Scott, David W.

Abstract The phase 1/2 Intergroup study E4412 investigated checkpoint blockade with nivolumab (Nivo) and ipilimumab (Ipi) combined with the CD30 targeting antibody-drug conjugate brentuximab vedotin (BV) in relapsed/refractory classic Hodgkin lymphoma. A total of 147 patients aged ≥12 years were randomized between BV/Nivo and BV/Ipi/Nivo; 132 patients were included in the primary efficacy analysis. The complete response (CR) rate, was 64.7% (52.2, 75.9) for BV/Nivo and 70.3% (57.6, 81.1) for BV/Ipi/Nivo (1-sided P = .29). The median survival follow-up was 38.0 months (interquartile range, 32.6-48.1). Progression-free survival (PFS) did not significantly differ between the 2 arms (hazard ratio [HR], 0.78, confidence interval [CI], 0.39-1.57; 1-sided P = .24). Treatment-related grade 3+ toxicities in the adult cohort, excluding rash, were similar between both arms (38.5% BV/Nivo and 39.3% BV/Ipi/Nivo); there was a higher frequency of grade 3 rash with BV/Ipi/Nivo (24.6%) compared with BV/Nivo (9.2%). We compared PFS by stem cell transplantation (SCT) status in a planned before hoc comparison; 58 patients received SCT, and 36-month PFS (from SCT) was >90% for both arms. Sixty-six patients were progression free after the first scan and did not undergo SCT. The 36-month PFS was 73.0% (54.5, 85.0) for BV/Ipi/Nivo compared with 45.8% (26.3, 63.4) for BV/Nivo (HR, 0.45; CI, 0.19-1.08; 1-sided P = .03). The study did not meet its primary end point of superior CR rate for the triplet, but it supports the use of checkpoint antibody-drug conjugate induction prior to auto SCT, and there is an intriguing signal of disease control for patients wishing to defer or avoid SCT with the triplet of BV/Ipi/Nivo. This trial was registered at www.clinicaltrials.gov as NCT01896999.

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.

Peripheral T cell lymphoma (PTCL) is a heterogeneous group of postthymic T cell neoplasms, with ~40% classified as PTCL–not otherwise specified (PTCL-NOS). PTCL-GATA3, a molecularly defined subtype, associated with T helper 2 (T H 2)–like differentiation and poor prognosis, has frequent co-occurrence of TP53 loss/mutation and heterozygous PTEN loss. CD4+ T cell conditional mouse models with Trp53 mutation/deletion and Pten loss demonstrated mature T cell lymphomas (mTCLs) with T H 2-like transcriptomic and immunophenotypic profiles. Molecular studies revealed that codeletion of Trp53/Pten induced T cell receptor and Janus kinase–signal transducer and activator of transcription signaling, promoting T H 2 differentiation while inhibiting T H 1 differentiation. These findings were validated by CRISPR editing of TP53/PTEN loss in human CD4+ T cells and mechanistically evaluated the p53 binding region in intron-3 of GATA3, resulting in transcriptional repression. Transcriptomic profiles of m-TCLs recapitulated human-PTCL-GATA3 transcriptome and distinguished PTCL-NOS subtypes. Preclinical assessment of m-TCLs with PI3Kγ/δ inhibitors significantly improved survival, supporting a therapeutic approach for the p53-aberrant PTCL-GATA3.

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.

Abstract Molecular characterization of high-grade B-cell lymphoma, not otherwise specified (HGBCL-NOS), is hindered by its rarity, evolving definition, and poor diagnostic reproducibility. To address this challenge, we analyzed 92 HGBCL-NOS tumors collected across Lymphoma/Leukemia Molecular Profiling Project sites. Leveraging comparison cohorts of diffuse large B-cell lymphoma, NOS (DLBCL-NOS) and Burkitt lymphoma (BL), and molecular frameworks described in these entities, our analysis revealed a heterogenous molecular landscape, reminiscent of DLBCL-NOS but with an enrichment of BL features. By cell-of-origin classification, 59% were germinal center B-cell–like (GCB), and 25% were activated B-cell–like (ABC). LymphGen, a genetic classifier for DLBCL-NOS, assigned a genetic subtype to 34% of HGBCL-NOS. Although classification rate was lower than in DLBCL-NOS (66%), assigned subtypes spanned the spectrum of LymphGen classes, including 31% of ABCs classified as MCD. Features differentiating HGBCL-NOS from DLBCL-NOS included MYC rearrangement (47% vs 6%); dark zone signature (DZsig) expression (45% vs 7%); and more frequent mutation of ID3, MYC, CCND3, and TP53, all common to BL. A genetic classifier that differentiates DLBCL-NOS from BL classified 53% of DZsig+ tumors as BL-like, and those classified as DLBCL-like were frequently BCL2-rearranged. Among DZsig− GCB tumors, 95% were DLBCL-like. Centralized pathology review reclassified almost half of tumors as DLBCL-NOS but did not identify a more homogenous HGBCL-NOS population, with no difference in features between confirmed and reclassified tumors. In conclusion, molecular testing enables a subset of HGBCL-NOS to be assigned to established categories. Based on rarity and diagnostic challenges, broader inclusion of HGBCL-NOS should be considered in biomarker-driven DLBCL trials.

Abstract Background: MCL is a B-cell non-Hodgkin lymphoma for which auto-HCT is often employed in first CR, based on non-randomized phase 2 trials, and a randomized trial (Dreyling et al, Blood 2005) conducted before the advent of more effective induction and maintenance regimens. The TRIANGLE trial (Dreyling et al, Lancet 2024) suggested that auto-HCT may not add benefit to more effective induction and maintenance regimens containing high-dose cytarabine, rituximab and BTK inhibitors. E4151 explored whether auto-HCT benefits pts achieving deep first remission, as measured by a highly sensitive immunoglobulin high throughput sequencing minimal residual disease (MRD) assay. Methods: EA4151 is a four arm trial conducted through the U.S. National Clinical Trials Network (NCTN) and Blood and Marrow Transplant Clinical Trials Network (BMT-CTN). Pts with MCL between the ages of 18 and 70 and in first remission were eligible. After induction, pts underwent PET/CT, bone marrow biopsy, and the clonoSEQ® MRD assay from peripheral blood. Pts in CR with uMRD at 1 in 10-6 sensitivity (uMRD6) were randomized 1:1 to Arm A (auto-HCT + 3 years of maintenance rituximab [MR]) or Arm B (3 years of MR alone), stratified by MIPI-c score and intensive vs non-intensive induction. Pts with either MRD-positive (MRD+) CR or MRD-indeterminate CR (Arm C and Arm D, respsectively) both received auto-HCT + 3 years of MR. Pts who were MRD+ pre-transplant had MRD repeated at day 100. Primary endpoint was to compare overall survival (OS) in Arms A and B, with secondary endpoints including progression-free survival (PFS). Primary analysis population included all randomized pts. As some pts refused their assigned treatment (N=65 [25.3%] for arm A and N=2 [0.8%] for arm B), a “treated as assigned” analysis was also performed. Stratified logrank test was used to compare survival distributions. P-values are two-sided. Results: From Aug 2017 to July 2024, 650 pts were assigned to a treatment arm with 257, 259, 49, and 85 pts enrolled on Arms A, B, C, and D, respectively. Pts were 79% male and 92% white race. Median age was 60 years (range 27-70). MIPI-c was low/low-intermediate (LI) in 63% and high/ high-intermediate (HI) in 37%. Induction was intensive (defined as high-dose cytarabine-containing) in 73% and non-intensive in 27%. A BTK inhibitor was given during induction in 7.2% of pts, and during maintenance in 0.3% of pts. The third pre-planned interim analysis was based on data as of 7/15/24, with median follow up of 2.7 years. The futility boundary was an OS hazard ratio (HR) of 0.984 for Arm A vs B. The estimated OS HR for Arm A vs B in all randomized (n=516) and pts treated as assigned (n=375) were 1.11 (CI 0.71-1.74, p=0.66) and 1.00 (CI 0.58-1.74, p=0.99), respectively and crossed the futility boundary. The 3 year (yr) OS for Arms A and B were 82.1% and 82.7% in all randomized pts, and 86.2% and 84.8% in pts treated as assigned. The estimated PFS HR for Arm A vs B in all randomized and pts treated as assigned were 1.05 (CI 0.71-1.56, p=0.79) and 0.95 (CI 0.59-1.54, p=0.84), respectively. The 3 yr PFS for Arms A and B were 76.6% and 77.4% in all randomized pts, and 81.5% and 80.4% in pts treated as assigned. For Arm C, 3 yr OS and PFS were 81.9% (CI 69.6-96.4%) and 76.9% (CI 64.4-91.7%), respectively. For Arm D, 3 yr OS and PFS were 85.1% (CI 76.0%-95.4%) and 73.4% (62.7-85.9%), respectively. For the MIPI-c low/LI group, 3 yr OS was 84.6% vs 85.7% for Arm A vs B (p=0.96), while in the MIPI-c high/HI group, 3 yr OS was 77.4% vs 77.6% for Arm A vs B (p=0.71). For the intensive induction group, 3 yr OS was 83.0% vs 86.2% for Arm A vs B (p=0.30), while in the non-intensive induction group, 3 yr OS was 79.5% vs 72.8% for Arm A vs B (p=0.48). Exploratory analysis of MRD+ pts (Arm C) showed that pts who converted to uMRD6 post auto-HCT (n=17) had 3 yr OS of 100% and PFS of 100%, whereas those who remained MRD+ (n=13) post auto-HCT had 3 yr OS of 63.6% and PFS of 48.8%. Causes of death (COD) in Arm A were 4.7% lymphoma, 5.1% COVID-19, and 5.0% other/unknown. COD in Arm B were 3.5% lymphoma, 6.6% COVID-19, and 4.6% other/unknown. In Sept 2024, the DSMC recommended termination of accrual and release of trial findings based on these results. Conclusion: In this interim analysis, in the era of highly effective induction and maintenance regimens, MCL pts in first CR with uMRD6 did not benefit from consolidative auto-HCT. Pts who remain MRD+ after induction may benefit from auto-HCT. Longer follow-up will be important to confirm these 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: HIV-associated diffuse large B-cell lymphoma (HIV-DLBCL) is a significant complication in HIV-positive individuals, presenting unique clinical challenges and a poorer prognosis compared to patients with de novo DLBCL. Epstein-Barr Virus (EBV) status is a critical factor influencing the pathogenesis, clinical behavior, and therapeutic responses in these lymphomas. Plasmablastic lymphomas (PBL) have shared some overlapping genomic abnormalities with HIV-DLBCL including MYC rearrangements. This study aims to elucidate the differences in the genetic profiles and pathway activations between EBV(+) and EBV(-) HIV-DLBCL through comprehensive genomic profiling. Methods: Cases of DLBCL were identified in patients living with HIV from a multi-institute cooperative group. Cases of Burkitt lymphoma and PBL were excluded. We performed whole exome sequencing (WES) on 106 tumors of HIV-DLBCL and RNA sequencing on 39 tumors and mapped the reads to hg38 reference genome using BWA and STAR, respectively. EBV status of cases was determined by in situ hybridization and/or reads mapped on EBV genome in sequencing data. Somatic mutations were identified using variant callers (Strelka2, Mutect2) with WES data. For those mutated genes in COSMIC, allele frequency cutoff was set at 0.03. Mutations with an artifact score larger than 0.7 in SOBDetector and those located in segmental duplications were further removed. They were annotated with Ensembl Variant Effect Predictor, manually checked, and plotted with maftools. RNAseq read counts aligned to each gene were obtained by featureCounts and DEseq2 was used for differential expression (DE) analysis between EBV positive and negative groups. Pathway analysis with the DE genes was performed using GSEA. FISH analysis was performed using MYC, BCL2, and BCL6 break-apart probes. Results: There were 41 cases of EBV(+) HIV-DLBCL and 65 cases of EBV(-) HIV-DLBCL. FISH of EBV(+) HIV-DLBCL identified 42% (3/7) with MYC rearrangement (MYCr), none with BCL2 rearrangements (BCL2r) (0/6), and 33% (2/6) positive for BCL6 rearrangement (BCL6r) with 1 tumor having a MYCr/BCL6r. For EBV(-) HIV-DLBCL, there were 35% with a MYCr (6/17), 18% with BCL2r (2/11), and 27% with a BCL6r (3/11) with 2 tumors with MYCr/BCL6r, and 1 tumor with MYCr/BCL2r/BCL6r. The most frequent mutations seen in EBV(+) HIV-DLBCL were STAT3 (32%), DDX3X (22%), and TP53 (15%). For EBV(-) HIV-DLBCL, the most frequent mutations were KMT2D (25%), TP53 (23%), GNA13 (22%), and TET2 (22%). Pathways that were most commonly associated with EBV(+) HIV-DLBCL were JAK/STAT (42%) (STAT3, JAK1) and NF-kB (39%)(DDX3X, TNFAIP3) while EBV(-) HIV-DLBCL was associated with epigenetic modifiers (62%)(KMT2D, TET2, EP300), JAK/STAT (45%)(SOCS1, JAK3) and PI3K/mTOR (40%)(GNA13, MTOR). Significant disparities between EBV(+) and EBV(-) were KMT2D (24% vs 50%, P=0.0025), TET2 (10% vs 26%, P=0.008), SOCS1 (29% vs 39%, P=0.04), DDX3X (15% vs 5%, P=0.03), and GNA13 (5% vs 24%, P=0.001). RAS mutations (KRAS, NRAS) were infrequent in HIV-DLBCL (EBV(+), 12%; EBV(-), 6%), in contrast to prior studies of PBL. Gene expression profiling showed enrichment of MYC targets, oxidative phosphorylation, and the mTORC pathway in EBV(+) as compared to EBV(-) HIV-DLBCL. Conclusion: EBV(+) HIV-DLBCL show a distinct mutational profile compared to EBV(-) HIV-DLBCL, with EBV(+) cases showing frequent mutations with STAT3 and DDX3X in contrast to EBV(-) cases with frequent mutations associated with epigenetic modification pathways (KMT2D, TET2). These genetic profiles underscore the need for tailored therapeutic approaches depending on EBV status. Although the analysis is limited and requires confirmation in a larger cohort, this is the largest study at present evaluating these tumors.