Citations

PURPOSE Although most patients with follicular lymphoma (FL) can expect an indolent course, progressive lymphoma remains the primary cause of death during the first decade after diagnosis. Progression of disease within 24 months (POD24) of starting first-line (1L) immunochemotherapy defines a high-risk population with poor survival, but better risk stratification at diagnosis is needed. METHODS The FLIPI24 model was developed and internally validated to predict 24-month event rates using individual data from 4,485 patients treated with 1L immunochemotherapy from 10 observational cohorts of FL. Overall and cause-specific survival was further evaluated in FLIPI24 risk groups. External validation in the 1L immunochemotherapy setting was performed using the prospective observational Lymphoma Epidemiology of Outcomes (LEO) cohort (N = 565) and three randomized phase III trials (N = 3,192); extension to all patients with FL (any 1L therapy) was performed in the LEO cohort (N = 1,445) and its Molecular Epidemiology Resource subcohort (N = 1,074). RESULTS The FLIPI24 model uses age and four blood-based variables (hemoglobin, lactate dehydrogenase, beta-2 microglobulin, and WBC count). FLIPI24 showed consistent performance across validation and extension data sets, which was superior to existing prognostic tools. Across the four external immunochemotherapy validation data sets, patients with high-risk FLIPI24 (23%-32% of patients) had significantly higher 24-month event rates (22%-35%) and inferior 5-year overall survival (77%-83%) compared with patients with low-risk FLIPI24 (29%-31% of patients, 24-month event rates: 10%-12%; 5-year OS: 96%-97%). Results were consistent when evaluating lymphoma-related death and when extended to all patients with FL. CONCLUSION The FLIPI24 model robustly stratifies, at diagnosis, patients with FL at increased risk of lymphoma-related death versus patients with very low lymphoma-related mortality during the first decade after diagnosis. FLIPI24 can be used to enrich future clinical trial designs in newly diagnosed FL.

Abstract Anaplastic large cell lymphomas (ALCLs) are CD30+ T-cell lymphomas that share pathologic features but differ in presentation, outcome, and genetics. Current classification incorporates clinical presentation and anaplastic lymphoma kinase (ALK) status but inadequately addresses molecular heterogeneity and therapeutic vulnerabilities. We studied 689 patients with ALCL in the LLMPP (Lymphoma/Leukemia Molecular Profiling Project) and performed expert consensus review, genetic subtyping (ALK, DUSP22, TP63, and triple negative), and immunohistochemistry for phosphorylated STAT3Tyr705. RNA sequencing with unsupervised gene expression profiling in 393 patients identified 2 main molecular types of ALCL that could be predicted with 91% accuracy based on the presence (type I) or absence (type II) of phosphorylated STAT3Y705 expression. Type I ALCLs included ALK+ ALCL and a subset of triple-negative ALCLs (TN-I); type II ALCLs included tumors with DUSP22 and/or TP63 rearrangements and the remaining triple-negative ALCLs (TN-II). Type I ALCLs were enriched for JAK-STAT3, whereas type II ALCLs were enriched for non–tyrosine kinase pathways, particularly epigenetic regulators such as EZH2. Immunohistochemistry showed overexpression of EZH2 and its trimethylated substrate H3K27. Prognosis in systemic ALCL was favorable for DUSP22-rearranged ALCL (5-year overall survival, 95%) and ALK+ ALCL (88%), intermediate for triple-negative ALCL (TN-I, 52% and TN-II, 37%), and poor for TP63-rearranged ALCL (0%). We introduce an integrated molecular classification that preserves currently diagnosed ALCL entities but identifies 4 molecularly distinct ALK− ALCL subtypes (DUSP22-rearranged, TP63-rearranged, TN-I, and TN-II). This classification can be easily implemented on paraffin tissue in routine practice or clinical trials, and stratifies ALCL into diagnostically, prognostically, biologically, and potentially therapeutically relevant subtypes.

Abstract Description Follicular Lymphoma (FL) is a germinal center-derived malignancy representing approximately 20% of all non-Hodgkin’s lymphomas. Despite a relatively indolent disease course, FL is a biologically and clinically heterogeneous disease which poses significant clinical challenges due to its propensity for relapse and transformation into aggressive and untreatable forms of lymphoma. Here, we have leveraged computational techniques with WES, RNA-sequencing, and immune profiling data from FL tumors to gain further insight into the biologic basis for FL heterogeneity. We performed computational reconstruction of the B cell receptor repertoire on B cell sorted RNA-sequencing data from normal lymph nodes (n = 5) and FL tumors (n = 89). To gain a better understanding of FL B cell biology at the single cell level, we further investigated the clonal architecture of dominant malignant clonotypes in FL tumors using computational analysis of paired single cell V(D)J and 5’ GEX sequencing data from FL tumors (n = 21) and non-malignant lymph nodes (n = 3), allowing us to characterize the germinal center programs in malignant versus non-malignant B cells. Our data suggest that a subset of FL tumors consist of complex polyclonal B cell populations, some of which represent a pre-malignant FL precursor. Our data provide a deepened understanding of FL biology, including precursor cells, and contributes a refined transcriptomic characterization of malignant and non-malignant B cells in Follicular Lymphoma. Funding Sources This work was supported by the National Institutes of Health (R01 CA212162-01A1 to A.J.N and J.R.C., and P50 CA097274 to J.R.C., and A.J.N) Topic Categories Computational and Systems Immunology (COMP)

Importance Transthyretin amyloid cardiomyopathy (ATTR-CM) is underdiagnosed despite expanding treatment options. Objective To develop and evaluate an artificial intelligence (AI)–augmented clinical program for ATTR-CM detection. Design, Setting, and Participants This nonrandomized clinical trial involved constructing an AI model, ATTRACTnet, using electrocardiogram waveforms, echocardiographic measurements, demographics, and diagnosis codes for orthopedic manifestations of amyloidosis. A single-system, multisite, single-arm, open-label trial was conducted to evaluate its real-world performance. The model was trained and validated at a large academic referral site for ATTR-CM with external validation at an academic site. The trial was conducted as a single-system, multisite trial. Patients with left ventricular (LV) wall thickness 12 mm or more and an ATTRACTnet score 0.5 or higher were eligible. Exclusions included prior ATTR-CM testing, hypertrophic cardiomyopathy, expected life span less than 1 year, nursing home residence, advanced dementia, or LV wall thickness less than 14 mm explained by uncontrolled hypertension or moderate/severe aortic stenosis. Intervention Eligible patients were notified and offered nuclear scintigraphy testing, monoclonal protein testing, and follow-up care on agreement from the treating physician. Main Outcomes and Measure The primary outcome was a diagnosis of ATTR-CM by consensus criteria. ATTR-CM testing positivity was compared with historical and contemporary controls. Results ATTRACTnet was developed in an internal test set of 799 patients (mean [SD] age, 75.1 [11.1] years; 516 [64.7%] male and 283 [35.3%] female) using 5-fold cross-validation with an additional external test set of 422 patients. It had good discrimination for ATTR-CM detection with an area under the receiver operator characteristic curve of 0.85 (5-fold cross-validation, 0.77-0.85) in the internal set and 0.82 (95% CI, .81-0.83) in the external test set with similar performance in Hispanic, non-Hispanic Black, and non-Hispanic White patients. A total of 1471 patients were identified with positive AI model scores 0.5 or more during the study period, with 256 eligible patients who met study criteria. Of these patients, 50 underwent amyloidosis testing after physician and patient approval; 24 (48%) were diagnosed with ATTR-CM, and 21 (88%) initiated treatment within 3 months. The positivity rate was more than 2.8 times higher than historical controls (15.3%; 95% CI, 13.1%-17.9%; P < .001), with an 18% relative increase in new diagnoses vs the prior year. Conclusions and Relevance AI-augmented screening may improve ATTR-CM detection and identify patients who are missed by usual care. Prospective randomized trials are needed to determine if outcomes are improved. Trial Registration ClinicalTrials.gov Identifier: NCT06469372

Importance Transthyretin amyloid cardiomyopathy (ATTR-CM), a progressive disease caused by misfolded transthyretin (TTR), occurs as wild-type (ATTRwt-CM) or variant (ATTRv-CM) forms. p.Val142Ile is the most common variant in the US, linked to rapid progression and increased mortality. Acoramidis achieves near-complete (≥90%) TTR stabilization and showed clinical benefit in the 30-month ATTRibute-CM trial and through month 42 in the ongoing open-label extension (OLE). Objective To evaluate the efficacy of acoramidis in ATTRwt-CM, ATTRv-CM, and variant subgroups (p.Val142Ile and non-p.Val142Ile). Design, Setting, and Participants This international, multicenter, phase 3, randomized placebo-controlled study took place from April 2019 to May 2023 with ongoing OLE (month 42). ATTRibute-CM enrolled 632 participants with ATTR-CM; 611 of 632 were included in the modified intention-to-treat (mITT) population. There were 380 participants who continued into the OLE. These data were analyzed from January 2025 to July 2025. Interventions Oral acoramidis, 712 mg, or placebo twice daily for 30 months, followed by 12 months of open-label treatment. Main Outcomes and Measures All-cause mortality (ACM), cardiovascular-related hospitalizations (CVH), serum TTR, 6-minute walk distance, Kansas City Cardiomyopathy Questionnaire Overall Summary score, and N-terminal pro B-type natriuretic peptide in participants with ATTRwt-CM and ATTRv-CM. Post-hoc analyses were conducted in variant subgroups, including p.Val142Ile. Results Overall, 552 participants with wild-type ATTR-CM (mean [SD] age, 78 [6.3] years; 92.0% male and 8.0% female) and 59 participants with variant ATTR-CM (mean [SD] age, 73 [7.7] years; 77.3% male and 22.7% female) were randomized (mITT population), including 35 with p.Val142Ile. Consistent efficacy was observed in wild-type and variant subgroups for ACM/CVH through month 30 and ACM through month 42. At month 30, acoramidis reduced the risk of ACM/first CVH vs placebo by 31% in ATTRwt-CM (hazard ratio [HR], 0.69; 95% CI, 0.52-0.90; P = .007) and by 59% in ATTRv-CM (HR, 0.41; 95% CI, 0.21-0.81; P = .01). ACM was reduced through month 42 with HRs of 0.70 (95% CI, 0.50-0.98; P = .04) and 0.41 (95% CI, 0.19-0.93; P = .03) in the ATTRwt-CM and ATTRv-CM groups, respectively. Consistent treatment benefit was observed in participants with ATTRwt-CM and ATTRv-CM for secondary end points. Within variant subgroups (p.Val142Ile vs non-p.Val142Ile), consistent treatment benefits were observed for ACM/CVH through month 30 and ACM through month 42. Conclusions and Relevance The beneficial effect of acoramidis was observed consistently in ATTRwt-CM and ATTRv-CM groups. These hypothesis-generating results indicate that further studies are warranted to better characterize the therapeutic benefit of acoramidis in variant subgroups. Trial Registration ClinicalTrials.gov Identifiers: NCT03860935 ; NCT04988386

Introduction: Acoramidis, an oral transthyretin (TTR) stabilizer achieving near-complete (≥90%) TTR stabilization, is now approved in the USA, EU, Japan, and UK for the treatment of TTR amyloid cardiomyopathy (ATTR-CM). In the 30-month phase 3 ATTRibute-CM study, acoramidis reduced all-cause mortality (ACM) or first cardiovascular-related hospitalization (CVH) vs placebo. Through Month 42 (M42) of the open-label extension (OLE; 30 months ATTRibute-CM + 12 months OLE), the clinical benefit of acoramidis was sustained, resulting in a 36% and 43% risk reduction in ACM and ACM/first CVH, respectively, compared with the placebo to acoramidis group Research Question: Are the long-term clinical benefits of acoramidis on ACM and ACM/first CVH in ATTR-CM over 42 months consistent across patient subgroups? Methods: ATTRibute-CM study design has been previously described. At the end of the 30-month double-blind period, patients enrolled into OLE continued acoramidis or switched from placebo to acoramidis; all patients received open-label acoramidis HCI 800 mg BID. Data cut was performed after 12 months in OLE. Subgroup analysis for ACM and ACM/first CVH through M42 were conducted for randomization stratification factors (genotype, NT-proBNP, and eGFR levels) and other pre-specified subgroups. ACM included death due to any cause, cardiac mechanical assist device placement, and heart transplant. CVH included CV hospitalizations (≥24 h) and urgent visits (<24 h) for decompensated heart failure requiring IV diuretics and was adjudicated by an independent Clinical Events Committee. Results: Overall, 389 patients enrolled in the OLE (263 continuous acoramidis and 126 placebo to acoramidis). The analyses of ACM/first CVH ( Figure 1 ) and ACM ( Figure 2 ) through M42 consistently favored patients initially randomized to acoramidis across all patient subgroups, including those based on sex, age, country, ATTR-CM genotype, baseline NT-proBNP, baseline eGFR, NYHA class, and NAC stage. The largest effect size for the reduction in ACM/first CVH (HR <0.50) was observed in the following subgroups: female, non-Caucasian patients, variant ATTR-CM, or NAC stage 2. Conclusions: The long-term benefit of acoramidis was consistently shown in patients initially randomized to acoramidis treatment compared with placebo to acoramidis switch after 30 months across multiple clinically relevant subgroups, underscoring the importance of early initiation of acoramidis to reduce the long-term risk of ACM or CVH.

ABSTRACT Newly diagnosed patients with non‐Hodgkin lymphoma (NHL) often have a history of other diseases, and these comorbidities can impact patient treatment and management options, as well as overall survival (OS). We developed the Lymphoma Epidemiology of Outcomes (LEO) comorbidity index (LCI) as a sum of 10 comorbidities adapted from the Self‐Report‐Generated Charlson Comorbidity Index (SRG‐CCI) for use in the LEO cohort, a national prospective study of newly diagnosed NHL. Of the 5502 participants with self‐reported comorbidity data, 3107 (56.4%) were male and the mean age at diagnosis was 60.9 years (range, 18–99 years). The LCI ranged from 0 to 6, with 48.6% having 0, 30.2% having 1, 21.2% having 2 or more comorbidities. With a median follow‐up of 62.4 months among surviving participants, 2099 patients had an event and 1219 died. Continuous LCI similarly predicted both 1‐year mortality (c‐statistic = 0.654) and OS (c‐statistic = 0.655), while it showed a weaker but still statistically significant predictive ability for lymphoma‐specific (c‐statistics = 0.617) and event‐free (c‐statistic = 0.574) survival. Participants with 1 (HR = 1.21, 95% CI 1.05–1.39) and 2+ (HR = 1.80, 95% CI 1.56–2.08) comorbidities had inferior OS compared to those with no comorbidities after adjustment for age and sex (c‐statistic = 0.654), and performance strengthened after adjustment for the International Prognostic Index (c‐statistic = 0.672). LCI predicted OS most strongly in marginal zone (c‐statistics = 0.748) and weakest in T‐cell (c‐statistic = 0.579) lymphoma. The cumulative incidence of death due to lymphoma, lymphoma treatment, and other causes all increased with increasing comorbidities, with the greatest increase observed for death due to other causes. The LCI performs comparable to other published comorbidity indices, supporting its use in the LEO cohort to better model real‐world outcomes and more generally providing an approach to implementing comorbidity indices in cancer survivorship cohorts.

Abstract Although follicular lymphoma (FL) typically follows an indolent course, patients with FL who experience early events, such as transformation or progression, have increased risk of death related to lymphoma. The FL24Cx is an algorithm based on a 45-target gene expression profiling (GEP) assay, which was developed and trained using 265 formalin-fixed, paraffin-embedded tissue samples on a reliable platform to predict, at the time of diagnosis, whether a patient will experience an event within 24 months. The modeling also confirmed and relied upon previously reported synergy between immune response (IR) gene expression signatures IR1 and IR2. Once locked, the 5-factor logistic regression FL24Cx model was independently validated in a retrospectively assessed cohort of 232 patients from 2 immunochemotherapy-treated arms of SWOG Cancer Research Network S0016 phase 3 clinical trial, in which it assigned 169 patients to the low-risk group with 29 events before 24 months (17.2%) and 63 patients to the high-risk group with 24 events before 24 months (38.1%). The relative risk of an event within 24 months after registration among patients who were classified into the high-risk group relative to patients who were classified into the low-risk group was 2.2 (95% confidence interval, 1.41 to 3.51). An up-front GEP biomarker, such as the FL24Cx, rigorously validated in a clinical laboratory and with a clinically relevant turnaround time, could identify and steer enrollment of patients at high risk for early events in clinical trials, thus enabling timely interpretation of such trials and increasing the pace of innovation.