Faculty Bibliography

Patients with hematologic malignancies are a high priority for SARS-CoV-2 vaccination, yet the benefit they will derive is uncertain. We investigated the humoral response to vaccination in 53 non-Hodgkin lymphoma (NHL), Hodgkin lymphoma (HL), or CLL patients. Peripheral blood was obtained 2 weeks after first vaccination and 6 weeks after second vaccination for antibody profiling using the multiplex bead-binding assay. Serum IgG, IgA, and IgM antibody levels to the spike specific receptor binding domain (RBD) were evaluated as a measure of response. Subsequently, antibody-positive serum were assayed for neutralization capacity against authentic SARS-CoV-2. Histology was 68% lymphoma and 32% CLL; groups were: patients receiving anti-CD20-based therapy (45%), monitored with disease (28%), receiving BTK inhibitors (19%), or chemotherapy (all HL) (8%). SARS-CoV-2 specific RBD IgG antibody response was decreased across all NHL and CLL groups: 25%, 73%, and 40%, respectively. Antibody IgG titers were significantly reduced (p < 0.001) for CD20 treated and targeted therapy patients, and (p = 0.003) for monitored patients. In 94% of patients evaluated after first and second vaccination, antibody titers did not significantly boost after second vaccination. Only 13% of CD20 treated and 13% of monitored patients generated neutralizing antibodies to SARS-CoV-2 with ICD50s 135 to 1767, and 445 and > 10240. This data has profound implications given the current guidance relaxing masking restrictions and for timing of vaccinations. Unless immunity is confirmed with laboratory testing, these patients should continue to mask, socially distance, and to avoid close contact with non-vaccinated individuals.

Background: Mosunetuzumab (M), a full-length, humanized, IgG1 bispecific antibody targeting CD20 and CD3, has shown promising efficacy and safety as monotherapy for relapsed/refractory (R/R) B-cell non-Hodgkin's lymphoma (B-NHL) (NCT02500407; Assouline, et al. ASH 2020). The combination of M with the anti-CD79b antibody-drug conjugate, polatuzumab vedotin (Pola), showed synergistic anti-lymphoma activity in a mouse xenograft model. These data supported a Phase Ib/II, open-label, multicenter trial of M-Pola for R/R B-NHL (GO40516, NCT03671018).
Aim(s): To present early clinical data from the Phase Ib cohort of the GO40516 study.
Method(s): Patients (pts) with R/R follicular lymphoma (FL; grade [Gr] 1-3a) or aggressive NHL (aNHL), including de novo diffuse large B-cell lymphoma (DLBCL), transformed FL (trFL) and FL Gr 3b (FL3b), received Cycle (C) 1 step-up doses of M on Day (D) 1 (1mg) and D8 (2mg), the target dose on C1D15, then continued at the target dose on C2D1 onwards. M was given every 21 days for eight cycles (or 17 cycles if stable disease or a partial response after C8). Pola (1.8mg/kg) was given with M on D1 of each cycle for six cycles.
Result(s): As of November 17, 2020, 22 pts had received M-Pola (M target doses: 9mg, n=7; 20mg, n=3; 40mg, n=6; 60mg [with D1 dose of 30mg from C3 onwards], n=6). Pts had DLBCL (n=12), FL (n=3), FL3b (n=3) and trFL (n=4). Pt characteristics include: median age of 70 (38-81) years; median of 3 (1-10) prior lines of therapy; 7 (32%) pts had prior chimeric antigen-receptor T-cell (CAR-T) therapy; 17 (77%) and 19 (86%) pts had disease refractory to last prior therapy and prior anti-CD20 therapy, respectively. Median follow-up duration was 9.6 (0.7-23.7) months. The most frequent treatment-related adverse events (AEs) were neutropenia (45.4%), fatigue, nausea and diarrhea (all 36.4%). Cytokine release syndrome (CRS) was observed in 2 pts (9.1%; both Gr 1 by American Society for Transplantation and Cellular Therapy 2019 criteria). One dose-limiting toxicity (Gr 3 new onset atrial fibrillation) was observed in the 40mg cohort. The maximum tolerated dose was not exceeded. The most common Gr >=3 and serious AEs were both neutropenia, observed in 8 (36.4%) and 3 (13.6%) pts, respectively. Two (9.3%) Gr 5 AEs occurred: sudden cardiac death (n=1) and respiratory failure (n=1); neither was deemed treatment related. No immune effector cell-associated neurotoxicity was observed. The Table shows preliminary efficacy data. Summary/Conclusion: These data indicate that M-Pola has an acceptable safety profile, with no Gr >=2 CRS observed, and promising efficacy in pts with R/R NHL with predominantly aggressive disease. The Phase II expansion cohort in R/R DLBCL is ongoing, with no mandatory hospitalization required. . 2021 American Society of Clinical Oncology, Inc. Reused with permission. This abstract was accepted and previously presented at the 2021 ASCO Annual Meeting. All rights reserved

PURPOSE/OBJECTIVE:Burkitt lymphoma (BL) has unique biology and clinical course but lacks a standardized prognostic model. We developed and validated a novel prognostic index specific for BL to aid risk stratification, interpretation of clinical trials, and targeted development of novel treatment approaches. METHODS:We derived the BL International Prognostic Index (BL-IPI) from a real-world data set of adult patients with BL treated with immunochemotherapy in the United States between 2009 and 2018, identifying candidate variables that showed the strongest prognostic association with progression-free survival (PFS). The index was validated in an external data set of patients treated in Europe, Canada, and Australia between 2004 and 2019. RESULTS:In the derivation cohort of 633 patients with BL, age ≥ 40 years, performance status ≥ 2, serum lactate dehydrogenase > 3× upper limit of normal, and CNS involvement were selected as equally weighted factors with an independent prognostic value. The resulting BL-IPI identified groups with low (zero risk factors, 18% of patients), intermediate (one factor, 36% of patients), and high risk (≥ 2 factors, 46% of patients) with 3-year PFS estimates of 92%, 72%, and 53%, respectively, and 3-year overall survival estimates of 96%, 76%, and 59%, respectively. The index discriminated outcomes regardless of HIV status, stage, or first-line chemotherapy regimen. Patient characteristics, relative size of the BL-IPI groupings, and outcome discrimination were consistent in the validation cohort of 457 patients, with 3-year PFS estimates of 96%, 82%, and 63% for low-, intermediate-, and high-risk BL-IPI, respectively. CONCLUSION/CONCLUSIONS:The BL-IPI provides robust discrimination of survival in adult BL, suitable for use as prognostication and stratification in trials. The high-risk group has suboptimal outcomes with standard therapy and should be considered for innovative treatment approaches.

B-cell non-Hodgkin lymphoma cell survival depends on poorly understood immune evasion mechanisms. In melanoma, the composition of the gut microbiota (GMB) is associated with immune system regulation and response to immunotherapy. We investigated the association of GMB composition and diversity with lymphoma biology and treatment outcome. Patients with diffuse large B-cell lymphoma (DLBCL), marginal zone (MZL), and follicular lymphoma (FL) were recruited at Mayo Clinic, Minnesota, and Perlmutter Cancer Center, NYU Langone Health. The pretreatment GMB was analyzed using 16S ribosomal RNA gene sequencing. We examined GMB compositions in 3 contexts: lymphoma patients (51) compared with healthy controls (58), aggressive (DLBCL) (8) compared with indolent (FL, MZL) (18), and the association of GMB with immunochemotherapy treatment outcomes (8 responders, 6 nonresponders). Respectively, we found that the pretreatment GMB in lymphoma patients had a distinct composition compared with healthy controls (P < .001); GMB compositions in DLBCL patients were significantly different than indolent patients (P = .01) with a trend toward reduced microbial diversity in DLBCL patients (P = .08); and pretreatment GMB diversity and composition were significant predictors of treatment responses (P = .01). The impact of these pilot results is limited by our small sample size, and should be considered a proof of principle. If validated, our results could lead toward improved treatment outcomes by improving medication stewardship and informing which GMB-targeted therapies should be tested to improve patient outcomes.

CONTEXT.—:The diagnostic workup of lymphoma continues to evolve rapidly as experience and discovery led to the addition of new clinicopathologic entities and techniques to differentiate them. The optimal clinically effective, efficient, and cost-effective approach to diagnosis that is safe for patients can be elusive, in both community-based and academic practice. Studies suggest that there is variation in practice in both settings. OBJECTIVE.—:To develop an evidence-based guideline for the preanalytic phase of testing, focusing on specimen requirements for the diagnostic evaluation of lymphoma. DESIGN.—:The American Society for Clinical Pathology, the College of American Pathologists, and the American Society of Hematology convened a panel of experts in the laboratory workup of lymphoma to develop evidence-based recommendations. The panel conducted a systematic review of literature to address key questions. Using the Grading of Recommendations Assessment, Development, and Evaluation approach, recommendations were derived based on the available evidence, strength of that evidence, and key judgements as defined in the Grading of Recommendations Assessment, Development, and Evaluation Evidence to Decision framework. RESULTS.—:Thirteen guideline statements were established to optimize specimen selection, ancillary diagnostic testing, and appropriate follow-up for safe and accurate diagnosis of indolent and aggressive lymphoma. CONCLUSIONS.—:Primary diagnosis and classification of lymphoma can be achieved with a variety of specimens. Application of the recommendations can guide decisions on specimen suitability, diagnostic capabilities, and correct use of ancillary testing. Disease prevalence in patient populations, availability of ancillary testing, and diagnostic goals should be incorporated into algorithms tailored to each practice environment.

We examined adults with untreated Burkitt lymphoma (BL) from 2009 to 2018 across 30 US cancer centers. Factors associated with progression-free survival (PFS) and overall survival (OS) were evaluated in univariate and multivariate Cox models. Among 641 BL patients, baseline features included the following: median age, 47 years; HIV+, 22%; Eastern Cooperative Oncology Group (ECOG) performance status (PS) 2 to 4, 23%; >1 extranodal site, 43%; advanced stage, 78%; and central nervous system (CNS) involvement, 19%. Treatment-related mortality was 10%, with most common causes being sepsis, gastrointestinal bleed/perforation, and respiratory failure. With 45-month median follow-up, 3-year PFS and OS rates were 64% and 70%, respectively, without differences by HIV status. Survival was better for patients who received rituximab vs not (3-year PFS, 67% vs 38%; OS, 72% vs 44%; P < .001) and without difference based on setting of administration (ie, inpatient vs outpatient). Outcomes were also improved at an academic vs community cancer center (3-year PFS, 67% vs 46%, P = .006; OS, 72% vs 53%, P = .01). In multivariate models, age ≥ 40 years (PFS, hazard ratio [HR] = 1.70, P = .001; OS, HR = 2.09, P < .001), ECOG PS 2 to 4 (PFS, HR = 1.60, P < .001; OS, HR = 1.74, P = .003), lactate dehydrogenase > 3× normal (PFS, HR = 1.83, P < .001; OS, HR = 1.63, P = .009), and CNS involvement (PFS, HR = 1.52, P = .017; OS, HR = 1.67, P = .014) predicted inferior survival. Furthermore, survival varied based on number of factors present (0, 1, 2 to 4 factors) yielding 3-year PFS rates of 91%, 73%, and 50%, respectively; and 3-year OS rates of 95%, 77%, and 56%, respectively. Collectively, outcomes for adult BL in this real-world analysis appeared more modest compared with results of clinical trials and smaller series. In addition, clinical prognostic factors at diagnosis identified patients with divergent survival rates.

OBJECTIVES:The diagnostic workup of lymphoma continues to evolve rapidly as experience and discovery lead to the addition of new clinicopathologic entities and techniques to differentiate them. The optimal clinically effective, efficient, and cost-effective approach to diagnosis that is safe for patients can be elusive, in both community-based and academic practice. Studies suggest that there is variation in practice in both settings. THE AIM OF THIS REVIEW IS TO:develop an evidence-based guideline for the preanalytic phase of testing, focusing on specimen requirements for the diagnostic evaluation of lymphoma. METHODS:The American Society for Clinical Pathology, the College of American Pathologists, and the American Society of Hematology convened a panel of experts in the laboratory workup of lymphoma to develop evidence-based recommendations. The panel conducted a systematic review of the literature to address key questions. Using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach, recommendations were derived based on the available evidence, the strength of that evidence, and key judgments as defined in the GRADE Evidence to Decision framework. RESULTS:Thirteen guideline statements were established to optimize specimen selection, ancillary diagnostic testing, and appropriate follow-up for safe and accurate diagnosis of indolent and aggressive lymphoma. CONCLUSIONS:Primary diagnosis and classification of lymphoma can be achieved with a variety of specimens. Application of the recommendations can guide decisions about specimen suitability, diagnostic capabilities, and correct utilization of ancillary testing. Disease prevalence in patient populations, availability of ancillary testing, and diagnostic goals should be incorporated into algorithms tailored to each practice environment.

The treatment of lymphomas has undergone a shift in the last few decades, from traditional cytotoxic chemotherapy toward immune-targeting agents that supplement or, in some cases, even supplant direct tumor killing with activation of antitumor systemic immunity. Since the introduction of the first known immunomodulatory modality, allogeneic hematopoietic cell transplantation, multiple immunotherapeutic approaches have been developed including monoclonal antibodies (mABs), antibody-drug conjugates, bispecific T-cell engagers, checkpoint inhibitors, small molecule inhibitors, chimeric antigen receptor (CAR) T-cell therapies, and vaccines. Many of these agents, either as monotherapies or as a component of a combination strategy, have shown impressive results, combining efficacy with tolerability. Immunotherapy ranging from mABs to checkpoint inhibitors and CAR T-cell therapy are now integrated into lymphoma treatment from the earliest lines of therapy to the relapsed and refractory setting for both Hodgkin (HL) and non-Hodgkin lymphoma (NHL). Although further studies are needed to improve our understanding of the unique side effects of immunomodulation, to determine the optimal sequence and combinations of these agent with targeted therapies and standard chemotherapy, and to identify predictive biomarkers, they clearly represent a growing list of treatment options for both HL and NHL and an important step on our road toward cure of these diseases.

[Formula presented] Background. BL is a rare, high-grade B-cell lymphoma that is often studied in trials with small sample sizes. Historical definitions of "low-risk BL" vary between studies, use arbitrary cutoffs for lactate dehydrogenase (LDH), and identify a small favorable group, leaving >80-90% of patients (pts) in an undifferentiated "high-risk" category. A validated prognostic index will help compare study cohorts and better define good-prognosis pts for whom reduced treatment would be appropriate vs a poor-prognosis group in need of new approaches. Herein, we constructed and validated a simplified prognostic model for BL applicable to diverse clinical settings across the world. Methods. We derived the BL-IPI from a large real-world evidence cohort of US adults treated for BL in 2009-2018 (Evens A, Blood 2020). Progression-free survival (PFS) from diagnosis until BL recurrence, progression, death, or censoring was the primary outcome. We first determined the best prognostic cutoffs for age, LDH (normalized to local upper limit normal, ULN), hemoglobin (Hgb), and albumin. Independent risk factors were ascertained by forward stepwise selection into Cox regression from candidate variables: age, sex, HIV+ status, ECOG performance status (PS) >=2, advanced stage (3/4), involvement of >1 extranodal site, bone marrow, central nervous system (CNS), values of LDH, Hgb, and albumin. Derivation models used multiple imputation to mitigate bias from missing data and reported hazard ratios (HR) with 95% confidence interval (CI). BL-IPI groups, defined by inspection of survival curves, were compared using log-rank test for trend. We validated performance of the BL-IPI in an external retrospective dataset of BL pts treated contemporaneously in centers from the United Kingdom, Scandinavia, Canada, and Australia. Results. Characteristics of pts in the derivation (N= 633) and validation (N=457) cohorts are shown in the Table. Age >=40 years (yr), LDH >3xULN, Hgb <11.5 g/dL, and albumin <3.5 g/dL were determined as optimal prognostic cutoffs. Age >=40 yr, PS >=2, stage 3/4, involvement of marrow, CNS, LDH >3xULN, low Hgb, and low albumin were associated with inferior PFS in univariate tests. In the multivariable model age >=40 yr, LDH >3xULN, PS >=2, and CNS involvement were selected as 4 independent prognostic factors; adding stage did not enhance the model. The model was simplified to 3 groups with 0 (low risk; 18% of pts), 1 (intermediate risk; 36% of pts; HR=3.14; 95%CI, 1.61-6.14), or 2-4 factors (high risk; 46% of pts; HR=6.52; 95%CI, 3.48-12.20; Fig A) with 3 yr PFS of 92%, 72%, and 53%, respectively (P<.001, Fig. B); median PFS was reached only in the high-risk group (46 months, 95%CI, 19-53). BL-IPI was similarly prognostic for overall survival (OS, P<.001; Fig. C). Among pts with stage III/IV (historically classified as "high-risk" and constituting 78% of all pts in the cohort), the BL-IPI further discriminated subgroups with 3 yr PFS of 87%, 71%, and 52%, respectively (P<.001; Fig. D), and OS of 95%, 75%, and 57%, respectively (P<.001; Fig. E). In addition, BL-IPI was prognostic regardless of HIV status, in the subcohort treated with rituximab (3 yr PFS: 92%, 73%, and 55%, respectively, P<.001), and among pts treated with specific regimens: CODOX-M/IVAC+/-R (3 yr PFS: 88%, 67%, 61%, respectively, P=.004), DA-EPOCH-R (3 yr PFS, 87%, 73%, 51%, respectively, P<.001), or hyperCVAD/MA+/-R (3yr PFS: 100%, 80%, 54%, respectively, P<.001). In the international validation cohort, fewer pts had CNS involvement; most received CODOX-M/IVAC+R; and PFS/OS estimates at 3 yr were higher. BL-IPI categories were of similar size (low-risk 15%, intermediate-risk 35%, high-risk 50%), and provided similar risk discrimination (Harrell's C=.65 in both datasets). PFS at 3 yr was 96%, 82%, and 63%, respectively (P<.001; Fig. F), and OS was 99%, 85%, and 64%, respectively (P<.001; Fig. G). In the validation cohort, BL-IPI remained prognostic in the subsets receiving rituximab (P<.001) and in advanced stage (P<.001). Conclusions. BL-IPI is a novel prognostic index specific to BL, which was validated to allow for simplified stratification and comparison of risk distribution in geographically diverse cohorts. The index identified a low-risk group with PFS >90-95%, which could be targeted with future strategies for treatment de-escalation. Conversely, only about 55-60% of pts in the high-risk group achieved cure with currently available immunochemotherapy. [Formula presented] Disclosures: Olszewski: Spectrum Pharmaceuticals: Research Funding; Genentech, Inc.: Research Funding; TG Therapeutics: Research Funding; Adaptive Biotechnologies: Research Funding. Jakobsen: Takeda: Honoraria. Collins: ADC Therapeutics: Consultancy, Honoraria; Celleron: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Speakers Bureau; Amgen: Research Funding; BeiGene: Consultancy; BMS: Consultancy, Honoraria, Research Funding, Speakers Bureau; Gilead: Consultancy, Honoraria, Speakers Bureau; MSD: Consultancy, Honoraria, Research Funding; Taekda: Consultancy, Honoraria, Other: travel, accommodations, expenses, Speakers Bureau; Roche: Consultancy, Honoraria, Other: travel, accommodations, expenses, Speakers Bureau; Pfizer: Honoraria; Celgene: Research Funding. Cwynarski: Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support; Atara: Consultancy, Membership on an entity's Board of Directors or advisory committees; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support, Speakers Bureau; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support, Speakers Bureau. Bachanova: Incyte: Research Funding; Karyopharma: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; FATE: Research Funding; Kite: Membership on an entity's Board of Directors or advisory committees; Gamida Cell: Membership on an entity's Board of Directors or advisory committees, Research Funding. Danilov: Abbvie: Consultancy; BeiGene: Consultancy; Nurix: Consultancy; Celgene: Consultancy; Gilead Sciences: Research Funding; Takeda Oncology: Research Funding; Pharmacyclics: Consultancy; Bayer Oncology: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; TG Therapeutics: Consultancy; Astra Zeneca: Consultancy, Research Funding; Verastem Oncology: Consultancy, Research Funding; Karyopharm: Consultancy; Aptose Biosciences: Research Funding; Bristol-Myers Squibb: Research Funding; Rigel Pharmaceuticals: Consultancy. Diefenbach: Trillium: Research Funding; Millenium/Takeda: Research Funding; MEI: Research Funding; Merck: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Genentech, Inc.: Consultancy, Research Funding; Incyte: Research Funding; LAM Therapeutics: Research Funding; Denovo: Research Funding. Epperla: Pharmacyclics: Honoraria; Verastem Oncology: Speakers Bureau. Farooq: Kite, a Gilead Company: Honoraria. Feldman: Pfizer: Research Funding; Portola: Research Funding; Janssen: Speakers Bureau; AstraZeneca: Consultancy; Cell Medica: Research Funding; Seattle Genetics, Inc.: Consultancy, Honoraria, Other: Travel expenses, Research Funding, Speakers Bureau; Viracta: Research Funding; Trillium: Research Funding; Rhizen: Research Funding; Corvus: Research Funding; BMS: Consultancy, Honoraria, Research Funding; Kite: Honoraria, Other: Travel expenses, Speakers Bureau; Celgene: Honoraria, Research Funding; Takeda: Honoraria, Other: Travel expenses; Amgen: Research Funding; Pharmacyclics: Honoraria, Other, Speakers Bureau; Abbvie: Honoraria; Bayer: Consultancy, Honoraria; Eisai: Research Funding; Kyowa Kirin: Consultancy, Research Funding. Gerrie: AbbVie: Consultancy, Honoraria, Research Funding; Astrazeneca: Consultancy, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Roche: Research Funding; Sandoz: Consultancy. Jagadeesh: Regeneron: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Debiopharm Group: Research Funding; MEI Pharma: Research Funding; Verastem: Membership on an entity's Board of Directors or advisory committees. Kamdar: BMS: Consultancy; Abbvie: Consultancy; Karyopharm: Consultancy; Celgene: Consultancy; AstraZeneca: Consultancy; Pharmacyclics: Consultancy; Seattle Genetics: Speakers Bureau. Karmali: Takeda: Research Funding; AstraZeneca: Speakers Bureau; BeiGene: Speakers Bureau; Karyopharm: Honoraria; BMS/Celgene/Juno: Honoraria, Other, Research Funding, Speakers Bureau; Gilead/Kite: Honoraria, Other, Research Funding, Speakers Bureau. Khan: Seattle Genetics: Research Funding; Janssen: Honoraria; Pharmacyclics: Honoraria; Bristol Myers Squibb: Research Funding; Celgene: Research Funding. Klein: Takeda: Membership on an entity's Board of Directors or advisory committees. Lossos: Verastem: Consultancy, Honoraria; Stanford University: Patents & Royalties; Seattle Genetics: Consultancy, Other; Janssen Biotech: Honoraria; NCI: Research Funding; Janssen Scientific: Consultancy, Other. Lunning: ADC Therapeutics: Consultancy; Legend: Consultancy; Acrotech: Consultancy; AstraZeneca: Consultancy, Honoraria; Aeratech: Consultancy, Honoraria; Beigene: Consultancy, Honoraria; Verastem: Consultancy, Honoraria; TG Therapeutics: Research Funding; Novartis: Consultancy, Honoraria; Kite: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Curis: Research Funding; Bristol Meyers Squibb: Consultancy, Honoraria, Research Funding. Martin: I-M Consultancy; Celgene: Consultancy; Teneobio: Consultancy; Karyopharm: Consultancy, Research Funding; Janssen: Consultancy; Sandoz: Consultancy; Bayer: Consultancy; Beigene: Consultancy; Cellectar: Consultancy; Incyte: Consultancy; Kite: Consultancy; Morphosys: Consultancy; Regeneron: Consultancy. Martinex-Calle: Abbvie: Other: Travel grant. Naik: Celgene: Other: advisory board; Sanofi: Other: advisory board. Palmisiano: Genentech: Research Funding; AbbVie: Research Funding. Phillips: Beigene: Honoraria; Roche: Research Funding. Phillips: Seattle Genetics: Consultancy; Incyte: Consultancy, Other: travel expenses; AstraZeneca: Consultancy; Karyopharm: Consultancy; Beigene: Consultancy; Bayer: Consultancy, Research Funding; BMS: Consultancy; Pharmacyclics: Consultancy; Abbvie: Consultancy, Research Funding; Cardinal Health: Consultancy. Portell: Roche/Genentech: Consultancy, Research Funding; Infinity: Research Funding; Bayer: Consultancy; Amgen: Consultancy; TG Therapeutics: Research Funding; AbbVie: Research Funding; Pharmacyclics: Consultancy; Janssen: Consultancy; Kite: Consultancy, Research Funding; Acerta/AstraZeneca: Research Funding; Xencor: Research Funding; BeiGene: Consultancy, Research Funding. Reddy: Celgene: Consultancy; BMS: Consultancy, Research Funding; Genentech: Research Funding; Abbvie: Consultancy; KITE Pharma: Consultancy. Yazdy: Abbvie: Consultancy; Genentech: Research Funding; Octapharma: Consultancy; Bayer: Honoraria. Smith: Bristol Meyers Squibb: Research Funding; Ayala: Research Funding; Seattle Genetics: Research Funding; Portola: Research Funding; Pharmacyclics: Research Funding; Merck: Research Funding; Incyte: Research Funding; Ignyta: Research Funding; Genentech: Research Funding; De Novo Biopharma: Research Funding; AstraZeneca: Consultancy; Millenium/Takeda: Consultancy; Beigene: Consultancy; Bayer: Research Funding; AstraZeneca: Research Funding; Acerta Pharma BV: Research Funding; Karyopharm: Consultancy. Cheah: Celgene, F. Hoffmann-La Roche, Abbvie, MSD: Research Funding; Celgene, F. Hoffmann-La Roche, MSD, Janssen, Gilead, Ascentage Pharma, Acerta, Loxo Oncology, TG therapeutics: Honoraria. El-Galaly: F. Hoffmann-La Roche: Current Employment, Other: Support of parent study and funding of editorial support. Evens: Research To Practice: Honoraria, Speakers Bureau; Mylteni: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Merck: Consultancy, Honoraria, Research Funding; Epizyme: Consultancy, Honoraria, Research Funding; MorphoSys: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria.
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Introduction: There are few data about prognostication and outcomes in patients (pts) with HIV-BL treated in the cART era. Optimal treatment strategies to minimize treatment-related mortality (TRM) remain unclear and current recommendations are based on small studies. We conducted a multicenter international analysis to identify prognostic factors and outcomes in pts with HIV-BL treated in the cART era.
Method(s): This retrospective analysis included a subcohort from a recent study across 30 US sites (Evens et al. Blood 2020) augmented by data from 5 UK centers treated 2009-2018. Progression-free (PFS) and overall survival (OS) were estimated by Kaplan-Meier & differences assessed by log-rank test. Univariate (UVA) associations were derived via Cox model and multivariable (MVA) models were constructed by forward selection of significant variables with P<0.05.
Result(s): 249 (US: 140 & UK: 109) pts with newly diagnosed HIV-BL were included. Clinical features included median age 43 (IQR 35-50 years [yrs]); male sex: 84%; ECOG PS: 2-4: 48%; elevated LDH: 85% (> 3x upper limit of normal (ULN) 49% & >5xULN 39%); >1 extranodal (EN) site: 60%; any CNS involvement (CNSinv) 25%; and +bone marrow (BM) 46%. MYC rearrangement was reported in 93% of pts with t(8;14) in 49%, break-apart probe in 41% and MYC-light chain in 3%; the rest had classical BL with negative MYC testing (4%) or missing result (3%) (otherwise classical BL). Median CD4 count was 217 (IQR 90-392 cells/microL) with 68% pts having CD4>100 cells/microL. At BL diagnosis, HIV viral load was detectable in 55%; 39% of pts were on cART. Baseline features were similar between the US & UK cohorts with significant differences only in ECOG PS 2-4 (32% vs 65%; P<0.001) & baseline CNSinv (30% vs 17%, respectively; P=0.02). Tx regimens included: CODOX-M/IVAC (Magrath) 60%, DA-EPOCH 25%, HyperCVAD/MA 13%, & other 1%; most pts (87%) received rituximab (R). Similar regimens were used in pts with baseline CNSinv: Magrath 64%, DA-EPOCH 24% & HyperCVAD 12%. In the US, pts most frequently received DA-EPOCH (42%) followed by Magrath (32%) & HyperCVAD/MA (24%), whereas in the UK, 96% received Magrath. R was more frequently given in the US (94% vs 79%, P<0.001). Similar baseline features were seen in US pts selected for DA-EPOCH as those selected for Magrath or HyperCVAD/MA except for lower median CD4 count (144 vs 260 cells/microL; P=0.04). Overall response to Tx was: CR 70%, PR 9%, PD 14%, not evaluable 7%. TRM was 18% following HyperCVAD/MA, 13% after DA-EPOCH & 7% in patients treated with Magrath. Overall, 33% of pts had a relapse of HIV-BL with 23% systemic only & 10% CNS. With median follow-up of 4.5 yrs, 3-yr PFS & OS were 61% & 66%, respectively, and nearly identical in both countries (Fig A). Pts with CD4>100 cells/microL had better 3-yr PFS (Fig B) & OS (68% vs 57% P=0.03). We observed significantly worse outcomes in pts with baseline CNSinv (3-yr PFS 36% vs 69%, P<0.001; OS 41% vs 73%, P<0.001; Fig C). Magrath was associated with the highest 3-yr PFS (66%) compared with 63% after HyperCVAD/MA & 51% after DA-EPOCH, but the difference was not significant (P=0.13; Fig D). Pts receiving R had numerically higher PFS, but also not statistically significant (63% vs 53% P=0.16). We observed poor outcomes in pts with baseline CNSinv regardless of frontline Tx (3-yr PFS HyperCVAD/MA 40%, Magrath 39%, DA-EPOCH 32%; P=0.93; Fig E). The incidence of CNS recurrence at 3 yr across all Tx was 11%. Higher incidence was observed with DA-EPOCH (P=0.032 vs other regimens; Fig F) with no difference according to CD4 count. Variables associated with PFS & OS on UVA included: ECOG PS 2-4, >1 EN, +BM, baseline CNSinv, LDH>ULN, CD4 <100 cells/microL. On MVA, the variables independently associated with inferior PFS were ECOG PS 2-4 (HR 1.87 P=0.007); baseline CNSinv (HR 1.70, P=0.023); LDH >5xULN (HR 2.09, P<0.001); and >1 EN sites (HR 1.58 P=0.043). The same variables were significant on MVA for OS. Adjusting for all of the prognostic variables, Tx with Magrath was associated with longer PFS (adjusted HR, 0.45, P=0.005).
Conclusion(s): These data represent the largest analysis of HIV-BL to date. There were favorable tolerance and outcomes with intensive R-containing regimens with Magrath being associated with lower TRM and the highest PFS. In addition, prognostic factors for pt outcomes were associated with lymphoma characteristics rather than with HIV-related features. Pts with baseline CNSinv represent a high-risk group with unmet therapeutic needs. [Formula presented] Disclosures: Alderuccio: Oncinfo: Honoraria; Puma Biotechnology: Other: Family member; ADC Therapeutics: Membership on an entity's Board of Directors or advisory committees; OncLive: Honoraria; Inovio Pharmaceuticals: Other: Family member; Foundation Medicine: Other: Family member; Forma Therapeutics: Other: Family member; Agios Pharmaceuticals: Other: Family member. Olszewski: Spectrum Pharmaceuticals: Research Funding; TG Therapeutics: Research Funding; Adaptive Biotechnologies: Research Funding; Genentech, Inc.: Research Funding. Evens: Epizyme: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria; Merck: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria; Mylteni: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria, Research Funding; MorphoSys: Consultancy, Honoraria; Research To Practice: Honoraria, Speakers Bureau; Novartis: Consultancy, Honoraria. Collins: Gilead: Consultancy, Honoraria, Speakers Bureau; BMS: Consultancy, Honoraria, Research Funding, Speakers Bureau; MSD: Consultancy, Honoraria, Research Funding; Taekda: Consultancy, Honoraria, Other: travel, accommodations, expenses, Speakers Bureau; BeiGene: Consultancy; Roche: Consultancy, Honoraria, Other: travel, accommodations, expenses, Speakers Bureau; Celleron: Consultancy, Honoraria, Research Funding; ADC Therapeutics: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Speakers Bureau; Celgene: Research Funding; Amgen: Research Funding; Pfizer: Honoraria. Danilov: Astra Zeneca: Consultancy, Research Funding; Verastem Oncology: Consultancy, Research Funding; Takeda Oncology: Research Funding; Gilead Sciences: Research Funding; Bayer Oncology: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; TG Therapeutics: Consultancy; Nurix: Consultancy; Celgene: Consultancy; Aptose Biosciences: Research Funding; Bristol-Myers Squibb: Research Funding; Rigel Pharmaceuticals: Consultancy; Karyopharm: Consultancy; Pharmacyclics: Consultancy; BeiGene: Consultancy; Abbvie: Consultancy. Jagadeesh: Seattle Genetics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Debiopharm Group: Research Funding; MEI Pharma: Research Funding; Verastem: Membership on an entity's Board of Directors or advisory committees; Regeneron: Research Funding. Reddy: Genentech: Research Funding; Abbvie: Consultancy; BMS: Consultancy, Research Funding; Celgene: Consultancy; KITE Pharma: Consultancy. Farooq: Kite, a Gilead Company: Honoraria. Bond: Seattle Genetics: Honoraria. Khan: Celgene: Research Funding; Janssen: Honoraria; Pharmacyclics: Honoraria; Bristol Myers Squibb: Research Funding; Seattle Genetics: Research Funding. Yazdy: Bayer: Honoraria; Genentech: Research Funding; Octapharma: Consultancy; Abbvie: Consultancy. Karmali: Karyopharm: Honoraria; Takeda: Research Funding; AstraZeneca: Speakers Bureau; BeiGene: Speakers Bureau; BMS/Celgene/Juno: Honoraria, Other, Research Funding, Speakers Bureau; Gilead/Kite: Honoraria, Other, Research Funding, Speakers Bureau. Martin: Janssen: Consultancy; Regeneron: Consultancy; Bayer: Consultancy; Sandoz: Consultancy; I-M Consultancy; Beigene: Consultancy; Cellectar: Consultancy; Incyte: Consultancy; Kite: Consultancy; Morphosys: Consultancy; Celgene: Consultancy; Teneobio: Consultancy; Karyopharm: Consultancy, Research Funding. Diefenbach: Bristol-Myers Squibb: Consultancy, Research Funding; Denovo: Research Funding; Genentech, Inc.: Consultancy, Research Funding; Incyte: Research Funding; LAM Therapeutics: Research Funding; MEI: Research Funding; Merck: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Millenium/Takeda: Research Funding; Trillium: Research Funding. Klein: Takeda: Membership on an entity's Board of Directors or advisory committees. Haverkos: Viracta THerapeutics: Consultancy. Epperla: Verastem Oncology: Speakers Bureau; Pharmacyclics: Honoraria. Caimi: Amgen: Other: Advisory Board; Bayer: Other: Advisory Board; Kite Pharma: Other: Advisory Board; ADC Therapeutics: Other: Advisory Board, Research Funding; Celgene: Speakers Bureau; Verastem: Other: Advisory Board. Kamdar: Roche: Research Funding. Feldman: Eisai: Research Funding; Pfizer: Research Funding; Kyowa Kirin: Consultancy, Research Funding; Portola: Research Funding; Janssen: Speakers Bureau; AstraZeneca: Consultancy; Trillium: Research Funding; Cell Medica: Research Funding; Amgen: Research Funding; Pharmacyclics: Honoraria, Other, Speakers Bureau; Abbvie: Honoraria; Bayer: Consultancy, Honoraria; Viracta: Research Funding; Rhizen: Research Funding; Corvus: Research Funding; BMS: Consultancy, Honoraria, Research Funding; Kite: Honoraria, Other: Travel expenses, Speakers Bureau; Celgene: Honoraria, Research Funding; Takeda: Honoraria, Other: Travel expenses; Seattle Genetics, Inc.: Consultancy, Honoraria, Other: Travel expenses, Research Funding, Speakers Bureau. Smith: AstraZeneca: Consultancy; Millenium/Takeda: Consultancy; Karyopharm: Consultancy; Beigene: Consultancy; Seattle Genetics: Research Funding; Ayala: Research Funding; Bayer: Research Funding; AstraZeneca: Research Funding; Acerta Pharma BV: Research Funding; Bristol Meyers Squibb: Research Funding; Portola: Research Funding; Pharmacyclics: Research Funding; Merck: Research Funding; Incyte: Research Funding; Ignyta: Research Funding; Genentech: Research Funding; De Novo Biopharma: Research Funding. Portell: Amgen: Consultancy; Pharmacyclics: Consultancy; AbbVie: Research Funding; Janssen: Consultancy; TG Therapeutics: Research Funding; Bayer: Consultancy; BeiGene: Consultancy, Research Funding; Xencor: Research Funding; Kite: Consultancy, Research Funding; Acerta/AstraZeneca: Research Funding; Infinity: Research Funding; Roche/Genentech: Consultancy, Research Funding. Naik: Celgene: Other: advisory board; Sanofi: Other: advisory board. Lossos: Janssen Biotech: Honoraria; Verastem: Consultancy, Honoraria; Stanford University: Patents & Royalties; NCI: Research Funding; Seattle Genetics: Consultancy, Other; Janssen Scientific: Consultancy, Other. Cwynarski: Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support; Roche: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support, Speakers Bureau; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Atara: Consultancy, Membership on an entity's Board of Directors or advisory committees; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support, Speakers Bureau.
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