Cunningham, Julie M.

Abstract Abstract 2295 Background: While interaction between Factor V Leiden and other VTE risk exposures compound VTE risk, whether other gene-environment interactions are associated with VTE is largely unknown. Objective: To test novel gene-environment interactions for an association with VTE. Methods: Genome-wide scan (Illumina 660Q [557,112 SNPs]) and candidate gene (12,551 SNPs in 764 genes within the anticoagulant, procoagulant, fibrinolytic and innate immunity pathways) genotypes from 1270 non-Hispanic adults of European ancestry with objectively-diagnosed VTE (cases; no cancer, venous catheter or antiphospholipid antibodies) and 1302 controls (frequency-matched on case age, gender, race, MI/stroke status) were merged and imputed to 2.5 million SNPs with MACH using HapMap Phase 2 (60 CEU). We tested pairwise interactions between these SNPS and each of 7 exposures (“stress” [defined as ever hospitalized, ever surgery, ever trauma, ever transfemoral procedure or ever leg paresis], ever surgery, ever neurosurgery, ever orthopedic surgery; and among women, ever pregnant, ≥3 pregnancies and ever oral contraceptives/hormone therapy), adjusted for age, gender, MI/stroke status and state of residence, using logistic regression. Results: The mean ± standard deviation case and control ages were 54.4 ± 16.2 and 55.6 ± 15.8 years, respectively and 51% were female. Among the 7 models, a significant interaction was found between stress and A2BP1 (rs735919, OR=0.2, p=2.8E-07), ever surgery and ABCG8 (rs6709904, OR=0.27, p=2.1E-07), orthopedic surgery and SLC24A3 (rs6081599, OR=0.53, p=2.8E-07), neurosurgery and TBC1D4 (rs17064485, OR=8.5, p=3.5E-07), every pregnant and MICALCL (rs11022321, OR=0.17, p=8.3E-07), >3 pregnancies and KLRB1 (rs2536929, OR=0.31, p=1.1E-07) and ever oral contraceptives/hormone therapy and LINGO2 (rs10969259, OR=2.02, p=4.9E-07). A2BP1 plays a role in RBC shape and size, ABCG8 is associated with severe hypercholesterolemia, SLC24A3 is important for intracellular calcium homeostasis, TBC1D4 participates in cell signaling in CD4+/CD45RO+ memory T-cells, MICALCL is an extracellular signal-regulated kinase 2-binding protein, KLRB1 regulates natural killer cell function, and LINGO2 encodes a leucine-rich repeat signaling protein. Our observed interaction of VTE risk factors with these genes to compound the risk of VTE requires replication in future studies. If confirmed, functional studies will be important to determine the roles of these gene products in the causal pathway to VTE. Conclusion: VTE risk factors and SNPs within several novel genes may interact to compound the risk of VTE. Disclosures: Heit: Daiichi Sankyo: Consultancy, Honoraria.

Abstract Background: Peripheral T-cell lymphomas (PTCLs) comprise a heterogeneous group of relatively uncommon and generally aggressive non-Hodgkin lymphomas. Recent studies have begun to reveal their mutational landscape, such as the recurrent mutations affecting RHOA, IDH2, and TET2 in angioimmunoblastic T-cell lymphomas (AITLs) and related PTCLs. Here, we studied the exomes of 63 PTCLs to address several unanswered questions: (1) How frequently do PTCLs have mutations with a strong rationale for being actionable using clinically available targeted therapies? (2) What pathways are enriched in PTCLs that could point to additional targeted therapies? (3) What additional recurrent mutations exist that may provide insight into PTCL pathogenesis and classification? Methods: We performed exome capture and sequencing in 63 frozen PTCL samples (with matched germline controls from 22), including 13 PTCLs, not otherwise specified; 6 AITLs; 37 anaplastic large cell lymphomas (ALCLs: 12 ALK+, 19 ALK-, and 6 cutaneous); 4 extranodal NK/T-cell lymphomas; 2 cutaneous T-cell lymphomas, and 1 enteropathy associated T-cell lymphoma. Following primary variant calling, data were filtered by removing variants that were: outside the capture region, polymorphic (0.1% in ExAC), seen in paired germline or 50 healthy controls, within repetitive elements, or synonymous. Potentially actionable variants required published evidence for altering protein function and demonstrated in vivo or in vitro sensitivity to an available targeted agent (including open clinical trials). Mutated cancer genes (COSMIC) were examined for enrichment of KEGG canonical signaling pathways. A recurrent mutation encoding MSC E116K was validated in tumor DNA and MSC protein was examined by immunohistochemistry. Results: Of 25 unique variants with published evidence suggesting actionability (including ErbB, JAK/STAT, and RAS genes), at least one variant was present in 16/63 patients (25%). Variants in any COSMIC gene were seen in 55/63 samples (87%). Top genes were STAT3, RHOA, ARID1A, MLL2 (KMT2D), PTPRB, TP53, and TET2. The KEGG ErbB pathway was most significantly enriched for mutated genes in our dataset (EGFR, ERBB2, ERBB3, ERBB4, and others; q=3.88x10-3), followed by neurotrophin and mitogen-activated protein kinase signaling pathways. Among 25 recurrent non-synonymous variants, we identified MSC E116K with predicted gain-of-function exclusively in 3 ALK-ALCLs and 1 cutaneous ALCL. All mutated cases strongly expressed MSC protein. Among 96 cases, 13/18 ALK-ALCLs (72%) were positive for MSC, compared to 9/78 (12%) from other PTCL subtypes. Conclusions: Mutations that were potentially actionable based on published variant-specific data were identified in 25% of PTCLs, indicating promise for the role of sequencing in individualizing therapy. Additionally, remaining variants were enriched for several targetable pathways, most significantly ErbB. A novel recurrent mutation, MSC E116K, was seen exclusively in ALK-ALCLs (systemic and cutaneous). MSC protein, a repressor of E2A-mediated transcription of targets such as MYC, was expressed in most ALK- ALCLs and generally was absent in other PTCL subtypes. This finding extends evidence for a genetic relationship between systemic and cutaneous ALCLs and suggests MSC may help distinguish ALK- ALCLs from other PTCLs. Disclosures Link: Genentech: Consultancy, Research Funding; Kite Pharma: Research Funding.

Abstract Abstract 480 Background: While interaction between Factor V Leiden and other VTE risk exposures (i.e., oral contraceptives, hormone therapy, pregnancy, cancer, minor trauma) compound VTE risk, whether other gene-environment interactions are associated with VTE is largely unknown. Objective: To test gene-environment interactions for an association with VTE. Methods: Cases (n=1488) were Mayo Clinic European-American patients of non-Hispanic ancestry with objectively-diagnosed VTE in the absence of active cancer, venous catheter or antiphospholipid antibodies. Controls (n=1439) were Mayo Clinic outpatients without VTE who were frequency-matched on case age, gender, race, MI/stroke status and state of residence. We selected candidate genes relevant to the anticoagulant, procoagulant, fibrinolytic and innate immunity pathways, focusing on platelet, monocyte, neutrophil and endothelial cell agonists, receptors, ligands, signal transduction and adhesion molecules, granule contents and effectors; plasma proteases and inhibitors; matrix metalloproteases; inflammatory cytokines and receptors; estrogen, progesterone and androgen receptors; co-regulators and enzymes related to estrogen metabolism; important enzymes for catechol, homocysteine, thromboxane A2 and prostacyclin biosynthesis and metabolism; and HMG CoA reductase. For these genes (n=750), we selected all non-synonymous coding single nucleotide polymorphisms (SNPs) with minor allele frequency ≥0.5%; the remaining SNPs were selected using an LD tagging algorithm (Carlson et al. AJHG 2004); 479 ancestry-informative markers were also included (total n=13,241 SNPs). Leukocyte genomic DNA was genotyped using a custom Illumina Infinium iSelect platform, including appropriate controls. We tested all pairwise interactions between 12,483 SNPs (12296 autosomal, 187 chromosome X) that passed quality control and each of seven exposures (ever “stress”[defined as ever hospitalized, ever surgery, ever trauma, ever transfemoral procedure or ever leg paresis], ever surgery, ever neurosurgery, ever orthopedic surgery; and among women, ever pregnant, ≥3 pregnancies and ever oral contraceptives/hormone therapy), adjusted for age, gender, MI/stroke status and state of residence, using logistic regression. False discovery rates (q-value) were calculated to estimate the expected fraction of false positive associations due to multiple statistical tests. Results: The mean ± SD case and control ages were 55.3 ± 16.4 and 56.5 ± 15.9 years, respectively, and 51% were female. Analysis of ancestry-informative markers revealed no evidence of population stratification. Among the seven models, a significant interaction (q≤0.05) was found between ever surgery and two genes located on chromosome 1: USF1 (rs2516840, OR=2.1, p=2.0E-06; q=0.02), and F11R (rs790055 & rs790056; OR∼2.29, p∼4E-06; q=0.02); the two F11R SNPs are in complete linkage dysequilibrium (LD), and the USF1 SNP is in moderate LD with the F11R SNPs (r2∼0.6). USF1 encodes for upstream transcription factor 1 which regulates many genes involved in lipid and glucose homeostasis. F11R encodes for the platelet F11 receptor (JAM [junctional adhesion molecule], a cell adhesion molecule important for platelet adhesion to cytokine-stimulated endothelial cells. Conclusion: Interaction between surgery and SNPs within USF1 and F11R are associated with significantly increased risks for VTE. These potential associations require confirmation in future replication studies. Disclosures: No relevant conflicts of interest to declare.

Abstract Abstract 2702 Background: CXCR5 [chemokine (C-X-C motif) receptor 5; also known as Burkitt lymphoma receptor 1 (BCR1)] is expressed on mature B-cells, subsets of CD4+ and CD8+ T-cells, and skin-derived migratory dendritic cells. CXCL13 is the ligand for CXCR5, and together they are involved in guiding B-cells into the B-cell zones of secondary lymphoid organs as well as T-cell migration. CXCR5 is also expressed in malignant cells in NHL patients. Single nucleotide polymorphisms (SNPs) in CXCR5 have not been extensively studied, although two studies reported that SNPs from this chromosome 11 gene were associated with risk of primary biliary cirrhosis in a European population and with risk of NHL in an Asian population. This study evaluated the role of common germline genetic variation in the risk and prognosis of the common NHL subtypes of diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), mantle cell lymphoma (MCL), marginal zone lymphoma (MZL) and peripheral T-cell lymphoma (PTCL). Methods: Ten SNPs in CXCR5 were genotyped using a clinic-based study of 2694 NHL cases (including 586 DLBCL, 588 FL, 137 MCL, 230 MZL, and 158 PTCL) and 1521 controls enrolled from 2002–2009 in the Molecular Epidemiology Resource of the University of Iowa/Mayo Clinic Lymphoma SPORE. Clinical data were abstracted for all NHL patients and all were prospectively followed for disease progression, retreatment, transformation and death. SNPs were selected from HapMap using a standard tagging approach (r2≥0.80 and minor allele frequency (MAF) ≥0.05) and included SNPs 5kb upstream and downstream from the gene. Genotyping was conducted using a custom Illumina iSelect panel. All SNPs were in Hardy-Weinberg equilibrium in the control group. The most prevalent homozygous genotype was used as the reference group, and each SNP was modeled as having a log-additive effect in the regression model. For risk of NHL, odds ratios (ORs) and 95% confidence intervals were calculated using unconditional logistic regression, adjusted for age and gender. For prognosis, hazard ratios (HR) were calculated for both event-free (EFS) and overall (OS) survival using Cox proportional hazards regression, adjusting for age, treatment type, and IPI (DLBCL, PTCL), FLIPI (FL), or MIPI (MCL). To determine risk of FL transformation to DLBCL, a competing risk Cox model was used. A p<0.05 was declared as statistically significant. Results: Of the 10 CXCR5 tag SNPs in our study, 5 were associated with risk of NHL, with rs1790192 (MAF=0.39 among controls) having the strongest association (OR=1.19, 95%CI 1.08–1.30; p=0.0003). This SNP was strongly associated with FL (OR=1.44, 95%CI 1.25–1.66; p=3.1×10−7), and was also associated with DLBCL (OR=1.16, 95%CI 1.01–1.33; p=0.038) and PTCL (OR=1.29, 95%CI 1.02–1.64; p=0.037) but not MCL or MZL. For FL, none of the SNPs were associated with EFS or OS, although rs1790192 was associated with better outcome among FL patients that were observed (HR=0.64; 95%CI 0.47–0.87; p=0.0039). For DLBCL, none of the SNPs were associated with EFS, while three SNPs were associated with OS: rs523604 (HR=0.79; 95%CI 0.64–0.97; p=0.028), rs12363277 (HR=0.57; 95%CI 0.33–0.99; p=0.046), and rs3922 (HR=0.79; 95%CI 0.65–0.97; p=0.027). The latter SNP was also associated with risk of transformation from FL to DLBCL (HR=0.61; 95%CI 0.40–0.91; p=0.016). For MCL, MZL and PTCL, none of the SNPs were associated with either EFS or OS. Conclusions: Several germline CXCR5 polymorphisms were associated with NHL risk, particularly rs1790192 with risk of FL as well as DLBCL and PTCL. CXCR5 SNPs did not appear to be important in prognosis for most NHL subtypes, although rs1790192 was associated with a better outcome in untreated FL and rs3922 was associated with OS in DLBCL patients and with risk of FL transformation. These results provide additional evidence for a role of host genetic variation in CXCR5 in lymphomagenesis, mainly in risk of disease development, although there may also be a role for CXCR5 in FL and DLBCL prognosis. Replications of these findings in additional studies are needed. Disclosures: Ansell: Seattle Genetics, Inc.: Research Funding.

Abstract Background Next Generation sequencing (NGS) is a powerful tool to identify somatic mutations associated with tumor onset and drug response. While it is well suited for high quality fresh/frozen samples, NGS is not proven for FFPE tissue which is the most common type of clinical specimen. Since the nucleic acids can be readily extracted from FFPE samples for a variety of genomic analyses, a comparative mutational analysis of paired frozen and FFPE tissues is urgently needed. Our long term goal is to establish a lab protocol to detect mutations in FFPE tumors using a targeted capture and sequencing approach for genes of interest. This pilot study focuses on the comparison of FFPE and frozen samples to test the validity of using FFPE tissues in such application. Methods Gene Selection: 128 genes associated with known pathogenic mutations in lymphoma Sample Selection: 9 diffuse large B-cell lymphoma (DLBCL) cases with FFPE, frozen and germline samples, as well as 10 frozen normal lymphatic tissues as references for CNV detections Capture Probe Design: We targeted coding exons and UTR, as well as the evolutionarily conserved intronic regions. The capture probes were designed using the Agilent eArray tool. The titling density of the probes was set to 3 probes overlapping with every base in the target region to improve the capture efficiency in FFPE samples. The least stringent masking of the repeat regions was allowed to include regions with small repeats that are shorter than the length of the sequencing reads (100-bp). In addition, boosting parameters were picked to set various levels of probe replication in different regions in order to minimize the local coverage differences (e.g. between regions of different GC contents) Sequencing and Bioinformatics: The target capture and sequencing were performed by the Mayo Clinic Medical Genome Facility. The reads were mapped to Human Reference Genome Build 37 using Novalign, and SNVs were called using GATK. The CNVs were identified using an in-house developed algorithm, patternCNV. Results The designed probes covered 99.65937% of the target regions. We generated 2.2-6.7 Gbp of reads per sample, 57.4-71.5% of which were on target. This equalled an average coverage of 2100-6700 folds which is 10-30 times higher than the minimal coverage recommended by Agilent. Due to this high coverage, we observed duplicate reads that accounted for 7.7-73.5% of the total reads. When we analysed the data with and without the duplicated reads, the concordance of the called SNVs was between 84-93% out of 207-249 mutated positions per trio-sample. There were 7.8-8.9% and 1.1-2.2% unique SNVs per sample by excluding or including duplicate reads, respectively. The dis-concordances were mostly missed calls, where a SNV was observed in only 1 or 2 of the trio samples. The missed calls from frozen samples ranged from 0-10.4% compared to 1.4-10.4% from the FFPE tissues, with 0.88-2.4% more SNVs missed in FFPE. Further analyses showed that all of the missing calls came from the lack of or low coverage of the corresponding positions. There were also differences of the called SNVs between the trio samples. However, this was extremely rare. Only 2 out of the 9 trio samples at a total of 3 positions had disagreements in called SNVs between FFPE and frozen tissues, all due to the allelic imbalance where the percentage of reads supporting the alternative alleles were below 20%. Therefore, this dis-concordance can be removed by back-filling of the read-level information for each position. Unfortunately only 11.9-47.4% of the CNVs called in frozen tissues were identified in FFPE samples, due to the widely various coverage in FFPE samples. The consequent large noises of the log ratio values between the FFPEs and normal references significantly reduced the sensitivity for CNV calling. Conclusions This pilot study compared the performance of SNV and CNV detection in FFPE and paired frozen tissues using a target capture and sequencing approach. With a capture probe design strategized to benefit FFPE samples, we observed SNV detection rates in FFPE that were only slightly lower (0.88-2.4%) than those of frozen tissues due to poor coverage of some positions in FFPE samples. With a proper back-filling step, there was no dis-concordance of the called SNVs between FFPE and frozen samples. However, CNV detections in FFPE were more problematic due to the un-predictable regional coverage in FFPE samples. Disclosures: No relevant conflicts of interest to declare.

Abstract Abstract 2443 Background: There is strong and consistent evidence that a genetic component contributes to the etiology of chronic lymphocytic leukemia (CLL). A recent genome-wide association (GWA) study of CLL identified genetic variants located on chromosomes 2q13, 2q37.1, 6p25, 11q24, 15q23, and 19q13 that increased the risk of CLL within a European population. We replicated 5 of these 6 loci in an independent sample of CLL cases and controls from the United States. We now investigate whether these loci also influences MBL, a reported precursor condition of CLL. In addition, a follow-up analysis of the initial GWA study identified four more CLL-susceptibility loci on 2q37.3, 8q24.21, 15q21.3, and 16q24.1. Herein, we also evaluate the association of these four loci with risk of CLL. Methods: Peripheral blood samples were obtained from three ongoing studies: the Genetic Epidemiology CLL (GEC) Consortium, the Mayo Clinic non-Hodgkin lymphoma (NHL)/ CLL study, and the Mayo Clinic Biobank. We implemented rigorous genotyping quality-control measures, and successfully genotyped a total of 407 CLL patients, 965 controls, and 60 MBLs from these studies. Within each locus, the previously reported single nucleotide variants (SNPs) or variants in high linkage disequilibrium (LD) with the previously reported SNPs were evaluated with risk of MBL or CLL. Tests for association was done using the Cochran-Armitage trend test, and unconditional logistic regression was used to estimate odds ratios (ORs) and corresponding 95% confidence intervals (CIs) for CLL or MBL risk Results: In our evaluation of the six initially reported CLL-susceptibility loci (2q13, 2q37.1, 6p25, 11q24, 15q23, and 19q13) with MBL risk, we found three of the six had suggestive associations (p-value < 0.20) with ORs comparable to and in the same direction as those observed from CLL risk. Our strongest finding was with rs13397985 at locus 2q37.1 (OR= 1.56; 95% CI: 1.05, 2.31; p-trend = 0.041), followed by rs17483466 at locus 2q13 (OR= 1.49; 95% CI: 0.99, 2.24; p-trend = 0.074). As expected given our previously reported findings with CLL risk, the association between rs11083846 on chromosome 19q13 and MBL risk was not significant (p-trend = 0.70). Of the four recently reported CLL-susceptibility loci SNPs located on 2q37.3, 8q24.21, 15q21.3, and 16q24.1, we found all to be associated with CLL risk but one. Specifically, the strongest association was seen for locus 8q24.21 (best tagged SNP rs1021955; OR = 1.37; 95% CI: 1.10, 1.70; p-trend = 0.005), followed by locus 16q24.1 (best tagged SNP rs305065; OR= 0.77; 95% CI: 0.61, 0.97; p-trend = 0.024). However, we found no associations for locus 15q21.3 for the previously reported SNP nor for any SNPs in LD with the previously reported SNP. Conclusions: Our MBL results provide additional robust genetic evidence that MBL is a precursor to CLL and that it shares similar underlying genetic predisposition. Also our results confirm three of the four recently reported CLL-susceptibility loci and further support the role of a genetic basis in the etiology of CLL. More research is needed to elucidate the potential manner in which these genetic loci function in CLL or MBL. Disclosures: No relevant conflicts of interest to declare.

Abstract Background: Archived formalin fixed paraffin embedded (FFPE) samples are valuable clinical resources to examine clinically relevant morphology features and also to study genetic changes. However, DNA quality and quantity of FFPE samples are sub-optimal, and resulting NGS-based genetics variant detections are prone to false positives. Evaluations of wet-lab and bioinformatics approaches are needed to optimize variant detection from FFPE samples. Results: As a pilot study, we designed within-subject triplicate samples of DNA derived from paired FFPE and fresh frozen breast tissues to highlight FFPE-specific artifacts. For FFPE samples, we tested two FFPE DNA extraction methods to determine impact of wet-lab procedures on variant calling: QIAGEN QIAamp DNA Mini Kit ("QA"), and QIAGEN GeneRead DNA FFPE Kit ("QGR"). We also used negative-control (NA12891) and positive control samples (Horizon Discovery Reference Standard FFPE). All DNA sample libraries were prepared for NGS according to the QIAseq Human Breast Cancer Targeted DNA Panel protocol and sequenced on the HiSeq 4000. Variant calling and filtering were performed using QIAGEN Gene Globe Data Portal. Detailed variant concordance comparisons and mutational signature analysis were performed to investigate effects of FFPE samples compared to paired fresh frozen samples, along with different DNA extraction methods. In this study, we found that five times or more variants were called with FFPE samples, compared to their paired fresh-frozen tissue samples even after applying molecular barcoding error-correction and default bioinformatics filtering recommended by the vendor. We also found that QGR as an optimized FFPE-DNA extraction approach leads to much fewer discordant variants between paired fresh frozen and FFPE samples. Approximately 92 % of the uniquely called FFPE variants were of low allelic frequency range (<5%), and collectively shared a “C>T|G>A” mutational signature known to be representative of FFPE artifacts resulting from cytosine deamination. Based on control samples and FFPE-frozen replicates, we derived an effective filtering strategy with associated empirical false-discovery estimates. Conclusions: Through this study, we demonstrated feasibility of calling and filtering genetic variants from FFPE tissue samples using a combined strategy with molecular barcodes, optimized DNA extraction, and bioinformatics methods incorporating genomics context such as mutational signature and variant allelic frequency.

Abstract Introduction: Several B-cell malignancies, including chronic lymphocytic leukemia (CLL) and multiple myeloma (MM), are known to have a familial component to disease risk. Although several common inherited genetic variants have been found to be associated with risk of these malignancies, a large proportion of the heritability remains unexplained. Here we set out to identify rare high-penetrance susceptibility variants for CLL and MM using the established Mayo Clinic family study of B-cell malignancies. Methods: We performed exome sequencing on germ line DNA of 106 families with either two or more members with CLL (n=93 families) or two or more members with MM or monoclonal gammopathy of undetermined significance (MGUS) (n=13 families) using Aglient capture kits. All samples were sequenced on an Illumina HiSeq2000. Bioinformatics analyses leveraged the following software packages: Novoalign, Picard, The Genome Analysis Toolkit (GATK), and the Biological Reference Repository (bioR). Quality control filters were implemented; subjects with mis-specified relationships were removed as were variants with <95% call rate or <20x coverage, and those variants identified as sequencing artifacts. To identify potentially functional single nucleotide variants, the following was required a) uncommon in public databases, b) cosegregating variants in multiple families, c) variants to be highly conserved and in coding regions, and d) functional prediction status of deleterious (SIFT score), damaging (PolyPhen score), and a moderate, or high variant impact (SNPEffect). The association of these variants with risk was then evaluated in large samples of cases and controls in genome-wide association studies (GWAS). Results: In the CLL familial cohort, there were 443 individuals (160 with CLL, 73 with monoclonal B cell lymphocytosis (MBL), and 210 relatives without CLL or MBL) sequenced; and in the MM cohort, there were 39 individuals (13 with MM, 13 with MGUS and 13 without MM or MGUS) sequenced. A total of 61,992 single nucleotide variants passed quality control filters in the CLL cohort and 39,456 single nucleotide variants in the MM cohort. From these, we identified 32 variants in the CLL cohort and 39 variants in the MM cohort that were cosegregating in multiple CLL or MM families, respectively, and were predicted to affect protein structure or function. Of these, one CLL variant was within 1 Mb of a known GWAS-discovered locus. Moreover, there were seven CLL and four MM variants that were within 1 Mb of single nucleotide polymorphisms (SNPs) that had statistical significance between p<10-4 and p<10-6 in GWAS. Finally, there were 3 variants found to be common between CLL and MM families (see Table). Conclusions: Through whole exome sequencing, we identified a number of rare, highly penetrant predisposition variants for CLL and MM. Several of these variants were common to both B-cell malignancies providing evidence of shared genetic components; one variant may be a functional driver of the GWAS-discovered loci. These identified variants provide a list for future validation studies. Table: Exome variants common in both Chronic Lymphocytic Leukemia and Multiple Myeloma SNP ID rs41314099 rs61754114 rs143025033 Chromosome 17 6 7 Gene WSCD1 BACH2 TNS3 Penetrance (CLL) 0.70 0.75 0.80 Penetrance (MM) 1.00 0.80 0.75 Allele Frequency (CLL) 0.015 0.025 0.017 Allele Frequency (MM) 0.077 0.077 0.058 1000 Genome Frequency (European) 0.010 0.020 0.010 SIFT Term Deleterious Deleterious Deleterious PolyPhen Term Probably Damaging Probably Damaging Probably Damaging SNP: Single Nucleotide Polymorphism; CLL: Chronic Lymphocytic Leukemia; MM: Multiple Myeloma; Penetrance is defined as proportion of individuals carrying the alternate allele that are also affected; SIFT: Sorting Intolerant From Tolerant, predicts whether an amino acid substitution affects protein function; PolyPhen: Polymorphism Phenotyping, predicts possible impact of an amino acid substitution on the structure of a human protein Disclosures No relevant conflicts of interest to declare.

Abstract Abstract 150 Background: While bivariate interactions between Factor V Leiden, prothrombin G20201A and hereditary deficiency of antithrombin, protein C and protein S compound VTE risk, whether other gene-gene interactions are associated with VTE is largely unknown. Objective: To test gene-gene interactions for an association with VTE. Methods: Cases (n=1004) were Mayo Clinic European-American patients of non-Hispanic ancestry with objectively-diagnosed VTE in the absence of active cancer, venous catheter or antiphospholipid antibodies. Controls (n=1066) were Mayo Clinic outpatients without VTE who were frequency-matched on case age, gender, race, MI/stroke status and state of residence. We selected candidate genes relevant to the anticoagulant, procoagulant, fibrinolytic and innate immunity pathways, focusing on platelet, monocyte, neutrophil and endothelial cell agonists, receptors, ligands, signal transduction and adhesion molecules, granule contents and effectors; plasma proteases and inhibitors; matrix metalloproteases; inflammatory cytokines and receptors; estrogen, progesterone and androgen receptors; co-regulators and enzymes related to estrogen metabolism; important enzymes for catechol, homocysteine, thromboxane A2 and prostacyclin biosynthesis and metabolism; and HMG CoA reductase. For these genes (n=780), we selected all non-synonymous coding single nucleotide polymorphisms (SNPs) with minor allele frequency ≥0.5%; the remaining SNPs were selected using an LD tagging algorithm (Carlson et al. AJHG 2004); 558 ancestry-informative markers were also included (total n=13,237 SNPs). Leukocyte genomic DNA was genotyped using a custom Illumina Infinium iSelect chemistry and platform, including appropriate controls. For these analyses, all pairwise SNP-SNP interactions for 12,313 SNPs were tested using logistic regression. In addition, we tested all Factor V Leiden -, prothrombin G20210A - and ABO non-O blood group - SNP interactions. Results: The mean ± SD case and control ages were 55.3 ± 16.4 and 56.5 ± 15.9 years, respectively, and 51% were female. Analysis of ancestry-informative markers gave no evidence of population stratification. Among almost 76 million pairwise interactions tested, 504, 44 and 8 SNP-SNP interactions reached p-values ≤ 1E-5, 1E-6, and 1E-7 respectively; none reached the Bonferroni statistical significance threshold (≤ 6.6E-10). The gene-gene pairwise interactions with p-values ≤ 1E-7 included: Leptin receptor - Estrogen receptor 1, α1 Antichymotrypsin - β2 glycoprotein 1, CRADD(caspace) - B1 Bradykinin receptor, ILF10 - Prolactin receptor and GFRA2(TGFβ neurotrophic factor receptor) - Bactericidal permeability-increasing protein. Gene-gene pairwise interactions with the lowest p-value for Factor V Leiden -, prothrombin G20210A - and ABO non-group O - were Protein kinase C beta (2.1E-5), von Willebrand Factor (7.6E-4) and Phospholipase C gamma-2 (6.7E-4), respectively. Conclusion: Pairwise gene-gene interaction analyses suggest potential associations between VTE and novel genes and gene pathways. These potential associations require confirmation in future replication studies. Disclosures: No relevant conflicts of interest to declare.

Abstract Background Archived formalin fixed paraffin embedded (FFPE) samples are valuable clinical resources to examine clinically relevant morphology features and also to study genetic changes. However, DNA quality and quantity of FFPE samples are often sub-optimal, and resulting NGS-based genetics variant detections are prone to false positives. Evaluations of wet-lab and bioinformatics approaches are needed to optimize variant detection from FFPE samples. Results As a pilot study, we designed within-subject triplicate samples of DNA derived from paired FFPE and fresh frozen breast tissues to highlight FFPE-specific artifacts. For FFPE samples, we tested two FFPE DNA extraction methods to determine impact of wet-lab procedures on variant calling: QIAGEN QIAamp DNA Mini Kit ("QA"), and QIAGEN GeneRead DNA FFPE Kit ("QGR"). We also used negative-control (NA12891) and positive control samples (Horizon Discovery Reference Standard FFPE). All DNA sample libraries were prepared for NGS according to the QIAseq Human Breast Cancer Targeted DNA Panel protocol and sequenced on the HiSeq 4000. Variant calling and filtering were performed using QIAGEN Gene Globe Data Portal. Detailed variant concordance comparisons and mutational signature analysis were performed to investigate effects of FFPE samples compared to paired fresh frozen samples, along with different library preparations. In this study, we found that five times or more variants were called with FFPE samples, compared to their paired fresh-frozen tissue samples even after applying molecular barcoding error-correction and default bioinformatics filtering recommended by the vendor. We also found that QGR as an optimized FFPE-DNA extraction approach leads to much fewer discordant variants between paired fresh frozen and FFPE samples. Approximately 92% of the uniquely called FFPE variants were of low allelic frequency range (<5%), and collectively shared a “C>T|G>A” mutational signature known to be representative of FFPE artifacts resulting fromcytosine deamination. Based on control samples and FFPE-frozen replicates, we derived an effective filtering strategy with associated empirical false-discovery estimates. Conclusions Through this study, we demonstrated feasibility of calling and filtering genetic variants from FFPE tissue samples using a combined strategy with molecular barcodes, optimized DNA extraction, and bioinformatics methods incorporating genomics context such as mutational signature and variant allelic frequency.