Next-generation sequencing studies have revealed genome-wide structural variation patterns in cancer, such as chromothripsis and chromoplexy, that do not engage a single discernable driver mutation, and whose clinical relevance is unclear. We devised a robust genomic metric able to identify cancers with a chromotype called tandem duplicator phenotype (TDP) characterized by frequent and distributed tandem duplications (TDs). Enriched only in triple-negative breast cancer (TNBC) and in ovarian, endometrial, and liver cancers, TDP tumors conjointly exhibit tumor protein p53 (TP53) mutations, disruption of breast cancer 1 (BRCA1), and increased expression of DNA replication genes pointing at rereplication in a defective checkpoint environment as a plausible causal mechanism. The resultant TDs in TDP augment global oncogene expression and disrupt tumor suppressor genes. Importantly, the TDP strongly correlates with cisplatin sensitivity in both TNBC cell lines and primary patient-derived xenografts. We conclude that the TDP is a common cancer chromotype that coordinately alters oncogene/tumor suppressor expression with potential as a marker for chemotherapeutic response.

Patient-Derived Xenografts (PDXs) are tumor-in-mouse models for cancer. PDX collections, such as those supported by the NCI PDXNet program, are powerful resources for preclinical therapeutic testing. However, variations in experimental design and analysis procedures have limited interpretability. To determine the robustness of PDX studies, the PDXNet tested temozolomide drug response for three pre-validated PDX models (sensitive, resistant, and intermediate) across four blinded PDX Development and Trial Centers (PDTCs) using independently selected SOPs. Each PDTC was able to correctly identify the sensitive, resistant, and intermediate models, and statistical evaluations were concordant across all groups. We also developed and benchmarked optimized PDX informatics pipelines, and these yielded robust assessments across xenograft biological replicates. These studies show that PDX drug responses and sequence results are reproducible across diverse experimental protocols. Here we share the range of experimental procedures that maintained robustness, as well as standardized cloudbased workflows for PDX exome-seq and RNA-Seq analysis and for evaluating growth.

ObjectivesFrom the first description by Leo Kanner [1], autism has been an enigmatic neurobehavioral phenomenon. The new genetic/genomic technologies of the past decade have not been as productive as originally anticipated in unveiling the mysteries of autism. The specific etiology of the majority of cases of autism spectrum disorder (ASD) is unknown, although numerous genetic/genomic variants and alterations of diverse cellular functions have been reported. Prompted by this failure, we have investigated whether the metabolomics approach might yield results which could simultaneously lead to a blood-based screening/diagnostic test and to treatment options. Methods Plasma samples from a clinically well-defined cohort of 100 male individuals, ages 2-16+ years, with ASD and 32 age-matched typically developing (TD) controls were subjected to global metabolomic analysis. ResultsWe have identified more than 25 plasma metabolites among the approximately 650 metabolites analyzed, representing over 70 biochemical pathways, that can discriminate children with ASD as young as 2 years from children that are developing typically. The discriminating power was greatest in the 2-10 year age group and weaker in older age groups. The initial findings were validated in a second cohort of 83 children, males and females, ages 2-10 years, with ASD and 76 age and gender-matched TD children. The discriminant metabolites were associated with several key biochemical pathways suggestive of potential contributions of increased oxidative stress, mitochondrial dysfunction, inflammation and immune dysregulation in ASD. Further, targeted quantitative analysis of a subset of discriminating metabolites using tandem mass spectrometry provided a reliable laboratory method to detect children with ASD. Conclusion Metabolic profiling appears to be a robust technique to identify children with ASD ages 2-10 years and provides insights into the altered metabolic pathways in ASD, which could lead to treatment strategies. ObjectivesTo uncover novel traits associated with nicotine and alcohol use genetics, we performed a phenome-wide association study in a large multi-ethnic cohort. Methods We investigated 7,688 African-Americans (AFR), 1,133 Asian-Americans (ASN), 14,081 European-Americans (EUR), and 3,492 Hispanic-Americans (HISP) from the Women's Health Initiative, analyzing risk alleles located in the CHRNA5-CHRNA3 locus (rs8034191, rs1051730, rs12914385, rs2036527, and rs16969968) for nicotine-related traits and ADH1B (rs1229984 and rs2066702) and ALDH2 (rs671) for alcohol-related traits with respect to anthropometric characteristics, dietary habits, social status, psychological circumstances, reproductive history, health conditions, and nicotine-and alcohol-related traits. ResultsThe investigated loci resulted associated with novel traits: rs1229984 were associated with family income (p=4.1*10 −12 ), having a pet (p=6.5*10 −11 ), partner education (p=1.8*10 −10 ), "usually expect the best" (p=2.4*10 −7), "felt calm and peaceful" (p=2.6*10 ), and num...

Patient-Derived Xenografts (PDX) are powerful models to study tumors' drug-response in the context of personalized medicine. In the PDX model settings, by virtue of expanding the patient's tumor sample, testing multiple drug or drug-combinations can be executed rapidly and has no ethical limitations. However, there are major issues around standards that need to be addressed to make these models widely accessible and usable. The overarching goal of the PDXNet is to coordinate the development of appropriate PDX models and methods for preclinical drug testing to advance CTEP clinical development of new cancer agents. In an effort to standardize protocols for PDX generation as well as data analysis and metadata harmonization, we are building a data storage, sharing, and analysis platform that harmonizes PDXNet data with other large datasets and analysis workflows. The PDX Data Commons is built on top of existing NCI resources, leveraging the Cancer Genomics Cloud maintained by Seven Bridges Genomics, where PDXNet data is co-located with TCGA and other large-scale datasets. The PDCCC is co-led by experts from The Jackson Laboratory, providing scientific leadership in xenograft methods and cancer biology to ensure the promulgation of standards that are well-suited for the PDX community. In addition, the PDCCC is responsible for establishing studies to identify best-practices for PDX data analysis and metadata schemas. The data collected as part of the PDXNet is currently stored on the PDXNet portal that has a query interface for identifying models for pre-clinical trials. Simultaneously, we administer training activities and research pilots to build synergies within the PDXNet, enhancing the ability of the PDXNet to develop clinical trials from PDX studies. In PDXNet, besides the PDCCC, there are 4 PDX Development and Trial Centers (PDTCs) responsible for executing specific pre-clinical trials focused around cancer types including breast cancer, melanoma, and lung cancer. Data generated by the PDTCs will be hosted by the PDCCC, and metadata will be collected based on schemas developed by the network for systematic ontological analysis. These PDX models, in coordination with the NCI Patient-Derived Models Repository (PDMR) at the Frederick National Laboratory for Cancer Research (FNLCR) will be shared with the broader community. In addition, PDTC's will collaborate with non-PDXNet investigators for PDX studies through an administrative supplement program supported by the NCI. The PDXNet is a strong step toward building a consensus around PDX models, so that the power for discovery can be expanded by making multi-institutional PDX cohorts a reality. The PDCCC is a central part of this process to systematically capture and analyze the variables most influential to PDX models and share protocols and tools to make PDXs an interchangeable research currency for pre-clinical discovery. Citation Format: Anurag Sethi, Anuj Srivastava, Xingyi Woo, Vishal Sarsani, Ziming Zhao, Javad Noorbakhsh, Christian French, Jack DiGiovanna, Ogan D. Abaan, Steve Neuhauser, Peter Robinson, Yvonne A. Evrard, Carol J. Bult, Jeffrey A. Moscow, Brandi Davis-Dusenbery, Jeffrey H. Chuang. The PDX Data Commons and Coordinating Center (PDCCC) for PDXNet [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1029.

Ovarian cancer affects ∼ 204,000 women each year, and it is one of the leading causes of cancer-related death among women world-wide, as most patients present with advanced stage (III/IV) tumours, and only 40% survive 5 years after diagnosis. Improvement in the clinical management of ovarian patients is likely to derive from a better understanding of the molecular aberrations which initiate and maintain tumour growth as well as from the discovery of novel drug-able targets for the development of personalised targeted therapies. In this study, we comprehensively characterised the cancer genome of a patient diagnosed with grade III serous ovarian carcinoma, using a combination of massive-parallel sequencing technologies, including long distance DNA Paired-End-Tag (DNA-PET) sequencing, RNA-sequencing and exome-capture sequencing. This approach ensures deep coverage (from 50-180X) of critical mutational elements in a cancer genome. By comparing the genomic abnormalities identified in the tumour sample with those found in its normal counterpart (peripheral blood lymphocytes), we were able to compile a catalogue of all of the somatic events which occurred during oncogenesis and to define the overall complexity of this specific cancer genome. Interestingly, chromosomal arm loss appeared to be the predominant feature of this tumour, with more than 30% of the haploid genome being affected by a decrease in copy number. This associated with a prevalence of inter-chromosomal rearrangements, suggesting chromosomal translocations as a preferred consequence of genomic instability. Here we describe some examples of somatic translocation events causing the loss of tumour suppressor genes. Using this genotype as a possible model of ovarian carcinoma evolution, we aim to explain a subset of ovarian cancers displaying a similar chromosomal profile by applying an analysis of copy number variations, and to define common mechanisms of cancer gene loss. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5107. doi:1538-7445.AM2012-5107