Hsi, Eric D.

PURPOSE Patients with bulky stage I/II classic Hodgkin lymphoma (cHL) are typically treated with chemotherapy followed by radiation. Late effects associated with radiotherapy include increased risk of second cancer and cardiovascular disease. We tested a positron emission tomography (PET)–adapted approach in patients with bulky, early-stage cHL, omitting radiotherapy in patients with interim PET-negative (PET−) disease and intensifying treatment in patients with PET-positive (PET+) disease. METHODS Eligible patients with bulky disease (mass > 10 cm or 1/3 the maximum intrathoracic diameter on chest x-ray) received two cycles of doxorubicin, bleomycin, vinblastine, and dacarbazine (ABVD) followed by interim fluorodeoxyglucose PET (PET2). Patients with PET2–, defined as 1-3 on the 5-point scale, received four additional cycles of doxorubicin, bleomycin, vinblastine, and dacarbazine. Patients with PET2+ received four cycles of escalated bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, and prednisone followed by 30.6 Gy involved-field radiation. RESULTS Of 94 evaluable patients, 53% were female with median age 30 years (range, 18-58 years). Eight-five (90%) had stage II disease, including 48 (51%) with stage IIB/IIBE. Seventy-eight (78%) were PET2– and 21 (22%) were PET2+. The predominant toxicity was neutropenia, with 9% of patients developing febrile neutropenia and one developing sepsis. The primary end point of 3-year progression-free survival (PFS) was 93.1% in PET2– and 89.7% in PET2+ patients. Three-year overall survival was 98.6% and 94.4%, respectively. The estimated hazard ratio comparing PFS of patients with PET2+ and patients with PET2− was 1.03 (85% upper bound 2.38) and was significantly less than the null hypothesis of 4.1 (one-sided P = .04). CONCLUSION Our study of PET-adapted therapy in bulky stage I/II cHL met its primary goal and was associated with an excellent 3-year PFS rate of 92.3% in all patients, with the majority being spared radiotherapy and exposure to intensified chemotherapy.

Abstract With the introduction of large-scale molecular profiling methods and high-throughput sequencing technologies, the genomic features of most lymphoid neoplasms have been characterized at an unprecedented scale. Although the principles for the classification and diagnosis of these disorders, founded on a multidimensional definition of disease entities, have been consolidated over the past 25 years, novel genomic data have markedly enhanced our understanding of lymphomagenesis and enriched the description of disease entities at the molecular level. Yet, the current diagnosis of lymphoid tumors is largely based on morphological assessment and immunophenotyping, with only few entities being defined by genomic criteria. This paper, which accompanies the International Consensus Classification of mature lymphoid neoplasms, will address how established assays and newly developed technologies for molecular testing already complement clinical diagnoses and provide a novel lens on disease classification. More specifically, their contributions to diagnosis refinement, risk stratification, and therapy prediction will be considered for the main categories of lymphoid neoplasms. The potential of whole-genome sequencing, circulating tumor DNA analyses, single-cell analyses, and epigenetic profiling will be discussed because these will likely become important future tools for implementing precision medicine approaches in clinical decision making for patients with lymphoid malignancies.

Abstract Since the publication of the Revised European-American Classification of Lymphoid Neoplasms in 1994, subsequent updates of the classification of lymphoid neoplasms have been generated through iterative international efforts to achieve broad consensus among hematopathologists, geneticists, molecular scientists, and clinicians. Significant progress has recently been made in the characterization of malignancies of the immune system, with many new insights provided by genomic studies. They have led to this proposal. We have followed the same process that was successfully used for the third and fourth editions of the World Health Organization Classification of Hematologic Neoplasms. The definition, recommended studies, and criteria for the diagnosis of many entities have been extensively refined. Some categories considered provisional have now been upgraded to definite entities. Terminology for some diseases has been revised to adapt nomenclature to the current knowledge of their biology, but these modifications have been restricted to well-justified situations. Major findings from recent genomic studies have impacted the conceptual framework and diagnostic criteria for many disease entities. These changes will have an impact on optimal clinical management. The conclusions of this work are summarized in this report as the proposed International Consensus Classification of mature lymphoid, histiocytic, and dendritic cell tumors.

Abstract Introduction: Multiple studies have demonstrated that diffuse large B-cell lymphoma (DLBCL) can be divided into subgroups based on their biology. However, these biological subgroups overlap clinically. While R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) remains the standard of care for treating patients with DLBCL, predicting which patients will not benefit from such therapy is important so that alternative therapy or clinical trials can be considered. Most of the studies stratifying patients select biomarkers first, then explore how these biomarkers can stratify patients based on outcome. We explored the potential of using machine learning to first group patients with DLBCL based on survival, then isolating the biomarkers necessary for predicting these survival subgroups. Methods: RNA was extracted from tissue paraffin blocks from 379 R-CHOP treated patients with de novo DLBCL, and from 247 patients with extranodal DLBCL. A targeted hybrid capture RNA panel of 1408 genes was used for next generation sequencing (NGS). Sequencing was performed using an Illumina NextSeq 550 System platform. Ten million reads per sample in a single run were required, and the read length was 2 × 150 bp. An expression profile was generated from the sequencing coverage profile of each individual sample using Cufflinks. A machine learning system was developed to classify patients into four groups based on their overall survival. This machine learning approach based on Naïve Bayesian algorithm was also used to discover the relevant subset of genes with which to classify patients into each of the four survival groups. To eliminate the underflow problem commonly associated with the standard Naïve Bayesian classifiers, we applied Geometric Mean Naïve Bayesian (GMNB) as the classifier to predict the survival group for each patient. Results: Using machine learning, patients were first divided into two groups: short survival (S) and long survival (L). To refine this model, we used the same approach and divided the patients in each group into two subgroups, generating four groups: long survival in the long group (LL), short survival in the long group (LS), long survival in the short group (SL), and short survival in the short group (SS). The hazard ratio for this model was 0.174 (confidence interval: 0.120-0.251), and P-value <0.0001. After defining these four groups, a machine learning algorithm was used to discover the biomarkers from the expression data of the 1408 genes from NGS data. To reduce the effects of noise and avoid overfitting, we employed a 12-step cross validation to obtain a robust measure. For an individual gene, a generalized Naïve Bayesian classifier was constructed on the training of one of the 12 subsets and tested on the other 11 testing subsets. This allowed us to limit the prediction process to 60 genes for each separation step. Using the selected biomarkers, we classified the patients in the original set (379 patients) into LL, LS, SL, and SS groups and then evaluated the survival pattern of these groups. As shown in Fig. 1A, the selected biomarkers predicted survival as expected in the overall survival groups prior to biomarker selection. For additional validation of the system, we used the selected biomarkers to classify a completely new set of 247 samples of patients with extranodal DLBCL. As shown in Fig. 1B, these selected biomarkers successfully predicted the overall survival in this group of patients with an HR of 0.530 (confidence interval: 0.234-1.197, P=0.005). This classification correlated with cell of origin classification, TP53 mutation status, MYC expression, and IRF4 expression. However, in a multivariate analysis, only TP53 mutation was independent in predicting prognosis (P=0.005) and age (below or over 60) (P=0.01) along with the survival grouping (P<0.000001). Conclusions: Using a novel machine learning approach with the expression levels of 180 genes, we developed a model that can reliably stratify patients with DLBCL treated with R-CHOP into four survival subgroups. This model can be used to identify patients who may not respond well to R-CHOP to be considered for alternative therapy and clinical trials. Figure 1 Figure 1. Disclosures Hsi: AbbVie Inc, Eli Lilly: Research Funding. Ferreri: Ospedale San Raffaele srl: Patents & Royalties; BMS: Research Funding; Pfizer: Research Funding; Beigene: Research Funding; Hutchison Medipharma: Research Funding; Amgen: Research Funding; Genmab: Research Funding; ADC Therapeutics: Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; PletixaPharm: Membership on an entity's Board of Directors or advisory committees; x Incyte: Membership on an entity's Board of Directors or advisory committees; Adienne: Membership on an entity's Board of Directors or advisory committees. Piris: Millenium/Takeda, EUSA, Jansen, NanoString, Kyowa Kirin, Gilead and Celgene.: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Winter: BMS: Other: Husband: Data and Safety Monitoring Board; Actinium Pharma: Consultancy; Janssen: Other: Husband: Consultancy; Agios: Other: Husband: Consultancy; Gilead: Other: Husband: Consultancy; Epizyme: Other: Husband: Data and Safety Monitoring Board; Ariad/Takeda: Other: Husband: Data and Safety Monitoring Board; Merck: Consultancy, Honoraria, Research Funding; Novartis: Other: Husband: Consultancy, Data and Safety Monitoring Board; Karyopharm (Curio Science): Honoraria.

Abstract Background: High grade B-cell Lymphoma with rearrangements of MYC and BCL2 and/or BCL6, also known as double hit lymphoma (DHL), and double expressing lymphomas (DEL; DLBCL with IHC expression of MYC and BCL2, but without double hit cytogenetics) are highly aggressive B cell non-Hodgkin lymphomas and patients (pts) often develop relapsed/refractory disease following standard first-line chemoimmunotherapy. DA-EPOCH-R is widely used in patients with DHL, and a phase 1 trial of venetoclax + DA-EPOCH-R established the dose of 600 mg for 5 days with each cycle as the recommended phase 2 dose (Rutherford, et al. Proc ASCO 2020). A051701 is a phase II/III randomized trial evaluating chemoimmunotherapy +/- the BCL2 inhibitor venetoclax in a DHL cohort (pts with DHL with BCL2 rearrangement and/or expression), and a DEL cohort. Here we report the initial results from the DHL cohort of A051701. Methods: Pts age ≥ 18 years with newly diagnosed DHL were stratified on International Prognostic Index (IPI) score and receipt of a single cycle of chemotherapy prior to registration, which was permitted, and randomized 1:1 to receive DA-EPOCH-R (Arm 1) or DA-EPOCH-R with venetoclax (Arm 2). Enrollment was based on local pathology results which were centrally confirmed. DA-EPOCH-R was administered as previously published. Venetoclax was dosed at 600 mg po daily on days 4-8 of cycle 1 and on days 1-5 of subsequent cycles for up to 6 total cycles. All cycles were supported by GCSF or peg-GCSF. For the interim analysis at the end of phase II, 53 events ensured 90% power to detect an improvement in 24-month progression-free survival (PFS) from 40% in Arm 1 to 60% in Arm 2 (HR=0.557) using a one-sided stratified log-rank test with type I error rate of 20%. The Alliance Data and Safety Monitoring Board approved the data release after accrual was stopped early due to excess toxicity in Arm 2. These data were frozen July 8, 2021. Results: 73 pts were registered (36 in Arm 1 and 37 in Arm 2) between 8/7/19 and 9/18/20. Analyses were performed for the modified intent-to-treat (mITT) population, defined as eligible pts with DHL histology confirmed (n=66; 30 Arm 1, 36 Arm 2). Median age was 65 years in both arms (range 37-80) and baseline demographic factors were well balanced. An allowed pre-protocol chemoimmunotherapy cycle was administered in 60% and 56% of Arm 1 and Arm 2 pts, respectively. The majority of pts had advanced stage disease (Arm 1 87%, Arm 2 86%), GCB immunophenotype (Arm 1 100%, Arm 2 88%), ECOG PS 0-1 (Arm 1 90%, Arm 2 86%), and high-intermediate/high risk IPI score (Arm 1 63%, Arm 2 64%). Therapy was completed per protocol in 70% and 47% of pts, respectively, for Arm 1 and 2, and disease progression on therapy occurred in 10% and 3%. Treatment was discontinued due to adverse events in 7% of pts in Arm 1 and 11% of pts in Arm 2. Consent was withdrawn after beginning treatment in 3% and 11% in Arm 1 and 2, respectively. Death on treatment occurred in 1 patient (3%; PJP pneumonia) in Arm 1 and 6 pts (17%; sepsis (n=4), cardiac arrest (n=2)) in Arm 2. The observation of excess deaths on Arm 2 prompted suspension of accrual to the DHL cohort. Treatment was discontinued for other reasons in 3 (10%) and 4 (11%) pts in Arms 1 and 2, respectively. Venetoclax was dose-reduced and/or omitted in 15 (42%) pts. Grade 4 neutropenia was not significantly different between arms (Arm 1 67%, Arm 2 71%), nor was grade ≥3 neutropenic fever (Arm 1 36%, Arm 2 40%). Sepsis occurred more frequently in Arm 2 (14% vs. 23%). The end of treatment overall and complete response rates in the mITT population were 73% and 67% in Arm 1, and 58% and 50% in Arm 2. The end of treatment overall and complete response rates among only pts evaluated for response (n=52) were 79% and 71% in Arm 1, and 88% and 75% in Arm 2. With a median follow-up of 7.4 months, the median PFS was not reached (NR) in Arm 1 (95% CI 6.1-NR) and was 6.7 months in Arm 2 (95% CI 4.5-NR) (two-sided p=0.13). The median OS was NR in Arm 1 and was 8.5 months in Arm 2 (95% CI 5.2-NR) (two-sided p=0.004). Conclusions: DA-EPOCH-R plus venetoclax in DHL pts resulted in excess treatment-related mortality compared to DA-EPOCH-R alone, prompting early discontinuation of accrual and venetoclax treatment for the study cohort. Based on these data, we recommend against combining venetoclax with DA-EPOCH-R in DHL. Robust accrual to this study shows that prospective trials are feasible in DHL, and the favorable outcome thus far in the DA-EPOCH-R arm may serve as a benchmark for future trials Figure 1 Figure 1. Disclosures Abramson: Astra-Zeneca: Consultancy; Allogene Therapeutics: Consultancy; Incyte Corporation: Consultancy; BeiGene: Consultancy; Kymera: Consultancy; Bluebird Bio: Consultancy; Genmab: Consultancy; EMD Serono: Consultancy; Bristol-Myers Squibb Company: Consultancy, Research Funding; C4 Therapeutics: Consultancy; Morphosys: Consultancy; Kite Pharma: Consultancy; Novartis: Consultancy; Seagen Inc.: Research Funding; AbbVie: Consultancy; Karyopharm: Consultancy; Genentech: Consultancy. Ruppert: Telios Pharma: Consultancy. Hsi: Seattle Genetics: Honoraria; Cytomx: Honoraria; Eli Lilly: Research Funding; AbbVie: Research Funding. Landsburg: Triphase: Research Funding; Takeda: Research Funding; Curis: Research Funding; Karyopharm: Membership on an entity's Board of Directors or advisory committees, Other: DSMB member; ADCT: Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees. Kahl: AbbVie, Adaptive, ADCT, AstraZeneca, Bayer, BeiGene, Bristol-Myers Squibb, Celgene, Genentech, Incyte, Janssen, Karyopharm, Kite, MEI, Pharmacyclics, Roche, TG Therapeutics, and Teva: Consultancy; AbbVie, Acerta, ADCT, AstraZeneca, BeiGene, Genentech: Research Funding. Friedberg: Acerta: Other: DSMC ; Novartis: Other: DSMC ; Bayer: Other: DSMC . Bartlett: Millennium: Research Funding; Janssen: Research Funding; Kite, a Gilead Company: Research Funding; Merck: Research Funding; Genentech: Research Funding; Celgene: Research Funding; Forty Seven: Research Funding; Bristol Myers Squibb: Research Funding; Roche/Genentech: Consultancy; Autolus: Research Funding; Seagen: Consultancy, Research Funding; Pharmacyclics: Research Funding; ADC Therapeutics: Consultancy, Research Funding. Leonard: ADC Therapeutics, AstraZeneca, Bayer, BMS/Celgene, Epizyme, Inc., Genmab, Gilead/Kite, Karyopharm, BMS/Celgene, Regeneron, MEI Pharma, Miltenyi, Roche/Genentech, Sutro: Consultancy; Roche/Genentech: Consultancy. OffLabel Disclosure: venetoclax in double hit lymphoma

In the last decade, a better understanding of the molecular pathogenesis of B-cell non-Hodgkin lymphomas has resulted in the development of novel targeted therapies, such as small molecule inhibitors of select kinases in the B-cell receptor pathway, antibody–drug conjugates, and small molecules that target a variety of proteins (eg, CD-19, EZH2, and XPO-1–mediated nuclear export). Anti-CD19 CAR T-cell therapy, first approved for relapsed/refractory (R/R) diffuse large B-cell lymphoma, has also emerged as a novel treatment option for R/R follicular lymphoma and mantle cell lymphoma. These NCCN Guideline Insights highlight the new targeted therapy options included in the NCCN Guidelines for B-Cell Lymphomas for the treatment of R/R disease.

7506 Background: Optimal consolidative therapy for primary central nervous system lymphoma (PCNSL) is not defined. Avoidance of whole brain radiation may reduce risk of neurotoxicity. Non-radiation consolidative options include myeloablative chemotherapy with autologous stem cell transplantation (HDT/ASCT) or non-myeloablative chemotherapy. Methods: This is a randomized phase 2, National Clinical Trials Network study of induction methotrexate (MTX) (8 g/m2 days 1, 15), temozolomide (TMZ) (150-200 mg/m2 D7-11), and rituximab (RTX) (C1 D3, 10, 17, 24 and C2 D3, 10 ) in four 28-day cycles followed by one cycle of cytarabine (ARA-C) (2 g/m2 BID, D1, 2) (MTRA). After induction, patients (pts) received consolidation with thiotepa (5 mg/kg BID, D -5, -4) plus carmustine (400 mg/m2, day -6) and ASCT (Arm A) or one cycle of ARA-C (2 g/m2 BID, D1-4) plus infusional etoposide (40 mg/kg over 96h) (Arm B). Pts were stratified on age and performance status and randomized 1:1 before induction. The primary endpoint was progression-free survival (PFS) from randomization. With 110 pts, there was 84% power to detect an improvement in PFS using a log-rank test (1-sided α= 10%), assuming a median PFS of 3 months for pts who progress during induction, and a median PFS of 2 years (yrs) for Arm B and 4.5 yrs for Arm A consolidation. This report includes the results for the primary endpoint analysis. Results: 113 pts (median age 61 yrs, range 33-75) were randomized (Arm A: 57, Arm B: 56) across 27 centers. 108 eligible pts who received induction therapy were included in the primary endpoint analysis (Arm A: 54, Arm B: 54). 72/108 pts started consolidation and 70/72 completed consolidation per protocol (Arm A: 36, Arm B: 34). With a median follow-up of 3.8 years, median PFS from randomization was 6 yrs (95% CI 3.9-not reached) in Arm A vs 2.4 yrs (95% CI 0.6-not reached) in Arm B (p = 0.02). However, more pts randomized to Arm B went off treatment before consolidation due to progression or death (28% vs 11%, p = 0.05). PFS landmarked at start of consolidation demonstrated a trend for improved PFS favoring Arm A (HR 0.58, 95% CI 0.25-1.36; p = 0.21). Median OS was not reached in either arm, and 3-yr estimates were 83% (95% CI 69-91; Arm A) vs 72% (95% CI 57-82; Arm B). Toxicities were similar between arms with no treatment-related mortality during consolidation. Conclusions: MTRA induction followed by myeloablative consolidation (Arm A) had improved PFS vs MTRA induction followed by non-myeloablative consolidation (Arm B), though more progressions or deaths leading to treatment discontinuation prior to consolidation in Arm B were noted. Both consolidation regimens were well-tolerated with encouraging PFS and OS in newly-diagnosed PCNSL. Support: U10CA180821, U10CA180882; https://acknowledgments.alliancefound.org . Clinical trial information: NCT01511562.