Citations

Abstract We used a rigorous PRoBE (prospective-specimen collection, retrospective-blinded-evaluation) study design to compare the ability of biomarkers of systemic inflammation and biomarkers of gastrointestinal (GI) tissue damage to predict response to corticosteroid treatment, the incidence of clinically severe disease, 6-month nonrelapse mortality (NRM), and overall survival in patients with acute graft-versus-host disease (GVHD). We prospectively collected serum samples of newly diagnosed GVHD patients (n = 730) from 19 centers, divided them into training (n = 352) and validation (n = 378) cohorts, and measured TNFR1, TIM3, IL6, ST2, and REG3α via enzyme-linked immunosorbent assay. Performances of the 4 strongest algorithms from the training cohort (TNFR1 + TIM3, TNFR1 + ST2, TNFR1 + REG3α, and ST2 + REG3α) were evaluated in the validation cohort. The algorithm that included only biomarkers of systemic inflammation (TNFR1 + TIM3) had a significantly smaller area under the curve (AUC; 0.57) than the AUCs of algorithms that contained ≥1 GI damage biomarker (TNFR1 + ST2, 0.70; TNFR1 + REG3α, 0.73; ST2 + REG3α, 0.79; all P < .001). All 4 algorithms were able to predict short-term outcomes such as response to systemic corticosteroids and severe GVHD, but the inclusion of a GI damage biomarker was needed to predict long-term outcomes such as 6-month NRM and survival. The algorithm that included 2 GI damage biomarkers was the most accurate of the 4 algorithms for all endpoints.

Abstract Background: As a key receptor tyrosine kinase (RTK) regulating tumorigenesis, AXL is overexpressed in multiple different types of cancers. AXL can be activated by homodimerization after binding with its ligand, growth arrest specific 6 protein (Gas6) or by heterodimerization with other RTKs. AXL overexpression and/or activation have been reported to promote cancer cell proliferation, migration/invasion and to induce resistance to targeted therapy. However, the AXL downstream signaling network has not yet been fully elucidated. Methods: We employed a CRISPR-CAS9-based knockout technology to create AXL inducible knockout cells in a human triple negative breast cancer (TNBC) cell line, HCC1395. In order to explore the downstream signaling events driven by AXL, we induced AXL knockout for 72 hours and used a 6-plex TMT-labeling-based quantitative proteomic approach to characterize the protein phosphorylation alterations induced by AXL knockout. IMAC and anti-phosphotyrosine antibody (pY1000)-based phosphopeptide enrichment methods were used to comprehensively quantify the phosphoproteome changes induced by AXL knockout. Results: In this study, we identified 38,525 phospho serine/threonine sites and 1,427 phosphotyrosine sites. In response to AXL knockout, 1,581 phospho serine/threonine sites and 40 phosphotyrosine sites showed significant differences between AXL-KO and AXL-WT groups with >1.5-fold change and p<0.05 as the cutoff. Among them, 982 phosphoserine/threonine sites and 30 phosphotyrosine sites were decreased, while 599 phospho serine/threonine sites and 10 phosphotyrosine sites were increased in AXL-KO cells. Signaling pathway enrichment analysis revealed that suppressing AXL expression could inhibit multiple important cancer related signaling pathways, including cell cycle, focal adhesion, MAPK signaling pathway, p53 signaling pathway and mTOR signaling pathway. Conclusion: Quantitative analysis of the phosphoproteome driven by AXL demonstrated that AXL plays a pivotal role in regulating canonical oncogenic signaling but also signaling pathways involved in RNA processing and DNA repair. Our data also suggest that targeting AXL could suppress these oncogenic signaling pathways and have therapeutic potential to improve clinical outcomes for patients with AXL overexpression. Citation Format: Li Wang, Nicole Pearson, Yuning Xiong, Santosh Renuse, Ran Cheng, Jodi M. Carter, Akhilesh Pandey, Xinyan Wu. Quantitative phosphoproteomic analysis of AXL signaling network in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3913.

BRAF-targeted kinase inhibitors (KIs) are used to treat malignancies including BRAF-mutant non–small cell lung cancer, colorectal cancer, anaplastic thyroid cancer, and, most prominently, melanoma. However, KI selection criteria in patients remain unclear, as are pharmacokinetic/pharmacodynamic (PK/PD) mechanisms that may limit context-dependent efficacy and differentiate related drugs. To address this issue, we imaged mouse models of BRAF-mutant cancers, fluorescent KI tracers, and unlabeled drug to calibrate in silico spatial PK/PD models. Results indicated that drug lipophilicity, plasma clearance, faster target dissociation, and, in particular, high albumin binding could limit dabrafenib action in visceral metastases compared to other KIs. This correlated with retrospective clinical observations. Computational modeling identified a timed strategy for combining dabrafenib and encorafenib to better sustain BRAF inhibition, which showed enhanced efficacy in mice. This study thus offers principles of spatial drug action that may help guide drug development, KI selection, and combination.

Abstract Glycolysis is essential for the classical activation of macrophages (M1), but how glycolytic pathway metabolites engage in this process remains to be elucidated. Glycolysis culminates in the production of pyruvate, which can be transported into the mitochondria by the mitochondrial pyruvate carrier (MPC) followed by conversion to citrate and utilization in the TCA cycle. Alternatively, pyruvate can be metabolized to lactate under aerobic conditions, which had been considered to be the dominant route in the setting of classical macrophage activation. However, based on studies that used UK5099 as a MPC inhibitor and showed reduction in key inflammatory cytokines, the mitochondrial route has been considered to be of significance for M1 activation as well. Herein, using a genetic depletion model, we found that MPC is dispensable for metabolic reprogramming and the activation of M1. In addition, MPC depletion in myeloid cells has no impact on inflammatory responses and macrophage polarization toward M1 phenotype in the endotoxemia mice model. While UK5099 reaches maximal MPC inhibitory capacity at approximately 2–5µM, higher concentrations are required to inhibit inflammatory cytokine production in M1 and this is independent of MPC expression. Taken together, this study found MPC-mediated metabolism is dispensable for the classical activation of macrophages, and UK5099 inhibits inflammatory responses in M1 macrophages due to effects other than MPC inhibition.

Abstract Background Previous prospective studies highlighted dairy intake as a risk factor for Parkinson's disease (PD), particularly in men. It is unclear whether this association is causal or explained by reverse causation or confounding. Objective The aim is to examine the association between genetically predicted dairy intake and PD using two‐sample Mendelian randomization (MR). Methods We genotyped a well‐established instrumental variable for dairy intake located in the lactase gene (rs4988235) within the Courage‐PD consortium (23 studies; 9823 patients and 8376 controls of European ancestry). Results Based on a dominant model, there was an association between genetic predisposition toward higher dairy intake and PD (odds ratio [OR] per one serving per day = 1.70, 95% confidence interval = 1.12–2.60, P = 0.013) that was restricted to men (OR = 2.50 [1.37–4.56], P = 0.003; P ‐difference with women = 0.029). Conclusions Using MR, our findings provide further support for a causal relationship between dairy intake and higher PD risk, not biased by confounding or reverse causation. Further studies are needed to elucidate the underlying mechanisms. © 2022 International Parkinson and Movement Disorder Society

Abstract Background: CD19 CAR T cell therapies have demonstrated high initial complete remission (CR) rates of 70-80% in relapsed or refractory (R/R) B-cell acute lymphoblastic leukemia (B-ALL) with durable remissions observed in a subset of patients. Despite the remarkable initial CR rate, relapses occur in 20-45% of patients and with increasing use of CD19 CAR T cell therapies in ALL, a key remaining clinical question is the role of post-CAR HCT. Methods: Patients up to 26 years treated with the commercial (i.e., tisagenlecleucel) or investigational CD19 CAR T cell products from 2014 to 2019 in the United States were included. The objectives were to examine the impact of post-CAR HCT on mortality, disease-free survival (DFS), leukemia relapse, GVHD and transplant related mortality among HCT recipients. The intent of HCT was analyzed as consolidation when there was no evidence of post-CAR T cell relapse prior to HCT. Cox regression multivariable analysis used HCT as time-dependent variable along with age, performance status and time from diagnosis to CAR-T cell infusion to evaluate its impact on outcomes. Results: We identified 347 patients who received CD19 CAR T cells from 86 centers. Median age was 13 (range, 0.4-26 years) with 39% of patients from Hispanic or Latino ethnic origin. Patients were high risk and heavily pretreated; 56% had poor cytogenetics, 51% had ≥3 line of prior therapies and 34% had prior HCT. Most of the patients (74%) received tisagenlecleucel. Disease burden at the time of CAR T cell infusion included morphologic disease (≥5% bone marrow blasts) (65%), measurable residual disease (MRD) positive (16%) and negative CR (18%). CRS of all grades was observed in 57.6% and grade 3-4 CRS in 13%; neurotoxicity of all grades was observed in 25% and grade 3-4 in 8%. With a median follow-up of 12.7 months (range, 2.2-60.3), DFS at 3, 6 and 12 months following CAR T cell infusion was 80.9% % (95% CI: 76.6%-84.9%), 71.2% (95% CI: 66.1%-76.0%) and 57.6% (95% CI: 51.8%-63.3%), respectively. OS at 3, 6 and 12 months was 93.6% (95% CI: 90.8%-96.0%), 89.8% (95% CI: 86.4%-92.8%) and 79.4% (95% CI: 74.7%-83.6%), respectively. Incidences of relapse without censoring at subsequent HCT at month 3, 6 and 12 were 18.5% (95% CI: 14.5%-22.8%), 28.2% (95% CI: 23.5%-33.3%), and 40.6% (95% CI: 35.0%-46.3%), respectively. Of the 347 patients who received CD19 CAR therapy, 62 patients (18%) had subsequent HCT in CR as a consolidation post-CAR with a median time from CAR T to HCT of 4.7 months (range, 1.1-18.6). 36% had a prior HCT (i.e., pre-CAR), 87% received myeloablative regimen and among the 57 patients with MRD evaluation 56 were negative (98%). Frequencies of post-CAR HCT did not change during the 5-year study period. Overall, transplant related mortality (TRM) at month 6 was 8.9% (95% CI: 3.6-16.1), and grade 2-4 acute GvHD was observed in 38.0% (95% CI: 28.4-48.2) of patients at month 6. Post-CAR HCT used as a consolidation was associated with lower relapse (hazard ratio (HR) 0.37; p=0.037) but no significant impact on mortality (HR 0.75, p=0.51) or DFS (HR=0.46, p=0.057). When accounting for any HCT performed after CAR T cell, HCT was associated with higher mortality (Figure). Conclusions: This is the largest clinical series to date examining the impact of post-CAR HCT on clinical outcome of R/R B-ALL patients. We found that 18% of the patients had post-CAR HCT as a consolidation therapy with a favorable safety profile of 8.9% TRM rate despite a prior transplant rate of 36%. Consolidation HCT after CAR T cell resulted in significant reduction in leukemia relapse with a trend towards better DFS. Consolidation HCT seem safe and incurred a benefit by reducing disease relapse. Further studies in understanding the role of consolidation HCT as planned, or post-CAR T cell response-based strategy or treatment for early B-cell recovery are needed to further define the best candidates for HCT after tisagenlecleucel . Figure 1 Figure 1. Disclosures Park: BMS: Consultancy; Affyimmune: Consultancy; Kite Pharma: Consultancy; Curocel: Consultancy; Novartis: Consultancy; Minerva: Consultancy; Innate Pharma: Consultancy; PrecisionBio: Consultancy; Intellia: Consultancy; Amgen: Consultancy; Autolus: Consultancy; Servier: Consultancy; Artiva: Consultancy; Kura Oncology: Consultancy. Nikiforow: Kite/Gilead: Other: ad HOC Advisory Boards; Novartis: Other: ad Hoc Advisory Boards; Iovance: Other: ad Hoc Advisory Boards; Glaxo Smith Kline (GSK): Other: ad Hoc Advisory Boards. Hu: Kite/Gilead: Research Funding; Novartis: Research Funding; Celgene: Research Funding. Ahmed: Merck: Research Funding; Tessa Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Seagen: Research Funding; Xencor: Research Funding. Badar: Pfizer Hematology-Oncology: Membership on an entity's Board of Directors or advisory committees. Cairo: Servier: Speakers Bureau; Sanofi: Speakers Bureau; Amgen: Speakers Bureau; Jazz Pharmaceutical: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sobi: Speakers Bureau; Omeros: Membership on an entity's Board of Directors or advisory committees; Nektar: Membership on an entity's Board of Directors or advisory committees. Dholaria: Janssen: Research Funding; Takeda: Research Funding; Jazz: Speakers Bureau; MEI: Research Funding; Pfizer: Research Funding; Angiocrine: Research Funding; Poseida: Research Funding; Celgene: Speakers Bureau. Grover: Kite: Other: Advisory Board; Genentech: Research Funding; ADC: Other: Advisory Board; Tessa: Consultancy; Novartis: Consultancy. Locke: Gerson Lehrman Group: Consultancy; Emerging Therapy Solutions: Consultancy; Moffitt Cancer Center: Patents & Royalties: field of cellular immunotherapy; Calibr: Consultancy, Other: Scientific Advisory Role; BMS/Celgene: Consultancy, Other: Scientific Advisory Role; Wugen: Consultancy, Other; Legend Biotech: Consultancy, Other; Janssen: Consultancy, Other: Scientific Advisory Role; Iovance Biotherapeutics: Consultancy, Other: Scientific Advisory Role; Novartis: Consultancy, Other, Research Funding; GammaDelta Therapeutics: Consultancy, Other: Scientific Advisory Role; Takeda: Consultancy, Other; EcoR1: Consultancy; Cowen: Consultancy; Umoja: Consultancy, Other; Kite, a Gilead Company: Consultancy, Other: Scientific Advisory Role, Research Funding; Cellular Biomedicine Group: Consultancy, Other: Scientific Advisory Role; Bluebird Bio: Consultancy, Other: Scientific Advisory Role; Amgen: Consultancy, Other: Scientific Advisory Role; Allogene Therapeutics: Consultancy, Other: Scientific Advisory Role, Research Funding. Murthy: CRISPR Therapeutics: Research Funding. Mussetti: GILEAD: Other: Clinical trials participation, Research Funding; TAKEDA: Honoraria; NOVARTIS: Honoraria, Other: Clinical trials participation. Pulsipher: Jasper Therapeutics: Honoraria; Adaptive: Research Funding; Equillium: Membership on an entity's Board of Directors or advisory committees. Qayed: Novartis: Membership on an entity's Board of Directors or advisory committees; Medexus: Membership on an entity's Board of Directors or advisory committees; Mesoblast: Membership on an entity's Board of Directors or advisory committees; Jazz: Membership on an entity's Board of Directors or advisory committees; BMS: Other. Reshef: Gilead, BMS, Precision, Immatics, Atara, Takeda, Shire, Pharmacyclics, Incyte: Research Funding; Gilead and Novartis: Honoraria; Bayer, BMS, Regeneron, TScan, Synthekine, Atara, Jasper: Consultancy. Rizzieri: Kite: Honoraria, Speakers Bureau; Incyte: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Stemline: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Seattle Genetics: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Acrotech: Membership on an entity's Board of Directors or advisory committees; Teva: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Bayer: Membership on an entity's Board of Directors or advisory committees; AROG: Membership on an entity's Board of Directors or advisory committees; AbbVie: Membership on an entity's Board of Directors or advisory committees; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Karyopharm: Honoraria, 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; Celltrion: Membership on an entity's Board of Directors or advisory committees; Mustang: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Jazz: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Honoraria; Pharmacyclics: Honoraria. Sharma: Vertex Pharmaceuticals/CRISPR Therapeutics: Other: Salary support paid to institution; Medexus Inc: Consultancy; CRISPR Therapeutics: Other, Research Funding; Vindico Medical Education: Honoraria; Spotlight Therapeutics: Consultancy; Novartis: Other: Salary support paid to institution. Schultz: BMS DSMB: Other: payment; Sobi: Membership on an entity's Board of Directors or advisory committees. Turtle: Century Therapeutics Ad hoc advisory boards (last 12 months): Nektar Therapeutics, Allogene, PACT Pharma, Astra Zeneca, Amgen: Membership on an entity's Board of Directors or advisory committees; Precision Biosciences: Membership on an entity's Board of Directors or advisory committees; Eureka Therapeutics: Membership on an entity's Board of Directors or advisory committees; Caribou Biosciences: Membership on an entity's Board of Directors or advisory committees; T-CURX: Membership on an entity's Board of Directors or advisory committees; Myeloid Therapeutics: Membership on an entity's Board of Directors or advisory committees; ArsenalBio: Membership on an entity's Board of Directors or advisory committees; Minerva: Other; Nektar Therapeutics: Other; Juno Therapeutics/BMS: Other: Payments, Patents & Royalties: Patent licensed to Juno Therapeutics.; Precision Biosciences, Eureka Therapeutics, Caribou Biosciences, Myeloid Therapeutics, ArsenalBio: Current holder of stock options in a privately-held company. Pasquini: Kite Pharma: Research Funding; Novartis: Research Funding; Bristol Myers Squibb: Consultancy, Research Funding; GlaxoSmithKline: Research Funding. Perales: MorphoSys: Honoraria; Cidara: Honoraria; Bristol-Myers Squibb: Honoraria; Miltenyi Biotec: Honoraria, Other; Sellas Life Sciences: Honoraria; Takeda: Honoraria; Karyopharm: Honoraria; Celgene: Honoraria; Omeros: Honoraria; Nektar Therapeutics: Honoraria, Other; Equilium: Honoraria; Novartis: Honoraria, Other; NexImmune: Honoraria; Incyte: Honoraria, Other; Kite/Gilead: Honoraria, Other; Merck: Honoraria; Servier: Honoraria; Medigene: Honoraria.

Abstract Background: A major cause of mortality in patients receiving hematopoietic stem cell transplantation (HCT) is acute graft-versus-host disease (GVHD), a multiorgan disorder that includes the skin, liver and gastrointestinal tract. We have previously identified elafin, a protease inhibitor overexpressed in inflamed epidermis, as a diagnostic biomarker of GVHD in the skin, the most commonly involved GVHD organ. However, our initial study was limited to a subset of patients with isolated skin GVHD. The main driver of nonrelapse mortality (NRM) in HCT patients is GI GVHD. Two biomarkers, Regenerating islet-derived 3a (REG3α) and Suppressor of tumorigenesis 2 (ST2), have since been validated as biomarkers of GI GVHD that predict long-term outcomes in patients treated for GVHD. We undertook this study to determine the utility of elafin as a prognostic biomarker of acute GVHD in the general population of previously unstudied acute GVHD patients, and to compare it to ST2 and REG3α. Study Design: 526 patients who received systemic corticosteroid treatment for skin GVHD were analyzed from the Mount Sinai Acute GVHD International Consortium (MAGIC), which includes patients from 25 HCT centers. We used ELISA to measure serum concentrations of elafin, ST2 and REG3α. Patients were divided randomly into equal training and validation sets; and we developed a competing risk regression model for 6-month NRM using elafin concentration in the training set. We developed additional models for 6-month NRM using concentrations of ST2 and REG3α, or the combination of all three biomarkers as predictors. We then constructed ROC curves to evaluate the predictive accuracy of each model and to analyze the ability of each model to stratify patients into high- and low-risk groups. We analyzed the cumulative incidence of 6-month NRM and overall survival in each model and compared the accuracy of each model in the validation set. Results: The area under the receiver operating curve (AUROC) for elafin alone was 0.55 whereas it was 0.75 and statistically superior (P = 0.02) for the combination of ST2 and REG3α. The combination of 3 biomarkers produced an AUROC of 0.76 that was not significantly better than the two biomarker model (P = 0.10). Elafin concentrations, either alone or in combination with ST2 and REG3α, did not produce higher hazard ratios of NRM (data not shown). Patients in the low-risk elafin group paradoxically demonstrated a higher incidence of 6-month NRM, although this difference was not statistically significant (17% vs. 11%, P=0.19), and both overall survival at 6 months (68% vs. 68%, P>0.99) and four-week response (78% vs. 78%, P=0.98) were similar in the low- and high-risk elafin groups (Figure 1). As demonstrated in previous data sets, the combination of ST2 and REG3α divided patients into two groups with a nearly five-fold difference in NRM (6.7% vs. 31%, P <0.001). Conclusion: We demonstrated that serum elafin concentrations measured at the initiation of systemic treatment for acute GVHD in a multicenter population of patients treated systemically for acute GVHD do not predict 6-month NRM, overall survival, or treatment response. As seen in previous studies, serum concentrations of the GI GVHD biomarkers ST2 and REG3α were significant predictors of NRM and the addition of elafin levels did not improve their accuracy. These results underscore the importance of GI disease in driving NRM in patients who develop acute GVHD. Figure 1. Cumulative incidence of nonrelapse mortality and overall survival in high and low risk groups Six-month cumulative incidences of nonrelapse mortality (NRM) in high (solid line) and low (dotted line) risk groups defined by optimized biomarker thresholds (upper panels) and six-month overall survival estimated using the Kaplan-Meier method (lower panels). (A) Cumulative incidence of NRM (14%) and overall survival (75%) in the total validation set (N=263). (B) Cumulative incidence of NRM in the low (N=150) and high (N=113) elafin group (17% vs. 11%, P=0.19). Overall survival in the low and high elafin group (68% vs. 68%, P > 0.99). (C) Cumulative incidence of NRM in the low (N=175) and high (N=88) ST2 + REG3a group (6.7 vs. 31%, P < 0.001). Overall survival in the low and high ST2 + REG3a group (77% vs. 51%, P < 0.001). (D) Cumulative incidence of NRM in the low (N=180) and high (N=83) elafin + ST2 + REG3a group (7.0 vs. 30%, P < 0.001). Overall survival in the low and high elafin + ST2 + REG3a group (79% vs. 64%, P < 0.001). Figure 1 Figure 1. Disclosures Ozbek: Viracor: Patents & Royalties: GVHD biomarker patent with royalties from Viracor. DeFilipp: Omeros, Corp.: Consultancy; Incyte Corp.: Research Funding; Regimmune Corp.: Research Funding; Syndax Pharmaceuticals, Inc: Consultancy. Grupp: Novartis, Kite, Vertex, and Servier: Research Funding; Jazz Pharmaceuticals: Consultancy, Other: Steering committee, Research Funding; Novartis, Roche, GSK, Humanigen, CBMG, Eureka, and Janssen/JnJ: Consultancy; Novartis, Adaptimmune, TCR2, Cellectis, Juno, Vertex, Allogene and Cabaletta: Other: Study steering committees or scientific advisory boards. Hexner: Blueprint medicines: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tmunity Therapeutics: Research Funding; PharmaEssentia: Membership on an entity's Board of Directors or advisory committees. Kitko: Co-investigator on two NIH grants as part of the cGVHD consortium: Research Funding; Vanderbilt University Medical Center: Current Employment; PER: Other: PER - CME educational talks about GVHD; Horizon: Membership on an entity's Board of Directors or advisory committees. Qayed: Novartis: Honoraria; Mesoblast: Honoraria; Medexus: Honoraria. Reshef: ilead, BMS, Precision, Immatics, Atara, Takeda, Shire, Pharmacyclics, Incyte: Research Funding; Bayer: Consultancy; Gilead and Novartis: Honoraria; BMS, Regeneron, TScan, Synthekine, Atara, Jasper, Bayer: Consultancy. Levine: Incyte: Consultancy, Research Funding; Viracor: Patents & Royalties: GVHD biomarker patent with royalties from Viracor; Mesoblast: Consultancy, Research Funding; Equillium Bio: Membership on an entity's Board of Directors or advisory committees; X4 Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Talaris Therapeutics: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Omeros: Membership on an entity's Board of Directors or advisory committees; Symbio: Membership on an entity's Board of Directors or advisory committees; Biogen: Research Funding; Kamada: Research Funding. Ferrara: Eurofins Viracor: Consultancy, Other: Royalties. Chen: Incyte: Consultancy; Gamida: Consultancy.

Background: Previous studies showed that lifestyle behaviors (cigarette smoking, alcohol, coffee) are inversely associated with Parkinson’s disease (PD). The prodromal phase of PD raises the possibility that these associations may be explained by reverse causation. Objective: To examine associations of lifestyle behaviors with PD using two-sample Mendelian randomisation (MR) and the potential for survival and incidence-prevalence biases. Methods: We used summary statistics from publicly available studies to estimate the association of genetic polymorphisms with lifestyle behaviors, and from Courage-PD (7,369 cases, 7,018 controls; European ancestry) to estimate the association of these variants with PD. We used the inverse-variance weighted method to compute odds ratios (ORIVW) of PD and 95%confidence intervals (CI). Significance was determined using a Bonferroni-corrected significance threshold (p = 0.017). Results: We found a significant inverse association between smoking initiation and PD (ORIVW per 1-SD increase in the prevalence of ever smoking = 0.74, 95%CI = 0.60–0.93, p = 0.009) without significant directional pleiotropy. Associations in participants ≤67 years old and cases with disease duration ≤7 years were of a similar size. No significant associations were observed for alcohol and coffee drinking. In reverse MR, genetic liability toward PD was not associated with smoking or coffee drinking but was positively associated with alcohol drinking. Conclusion: Our findings are in favor of an inverse association between smoking and PD that is not explained by reverse causation, confounding, and survival or incidence-prevalence biases. Genetic liability toward PD was positively associated with alcohol drinking. Conclusions on the association of alcohol and coffee drinking with PD are hampered by insufficient statistical power.