Mou, Eric

7073 Background: The Optimized HLH Inflammatory (OHI) index (Zoref-Lorenz et al., Blood , 2022), based on serum soluble CD25 (sCD25) and ferritin, is a prognostic tool identifying inflammation in the hemophagocytic lymphohistiocytosis (HLH) spectrum and early mortality risk. Prior studies were retrospective and enriched with HLH-suspected cases. We hypothesized that the OHI index would predict outcomes in unselected diffuse large B-cell lymphoma (DLBCL). Methods: 670 newly diagnosed DLBCL patients from a prospective cohort (2002–2015) were analyzed: 335 for discovery and 335 for validation. Pre-treatment serum sCD25 and ferritin levels were quantified via ELISA, and we evaluated original thresholds (sCD25 ≥ 3,900 U/mL; ferritin ≥ 1,000 ng/mL) and developed DLBCL-optimized thresholds (using receiver operator curves) for predicting 500-day mortality. We also evaluated event-free survival (EFS), EFS at 24 months (EFS24), and overall survival (OS). Results: The median age was 64, and 51% were male. Using original thresholds, 4.2% (n=14) of patients were OHI+, with a 5.6-fold higher risk of 500-day mortality (95% CI 1.6–17; p<0.001). Optimized thresholds identified 23.6% (n=78) as OHI+ with a 9.2-fold higher 500-day mortality risk (95% CI 4.0–24; p<0.001). Optimized OHI+ also had a higher risk of EFS24 failure and inferior EFS and OS (Table). OHI+ patients had higher rates of B symptoms (35% vs. 20%; p=0.027), elevated LDH (76% vs. 34%; p<0.001), worse performance status (ECOG ≥2: 38% vs. 12%; p<0.001), and advanced-stage disease (Stage IV: 57% vs 38%; p<0.001), though IPI scores (3–5) were not significantly different (44% vs 36%; p=0.28). Adjusting for age and IPI, OHI+ independently predicted 500-day mortality (OR=5.0; CI 1.9–13; p=0.001), EFS24 failure (OR=3.2; CI 1.5–6.5; p=0.0018), and inferior long-term EFS (HR=2.2; CI 1.1–4.3; p=0.030) and OS (HR=2.0; CI 1.0–4.0; p=0.040) at a median follow up of 9.8 years in living patients. In validation, optimized OHI predicted 500-day mortality, EFS24 failure, and long-term OS and EFS (Table). Cytokine profiling revealed elevated inflammatory markers in OHI+ patients, including IL1a (p=0.008), CCL2 (p=0.03), CXCL10 (p<0.001), and CXCL9 (p<0.001), reflecting systemic hyperinflammation. Conclusions: The OHI index is a powerful predictor of early and long-term outcomes in DLBCL patients. Optimized thresholds identify a larger OHI+ group, highlighting hyperinflammation’s critical role in poor outcomes. These findings support its use in routine management and clinical trial design for novel therapies. Odds ratios or hazard ratios and p-values for optimized OHI+ and DLBCL prognosis. Outcome Discovery Validation OR for 500-day mortality 9.2; p<0.001 4.0; p=0.002 OR for EFS24 failure 5.2; p <0.001 6.3; p<0.001 HR for OS 2.3; p<0.001 1.8; p=0.006 HR for EFS 2.4; p<0.001 2.5; p<0.001

7052 Background: Secondary central nervous system lymphoma (SCNSL) is a rare but serious complication that occurs in 2-10% of patients (pts) with diffuse large B-cell lymphoma (DLBCL). The CNS-International Prognostic Index (CNS-IPI) is a validated tool used to assess the risk of CNS relapse. However, with advancements in molecular profiling and treatments, the predictors of SCNSL, including the predictive ability of CNS-IPI combined with other molecular markers and treatment regimens, have become uncertain. This study evaluates new prognostic markers for CNS relapse in DLBCL, focusing on cell-of-origin (COO), gene rearrangements, and treatment types. Methods: DLBCL pts enrolled from 2002-2015 in the Mayo Clinic and University of Iowa Lymphoma Molecular Epidemiology Resource were prospectively followed for incidence of CNS relapse. Pts with primary CNS lymphoma or CNS involvement at diagnosis, and pts treated with high-dose methotrexate containing regimens were excluded. COO classification (GCB vs non-GCB) was determined using Hans algorithm and/or NanoString data. Multivariate Cox regression models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for predictors of SCNSL. Survival analysis was performed from time of DLBCL diagnosis using Cox proportional hazards model. Results: Among 1278 newly diagnosed DLBCL pts included (median age: 63 years; 43.6% female), SCNSL occurred in 46 (3.6%) pts. Pts with SCNSL were more likely to have intermediate/high CNS-IPI scores (80.4%) than pts without SCNSL (63.9%, p=0.02). The 3 yr SCNSL incidence was 0.016 (0.008-0.0033) in low CNS-IPI vs 0.043 (0.031-0.0060) in high CNS-IPI. Those with SCNSL were significantly more likely at baseline to have non-GCB COO (56.3% vs. 36.5%, p=0.02), and advanced stage (76.1% vs. 59.9%, p=0.03) compared to those without SCNSL. The incidence of SCNSL was not influenced by MYC, BCL2, or BCL6 rearrangements, nor by treatment with R-CHOP, R-EPOCH, or other immunochemotherapy regimens. MVA showed that intermediate CNS-IPI (HR 2.32, 95% CI 1.09-4.92, p=0.029), high CNS-IPI (HR 3.59, 95% CI 1.49-8.62, p=0.004), and non-GCB COO (HR 2.19, 95% CI 1.08-4.42, p=0.0029) were independently associated with increased SCNSL risk. After a median follow-up of 12 years (IQR 9–15), overall survival (OS) was 52.1% for the cohort, with OS significantly worse for SCNSL pts (17.4% vs. 53.4%, p<0.001). Conclusions: This large cohort with extensive follow-up highlights the role of COO as an additional prognostic factor for SCNSL while reinforcing CNS-IPI’s prognostic value. Non-GCB COO pts had a higher risk of SCNSL even when adjusted by CNS-IPI risk. Despite advancements in management, SCNSL pts continue to have poor outcomes. Strategies to reduce the incidence of CNS relapse in high-risk pts require further investigation. MER CNS relapse hazard ratios (95% CI). Characteristics Hazard Ratio 95% CI P-Value CNS-IPI Group 0-1 Low Ref Ref Ref 2-3 Intermediate 2.32 1.09 - 4.92 0.029 4+ High 3.59 1.49 - 8.62 0.004 Cell of Origin* GCB Ref Ref Ref non-GCB 2.19 1.08 - 4.42 0.029 Double Hit* non-DHL Ref Ref Ref DHL 1.08 0.14 - 8.3 0.941 Not Done/Missing 1.38 0.76 - 2.51 0.297 Double Expressor* Negative Ref Ref Ref Positive 2.36 0.71 - 7.85 0.162 Not Done/Missing 1.91 0.89 - 4.08 0.097 IC Group* R-CHOP R-EPOCH Ref 1.63 Ref 0.71 - 3.74 Ref 0.249 Other IC 1.18 0.57 - 2.46 0.660 Any EN Involvement* No Ref Ref Ref Yes 0.98 0.51 - 1.88 0.946 Albumin Group* >=Normal Ref Ref Ref <Normal 1.70 0.75 - 3.85 0.201 Head & Neck Involvement* No Ref Ref Ref Yes 0.35 0.048 - 2.63 0.310 *Adjusted by CNS-IPI risk group.

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 &lt; 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&lt;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&lt;0.001), elevated LDH (27% vs 22%, p=0.029), bulky disease &gt; 7 cm (44% vs 37%, p=0.019), lower ECOG PS (PS &gt;1 7% vs 13%, p&lt;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: The Optimized Hemophagocytic Lymphohistiocytosis (HLH) Inflammatory (OHI, Zoref-Lorenz et al, Blood, 2022) index, defined by the combined elevation in serum soluble CD25 (sCD25) and ferritin, has emerged as a promising prognostic tool for patients with newly diagnosed hematologic malignancies, demonstrating superior performance in predicting 500-day mortality compared to HLH-2004 criteria. However, previous studies were retrospective, with a substantial proportion (65%) of patients suspected clinically to have HLH. We evaluated the performance of the OHI index in an unselected, prospective cohort of newly diagnosed diffuse large B-cell lymphoma (DLBCL) patients. Methods: We analyzed the predictive utility of the OHI index in 331 newly diagnosed DLBCL patients enrolled from 2002-2015 in the prospective Iowa/Mayo Molecular Epidemiology Resource cohort. Ferritin and sCD25 levels were quantified in pre-treatment serum samples using R&D ELISA kits. OHI values were also optimized based on 500-day mortality in the cohort using receiver operating curve analysis with internal validation. Patients were classified as OHI+ using the original and newly optimized thresholds for sCD25 and ferritin. The primary endpoint was 500-day survival, and the secondary endpoints were event-free survival (EFS) at 24 months (EFS24), EFS, and overall survival (OS). The predictive capability of the OHI index was evaluated using logistic regression to determine odds ratios (OR) and 95% confidence intervals (CI) for 500-day mortality and EFS24, as well as Cox regression to calculate hazard ratios (HR) and 95% CIs for EFS and OS. Results: The median age at diagnosis of the 331 patients was 63.5 years (interquartile range, 54.5-74), and 168 (51%) were male. Using the original thresholds (sCD25 3,900 U/mL and ferritin 1,000 ng/mL), 4.2% of patients were OHI+, with a more than 5-fold higher 500-day mortality risk compared to OHI- patients (OR=5.6; CI 1.6-17, p&lt;0.001). After optimizing thresholds for sCD25 (2,400 U/mL) and ferritin (250 ng/mL) to predict 500-day mortality, 23.6% were OHI+ and the OHI+ group had a 9-fold higher 500-day mortality risk (OR=9.2; CI 4.0-24, p&lt;0.001), with 31% mortality in the OHI+ vs 5% in the OHI- group. OHI+ patients exhibited more B-symptoms (35% vs 20%, p=0.027), elevated LDH levels (76% vs 34%, p&lt;0.001), poorer performance status (Eastern Cooperative Oncology Group 2+: 38% vs 12%, p&lt;0.001), and more advanced stage disease (Stage IV: 57% vs 38%, p&lt;0.001), although the frequency of a high International Prognostic Index (IPI) was not statistically different between the groups (IPI 3-5: 44% vs 36%, p=0.28). After adjusting for age and IPI, OHI+ remained a strong predictor of 500-day mortality (OR=5.0; CI 1.9-13, p=0.001). Further adjustment for the cell of origin did not impact the results. The association of OHI+ status with 500-day mortality was similar in IPI 0-2 (OR=5.8; CI 1.2-29, p=0.033) and 3-5 (OR=6.8; CI 0.6-73, p=0.11). OHI+ patients were also more likely to not achieve EFS24 (45% vs 13%), with a 3-fold greater risk after adjusting for age and IPI (OR=3.2; CI 1.5-6.5, p=0.0018). The OHI index also retained its predictive ability over longer-term follow-up (median 9.8 years for living patients) for both OS (HR=2.0; CI 1.0-4.0, p=0.040) and EFS (HR=2.2; CI 1.1-4.3, p=0.030) after adjusting for age and IPI. Cytokine profiling using multiplex ELISA identified elevated inflammatory markers in OHI+ patients, underscoring the role of systemic inflammation in prognosis. The distribution of cytokines was significantly different between the groups, with consistently higher means in the OHI+ vs OHI- group, including IL1a (1,628 vs 759 pg/ml, p=0.008), CCL2 (1,020 vs 724 pg/ml, p=0.028), CXCL10 (165 vs 53 pg/ml, p&lt;0.0001), and CXCL9 (380 vs 150 pg/ml, p&lt;0.0001). Conclusions: While the original OHI index for prediction of 500-day mortality was validated in this unselected cohort of newly diagnosed DLBCL patients, optimization of the OHI thresholds identified a larger OHI+ group (24%), enhanced the identification of hyperinflammation, improved predictive ability for early (500-day) mortality, and predicted EFS24 status and long-term OS. This underscores the clinical relevance of the OHI index, supports the need to evaluate disease-specific thresholds, and suggests its potential for use in routine management and as enrollment criteria in clinical trials for novel therapies.

Background: Indolent non-Hodgkin lymphomas (iNHL) are incurable and disproportionately affect adults age &gt;65 in whom frailty and functional impairment can impact disease management. The Vulnerable Elders Survey (VES-13) is a 13-item screening tool that incorporates age, self-rated health, physical function limitations, and functional disabilities to identify vulnerable older community-dwelling adults. Adults with a VES-13 score of &gt;3 have a fourfold increased risk of death or functional decline over 2 years as compared to adults with a score &lt;3 (Saliba et al, JAGS 2001). Our team previously showed that VES-13 &gt;3 was associated with a higher risk of 1-year mortality in NHL (OR 3.26, 95% CI 2.15-4.96, Fama et al, ASH 2019), including in adults age &lt;65. In this study, we determined whether VES-13 scores &gt;3 were associated with quality of life (QOL) changes in adults with iNHL across the age spectrum. Methods: We used the Lymphoma Epidemiology of Outcomes (LEO) Cohort Study to identify adults enrolled from 2015-2020 with grade 1-3A follicular and marginal zone lymphoma (FL, MZL) who completed the VES-13 at enrollment and QOL surveys at enrollment and follow-up. The LEO Cohort Study enrolls participants within 6 months of diagnosis and collects information on disease status, QOL, health behaviors, and patient-reported functional assessment. For this analysis we included QOL measured using the Functional Assessment of Cancer Therapy-General (FACT-G) at years 1 and 2 post-enrollment. The FACT-G total score was the sum of 4 subscales (range): physical (PWB, 0-28), social/family (SFWB, 0-28), emotional (EWB, 0-24), and functional (FWB, 0-28) well-being. A higher FACT-G total score (range 0-108) indicated better overall QOL. VES-13 scores ranged from 0 (lowest risk for death or decline) to 10+ (highest risk). Generalized linear mixed models evaluated changes in QOL from baseline based on VES-13 score &gt;3 (vulnerable) vs &lt;3 (non-vulnerable), within groups by age at enrollment &gt;65 and &lt;65. Results: Our study included 1,186 participants: 42% age &gt;65, 54% female, 84% non-Hispanic White, 10% Hispanic, 3.3% Black, and 1.4% Asian/Pacific Islander. Most (68%) were diagnosed with FL. FLIPI was high risk for 20%, and MALT-IPI was high risk for 23%. In the VES-13 &lt;3 group, the mean (SD) baseline FACT-G total score was 86.5 (12.0), PWB 21.0 (3.5), SFWB 24.3 (4.3), EWB 18.8 (3.7), and FWB 22.3 (4.9). In the VES-13 &gt;3 group, the mean (SD) baseline FACT-G total score was 70.4 (16.5), PWB 15.4 (5.6), SFWB 22.3 (5.5), EWB 17.5 (4.5), and FWB 15.3 (6.4); all significantly lower than the VES-13 &gt;3 group (all p&lt;0.01). In adults &lt;65, 63 (9%) had a VES-13 &gt;3, which was associated with a significant improvement from baseline in PWB at years 1 and 2 when compared to VES-13 &lt;3 (changes in baseline PWB were 2.1, 2.9 for VES-13 &gt;3 vs 0.2, 0.1 for VES-13 &lt;3, all p&lt;0.05). Similarly, we observed improvement in FWB from baseline at years 1 and 2 in the VES-13 &gt;3 vs &lt;3 group (2.4, 2.9 vs 0.4, -0.1, respectively all p&lt;0.05). The VES-13 &gt;3 group had a decline in their EWB at 1 year vs the VES &lt;3 group (-0.5 vs 1.2, respectively, p=0.003). At year 2, total FACT-G improved from baseline in the VES-13 &gt;3 group (5.6 for VES-13 &gt;3 vs -0.5 for VES-13 &lt;3, p=0.002). SFWB changes from baseline did not differ between groups. In adults &gt;65, 83 (17%) had a VES-13 &gt;3, which was associated with a significant improvement from baseline PWB (1.3, 1.5 for VES-13 &gt;3 vs. 0.1, -0.1 for VES-13 &lt;3), FWB (3.3, 2.2 for VES-13 &gt;3 vs. 0.5, 0.1 for VES-13 &lt;3), and total FACT-G score (5.2, 3.4 for VES-13 &gt;3 vs. 1.0, -0.4 for VES-13 &lt;3) when compared to VES-13 &lt;3 at years 1 and 2 (all p&lt;0.05). SFWB and EWB changes from baseline did not differ between groups. While there was improvement from baseline PWB and FWB in the VES-13 &gt;3 group, QOL scores across all subscales remained below the VES-13 &lt;3 group in the study period for both age groups. Statistical significance was maintained in models adjusted for baseline characteristics, including initial treatment strategy (observation, systemic, vs local) for both age groups. Conclusion: Although the vulnerable group reported worse QOL throughout the study period compared to the non-vulnerable group, survivors in the vulnerable group saw greater improvement in their physical and functional well-being at 1- and 2-years post-diagnosis. Additional analyses are underway to identify drivers of improvement in physical and functional well-being in vulnerable adults diagnosed with iNHL.

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&lt;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&lt;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.

Background: Anaplastic large cell lymphomas (ALCLs) represent a heterogeneous group of T-cell lymphomas that currently are classified by the presence or absence of ALK tyrosine kinase (TK) fusion genes (ALK+ or ALK−) and clinical presentation (systemic, cutaneous, or breast implant-associated). Two overarching molecular types of ALCL recently were discovered, defined by the presence (Type I) or absence (Type II) of a gene expression signature highly enriched for JAK-STAT3 activation. Fusions involving non-ALK TK genes occur in some ALK− cases, but the fusion landscape of ALCL remains incompletely characterized. Methods: Expert consensus pathology review was conducted in the Lymphoma/Leukemia Molecular Profiling Project (LLMPP). RNAseq was performed and fusions were identified using FusionCatcher. Here, we focused on recurrent in-frame coding fusions. Previously unreported fusions were validated by RT-PCR. Type I/II was assigned using a previously validated gene expression-based model. Genes with adjusted (adj) P&lt;0.05 were considered differentially expressed. Overall survival (OS) was assessed for systemic ALCL, when available. Results: We evaluated 379 ALCLs (229M/150F; mean age, 56 y). Of 199 candidate fusions (excluding reciprocal events), 28 were recurrent and passed quality metrics. At least 1 of these 28 fusions was present in 150 cases (40%). ALK fusions were present in 106 ALK+ ALCLs (all Type I; P&lt;0.0001). Of these, NPM1::ALK was seen in 68/106 (64%). Alternate partners (X::ALK) included ATIC (N=24) and CTLC, COL1A2, MSN, MYH9, RNF213, SATB1, TFH, TPM3, and TRAF1 (1-3 cases each). X::ALK was associated with older age (mean, 52 y) than NPM1::ALK (32 y; P&lt;0.0001) and showed relative overexpression of 49 genes, including multiple activators of small GTPases such as CGNL1 (FC, 5.6; Padj=2.4×10-6), SRGAP1, ALS2, DOCK1, and NCKAP1. ALK expression was similar in cases with NPM1::ALK and X::ALK and there was no significant difference in OS. Non-ALK TK fusions were seen in 17/273 ALK− cases (6%), including TYK2 (N=9); JAK2 (N=6); and ROS1 (N=2). All were Type I ALCLs (P&lt;0.0001). ALK− cases with non-ALK TK fusions overexpressed 38 genes compared to ALK− cases without TK fusions, including cytokines involved in the IL17 signaling pathway such as CXCL6 (FC, 12.5; Padj=4.2×10-3), CXCL1, and CSF3. We then assessed the relationship between ALK− cases with non-ALK TK fusions and ALK+ cases by separately comparing each subset to ALK− cases without TK fusions. FC values for ALK− cases with non-ALK TK fusions were significantly correlated with FC values for ALK+ ALCLs over the set of expressed genes (R=0.54; P&lt;0.0001). At a median follow-up of 32 months, 0/4 systemic ALCL patients with non-ALK TK fusions had died, while 30% of those with ALK− ALCL without TK fusions had died, but this was not statistically significant (P=0.26). The other 13 ALK− cases with non-ALK TK fusions were either localized ALCLs or did not have outcome data available. Of 20 TP63 fusions, 17 (85%) were TBL1XR1::TP63 and 3 were X::TP63. As previously reported, TP63 fusions were associated with poor OS (median OS, 10 months; not reached for other ALK− ALCLs; P=0.007). Four ALCLs had a novel NCL::UBTF fusion involving genes encoding the nucleolar proteins nucleolin and nucleolar (upstream binding) transcription factor-1. Four cases each had PARG::BMS1 and TNK1::GPS2 (the latter seen only in Type I ALCLs). Conclusions: This large consortium-based analysis of coding fusions further elucidates the molecular distinction between Type I and Type II ALCLs. Both ALK and non-ALK TK fusions were seen exclusively in Type I ALCLs, which we have shown previously are enriched for JAK-STAT3 pathway genes and correlate strongly with pSTAT3Y705 positivity by immunohistochemistry. These fusions were not identified in Type II ALCLs, which previous data suggest are associated with epigenetic alterations. ALK+ ALCLs with NPM1::ALK and X::ALK showed differences in gene expression, suggesting biological differences. Furthermore, ALCLs with non-ALK TK fusions shared gene expression features with ALK+ ALCL. Clinical fusion detection could enhance molecular classification of Type I and Type II ALCLs and may guide precision therapy.

Background: Although advanced stage cHL typically has excellent disease outcomes, 20-30% of patients (pts) experience treatment failure. In addition, decision-making is challenging given varying treatment options and incomplete prognostic data across the disease course. Pre-treatment factors, such as those incorporated into the Advanced Stage Hodgkin Lymphoma International Prognostication Index (A-HIPI), predict 5 year (y) overall survival (OS) and progression-free survival (PFS) (Rodday JCO 2023). Other than interim PET (iPET) results, it is unknown what factors influence cHL disease states mid-therapy. Using multistate modeling (MSM) and individual pt data from the HoLISTIC Consortium, we refined prognostication across the cHL disease course, specifically assessing the relationships between A-HIPI, iPET and end of treatment (EOT) response, and whether the A-HIPI and iPET provide independent prognostic information. Methods: We analyzed 1240 pts aged 18-65y with newly diagnosed Stage IIB, III or IV cHL treated on 2 advanced stage PET-adapted trials (RATHL, SWOG0816). All pts first received 2 cycles of ABVD; those with a negative iPET (Deauville score (DS) ≤3) received ABVD or AVD, and those with a positive iPET (DS &gt;3) received BEACOPP. In contrast to other methods, MSMs incorporate multiple disease states into one model, account for censoring and competing risks, and estimate transitions between disease states using different covariates. Our MSM had 6 states: diagnosis, negative iPET, positive iPET, EOT non-progression (e.g., DS ≤ 3), treatment failure, and death. Treatment failure included progression or relapse. All pts start at the diagnosis state and can transition to other states without return to prior states. Transition states with &lt;5 events were excluded. The MSM was estimated using a multivariable Cox model with censoring at 5y. Covariates were the 5y PFS A-HIPI (comprised of stage and continuous age, lymphocyte count and albumin), which was included for all transitions, and iPET, which was included for transitions from EOT non-progression and from treatment failure. Higher scores on the 5y PFS A-HIPI indicate lower risk of progression or death; the A-HIPI was modeled for a 1 standard deviation (SD) increase. We calculated probabilities of 5y treatment failure for 4 sample pts based on low (70) and high (85) A-HIPI scores, positive and negative iPET, and EOT non-progression. Results: Median age was 33y (Q1=25, Q3=45), 56% were male, 23% were stage IIB, 40% III and 37% IV, and mean A-HIPI was 77 (SD=7). Median follow-up was 75 months (Q1=53, Q3=92). At iPET, 80% (95% CI 78%, 83%) were negative and 16% (95% CI 14%, 18%) were positive. At EOT, 90% (95% CI 89%, 92%) were in the non-progression state. At 5y, 75% (95% CI 73%, 78%) were in the non-progression state, 16% (95% CI 14%, 19%) had experienced treatment failure, and 9% (7%, 11%) had died. Better A-HIPI scores (per SD) were associated with lower rates of transitioning from diagnosis to positive iPET (HR=0.80, p&lt;0.01) and from positive iPET to treatment failure (HR=0.58, p=0.04). Better A-HIPI scores (per SD) were also associated with lower rates of transitioning from EOT non-progression to treatment failure (HR=0.78, p&lt;0.01) and from treatment failure to death (HR=0.64, p&lt;0.01), adjusting for iPET. Positive iPET was associated with an increased rate of transitioning from EOT non-progression to treatment failure (HR=1.90, p&lt;0.01), adjusting for A-HIPI. The probability of 5y treatment failure for 4 sample pts was: 25% for low A-HIPI and positive iPET; 20% for high A-HIPI and positive iPET; 13% for low A-HIPI and negative iPET; and 12% for high A-HIPI and negative iPET. Conclusions: We created a novel MSM that refines prognostication across the cHL disease course by incorporating pre- and post-treatment factors (e.g., iPET) to estimate transitions to future disease states. Although the A-HIPI was developed using pre-treatment factors to predict 5y OS and PFS, we found that it was also associated with transitions to interim disease states, including iPET and EOT response. The A-HIPI and iPET each provided independent prognostic information, supporting the use of both in estimating cHL outcomes. Future analyses will develop clinical prediction models using MSMs that also incorporate varying frontline and salvage treatments and treatment-specific late effects, with the goal of providing individualized information across the disease course.