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Background: Financial toxicity (FT), or the negative impact of medical expenses experienced by patients, is a known adverse effect of cancer treatment that result in worse health outcomes for patients. However, there is limited literature investigating FT in lymphoma with even less data on predictors such as age, gender, and socioeconomic factors. As lymphoma treatment options expand, it is vital to understand the impact of these treatments on patients' financial well-being. We investigate FT through patient reported outcomes from the Lymphoma Epidemiology of Outcomes (LEO), a large multi-institution prospective cohort study of non-Hodgkin lymphoma (NHL). Methods: Patients with newly diagnosed NHL were enrolled into the LEO cohort from 2015-2020. Clinical, pathology and treatment data were abstracted from medical records, and participants were offered self-administered surveys at enrollment and selected follow-up time points. Participants were included if they completed at least four of six survey questions on FT adapted from the Medical Expenditure Panel Survey (MEPS) within the first year after diagnosis. Questions focused on either material hardship (unable to cover costs; file for bankruptcy; borrow money; work extra job or hours) or psychological stress (worried about paying large medical bills). We evaluated FT as answering positively to any question (any vs none). Associations with FT were evaluated using chi-squared tests. All analyses were performed using R (v4.2.2). Results: 3202 patients completed at least four survey questions relating to FT. Mean age at diagnosis was 62 years (18-94), 56% were male, and majority identified as white (90%). Characteristics associated with FT are reported (p<0.001). FT was more likely in younger patients: 18-25 years (64%, 30/47), 26-44 years (73%, 299/313), and 45-64 years (58%, 799/1375) as compared to those 65-79 years (36%, 459/1272) and ≥80 years (35%, 69/195). FT was highest in patients without a high school diploma (54%, 45/84), followed by patients with a high school diploma (52%, 209/402), and an associate's degree (57%, 358/627). It was lowest in patients with a bachelor's (45%, 224/296) or post-graduate degree (39.6%, 216/545). Higher FT was seen in those receiving disability income (65%, 47/72); whereas less FT was seen in retired patients (36%, 299/828). FT rates inversely correlated with income: 65% for income < $21,000 (184/283), 61% for $21,000- $39,999 (224/370) and 60% for $40,000-$65,999 (293/491). Lower FT noted for income range of $66,000-$106,000 (47%, 323/681) or > $106,000 (37%, 324/872). Patients separated from a partner (82.9%, 19/23) had the highest FT, followed by patients living with a partner (64%, 48/75), divorced (56%, 103/184), and never married (53.4%, 79/148). Patients who were married (47%, 793/1672) or widowed (41%, 60/146) reported the lowest FT. Higher FT was reported in DLBCL (55%, 631/1146), MCL (55%, 172/315), and TCL (53%, 119/226) when compared to FL (44%, 327/746), MZL (44%, 137/316), and other NHL (44%, 200/453). This is likely related to treatment as more FT was seen in patients who received an anthracycline and/or alkylator containing therapy (54%, 1066/1963) versus non-systemic (43%, 120/278) and observation (35%, 168/483). In multivariable analyses, FT strongly correlated with age at diagnosis, household income, and treatment type. The odds ratio and 95% confidence intervals for the most significant category within each variable are as follows: age per 1 year (0.96, [0.95-0.97]), income > $106,000 vs < $21,000 (0.28, [0.21-0.38]), and observation vs anthracycline and/or alkylator (0.56, [0.44-0.71]). Conclusion: Nearly 50% of patients with lymphoma experience FT, seen as material hardship and psychological stress, within the first year of diagnosis. Patients with a younger age (18-44), less education, and lower household income reported higher levels of FT; whereas those who were retired or married/widowed reported lower FT. Lymphoma subtypes that require more anthracycline and/or alkylator containing therapy imposed higher FT. Understanding these risk factors can help clinicians identify patients who may benefit from additional resources during lymphoma management.

Background: Indolent non-Hodgkin lymphomas (iNHL) are incurable and disproportionately affect adults age >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 >3 have a fourfold increased risk of death or functional decline over 2 years as compared to adults with a score <3 (Saliba et al, JAGS 2001). Our team previously showed that VES-13 >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 <65. In this study, we determined whether VES-13 scores >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 >3 (vulnerable) vs <3 (non-vulnerable), within groups by age at enrollment >65 and <65. Results: Our study included 1,186 participants: 42% age >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 <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 >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 >3 group (all p<0.01). In adults <65, 63 (9%) had a VES-13 >3, which was associated with a significant improvement from baseline in PWB at years 1 and 2 when compared to VES-13 <3 (changes in baseline PWB were 2.1, 2.9 for VES-13 >3 vs 0.2, 0.1 for VES-13 <3, all p<0.05). Similarly, we observed improvement in FWB from baseline at years 1 and 2 in the VES-13 >3 vs <3 group (2.4, 2.9 vs 0.4, -0.1, respectively all p<0.05). The VES-13 >3 group had a decline in their EWB at 1 year vs the VES <3 group (-0.5 vs 1.2, respectively, p=0.003). At year 2, total FACT-G improved from baseline in the VES-13 >3 group (5.6 for VES-13 >3 vs -0.5 for VES-13 <3, p=0.002). SFWB changes from baseline did not differ between groups. In adults >65, 83 (17%) had a VES-13 >3, which was associated with a significant improvement from baseline PWB (1.3, 1.5 for VES-13 >3 vs. 0.1, -0.1 for VES-13 <3), FWB (3.3, 2.2 for VES-13 >3 vs. 0.5, 0.1 for VES-13 <3), and total FACT-G score (5.2, 3.4 for VES-13 >3 vs. 1.0, -0.4 for VES-13 <3) when compared to VES-13 <3 at years 1 and 2 (all p<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 >3 group, QOL scores across all subscales remained below the VES-13 <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 Advancements in imaging techniques, diagnostic procedures, and increased routine health care visits are anticipated to lead to a higher number of patients with an incidental diagnosis of cancer, including the most common lymphoma DLBCL. In parallel, multiple cancer early detection (MCED) blood assays are emerging to screen for cancer in asymptomatic, average risk individuals. Assays that use methylated DNA markers (MDM) are of special interest and have been shown to detect evidence of DLBCL in the blood in 80% of DLBCL cases (Witzig TE et al ASCO 2022). While these MDMs are candidates for inclusion in MCED assays, the impact of early detection of lymphoma in general and DLBCL specifically remains controversial. Since the outcomes for patients with an incidental diagnosis of DLBCL have not been previously studied and to give further insight to the early detection question, we investigated clinical characteristics and outcome differences between patients with DLBCL found incidentally and those with symptoms or obvious signs of disease that led them to seek medical evaluation. Methods We conducted a retrospective analysis of newly diagnosed DLBCL patients enrolled into the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence from 2002-2015. We reviewed the medical records of the Mayo component to assign a mode of diagnosis (incidental vs symptomatic). We examined disease characteristics and impact on event-free survival (EFS) and overall (OS) survival according to the mode of diagnosis. EFS was the time from diagnosis to relapse, progression, retreatment, or death from any cause. OS was defined as the time from diagnosis to death from any cause. Results 1140 patients with DLBCL (90.6%), high grade B cell lymphoma (2.4%) or primary mediastinal B cell lymphoma (7.0%) were reviewed; 85 (7.5%) patients presented incidentally (incidental group), while 1055 (92.5%) presented with a lymphoma-related symptom or sign, e.g., palpation of a lump/mass, B symptoms and/or other symptoms caused by the lymphoma such as abdominal pain (symptomatic group). In the incidental group, 67.5% (65/85) of patients were diagnosed after an incidental finding on imaging or diagnostic/surgical procedure, 14.1% (12/85) were diagnosed after an incidental lab finding, and 9.4% (8/85) were diagnosed after a clinical health care professional incidentally identified a lump/mass. Compared to the symptomatic group, patients with incidental diagnosis were older at diagnosis (median age 66 vs 63 years, P=0.01) and more likely to present with an earlier stage I/II (62.4% vs 38.9%, P<0.001). Patients who presented with symptoms were more likely to have ≥ 1 extra-nodal site involvement (29.8% vs 15.5%, P=0.005), elevated LDH level (55.4% vs 40.8%, P=0.01), and high-risk IPI score (13.7% vs 5.9% P=0.03) compared to the incidental group. The median diagnosis to treatment interval (DTI) was significantly longer in the incidental group (21 days, IQR 12-34) compared to the symptomatic group (15 days, IQR 9-24) (P = 0.0014). The median follow-up time was 152.3 months in the incidental group and 123.7 months in the symptomatic group. We observed no difference in early failure rates as measured by failure to achieve event-free status at 24 months (EFS24) for incidentally vs symptomatic diagnosis (25.9% vs 31.9%, P=0.25). There was no difference in EFS between the two groups in univariate (HR=1.08, 95%CI=0.8-1.45) or multivariable analysis adjusted for IPI (aHR=0.97, 95%CI=0.72-1.31). There was also no difference in OS in univariate (HR=0.98, 95%CI=0.72-1.34) or multivariable analysis adjusted for IPI (aHR=0.87, 95%CI=0.64-1.19). Conclusion In this large cohort of aggressive B-cell lymphoma, incidental diagnosis occurred in only 7.5% of cases and these patients had earlier stage, lower IPI score, lower LDH levels and less extranodal involvement. These differences in clinical characteristics did not translate into EFS or OS differences. Our findings are the first to describe presentation and outcomes of incidentally diagnosed DLBCL and provide data regarding what investigators may find with MCED assays. Finding more early stage DLBCL patients with lower tumor burden may influence type of treatment, enable the use of fewer chemotherapy cycles, and avoid symptoms and complications of advanced stage disease such as tumor lysis syndrome and organ compression.

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.

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<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<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<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<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<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.

Introduction: Diffuse large B cell lymphoma (DLBCL) patients experience favorable outcomes following frontline immunochemotherapy, however, 30-40% of patients will exhibit primary refractory disease or relapse (rrDLBCL). Recent studies have expanded upon well-defined characteristics of newly diagnosed DLBCL (ndDLBCL) to elucidate genomic landscapes underlying rrDLBCL; yet, high-throughput examination of resistance and relapse mechanisms within rrDLBCL subtypes, including the newly defined dark zone signature (DZsig) addition to cell of origin (COO), have yet to be described. To expand upon the current tumor etiology and define subtype-specific driving mechanisms, we describe a multi-omic analysis leveraging clinical and biologic variables in rrDLBCL. Methods: Tumor biopsies (N=228) collected at the time of relapse were obtained from n=85 participants in the Mayo Clinic/University of Iowa Lymphoma Molecular Epidemiology Resource (MER), n=73 in the Mayo Clinic Biobank, and n=38 from CC-122-DLBCL-001 or n=32 CC-122-ST-001 trials. Tumor samples were analyzed by WES (n=158) or WGS (n=70) and by RNAseq (n=145). Comparative analysis against ndDLBCL MER samples (N=444) utilized WES (n=404) and RNAseq (n=321). Chi Square or Fisher's exact test was used for enrichment analysis of categorical variables, while Wilcoxon or Kruskal-Wallis test was used for continuous variables. Results: rrDLBCL samples were enriched for ABC (46% vs 33% in ndDLBCL, p=0.01), and fewer GCB (27% vs 40%, p=0.006). Amongst rrDLBCL patients, variance in overall survival (from time of original diagnosis; log-rank p=0.001) was observed between COO subtypes, with poor outcomes in patients with DZsig (median 15.0 mo) and GCB (36.5 mo) compared to ABC tumors (74.4 mo). Patients with progression or relapse within 6 months featured a more even COO distribution (ABC - 22%, GCB - 34%, DZsig - 28%) than patients with early (6-24 mo; 51%, 22%, 22%) or late relapses (>24 mo; 62%, 19%, 4%), which were predominantly ABC. Comparing mutation frequency within subtypes, rrDLBCL GCB tumors were enriched for B2M (41% vs 15% in ndDLBCL, p=0.002), EP300 (19% vs 7%, p=0.059), CIITA (19% vs 5%, p=0.02), PRKCB (11% vs 2%, p=0.035), CD79B (11% vs 0%, p=0.004), and CD83 (8% vs 1%, p=0.049) mutations, and CNV events including gains at 8q24.21 (MYC locus; 48% vs 14%, p<0.001) and 18q21.33 (BCL2, TCF4 locus; 36% vs 15%, p=0.021), and losses at 17p13.1 (TP53 locus; 40% vs 19%, p=0.035). rrDLBCL DZsig tumors featured enrichment for BCL6 (22% vs 3%, p=0.025), and SPEN (17% vs 3%, p=0.062) mutations and 2q22.2 losses (LRP1B locus; 20% vs 0%, p=0.019). Despite differential mutation and CNV profiles, gene expression analysis revealed minimal variance within COO subtypes in rrDLBCL and ndDLBCL, however upregulation of lymphoma driving genes including MYBPC2 (LogFC=1.68, FDR<0.001), MYOM2 (LogFC=1.33, FDR=0.007), MYC (LogFC=0.61, FDR=0.04), and CELSR2 (LogFC=0.53, FDR=0.02) were observed in ABC rrDLBCL. Increased frequency of LymphGen classifier A53 was observed in ABC (43% vs 17%, p=0.005) and GCB (40% vs 14%, p=0.009) rrDLBCL. Using CIBERSORTx to infer lymphoma microenvironment (LME) cell type abundance, an LME-depleted class was evident in rrDLBCL tumors, including reduction of CD8+ T cells (effect size (r)=-0.78, p<0.001) and T follicular helper cells (r=-0.48, p=0.012) in GCB rrDLBCL and reduction of T follicular helper cells (r=-0.43, p=0.023) and Tregs (r=-0.32, p=0.065) in DZsig rrDLBCL. Significance: We present an update to the genomic landscape of rrDLBCL, highlighting GCB and DZsig tumors with differential biology and advanced clinical course. Our data supports increased implementation of sequencing efforts in rrDLBCL to more broadly evaluate relapse mechanisms, with optimism that a more detailed understanding of r/r disease may guide trials for targeted treatment strategies. For example, rrDLBCL patients may be strong candidates for MYC pathway targeting agents (BETi), or a Lenalidomide-based regimen (R2 or Tafa-Len), as Lenalidomide has been shown to reprogram the LME via PD-L1 expression in addition to downregulation of MYC and MYC target genes. Or, due to enrichment of variants affecting anti-apoptotic proteins including BCL6 or BCL2 in DZsig and GCB rrDLBCL, a venetoclax-based therapeutic may be considered. Further targeted strategies should be considered as development of a more personalized approach remains an unmet need.

Background: Monoclonal B-cell lymphocytosis (MBL) is a common premalignant condition that can be classified into chronic lymphocytic leukemia (CLL)-type (CD5, CD23, and CD20 co-expression), and non-CLL-type (CD5-negative) based on flow cytometry immunophenotypes. CLL-type MBL is relatively well characterized with increased risk of hematologic (especially lymphoid) malignancies, serious infections, and decreased response to vaccinations. However, the prevalence, genetic predispositions, and clinical consequences of non-CLL-type MBL remain to be fully elucidated. Herein, we report findings in non-CLL-type MBL in a large screening cohort. Methods: We screened 10,330 individuals (40 years or older with no prior history of hematologic cancers) for MBL with highly sensitive flow cytometry, in the Mayo Clinic Biobank, with patients recruited from primary care-based clinics. Individuals were genotyped by Regeneron Genetics. Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI). Among those individuals residing in the 27 counties surrounding Rochester, Minnesota, we identified incident hematologic malignancies and melanomas using ICD codes and the Rochester Epidemiology Project, a population based medical records linkage system. All diagnoses were manually reviewed for confirmation. Cox regression was used to calculate hazard ratios (HR) and 95% CIs, adjusted for age and sex. Results: We identified 1,736 individuals with MBL and 8,341 individuals without MBL. Among those with MBL, 246 (14.2%) had non-CLL-type MBL as the major clone. The median age of non-CLL-type MBL was 77 years (range, 47-95). The prevalence of non-CLL-type MBL significantly increased with age, with 0.5% prevalence among those between 40 to 59 years of age, 2.3% prevalence among those between 60 and 79 years, and 7.8% prevalence in those 80 years of age or older. The prevalence of non-CLL-type MBL was significantly higher (P < .001) among males (3.1%) than females (2.0%). Using the genome-wide complex trait analysis tool, the heritability of non-CLL-type MBL was estimated to be ~26%, suggesting inherited genetics plays a role in etiology. To identify common inherited variants, we first evaluated the known CLL susceptibility variants, many of which also associated with CLL-type MBL. Among the 42 variants, none were statistically associated (all P > .05) with non-CLL-type MBL. We next conducted a genome-wide association study to identify novel variants, and the most significant variant resided in 10q26.3 (minor allele frequency [MAF]control = 0.7%, MAFMBL = 3.5%, OR = 6.4, 95% CI 3.6-11.3, P = 5.7 * 10-8). Among the 6,478 individuals (192 with non-CLL-type MBL and 6,286 without MBL) followed for incident hematologic malignancies, 36 developed lymphoid and 23 developed myeloid malignancies after a median of 4.2 years of follow up. Similar to CLL-type MBL, non-CLL-type MBL was associated with a 5.2-fold risk for lymphoid malignancies (95% CI 2.0-13.0, P < .001). Elevated but not significant risks were observed for myeloid malignancies (HR = 2.3, 95% CI 0.5-10.3, P = 0.27). The lymphoid malignancies developed by individuals with non-CLL-type MBL included diffuse large B-cell lymphoma, multiple myeloma, marginal zone lymphoma, and other low-grade lymphomas. We recently reported that individuals with CLL-type MBL are at higher risk for developing melanoma (HR = 1.9, 95% CI 1.3-2.8). Interestingly, non-CLL-type MBL had no evidence of an association with melanoma risk (HR = 0.4, 95% CI 0.1-1.8). We did not observe a significant difference in overall survival between individuals with non-CLL-type MBL vs those without MBL. Conclusions: In the largest screening cohort to date, we evaluated the etiology and clinical outcomes of non-CLL-type MBL, an understudied yet common condition that occurs in ~4 million adults in the United States. Non-CLL-type MBL is more common in men and older individuals, and evidence supports that inherited genetics plays an etiological role with 10q26.3 locus identified as a novel susceptibility locus. Individuals with non-CLL-type MBL had significantly increased risk of lymphoid malignancies, similar to CLL-type MBL, but, distinct from CLL-type MBL, no evidence of an increased risk of melanoma. Our study provides insights into the prevalence, genetic predispositions and clinical consequences of non-CLL-type MBL and how it differs from the classic CLL-type MBL.

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 >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 <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<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<0.01) and from treatment failure to death (HR=0.64, p<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<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.

Background: Immune dysfunction is a hallmark of chronic lymphocytic leukemia (CLL), and several studies suggest that hypogammaglobulinemia at the time of CLL diagnosis may predict shorter time to first therapy (TTFT; Parikh et al, 2015). However, it is unclear if hypogammaglobulinemia can predict TTFT independent of the CLL-International Prognostic Index (CLL-IPI) and the tumor mutational load (TML). We sought to determine if serum immunoglobulin (Ig) levels at diagnosis can independently predict outcomes in patients with CLL and its precursor, high-count monoclonal B-cell lymphocytosis (MBL). Methods: We used the Mayo Clinic CLL Database to identify newly diagnosed CLL/MBL consented within 2 years of diagnosis. Next generation sequencing (NGS) was performed using a 59 gene panel (SureSelect) to detect mutated CLL genes. Serum Ig levels were quantitated by radial immunodiffusion (Immunoplates). Serum Ig levels below the lower limit of normal were defined as hypogammaglobulinemia. We used Chi-squared or Fisher's exact tests to compare discrete variables and the Kruskal Wallis test for continuous variables. TTFT was calculated from date of diagnosis to date of first treatment or date last known to be untreated. We used Cox regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for TTFT and overall survival (OS) associations. Results: We identified 895 newly diagnosed patients (588 CLL and 307 MBL) who had undergone NGS for recurrent somatic mutations and had serum Ig levels available at the time of diagnosis. The median absolute B-cell count was 6.7 x109/L; 613 (69%) were male, 375 (42%) had unmutated IGHV genes, and 81 (9%) had TP53 disruption. Based on CLL-IPI, 412 (46%) patients had low risk, 285 (32%) had intermediate risk, and 198 (22%) had high/very high risk. The most commonly mutated genes were NOTCH1 (14%), SF3B1 (11%), TP53 (10%), NFKBIE (8%), ATM (6%), and BIRC3 (6%). Based on the number of genes with high impact or hotspot mutations, we classified 40%, 29%, and 31% patients with TML scores of 0, 1, or 2+, respectively. At CLL/MBL diagnosis, the median serum IgG, IgA, and IgM was 891 mg/dL (111-2620), 121 mg/dL (1-1150), and 44 mg/dL (5-1320), respectively; 284 (32%) patients had low serum IgG, 128 (17%) had low serum IgA, and 322 (42%) had low serum IgM. Low serum IgA was associated with mutations in NOTCH1, SF3B1, ATM, BIRC3 and NFKBIE, low IgM was associated with SF3B1 and BIRC3 mutations (all comparisons, P < .05), and low IgG was associated with BIRC3 mutation (P = .05). The median follow-up for the cohort was 8.4 years. A total of 359 patients progressed requiring therapy (299 CLL and 60 MBL), and 265 patients died (177 CLL and 88 MBL). The TTFT was significantly shorter for patients with low serum IgG compared to normal serum IgG at diagnosis (median 4.4 vs 10.8 years) and similar findings were observed in individuals with low serum IgA (1.7 vs 10.8 years) and those with low serum IgM (4.6 vs 11.1 years). On univariable analysis, the following were associated with a shorter TTFT: low serum IgG (HR: 1.8, 95% CI 1.4-2.2), low serum IgA (HR: 3.5, 95% CI 2.7-4.4), low serum IgM (HR: 1.9, 95% CI 1.5-2.3), TML score of 1 (HR: 1.7, 95% CI 1.3-2.2), TML of 2+ (HR: 4.4, 95% CI 3.4-5.7), CLL-IPI intermediate risk (HR: 3.7, 95% CI 2.8-4.8), and CLL-IPI high/very high risk (HR: 7.6, 95% CI 5.8-10.1) (all comparisons, P < .0001). In multivariable analyses after adjusting for CLL-IPI and TML, low serum levels of IgG (HR: 1.8, 95% CI: 1.5-2.3), IgA (HR: 2.4, 95% CI: 1.8-3.0), and IgM (HR: 1.8, 95% CI: 1.5-2.3) were independently associated with a shorter TTFT (all comparisons, P < .0001). No significant differences in OS were detected between patients with low and normal serum IgG, IgM, and IgA on univariate analysis. Similar results for TTFT and OS were observed after adding clonal B-cell count to the Cox model. Conclusions: Our study shows that low serum Ig levels at the time of initial diagnosis is predictive of TTFT independently of the CLL-IPI and TML, which can further risk stratify individuals with newly diagnosed CLL/MBL. Additionally, low serum IgA, IgM, and IgG levels at diagnosis are significantly associated with specific somatic mutations in individuals with newly diagnosed CLL/MBL.

Background: Approximately20% of patients with follicular lymphoma (FL) will experience disease progression or death within two years of years (POD24) of immunochemotherapy (IC) (Casulo et al, JCO 2015). A proportion of patients with POD24 have histologic transformation (HT), which heightens the risk of lymphoma-related death (Freeman et al, Blood 2019). To clarify the impact of HT on prognosis in POD24, we analyzed data from the Lymphoma Epidemiology of Outcomes (LEO) Consortium of Real-World Evidence (CReWE) to compare differences in outcomes among patients with POD24 with retained FL histology versus POD24 with HT. Methods: This was a multicenter study among 8 academic cancer centers. Eligible patients hadgrade 1-3a FL at diagnosis between 2003 and 2022, with POD24 following first line IC treatment with BR, R-CHOP, or R-CVP. POD24 was defined as disease recurrence within 24 months of IC. Patients with prior lines of treatment for FL were excluded. Patients with HT at first line IC were excluded. Biopsy at POD24 was obtained at the discretion of the treating physician. Three groups of POD24 were defined: POD24 with biopsy-proven retained FL histology (POD24_FL), POD24 with biopsy-proven HT to aggressive lymphoma (POD24_HT), and POD24 either without a biopsy or indeterminate histology on biopsy (POD24_I). We evaluated second-line treatments at time of POD24. Survival probabilities from second-line treatment initiation were estimated by the Kaplan-Meier method. Results A total of1,122 patients with FL were screened and308 patients with POD24 were identified.Median age at POD24 was 58 years (IQR 50-65).Among available data, race and ethnicity was reported as 76% non-Hispanic White, 3% non-Hispanic Black/African American, 9% Hispanic/Latino (any race), and 1% other minorities. First line therapy was R-CHOP in45% (140/308), BR in 33% (102/308) and R-CVP in 21% (66/308). Most patients (52%) had biopsy-proven retained FL at POD24 (162/308); 30% had no biopsy or indeterminate histology at POD24 (94/308), and 17% had biopsy-proven HT at POD24 (52/308). In the POD24_HT group, 52% received R-CHOP and 21% received intensive salvage chemotherapy as second line therapy. Patients with POD24_FL and POD24_I had similar rates of salvage chemotherapy (21% and 18%), BR (13% and 18%), R-CHOP (19% and 24%), and targeted therapies (8% and 5%), respectively, at second line. Consolidative autologous stem cell transplant was more common in POD24_HT patients at 21% versus 16% in POD24_FL and 6% in POD24_I. Patients with POD24_HT had significantly worse overall survival (OS) compared to POD24_FL (ref), with a hazard ratio of 3.79 (95% confidence interval [CI] 2.46-5.84), even after adjusting for age, FLIPI score, and first-line IC treatment. At 5 years, patients with POD24_HT had OS of 31% compared with an OS of 71% for patients with POD24_FL. Patients with POD24_I had similar outcomes to those with POD24_FL (5-year OS 75% versus 71%, respectively). Event-free survival (EFS) was also significantly lower in POD24_HT compared to POD24_FL: At 2 years, EFS was 27% for POD24_FL (21-35%) and 10% for POD24 _HT (4-23%). At 5 years, lymphoma remained the leading cause of death among patients with POD24, with 20% probability of lymphoma-related death in those with POD24_FL (95% CI 14.2-27.8) compared to 53% probability of lymphoma-related death in patients with POD24_HT (95% CI 40.6-70.7). Conclusions: In our multicenter cohort, we identified remarkable differences between the two biopsy-demonstrated entities of POD24 (i.e., with and without HT). Patients without a biopsy or indeterminate histology had similar outcomes as those with POD24_FL. Patients withearlyrelapse of FL after first-line IC remain a heterogeneous group with poor outcomes and a heightened risk of lymphoma-related mortality particularly compared with nonPOD24 groups. Even with retained FL histology, patients with POD24 had 20% probability of lymphoma-related death within 5 years of second-line treatment initiation. We demonstrate that poor survival is driven in part, but not entirely, by HT. Despite most patients receiving appropriate aggressive treatment, those with POD24_HT represent a distinctly vulnerable population warranting further study in directed prospective studies of both FL biology and clinical trials.