Citations

Abstract Background: Use of direct-acting oral anticoagulants (DOAC) is increasingly common among patients with atrial fibrillation and venous thromboembolic disease. Differences in the mechanisms of action as compared to warfarin could impact transfusion patterns and clinical outcomes in patients, especially for those presenting with major hemorrhage. The management of patients taking these newer medications and corresponding outcomes are relevant to optimizing clinical decision making in situations of major hemorrhage. Methods: We tested the hypothesis that inpatient all-cause mortality among patients presenting with major hemorrhage differs based on the home-administered anticoagulant medication class (DOAC versus warfarin). A cohort of patients presenting to twelve US hospitals from 2013 to 2016 was identified using the Recipient Epidemiology and Donor Evaluation Study (REDS)-III Recipient Database. Primary ICD diagnosis codes, issued blood products, laboratory data, and early mortality events were used in the application of the International Society on Thrombosis and Hemostasis definition of major hemorrhage. Exposure status was defined as a record of home-administered DOAC (apixaban, dabigatran, edoxaban, or rivaroxaban; exposed) or warfarin (non-exposed). Patients with multiple encounters and those transferred into or out of network were excluded from the analysis. Proportional hazards regression was used to compare all-cause mortality and hospital length of stay. We then repeated the analysis using a cohort matched on propensity scores to account for confounding by age, gender, concurrent aspirin and anti-platelet use, liver and renal dysfunction, cancer, CHA2DS2-VASc score, traumatic injury, and hospital. We then repeated the propensity score matched analysis stratified by anatomic location of bleed and traumatic injury. Results: More than 1.5 million hospitalizations were screened for eligibility. Exclusion of minors, outpatients, hospitalizations without a medication of interest, absence of major hemorrhage, multiple hospitalizations, and hospital transfers resulted in 3,731 patients available for the unadjusted analysis. Inpatient all-cause mortality was lower among DOAC users when the entire cohort was considered (HR = 0.60, 95%CI 0.45 - 0.80, p=0.0005). Implementation of propensity score matching to account for confounding abrogated this difference (HR=0.84, 95%CI 0.58 - 1.22, p=0.36). Time to hospital discharge was shorter for DOAC users (HR = 1.17, 95%CI 1.05 - 1.30, p=0.0034). Transfusion patterns were similar by medication, except for plasma transfusion occurring in 42% of warfarin encounters and 11% of DOAC encounters. Vitamin K was administered in 63% of warfarin encounters, whereas specific DOAC reversal agents were largely unavailable during the analysis period [used in 5 (1%) DOAC encounters]. There were no statistically significant differences in inpatient all-cause mortality in the stratified analysis (warfarin as reference): HR = 0.69 (95%CI 0.31 - 1.55) for traumatic head injuries; HR = 1.10 (95%CI 0.62 - 1.95) for non-traumatic head injuries; HR = 0.62 (95%CI 0.20 - 1.94) for traumatic, non-head injuries; and HR = 0.69 (95%CI 0.29 - 1.63) for non-traumatic, non-head injuries. Conclusions: Analysis of a population taking oral anticoagulation and presenting with major hemorrhage showed that transfusion of plasma was more commonly employed to treat major hemorrhage among warfarin users than DOAC users. Inpatient all-cause mortality was lower among DOAC users in the overall cohort; however, accounting for potential confounding factors using propensity score matching abrogated this difference. Hospital length of stay was shorter for DOAC users compared to warfarin users. Stratification by location of bleed and traumatic injury did not alter these findings. Less plasma use and a shorter length of hospitalization in this study, combined with no observable difference in inpatient all-cause mortality, suggests that outcomes following major hemorrhage are at least no different for DOAC users as compared to warfarin users. Disclosures Mast: Novo Nordisk: Research Funding. Kor:NIH: Consultancy; NIH: Research Funding; UpToDate: Patents & Royalties; CSL Behring: Honoraria.

Abstract Abstract 4048 Poster Board III-983 Introduction Iron deficiency is the most common nutritional disorder in the world with an estimated two billion affected persons. Although commonly considered environmental in origin, the existence of multiple genetic disorders of iron metabolism in man, rodents and other vertebrates suggest a genetic contribution to iron deficiency. Methods: The Hemochromatosis and Iron Overload Screening (HEIRS) Study is a multi-center, multi-ethnic study in which transferrin saturation (TS), serum ferritin (SF), and HFE mutations were determined in 101,168 adults. To identify genomic locations associated with iron deficiency, we performed a genome-wide association study (GWAS) using DNA collected from white HEIRS Study participants who had SF ≤ 12 μg/L (cases) and an equal number of white controls (SF > 100 μg/L in men, SF > 50 μg/L in women) frequency-matched to cases by sex and geographic location. Men aged ≥ 25 y and women ≥ 50 y were included in both groups. Tissue body iron, an index of iron deficiency, was estimated from serum transferrin receptor (sTfR) and SF. Genotyping was performed with the Illumina HumanCNV370K Beadchip platform. Quality control filters excluded single nucleotide polymorphisms (SNPs) or samples with > 5% missing genotypes, SNPs showing heterozygosity or Hardy-Weinberg deviations (P<10−7), and SNPs with minor allele frequency < 0.02. Population admixture/structure was assessed using principal component analysis. Regression analysis was used to examine the association between outcomes (case-control status, tissue body iron, serum ferritin, transferrin receptor, serum iron, total iron-binding capacity [TIBC], and unsaturated iron-binding capacity [UIBC]) and each SNP genotype variable; covariates included age, sex, and geographic location. Replication for 56 SNPs was conducted in a population attending primary care clinics at a Veterans Affairs (VA) medical center using the iPlex platform. Eligibility within the VA replication population was restricted to age and self-reported white ethnicity as for the HEIRS subset from a total of 2559 people (138 women). VA participants with SF ≤ 20 μg/L were classified as iron-deficient cases and frequency matched 1:2 with controls (men with SF > 100 μg/L and women with SF > 50 μg/L). Results The GWAS genomic control parameter was not significantly different from 1.0. There were 392 cases (96 men) and 390 controls (96 men) in the HEIRS subset GWAS with average age (SD) of 59 (10) y and 61 (11) y, respectively. Geometric mean SF (minimum, maximum), and mean (SD) for sTfR and tissue body iron in the HEIRS subset were 7.5 (1.2, 12) μg/L, 6.4 (3.77) mg/kg and -2.0 (2.50) for cases and 141 (51, 881) μg/L, 3.0 (0.98) mg/kg and 10.8 (2.5) for controls. After quality control tests, GWAS analysis included genotype data for 331,060 SNPs in 734 individuals (364 cases, 370 controls). For the VA replication population there were 67 male and 11 female cases, and 136 male and 27 female controls for whom DNA was successfully prepared; the average age (SD) was 68 (12) y for cases and 65 (11) y for controls. Regression analysis identified seven SNPs within four independent regions that replicated associations found in the GWAS (GWAS P<1×10-4 and VA P<0.05).The SNP rs6735681 on chromosome 2p24 was associated with serum iron (GWAS P<3.9×10-5, VA P=0.038). Three SNPs on chromosome 2p14 (rs6750096, rs2698541 and rs2698530) significantly influenced both TIBC and UIBC (GWAS P<2.9×10-5, VA P< 0.04 for all). Two SNPs in the TF gene region on chromosome 3q22 also showed significant effects on TIBC and UIBC (GWAS P<4.7×10-6, VA P<0.03 for all). The SNP rs10512064 on chromosome 9q21 was associated with serum ferritin concentration and tissue body iron (GWAS P<2.5×10-5, VA P<0.05 for both). Conclusion: From these GWAS and replication studies, we have identified three new genetic loci and one known iron gene, TF, associated with iron phenotype variability. These results point to specific loci as targets for gene identification and TF polymorphisms as determinants of iron metabolism, which in turn may play a role in regulation of body iron status. Disclosures: No relevant conflicts of interest to declare.

Abstract Introduction Over the last 15 years gene expression profiling (GEP) has been used to define myeloma molecular subgroups and to determine clinical prognosis. Two major molecular subgroup classifications have been used: the UAMS which determines 7 subgroups and the TC classification based on the presence of IgH translocations and expression of D group cyclins. For prognosis, although a number of different GEP signatures have been defined, the widely used GEP70 identifies 15% of patients with high risk (HR) disease who have a median PFS and OS of 1.75 and 2.83 years. An ideal classification system would identify clinically relevant subgroups with distinct etiology and biology using standardized techniques. We have examined a large group of patients characterized at multiple genetic levels to optimize the diagnostic approach of newly diagnosed patients going forward. Materials and methods Study subjects included 1349 cases enrolled in Total Therapy trials (median follow up 7.5 years). Gene expression profiling was used to determine GEP70 risk status, molecular subgroup by UAMS and TC classifications, and to devise a new and extended TC classification (TC10). Interphase FISH associated with IgH translocations and 1q+ and 17p- were used to build GEP proxies. Data from mutational analysis generated by the FoundationOne targeted sequence panel was also incorporated. Results were validated on the UK MRC MyelomaIX and Hovon65/GMMG-4 studies. Results An initial agnostic analysis of GEP data using sparse k-means clustering verified the existence of TC based groups. Six groups were identified that corresponded overwhelmingly with known TC subgroups; CCND1-t(11;14), D1-HRD, D2-HRD, MMSET, MAF/CCND2, and CCND3. Further comparisons between the molecular subgroup and TC classifier revealed that the UAMS 7 subgroups clustered strongly within one predominant TC group: CD-1 and CD-2 to t(11;14), HY to D1, LB and PR to D2, MF to t(14;16) or t(14;20), and MS to t(4;14). As the UAMS molecular subgroups are largely contained within the TC framework, we aimed to extend the TC by developing the TC10. To extend the known TC subgroups, unsupervised clustering was applied to the 3 largest subgroups [t(11;14), D1, and D2] to determine the strongest single divisor within each respective subgroup. The dominant feature within the t(11;14) cases was CD20 expression, while the D1 and D2 subgroups both split according to RRAS2. CD20 is associated with PAX5 and VPREB3 expression, and RRAS2 is associated with decreased PTP4A3 and increased TNFAIP3 and BIRC3 expression. RRAS2 activation within D1 subgroup and CD20 activation within t(11;14) cases corresponds to an increased time to response to induction therapy suggesting they constitute important biological subgroups. The TC10 combines the known etiologic subgroups of the TC with functionally relevant subdivisions to create 10 novel subgroups: t(11;14) CD20+/-, D1: RRAS2+/-, D2: RRAS2+/-, t(4;14), t(14;16), t(14;20), and t(6;14). Analysis of mutational data revealed that RRAS2 and CD20 activation within the D1, D2, and t(11;14) subgroups reduced the number of mutations in the MAPK pathway. Further mutational analysis revealed that median mutational load was highest in t(14;16) and lowest in D2: RRAS2+ subgroups. The GEP70 score identifies 15% of patients with HR disease and is specific for this purpose. In an analysis of risk assessment methods, we compared GEP detected adverse lesions [t(4;14), t(14;16), t(14;20), 17p- and 1q+] with the GEP70 and revealed that GEP70 HR identified samples have lower OS rates than cases with more than one adverse lesion (validated in external sets). GEP70 HR segregates non-uniformly across molecular subgroups as over 40% of all HR cases are found in the TC10 t(4;14), t(14;16), and t(14;20) subgroups. GEP70 HR cases also have a higher mutational load than low risk cases. Furthermore, GEP70 HR is uniquely associated with 1q+ and 17p- as cases with at least one of these adverse lesions are 4.9 times as likely to be GEP70 HR as cases with neither. Conclusion GEP profiling has a central role in simplifying and standardizing the molecular subgroup designation and risk stratifying of MM patients. The GEP70 risk score reliably identifies HR cases and outperforms FISH in risk assessment, even in validation data sets. The TC10 provides a classification system that improves upon previous methods by defining both etiological and functionally meaningful subgroups. Disclosures Stein: University of Arkansas for Medical Sciences: Employment. Davies:University of Arkansas for Medical Sciences: Employment; Celgene: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Onyx: Consultancy. Heuck:University of Arkansas for Medical Sciences: Employment; Celgene: Consultancy; Janssen: Other: Advisory Board; Millenium: Other: Advisory Board; Foundation Medicine: Honoraria. Weinhold:University of Arkansas for Medical Sciences: Employment; Janssen Cilag: Other: Advisory Board. Chavan:University of Arkansas for Medical Sciences: Employment. Thanendrarajan:University of Arkansas for Medical Sciences: Employment. Epstein:University of Arkansas for Medical Sciences: Employment. Yaccoby:University of Arkansas for Medical Sciences: Employment. Zangari:University of Arkansas for Medical Sciences: Employment; Novartis: Research Funding; Onyx: Research Funding; Millennium: Research Funding. van Rhee:University of Arkansa for Medical Sciences: Employment. Kaiser:Janssen: Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; BristolMyerSquibb: Consultancy; Chugai: Consultancy. Sonneveld:Janssen-Cilag, Celgene, Onyx, Karyopharm: Honoraria, Research Funding; novartis: Honoraria. Goldschmidt:Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Onyx: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen-Cilag: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Millenium: Honoraria, Research Funding, Speakers Bureau; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Chugai: Honoraria, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Barlogie:University of Arkansas for Medical Sciences: Employment. Morgan:Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; MMRF: Honoraria; CancerNet: Honoraria; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; University of Arkansas for Medical Sciences: Employment; Weismann Institute: Honoraria; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees.

AbstractBackgroundThe extent to which obesity and genetics determine postoperative complications is incompletely understood.MethodsWe performed a retrospective study using two population cohorts with electronic health record (EHR) data. The first included 736,726 adults with body mass index (BMI) recorded between 1990 and 2017 at Vanderbilt University Medical Center. The second cohort consisted of 65,174 individuals from 12 institutions contributing EHR and genome‐wide genotyping data to the Electronic Medical Records and Genomics (eMERGE) Network. Pairwise logistic regression analyses were used to measure the association of BMI categories with postoperative complications derived from International Classification of Disease‐9 codes, including postoperative infection, incisional hernia, and intestinal obstruction. A genetic risk score was constructed from 97 obesity‐risk single‐nucleotide polymorphisms for a Mendelian randomization study to determine the association of genetic risk of obesity on postoperative complications. Logistic regression analyses were adjusted for sex, age, site, and race/principal components.ResultsIndividuals with overweight or obese BMI (≥25 kg/m2) had increased risk of incisional hernia (odds ratio [OR] 1.7–5.5, p < 3.1 × 10−20), and people with obesity (BMI ≥ 30 kg/m2) had increased risk of postoperative infection (OR 1.2–2.3, p < 2.5 × 10−5). In the eMERGE cohort, genetically predicted BMI was associated with incisional hernia (OR 2.1 [95% CI 1.8–2.5], p = 1.4 × 10−6) and postoperative infection (OR 1.6 [95% CI 1.4–1.9], p = 3.1 × 10−6). Association findings were similar after limitation of the cohorts to those who underwent abdominal procedures.ConclusionsClinical and Mendelian randomization studies suggest that obesity, as measured by BMI, is associated with the development of postoperative incisional hernia and infection.

Abstract Introduction High expression of the H3K27 histone methyltransferase EZH2 mRNA in myeloma (MM) patient samples is associated with molecular features of high risk disease, including increased proliferation, and adverse outcomes (1). Mutations or deletions in the H3K27 demethylase KDM6A are associated with similar findings (2) and would be expected to have the same epigenetic effect, increasing H3K27me3 levels, a mark associated with repression of gene expression. We, therefore, sought to identify the role EZH2 plays in controlling myeloma cell proliferation. Methods A panel of MM cell lines and primary patient samples (CD138 selected from bone marrow with consent) representing a variety of different MM molecular subgroups were used. Cell viability (WST-1), cell cycle (PI) and apoptosis (AnnexinV/PI, Caspase-Glo 3/7) assays were performed. Affymetrix gene expression arrays followed by validation with RT-PCR were used to identify patterns of gene expression change with EZH2i. Western blotting confirmed changes at the protein level and Chip-PCR was performed using a validated antibody and isotype control to identify H3K27me3 changes at the relevant gene promotors. Affymetrix gene expression data for 1213 patients enrolled in the Total Therapy studies were used to investigate the relevance of our findings in myeloma patient samples. Results We confirmed a reduction in viability following EZH2i using two chemically distinct, specific small molecule inhibitors (EPZ005687 and UNC1999) and the negative control compound UNC2400. There was a reduction in viability in 6/8 cell lines and 5/6 patient samples. Response to inhibition was not related to molecular subgroup or the presence of high-risk molecular features including del17p. Global levels of H3K27me3 measured by Western blot were reduced in all cell lines regardless of response to EZH2i. In responding cell lines EZH2i induced cell cycle arrest at G1/S followed by induction of apoptosis. Gene expression arrays performed using mRNA from KMS11 and KMM1 cell lines highlighted a change in expression of cell cycle control genes associated with EZH2i. This finding was validated using qRT-PCR, which demonstrated upregulation of the cyclin dependent kinase inhibitors CDKN2B, CDKN1A or both. These findings were confirmed at the protein level by Western blotting. Chip-PCR experiment using cell lysates from KMS11 cells following incubation with EZH2i over 6 days identified changes in H3K27me3 at the promoter and transcriptional start site (PROM/TSS) regions of the CDKN2B and CDKN1A genes. The most specific changes occurred at the CDKN1A PROM/TSS, which were more heavily marked with H3K27me3 at baseline compared to a region approx. 5KB upstream. Given these results, which suggest that CDKN1A expression may be controlled by changes in H3K27me3, we explored the effect of CDKN1A mRNA expression in our patient datasets. We found the expression of EZH2 and CDKN1A to be inversely correlated (R=-0.170, p<0.0001) and that low expression of CDKN1A was associated with a significantly shorter progression free and overall survival (p<0.001). In order to confirm whether these gene expression changes could be used as a potential biomarker of response we looked at our panel of cell lines with variable responses to EZH2i. We identified a consistent increase in expression of CDKN1A only in responding cell lines suggesting it could be used as a biomarker of efficacy in the clinic. Conclusions These data support the hypothesis that CDKN1A expression is suppressed by increased H3K27me3, due to high expression of EZH2 and that this can be reversed with pharmacological EZH2 inhibition leading to a reduction in proliferation of myeloma cells. We provide data which supports the investigation of EZH2i in clinical trials of myeloma patients, which has the potential to be an effective therapeutic strategy even for those with high-risk disease, for whom current treatment approaches are ineffective.Pawlyn et al, EZH2 Overexpression in Myeloma Patients Shortens Survival and in-vitro Data Supports a Potential New Targeted Treatment Strategy. AACR and IMW abstracts, 2015Pawlyn et al, The Spectrum and Clinical Impact of Epigenetic Modifier Mutations in Myeloma. Clinical Cancer Research, 2016 Disclosures Pawlyn: Celgene: Consultancy, Honoraria, Other: Travel Support; Takeda Oncology: Consultancy. Kaiser:Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; BMS: Consultancy, Other: Travel Support; Takeda: Consultancy, Other: Travel Support; Chugai: Consultancy. Jones:Celgene: Honoraria, Research Funding. Jackson:Amgen: Consultancy, Honoraria, Speakers Bureau; Roche: Consultancy, Honoraria, Speakers Bureau; MSD: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau. Bergsagel:Novartis: Research Funding; Amgen, BMS, Novartis, Incyte: Consultancy. Morgan:Univ of AR for Medical Sciences: Employment; Janssen: Research Funding; Bristol Meyers: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding. Davies:Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria.

Abstract Abstract 873 Introduction: Clonal chromosomal anomalies have been well-characterized in the blood cells of patients with Chronic Lymphocytic Leukemia (CLL) and other hematological malignancies. However, the frequencies and types of chromosomal anomalies found in “normal” people (i.e. no history of hematological cancer) are much less well known. Here we compare chromosomal anomalies in untreated CLL patients with those found in a large sample of people (n>50,000) recruited for genome-wide association studies in the Gene Environment Association Studies (GENEVA) consortium. This comparison provides information about the extent to which people without a prior history of hematological cancer have chromosomal anomalies that are characteristic of CLL. Methods and Results: The anomaly detection method focused on acquired, rather than inherited, chromosomal anomalies identified in whole blood samples. The method and results for the GENEVA subjects were published recently (Laurie et al. 2012, Nat. Gen. 44:642–650). The same method was applied to pre-treatment samples from 220 CLL patients from US Intergroup Trial E2997 of the Eastern Cooperative Oncology Group (ECOG). Acquired anomalies were detected as mosaic mixtures of normal disomic cells along with cells containing autosomal anomalies consisting of large (generally > 1 Mb) deletions, duplications and/or acquired uniparental disomy (UPD). Detection requires a large fraction of cells with the same abnormal karyotype (>5–10%), which is presumably of clonal origin. The subjects (of all ages) came from 12 different GENEVA studies, which addressed a variety of health conditions not directly related to hematological cancer (e.g. addiction and glaucoma). DNA was extracted mainly from blood samples (or, in 8% of subjects, from buccal/saliva samples) and genotyped on Illumina SNP mircroarrays. Clonal mosaic anomalies were detected from change points in intensity and genotype data (B Allele Frequency and Log R Ratio). We found 514 anomalies in 404 GENEVA subjects out of 50,222 examined (0.8% of subjects), although the prevalence increased with age. Clinical data from about 75% of subjects with a clonal chromosomal abnormality revealed that only a small fraction (2.8%, 95% CI=1.0 – 4.7%) had a record of prior hematological cancer. In the ECOG study, blood samples from 220 CLL patients enrolled in protocol E2997 were genotyped on the Illumina HumanOmni1-Quad_v1 array and clonal mosaic chromosome anomalies were detected using the GENEVA method. Chromosomal anomalies typical of CLL also were identified using interphase FISH (i.e. 6q23.3-, 11q22-, 12q15+, 13q14-, 17p13-) as previously reported (Grever, JCO 25:799-804). We found 489 anomalies in 148 patients of the 220 examined (67% of patients). The anomalies detected by FISH and SNP microarray were highly concordant (>98%). Although, the most common recurrent anomalies occurred on chromosomes 6, 11, 12, 13 and 17 in regions that span the FISH probes (except for chromosome 6), a majority of the anomalies detected by SNP data (74% of 489) were not detected by the CLL FISH panel. The frequency of large chromosomal anomalies was much higher in CLL patients than in GENEVA (67% versus 0.8% of subjects) and the overall composition of anomaly types found was also different. The fraction of anomalies consisting of acquired UPD was substantially higher in GENEVA subjects than in ECOG (34.0% versus 10.6%), while deletions were less common in GENEVA (50.1% versus 72.4%) and duplications were similar (15.6% versus 17.0%) (Chi-squared test p-value=3 × 10−18). We compared the most commonly deleted regions (CDR) on each chromosome across studies. In the ECOG CLL patients, four chromosomes each had 10 or more overlapping deletions in their top CDR (6, 11, 13 and 17), which span the FISH probes (except for chromosome 6). The GENEVA subjects also had CDR on chromosomes 11, 13 and 17, which overlap those in ECOG. In addition, GENEVA had CDR spanning genes or regions implicated in myeloid malignancies, including DNMT3a on 2p, TET2 on 4q, PRAME on 22q, and a region on 20q13. Conclusions: These genotyping results of >50,000 subjects suggest clonal chromosomal anomalies may represent precursor states, or perhaps undiagnosed or unrecorded cases, of a variety of hematological malignancies. Further studies to validate these findings and explore the natural history of these genetic defects in individuals without hematologic malignancy are warranted. Disclosures: Shanafelt: Genentech: Research Funding; Glaxo-Smith-Kline: Research Funding; Cephalon: Research Funding; Hospira: Research Funding; Celgene: Research Funding; Polyphenon E International: Research Funding.

INTRODUCTION: Treatment options for patients with moderate-severe ulcerative colitis (UC) are increasing. The appropriateness of their use in mild-moderate UC is less clear, as is the role of surgical intervention. Our aim was to define the appropriateness of therapeutic options in a range of clinical scenarios in mild-moderate UC METHODS: We applied the RAND/UCLA Appropriateness Method to rate appropriateness of treatment choices in patients with mild-moderate UC; severe disease (likely to need hospitalization or surgery within 2 weeks) was excluded. A literature review was presented to the BRIDGe group, a panel of 13 IBD specialists, as well as 1 external IBD expert and 2 IBD surgical experts. 12 chapters were constructed including failed 5-ASA, steroid or thiopurine therapy, and primary/secondary non-response to biologics or tofacitinib. Scenarios were also divided by disease extent (proctitis vs more extensive) and activity (mild vs moderate) creating 260 scenarios in all. Panellists used a modified Delphi method to anonymously rate the appropriateness of a therapy on a 1-9 scale (1-3 not appropriate, 4-6 uncertain, 7-9 appropriate). Disagreement was assessed using a validated index (DI) and was defined as a DI > 1. The panellists then convened in person to discuss areas of disagreement, followed by a second round of rating. Scenarios rated a median of 7 or higher were deemed appropriate and those rated 3 or lower were deemed inappropriate. Scenarios rated 4-6 were considered uncertain, as were any with a DI > 1 RESULTS: Appropriateness was agreed in 131 and inappropriateness in 47 scenarios. Greatest disagreement was around the use of thiopurines, but no scenario had a DI > 1. There were 82 scenarios were rated uncertain. Biologics and tofacitinib were more likely to be rated appropriate for treatment resistance and greater disease activity; disease extent less likely to influence decision making. Surgery was felt to be appropriate in 8 scenarios, generally in more extensive and active disease that had failed to respond to medical therapy. Surgery was felt to be inappropriate in 16 scenarios and was uncertain in 24 CONCLUSION: Although there was little disagreement amongst an expert panel of IBD physicians and surgeons, there remained uncertainty about the use of medical and surgical therapy in nearly one third of situations in patients with mild-moderate UC. These results could be used to guide the design of clinical trials for these patient groups in order to guide best practice.

INTRODUCTION: Colorectal cancer (CRC) screening is broadly endorsed for individuals 50–75 years of age. Given recent increases in CRC incidence and mortality for patients <50 years old, the American Cancer Society now recommends initiation of average-risk CRC screening at age 45 (Wolf AMD CA Cancer J Clin 2018). The performance of recommended tests, including the multi-target stool DNA (mt-sDNA; marketed as Cologuard™) test, has been minimally described in populations under 50 years old. To address this knowledge gap, we conducted a retrospective study of DNA markers (KRAS, BMP3, NDRG4) included in the mt-sDNA assay to quantify and compare tissue marker levels in sporadic CRC cases and normal colon controls from patients in 45–49 and 50–64 year-old age groups. Our primary hypothesis was that mt-sDNA marker levels would be statistically similar between cases in both age groups. METHODS: Patient samples were identified from a pathology tissue archive at Mayo Clinic, excluding those with inflammatory bowel disease or familial cancer syndromes. Formalin-fixed, paraffin-embedded tissues were reviewed by a GI pathologist for 45–49 year-old (n = 90) and 50–64 year-old (n = 96) CRC cases; normal colon samples (45–49, n = 76; 50–64, n = 92) were also included for comparison. DNA extracted from core punches was blinded and processed in a randomized order. Quantitative Allele specific Realtime Target and Signal amplification (QuARTS) assays were used to quantify KRAS mutations, and BMP3 and NDRG4 methylation. Marker level in each sample was reported as the QuARTS product of mutant KRAS , methylated BMP3 or NDRG4 relative to the ACTB product in each sample, reported as a percentage. At least 60 patients per group provided sufficient power to demonstrate differences in marker levels of ±25% between case groups and between control groups using the Wilcoxon Rank Sum test. RESULTS: CRC cases in the 45–49 vs 50–64 year-old groups did not differ significantly with respect to sex ( P = 0.77) or race/ethnicity ( P = 0.06); left-sided CRC was more common in the 45–49 vs 50–64 year-old group (87% vs. 72%; P = 0.02). None of the marker levels were statistically different between cases of the younger vs older groups or between controls of these groups (Figure 1). CONCLUSION: These data support the expectation of no age-related differences in CRC detection by mt-sDNA, as the relevant molecular markers, when they occur, are present at similar levels across age groups 45–49 and 50–64 years.

Abstract Background: Pancreatic cancer is associated with development of cachexia, a wasting syndrome thought to limit survival. Few studies have longitudinally quantified peripheral tissues or identified biomarkers predictive of future tissue wasting. Methods: Adipose and muscle tissue were measured by computed tomography (CT) at diagnosis and 50 to 120 days later in 164 patients with advanced pancreatic cancer. Tissue changes and survival were evaluated by Cox proportional hazards regression. Baseline levels of circulating markers were examined in relation to future tissue wasting. Results: Compared with patients in the bottom quartile of muscle change per 30 days (average gain of 0.8 ± 2.0 cm2), those in the top quartile (average loss of 12.9 ± 4.9 cm2) had a hazard ratio (HR) for death of 2.01 [95% confidence interval (CI), 1.12–3.62]. Patients in the top quartile of muscle attenuation change (average decrease of 4.9 ± 2.4 Hounsfield units) had an HR of 2.19 (95% CI, 1.18–4.04) compared with those in the bottom quartile (average increase of 2.4 ± 1.6 Hounsfield units). Changes in adipose tissue were not associated with survival. Higher plasma branched chain amino acids (BCAA; P = 0.004) and lower monocyte chemoattractant protein-1 (MCP-1; P = 0.005) at diagnosis were associated with greater future muscle loss. Conclusions: In patients with advanced pancreatic cancer, muscle loss and decrease in muscle density in 2 to 4 months after diagnosis were associated with reduced survival. BCAAs and MCP-1 levels at diagnosis were associated with subsequent muscle loss. Impact: BCAAs and MCP-1 levels at diagnosis could identify a high-risk group for future tissue wasting.

Abstract Purpose: The heterogeneity of androgen receptor (AR)-activity (AR-A) is well-characterized in heavily treated metastatic castration-resistant prostate cancer (mCRPC). However, the diversity and clinical implications of AR-A in treatment-naïve primary prostate cancer is largely unknown. We sought to characterize AR-A in localized prostate cancer and understand its molecular and clinical implications. Experimental Design: Genome-wide expression profiles from prostatectomy or biopsy samples from 19,470 patients were used, all with independent pathology review. This was comprised of prospective discovery (n = 5,239) and validation (n = 12,728) cohorts, six retrospective institutional cohorts with long-term clinical outcomes data (n = 1,170), and The Cancer Genome Atlas (n = 333). Results: A low AR-active subclass was identified, which comprised 9%–11% of each cohort, and was characterized by increased immune signaling, neuroendocrine expression, and decreased DNA repair. These tumors were predominantly ERG and basal subtype. Low AR-active tumors had significantly more rapid development of recurrence or metastatic disease across cohorts, which was maintained on multivariable analysis [HR, 2.61; 95% confidence interval (CI), 1.22–5.60; P = 0.014]. Low AR-active tumors were predicted to be more sensitive to PARP inhibition, platinum chemotherapy, and radiotherapy, and less sensitive to docetaxel and androgen-deprivation therapy. This was validated clinically, in that low AR-active tumors were less sensitive to androgen-deprivation therapy (OR, 0.41; 95% CI, 0.21–0.80; P = 0.008). Conclusions: Leveraging large-scale transcriptomic data allowed the identification of an aggressive subtype of treatment-naïve primary prostate cancer that harbors molecular features more analogous to mCRPC. This suggests that a preexisting subgroup of patients may have tumors that are predisposed to fail multiple current standard-of-care therapies and warrant dedicated therapeutic investigation.