Citations

Introduction: Cerebrovascular small vessel disease, seen on brain imaging as lacunes and white matter hyperintensities (WMH), is a substrate for dementia in older adults. Diffusion tensor imaging (DTI) is thought to provide early signs of loss of white matter (WM) integrity due to microvascular disease and predicts WM hyperintensity volume. Retinal fundus photography provides surrogate measures of cerebral microvasculature. No studies have quantified the long-term association between retinal signs and DTI measures. Hypothesis: Microvascular retinal signs measured in midlife are associated with small vessel disease measured on brain magnetic resonance imaging (MRI) 18 years later, including reduced WM microstructural integrity (lower fractional anisotrophy [FA] and greater mean diffusivity [MD] by DTI), greater WM hyperintensity volume and greater lacune prevalence. Methods: In a biracial prospective cohort study, retinal signs were measured using fundus photography (1993-1995) with 3-T magnetic resonance imaging conducted in 2011-13. Multivariable-adjusted linear regression was used to quantify the relationships of retinal signs with WM measures. Prevalence of lacunar infarcts by retinal sign status was estimated using log binomial regression. Analyses were adjusted for age [linear and quadratic terms], education, sex, race, intracranial volume, body mass index, smoking, diabetes, hypertension, and ≥1 APOE ε4 alleles. Results: In 1829 men and women (60% [N=1100] female, 27% [N=489] black race, aged 50-72 years when retinal signs were measured), a binary measure comprised of two retinal signs suggestive of arteriolar damage due to hypertension (focal arteriolar narrowing and/or arteriovenous nicking) was associated with worse (lower) FA (standardized β=-0.19, 95% confidence interval [CI]=-0.35, -0.02), worse (higher) MD (β=0.15, 95% CI=0.00, 0.30), greater WM hyperintensity volume (β=0.15, 95% CI=0.01, 0.30), and greater prevalence of lacunes (prevalence ratio=1.33, 95% CI: 0.99, 1.80). Generalized arteriolar narrowing, measured as the central retinal arteriolar equivalent (CRAE, narrowest quartile vs. widest three quartiles) was associated with worse FA (β=-0.13, 95% CI=-0.24, -0.01) and worse MD (β=0.12, 95% CI=0.01, 0.23). Results did not differ by sex, race, hypertension status or APOE ε4 genotype. No associations were found for retinopathy, but only 56 participants had retinopathy. Conclusions: Consistent with prior work, and as expected based on a common underlying pathology, retinal signs predicted WM disease and lacunar infarcts 18 years later. Novel to this study, we found that retinal signs related to arteriolar damage also predicted loss of white matter microvascular integrity measured using DTI.

Background: Invasive hemodynamic evaluation through right heart catheterization plays an essential role in the diagnosis, categorization, and risk stratification of patients with pulmonary hypertension. Methods: Subjects enrolled in the PVDOMICS (Redefining Pulmonary Hypertension through Pulmonary Vascular Disease Phenomics) program undergo an extensive invasive hemodynamic evaluation that includes repeated measurements at rest and during several provocative physiological challenges. It is a National Institutes of Health/National Heart, Lung, and Blood Institute initiative to reclassify pulmonary hypertension groups based on clustered phenotypic and phenomic characteristics. At a subset of centers, participants also undergo an invasive cardiopulmonary exercise test to assess changes in hemodynamics and gas exchange during exercise. Conclusions: When coupled with other physiological testing and blood -omic analyses involved in the PVDOMICS study, the comprehensive right heart catheterization protocol described here holds promise to clarify the diagnosis and clustering of pulmonary hypertension patients into cohorts beyond the traditional 5 World Symposium on Pulmonary Hypertension groups. This article will describe the methods applied for invasive hemodynamic characterization in the PVDOMICS program. Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT02980887.

208 Background: Treatment of high risk prostate cancer (HRPCa) with external beam radiotherapy (EBRT) plus brachytherapy (BT) boost (EBRT+BT) has been prospectively associated with lower rates of BCR, albeit potentially with increased toxicity, and retrospectively linked to decreased distant metastasis (DM) and PCa-specific mortality (PCSM) compared to EBRT alone. However, it is unclear whether patients who develop BCR following either approach have similar downstream oncologic outcomes. Methods: We identified 706 out of 3820 men with HRPCa treated at 13 institutions from 1998-2015 with EBRT (n=468/2134) or EBRT+BT (n=238/1686) who developed BCR. We compared rates of DM, PCSM, and all-cause mortality (ACM) after BCR between treatment groups using Fine-Gray competing risk regression. Models were adjusted for age, Gleason grade group, initial PSA (iPSA), clinical T stage, time-dependent use of systemic salvage, and interval to BCR using inverse probability of treatment weighting. Results: Median follow-up was 9.9 years from RT and 4.8 years from BCR. Groups were similar in age, iPSA, presence of ≥2 HR features, and median interval to BCR (3.3 years). Most men received neoadjuvant/concurrent androgen deprivation therapy (ADT), 92.5% and 91.0% for EBRT and EBRT+BT, respectively, though for a longer duration with EBRT (median 14.7 vs. 9.0 months, p=0.0012). Local and systemic salvage rates were 2.3% and 36.3% after EBRT, and 2.6% and 43.6% after EBRT+BT, respectively. Initial EBRT+BT was associated with significantly lower rates of DM after BCR (HR 0.48, 95% CI 0.36-0.64, p<0.001). Rates of PCSM and ACM did not significantly differ (HR 0.93, 95% CI 0.67-1.30, p=0.93, and HR 0.8, 95% CI 0.6-1.1, p=0.11, respectively). Conclusions: In this large retrospective series of radiorecurrent HRPCa, initial treatment with EBRT+BT was associated with significantly lower rates of DM after BCR compared with EBRT, despite shorter ADT use and a similar median interval to BCR. Local salvage was widely underutilized in both groups. In the absence of salvage for local failure after EBRT, upfront treatment intensification with BT may reduce DM, though not PCSM or ACM, even after development of BCR.

Background: DLBCL has traditionally been classified by cell of origin (COO) subcategories based on tumor gene expression profiles which include Activated B-Cell (ABC) and Germinal Center B-Cell (GCB). Recently, using tumor samples from patients treated with RCHOP, new classification models have focused on DNA alterations. However, a comprehensive integrative approach using a large transcriptomic data set across both newly diagnosed (nd) and relapsed/refractory (r/r) DLBCL is yet to be accomplished. A robust clustering of this type will allow for identification of biologically driven DLBCL patient subgroups and may predict patient outcome and inform treatment approaches. Methods: RNAseq was performed on a total of 882 DLBCL tumor FFPE biopsies from 2 ndDLBCL cohorts (cohort 1 and 2) and 2 r/r DLBCL cohorts (cohorts 3 and 4). Cohort 1 (N=267) was commercially sourced and served as the discovery cohort. Cohort 2 (N=340) was from the Mayo/Iowa Lymphoma SPORE Molecular Epidemiology Resource (MER) and served as the replication cohort. Cohort 3 (N=189) was from the CC-122-ST-001 and CC-122-DLBCL-001 clinical trials (NCT01421524 and NCT02031419), and cohort 4 (N=86) was from r/r patients from the MER. Clustering input consisted of gene expression data, gene set variation analysis (GSVA) scores computed from the hallmark gene sets of MSigDB gene sets, as well as immune cell abundance estimates from a DLBCL-specific deconvolution method. An integrative clustering method iClusterPlus was applied to the input data to identify patient subgroups. A multinomial generalized linear model classifier was trained on the discovery dataset and applied to cohorts 2, 3, and 4 to assess patterns of gene expression and clinical features among the subgroups. Results: Integrative clustering identified 8 subgroups of ndDLBCL patients (DLBCL1-8; D1-D8) in cohort 1. Classifiers trained on cohort 1 were applied to cohort 2 and the same 8 clusters were identified. Among RCHOP treated patients in cohort 2, subgroups D4 (p&lt;0.01) and D8 (p&lt;0.0001) had significantly worse survival outcomes than the rest of the population. D4 comprised 21% of the MER ndDLBCL replication cohort (cohort 2) with a median event-free survival (mEFS) of 38.2 months and a median overall survival (mOS) of 80.3 months. D8 comprised 5% of the cohort with a mEFS of 7.5 months and a mOS of 12.1 months. The remaining 6 subgroups were standard risk, with mEFS ranging from 82.1 months to not reached, and none reaching mOS. The subgroups were not uniquely defined by previously known molecular classification methods such as COO or double hit signature (DHITsig), nor by clinical risk factors such as age or international prognostic index (IPI). Within D4, 92% of patients were ABC, representing a high risk subset of ABC patients. The mEFS in D4 ABCs was 38.2 months, while mEFS of non-D4 ABCs was not reached (p&lt;0.005). Transcriptomic analysis revealed a lower abundance of immune infiltration. D4 was associated with high IPI, with 49% of D4 having IPI&gt;2, compared to 33% of non-D4 with IPI&gt;2 (p&lt;0.05). D8 represented a high-risk subset which was 73% GCB. The mEFS of D8 GCBs was 5.4 months, while mEFS of non-D8 GCBs was not reached (p&lt;0.0001). Transcriptomic analysis revealed low expression of immune response and cytokine signaling pathways, consistent with the low abundance of immune cells in D8. This subgroup consisted of 63% DHITsig positive patients. Although only 20% of all DHITsig positive patients were in D8, these D8 DHITsig patients showed significantly worse survival than non-D8 DHITsig patients (mEFS 11.3 months vs. not reached, p&lt;0.0001). In the r/r DLBCL setting, D1-D8 were all present, with an increased prevalence of D4 and D8 in Cohort 3 (30% and 17%, respectively) and Cohort 4 (30% and 14%) compared to the newly diagnosed setting. Mutational data for these cohorts has been collected and is being interpreted in the context of the discovered subgroups. Conclusion: A novel integrative clustering of transformed gene expression data revealed 8 biologically homogeneous groups, two of which had inferior outcomes when treated with RCHOP therapy. Furthermore, these two subgroups were more prevalent in r/r DLBCL. This classification allows for the transcriptomic identification of high-risk patients underserved by RCHOP therapy. *Ortiz, Wenzl and Stokes contributed equally **Gandhi and Novak contributed equally Disclosures Ortiz: Celgene Corporation: Employment, Equity Ownership. Stokes:Celgene Corporation: Employment, Equity Ownership. Huang:Celgene Corporation: Employment, Equity Ownership. Maurer:Celgene: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Towfic:Celgene Corporation: Employment, Equity Ownership. Hagner:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Ratushny:Celgene Corporation: Employment, Equity Ownership. McConnell:Celgene Corporation: Employment, Equity Ownership. Danziger:Celgene Corporation: Employment, Equity Ownership. Stong:Celgene Corporation: Employment, Equity Ownership. Lata:Celgene Corporation: Employment, Equity Ownership. Kamalakaran:Celgene Corporation: Employment, Equity Ownership. Mavrommatis:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Czuczman:Celgene Corporation: Employment, Equity Ownership. Ansell:Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding. Cerhan:NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding. Nowakowski:Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding. Gandhi:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Novak:Celgene Coorperation: Research Funding.

Objectives: To develop evidence-based recommendations for clinicians caring for adults with acute or acute on chronic liver failure in the ICU. Design: The guideline panel comprised 29 members with expertise in aspects of care of the critically ill patient with liver failure and/or methodology. The Society of Critical Care Medicine standard operating procedures manual and conflict-of-interest policy were followed throughout. Teleconferences and electronic-based discussion among the panel, as well as within subgroups, served as an integral part of the guideline development. Setting: The panel was divided into nine subgroups: cardiovascular, hematology, pulmonary, renal, endocrine and nutrition, gastrointestinal, infection, perioperative, and neurology. Interventions: We developed and selected population, intervention, comparison, and outcomes questions according to importance to patients and practicing clinicians. For each population, intervention, comparison, and outcomes question, we conducted a systematic review aiming to identify the best available evidence, statistically summarized the evidence whenever applicable, and assessed the quality of evidence using the Grading of Recommendations Assessment, Development, and Evaluation approach. We used the evidence to decision framework to facilitate recommendations formulation as strong or conditional. We followed strict criteria to formulate best practice statements. Measurements and Main Results: In this article, we report 29 recommendations (from 30 population, intervention, comparison, and outcomes questions) on the management acute or acute on chronic liver failure in the ICU, related to five groups (cardiovascular, hematology, pulmonary, renal, and endocrine). Overall, six were strong recommendations, 19 were conditional recommendations, four were best-practice statements, and in two instances, the panel did not issue a recommendation due to insufficient evidence. Conclusions: Multidisciplinary international experts were able to formulate evidence-based recommendations for the management acute or acute on chronic liver failure in the ICU, acknowledging that most recommendations were based on low-quality indirect evidence.

Background: Diffuse large B cell lymphoma (DLBCL) is the most common aggressive lymphoid malignancy in adults. Though standard immunochemotherapy regimens can result in clinical remission and cure in a majority of patients, approximately 30% of patients are primary refractory or eventually relapse, and their prognosis is very poor. Recent progress by large scale genomic and transcriptomic profiling of DLBCL patients has resulted in a deep understanding of disease drivers in newly diagnosed DLBCL (ndDLBCL). However, previous genomic studies of relapsed and/or refractory DLBCL (rrDLBCL) are limited by small sample sizes and much less is known about the genomic landscape or the changes in clonal populations that occur within a patient undergoing R-CHOP therapy. Analysis of a large cohort of rrDLBCL is needed to uncover the importance of known drivers, define clonal shifts that occur in relapsing patients, and discover novel mutations that impact resistance to therapy. Methods: DNA sequencing (whole exome or genome sequencing) was performed on tumor and matching germline samples from two rrDLBCL patient cohorts (N=143). The first rrDLBCL cohort consisted of 68 formalin fixed paraffin embedded (FFPE) tumor samples with matched germline obtained from the Mayo/Iowa Lymphoma SPORE Molecular Epidemiology Resource (MER); including 18 diagnostic/relapse paired samples. The second cohort consisted of 75 FFPE samples from 2 rrDLBCL clinical trials CC-122-ST-001 and CC-122-DLBCL-001 (NCT01421524 and NCT02031419). In addition, a ndDLBCL cohort (n=364) consisting of MER front line samples were also analyzed. CNV and SV calling was performed with Sclust and Manta, respectively. Mutational drivers were identified with cDriver. Cosmic mutational signatures were calculated for all samples. Results: For comparison of overall mutation rates between the ndDLBCL and rrDLBCL we analyzed the frequency of mutations in known driver genes [n=211, Chapuy et al, 2018; Schmitz et al, 2018; Reddy et al 2017]. Of these, the top 5 most mutated genes found in the relapse cohort were KMT2D (34.3%), IGLL5 (25.9%), CREBBP (22.4%), TP53 (21.0%), and HIST1H1E (19.6%) all of which were previously reported as mutated genes in rrDLBCL [Morin et al. 2016; Park et al. 2016;Greenawalt et al. 2017]. None of these frequencies changed significantly from the MER ndDLBCL cohort. However, frequencies of 2 genes NCOR1 and ACTB were increased significantly from 3.3% to 13.3% (p= 5.85 x 10-05) and 0.5% to 6.3% (p=2.56 x 10-04), respectively, in the rrDLBCL cohorts. Analysis of the previously reported driver mutations in 18 paired samples revealed a significant increase in the mean variant allele frequency (VAF) from diagnosis to relapse of mutations in BCL2 and BIRC6 from 18.5% to 28.8%(p=0.016) and 4.3% to 13.7%(p=0.011), respectively. In our analysis of novel drivers in rrDLBCL cohorts, 59 significant driver mutations (FDR 0.1) were identified, 23 were novel driver mutations while the remaining 36 have been previously reported as drivers of newly diagnosed DLBCL. These include novel mutations in genes such as IL4R (8.8%), CDK5R1 (6.7%), LAPTM5 (3.7%) and others whose function in lymphoma are unknown. In a secondary analysis, we performed mutational signature analysis for rrDLBCL and identified groups of patients with COSMIC mutational signatures that are associated with aging, defects in homologous end joining resulting in large insertions/deletions, and with defective mismatch repair often found in microsatellite unstable tumors. Together with the high prevalence of TP53 mutations, these results indicate genetic instability as a hallmark of relapse/refractory DLBCL. Analysis of CNV and SV will also be reported. Conclusion: Here we report to our knowledge the largest genomic dataset in relapsed and /or refractory DLBCL using tumor biopsies. Previously identified mutated genes in rrDLBCL were confirmed and novel drivers identified. Paired samples analysis reveals clonal shifts which occur during treatment and at time of relapse. Whether the novel driver mutations appeared as a function of selection of a rare clone over the course of therapy or a therapy induced mutation will be the focus of ongoing work. Novel variants including SNV, CNV and SV can potentially lead to new insights and deepen our understanding of the mechanisms of resistance in DLBCL. * Wenzl, Ortiz and Stong contributed equally **Novak and Gandhi contributed equally Disclosures Ortiz: Celgene Corporation: Employment, Equity Ownership. Stong:Celgene Corporation: Employment, Equity Ownership. Huang:Celgene Corporation: Employment, Equity Ownership. Maurer:Morphosys: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Nanostring: Research Funding. Towfic:Celgene Corporation: Employment, Equity Ownership. Hagner:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Nikbakht:Celgene Corporation: Employment, Equity Ownership. Stokes:Celgene Corporation: Employment, Equity Ownership. Lata:Celgene Corporation: Employment, Equity Ownership. Kamalakaran:Celgene Corporation: Employment, Equity Ownership. Mavrommatis:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Czuczman:Celgene Corporation: Employment, Equity Ownership. Ansell:Regeneron: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding. Cerhan:Celgene: Research Funding; NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees. Nowakowski:Bayer: Consultancy, Research Funding; Curis: Research Funding; Celgene: Consultancy, Research Funding; MorphoSys: Consultancy, Research Funding; Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding. Novak:Celgene Coorperation: Research Funding. Gandhi:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties.

*equal contribution of AB, RLL, BD and JCB Background: Acute myeloid leukemia (AML) is common with increasing age, and older adults with AML rarely achieve long-term remission with chemotherapy. Gene discovery studies in older adults with AML have shown that this malignancy is characterized by a multitude of somatic genomic alterations beginning with initiating somatic events followed by acquisition of collaborative transforming mutations. Despite these important biologic insights, current therapeutic approaches to AML remain limited, particularly in adults ≥ 60 years of age. The Beat AML trial was designed to assess the feasibility of using genetic profiling to assign patients (pts) to molecularly defined, subtype-specific therapies within 7 days of the initial diagnosis in a multi-center clinical trial setting, and to delineate the role of molecularly informed first-line treatment of AML with mechanism based novel therapies. Methods: Treatment-naïve patients with AML were enrolled in this prospective trial which offered accelerated cytogenetic and comprehensive mutational testing within 7 days followed by treatment assignment using these molecular data. Pt eligibility included age ≥ 60 years with non-APML AML, no known CNS leukemia, no prior hypomethylating agent (HMA) therapy and no clinical need for emergent therapy. Eligible pts were profiled by local cytogenetics analysis and using a central next generation sequencing (NGS) assay (Foundation Medicine, Inc.) with all molecular data required for treatment assignment (TA) obtained &lt; 7 days. TA was made centrally using a pre-determined algorithm considering somatic cytogenetic and molecular alterations in the dominant clone, available targeted therapeutics for specific AML subsets, and the likelihood of cure with intensive chemotherapy. Results: From November 2016 to January 2019, 487 pts with a suspected diagnosis of AML had enrolled at 14 clinical sites; 395 were eligible for the study (77% of the patients not eligible for the study had an alternative diagnosis). The median age of eligible patients was 72 years (range: 60 to 92) with 38% being≥ 75 years and 16% with treatment-related AML. From the 395 eligible patients, 374 (94.7%) were centrally assigned to the different cytogenetic/genomic groups within 7 days. The most common groups were TP53 mutated (19%) and marker negative (18%) molecular groups. The Beat AML trial is dynamic by design, thereby allowing different arms to open over time; all trial arms are designed to evaluate for substantial clinical efficacy in small, molecularly defined patient subsets. As shown in Figure 1, 224 patients (57%) had a TA and consented to a BEAT AML sub-study. Of the remaining 171 patients, 103 received standard therapy defined as induction therapy (7+3) or hypomethylation agent (25 before TA and 78 after TA), 28 received an alternative investigational agent (5 before TA and 23 after TA), 38 received palliative care, and 2 had an unknown treatment status and are grouped with the palliative care patients in subsequent analyses; 9 patients died before TA (2 who received standard therapy and 7 in the palliative care group). Demographic, clinical, performance and molecular characteristics were not largely different between pts who elected targeted therapy as part of the BEAT AML trial versus those who elected standard therapy. As shown in Figure 2, the overall survival was significantly longer for patients enrolled in a targeted therapy arm as part of the BEAT AML trial compared to those who elected standard therapy. (p&lt;0.0001). The 30-day estimated death rate was 3.7% (95% CI: 1.9-7.2) for patients electing treatment on a BEAT AML sub-study, 20.4% (95% CI: 13.0-31.2) for those receiving standard therapy, 0% for those receiving an investigational therapy, and 72.6% (95% CI: 57.8-85.7) for the palliative care group. Conclusion: Our data support the feasibility of a rapid precision medicine approach in older patients with previously untreated AML. Patients with AML who elected treatment assigned based upon cytogenetic and molecular alterations in the dominant clone using a novel precision medicine approach had significantly improved overall survival versus those who elected standard of care treatment. Disclosures Levine: Gilead: Consultancy; Prelude Therapeutics: Research Funding; Celgene: Consultancy, Research Funding; Novartis: Consultancy; Isoplexis: Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Research Funding; Qiagen: Membership on an entity's Board of Directors or advisory committees; Loxo: Membership on an entity's Board of Directors or advisory committees; Imago Biosciences: Membership on an entity's Board of Directors or advisory committees; C4 Therapeutics: Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria; Lilly: Honoraria. Mims:Abbvie: Membership on an entity's Board of Directors or advisory committees; Agios Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Astellas Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; PTC 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. Borate:Novartis: Consultancy; Takeda: Consultancy; Pfizer: Consultancy; Daiichi Sankyo: Consultancy; AbbVie: Consultancy. Stein:Agios: Consultancy, Membership on an entity's Board of Directors or advisory committees; Astellas Pharma US, Inc: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; PTC Therapeutics: Membership on an entity's Board of Directors or advisory committees; Syros: Membership on an entity's Board of Directors or advisory committees; Daiichi Sankyo, Inc.: Membership on an entity's Board of Directors or advisory committees; Bioline: Membership on an entity's Board of Directors or advisory committees; Genentech: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees. Patel:France Foundation: Honoraria; Dava Oncology: Honoraria; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Baer:Takeda: Research Funding; Incyte: Research Funding; Kite: Research Funding; Forma: Research Funding; AI Therapeutics: Research Funding; Abbvie: Research Funding; Astellas: Research Funding. Stock:Daiichi: Membership on an entity's Board of Directors or advisory committees; Agios: Membership on an entity's Board of Directors or advisory committees; Astellas: Membership on an entity's Board of Directors or advisory committees; Research to Practice: Honoraria; UpToDate: Honoraria; Kite, a Gilead Company: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees. Deininger:Pfizer: Consultancy, Honoraria, Research Funding; Ascentage Pharma: Consultancy, Honoraria; TRM: Consultancy; Sangoma: Consultancy; Fusion Pharma: Consultancy; Adelphi: Consultancy; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Humana: Honoraria; Incyte: Honoraria; Novartis: Honoraria; Sangamo: Consultancy; Blueprint: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Blum:Boehringer Ingelheim: Research Funding; Celgene: Research Funding; Astellas,: Research Funding; Xencor: Research Funding; Forma: Research Funding; AmerisourceBergen: Consultancy. Schiller:Amgen: Other, Research Funding; Agios: Research Funding, Speakers Bureau; Celgene: Research Funding, Speakers Bureau; Constellation Pharmaceutical: Research Funding; Daiichi Sankyo: Research Funding; Eli Lilly and Company: Research Funding; FujiFilm: Research Funding; Genzyme: Research Funding; Gilead: Research Funding; Incyte: Research Funding; J&J: Research Funding; Jazz Pharmaceuticals: Honoraria, Research Funding; Karyopharm: Research Funding; Novartis: Research Funding; Onconova: Research Funding; Pfizer Pharmaceuticals: Equity Ownership, Research Funding; Sangamo Therapeutics: Research Funding; Bristol Myer Squibb: Research Funding; Astellas: Research Funding; Biomed Valley Discoveries: Research Funding. Olin:Daiichi Sankyo: Research Funding; Astellas: Research Funding; Genentech: Consultancy, Research Funding; Pfizer: Research Funding; Jazz Pharmaceuticals: Consultancy, Honoraria; Revolution Medicine: Consultancy; AstraZeneca: Research Funding; Clovis: Research Funding; Ignyta: Research Funding; MedImmune: Research Funding; Mirati Therapeutics: Research Funding; Novartis: Research Funding; Spectrum: Research Funding. Foran:Agios: Honoraria, Research Funding. Lin:Pfizer: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Honoraria. Traer:AbbVie: Consultancy; Agios: Consultancy; Astellas: Consultancy; Daiichi Sankyo: Consultancy; Notable Labs: Equity Ownership. Odenike:AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Research Funding; Astra Zeneca: Research Funding; Astex Pharmaceuticals: Research Funding; NS Pharma: Research Funding; Gilead Sciences: Research Funding; Janssen Oncology: Research Funding; Oncotherapy: Research Funding; Agios: Research Funding; CTI/Baxalta: Research Funding. Arellano:Gilead: Consultancy. Vergilio:Roche Holding AG: Equity Ownership; Foundation Medicine: Employment. Brennan:Foundation Medicine: Employment. Vietz:Foundation Medicine: Employment. Druker:ALLCRON: Membership on an entity's Board of Directors or advisory committees; Pfizer: Research Funding; Aileron Therapeutics: #2573, Constructs and cell lines harboring various mutations in TNK2 and PTPN11, licensing fees , Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Patents & Royalties, Research Funding; Burroughs Wellcome Fund: Membership on an entity's Board of Directors or advisory committees; Cepheid: Consultancy, Honoraria; The RUNX1 Research Program: Membership on an entity's Board of Directors or advisory committees; GRAIL: Equity Ownership, Other: former member of Scientific Advisory Board; Patient True Talk: Consultancy; Dana-Farber Cancer Institute (antibody royalty): Patents & Royalties: #2524, antibody royalty; Pfizer: Other: PI or co-investigator on clinical trial(s) funded via contract with OHSU., Research Funding; Merck & Co: Patents & Royalties: Dana-Farber Cancer Institute license #2063, Monoclonal antiphosphotyrosine antibody 4G10, exclusive commercial license to Merck & Co; OHSU (licensing fees): Patents & Royalties: #2573, Constructs and cell lines harboring various mutations in TNK2 and PTPN11, licensing fees ; Amgen: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Aptose Biosciences: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Beta Cat: Membership on an entity's Board of Directors or advisory committees, Other: Stock options; Blueprint Medicines: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Vivid Biosciences: Membership on an entity's Board of Directors or advisory committees, Other: Stock options; Beat AML LLC: Other: Service on joint steering committee; CureOne: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; Gilead Sciences: Other: former member of Scientific Advisory Board; ICON: Other: Scientific Founder of Molecular MD, which was acquired by ICON in Feb. 2019; Monojul: Other: former consultant; Novartis: Other: PI or co-investigator on clinical trial(s) funded via contract with OHSU., Patents & Royalties: Patent 6958335, Treatment of Gastrointestinal Stromal Tumors, exclusively licensed to Novartis, Research Funding; Bristol-Myers Squibb: Other: PI or co-investigator on clinical trial(s) funded via contract with OHSU., Research Funding. Byrd:Novartis: Other: Travel Expenses, Speakers Bureau; BeiGene: Research Funding; Pharmacyclics LLC, an AbbVie Company: Other: Travel Expenses, Research Funding, Speakers Bureau; Acerta: Research Funding; Ohio State University: Patents & Royalties: OSU-2S; Genentech: Research Funding; Gilead: Other: Travel Expenses, Research Funding, Speakers Bureau; TG Therapeutics: Other: Travel Expenses, Research Funding, Speakers Bureau; Janssen: Consultancy, Other: Travel Expenses, Research Funding, Speakers Bureau.

Objective To determine cross-sectional adherence with the multi-target stool DNA test used for colorectal cancer screening in a large, fully insured Medicare population. Methods All patients aged 65–85 with a valid multi-target stool DNA test order from 1 September 2016 to 31 August 2017 identified from the Exact Sciences Laboratories (Madison, WI; sole-source national multi-target stool DNA test provider) database were evaluated for test adherence. Cross-sectional adherence, defined as multi-target stool DNA test completion within 365 days from order date, was analyzed overall and by time to adherence, as well as by available patient (age, sex, test order date, Medicare coverage type) and provider (specialty, year of first multi-target stool DNA test order, multi-target stool DNA test order frequency, and practice location) factors. Results Among 368,494 Medicare beneficiaries (64% female), overall cross-sectional adherence was 71%. Cumulative adherence rates increased more rapidly at 30 (44%) and 60 (65%) days, followed by more gradual increases at 90 (67%), 180 (70%), and 365 (71%) days. By provider specialty, primary care clinicians represented a higher percentage of multi-target stool DNA orders than gastroenterologists (88% vs. 6%), but had a lower associated patient adherence rate (71% vs. 78%). Conclusions In this large, national sample of Medicare insured older adults, nearly three-quarters of patients adhered with a multi-target stool DNA order for colorectal cancer screening. These real-world data should inform further clinical and population health applications, reimbursement model simulations, and guideline-endorsed colorectal cancer screening strategies adherence.