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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.

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: Anaplastic large cell lymphomas (ALCLs) are CD30-positive T-cell lymphomas that share pathologic features but differ in clinical presentation, outcome, and molecular features. The World Health Organization (WHO) and International Consensus Classification (ICC) classify ALCLs by presence or absence of ALK rearrangements (R) and clinical presentation (systemic, cutaneous [c], or breast implant-associated [BIA]). ICC, but not WHO, recognizes DUSP22-R as defining a new genetic subtype of ALK- ALCL. The classifications otherwise do not reflect additional molecular heterogeneity in genetics (e.g., TP63-R) or therapeutic vulnerabilities (e.g., JAK-STAT3 pathway activation). Methods: ALCLs (N=689) underwent expert consensus review (WHO/ICC) through the Lymphoma/Leukemia Molecular Profiling Project (LLMPP). All cases also underwent genetic subtyping (ALK, DUSP22, TP63, and triple-negative [TN]) using fluorescence in situ hybridization (FISH) and immunohistochemistry (IHC), as well as IHC for phospho-STAT3 Tyr705 (pSTAT3). RNAseq was performed and evaluable in 393 cases; the remaining cases had insufficient tissue, tumor content, RNA yield or quality, and/or sequencing data quality. Sequenced and non-sequenced sub-cohorts had similar demographics and subtype distribution. Results: Unsupervised gene expression profiling (GEP) identified 2 main molecular types of ALCL. Type I ALCLs predominantly included ALK+ ALCLs, BIA-ALCLs, and a subset of TN ALCLs (designated TN-I), whereas Type II ALCLs predominantly included ALCLs with DUSP22-R, TP63-R, or both (double-hit; DH), and the remaining TN ALCLs (TN-II). Type I ALCLs were strongly associated with pSTAT3 expression (74.2±26.4% positive malignant cells vs 9.9±22.7% for Type II; P<0.0001). An independently derived pSTAT3 staining threshold of 30% assigned Type I vs II with 91% accuracy. A third cluster of ALCLs, mostly cALCL, showed an epithelial GEP signature rather than a lymphoma signature; these cases were assigned to Types I or II based on pSTAT3 IHC. Distinct sub-signatures were identified for ALK+, DUSP22-R, TP63-R, and BIA ALCL (Fig. 1), but not ALK- ALCL or cALCL. Gene set enrichment analysis showed Type I ALCLs to be enriched for JAK-STAT3 signaling genes (normalized enrichment score [NES], 2.22; FDR<0.0001) and related pathways, such as TNFα-NFκB signaling (NES, 2.21; FDR<0.0001). In contrast, Type II ALCLs were enriched for cell cycle, DNA repair, epigenetic, and metabolic pathway genes, but not for major tyrosine kinase-mediated signaling pathway genes. Enriched epigenetic pathways included chromatin modifying enzymes (NES, -1.86; FDR=0.002) and histone methylation (NES, -1.71; FDR=0.002). EZH2 was the most overexpressed gene in Type II ALCLs (fold-change, 7.74; FDR=8.84×10 -306). At the protein level, EZH2 IHC H-scores were 275±45 in Type II and 168±70 in Type I ALCLs (P<0.0001); H3K27me3 H-scores were 170±81 and 76±63, respectively (P<0.001). The top metabolic gene set involved cholesterol biosynthesis (NES, -1.88; FDR=0.002). Overall survival (OS) data were available in 257 systemic ALCL patients (145 sequenced and 112 non-sequenced; cALCL and BIA-ALCL were excluded). Non-sequenced TN ALCLs were assigned to TN-I or TN-II using pSTAT3 IHC. The results supported earlier data indicating favorable prognosis of DUSP22-R ALCL (5 y OS, 95%) and ALK+ ALCL (87%), intermediate prognosis of TN ALCL (TN-I, 52% and TN-II, 38%; P=NS), and poor prognosis of TP63-R/DH ALCL (0%)(P<0.0001; Fig. 2). Conclusions: Two overarching molecular types of ALCL exist, predominantly associated with the presence (Type I) or absence (Type II) of the JAK-STAT3 signaling program. Distinct GEP signatures exist for ALK+ ALCL and BIA-ALCL (predominantly Type I), and DUSP22-R ALCL and TP63-R ALCL (predominantly Type II). TN ALCLs lacking ALK-R, DUSP22-R, and TP63-R can be stratified into TN-I and TN-II subtypes. pSTAT3 IHC has >90% accuracy as a surrogate for GEP-based subtyping. ALK- ALCL and cALCL cluster by molecular subtype rather than by defining GEP signatures. Type II ALCLs are enriched for targetable epigenetic and metabolic pathways, including EZH2/histone methylation and cholesterol biosynthesis. This molecular classification is diagnostically, prognostically, and potentially therapeutically relevant, and can be applied using FISH and IHC in routine practice and in the clinical trial setting.

Background: QOL can be affected by disease burden, side effects of treatment, and psychosocial effects of living with cancer. Although MZL is considered incurable, we previously found that patients diagnosed from 2002-2015 had a good QOL both at baseline (BL) and the first decade after diagnosis, regardless of treatment choice (Bommier et al., submitted). Here, we evaluate whether these findings can be extended to the 2015-2020 treatment era and a more diverse US cohort. Methods: Adults with newly diagnosed MZL from 8 academic centers were prospectively enrolled within 6 months of diagnosis from 2015-2020 in the LEO cohort (NCT02736357). Participants were actively followed for disease progression/relapse, retreatment and death. QOL was measured at BL, and at 1, 2, 3 and 5 years after diagnosis using the Functional Assessment of Cancer Therapy-Lymphoma (FACT-Lym) scale (range 0-164), which consists of the FACT-General (FACT-G, range 0-104) plus a lymphoma-specific scale (range 0-60). Total FACT-G measures 4 QOL domains (range): physical (0-28), social/family (0-28), emotional (0-24), and functional (0-28) well-being (WB). We also used the Trial Outcome Index (TOI; range 0-112), which consists of FACT-G physical and functional WB plus the lymphoma-specific scale and is commonly used as an endpoint in treatment trials. All MZL patients with both BL and at least one follow-up QOL questionnaire were eligible for analysis. Patients were grouped by initial management: active surveillance (observation), local/antibiotic therapy (radiation, surgery, or anti- H. pylori therapy), or systemic therapy (chemotherapy, targeted agents, or antibody therapy). Change in QOL scores over time was analyzed using mixed models for repeated measurements, and initial treatment groups were compared. Results: Of 384 patients, 60% were female, 91% were White, 6.6% were Black/African American, and 13% were Hispanic (all races). The median age was 64 years (range 19-94). For initial management, 28% (N=106) were observed, 31% (N=118) received local/antibiotic therapy, and 42% (N=160) received systemic therapy. Across treatment groups, there were no significant differences by age, gender, race, or performance status, while there were differences by Hispanic ethnicity (p=0.006), Ann Arbor stage (p<0.001) and MALT-IPI score (p<0.001) (Table). At BL, patients receiving systemic therapy had lower physical WB (p=0.008), emotional WB (p=0.04), functional WB (p=0.03), FACT-G total score (p=0.008), lymphoma subscale (p<0.001), FACT-Lym (p=0.001) and TOI (p<0.001) compared to observation and local/antibiotic therapy (Table). While FACT-G total scores were largely stable through 5 years after diagnosis, social/family WB declined from BL for patients initially receiving either local/antibiotic therapy (mean at 1-year -1.4, p<0.05; 2-year -2.1, p<0.05; 3-year -1.6, p<0.05; 5-year-2.9, p<0.05) or systemic therapy (mean at 2-year -1.2 , p<0.05; 3-year -1.4, p<0.05; and 5-year -1.2, p<0.05), while this was stable for observed patients. In patients receiving initial systemic therapy, there was a transient improvement in the lymphoma-specific scale (mean +1.7, p<0.05) and TOI (mean +3.7, p<0.05). Otherwise, QOL remained stable over time and global QoL (FACT-G, FACT-Lym), TOI, Lymphoma-specific scale (Figure), and the other FACT-G subscales (physical, emotional, or functional WB) did not differ by initial treatment strategy. Conclusions: In the diverse LEO cohort from the recent treatment era, we confirm our prior findings for newly diagnosed MZL patients that QOL is globally stable over the first 5 years after diagnosis; emotional WB is not associated with initial treatment type, providing evidence that being initially observed does not negatively impact emotional health; and there is a decline in social/family WB (support from friends and family, family acceptance of illness, family communication, close to partner), which supports the need to consider interventions for patients to address these unmet needs. In addition, this study extends these findings to the lymphoma-specific scale, FACT-Lym, and TOI, which all showing largely stable trends in the 5 years after diagnosis and suggest that recent MZL treatment strategies, including systemic therapies, neither impair nor improve QOL.

Background Peripheral T-cell lymphomas (PTCLs) comprise a rare, heterogeneous group of non-Hodgkin lymphomas of mature T-cell origin. Most systemic (s) PTCL subtypes typically have poor outcomes with conventional anthracycline-based chemotherapy. Biomarker-driven and subtype-specific treatments are promising but unmet needs. Few benchmark studies exist to characterize the current real-world landscape of clinical and pathologic practice. We report the patterns of care and updated outcome data for PTCL patients enrolled in the LEO-MER multi-center prospective cohort study (ClinicalTrials.gov NCT02736357). Methods Patients aged 18 years or older with newly diagnosed PTCL were prospectively enrolled in the University of Iowa/Mayo Clinic MER cohort (2002-2015) or the expanded LEO cohort (2015-2020). Clinical, pathology, treatment, and outcome data were abstracted from medical records using a standard protocol in both cohorts. Pathology underwent expert re-review based on WHO criteria. Overall survival (OS) was calculated from date of diagnosis to date of death or last follow-up. Event-free survival (EFS) was calculated from date of diagnosis to disease progression, initiation of 2 nd line therapy, or death from any cause. EFS and OS were evaluated using Cox model and Kaplan-Meier estimator. Log-rank test was used to test the significance of difference between groups. Results LEO-MER enrolled 1132 PTCL patients during the study period (462 MER and 670 LEO). Of these, 722 were sPTCLs (281 MER and 441 LEO), including PTCL-NOS (N=253, 35%), anaplastic large cell lymphoma (ALCL; N=180, 24.9% [74 ALK+, 106 ALK-]), angioimmunoblastic T-cell lymphoma (AITL, N=164, 22.7%), extranodal NK-TCL (N=50, 6.9%), adult T-cell leukemia/lymphoma (N=33, 4.6%), enteropathy-associated TCL (N=19, 2.6%), and hepatosplenic TCL (N=13, 1.8%) ( Table 1). Median age at diagnosis was 60 years. M:F ratio was 1.5. Comparing LEO to MER, African American population was 15.4% vs 0.7%, and Hispanic was 10% vs 2.2%. Clinical characteristics were comparable between MER and LEO overall: PS≥2, 20.9%; elevated LDH, 50.7%; stage III-IV, 67.6%; and IPI 2-5, 65.7%. The most common 1 st line chemotherapy regimens overall were CHOP-based (N=506, 70%), including 60% receiving CHOP-like chemotherapy in both MER and LEO cohorts (CHOP [N=268, 37%], CHOEP [N=91, 13%] or EPOCH [N=75, 10%]), and 16% with CHOP-like in combination with novel agents in LEO cohort (BV+ [N=41, 9.3%], azacitidine+ [N=11, 2.5%], pralatrexate+ [N=10, 2.3%], lenalidomide+ [N=8, 1.9%]) ( Table 1). More patients received etoposide in LEO (40.3%) than in MER (20.3%). 74 patients (10.3%) underwent consolidative SCT following induction chemotherapy (71 auto and 3 allo). 65 patients (11.2%) received frontline therapy on clinical trials. At a median follow-up of 9.4 years in MER and 4 years in LEO, 177 (MER) and 269 (LEO) events, and 150 (MER) and 206 (LEO) deaths were observed, respectively. The 3-year EFS and 4-year OS estimates for sPTCL were 46.3% and 59.1% in MER, and 40.4% and 51.3% in LEO. EFS and OS correlated with IPI scores and differed by sPTCL subtypes ( Figure 1). ALCL had superior survival, with 3-year EFS and 4-year OS of 78.2% and 90.5% for ALK+, and 65.9% and 76.9% for ALK-. Non-ALCL subtypes including PTCL-NOS and AITL had inferior outcomes, with 3-year EFS and 4-year OS of 31.4% and 42.8% for PTCL-NOS, and 33.4% and 50.7% for AITL. In subset analysis, the addition of etoposide to CHOP chemotherapy as either CHOEP or EPOCH was associated with improved OS for ALK- ALCL (p=0.038, log-rank), but not for ALK+ ALCL (p=0.75) or non-ALCL subtypes of PTCL-NOS and AITL (p=0.88). Conclusion The LEO-MER study is the largest prospective cohort study of PTCL to date. Patterns of care in the LEO cohort begin to incorporate novel agents in frontline therapy. Outcomes continue to mature with longitudinal follow-up and ongoing accrual, which poise to shape benchmarks in the contemporary era. The lack of benefit of etoposide adding to CHOP induction and poor overall survival of non-ALCL subtypes underscores the unmet need of therapeutic breakthrough for non-ALCL frontline treatment, particularly through clinical trials with biomarker-guided approaches incorporating novel agents.

Background: Marginal Zone Lymphoma (MZL) is considered indolent but incurable, with a variety of initial treatment strategies including observation. Quality of life (QOL) can be affected by disease burden, side effects of treatment, and psychosocial effects of living with an incurable cancer. However, little is known about long-term QOL in patients with MZL. We investigated QOL at baseline (BL), and up to nine years after a diagnosis of MZL. Methods: Newly diagnosed MZL patients aged 18 years and older were prospectively enrolled within 9 months of diagnosis in the Iowa/Mayo Clinic Molecular Epidemiology Resource from 9/2002 - 6/2015. Pathology was reviewed by a hematopathologist. All MZL participants with both BL and at least one follow-up (FU) QOL questionnaire were eligible for this analysis. QOL was measured at BL and years 1-, 2-, 3-, 6- and 9-year FU with the Functional Assessment of Cancer Therapy-General (FACT-G) scale (range 0-108), which measures 4 QOL domains (range): physical (0-28), social/family (0-28), emotional (0-24), and functional (0-28) well-being (WB). Clinical data were abstracted according to a standard protocol and patients were systematically followed for events (progression, re-treatment, and death). Patients were grouped into initial management with active surveillance (observation), local/antibiotic therapy (including radiation, surgery, and anti- H. pylori therapy), or systemic therapy (including chemotherapy, targeted agents, or antibody therapy). Change in QOL scores over time was analyzed using mixed models for repeated measurements, and comparisons were made according to initial treatment group. Results: Of 324 participants in the analysis, 59% were female, 98% were White and the median age was 63 years. For initial management, 36% (N=118) were observed, 30% (N=96) received local/antibiotic therapy and 34% (N=110) received systemic therapy. Across the initial treatment groups, there were no statistically significant differences by age, gender, or performance status, while there were differences for Ann Arbor Stage IV (observation 50%, local/antibiotic 27%, systemic 67%, p<0.001) and MALT-IPI score 2+ (observation 23%, local/antibiotic 18%, systemic 28%, p<0.001). At BL, mean FACT-G total score was 85.7 (SD 13.1), and physical, social/family, emotional, and functional WB scores were 20.3 (SD 4.2), 24.4 (SD 3.9), 18.9 (SD 3.5), 22.1 (SD 5.3); these scores were similar across initial treatment groups (all p>0.05). FACT-G total score was largely stable over time by treatment group (Panel 1). However, it declined at years 6 (mean -4.0, p<0.05) and 9 (mean -3.7, p<0.05) in the observation group (Panel 2), driven by a decline in functional WB, although this change was not significantly different from the other treatment groups. Further, in comparison with other treatment groups, local therapy was associated with a short-term 1-year improvement of FACT-G total score (mean +2.8, p=0.04), driven by improvements in both functional and emotional WB. For the subscales, we identified two major trends irrespective of treatment group: an overall improvement from BL of emotional WB at years 1 (mean +0.6, p<0.05), 2 (mean +0.7, p<0.05), 3 (+1.3, p<0.05), 6 (+1, p<0.05), and 9 (+1.3, p<0.05) and an overall decline of social/family WB at years 1 (mean -1.1, p<0.05), 2 (mean -1.7, p<0.05), 3 (-2.1, p<0.05), 6 (-2.7, p<0.05), and 9 (-2.2, p<0.05). There was no notable change from BL for physical or functional WB. Conclusions: This analysis is the first to provide prospective real-world QOL data on a longitudinal cohort of MZL patients with long follow-up. At BL, initial treatment was associated with stage and MALT-IPI score, but not QOL. Except for the transient 1-year QOL improvement in patients under local/antibiotic therapy, initial treatment groups had no impact on QOL over time. Irrespective of treatment group, emotional WB showed improvement over time, suggesting psychosocial adaptation by the patient, as it was found in aggressive lymphomas (Thompson et al., 2018). Further, emotional WB over time did not vary across treatment groups, suggesting that being initially observed did not lead to more emotional stress than being actively treated. Finally, decline in social/family WB (support from friends, family, family acceptance of illness, family communication, close to partner) suggests a need to consider interventions to patients and families to address these needs.

Background: Understanding the symptoms and quality of life (QOL) over time of adults with follicular lymphoma (FL) is important for treatment decision-making and clinical management. However, there are limited population-level data on the long-term QOL of adults with FL. We aim to describe the real-world, long-term QOL of adults with newly diagnosed FL up to 5/6 years after diagnosis. Methods: We used the Mayo Clinic/Iowa Molecular Epidemiology Resource (MER) and the multi-institutional Lymphoma Epidemiology of Outcomes (LEO) to identify adults with grade 1-3A FL who completed QOL surveys at baseline and follow-up. Participants with lymphoma were prospectively enrolled within 9 months of diagnosis in the MER cohort from 2002-2015 and within 6 months of diagnosis in the multi-institutional LEO cohort study across 8 cancer centers from 2015-2020. The LEO/MER cohort studies systematically collected information on disease status, QOL, health behaviors, and functional assessment. Treatments, disease relapses, and deaths were verified by medical record review. Treating physicians determined clinical management. QOL was measured using the Functional Assessment of Cancer Therapy-General (FACT-G) at years 1, 2, 3, and 5/6 post-diagnosis. A higher FACT-G total score (range 0-108) indicated better QOL across 4 subscales (range): physical (0-28), social/family (0-28), emotional (0-24), and functional (0-28) well-being. We categorized participants based on their frontline management at time of diagnosis: “observation” (surveillance), “treatment” (systemic treatment with immunotherapy +/- chemotherapy), and “local” (radiation) groups. We employed a generalized linear mixed model to evaluate and compare the changes in QOL scores (as a continuous variable) from baseline for the observation, treatment, and local groups, adjusting for sex, race/ethnicity, age, FLIPI risk, and cohort (i.e., LEO or MER). We also evaluated QOL changes by FLIPI risk. Results: Our study included 1,544 participants with FL and QOL data. At the time of enrollment, median age was 61 years (range 19-91), 88% were non-Hispanic White, and 49% were female. Based on initial management, 529 (34%) were in the observation group, 880 (57%) were in the treatment group, and 135 (9%) were in the local group. FLIPI risk was high for 17% in the observation group, 29.3% in the treatment group, and 3% in the local group. The mean (standard deviation) baseline FACT-G total score was lowest in the treatment group with the following baseline scores: 86 (13) for the observation group, 83 (14) for the treatment group, and 88 (12) for the local group (Table 1). The observation and local groups reported a greater worsening of their baseline FACT-G total score vs the treatment group at both the 2-year (-1.2, -0.8 vs +1.8, respectively) and 5/6 year (-5.4, -6 vs. -1.6, respectively) timepoints (Figure 1). This appeared to be driven by worsened physical and functional well-being in the observation (-1.3, -2.2, respectively) and local (-0.9, -2.4, respectively) groups vs the treatment group (0, -0.3, respectively). These differences were statistically significant between the observation vs treatment groups. There were no statistically significant differences in the other subscales, i.e., social/family and emotional well-being, by frontline management. Social/family well-being decreased across all groups by 5/6 years (-2.3 in observation, -2.2 in treatment, and -3 in local, p<0.05). Regardless of FLIPI risk, the total FACT-G scores decreased within each FLIPI risk group at 5/6 years (-4.4 for low, -3.8 for intermediate, and -4.9 for high, p<0.05). This was primarily due to worsened social/family well-being (-2.4 for low, -2.2 for intermediate, and -3 for high, p<0.05) at 5/6 years. There was no statistically significant difference in QOL between FLIPI risk groups. Conclusion: This is one of the first and largest studies with real-world longitudinal QOL data for FL. Our study suggests that frontline systemic treatment initially improved QOL and resulted in a lower degree of QOL decline over time. Social/family well-being decreased over time regardless of frontline management or FLIPI risk. Further research is warranted to explore the impact of timing and specific systemic treatments on QOL, incorporate the patient/caregiver experience to address social/family well-being, and identify the clinical significance of our findings.

PURPOSE The International Prognostic Score (IPS) has been used in classic Hodgkin lymphoma (cHL) for 25 years. However, analyses have documented suboptimal performance of the IPS among contemporarily treated patients. Harnessing multisource individual patient data from the Hodgkin Lymphoma International Study for Individual Care consortium, we developed and validated a modern clinical prediction model. METHODS Model development via Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines was performed on 4,022 patients with newly diagnosed advanced-stage adult cHL from eight international phase III clinical trials, conducted from 1996 to 2014. External validation was performed on 1,431 contemporaneously treated patients from four real-world cHL registries. To consider association over a full range of continuous variables, we evaluated piecewise linear splines for potential nonlinear relationships. Five-year progression-free survival (PFS) and overall survival (OS) were estimated using Cox proportional hazard models. RESULTS The median age in the development cohort was 33 (18-65) years; nodular sclerosis was the most common histology. Kaplan-Meier estimators were 0.77 for 5-year PFS and 0.92 for 5-year OS. Significant predictor variables included age, sex, stage, bulk, absolute lymphocyte count, hemoglobin, and albumin, with slight variation for PFS versus OS. Moreover, age and absolute lymphocyte count yielded nonlinear relationships with outcomes. Optimism-corrected c-statistics in the development model for 5-year PFS and OS were 0.590 and 0.720, respectively. There was good discrimination and calibration in external validation and consistent performance in internal-external validation. Compared with the IPS, there was superior discrimination for OS and enhanced calibration for PFS and OS. CONCLUSION We rigorously developed and externally validated a clinical prediction model in > 5,000 patients with advanced-stage cHL. Furthermore, we identified several novel nonlinear relationships and improved the prediction of patient outcomes. An online calculator was created for individualized point-of-care use.