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Integrated sequencing strategies have provided a broader understanding of the genomic landscape and molecular classifications of multiple cancer types and have identified various therapeutic opportunities across cancer subsets. Despite pivotal advances in the characterization of genomic alterations in glioblastoma, targeted agents have shown minimal efficacy in clinical trials to date, and patient survival remains poor. In this review, we highlight potential reasons why targeting single alterations has yielded limited clinical efficacy in glioblastoma, focusing on issues of tumor heterogeneity and pharmacokinetic failure. We outline strategies to address these challenges in applying precision medicine to glioblastoma and the rationale for applying targeted combination therapy approaches that match genomic alterations with compounds accessible to the central nervous system.

Canine malignant melanoma, a significant cause of mortality in domestic dogs, is a powerful comparative model for human melanoma, but little is known about its genetic etiology. We mapped the genomic landscape of canine melanoma through multi-platform analysis of 37 tumors (31 mucosal, 3 acral, 2 cutaneous, and 1 uveal) and 17 matching constitutional samples including long- and short-insert whole genome sequencing, RNA sequencing, array comparative genomic hybridization, single nucleotide polymorphism array, and targeted Sanger sequencing analyses. We identified novel predominantly truncating mutations in the putative tumor suppressor genePTPRJin 19% of cases. NoBRAFmutations were detected, but activatingRASmutations (24% of cases) occurred in conserved hotspots in all cutaneous and acral and 13% of mucosal subtypes.MDM2amplifications (24%) andTP53mutations (19%) were mutually exclusive. Additional low-frequency recurrent alterations were observed amidst low point mutation rates, an absence of ultraviolet light mutational signatures, and an abundance of copy number and structural alterations. Mutations that modulate cell proliferation and cell cycle control were common and highlight therapeutic axes such as MEK and MDM2 inhibition. This mutational landscape resembles that seen inBRAFwild-type and sun-shielded human melanoma subtypes. Overall, these data inform biological comparisons between canine and human melanoma while suggesting actionable targets in both species.AUTHOR SUMMARYMelanoma, an aggressive cancer arising from transformed melanocytes, commonly occurs in pet dogs. Unlike human melanoma, which most often occurs in sun-exposed cutaneous skin, canine melanoma typically arises in sun-shielded oral mucosa. Clinical features of canine melanoma resemble those of human melanoma, particularly the less common sun-shielded human subtypes. However, whereas the genomic basis of diverse human melanoma subtypes is well understood, canine melanoma genomics remain poorly defined. Similarly, although diverse new treatments for human melanoma based on a biologic disease understanding have recently shown dramatic improvements in outcomes for these patients, treatments for canine melanoma are limited and outcomes remain universally poor. Detailing the genomic basis of canine melanoma thus provides untapped potential for improving the lives of pet dogs while also helping to establish canine melanoma as a comparative model system for informing human melanoma biology and treatment. In order to better define the genomic landscape of canine melanoma, we performed multi-platform characterization of 37 tumors. Our integrated analysis confirms that these tumors commonly contain mutations in canine orthologs of human cancer genes such asRAS,MDM2, andTP53as well mutational patterns that share important similarities with human melanoma subtypes. We have also found a new putative cancer gene,PTPRJ, frequently mutated in canine melanoma. These data will guide additional biologic and therapeutic studies in canine melanoma while framing the utility of comparative studies of canine and human cancers more broadly.

There has been a growing interest in using next-generation sequencing (NGS) to profile extracellular small RNAs from the blood and cerebrospinal fluid (CSF) of patients with neurological diseases, CNS tumors, or traumatic brain injury for biomarker discovery. Small sample volumes and samples with low RNA abundance create challenges for downstream small RNA sequencing assays. Plasma, serum, and CSF contain low amounts of total RNA, of which small RNAs make up a fraction. The purpose of this study was to maximize RNA isolation from RNA-limited samples and apply these methods to profile the miRNA in human CSF by small RNA deep sequencing. We systematically tested RNA isolation efficiency using ten commercially available kits and compared their performance on human plasma samples. We used RiboGreen to quantify total RNA yield and custom TaqMan assays to determine the efficiency of small RNA isolation for each of the kits. We significantly increased the recovery of small RNA by repeating the aqueous extraction during the phenol-chloroform purification in the top performing kits. We subsequently used the methods with the highest small RNA yield to purify RNA from CSF and serum samples from the same individual. We then prepared small RNA sequencing libraries using Illumina’s TruSeq sample preparation kit and sequenced the samples on the HiSeq 2000. Not surprisingly, we found that the miRNA expression profile of CSF is substantially different from that of serum. To our knowledge, this is the first time that the small RNA fraction from CSF has been profiled using next-generation sequencing.

A retrospective analysis of 130 patients was conducted in a Phase I oncology clinic to assess the effect of QTc formula selection on clinical trial eligibility. QTc values were calculated from screening electrocardiograms using 7 formulae (Bazett, Fridericia, Framingham, Hodges, Mayeda, Van de Water and Wohlfart). QTc values > 470 ms for females and > 450 ms for males were used to define prolongation. Concomitant medication potential for QTc prolongation was determined using a public database (AzCert). Ineligibility rates ranged from 3.1 % to 17.7 % (Framingham: 3.1 %, Van de Water: 3.1 %, Hodges: 3.1 %, Wohlfart: 3.1 %, Fridericia: 3.9 %, Bazett: 10.8 % and Mayeda: 17.7 %). A consistent ineligibility rate was achieved by using formulae-specific thresholds. Fifty one percent of patients were taking concomitant medications with QTc prolongation potential. The proportion of concomitant medications with the potential to prolong QTc was 11.57 % (96 of 830). Uniform criteria and guidelines for selection of QTc formulae need to be developed. Formulae-specific QTc thresholds also need to be specified.

Large-scale multiplexed identification of somatic alterations in cancer has become feasible with next generation sequencing (NGS). However, calibration of NGS somatic analysis tools has been hampered by a lack of tumor/normal reference standards. We thus performed paired PCR-free whole genome sequencing of a matched metastatic melanoma cell line (COLO829) and normal across three lineages and across separate institutions, with independent library preparations, sequencing, and analysis. We generated mean mapped coverages of 99X for COLO829 and 103X for the paired normal across three institutions. Results were combined with previously generated data allowing for comparison to a fourth lineage on earlier NGS technology. Aggregate variant detection led to the identification of consensus variants, including key events that represent hallmark mutation types including amplified BRAF V600E, a CDK2NA small deletion, a 12 kb PTEN deletion, and a dinucleotide TERT promoter substitution. Overall, common events include >35,000 point mutations, 446 small insertion/deletions, and >6,000 genes affected by copy number changes. We present this reference to the community as an initial standard for enabling quantitative evaluation of somatic mutation pipelines across institutions.

The longitudinal evolution of a myeloma genome from diagnosis to plasma cell leukemia has not previously been reported. We used whole-genome sequencing (WGS) on 4 purified tumor samples and patient germline DNA drawn over a 5-year period in a t(4;14) multiple myeloma patient. Tumor samples were acquired at diagnosis, first relapse, second relapse, and end-stage secondary plasma cell leukemia (sPCL). In addition to the t(4;14), all tumor time points also shared 10 common single-nucleotide variants (SNVs) on WGS comprising shared initiating events. Interestingly, we observed genomic sequence variants that waxed and waned with time in progressive tumors, suggesting the presence of multiple independent, yet related, clones at diagnosis that rose and fell in dominance. Five newly acquired SNVs, including truncating mutations of RB1 and ZKSCAN3, were observed only in the final sPCL sample suggesting leukemic transformation events. This longitudinal WGS characterization of the natural history of a high-risk myeloma patient demonstrated tumor heterogeneity at diagnosis with shifting dominance of tumor clones over time and has also identified potential mutations contributing to myelomagenesis as well as transformation from myeloma to overt extramedullary disease such as sPCL.

Next-generation sequencing enables use of whole-genome sequence typing (WGST) as a viable and discriminatory tool for genotyping and molecular epidemiologic analysis. We used WGST to confi rm the linkage of a cluster of Coccidioides immitis isolates from 3 patients who received organ transplants from a single donor who later had positive test results for coccidioidomycosis. Isolates from the 3 patients were nearly genetically identical (a total of 3 singlenucleotide polymorphisms identifi ed among them), thereby demonstrating direct descent of the 3 isolates from an original isolate. We used WGST to demonstrate the genotypic relatedness of C. immitis isolates that were also epidemiologically linked. Thus, WGST offers unique benefi ts to public health for investigation of clusters considered to be linked to a single source.

Canine malignant melanoma, a significant cause of mortality in domestic dogs, is a powerful comparative model for human melanoma, but little is known about its genetic etiology. We mapped the genomic landscape of canine melanoma through multi-platform analysis of 37 tumors (31 mucosal, 3 acral, 2 cutaneous, and 1 uveal) and 17 matching constitutional samples including long- and short-insert whole genome sequencing, RNA sequencing, array comparative genomic hybridization, single nucleotide polymorphism array, and targeted Sanger sequencing analyses. We identified novel predominantly truncating mutations in the putative tumor suppressor gene PTPRJ in 19% of cases. No BRAF mutations were detected, but activating RAS mutations (24% of cases) occurred in conserved hotspots in all cutaneous and acral and 13% of mucosal subtypes. MDM2 amplifications (24%) and TP53 mutations (19%) were mutually exclusive. Additional low-frequency recurrent alterations were observed amidst low point mutation rates, an absence of ultraviolet light mutational signatures, and an abundance of copy number and structural alterations. Mutations that modulate cell proliferation and cell cycle control were common and highlight therapeutic axes such as MEK and MDM2 inhibition. This mutational landscape resembles that seen in BRAF wild-type and sun-shielded human melanoma subtypes. Overall, these data inform biological comparisons between canine and human melanoma while suggesting actionable targets in both species., Author summary Melanoma, a cancer arising from transformed melanocytes, commonly occurs in pet dogs. Unlike human melanoma, most often occurring in sun-exposed cutaneous skin, canine melanoma typically arises in sun-shielded oral mucosa. Canine melanoma clinically resembles human melanoma, particularly sun-shielded subtypes. However, canine melanoma genomics remain poorly defined. Similarly, although new treatments for human melanoma based on genomic understanding have shown improvements in outcomes for these patients, treatments for canine melanoma are limited and outcomes remain poor. Detailing the genomic basis of canine melanoma thus provides untapped potential for improving lives of pet dogs and helping to define canine melanoma’s role in comparative studies that also inform human melanoma understanding. To better define the genomic landscape of canine melanoma, we performed multi-platform characterization of 37 tumors. Our integrated analysis confirms that these tumors commonly contain mutations in canine orthologs of human cancer genes such as RAS, MDM2, and TP53 alongside mutational patterns sharing important similarities with human melanoma subtypes. We have also found a new putative cancer gene, PTPRJ, frequently mutated in canine melanoma. These data will guide biologic and therapeutic studies in canine melanoma while broadly framing the utility of comparative studies of canine and human cancers.

As a first step in analyzing high-throughput data in genome-wide studies, several algorithms are available to identify and prioritize candidates lists for downstream fine-mapping. The prioritized candidates could be differentially expressed genes, aberrations in comparative genomics hybridization studies, or single nucleotide polymorphisms (SNPs) in association studies. Different analysis algorithms are subject to various experimental artifacts and analytical features that lead to different candidate lists. However, little research has been carried out to theoretically quantify the consensus between different candidate lists and to compare the study specific accuracy of the analytical methods based on a known reference candidate list. Within the context of genome-wide studies, we propose a generic mathematical framework to statistically compare ranked lists of candidates from different algorithms with each other or, if available, with a reference candidate list. To cope with the growing need for intuitive visualization of high-throughput data in genome-wide studies, we describe a complementary customizable visualization tool. As a case study, we demonstrate application of our framework to the comparison and visualization of candidate lists generated in a DNA-pooling based genome-wide association study of CEPH data in the HapMap project, where prior knowledge from individual genotyping can be used to generate a true reference candidate list. The results provide a theoretical basis to compare the accuracy of various methods and to identify redundant methods, thus providing guidance for selecting the most suitable analysis method in genome-wide studies.

Contemporary biological technologies produce extremely high-dimensional data sets from which to design classifiers, with 20,000 or more potential features being common place. In addition, sample sizes tend to be small. In such settings, feature selection is an inevitable part of classifier design. Heretofore, there have been a number of comparative studies for feature selection, but they have either considered settings with much smaller dimensionality than those occurring in current bioinformatics applications or constrained their study to a few real data sets. This study compares some basic feature-selection methods in settings involving thousands of features, using both model-based synthetic data and real data. It defines distribution models involving different numbers of markers (useful features) versus non-markers (useless features) and different kinds of relations among the features. Under this framework, it evaluates the performances of feature-selection algorithms for different distribution models and classifiers. Both classification error and the number of discovered markers are computed. Although the results clearly show that none of the considered feature-selection methods performs best across all scenarios, there are some general trends relative to sample size and relations among the features. For instance, the classifier-independent univariate filter methods have similar trends. Filter methods such as the t-test have better or similar performance with wrapper methods for harder problems. This improved performance is usually accompanied with significant peaking. Wrapper methods have better performance when the sample size is sufficiently large. ReliefF, the classifier-independent multivariate filter method, has worse performance than univariate filter methods in most cases; however, ReliefF-based wrapper methods show performance similar to their t-test-based counterparts.

Contains fulltext : 80661.pdf (Publisher’s version ) (Closed access) Age-related hearing impairment (ARHI), or presbycusis, is the most prevalent sensory impairment in the elderly. ARHI is a complex disease caused by an interaction between environmental and genetic factors. Here we describe the results of the first whole genome association study for ARHI. The study was performed using 846 cases and 846 controls selected from 3434 individuals collected by eight centers in six European countries. DNA pools for cases and controls were allelotyped on the Affymetrix 500K GeneChip for each center separately. The 252 top-ranked single nucleotide polymorphisms (SNPs) identified in a non-Finnish European sample group (1332 samples) and the 177 top-ranked SNPs from a Finnish sample group (360 samples) were confirmed using individual genotyping. Subsequently, the 23 most interesting SNPs were individually genotyped in an independent European replication group (138 samples). This resulted in the identification of a highly significant and replicated SNP located in GRM7, the gene encoding metabotropic glutamate receptor type 7. Also in the Finnish sample group, two GRM7 SNPs were significant, albeit in a different region of the gene. As the Finnish are genetically distinct from the rest of the European population, this may be due to allelic heterogeneity. We performed histochemical studies in human and mouse and showed that mGluR7 is expressed in hair cells and in spiral ganglion cells of the inner ear. Together these data indicate that common alleles of GRM7 contribute to an individual's risk of developing ARHI, possibly through a mechanism of altered susceptibility to glutamate excitotoxicity.

Summary: For many genome-wide association (GWA) studies individually genotyping one million or more SNPs provides a marginal increase in coverage at a substantial cost. Much of the information gained is redundant due to the correlation structure inherent in the human genome. Pooling-based GWA studies could benefit significantly by utilizing this redundancy to reduce noise, improve the accuracy of the observations and increase genomic coverage. We introduce a measure of correlation between individual genotyping and pooling, under the same framework that r2 provides a measure of linkage disequilibrium (LD) between pairs of SNPs. We then report a new non-haplotype multimarker multi-loci method that leverages the correlation structure between SNPs in the human genome to increase the efficacy of pooling-based GWA studies. We first give a theoretical framework and derivation of our multimarker method. Next, we evaluate simulations using this multimarker approach in comparison to single marker analysis. Finally, we experimentally evaluate our method using different pools of HapMap individuals on the Illumina 450S Duo, Illumina 550K and Affymetrix 5.0 platforms for a combined total of 1 333 631 SNPs. Our results show that use of multimarker analysis reduces noise specific to pooling-based studies, allows for efficient integration of multiple microarray platforms and provides more accurate measures of significance than single marker analysis. Additionally, this approach can be extended to allow for imputing the association significance for SNPs not directly observed using neighboring SNPs in LD. This multimarker method can now be used to cost-effectively complete pooling-based GWA studies with multiple platforms across over one million SNPs and to impute neighboring SNPs weighted for the loss of information due to pooling. Contact: dcraig@tgen.org Supplementary information: Supplementary data are available at Bioinformatics online.

We report the development and validation of experimental methods, study designs, and analysis software for pooling-based genomewide association (GWA) studies that use high-throughput single-nucleotide–polymorphism (SNP) genotyping microarrays. We first describe a theoretical framework for establishing the effectiveness of pooling genomic DNA as a low-cost alternative to individually genotyping thousands of samples on high-density SNP microarrays. Next, we describe software called “GenePool,” which directly analyzes SNP microarray probe intensity data and ranks SNPs by increased likelihood of being genetically associated with a trait or disorder. Finally, we apply these methods to experimental case-control data and demonstrate successful identification of published genetic susceptibility loci for a rare monogenic disease (sudden infant death with dysgenesis of the testes syndrome), a rare complex disease (progressive supranuclear palsy), and a common complex disease (Alzheimer disease) across multiple SNP genotyping platforms. On the basis of these theoretical calculations and their experimental validation, our results suggest that pooling-based GWA studies are a logical first step for determining whether major genetic associations exist in diseases with high heritability.

Motivation: The technology to genotype single nucleotide polymorphisms (SNPs) at extremely high densities provides for hypothesis-free genome-wide scans for common polymorphisms associated with complex disease. However, we find that some errors introduced by commonly employed genotyping algorithms may lead to inflation of false associations between markers and phenotype.Results: We have developed a novel SNP genotype calling program, SNiPer-High Density (SNiPer-HD), for highly accurate genotype calling across hundreds of thousands of SNPs. The program employs an expectation-maximization (EM) algorithm with parameters based on a training sample set. The algorithm choice allows for highly accurate genotyping for most SNPs. Also, we introduce a quality control metric for each assayed SNP, such that poor-behaving SNPs can be filtered using a metric correlating to genotype class separation in the calling algorithm. SNiPer-HD is superior to the standard dynamic modeling algorithm and is complementary and non-redundant to other algorithms, such as BRLMM. Implementing multiple algorithms together may provide highly accurate genotyping calls, without inflation of false positives due to systematically miss-called SNPs. A reliable and accurate set of SNP genotypes for increasingly dense panels will eliminate some false association signals and false negative signals, allowing for rapid identification of disease susceptibility loci for complex traits.Availability: SNiPer-HD is available at TGen's website: .Contact: dstephan@tgen.org

Individuals with type 2 diabetes have an increased risk of developing non-alcoholic fatty liver disease (NAFLD), and NAFLD patients are also at greater risk for developing type 2 diabetes. Although the relationship between type 2 diabetes and NAFLD is highly interconnected, the pathogenic mechanisms linking the two diseases are poorly understood. The goal of this study was to identify genetic determinants of hepatic lipid accumulation through association analysis using histological phenotypes in obese individuals. Using the Illumina HumanOmniExpress BeadChip assay, we genotyped 2,300 individuals on whom liver biopsy data were available. We analyzed total bilirubin levels, which are linked to fatty liver in severe obesity, and observed the strongest evidence for association with rs4148325 in UGT1A (P

Small cell carcinoma of the ovary, hypercalcemic type (SCCOHT), is a rare and understudied cancer with a dismal prognosis. SCCOHT's infrequency has hindered empirical study of its biology and clinical management. However, we and others have recently identified inactivating mutations in the SWI/SNF chromatin remodeling gene SMARCA4 with concomitant loss of SMARCA4 protein in the majority of SCCOHT tumors.(1-4) Here we summarize these findings and report SMARCA4 status by targeted sequencing and/or immunohistochemistry (IHC) in an additional 12 SCCOHT tumors, 3 matched germlines, and the cell line SCCOHT-1. We also report the identification of a homozygous inactivating mutation in the gene SMARCB1 in one SCCOHT tumor with wild-type SMARCA4, suggesting that SMARCB1 inactivation may also play a role in the pathogenesis of SCCOHT. To date, SMARCA4 mutations and protein loss have been reported in the majority of 69 SCCOHT cases (including 2 cell lines). These data firmly establish SMARCA4 as a tumor suppressor whose loss promotes the development of SCCOHT, setting the stage for rapid advancement in the biological understanding, diagnosis, and treatment of this rare tumor type.

The discovery and reliable detection of markers for neurodegenerative diseases have been complicated by the inaccessibility of the diseased tissue--such as the inability to biopsy or test tissue from the central nervous system directly. RNAs originating from hard to access tissues, such as neurons within the brain and spinal cord, have the potential to get to the periphery where they can be detected non-invasively. The formation and extracellular release of microvesicles and RNA binding proteins have been found to carry RNA from cells of the central nervous system to the periphery and protect the RNA from degradation. Extracellular miRNAs detectable in peripheral circulation can provide information about cellular changes associated with human health and disease. In order to associate miRNA signals present in cell-free peripheral biofluids with neurodegenerative disease status of patients with Alzheimer's and Parkinson's diseases, we assessed the miRNA content in cerebrospinal fluid and serum from postmortem subjects with full neuropathology evaluations. We profiled the miRNA content from 69 patients with Alzheimer's disease, 67 with Parkinson's disease and 78 neurologically normal controls using next generation small RNA sequencing (NGS). We report the average abundance of each detected miRNA in cerebrospinal fluid and in serum and describe 13 novel miRNAs that were identified. We correlated changes in miRNA expression with aspects of disease severity such as Braak stage, dementia status, plaque and tangle densities, and the presence and severity of Lewy body pathology. Many of the differentially expressed miRNAs detected in peripheral cell-free cerebrospinal fluid and serum were previously reported in the literature to be deregulated in brain tissue from patients with neurodegenerative disease. These data indicate that extracellular miRNAs detectable in the cerebrospinal fluid and serum are reflective of cell-based changes in pathology and can be used to assess disease progression and therapeutic efficacy.

Recent advances in sample preparation and analysis for next generation sequencing have made it possible to profile and discover new miRNAs in a high throughput manner. In the case of neurological disease and injury, these types of experiments have been more limited. Possibly because tissues such as the brain and spinal cord are inaccessible for direct sampling in living patients, and indirect sampling of blood and cerebrospinal fluid are affected by low amounts of RNA. We used a mouse model to examine changes in miRNA expression in response to acute nerve crush. We assayed miRNA from both muscle tissue and blood plasma. We examined how the depth of coverage (the number of mapped reads) changed the number of detectable miRNAs in each sample type. We also found that samples with very low starting amounts of RNA (mouse plasma) made high depth of mature miRNA coverage more difficult to obtain. Each tissue must be assessed independently for the depth of coverage required to adequately power detection of differential expression, weighed against the cost of sequencing that sample to the adequate depth. We explored the changes in total mapped reads and differential expression results generated by three different software packages: miRDeep2, miRNAKey, and miRExpress and two different analysis packages, DESeq and EdgeR. We also examine the accuracy of using miRDeep2 to predict novel miRNAs and subsequently detect them in the samples using qRT-PCR.

The development of accurate clinical biomarkers has been challenging in part due to the diversity between patients and diseases. One approach to account for the diversity is to use multiple markers to classify patients, based on the concept that each individual marker contributes information from its respective subclass of patients. Here we present a new strategy for developing biomarker panels that accounts for completely distinct patient subclasses. Marker State Space (MSS) defines ‘‘marker states’’ based on all possible patterns of high and low values among a panel of markers. Each marker state is defined as either a case state or a control state, and a sample is classified as case or control based on the state it occupies. MSS was used to define multi-marker panels that were robust in cross validation and training-set/test-set analyses and that yielded similar classification accuracy to several other classification algorithms. A three-marker panel for discriminating pancreatic cancer patients from control subjects revealed subclasses of patients based on distinct marker states. MSS provides a straightforward approach for modeling highly divergent subclasses of patients, which may be adaptable for diverse applications.

In the U.S., the incidence rates of breast cancer (BC) among Caucasian (CA) women are lower than those of African American (AA), however, AA women have a significantly higher mortality rate. AA women often present at a younger age, with later stage and higher grade tumors. In fact, AA women are 3x more likely to be diagnosed with the more aggressive molecular subtype, triple-negative breast cancer (TNBC), than CA women. Socioeconomic and lifestyle factors may be contributory; however, it is imperative that we investigate the underlying molecular biology that may be the cause of health disparities between AA and CA with TNBC. In this study, gene expression profiling, using the Almac BC DSA Research Tool, was performed on archived FFPE samples, obtained from CA and AA women diagnosed with early stage (Node 0) TNBC. Unsupervised hierarchical clustering revealed a pattern of differential gene expression in the AA cohort compared to CA. Using the TNBC type: A Subtyping Tool for TNBC, we found a distinct distribution pattern of TNBC molecular subtypes in the AA cohort, which was very different than the CA cohort; Basal-like (14%), Immunomodulatory (43%) and Mesenchymal (43%). Gene expression analyses, comparing the AA and CA cohort (fold change > 2.0, p-value Citation Format: Julie E. Getz, Davyd B. Teoh, Sara Nasser, Christophe R. Legendre, Waibhav Tembe, Venkata Yellapantula, Mary Ellen Ahearn, Carmen R. Gomez, Merce Jorda, Shuk Mei Wong, Mark D. Pegram, John D. Carpten, Lisa L. Baumbach-Reardon. High caveolin-1 expression in a cohort of African American women with triple-negative breast cancer. [abstract]. In: Proceedings of the Eighth AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 13-16, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2016;25(3 Suppl):Abstract nr C54.

Formalin fixed paraffin embedded (FFPE) tissues are a vast resource of annotated clinical samples. As such, they represent highly desirable and informative materials for the application of high definition genomics for improved patient management and to advance the development of personalized therapeutics. However, a limitation of FFPE tissues is the variable quality of DNA extracted for analyses. Furthermore, admixtures of non-tumor and polyclonal neoplastic cell populations limit the number of biopsies that can be studied and make it difficult to define cancer genomes in patient samples. To exploit these valuable tissues we applied flow cytometry-based methods to isolate pure populations of tumor cell nuclei from FFPE tissues and developed a methodology compatible with oligonucleotide array CGH and whole exome sequencing analyses. These were used to profile a variety of tumors (breast, brain, bladder, ovarian and pancreas) including the genomes and exomes of matching fresh frozen and FFPE pancreatic adenocarcinoma samples.

Pancreatic adenocarcinoma (PAC) is among the most lethal malignancies. While research has implicated multiple genes in disease pathogenesis, identification of therapeutic leads has been difficult and the majority of currently available therapies provide only marginal benefit. To address this issue, our goal was to genomically characterize individual PAC patients to understand the range of aberrations that are occurring in each tumor. Because our understanding of PAC tumorigenesis is limited, evaluation of separate cases may reveal aberrations, that are less common but may provide relevant information on the disease, or that may represent viable therapeutic targets for the patient. We used next generation sequencing to assess global somatic events across 3 PAC patients to characterize each patient and to identify potential targets. This study is the first to report whole genome sequencing (WGS) findings in paired tumor/normal samples collected from 3 separate PAC patients. We generated on average 132 billion mappable bases across all patients using WGS, and identified 142 somatic coding events including point mutations, insertion/deletions, and chromosomal copy number variants. We did not identify any significant somatic translocation events. We also performed RNA sequencing on 2 of these patients' tumors for which tumor RNA was available to evaluate expression changes that may be associated with somatic events, and generated over 100 million mapped reads for each patient. We further performed pathway analysis of all sequencing data to identify processes that may be the most heavily impacted from somatic and expression alterations. As expected, the KRAS signaling pathway was the most heavily impacted pathway (P

Background Olfactory neuroblastoma (ONB) is a rare cancer of the sinonasal tract with little molecular characterization. We performed whole genome sequencing (WGS) on paired normal and tumor DNA from a patient with metastatic-ONB to identify the somatic alterations that might be drivers of tumorigenesis and/or metastatic progression. Methodology/Principal Findings Genomic DNA was isolated from fresh frozen tissue from a metastatic lesion and whole blood, followed by WGS at >30X depth, alignment and mapping, and mutation analyses. Sanger sequencing was used to confirm selected mutations. Sixty-two somatic short nucleotide variants (SNVs) and five deletions were identified inside coding regions, each causing a non-synonymous DNA sequence change. We selected seven SNVs and validated them by Sanger sequencing. In the metastatic ONB samples collected several months prior to WGS, all seven mutations were present. However, in the original surgical resection specimen (prior to evidence of metastatic disease), mutations in KDR, MYC, SIN3B, and NLRC4 genes were not present, suggesting that these were acquired with disease progression and/or as a result of post-treatment effects. Conclusions/Significance This work provides insight into the evolution of ONB cancer cells and provides a window into the more complex factors, including tumor clonality and multiple driver mutations.

Summary Genomic structural variants (SVs) are abundant in humans, differing from other variation classes in extent, origin, and functional impact. Despite progress in SV characterization, the nucleotide resolution architecture of most SVs remains unknown. We constructed a map of unbalanced SVs (i.e., copy number variants) based on whole genome DNA sequencing data from 185 human genomes, integrating evidence from complementary SV discovery approaches with extensive experimental validations. Our map encompassed 22,025 deletions and 6,000 additional SVs, including insertions and tandem duplications. Most SVs (53%) were mapped to nucleotide resolution, which facilitated analyzing their origin and functional impact. We examined numerous whole and partial gene deletions with a genotyping approach and observed a depletion of gene disruptions amongst high frequency deletions. Furthermore, we observed differences in the size spectra of SVs originating from distinct formation mechanisms, and constructed a map constructed a map of SV hotspots formed by common mechanisms. Our analytical framework and SV map serves as a resource for sequencing-based association studies.

We use high-density single nucleotide polymorphism (SNP) genotyping microarrays to demonstrate the ability to accurately and robustly determine whether individuals are in a complex genomic DNA mixture. We first develop a theoretical framework for detecting an individual's presence within a mixture, then show, through simulations, the limits associated with our method, and finally demonstrate experimentally the identification of the presence of genomic DNA of specific individuals within a series of highly complex genomic mixtures, including mixtures where an individual contributes less than 0.1% of the total genomic DNA. These findings shift the perceived utility of SNPs for identifying individual trace contributors within a forensics mixture, and suggest future research efforts into assessing the viability of previously sub-optimal DNA sources due to sample contamination. These findings also suggest that composite statistics across cohorts, such as allele frequency or genotype counts, do not mask identity within genome-wide association studies. The implications of these findings are discussed.

Contemporary biological technologies produce extremely high-dimensional data sets with limited samples which demands feature selection in classifier design. Heretofore, dimensionalities considered in the existing comparative studies for feature selection are nowhere near those currently being used. This study compares some basic feature-selection methods in settings of 20,000 features, where it defines distribution models involving different kinds of relations among the features. The study evaluates the performances of different feature selection algorithms, which show some general trends relative to sample size and relations among the features.

Breast cancer detection has not resulted in the expected decrease in mortality suggesting that current screening strategies are not finding metastasis-prone lesions before metastasis occurs and instead are detecting many lesions that will not progress to metastatic disease. Although several improvements in the management of breast cancer have been made, an estimated 90% of breast cancer related deaths are due to the development of distant metastasis to organs such as lung, bone, liver, and brain. Metastatic breast cancer affects 10-15% of breast cancer patients, is often refractory to therapy and associated with poor outcomes. However, a blood test indicating a high-risk population at the time of breast cancer diagnosis might shift the current strategy and improve outcomes. Efforts for identifying predictive markers of breast cancer metastasis have been impeded by a lack of data defining the genetic and epigenetic determinants of the disease. Numerous studies suggest that DNA methylation could be a useful biomarker for improving the clinical management. Recently circulating tumor (ct)DNA has attracted attention for clinical use in the context of risk prediction, prognostication and prediction in human cancer. Various types of DNA alterations have been reported in ctDNA but methylated DNAs are chemically stable and are among the most sensitive and specific ctDNA cancer markers. The objective of our study was to identify and validate a blood-based DNA methylation biomarker for breast cancer patients at high risk of distant metastasis. Using the HumanMethylation27K bead array, we recently identified RUNX3 hypermethylation involving 3 CpG probes in 32 breast brain metastasis tumor tissue compared to a series of 50 unmatched early stage breast cancer samples. We calculated the area under the curve (AUC) to exceed 0.8, with a higher AUC achieved in Her2-positive samples. In an independent, first of its kind analysis, we performed whole genome bisulfite sequencing (WGBS) of ctDNA from plasma samples representing three pools of 40 women with distant metastatic breast cancer to various organs, 40 women with metastasis-free breast cancer, and 40 non-breast cancer controls. In this unbiased analysis, these data demonstrated RUNX3 hypermethylation in the plasma of patients with metastatic breast cancer in the same region corresponding to the CpG probes of the methylation array. We are now performing bisulfite pyrosequencing of each individual plasma sample, and in an additional cohort of 50 archival primary/brain metastasis tumor pairs with matching serum and clinical outcomes data. To determine regional specific differences we have also selected two additional regions of RUNX3 not differentially methylated for validation by bisulfite pyrosequencing. Determining the extent to which RUNX3 hypermethylation occur in the context of brain metastasis versus other distant sites are ongoing studies. Together these studies point towards RUNX3-regional specific methylation as a potential blood-based biomarker of breast cancer metastasis. Such a biomarker will help establish surveillance methods and eventually preventative regimens that will increase the likelihood of long-term progression-free survival for breast cancer patients at risk of distant relapse. Citation Format: Bodour Salhia, Gerald C Gooden, Kyle N Johnson, Waibhav Tembe, Garrick Wallstrom, Winnie Liang, Manmeet Ahluwalia, Steven N Kalkanis, Tom Mikkelsen, Steven A Toms. RUNX3 hypermethylation in circulating tumor DNA is a biomarker of breast cancer distant metastasis [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P4-02-09.

Motivation: Advances in DNA microarray technology and computational methods have unlocked new opportunities to identify ‘DNA fingerprints’, i.e. oligonucleotide sequences that uniquely identify a specific genome. We present an integrated approach for the computational identification of DNA fingerprints for design of microarray-based pathogen diagnostic assays. We provide a quantifiable definition of a DNA fingerprint stated both from a computational as well as an experimental point of view, and the analytical proof that all in silico fingerprints satisfying the stated definition are found using our approach.Results: The presented computational approach is implemented in an integrated high-performance computing (HPC) software tool for oligonucleotide fingerprint identification termed TOFI. We employed TOFI to identify in silico DNA fingerprints for several bacteria and plasmid sequences, which were then experimentally evaluated as potential probes for microarray-based diagnostic assays. Results and analysis of approximately 150 in silico DNA fingerprints for Yersinia pestis and 250 fingerprints for Francisella tularensis are presented.Availability: The implemented algorithm is available upon request.Contact: jaques.reifman@us.army.mil.

Measuring the likeness between data in different ways is an important part of pattern recognition and, over the years, many such measures have been developed. This paper proposes an asymmetric measure of likeness based on the concept of context dependent divergence. This is used to construct a numerical descriptor for images and, in conjunction with fuzzy sets, to develop a supervised learning algorithm. When applied to the problem of handwritten digit recognition, the algorithm produces promising and highly accurate results.

Oncogenic fusion genes underlie the mechanism of several common cancers and also constitute or encode important diagnostic and therapeutic targets. Studies using next-generation sequencing technologies, particularly RNA sequencing (RNA-seq), have revealed a growing number of recurrent fusions in a variety of cancers. As such, establishing analytic parameters, including the limit of detection, sensitivity, and specificity of detecting fusions in tumor RNA specimens is important. We have developed reference materials useful in assessing the ability to detect fusion events in RNA or DNA samples. These consist of a set of nine synthetic poly-adenylated RNA transcripts that correspond to common cancer fusion gene sequences. These synthetic fusion RNAs can be spiked at known concentrations into total RNA in the first stage of the mRNA sample prep and used as in-line controls to evaluate the sensitivity and specificity of the detection of these mutations. As a demonstration of the utility of these controls, we performed a series of spike-in experiments to investigate performance characteristics of three commonly used fusion-detection software algorithms. Our results show all three algorithms to be capable of detecting fusions in a dose-dependent manner. The results also show differences in performance among the algorithms for detecting specific fusions. Citation Format: Winnie Liang, Stephanie JK Pond, Waibhav D. Tembe, Han-Yu Chuang, Christophe Legendre, Nancy Kim, Valerie Montel, Shukmei Wong, Timothy K. McDaniel, David Craig, John Carpten. mRNA spike-in control materials for cancer fusion gene detection assays. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3571. doi:10.1158/1538-7445.AM2014-3571

Despite remarkable therapeutic advances in malignant melanoma, an incomplete understanding of melanoma's extraordinary genetic diversity, complex disease course and ability to resist treatment continues to impede rational, curative clinical intervention. This is particularly true for the understudied “dark matter” of the melanoma genome, i.e. cases that lack mutations in the three most commonly-mutated melanoma genes (BRAF, NRAS, NF1) and that display extensive mutational heterogeneity. Clearly a pressing need exists for transformative approaches to melanoma medicine. We have therefore broadened our traditional focus on melanoma in human patients to include canine melanoma as a model of human disease. Given canine melanoma's clinicopathological resemblance to human non-cutaneous triple-negative subtypes, study of this disease in each species may inform the other. Further, the accelerated clinical course of canine melanoma and flexibility in veterinary clinical trial design may allow for evaluation of novel treatment designs that would not be possible in human cancer trials. Thus, we have undertaken a first-in-dog genomic characterization study of canine melanomas from a cohort of 37 dogs to further explore the landscape of this comparative model. This analysis combines comprehensive genomics (whole genome, whole genome long-insert, RNA sequencing, and array comparative genomic hybridization) in 7 matched canine melanoma/normal tissues with targeted analysis in an additional 27 tumors and 3 cell lines. These canine tumors display striking patterns of mutation and subtype heterogeneity resembling that of their non-cutaneous human counterparts with the majority being BRAF, NRAS, and NF1 wild type and lacking recurrent single nucleotide variants. Despite heterogeneity at the single nucleotide level, shared patterns of structural variation and gene expression exist and these patterns suggest core pathways that may define therapeutic vulnerabilities in these disease subtypes. These data provide the genomic framework for further clinical pursuit of melanoma in the canine model as a paradigm for drug development. Citation Format: William P. Hendricks, Victoria Zismann, Berry Button, Christophe Legendre, Waibhav Tembe, Holly Yin, Michael Bittner, Donald Berry, Matthew Huentelman, Chand Khanna, Patricia LoRusso, Matthew Breen, Aleksandar Sekulic, Jeffrey Trent. The genomic landscape of canine melanoma reveals broad mutational heterogeneity and recurrent patterns of structural variation. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3417. doi:10.1158/1538-7445.AM2014-3417