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PURPOSE To develop recommendations concerning the management of male breast cancer. METHODS ASCO convened an Expert Panel to develop recommendations based on a systematic review and a formal consensus process. RESULTS Twenty-six descriptive reports or observational studies met eligibility criteria and formed the evidentiary basis for the recommendations. RECOMMENDATIONS Many of the management approaches used for men with breast cancer are like those used for women. Men with hormone receptor–positive breast cancer who are candidates for adjuvant endocrine therapy should be offered tamoxifen for an initial duration of five years; those with a contraindication to tamoxifen may be offered a gonadotropin-releasing hormone agonist/antagonist plus aromatase inhibitor. Men who have completed five years of tamoxifen, have tolerated therapy, and still have a high risk of recurrence may be offered an additional five years of therapy. Men with early-stage disease should not be treated with bone-modifying agents to prevent recurrence, but could still receive these agents to prevent or treat osteoporosis. Men with advanced or metastatic disease should be offered endocrine therapy as first-line therapy, except in cases of visceral crisis or rapidly progressive disease. Targeted systemic therapy may be used to treat advanced or metastatic cancer using the same indications and combinations offered to women. Ipsilateral annual mammogram should be offered to men with a history of breast cancer treated with lumpectomy regardless of genetic predisposition; contralateral annual mammogram may be offered to men with a history of breast cancer and a genetic predisposing mutation. Breast magnetic resonance imaging is not recommended routinely. Genetic counseling and germline genetic testing of cancer predisposition genes should be offered to all men with breast cancer.

Introduction: Rituximab-based high-dose therapy (HDT) is frequently prescribed to DLBCL patients (pts) undergoing auto-HCT. However data supporting the benefit of adding R to auto-HCT conditioning are not available. Herein, we report the impact of R-based conditioning on auto-HCT outcomes of DLBCL pts. Methods: Using the Center for International Blood and Marrow Transplant Research registry, 862 adult (≥18 years) DLBCL pts undergoing auto-HCT, between 2003-2017 were included. Analysis was limited to pts receiving BEAM (BCNU, etoposide, cytarabine, melphalan)-based HDT, as R was infrequently used with non-BEAM conditioning regimens. All pts received R-containing chemoimmunotherapy in the frontline setting and had chemosensitive disease prior to HCT. Early chemoimmunotherapy failure (ECitF) was defined as not achieving a complete remission (CR) after frontline chemoimmunotherapy or relapsing within 1 year of initial diagnosis. Primary outcome was overall survival (OS). Secondary outcomes included non-relapse mortality (NRM), relapse, progression-free survival (PFS) and infectious complications within 100 days post-HCT. Results: The study cohort was divided into 2 groups; BEAM (n=667) and R-BEAM (n=195). The baseline characteristics of the 2 cohorts were comparable including age at auto-HCT, disease stage, Karnofsky performance score, extranodal involvement, time from diagnosis to auto-HCT, number of prior therapies, remission status, and ECitF. However, significantly more R-BEAM cohort patients received R as part of last therapy line before auto-HCT (75% vs. 86%; P=0.001). Median follow-up of survivors was 48 (range 1-171) and 64 (range 3-142) months in the BEAM and R-BEAM cohorts, respectively. On univariate analysis, the 4 year cumulative incidence of relapse (41% vs 44%), NRM (11% vs 9%), PFS (48% vs 47%; Figure 1) and OS (58% vs 61%; Figure 2) were similar in the R-BEAM and BEAM groups, respectively (Table 1). On multivariate analysis, no significant difference was seen in OS (HR 0.81; 95% CI 0.81-1.31; P=0.83) or PFS (HR 0.94; 95% CI 0.76-1.18; P=0.61) (Table 1) between the two cohorts. Addition of R had no impact on risk of relapse (HR 0.83; 95% CI 0.65-1.07; P=0.15) or NRM (HR 1.43; 95% CI 0.909-2.26; P=0.12). Variables independently associated with lower OS included older age (HR 3.05; 95% CI 1.81-5.13; P<0.001), not being in CR at auto-HCT (HR 1.67, 95% CI 1.39-2.07; P<0.001) and presence of ECitF (HR 1.52, 95% CI 0.54-3.26; P<0.001). Older age (HR 2.26, 95% CI 1.48-3.45; P<0.0002) and not being in CR at auto-HCT (HR 1.78, CI 1.47-2.14; P<0.0001) were also associated with inferior PFS. There was no significant difference in the 100-day cumulative incidence of bacterial, viral or fungal infections between the two cohorts. Disease relapse was the main cause of death in both BEAM and R-BEAM cohorts (66% vs 55%). Conclusion: In this large registry analysis of DLBCL pts undergoing auto-HCT, adding R to BEAM conditioning had no impact on transplantation outcomes. Older age, absence of CR and ECitF were associated with inferior survival. Disclosures Majhail: Mallinckrodt: Honoraria; Incyte: Consultancy; Nkarta: Consultancy; Anthem, Inc.: Consultancy; Atara Bio: Consultancy. Sureda:Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support; Gilead: Honoraria; Amgen: Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Speakers Bureau; Novartis: Honoraria; BMS: Honoraria; Roche: Honoraria; Sanofi: Honoraria; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees. Kharfan-Dabaja:Daiichi Sankyo: Consultancy; Pharmacyclics: Consultancy. Hamadani:ADC Therapeutics: Consultancy, Research Funding; Merck: Research Funding; Takeda: Research Funding; Pharmacyclics: Consultancy; Celgene: Consultancy; Medimmune: Consultancy, Research Funding; Janssen: Consultancy; Otsuka: Research Funding; Sanofi Genzyme: Research Funding, Speakers Bureau.

Introduction: Anti-PD-1 monoclonal antibodies (mAbs) are highly active in relapsed/refractory classical Hodgkin lymphoma (cHL), but most patients (pts) will still relapse. Given this, allogeneic stem cell transplantation (alloHSCT) remains an important option for pts after PD-1 blockade, as it offers the possibility of cure. Prior reports have suggested that alloHSCT after PD-1 mAbs may be associated with severe immune-related complications including acute graft-versus-host disease (GVHD), veno-occlusive disease (VOD) and cytokine release/febrile non-infectious syndrome (CRS). Prior studies of alloHSCT after PD-1 blockade in cHL have been limited by the small number of pts and short follow-up, preventing an accurate assessment of long-term outcomes and complications, risk factors for early toxicity, and the impact of transplant strategies such as choice of GVHD prophylaxis. We therefore assembled a large retrospective international cohort of cHL pts who underwent alloHSCT after PD-(L)1 blockade to better answer these questions, including an assessment of the impact of post-transplant cyclophosphamide (PTCy) on efficacy and toxicity. Methods: Medical records and databases were reviewed at 26 European and United States transplant centers to identify pts with cHL who underwent an alloHSCT any time after receiving a PD-1 or PD-L1 mAb. Response assessment was performed by local investigators according to Lugano 2014 criteria. Overall survival (OS), progression-free survival (PFS), cumulative incidence (CumInc) of relapse (CIR), non-relapse mortality (NRM), acute (a) and chronic (c) GVHD were estimated, as was the association between baseline variables and these outcomes. Results: Between 2014 and 2019, 150 pts were identified who underwent alloHSCT after a median of 10 (range, 1-74) doses of nivolumab (n=118), pembrolizumab (n=31), or avelumab (n=1). The median age was 31 (range 17-68) and pts had received a median of 4 (range, 2-11) lines of therapy prior to PD-(L)1 blockade. 138 pts (92%) had failed BV and 111 (74%) autologous HSCT. The best overall response to PD-(L)1 mAbs was CR for 62 pts (41%), PR for 55 (37%), SD for 17 (11%), PD for 15 (10%) and unknown for 1 (1%). Median time from last dose of PD-(L)1 mAb to alloHSCT was 80 days (range, 17-756) with 70 pts (47%) receiving intervening systemic therapy. At alloHSCT, 90 pts were in CR (60%), 45 in PR (30%), 5 in SD (3%), and 10 in PD (7%). Donors were haploidentical (n=71, 47%), matched sibling (n=29, 19%), matched unrelated (n=39, 26%), mismatched unrelated (n=7, 5%), cord blood (n=2, 1%), or unknown (n=2, 1%). Stem cell source was bone marrow (n=38, 25%), peripheral blood (n=110, 73%), or cord blood (n=2, 1%). GVHD prophylaxis included PTCy in 88 pts (59%) (69/71 (97%) with haploidentical donors; 19/79 (24%) with other donors). With a median post-alloHSCT follow-up for survivors of 23.8 months (range, 1-67), the 2y OS and PFS were 79% (95CI 71-86%) and 65% (95CI 55-73%), respectively, while the 2y CumIncs of relapse and NRM were 21% (95CI 13-29%) and 14%, (95CI, 8-22%), respectively (Fig. 1A-B). 27 pts have died, 3 due to disease and 24 to NRM, including aGVHD (n=7) and VOD (n=2). Veno-occlusive disease (VOD) occurred in 5 pts (day 100 CumInc 4%) and 29 pts (19%) developed CRS (grade 1 n=16; grade 2 n=7; grade 3 n=4; grade 4 n=2). The 6-month CumIncs of grade 2-4, grade 3-4 and grade 4 aGVHD were 39%, 16% and 8%, respectively. Hyperacute GVHD (onset ≤ 14 days after alloHSCT) occurred in 4% of pts and was fatal in 2 pts. The 2y CumInc of cGVHD was 45%. Neither receipt of > 10 doses (median) of anti-PD-(L)1 mAb nor undergoing alloHSCT ≤80 days (median) after last dose of PD-(L)1 mAb were associated with PFS or OS. However, pts with a shorter time to transplant (≤80 days) appeared to have a higher risk of severe (grade 3-4) aGVHD (6m CumInc 24% vs 9%, p=0.006). Recipients of PTCy in this cohort had lower 2y CumIncs of cGVHD (34% vs 58%, p=0.01) and relapse (12% vs 31%, p=0.02), superior 2y PFS (76% vs 54%, p=0.015), and similar rates of severe aGVHD (15% vs 18%, p=0.5), 2y NRM (12% vs 16%, p=0.5), and 2y OS (82% vs 78%, p=0.6). Conclusions: With extended follow-up of a large international cohort, our results argue that alloSCT performed after PD-(L)1 mAbs is a feasible strategy associated with an excellent PFS and a very low CIR for this disease. The use of PT-Cy appears to be associated with improved outcomes and may at present represent the optimal transplant strategy in this pt population. Figure Disclosures Corradini: kite: Honoraria; Abbvie: Honoraria; Servier: Honoraria; Sanofi: Honoraria; Takeda: Honoraria; Roche: Honoraria; Novartis: Honoraria; KiowaKirin: Honoraria; Janssen: Honoraria; Gilead: Honoraria; Daiichi Sankyo: Honoraria; Celgene: Honoraria; Amgen: Honoraria. Ho:Jazz Pharmaceuticals: Research Funding; Jazz Pharmaceuticals: Consultancy; Omeros Corporation: Membership on an entity's Board of Directors or advisory committees. Jaglowski:Kite: Consultancy, Other: advisory board, Research Funding; Novartis: Consultancy, Other: advisory board, Research Funding; Juno: Consultancy, Other: advisory board; Unum Therapeutics Inc.: Research Funding. Herrera:Adaptive Biotechnologies: Consultancy; Bristol-Myers Squibb: Consultancy, Research Funding; Gilead Sciences: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; AstraZeneca: Research Funding; Merck: Consultancy, Research Funding; Genentech, Inc.: Consultancy, Research Funding; Pharmacyclics: Research Funding; Immune Design: Research Funding; Kite Pharma: Consultancy, Research Funding. Blaise:Jazz Pharmaceuticals: Honoraria; Sanofi: Honoraria; Pierre Fabre medicaments: Honoraria; Molmed: Consultancy, Honoraria. Hamadani:Pharmacyclics: Consultancy; Sanofi Genzyme: Research Funding, Speakers Bureau; Merck: Research Funding; Otsuka: Research Funding; Janssen: Consultancy; Medimmune: Consultancy, Research Funding; ADC Therapeutics: Consultancy, Research Funding; Takeda: Research Funding; Celgene: Consultancy. Ansell:LAM Therapeutics: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Affimed: Research Funding. Nieto:Astra-Zeneca: Research Funding; Affimed: Consultancy; Affimed: Research Funding; Novartis: Research Funding. Feldman:Celgene: Honoraria, Research Funding, Speakers Bureau; Seattle Genetics: Consultancy, Honoraria, Other: Travel expenses, Speakers Bureau; Pfizer: Research Funding; AbbVie: Honoraria, Other: Travel expenses, Speakers Bureau; Portola Pharma: Research Funding; Kite Pharma: Honoraria, Other: Travel expenses, Speakers Bureau; Janssen: Honoraria, Speakers Bureau; Pharmacyclics: Honoraria, Other: Travel expenses, Speakers Bureau; Kyowa Hakko Kirin: Research Funding; Eisai: Research Funding; Bayer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding; Cell Medica: Research Funding; Roche: Research Funding; Corvus: Research Funding; Viracta: Research Funding; Trillium: Research Funding; Roche: Research Funding; Takeda: Honoraria, Speakers Bureau. McGuirk:ArticulateScience LLC: Other: Assistance with manuscript preparation; Bellicum Pharmaceuticals: Research Funding; Kite Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Gamida Cell: Research Funding; Pluristem Ltd: Research Funding; Juno Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astellas: Research Funding; Novartis: Research Funding; Fresenius Biotech: Research Funding. Mohty:Jazz Pharmaceuticals: Honoraria, Research Funding. Stamatoulas Bastard:Celgene: Honoraria; Takeda: Consultancy. Houot:Bristol Myers Squibb: Honoraria; Merck Sharp Dohme: Honoraria. Manson:Bristol Myers Squibb: Honoraria. Orvain:Incyte: Honoraria; Novartis: Honoraria; Jazz Pharmaceuticals: Other: Travel & accommodations; Pfizer: Other: Travel & accommodations. Bouabdallah:Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Frigault:Novartis: Consultancy; Kite/Gilead: Honoraria; Nkarta: Consultancy; Incyte: Consultancy; Juno/Celgene: Consultancy; Foundation Medicine: Consultancy; Xenetic: Consultancy. Chen:Takeda: Consultancy; Kiadis: Consultancy; Magenta: Consultancy; Abbvie: Consultancy; Incyte: Consultancy. Lynch:T.G. Therapeutics: Research Funding; Rhizen Pharmaceuticals S.A: Research Funding; Takeda Pharmaceuticals: Research Funding; Juno Therapeutics: Research Funding; Incyte Corporation: Research Funding; Johnson Graffe Keay Moniz & Wick LLP: Consultancy. Smith:AstraZeneca: Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck Sharp & Dohme Corp: Consultancy, Research Funding; Acerta Pharma BV: Research Funding; Portola Pharmaceuticals: Research Funding; Pharmacyclics: Research Funding; Bristol-Myers Squibb (spouse): Research Funding; Denovo Biopharma: Research Funding; Genentech: Research Funding; Ignyta (spouse): Research Funding; Incyte Corporation: Research Funding; Ayala (spouse): Research Funding; Seattle Genetics: Research Funding. Byrne:Karyopharm: Research Funding. Cohen:Hutchison: Research Funding; Astra Zeneca: Research Funding; Janssen Pharmaceuticals: Consultancy; Seattle Genetics, Inc.: Consultancy, Research Funding; Bristol-Meyers Squibb Company: Research Funding; Takeda Pharmaceuticals North America, Inc.: Research Funding; Gilead/Kite: Consultancy; Genentech, Inc.: Consultancy, Research Funding; UNUM: Research Funding; ASH: Research Funding; LAM Therapeutics: Research Funding; Lymphoma Research Foundation: Research Funding. Svoboda:AstraZeneca: Consultancy; Celgene: Research Funding; Incyte: Research Funding; Pharmacyclics: Consultancy, Research Funding; Kyowa: Consultancy; Merck: Research Funding; BMS: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding. Santoro:Bayer: Consultancy, Speakers Bureau; MSD: Speakers Bureau; Arqule: Consultancy, Speakers Bureau; Pfizer: Consultancy, Speakers Bureau; AstraZeneca: Speakers Bureau; Gilead: Consultancy, Speakers Bureau; Servier: Consultancy, Speakers Bureau; Takeda: Speakers Bureau; BMS: Speakers Bureau; Roche: Speakers Bureau; Abb-Vie: Speakers Bureau; Amgen: Speakers Bureau; Celgene: Speakers Bureau; Novartis: Speakers Bureau; BMS: Consultancy; Lilly: Speakers Bureau; Sandoz: Speakers Bureau; Eisai: Consultancy, Speakers Bureau. Armand:Sigma Tau: Research Funding; Otsuka: Research Funding; Pfizer: Consultancy; ADC Therapeutics: Consultancy; Tensha: Research Funding; Affimed: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Research Funding; Adaptive: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Infinity: Consultancy; Genentech: Research Funding. Zinzani:Roche: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Portola: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Immune Design: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sandoz: Membership on an entity's Board of Directors or advisory committees; Servier: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen-Cilag: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celltrion: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sanofi: Consultancy; Eusapharma: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Verastem: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; MSD: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kyowa Kirin: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; TG Therapeutics: Honoraria, Speakers Bureau. Carlo-Stella:Servier: Consultancy, Honoraria, Other: Travel, accommodations; Genenta Science srl: Consultancy; Boehringer Ingelheim: Consultancy; F. Hoffmann-La Roche Ltd: Honoraria, Other: Travel, accommodations, Research Funding; Novartis: Consultancy, Research Funding; ADC Therapeutics: Consultancy, Other: Travel, accommodations, Research Funding; Sanofi: Consultancy, Research Funding; Rhizen Pharmaceuticals: Research Funding; Celgene: Research Funding; Amgen: Honoraria; AstraZeneca: Honoraria; Janssen Oncology: Honoraria; MSD: Honoraria; BMS: Honoraria; Janssen: Other: Travel, accommodations; Takeda: Other: Travel, accommodations.

Introduction: Reduced-intensity and non-myeloablative (RIC/NMA) conditioning regimens are frequently used in alloHCT for NHL, because they are associated with decreased non-relapse mortality (NRM) risk in comparison with myeloablative conditioning regimens and allow older patients and patients with comorbidities to receive alloHCT. However, the optimal RIC/NMA regimen in allo-HCT for NHL is not known. Using the CIBMTR database we compared the transplant outcomes between the commonly used RIC/NMA regimens in NHL. Methods: 1823 adult (≥18 years) NHL patients in CIBMTR registry undergoing alloHCT using matched related or unrelated donors, between 2008-2016 were included. Analysis was limited to patients receiving four commonly used RIC/NMA regimens: fludarabine/ i.v. busulfan (6.4mg/kg) (Flu/Bu), fludarabine/melphalan (140mg/m^2) (Flu/Mel 140), fludarabine/cyclophosphamide (Fly/Cy) and Flu/Cy with 2Gy Total Body Irradiation (Flu/Cy/TBI). Graft-versus-host disease (GVHD) prophylaxis was limited to calcineurin inhibitor-based regimens. Patients who received post transplantation cyclophosphamide for GVHD prophylaxis were excluded. The primary outcome was overall survival (OS). Secondary outcomes included cumulative incidence of NRM, relapse, progression-free survival (PFS) and cumulative incidence of acute and chronic GVHD. Probabilities of OS and PFS were calculated using the Kaplan-Meier estimator. Multivariable regression analysis was performed for GVHD, relapse/progression, NRM, PFS, and OS. Covariates with a p<0.01 were considered significant. Results: The study cohort was divided into 4 groups; Flu/Bu (n=458), Flu/Cy/TBI (n=89), Flu/Mel 140 (n=885) and Flu/Cy (n=391). The baseline characteristics are shown in Table 1. The 4 cohorts were comparable with respect to median patient age, gender, donor type, remission status at alloHCT, and use of prior auto HCT. There was no interaction between NHL histological subtype and type of conditioning regimen. Results of multivariate analysis are shown in Table 2. The Flu/Mel 140 regimen was associated with a higher NRM (HR 1.78, 95% CI 1.37-2.31; p<0.001) when compared to Flu/Bu. Although the risk of relapse with Flu/Mel 140 was lower when compared to Flu/Bu (HR 0.79, 95% CI 0.66-0.94); p=0.007), this did not result in an improvement in PFS. Moreover, the Flu/Mel 140 cohort had an inferior OS (HR 1.34, 95% CI 1.13-1.59; p<0.001) when compared to Flu/Bu. There was no significant difference in terms of OS, PFS, relapse and NRM between Flu/Bu, Flu/Cy and Flu/Cy/TBI. There was no difference in risk of grade 3-4 acute GVHD across the four cohorts and compared to Flu/Cy/TBI, Flu/Mel 140 had a higher risk of chronic GVHD (HR 1.38, 95% CI 1.15-1.65; p<0.001). Four year adjusted PFS was 38% for Flu/Bu, 51% for Flu/Cy/TBI, 39% for Flu/Mel 140 and 35% for Flu/Cy (p=0.07). Four year adjusted OS was 58% for Flu/Bu, 67% for Flu/Cy/TBI, 49% for Flu/Mel 140 and 63% for Flu/Cy (p<0.001). Disease relapse was the most common cause of death across all 4 cohorts. Conclusion: This is the largest analysis comparing the impact of various RIC/NMA conditioning regimens on the outcomes of NHL patients undergoing alloHCT. We report that the choice of RIC/NMA conditioning regimen significantly impacted OS. The most commonly used conditioning regimen, Flu/Mel 140 was associated with a higher NRM and an inferior OS. The Flu/Bu, Flu/Cy, and Flu/CY/TBI conditioning regimens appear to provide comparable OS. Disclosures Ghosh: Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Pharmacyclics LLC, an AbbVie Company: Consultancy, Honoraria, Research Funding, Speakers Bureau; Bristol-Myers Squibb: Honoraria, Speakers Bureau; Gilead: Consultancy, Honoraria, Speakers Bureau; SGN: Consultancy, Honoraria, Research Funding, Speakers Bureau; TG Therapeutics: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Research Funding; Genentech: Research Funding; Forty Seven Inc: Research Funding; AstraZeneca: Honoraria, Speakers Bureau. Kharfan-Dabaja:Pharmacyclics: Consultancy; Daiichi Sankyo: Consultancy. Sureda:Amgen: Membership on an entity's Board of Directors or advisory committees; Gilead: Honoraria; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Honoraria; Sanofi: Honoraria; Takeda: Consultancy, Honoraria, Speakers Bureau; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support; Novartis: Honoraria; Roche: Honoraria. Hamadani:Merck: Research Funding; ADC Therapeutics: Consultancy, Research Funding; Celgene: Consultancy; Otsuka: Research Funding; Medimmune: Consultancy, Research Funding; Takeda: Research Funding; Pharmacyclics: Consultancy; Janssen: Consultancy; Sanofi Genzyme: Research Funding, Speakers Bureau.

Abstract Background: Results from epidemiologic studies examining polyunsaturated fatty acids (PUFA) and colorectal cancer risk are inconsistent. Mendelian randomization may strengthen causal inference from observational studies. Given their shared metabolic pathway, examining the combined effects of aspirin/NSAID use with PUFAs could help elucidate an association between PUFAs and colorectal cancer risk. Methods: Information was leveraged from genome-wide association studies (GWAS) regarding PUFA-associated SNPs to create weighted genetic scores (wGS) representing genetically predicted circulating blood PUFAs for 11,016 non-Hispanic white colorectal cancer cases and 13,732 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). Associations per SD increase in the wGS were estimated using unconditional logistic regression. Interactions between PUFA wGSs and aspirin/NSAID use on colorectal cancer risk were also examined. Results: Modest colorectal cancer risk reductions were observed per SD increase in circulating linoleic acid [ORLA = 0.96; 95% confidence interval (CI) = 0.93–0.98; P = 5.2 × 10−4] and α-linolenic acid (ORALA = 0.95; 95% CI = 0.92–0.97; P = 5.4 × 10−5), whereas modest increased risks were observed for arachidonic (ORAA = 1.06; 95% CI = 1.03–1.08; P = 3.3 × 10−5), eicosapentaenoic (OREPA = 1.04; 95% CI = 1.01–1.07; P = 2.5 × 10−3), and docosapentaenoic acids (ORDPA = 1.03; 95% CI = 1.01–1.06; P = 1.2 × 10−2). Each of these effects was stronger among aspirin/NSAID nonusers in the stratified analyses. Conclusions: Our study suggests that higher circulating shorter-chain PUFAs (i.e., LA and ALA) were associated with reduced colorectal cancer risk, whereas longer-chain PUFAs (i.e., AA, EPA, and DPA) were associated with an increased colorectal cancer risk. Impact: The interaction of PUFAs with aspirin/NSAID use indicates a shared colorectal cancer inflammatory pathway. Future research should continue to improve PUFA genetic instruments to elucidate the independent effects of PUFAs on colorectal cancer.

Background Although rituximab‐based high‐dose therapy is frequently used in diffuse large B cell lymphoma (DLBCL) patients undergoing autologous hematopoietic cell transplantation (auto‐HCT), data supporting the benefits are not available. Herein, we report the impact of rituximab‐based conditioning on auto‐HCT outcomes in patients who have DLBCL. Methods Using the Center for International Blood and Marrow Transplant Research (CIBMTR) registry, 862 adult DLBCL patients undergoing auto‐HCT between 2003 and 2017 using BEAM (BCNU, etoposide, cytarabine, melphalan) conditioning regimen were included. All patients received frontline rituximab‐containing chemoimmunotherapy and had chemosensitive disease pre‐HCT. Early chemoimmunotherapy failure was defined as not achieving complete remission (CR) after frontline chemoimmunotherapy or relapse within 1 year of initial diagnosis. The primary outcome was overall survival (OS). Results The study cohort was divided into 2 groups: BEAM (n = 667) and R‐BEAM (n = 195). On multivariate analysis, no significant difference was seen in OS ( P = .83) or progression‐free survival (PFS) ( P = .61) across the 2 cohorts. No significant association between the use of rituximab and risk of relapse ( P = .15) or nonrelapse mortality ( P = .12) was observed. Variables independently associated with lower OS included older age at auto‐HCT ( P < .001), absence of CR at auto‐HCT ( P < .001) and early chemoimmunotherapy failure ( P < .001). Older age ( P < .0002) and non‐CR pre‐HCT ( P < .0001) were also associated with inferior PFS. There was no significant difference in early infectious complications between the 2 cohorts. Conclusion In this large registry analysis of DLBCL patients undergoing auto‐HCT, the addition of rituximab to the BEAM conditioning regimen had no impact on transplantation outcomes. Older age, absence of CR pre auto‐HCT, and early chemoimmunotherapy failure were associated with inferior survival.

ObjectivePublished predictive models of disease outcomes in idiopathic inflammatory myopathies (IIMs) are sparse and of limited accuracy due to disease heterogeneity. Computational methods may address this heterogeneity by partitioning patients based on clinical and biological phenotype.MethodsTo identify new patient groups, we applied similarity network fusion (SNF) to clinical and biological data from 168 patients with myositis (64 adult polymyositis [PM], 65 adult dermatomyositis [DM], and 39 juvenile DM [JDM]) in the Rituximab in Myositis trial. We generated a sparse proof‐of‐concept bedside classifier using multinomial regression and identified characteristics that distinguished these groups. We conducted χ2 tests to link new patient groups with the myositis subtypes.ResultsSNF identified five patient groups in the discovery cohort that subdivided the myositis subtypes. The sparse multinomial regressor to predict patient group assignments (areas under the receiver operating characteristic curve = [0.78, 0.97]; areas under the precision‐recall curve = [0.55, 0.96]) found that autoantibody enrichment defined four of these groups: anti–Mi‐2, anti–signal recognition peptide (SRP), anti–nuclear matrix protein 2 (NXP2), and anti‐synthetase (Syn). Depletion of immunoglobulin M (IgM) defined the fifth group. Each group was associated with one subtype, with adult DM being associated with anti–Mi‐2 and anti‐Syn autoantibodies, JDM being associated with anti‐NXP2 autoantibodies, and adult PM being associated with IgM depletion and anti‐SRP autoantibodies. These associations enabled us to further resolve the current myositis subtypes.ConclusionUsing unsupervised machine learning, we identified clinically and biologically homogeneous groups of patients with IIMs, forming the basis of an integrated disease classification based on both clinical and biological phenotype, thus validating other approaches and what has been previously described.

PURPOSETo compare the outcomes of patients with Hodgkin or non-Hodgkin lymphoma undergoing nonmyeloablative haploidentical or unrelated cord blood (UCB) hematopoietic cell transplantation.PATIENTS AND METHODSWe retrospectively studied 740 patients with Hodgkin lymphoma (n = 283, 38%) and non-Hodgkin lymphoma (n = 457, 62%) age 18-75 years who received transplantations from 2009 to 2016. Data were reported to the Lymphoma Working Party of the European Society for Blood and Marrow Transplantation, Eurocord, or Center for International Blood and Marrow Transplant Research. Of the 526 patients who received haploidentical transplantation, 68% received bone marrow and 32% received peripheral blood. All patients received a uniform transplantation conditioning regimen (2 Gy of total-body irradiation, cyclophosphamide, and fludarabine) and graft-versus-host disease prophylaxis (calcineurin inhibitor and mycophenolate). In addition, patients who received a haploidentical transplantation received posttransplantation cyclophosphamide.RESULTSCompared with haploidentical bone marrow and peripheral-blood transplantations and adjusted for age, lymphoma subtype, and disease status, survival was lower after UCB transplantation (hazard ratio [HR], 1.55; P = .001; and HR, 1.59; P = .005, respectively). Similarly, progression-free survival was lower after UCB transplantations compared with haploidentical bone marrow and peripheral-blood transplantations (HR, 1.44; P = .002; and HR, 1.86; P < .0001), respectively. The 4-year overall and progression-free survival rates after UCB transplantation were 49% and 36%, respectively, compared with 58% and 46% after haploidentical bone marrow transplantation and 59% and 52% after peripheral-blood transplantation, respectively. Lower survival was attributed to higher transplantation-related mortality after UCB transplantation compared with haploidentical bone marrow and peripheral-blood transplantation (HR, 1.91; P = .0001; and HR, 2.27; P = .0002, respectively).CONCLUSIONWhen considering HLA-mismatched transplantation for Hodgkin or non-Hodgkin lymphoma, the data support haploidentical related donor transplantation over UCB transplantation.

Background Surveillance of sepsis incidence is important for directing resources and evaluating quality-of-care interventions. The aim was to develop and validate a fully-automated Sepsis-3 based surveillance system in non-intensive care wards using electronic health record (EHR) data, and demonstrate utility by determining the burden of hospital-onset sepsis and variations between wards. Methods A rule-based algorithm was developed using EHR data from a cohort of all adult patients admitted at an academic centre between July 2012 and December 2013. Time in intensive care units was censored. To validate algorithm performance, a stratified random sample of 1000 hospital admissions (674 with and 326 without suspected infection) was classified according to the Sepsis-3 clinical criteria (suspected infection defined as having any culture taken and at least two doses of antimicrobials administered, and an increase in Sequential Organ Failure Assessment (SOFA) score by >2 points) and the likelihood of infection by physician medical record review. Results In total 82 653 hospital admissions were included. The Sepsis-3 clinical criteria determined by physician review were met in 343 of 1000 episodes. Among them, 313 (91%) had possible, probable or definite infection. Based on this reference, the algorithm achieved sensitivity 0.887 (95% CI: 0.799 to 0.964), specificity 0.985 (95% CI: 0.978 to 0.991), positive predictive value 0.881 (95% CI: 0.833 to 0.926) and negative predictive value 0.986 (95% CI: 0.973 to 0.996). When applied to the total cohort taking into account the sampling proportions of those with and without suspected infection, the algorithm identified 8599 (10.4%) sepsis episodes. The burden of hospital-onset sepsis (>48 hour after admission) and related in-hospital mortality varied between wards. Conclusions A fully-automated Sepsis-3 based surveillance algorithm using EHR data performed well compared with physician medical record review in non-intensive care wards, and exposed variations in hospital-onset sepsis incidence between wards.

There is an increasing consumption of energy drinks both in the United States and worldwide. The components of these beverages are sometimes unclear but commonly include caffeine, sugars, taurine, and B-vitamins. Young people, particularly those engaged in sports, studying, and in the military are especially likely to be consumers of energy drinks. While limited data are available regarding their autonomic and hemodynamic effects, current literature suggests that energy drink consumption is accompanied by increases in blood pressure, sympathetic drive, and also in QT prolongation. There are no systematic long term studies identifying consequences of frequent energy drink consumption. However, multiple anecdotal reports implicate energy drinks in adverse cardiovascular events including atrial fibrillation, ventricular arrhythmia, myocardial infarction, and sudden death. Events such as atrial fibrillation may even occur in otherwise healthy subjects with structurally normal hearts. It is likely that these cardiovascular outcomes are triggered by the hemodynamic, autonomic, and electrocardiographic responses to energy drink consumption. What remains unclear is how concomitant use of other stimulants such as amphetamines and nicotine may interact to potentiate neural and circulatory responses and cardiovascular consequences when combined with energy drinks.