Citations

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.

Significance Highly selective and sensitive detection of microRNA is a key challenge in the development of liquid-biopsy approaches. Technologies that can achieve high diagnostic performance without the requirement of complicated processing steps or expensive equipment are necessary for broad use. With these features in mind, we demonstrate a digital-readout microRNA diagnostic that fundamentally relies on microRNA-activated nanoparticle-photonic crystal hybrid coupling. The hybrid formation allows for clear detection of single-particle binding events due to enhanced nanoparticle absorption at the binding location. Whereas the applied photonics lend the assay concentration sensitivity, we additionally demonstrate broad placement single-base mismatch selectivity and complex media detection by applying free-energy tuned toehold probes.

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.