Citations

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.

Background: The combination of lenalidomide+rituximab (R2) recently showed superior efficacy vs R-placebo in patients (pts) with R/R iNHL (Leonard et al. J Clin Oncol 2019). Based on these AUGMENT study results, R2 was approved by the US FDA for treatment of adult pts with previously treated follicular lymphoma (FL) or marginal zone lymphoma (MZL). Advanced age at diagnosis is a risk factor in pts with iNHL. We performed post-hoc subgroup analyses by age from AUGMENT and data here focus on pts age ≥ 70 y. Methods: AUGMENT (NCT01938001) is a multicenter, double-blind, randomized phase III study of R2 vs R-placebo in pts with FL grade 1-3a or MZL previously treated with ≥ 1 systemic therapy with R/R disease but not refractory to rituximab. Pts were randomized 1:1 to R2 or R-placebo. R2 was oral lenalidomide 20 mg/d, d1-21/28 for 12c plus rituximab IV 375 mg/m2 weekly in c1 and d1 of c2-5. R-placebo was rituximab+placebo on the same schedule. The primary endpoint was progression-free survival (PFS) per 2007 IWG response criteria (without PET) as assessed by IRC (central review). Secondary endpoints included overall response rate (ORR), complete response (CR), time to next anti-lymphoma treatment (TTNLT) and safety. Post-hoc analyses were performed by dividing pts into age < 70 y and ≥ 70 y subgroups, the latter group considered unfit for chemotherapy. Results: Of 358 pts randomized (R2, n = 178; R-placebo, n = 180), 267 pts were age < 70 y (R2, n = 131; R-placebo, n = 136), and 91 pts were age ≥ 70 y (R2, n = 47; R-placebo, n = 44). Baseline characteristics including histology, disease status, and prior treatments are shown in the table and were similar across treatment arms in pts ≥ 70 y. At a median follow-up of 28.3 mo, the study met its primary endpoint of PFS, with a hazard ratio (HR) of 0.46 (95% CI, 0.34-0.62; P < 0.0001) in the overall population. R2 had superior PFS vs R-placebo in both < 70 and ≥ 70 y subgroups, with HR of 0.41 (95% CI, 0.29-0.59) and HR of 0.66 (95% CI, 0.37-1.18), respectively. In pts ≥ 70 y, median PFS with R2 vs R-placebo was 24.9 vs 14.3 mo; ORR/CR was 81%/26% vs 59%/16%; and TTNLT was not reached in either arm. Efficacy results for all pts and those < 70 y are reported in the table; notably in pts receiving R2, mPFS was longer in pts < 70 y vs ≥ 70 y (39.4 mo [95% CI, 22.9-NE] vs 24.9 mo [95% CI, 16.4-NE]). In pts ≥ 70 y, any-grade adverse events (AEs) with a ≥ 10 % difference between R2 vs R-placebo included neutropenia (63% vs 11%), constipation (33% vs 16%), cough (33% vs 16%), leukopenia (26% vs 2%), anemia (24% vs 9%), pyrexia (24% vs 9%), pruritus/pruritus generalized (24% vs 2%), muscle spasms (22% vs 11%), rash/rash maculopapular (22% vs 5%), headache (20% vs 9%), thrombocytopenia (17% vs 2%), dyspepsia (13% vs 2%), influenza (13% vs 2%), back pain (7% vs 18%), and nasopharyngitis (4% vs 16%). Also, tumor flare was reported in 9% vs 0% of pts, respectively. In pts ≥ 70 y, 75% of R2 pts vs 36% of R-placebo pts had ≥ 1 grade 3/4 AE, mainly due to neutropenia (50% vs 7%). All other grade 3/4 AEs occurred in < 10% of pts ≥ 70 y in both treatment arms. One grade 5 AE occurred in pts ≥ 70 y (R-placebo arm). In the R2 arm, the median number of treatment cycles was 12 for both the < 70 y vs ≥ 70 y subgroups; however, fewer older pts completed 12 cycles of lenalidomide (76% vs 57%), and more started lenalidomide at the lower dose of 10 mg (6% vs 35%) because of low creatinine clearance, respectively. In the R2 < 70 y and ≥ 70 y subgroups, the average daily dose of lenalidomide was 17.9 mg/d (range, 5.6-20.0) and 14.4 mg/d (range 4.2-20.0), and median relative dose intensity was 95% and 86%, respectively. Conclusions: Similar to the results in the original population, R2 showed superior efficacy vs rituximab monotherapy (plus placebo) as measured by the primary end point of PFS and secondary end points of ORR and CR in pts with R/R FL grade 1-3a and MZL irrespective of age. The efficacy and safety profiles of R2 and R-placebo in pts ≥ 70 y were similar to those reported in the overall population. Older pts treated with R2 vs R-placebo had superior mPFS (24.9 vs 14.3 mo). They were more likely to start lenalidomide at a lower dose and had lower median dose intensity which may have contributed to their shorter mPFS vs younger pts receiving R2. These data show that R2 maintained efficacy improvements vs R-placebo in pts ≥ 70 y, despite higher unfit status and lower overall lenalidomide treatment/exposure. Thus, R2 is an effective and available treatment option for pts with iNHL, including those with advanced age. Disclosures Trněný: Amgen: Consultancy, Honoraria; F. Hoffmann-La Roche: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Celgene: Consultancy; MorphoSys: Consultancy, Honoraria; Incyte: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Gilead Sciences: Consultancy, Honoraria. Leonard:Nordic Nanovector: Consultancy; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy; AstraZeneca: Consultancy; Celgene: Consultancy; Bayer Corporation: Consultancy; Epizyme, Inc: Consultancy; Karyopharm Therapeutics: Consultancy; Sutro Biopharma: Consultancy; Merck: Consultancy; Gilead: Consultancy; BeiGene: Consultancy; ADC Therapeutics: Consultancy; Sandoz: Consultancy; Akcea Therapeutics: Consultancy; Miltenyi: Consultancy; MorphoSys: Consultancy. Nowakowski:F. Hoffmann-La Roche Ltd: Research Funding; NanoString: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Genentech, Inc.: Research Funding; MorphoSys: Consultancy, Research Funding. Izutsu:Chugai, Celgene, Daiichi Sankyo, Astra Zeneca, Eisai, Symbio, Ono, Bayer, Solasia, Zenyaku, Incyte, Novartis, Sanofi, HUYA Bioscience, MSD, Astellas Amgen, Abbvie, ARIAD, Takeda, Pfizer: Research Funding; Kyowa Kirin, Eisai, Takeda, MSD, Chugai, Nihon Medi-physics, Janssen, Ono, Abbvie, Dainihon Sumitomo, Bayer, Astra Zeneca, HUYA Japan, Novartis, Bristol-Byers Squibb, Mundi, Otsuka, Daiichi Sankyo, Astellas, Asahi Kasei: Honoraria; Celgene: Consultancy; Eisai, Symbio, Chugai, Zenyaku: Research Funding; Eisai, Chugai, Zenyaku: Honoraria. Fowler:TG Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: 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; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding. Thieblemont:Roche: Honoraria, Research Funding; Gilead: Honoraria; Novartis: Honoraria; Kyte: Honoraria; Janssen: Honoraria; Celgene: Honoraria; Cellectis: Membership on an entity's Board of Directors or advisory committees. Zinzani: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; Eusapharma: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sanofi: Consultancy; Celltrion: 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; Janssen-Cilag: Honoraria, 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; Servier: 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; Immune Design: 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; Portola: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Roche: 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. Kalambakas:Celgene Corporation: Employment, Equity Ownership. Czuczman:Celgene Corporation: Employment, Equity Ownership. Fustier:Celgene Corporation: Employment, Equity Ownership. Wu:Celgene Corporation: Employment, Equity Ownership. Gribben:Janssen: Consultancy, Honoraria, Research Funding; Acerta/Astra Zeneca: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding.

BACKGROUND Determination of blood donor hemoglobin (Hb) levels is a pre‐requisite to ensure donor safety and blood product quality. We aimed to identify Hb measurement practices across blood donation services and to what extent differences associate with low‐Hb deferral rates. METHODS An online survey was performed among Biomedical Excellence for Safer Transfusion (BEST) Collaborative members, extended with published data. Multivariable negative‐binomial regression models were built to estimate adjusted associations of minimum donation intervals, Hb cut‐offs (high, ≥13.5 g/dL in men or ≥ 12.5 g/dL in women, vs. lower values), iron monitoring (yes/no), providing or prescribing iron supplementation (yes/no), post‐versus pre‐donation Hb measurement and geographical location (Asian vs. rest), with low‐Hb deferral rates. RESULTS Data were included from 38 blood services. Low‐Hb deferral rates varied from 0.11% to 8.81% among men and 0.84% to 31.85% among women. Services with longer minimum donation intervals had significantly lower deferral rates among both women (rate ratio, RR 0.53, 95%CI 0.33‐0.84) and men (RR 0.53, 95%CI 0.31‐0.90). In women, iron supplementation was associated with lower Hb deferral rates (RR 0.47, 95%CI 0.23‐0.94). Finally, being located in Asia was associated with higher low‐Hb deferral rates; RR 9.10 (95%CI 3.89‐21.27) for women and 6.76 (95%CI 2.45‐18.68) for men. CONCLUSION Differences in Hb measurement and eligibility criteria, particularly longer donation intervals and iron supplementation in women, are associated with variations in low‐Hb deferral rates. These insights could help improve both blood donation service efficiency and donor care.

Abstract Background: Traditionally, mutational burden and mutational signatures have been assessed by tumor-normal pair DNA sequencing. The requirement of having both normal and tumor samples is not always feasible from a clinical perspective, and led us to investigate the efficacy of using RNA sequencing of only the tumor sample to determine the mutational burden and signatures, and subsequently molecular cause of the cancer. The potential advantages include reducing the cost of testing, and simultaneously providing information on the gene expression profile and gene fusions present in the tumor.Results: In this study, we devised supervised and unsupervised learning methods to determine mutational signatures from tumor RNA-seq data. As applications, we applied the methods to a training set of 587 TCGA uterine cancer RNAseq samples, and examined in an independent testing set of xxx TCGA colorectal cancer RNAseq samples. Both diseases are known associated with microsatellite instable high (MSI-H) and driver defects in DNA polymerase ɛ (POLɛ). From RNAseq called variants, we found majority (>95%) are likely germline variants, leading to C>T enriched germline variants (dbSNP) widely applicable in tumor and normal RNAseq samples. We found significant associations between RNA-derived mutational burdens and MSI/POLɛ status, and insignificant relationship between RNAseq total coverage and derived mutational burdens. Additionally we found that over 80% of variants could be explained by using the COSMIC mutational signature-5, -6 and -10, which are implicated in natural aging, MSI-H, and POLɛ, respectively. For classifying tumor type, within UCEC we achieved a recall of 0.56 and 0.78, and specificity of 0.66 and 0.99 for MSI and POLɛ respectively. By applying learnt RNA signatures from UCEC to COAD, we were able to improve our classification of both MSI and POLɛ. Conclusions: Taken together, our work provides a novel method to detect RNAseq derived mutational signatures with effective procedures to remove likely germline variants. It can leads to accurate classification of underlying driving mechanisms of DNA damage deficiency.

Abstract Based on the currently available literature, total denervation of the elbow joint is considered impossible. However, consensus with respect to the anatomic location of sensory branches is lacking. The aim of this literature review was to establish consensus with respect to the anatomic features of the articular branches innervating the elbow joint, as well as the distribution of sensory receptors about its capsule. Four electronic databases were queried, between January 1945 and June 2019. Twenty‐one original articles providing a detailed description of the distribution of sensory receptors about the elbow joint capsule (5) and its articular branches (16) were reviewed. The posterior capsule was found to be primarily innervated by the ulnar and radial nerves via combined articular branches and sensory branches of the medial antebrachial cutaneous nerve. The anterior capsule was found to be primarily innervated by a plexus of articular ramifications from muscular branches of mixed nerves (ulnar, musculocutaneous, radial, and median nerves). A higher density of nociceptors and mechano‐receptors was identified within the posterior and anterior capsules, respectively. Thorough denervation, via the technique proposed herein, is likely to be sufficient in eliminating pain from degenerative conditions of the elbow joint.

AbstractIntroductionWe previously characterized associations between brain imaging measurements of amyloid‐β (Aβ) plaque burden and apolipoprotein E (APOE) ε4 gene dose in a small number of cognitively unimpaired late‐middle‐aged APOE ε4 homozygotes (HMs), heterozygotes (HTs), and noncarriers (NCs). We now characterize cross‐sectional Aβ plaque, tau tangle, and cortical atrophy (neurodegeneration) measurements, classifications, and associations with age in a larger number of unimpaired HMs, HTs, and NCs over a wider age range.MethodsWe analyzed 11C Pittsburgh compound B (Aβ) positron emission tomography (PET), flortaucipir (tau) PET, and volumetric magnetic resonance imaging data from 164 study participants of age 47–86 years, including 26 APOE ε4 HMs, 48 HTs, and 90 NCs matched for age and sex.ResultsAβ PET measurements rose, plateaued at the respective ages of 68 and 76, and then declined with age in unimpaired HM and HT groups. Compared with NCs, these two groups began to have significantly higher Aβ PET measurements at ages 62 and 70, respectively, and no longer had significantly higher measurements by ages 71 and 78, respectively. They began to have significantly higher entorhinal cortex tau PET measurements at ages 66 and 70, respectively, and no longer had significantly higher measurements by ages 74 and 78, respectively. Brain atrophy measurements tended to decline slowly with age in all three genetic groups. Their elevated tau PET measurements were attributable to those with positive Aβ PET scans. 41.0%, 18.0%, and 5.0% of the 47‐ to 70‐year‐old HMs, HTs, and NCs and 25.0%, 79.0%, and 38.0% of the 71‐ to 86‐year‐old HMs, HTs, and NCs had positive Aβ PET scans, and the long‐term recall memory scores are significantly higher in the older HMs than in HT and NC groups, suggesting resistance to Aβ deposition in those HMs who remained unimpaired at older ages.ConclusionsThis study provides information about Aβ plaque burden, tau tangle burden, and neurodegeneration in cognitively unimpaired persons at three levels of genetic risk for AD. Unimpaired APOE ε4 HMs can be studied before their 70s to evaluate the understanding of factors, processes, and interventions involved in the predisposition to and prevention of AD, and after their 70s, to discover factors, processes, and interventions involved in the resilience or resistance to and prevention of AD.

Abstract Aims Most clinical risk stratification models are based on measurement at a single time-point rather than serial measurements. Artificial intelligence (AI) is able to predict one-dimensional outcomes from multi-dimensional datasets. Using data from Global Anticoagulant Registry in the Field (GARFIELD)-AF registry, a new AI model was developed for predicting clinical outcomes in atrial fibrillation (AF) patients up to 1 year based on sequential measures of prothrombin time international normalized ratio (PT-INR) within 30 days of enrolment. Methods and results Patients with newly diagnosed AF who were treated with vitamin K antagonists (VKAs) and had at least three measurements of PT-INR taken over the first 30 days after prescription were analysed. The AI model was constructed with multilayer neural network including long short-term memory and one-dimensional convolution layers. The neural network was trained using PT-INR measurements within days 0–30 after starting treatment and clinical outcomes over days 31–365 in a derivation cohort (cohorts 1–3; n = 3185). Accuracy of the AI model at predicting major bleed, stroke/systemic embolism (SE), and death was assessed in a validation cohort (cohorts 4–5; n = 1523). The model’s c-statistic for predicting major bleed, stroke/SE, and all-cause death was 0.75, 0.70, and 0.61, respectively. Conclusions Using serial PT-INR values collected within 1 month after starting VKA, the new AI model performed better than time in therapeutic range at predicting clinical outcomes occurring up to 12 months thereafter. Serial PT-INR values contain important information that can be analysed by computer to help predict adverse clinical outcomes.

Abstract Motivation DNA methylation can be measured at the single CpG level using sodium bisulfite conversion of genomic DNA followed by sequencing or array hybridization. Many analytic tools have been developed, yet there is still a high demand for a comprehensive and multifaceted tool suite to analyze, annotate, QC and visualize the DNA methylation data. Results We developed the CpGtools package to analyze DNA methylation data generated from bisulfite sequencing or Illumina methylation arrays. The CpGtools package consists of three types of modules: (i) ‘CpG position modules’ focus on analyzing the genomic positions of CpGs, including associating other genomic and epigenomic features to a given list of CpGs and generating the DNA motif logo enriched in the genomic contexts of a given list of CpGs; (ii) ‘CpG signal modules’ are designed to analyze DNA methylation values, such as performing the PCA or t-SNE analyses, using Bayesian Gaussian mixture modeling to classify CpG sites into fully methylated, partially methylated and unmethylated groups, profiling the average DNA methylation level over user-specified genomics regions and generating the bean/violin plots and (iii) ‘differential CpG analysis modules’ focus on identifying differentially methylated CpGs between groups using different statistical methods including Fisher’s Exact Test, Student’s t-test, ANOVA, non-parametric tests, linear regression, logistic regression, beta-binomial regression and Bayesian estimation. Availability and implementation CpGtools is written in Python under the open-source GPL license. The source code and documentation are freely available at https://github.com/liguowang/cpgtools. Supplementary information Supplementary data are available at Bioinformatics online.