYC, BCL2, BCL6) but negative, (2) classification biomarkers for other lymphomas (CCND1, MALT, IRF4), and (3) those not tested because the biomarkers are not common and not included in the FISH panel. These additional biomarkers were supportive of suspected diagnosis of DLBCL versus double-hit lymphoma (24 cases), prompted reconsideration of a different diagnosis that may have led to different treatments (6 cases, e.g. mantle cell lymphoma), or biomarkers such as PDL1, ALK, and ATM which have implications for targeted therapies and/or inclusion in clinical trials (5 cases). Conclusion The Hi-C FFPE sequencing technology has the potential to improve the workflow of pathology labs for detection of a wide range of structural variants informing precision diagnosis and treatment for lymphomas.

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 Background: Hypomethylating agent (HMA) with venetoclax (VEN) is used in clinical practice for frontline management of patients with myelodysplastic syndrome (MDS) based on encouraging data from phase 1 clinical trial (Garcia JS et al. Blood 2025). While the randomized clinical trial (VERONA-NCT04401748) did not show a survival benefit of azacitidine-VEN, the study had important differences such as exclusion of therapy related MDS and being conducted in several non-academic US centers. To better understand the impact of this regimen and its effects in subgroups, we conducted a real-world analysis of frontline MDS patients treated mainly at academic medical centers with HMA-VEN and compared to HMA monotherapy. Methods: This is a multicenter retrospective study of 19 US academic medical centers. We included contemporary cohorts of adults (age≥18 years) with MDS (intermediate, high or very high risk by IPSS-R) who were treated outside of clinical trials with either upfront combination of HMA-VEN or HMA monotherapy. Data on patient-related, disease-related and treatment-related variables were collected through chart review. Descriptive statistics were used for baseline characteristics. Overall survival (OS) and event free survival (EFS: progression, leukemic transformation, death as events) analysis was conducted using Kaplan-Meier method. Determinants of outcomes were analyzed using multivariable regression models. All analyses were conducted at a significance level of p<0.05. Results: Among 1198 patients with intermediate, high or very-high risk MDS, 313 patients received upfront VEN in combination with HMA [HMA-VEN] and 885 patients received HMA monotherapy. Median age was 70 (63-76) years and majority of patients had MDS with excess blasts-1 (34% overall, HMA-VEN=26%, HMA=37%) or MDS with excess-blasts-2 (41% overall, HMA-VEN=60%, HMA=34%). Disease risk by IPSS-R was intermediate (29% overall, HMA-VEN=18%, HMA=33%), high (30% overall, HMA-VEN=31%, HMA=30%) or very-high risk (41% overall, HMA-VEN=51%, HMA=37%, p<0.001). Therapy related MDS (t-MDS) was seen in 27% of patients (HMA-VEN=33%, HMA=25%, p=0.004), 34% had TP53 mutation (HMA-VEN=40%, HMA=32%, p=0.005) and 33% underwent allogeneic hematopoietic stem cell transplantation (AHSCT) (HMA-VEN=42%, HMA=30%, p<0.001). The most common mutations detected at baseline included TP53 (34%), ASXL1 (22%), DNMT3A (16%), RUNX1 (16%), TET2 (14%), SRSF2 (13%), and U2AF1 (11%). The response rate by IWG 2006 criteria was significantly higher with HMA-VEN than HMA monotherapy (overall 81% vs 39%, CR: 17% vs 9.6%; marrow CR: 56% vs 14%, p<0.001). In multivariable analysis accounting for upfront treatment, AHSCT, genomic mutations, baseline bone marrow blasts and t-MDS, patients treated with HMA-VEN in the upfront setting had significantly improved EFS (median EFS 10.7 vs. 7.8 mon, HR 0.83, 95%CI 0.72-0.96, p=0.01) and OS (median OS 22.4 vs. 18.1 mon, HR 0.79, 95%CI 0.64-0.97, p=0.02). In addition, TP53 mutation status (OS: HR 1.88, 95% CI 1.57-2.26, p<0.001, EFS: HR 1.43, 95% CI 1.23-1.65, p<0.001), subsequent AHSCT (OS: HR 0.34, 95% CI 0.27-0.41, p<0.001; EFS: HR 0.47, 95% CI 0.40-0.54, p<0.001), and therapy related MDS (OS: HR 1.20, 95% CI 1.00-1.44, p=0.04, EFS: HR 1.10, 95% CI 0.96-1.27, p=0.2) were significant predictors on multivariable analysis. When separately investigated in subgroups, an OS benefit of HMA-VEN over HMA was seen in patients undergoing subsequent AHSCT (median OS 40.6 vs. 30 mon, p=0.05), bone marrow blast 10-19% (median OS 25.1 vs. 17.4 mon, P=0.01), de novo MDS (median OS 30 vs. 20.1 mon, P=0.003) and absence of TP53 mutation (median OS 34.9 mon vs. 23.1 mon, p=0.004). The benefit of HMA-VEN over HMA was also seen across molecular mutations including presence of ASXL1 mutation (median OS 35.2 vs 20.9 mon, p=0.03). Conclusions: In this large multicenter retrospective analysis from US academic medical centers, we found that HMA-VEN therapy significantly improved OS in certain subsets of high risk MDS including patients with 10-19% blasts, denovo MDS, subsequent AHSCT and those without TP53 mutation. While our analysis is limited by its retrospective design, prospectively exploring subgroups of MDS patients who could benefit from HMA-VEN therapy is needed.

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: 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.

Abstract Introduction: T-PLL is a rare, aggressive leukemia with poor prognosis and limited therapies. Previous studies on T-PLL patients receiving an allogeneic stem cell transplant (alloSCT) are limited by small numbers with limited data on disease and transplant characteristics. Further, recent data from our large national cohort of patients demonstrated significant survival difference between TCL1A+ and TCL1A- T-PLL, and CD8+ T-PLL vs CD8- T-PLL. In this large multi-center series, utilizing comprehensive disease subtype and alloSCT clinical data, we evaluated prognostic and survival therapeutic outcomes in T-PLL patients that had an alloSCT with a particular focus on T-PLL disease subtypes. Methods: We retrospectively evaluated 572 diagnosed with T-PLL at 21 academic cancer centers throughout the USA between 2000-2023, of which we performed a further evaluation on the169 that proceeded with alloSCT. Overall survival (OS) and progression-free survival (PFS) were assessed using Kaplan-Meier methods and Cox proportional hazard models. Results: Among n=169 alloSCT recipients with T-PLL, the median follow-up time was 20.9 months post-alloSCT (range: 0.2-182.9), the median age at transplant was 59 years (range: 35-77) and 52% were male. In evaluated patients (n=109), TCL1A was positive by cytogenetics or FISH in 69%. As our recent analysis demonstrated significant differences in survival among specific subtypes of T-PLL, we evaluated outcomes in patients with CD4+CD8- T-PLL (n=90, 53%), CD4-CD8+ (n=17, 10%) T-PLL, and CD4+CD8+ T-PLL (n=52, 31%). 53% (87/164) had known nodal involvement at diagnosis identified by PET or CT scan and 45% (43/96) of known patients had minimal residual disease (MRD) at the time of transplant [defined as <5% T-PLL in marrow by flow, or +cytogenetics (e.g. TCL1A)] prior to alloSCT. At the time of alloSCT, 136 (80%) patients were in CR and 23 (14%) patients were in PR. Forty-nine (30%) had a myeloablative (MAC)conditioning regimen and 114 (70%) had a reduced intensity chemotherapy (RIC) conditioning regimen. Eighty (49%) patients had TBI as part of their regimen. The most common regimens were fludarabine (flu)/melphalan based regimens (n=44; 26.0%), flu/cyclophosphamide (cy) based regimens (n=33; 19.5%), flu/busulfan based regimens (n=22; 13.0%), flu/total body irradiation (TBI) (n=8; 4.7%), and cy/TBI (n=4; 2.4%). Across the whole cohort, the median OS and PFS post-alloSCT was 32.6 months (95% CI: 21.0-41.9) and 20.8 months (95% CI: 14.4-30.5) months while the 1-year cumulative incidences (CI) of NRM and relapse were 17% and 20%, respectively. The incidences of grade II-IV acute GVHD and cGVHD were 19% and 23%. There was no difference between patients with myeloablative conditioning (MAC) (n=49, 29%) when compared to patients that received reduced intensity conditioning (RIC) (n=114, 67%) in OS (33.1 vs 29.2; p=0.68) and PFS (20.2 vs 20.8; p=0.97). Patients that were MRD+ (n=31) had worse OS (22.2 vs 48.6; p=0.1) and PFS (14.1 vs 32.5; p=0.04) and higher rates of relapse (1-year relapse 20% vs 8%; p=0.08) than patients that were MRD- (n=50). PFS was improved in those who received alloSCT though only trended towards significance due to low numbers (HR 0.76, p=0.09). In subtype analysis, TCL1A+ T-PLL had significantly worse outcomes compared to TCL1A negative T-PLL. TCL1A+ disease had significantly worse OS (21.6 vs 63.1; p=0.01) and PFS (12.4 vs 50.7; p=0.01) compared to TCL1A-. Additionally, in TCL1A+ disease CI of NRM (24% vs 3%; p=0.02) and relapse (25% vs 12%; p=0.86) was higher vs TCL1A- PLL. CD4+CD8-, CD4-CD8+, and CD4+CD8+ disease had no difference in survival via either OS (28.0 vs 34.4 vs 21.0; p=0.17) or PFS (29.4 vs 23.5 vs 13.6; p=0.09) as well as no difference 1-year CI of NRM ( 11.6 vs 23.5 vs 26.2; p=0.35) and relapse (18.5 vs 23.5 vs 20.0; p=0.67).Conclusions: AlloSCT significantly improved outcomes for patients with T-PLL compared to those who did not proceed to alloSCT and remains he only curative therapy for those with T-PLL. OS, PFS, and CI relapse were significantly worse for patients with TCL1A+ subtype of T-PLL, or those with MRD positivity or PR prior to alloSCT. Novel strategies to decrease relapse such as post-alloSCT maintenance therapy to improve outcomes are urgently needed in these patients.

Abstract Background The expansion of hematopoietic stem and progenitor cells (HSPCs) in the bone marrow is universally associated with adverse outcomes and disease progression in all chronic myeloid neoplasms, to include chronic myelomonocytic leukemia (CMML). However, HSPCs are a heterogeneous cellular compartment and the impact of its composition on clinical outcomes remains poorly defined. Single cell RNA-sequencing (scRNA-seq) represents an optimal strategy to annotate cellular composition but no large clinically annotated scRNA-seq datasets are currently available. To address this, we have embarked to generate a clinically annotated 500 patient CMML scRNA-seq atlas from CD34+ enriched BMNCs to establish among the largest clinically and genetically annotated single cell references for any chronic myeloid malignancy. Here we report the interim analysis of 113 clinically annotated cases that demonstrates massive reshaping of HSPC composition that is independently a predictor of overall survival. Methods Single-cell RNA expression profiling was performed on 113 CD34-enriched CMML BMNC samples using 10x Genomics GEM-X Universal 3' Gene Expression v4 (4-plex) chemistry. Libraries were sequenced on NovaSeq S4 flow cells in two runs, and data were processed using Cell Ranger to generate gene expression matrices. A total of 502,659 single cells from 113 samples were processed using Seurat. The upper threshold for gene count was defined as the mean number of detected genes plus two standard deviations data normalization was performed using the log-normalization method. Batch correction and integration were conducted with Harmony to mitigate technical variation across pools. Descriptive statistics were used to tabulate clinical and genetic parameters. KM curves, log-rank test, and cox regression were used to calculate survival, test for statistical differences, and perform multivariate analysis, respectively. Results To derive high resolution cell annotations, we employed the SingleR algorithm using a large hematopoietic reference of over 300,000 single cells (Zhang, X. Nature Immunology, 2024). Using this approach a diverse set of cell types were identified that included HSCs, 23 myeloid progenitors, 18 erythroid progenitors, and 18 lymphoid progenitors. This diverse cellular repertoire displayed massive reshaping relative to the normal CD34 enriched reference including a >53x expansion of multilineage GMPs and a >100x depletion of B-cell progenitors consistent with the known myeloid skewing seen in this disease. To understand whether expansion of specific progenitors was associated with outcomes we iteratively evaluated key progenitor populations and observed that expansions of HSC (HR 0.468 p<0.005) and MEP (HR 0.485, p<0.005) were associated with improved survival while GMP (HR 1.729, p=0.029) expansions were associated with inferior survival when using a cut point derived with a maximally selected ranks statistics approach. To achieve a more comprehensive metric of HSPC composition we explored GMP ratios to include GMP/HSC ratios and GMP/MEP ratios and observed that both were associated with inferior survival in univariate and multivariate when considering clinical and genetic features such as the CPSS-molecular prognostic score and FAB classification. When considering both GMP/HSC and GMP/MEP ratios in a multivariate model only GMP/MEP ratios and CPSS molecular remained independently prognostic. As an orthogonal approach, pseudotime projections were carried out using a reference map from Setty et al., 2019 to calculate differentiation potential and branch probabilities. This approach enabled both cell identity assignment and placement of cells along the hematopoietic differentiation trajectory. Samples were classified as monocyte-biased, MEP-biased, or normal-like, as previously described (Ferrall-Fairbanks et al. 2022) and demonstrated that increased GMP ratios are associated with a monocytic biased trajectory and that this trajectory is also associated with overall survival. Conclusion This study represents an interim analysis of among the largest scRNA-seq cohorts generated in chronic myeloid malignancies. This enabled us, for the first time, to demonstrate that HSPC composition is independently prognostic even when considering modern CMML prognostic models. As this cohort continues to increase, validation of these findings and examining the impact of therapy on HPSC composition in serial samples will be prioritized.