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• BAP1 Project

  For our BAP1 manuscript

  Dac EGAC50000000006

• EGAD00010000608

  SNP6 data for seminoma samples

  Dataset EGAD00010000608

• PPGL WES dataset

  Dataset includes fastq files for WES experiments for tumor samples of PPGL patients. Paired end reads fastq files are available for 87 different samples

  Dataset EGAD00001008579

• WGS data for MMML for Study EGAS00001002199

  Whole genome sequencing data for MMML (24 tumor/control pairs), fastq-files

  Dataset EGAD00001003276

• WGS data for MMML for Study EGAS00001002198

  Whole genome sequencing data for MMML (28 tumor/control pairs)

  Dataset EGAD00001003207

• WGS data for MMML for Study EGAS00001002198

  Whole genome sequencing data for MMML (12 tumor/control pairs)

  Dataset EGAD00001003208

• WGS data for MMML for Study EGAS00001002198

  Whole genome sequencing data for MMML (cell_line)

  Dataset EGAD00001003210

• Integrative genomic profiling of large-cell neuroendocrine carcinomas reveals distinct subtypes of high-grade neuroendocrine lung tumors

  Large-cell neuroendocrine lung carcinomas (LCNEC) have similarities with other lung cancers, but their precise relationship has remained unclear. We conducted comprehensive genomic (n=60) and transcriptomic (n=69) analyses of 75 LCNECs and identified two molecular subgroups: “type I LCNECs” with bi-allelic TP53 and STK11/KEAP1 alterations (37%), and “type II LCNECs” enriched for bi-allelic inactivation of TP53 and RB1 (42%). Despite sharing genomic alterations with adenocarcinomas and squamous cell carcinomas, no transcriptional relationship was found; instead LCNECs form distinct transcriptional subgroups with closest similarity to SCLC. While type I LCNECs and SCLCs exhibit a neuroendocrine profile with ASCL1high/DLL3high/NOTCHlow, type II LCNECs bear TP53 and RB1 alterations and differ from most SCLC tumors with reduced neuroendocrine markers, a pattern of ASCL1low/DLL3low/NOTCHhigh and upregulation of immune-related pathways. In conclusion, LCNECs are comprised of two molecularly defined subgroups, and distinguishing them from SCLC may allow for stratified targeted treatments for high-grade neuroendocrine lung tumors.

  Study EGAS00001000708

• DAC for achalasia risk consortium (arc)

  Dac EGAC00001000394

• DAC for aflatoxin project Qidong patient samples

  Dac EGAC00001000679

• DAC for seminoma genomic data

  Dac EGAC00001000231

• DAC for study WES of pituitary tumors

  Dac EGAC00001000443

• Data Access Committee for Hui Lab

  Dac EGAC00001000454

• INRP - Italian Network for Research on Psychoses

  Dac EGAC00001000576

• DAC for MOMA, Short QT Exome data

  Dac EGAC00001000730

• DAC for Dissecting Tumor Microenvironment with Tumorgrafts

  Dac EGAC00001000809

• Data Access Committee for data from EGAS00001002840

  Dac EGAC00001000834

• DAC for human single nuclei RNAseq of Epilepsy

  Dac EGAC00001000860

• Laboratory for Neurobiology (VIB-KU Leuven)

  Dac EGAC00001001282

• DAC for HSTCL study. National Cancer Centre Singapore.

  Dac EGAC00001001302

• DAC for MEITL study. National Cancer Centre Singapore.

  Dac EGAC00001001323

• DAC for Phylogenetic reconstruction of breast cancer

  Dac EGAC00001001572

• Center for Technology Licensing at Cornell University

  Dac EGAC00001001587

• Data Access Commitee for single-cell analysis

  Dac EGAC00001001600

• DAC for Stuttgart Breast Cancer Cohort

  Dac EGAC00001001693