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• Combined MEK and BCL-2/XL inhibition is effective in high-grade serous ovarian cancer patient-derived xenograft models and BIM levels are predictive of responsiveness

  Most patients with late stage high-grade serous ovarian cancer (HGSOC) initially respond to chemotherapy but inevitably relapse and develop resistance, highlighting the need for novel therapies to improve patient outcomes. The MEK/ERK pathway is activated in a large subset of HGSOC, thus making it an attractive therapeutic target. Here, we systematically evaluated the extent of MEK/ERK pathway activation and efficacy of pathway inhibition in a large panel of well-annotated HGSOC patient-derived xenograft (PDX) models. The vast majority of models were nonresponsive to the MEK inhibitor cobimetinib (GDC-0973) despite effective pathway inhibition. Proteomic analyses of adaptive responses to GDC-0973 revealed that GDC-0973 upregulated the pro-apoptotic protein BIM, thus priming the cells for apoptosis regulated by BCL2-family proteins. Indeed, combination of both MEK inhibitor and dual BCL-2/XL inhibitor (ABT-263) significantly reduced cell number, increased cell death and displayed synergy in vitro in most models. In vivo, the GDC-0973 and ABT-263 combination was well tolerated and resulted in greater tumor growth inhibition than single agents. Detailed proteomic and correlation analyses identified two subsets of responsive models – those with high BIM at baseline that was increased with MEK inhibition and those with low basal Bim and high pERK levels. Models with low BIM and low pERK were non-responsive. Our findings demonstrate that combined MEK and BCL-2/XL inhibition has therapeutic activity in HGSOC models and provide a mechanistic rationale for clinical evaluation of this drug combination as well as the assessment of the extent to which BIM and/or pERK levels predict drug combination effectiveness in chemoresistant HGSOC.

  Dataset EGAD00001005111

• NHLBI TOPMed - NHGRI CCDG: The Vanderbilt University BioVU Atrial Fibrillation Genetics Study

  Atrial fibrillation (also called AFib or AF) is a quivering or irregular heartbeat (arrhythmia) that can lead to blood clots, stroke, heart failure and other heart-related complications. At least 2.7 million Americans are living with AFib. Individuals with early onset atrial fibrillation (AF) are included in this study of cases from the BioVU sample repository. BioVU is Vanderbilt's biobank of DNA extracted from leftover and otherwise discarded clinical blood specimens. BioVU operates as a consented biorepository; all individuals must sign the BioVU consent form in order to donate future specimens. BioVU subjects are de-identified and linked to the Synthetic Derivative enabling researchers to access genetic data/DNA material as well as dense, longitudinal electronic medical record (EMR) information.

  Study phs001624

• DNA Methylation Characterization of Fusion-Positive and Fusion-Negative Rhabdomyosarcoma

  Rhabdomyosacoma (RMS) is a common pediatric soft tissue tumor that is associated with the skeletal muscle lineage. RMS comprises two major subtypes: fusion-positive (FP, most commonly PAX3- FOXO1 or PAX7-FOXO1) and fusion-negative (FN, frequently associated with mutations in RAS pathway). Our previous study demonstrated that FP and FN tumors exhibit distinct DNA methylation profiles. In this study, further investigation of genome-wide DNA methylation profiles in RMS tumors revealed DNA methylation differences between AX3-FOXO1 and PAX7-FOXO1 subsets in FP RMS, and between RAS mutation and RAS wild-type subsets in FN RMS. These data provide new insight into the relationship of DNA methylation with mutational changes and transcriptional organization in FP and FN RMS.

  Study phs001970

• Spatiotemporal Charting of Human Esophageal Development for Epidermolysis Bullosa Cell Therapy

  Human fetal esophageal development is not well understood. We present a spatio-temporal single-cell multi-omics atlas to close such a long-standing knowledge gap. With such an atlas, we describe the cellular heterogeneity and lineage transitions of esophageal epithelium and stroma. We delineate the cellular dynamics of both epithelial and stromal populations during the establishment of esophageal tissue architecture. We identify niche paracrine signals that drive the development of the epithelium. We develop an in silico perturbation deep learning algorithm to further enlighten the in vitro derivation of esophageal basal cells (eBCs) and we use the algorithm to screen for effective and efficient signaling pathway perturbations that could guide in vitro eBC differentiation. We observe that chemically perturbing prioritized pathways yields functional eBCs. Such success sheds light on cell replacement therapy of eBC dysfunction, such as Epidermolysis Bullosa.

  Study phs003281

• Genome Diversity in Africa Project - GemCode libraries (2017-07-05)

  High depth whole genome sequencing from GemCode (10x Genomics) DNA libraries containing long range linkage information for one Baganda trio and one Baganda child (parent already sequenced at high depth). This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ This dataset contains all the data available for this study on 2017-07-05.

  Dataset EGAD00001003426

• Rare disruptive mutations in ciliary function genes contribute to testicular cancer susceptibility

  Testicular germ cell tumor (TGCT) is the most common cancer in young men1,2. Here we aimed to identify novel risk factors for TGCT using whole-exome sequencing, which was performed on 328 affected individuals from 153 families, 634 sporadic cases and 1,644 controls. We searched for genes that were recurrently affected by rare variants (minor allele frequency <0.01) with potentially damaging effects and evidence of segregation in families. 8.7% of families carried rare disruptive mutations in the cilia-microtubule genes (CMG) as compared to 0.5% of controls3 (P=2.1x10-8). The most significantly mutated CMG was DNAAF1 with biallelic inactivation and loss of DNAAF1 expression shown in tumors from carriers. DNAAF1 as a cause of TGCT was supported by a DNAAF1Hu255h(+/-) zebrafish model with 94% penetrance for TGCT compared to 14% in wildtype fish. These data implicate cilia-microtubule inactivation as a cause of TGCT development and are the first evidence for CMGs as cancer susceptibility genes.

  Study EGAS00001001789

• Raw sequencing of single-cell RNA-seq data of a phase II clinical trial (NCT03419481)

  This is a single-arm Phase II trial of pembrolizumab in patients with hepatitis B virus-related hepatocellular carcinoma with parallel study on baseline and serial change in the immune environment.

  Study EGAS50000001315

• Targeted sequencing data of cfDNA, archival tissue, and WBC from 208 patients with mUC

  Targeted tissue (n=81), cfDNA (n=319), and WBC (n=208) Illumina sequencing of metastatic urothelial carcinoma patients. All cfDNA is collected in the metastatic setting, while archival tissue is from across the disease course. White blood cell (WBC) samples are used as patient matched germline samples. All files are in paired-end fastq format.

  Dataset EGAD50000002089

• Targeted sequencing data of cfDNA, archival tissue, and WBC from 226 patients with mUC

  Targeted tissue (n=210), cfDNA (n=402), and WBC (n=245) Illumina sequencing of metastatic urothelial carcinoma patients. All cfDNA is collected in the metastatic setting, while archival tissue is from across the disease course. White blood cell (WBC) samples are used as patient matched germline samples. All files are in paired-end fastq format.

  Dataset EGAD50000001571

• RNA-seq data of breast cancer HCI011 model from Ros et al (2020)

  RNA-seq data of the HCI011 and HCI011R models, GDC032 treated and control (total of 21 samples), from the paper: FOXM1 is a biomarker of resistance to PI3Kα inhibition in ER+ breast cancer that is detectable using metabolic imaging (Ros et al, 2020)

  Dataset EGAD00001006245