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• Genomic analysis of patient-derived xenograft models reveals intratumor-heterogeneity in endometrial cancer and can predict tumor growth inhibition with talazoparib

  Background: Endometrial cancer (EC) is a major gynecological cancer with increasing incidence. It comprised of four molecular subtypes with differing etiology, prognoses, and response to chemotherapy. In the future, clinical trials testing new single agents or combination therapies will be targeted to the molecular subtype most likely to respond. Pre-clinical models that faithfully represent the molecular subtypes of EC are urgently needed, we sought to develop and characterize a panel of novel EC patient-derived xenograft (PDX) models. Methods: Here, we report whole exome or whole genome sequencing of 11 PDX models and the matched primary tumor. Analysis of multiple PDX lineages and passages was performed to study tumor heterogeneity across lineages and/or passages. Based on recent reports of frequent defects in the homologous recombination (HR) pathway in EC, we assessed mutational signatures and HR deficiency scores and correlated these with in vivo responses to the PARP inhibitor (PARPi) talazoparib in six PDXs representing the copy number high/p53-mutant and mismatch-repair deficient molecular subtypes of EC. Results: PDX models were successfully generated with similar histomorphology from all four molecular subtypes of EC and uterine carcinosarcomas. We also observed a wide range of inter-tumor and intra-tumor heterogeneity across different PDX lineages, however one arm of most PDX models recapitulated the molecular landscape of the primary tumor without major genomic drift. An in vivo response to talazoparib was detected in four copy number high models. Two models (carcinosarcomas) showed a response consistent with stable disease and two models (one copy number high serous EC and another carcinosarcoma) showed significant tumor growth inhibition, albeit one consistent with progressive disease, however all lacked the HR deficiency genomic signature. Conclusions: EC PDX models represent the four molecular subtypes of disease and can capture intra-tumoral heterogeneity of the original primary tumor. PDXs of the copy number high molecular subtype showed sensitivity to PARPi, however, deeper and more durable responses will likely require combination of PARPi with other agents.

  Study EGAS00001004666

• Alopecia and Vitiligo

  exome sequencing data captured with agilent v4 (71k) and sequenced on illumina technology. data from a total of 40 samples, of which 14 are vitiligo cases with familial history of vitiligo or immune disease, and the remaining are alopecia areata cases.

  Dataset EGAD00001005289

• ECCO-GEN Valve Methylation Dataset (non-diseased)

  The valve methylation dataset consists of 12 bam files of human non-diseased valve tissue samples that are free from calcification (6 aortic and 6 mitral valves - matched; 10 males: 2 females; age range 42 – 64 years, mean age 52.2 years, SD 9.9682). Donor hearts are free from cardiovascular and valvular complications.

  Dataset EGAD00001006303

• Flemish_Gut_Flora_Project_phenotype

  Here, we studied well-phenotyped individuals from the Flemish Gut Flora Project (FGFP, N=1,106, Belgium) and the effect of environments on microbiome. The 69 major significant phenotypes found in this study are provided.

  Dataset EGAD00001001943

• Comprehensive genomic analysis for AYA with acute lymphoblastic leukemia

  AYA with B-ALL showed extremely inferior outcomes when compared to children. Genetic basis undelying AYA B-ALL is largely unknown. We performed integrated analysis of RNA-seq (hum0045���JGAS000047) and target DNA-seq.

  Study JGAS000276

• Genome-Wide Association Analysis of Biomarkers in the InCHIANTI and BLSA

  Version 1 The aim of this study was to identify gene(s) associated with serum iron concentrations. This dataset contains the results from a meta-analysis of 1919 subjects from the InCHIANTI and Baltimore Longitudinal Study of Aging (BLSA). Both studies are population based prospective studies that evaluate the contributors of normal aging. Version 2 We performed genome wide analysis of 93 circulating biomarkers in the InCHIANTI study of 1210 individuals, after imputation from the 1000 genomes project.

  Study phs000215

• Clinical and ctDNA data for IMpassion031

  Clinical and ctDNA data for IMpassion031, including survival, response, and ctDNA data from baseline through post-surgery time points. 222 samples run on Signatera assay. File type is csv.

  Dataset EGAD50000001420

• Longitudinal copy number variation analysis from plasma DNA of head and neck cancer patients under re-radiotherapy

  Recurrent locally advanced head and neck cancer (HNC) patients, receiving re-radiotherapy (re-RT), are presented with short progression-free (PFS) and overall survival. Hence, biomarkers predicting therapy failure are urgently needed. We analyzed copy number variations (CNVs) in blood plasma of HNC patients collected before, during and after re-RT. Detectable CNVs after five and ten fractions of re-irradiation as well as at the end of re-RT were associated with short PFS. Furthermore, we found that CNV kinetics in longitudinally collected plasma reflected therapy response and could detect disease progression in advance of radiographic tumor assessments.

  Study EGAS50000000163

• Characterization of Neoantigen-reactive T cells by Single-Cell Analysis

  Through CITE-seq and TCR-seq analysis of non-small cell lung cancer (NSCLC) tumor-infiltrating lymphocytes (TIL), we developed a neoantigen-reactive T-cell signature based on clonotype frequency and CD39 protein and CXCL13 mRNA expression. There are four datasets from four NSCLC cases. Each dataset includes (1) Transcriptome of tumor infiltrating lymphocytes (TIL), (2) Cell surface protein expression on TIL by Feature barcoding, (3) T-cell receptor analysis of TIL, and (4) Reference file for feature-barcoding antibodies.

  Study phs002792

• Genomic features of renal cell carcinoma developed in end-stage renal disease and dialysis

  Patients with end-stage renal disease (ESRD) or receiving dialysis have 3 ���24-fold higher risk for renal cell carcinoma (RCC). Long-term hemodialysis cause acquired cystic disease (ACD) and RCC.���But its carcinogenic mechanism and genomic features are not so clear. In this study, we analyzed whole genome and transcriptome of 38 RCCs, which were developed in ESRD patients receiving dialysis and characterized their genomic features and associations with their histology and dialysis or end-stage renal diseases.

  Study JGAS000533