Maurer, Matthew J.

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: 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: 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.

Background: Due to recent advances, patients with follicular lymphoma (FL) have multiple treatment options with high response rates and durability. However, there is a gap in understanding how patient preferences influence treatment selection. To close this gap, we conducted a qualitative study using focus groups of adults with FL to understand their knowledge of treatment options, priorities, and decision-making process. We designed this study to inform the content of a planned discrete choice experiment in a larger population of patients with FL to better understand patient preferences and trade-offs around treatment delivery, side effects, outcomes, and quality of life. Methods: We conducted focus groups lasting 60-90 minutes, each with 3-5 participants recruited from the Lymphoma Epidemiology of Outcomes (LEO) Cohort Study. The LEO Cohort Study is a multi-institutional prospective cohort study of newly diagnosed adults with non-Hodgkin lymphoma from July 2015 to present. Each focus group was co-led by two investigators (U.D. and M.T.) and conducted using Zoom video technology. Participants were asked to comment on their understanding about FL and available treatments, their ideal treatment, their decision-making process about prior therapies, and quality of life and side effects associated with treatment. We performed exploratory content analysis of participant understanding of FL and their treatment preferences. We will continue to conduct focus groups until we reach thematic saturation, which we anticipate will occur with one additional focus group. Themes from all focus groups were identified and agreed upon by both investigators (U.D. and M.T.) and were verified by additional investigators (J.D., M.L, M.H., B.K.L, C.C., M.M.). Results: To date, we conducted five focus groups with a total of N=21 participants (median age 60 [range 41-88], 66.7% female, 71.4% non-Hispanic White, 14.3% Hispanic, 9.5% Asian, 4.8% Black/Hispanic). We identified the following themes and representative quotes: 1) Holistic approach:Participants wanted their oncologists to view their care from a holistic perspective, discussing their individual priorities and options that would accommodate their fitness routine and ability to work. “[I wish] if doctors can consider not just the physical choices, but also the mental and emotional choices as well, the holistic person.” - Participant 13 “There's zero chance I can go back to working in the type of career where other people rely on me for things. I can't keep things straight.” - Participant 16 2) Ideal treatment:An ideal treatment would limit impact on fatigue, cognitive function, and have minimal toxicity to preserve quality of life and an active lifestyle. “I would say something that has minimal impact on daily life would be ideal and something that would make you feel better.” - Participant 6 3) Shared decision-making: Participants wanted to be better informed about available treatment options and often utilized second opinions to inform their decisions. “It helped to get the second opinion versus not. It would've been a completely different set of events.” - Participant 17 4) Treatment burden:Financial impact and effort required by patients to receive care (e.g., travel, number of office visits, and duration of therapy) were key factors in patient decision-making. “Part of my quality of life went down because I had to fight with my insurance company consistently, and we pay a lot of money.” - Participant 11 “Really explain the time options and calendarize the treatment and be more specific about the amount of time that you're going to spend at the hospital and the process.” - Participant 20 5) Representation: Minority participants raised concerns about not knowing if the available treatment data pertained to them. “[My end-goal would be] having an understanding of how the treatment is going to be for minority people. Many times the treatments are tested, they are not tested for minorities.” - Participant 21 Conclusions: The patient perspective is critical for treatment decision-making and education. People living with FL expressed a desire for a holistic and individualized approach to treatment decision-making that incorporated their lifestyle, minimized side effects, and limited overall treatment burden. These focus groups identified emerging themes that warrant further investigation for survivors with FL.

ABSTRACTDiffuse large B‐cell lymphoma (DLBCL) patients that fail to achieve a complete metabolic response with frontline immunochemotherapy have a poor prognosis. Genomic profiling has led to a broader understanding of the molecular drivers in DLBCL, but it is unknown how well current classifiers identify patients that will experience primary treatment resistance (PTR). Using whole exome and RNA sequencing data from newly diagnosed DLBCL patients, we evaluated the genomic landscape of PTR and compared it to that of non‐PTR DLBCL. We found a significant increase in the frequency of TP53 (34% vs. 15%, p = 0.005) and ARID1A mutations (21% vs. 7%, p = 0.007) in PTR cases, with pathway analysis further demonstrating a downregulation of TP53 and an increase in chromatin modifying pathways. These results suggest that TP53 and ARID1A may be key mediators of PTR and important pathways contributing to the poor outcomes. We found that the current molecular classifiers were unable to identify PTR cases at diagnosis. However, our newly identified high‐risk signature identified 46% of PTR cases at diagnosis. Overall, these results contribute to our understanding of the genomic landscape of patients with primary treatment resistance.

Abstract Progression of disease within 24 months of initial immunochemotherapy (POD24) is a negative prognostic factor for patients with follicular lymphoma (FL). There is no standard treatment after POD24. Assembling an academic-based cohort from the Lymphoma Epidemiology of Outcomes Consortium for Real World Evidence, we evaluated patterns of care and outcomes for 220 patients with FL with POD24 and retained FL histology. Therapy after POD24 was heterogeneous, with no treatment category accounting for >25% of the total. Among patients initially treated with bendamustine-rituximab, rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone (R-CHOP) was the predominant second-line choice (48%). Among patients initially treated with R-CHOP, aggressive salvage therapy was the predominant second-line choice (38%). Overall response rate to therapy after POD24 was 64% (95% confidence interval [CI], 56-70); complete response rate was 39% (95% CI, 32-46). The median event-free survival for therapy after POD24 was 9.8 months (95% CI, 7.3-12.1); 5-year overall survival (OS) was 71% (95% CI, 65-78). OS was inferior for patients aged >70 years (hazard ratio [HR], 2.31; 95% CI, 1.27-4.20) and those with high-risk FL International Prognostic Index scores at diagnosis (HR, 2.10; 95% CI, 1.23-3.60). No treatment category stood out with more favorable results. Cause of death was predominantly lymphoma related. Patients with follicular histology at their POD24 event had a low cumulative incidence of transformation (1.1% at 5 years). Our study is among the largest cohorts describing contemporary patterns of care for patients with POD24, providing a focused data set useful for interpreting and designing prospective clinical trials in this population.

Abstract Background/Introduction Patients with ATTR-CM can have lower circulating TTR levels, which is associated with worsening of cardiac function and increased risk of cardiovascular mortality.[1] Acoramidis has been shown to reduce cardiovascular mortality and hospitalization, while increasing circulating TTR levels, in the ATTRibute-CM phase 3 study.[2] Purpose I. Evaluate serum TTR levels in participants (pts) receiving acoramidis vs those who received tafamidis in the placebo group in ATTRibute-CM at Month 30. II. Evaluate serum TTR levels in pts who transitioned from placebo+tafamidis to acoramidis in the open-label long term extension (OLE) study. Methods ATTRibute-CM study: Eligible pts with ATTR-CM were randomized in a 2:1 ratio to receive acoramidis or placebo for 30 months. Pts in both groups had the option of initiating open-label, commercially available tafamidis after 12 months in the study. Upon completion of Month 30, pts were invited to participate in the OLE study. In OLE, pts who had previously received acoramidis continued acoramidis, and those who had previously received placebo+tafamidis were switched to acoramidis-only treatment. All pts in the OLE study received acoramidis only. Change in serum TTR levels in ATTRibute-CM and OLE at data cutoff (Oct 9, 2023) are summarised using descriptive statistics. Results In the phase 3 study, efficacy analyses were performed in 611 pts, of which 61 of 409 (14.9%) and 46 of 202 (22.8%) received tafamidis in acoramidis and placebo groups, respectively. The median time until tafamidis initiation was 17.8 and 16.1 months, respectively; median exposure duration was 11.6 and 10.5 months, for acoramidis and placebo groups, respectively.[3] Change from baseline (CFB) in serum TTR levels remained unchanged at Month 30 in the placebo-only group (mean [SD] = –0.4 [4.92] mg/dL). Characteristics of pts analysed are described in Fig. 1. A total of 380/611 pts (62.2%) entered the OLE study (229 pts continued acoramidis only, and 23 switched from placebo+tafamidis to acoramidis). At the time of data cutoff, 214 pts who had received acoramidis only completed Month 6 in OLE versus 18 who switched from placebo+tafamidis to acoramidis. At Month 30 in ATTRibute-CM, CFB in serum TTR levels were higher for patients who received acoramidis only than those receiving placebo+tafamidis (Fig 2A). Serum TTR levels increased further in pts who switched from placebo+tafamidis to acoramidis in OLE (Fig. 2B). The OLE study is ongoing, and this analysis includes pts who completed the Month 6 visit at time of data-cut. Conclusion(s) CFB in serum TTR levels were higher in pts receiving acoramidis only than those receiving placebo+tafamidis at Month 30 in the double-blind study. A further significant increase in serum TTR levels was observed in those who received placebo+tafamidis and switched to acoramidis in OLE.