Savage, Kerry J.

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: Prognostic modeling allows personalized risk prediction for individual patients (pt). The A-HIPI model in advanced stage classical Hodgkin lymphoma (AS-HL), developed and validated by the HoLISTIC Consortium (www.hodgkinconsortium.org), generates the individualized probability of a progression-free survival (PFS) event or death (OS) within the first 5 years (y) from diagnosis in pts based on continuous variables (www.qxmd.com/calculate/calculator_869/a-hipi). Clinical prognostic tools in lymphoma (eg, IPS, IPI, FLIPI, etc) typically use groupings of categorical values to define risk. Grouping a continuous value often results in loss of information, and most tools are not predictive for individual pts. However, discrete groupings have clinical utility & practicality for a) defining pt populations for clinical trials & real world studies, b) stratification within clinical trials, and c) crafting treatment guidelines. We studied potential approaches for utilizing the A-HIPI model to generate risk groups with input on strengths & limitations from the HoLISTIC modeling team & clinical experts. Methods: The A-HIPI model was developed via TRIPOD guidelines on 4,022 pts treated on 8 international clinical trials for AS-HL (Rodday. JCO 2023). External validation was performed on a dataset of 1,431 pts from 4 prospective registries. The 5y PFS (PFS5) in the A-HIPI development dataset was 77% (95% CI: 76-78); the 5y OS (OS5) was 92% (95% CI: 91-93). This represented the average outcome for a pt with AS-HL pt naïve to other clinical data at presentation. The distribution of PFS5 & OS5 predictions were heavily skewed (ie, asymmetric distribution) in both the A-HIPI discovery and validation dataset. While not unexpected due to the excellent PFS & OS in this disease setting, this presents challenges in the delineation of risk groups as depicted below . Three approaches were examined for the generation of A-HIPI risk groups. Proposed cutoffs were defined using the distribution of A-HIPI risk scores and data from the model-building cohort (ie, clinical trials). Validation was done using the A-HIPI validation cohort (ie, HL registries). Results: Approach 1: Risk groups based on clinical thresholds. Clinicians were queried what estimates of PFS5 would constitute high vs low risk. The positive, right-skewed distribution of A-HIPI risk scores limited this approach ( Figure), as cutoffs of PFS5 <70 and PFS5> 90 would only identify 15% and <1% of the population, respectively. Approach 2: Risk groups based on deviation from “average” pt. The 5-y PFS was 77% (95% CI: 76-78). We explored defining “standard risk” based on this confidence interval as well as clinical boundaries, with pts above or below this classified as decreased or increased risk, respectively. Results of a +/-5% clinical boundary are presented in the Table. Approach 3: Risk groups based on “ranking” of pts. Here we ranked the A-HIPI risk scores of the 4022 AS-HL pts in the model building cohort and used the distribution of the risk scores as a benchmark. The risk profile for a future pt is then compared to this distribution (eg, how do you rank compared to your peers). Continuous results are often presented this way (eg, tertiles or quartiles). It also allows flexibility for the user to define the size of the risk groups and/or clinical threshold of interest. Application of this approach showed good alignment between the predicted model percentiles and the observed distribution of scores in the validation cohort ( Table). This is also reflected in calibration curves presented in the primary manuscript. An online application (eg, R-Shiny) will be provided at the meeting to allow users to define their own cutoffs to aid in pt prognostication as well as identify populations for clinical trial development. Conclusions: There are challenges with defining risk groups from individual risk prediction modeling in AS-HL. Different applications and purposes, the skewed distribution of events/risk estimates, as well as varying clinical definitions of high risk, make it challenging to define consensus expert-based risk groupings for AS-HL. A flexible “rank-based” approach appeared to provide the most clinical utility & data granularity, which may be leveraged for clinical trial design and pt stratification. Further analysis and discussion of how to optimally define high-risk and low-risk populations in AS-HL will be needed as new therapeutic options emerge.

Peripheral T-cell lymphoma (PTCL) encompasses a diverse group of post-thymic lymphomas, with ~40% of cases not further classifiable and designated as PTCL-not otherwise specified (PTCL-NOS). Two molecular prognostic subtypes of PTCL-NOS, PTCL-TBX21 and PTCL-GATA3, were identified through gene expression profiling (GEP). A subset of PTCL-NOS with T-follicular helper (TFH) differentiation was subsequently categorized as nodal PTCL with TFH phenotype (PTCL-TFH), also known in current classifications as nodal PTCL-TFH, NOS, or TFH lymphoma, NOS. However, the boundary between PTCL-NOS and PTCL-TFH remains poorly defined. To refine these subtypes, 101 PTCL-NOS with centralized pathology review and extensive immunophenotyping by immunohistochemistry (IHC) using 22 antibodies, digital GEP (nCounter, NanoString Inc) (n= 70), and RNA sequencing (n= 73) were included in this study. IHC was used to identify PTCL-TFH cases (those with strong expression of more than two TFH markers); the remaining patients were classified as PTCL-GATA3 and PTCL-TBX21 using pre-defined GEP signatures. Cases were reclassified into PTCL-NOS (n= 63) and PTCL-TFH (n= 38), with PTCL-NOS subclassified into PTCL-GATA3 (n= 22; 34%) and PTCL-TBX21 (n= 41; 66%) and showing significant differences in OS (p<0.001). PTCL-GATA3 was characterized by medium to large transformed cells (average= 90%, 70-100%) and had minimal tumor microenvironment (TME) (TME-poor: 100%). In contrast, PTCL-TBX21 was more heterogeneous, associated with pleomorphic cells in a polymorphous background (TME-rich: 78%), including Lennert lymphoma-like cases (22%). mRNA and CIBERSORT analysis substantiated the findings and identified a subset enriched in cytotoxic features in the PTCL-TBX21 subtype. IHC indicated that PTCL-GATA3 cases were CD4+/CD8- (83%) or CD4-/CD8- (17%) and lacked expression of CD8 or cytotoxic markers compared to PTCL-TBX21 (p<0.01). PTCL-GATA3 showed significantly higher expression of LEF1 (average= 80%), Ki67 (80%), and MYC (25%) than PTCL-TBX21 (25%, 30%, <5%, respectively, p<0.01). mRNA and protein expression of these biomarkers showed a significant positive correlation (r=0.5, p<0.001), and expectedly higher mRNA expression of LEF1 and MYC was observed in the PTCL-GATA3 versus PTCL-TBX21 (p<0.05). Strong expression of CD30 (>50% of cells) was only seen in PTCL-GATA3 cases. EBER positivity, found only in rare background cells, was not significantly different (18% of PTCL-GATA3 vs. 12% of PTCL-TBX21). Within PTCL-TBX21, we identified cytotoxic and non-cytotoxic subsets with divergent morphological, phenotypic, and clinical findings. The cytotoxic PTCL-TBX21 exhibited an activated cytotoxic phenotype (23/25, 96%), denoted by TIA1 and granzyme-B and/or perforin expression. Extranodal involvement and single-cell apoptosis were observed in 17% and 45% of the cytotoxic cases, respectively, but absent in non-cytotoxic PTCL-TBX21 cases. A trend towards higher Ki67 expression (average= 40% vs. 20%, p= 0.08) was seen in the cytotoxic subgroup. In contrast, the non-cytotoxic PTCL-TBX21 was associated with a CD4+/CD8- phenotype and higher ICOS expression (average= 30%) and CCR4 (60%) compared to the cytotoxic PTCL-TBX21 (p= 0.001). PTCL-TFH cases showed a CD4+/CD8- phenotype (90%) and rarely a CD4-/CD8- phenotype (10%). While a subset of PTCL-TFH cases had AITL-like features such as numerous clear cells and/or prominent vasculature (31% of cases), which were not seen in the other subgroups, the remaining cases exhibited morphology that was indistinguishable from other PTLC-NOS cases including sheets of transformed cells or pleomorphic cells in a polymorphous background. Morphologically, PTCL-TFH cases, like PTCL-TBX21 were associated with a rich TME (TME-rich: 75%). CIBERSORT analysis showed enrichment of plasma cells (p<0.01) in PTCL-TFH, compared to PTCL-TBX21, and validated by morphology (30% of cases vs. 5%). Expression of one TFH marker was frequent in some PTCL-NOS cases (PTCL-GATA3= 41% of cases, PTCL-TBX21-non cytotoxic= 47%, PTCL-TBX21-cytotoxic= 5%). Conclusion: Our comprehensive evaluation underscores the importance of integrating morphology, immunophenotyping, and GEP in achieving an accurate diagnosis, potentially leading to more tailored treatment strategies for PTCL. Correlation with pending whole-exome sequencing studies will be provided at the time of presentation.

PURPOSE CNS progression or relapse is an uncommon but devastating complication of aggressive B-cell lymphoma. There is no consensus regarding the optimal approach to CNS prophylaxis. This study was designed to determine whether high-dose methotrexate (HD-MTX) is effective at preventing CNS progression in patients at high risk of this complication. PATIENTS AND METHODS Patients age 18-80 years with aggressive B-cell lymphoma and high risk of CNS progression, treated with curative-intent anti–CD20-based chemoimmunotherapy, were included in this international, retrospective, observational study. Cause-specific hazard ratios (HRs) and cumulative risks of CNS progression were calculated according to use of HD-MTX, with time to CNS progression calculated from diagnosis for all patients (all-pts) and from completion of frontline systemic lymphoma induction therapy, for patients in complete response at completion of chemoimmunotherapy (CR-pts). RESULTS Two thousand four hundred eighteen all-pts (HD-MTX; n = 425) and 1,616 CR-pts (HD-MTX; n = 356) were included. CNS International Prognostic Index was 4-6 in 83.4% all-pts. Patients treated with HD-MTX had a lower risk of CNS progression (adjusted HR, 0.59 [95% CI, 0.38 to 0.90]; P = .014), but significance was not retained when confined to CR-pts (adjusted HR, 0.74 [95% CI, 0.42 to 1.30]; P = .29), with 5-year adjusted risk difference of 1.6% (95% CI, –1.5 to 4.4; all-pts) and 1.4% (95% CI, –1.5 to 4.1; CR-pts). Subgroups were underpowered to draw definitive conclusions regarding the efficacy of HD-MTX in individual high-risk clinical scenarios; however, there was no clear reduction in CNS progression risk with HD-MTX in any high-risk subgroup. CONCLUSION In this large study, high-risk patients receiving HD-MTX had a 7.2% 2-year risk of CNS progression, consistent with the progression risk in previously reported high-risk cohorts. Use of HD-MTX was not associated with a clinically meaningful reduction in risk of CNS progression.

AbstractAdolescents and young adults (AYA) with lymphoma experience treatment‐related effects in the short and long term that impact their quality of life and survivorship experience. The effort to improve outcomes for AYA lymphoma survivors requires understanding the available literature, identifying current knowledge deficits, designing better clinical trials incorporating the patient perspective, using novel tools to bridge data gaps and building survivorship guidelines that translate research to clinical practice. This review article summarizes the current state of lymphoma treatment‐related outcomes in AYAs and provides future direction.

Abstract Patients with relapsed/refractory (R/R) classical Hodgkin lymphoma (cHL) for whom autologous hematopoietic cell transplantation (auto-HCT) had failed experienced frequent and durable responses to nivolumab in the phase 2 CheckMate 205 trial. We present updated results (median follow-up, ∼5 years). Patients with R/R cHL who were brentuximab vedotin (BV)–naive (cohort A), received BV after auto-HCT (cohort B), or received BV before and/or after auto-HCT (cohort C) were administered with nivolumab 3 mg/kg IV every 2 weeks until progression or unacceptable toxicity. Patients in cohort C with complete remission (CR) for 1 year could discontinue nivolumab and resume upon relapse. Among 243 patients (cohort A, n = 63; B, n = 80; and C, n = 100), the objective response rate (ORR) was 71.2% (95% confidence interval [CI], 65.1-76.8); the CR rate was 21.4% (95% CI, 16.4-27.1). Median duration of response, CR, and partial remission were 18.2 (95% CI, 14.7-26.1), 30.3, and 13.5 months, respectively. Median progression-free survival was 15.1 months (95% CI, 11.3-18.5). Median overall survival (OS) was not reached; OS at 5 years was 71.4% (95% CI, 64.8-77.1). In cohort C, all 3 patients who discontinued in CR and were subsequently re-treated achieved objective response. No new or unexpected safety signals were identified. This 5-year follow-up of CheckMate 205 demonstrated favorable OS and confirmed efficacy and safety of nivolumab in R/R cHL after auto-HCT failure. Results suggest patients may discontinue treatment after persistent CR and reinitiate upon progression. This trial was registered at www.clinicaltrials.gov as #NCT02181713.

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.