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• DNA sequencing of sgRNAs enriched from the CRISPR-Cas9 screened HCC organoids

  Project aims to find enriched or depleted sgRNAs to identify functional genes in HCC progression and lenvatinib resistance

  Study EGAS50000000848

• DNA Double Strand Breaks in KMT2A-Rearranged AML patients

  DNA breakage at specific rearrangement-participating genes, which are more sensitive to a range of environmental and chemotherapeutic agents than other regions of the human genome, contributes to the generation of leukemia-causing rearrangements, and can be used as a predictor of susceptibility to cancer caused by gene rearrangements. As a direct proof-of-principle regarding the feasibility of employing fragile site breakage to identify and monitor high-risk populations to allow personalized treatments, genome-wide DNA break mapping/sequencing was used to determine whether preferential DNA breaks are generated at the major breakpoint regions of KMT2A in AML remission patients with previously diagnosed KMT2A rearrangement. We are submitting break mapping data from three patients with KMT2A rearrangements and three non-fusion patients, with and without etoposide treatments.

  Study phs002804

• Health Professionals Follow-Up Study

  Health Professionals Follow-up Study (HPFS) is a prospective cohort study of men designed to evaluate hypotheses about men’s health related to diet and lifestyle to the incidence of serious illnesses, such as cancer, heart disease, and other vascular diseases. It consisted of 51,529 U.S. male health professionals who were 40-75 years old at baseline in 1986 and completed a mailed six-page baseline questionnaire. Information on age, height and weight, ancestry, medications, disease history, physical activity, lifestyle factors, and diet were gathered, with follow-up questionnaires sent biennially to update information and record health outcomes with every four years to update their diet. This datafile includes responses to the 2016 questionnaire, which is available at: https://cdn1.sph.harvard.edu/wp-content/uploads/sites/2471/2017/09/16L.pdf

  Study phs002460

• Hereditary Cancer Predisposition Syndromes and Uveal Melanoma

  Uveal melanoma (UM) is a subtype of melanoma characterized by the strong contribution of genetic rather than environmental factors in the pathogenesis of the disease, making it an ideal model disease to study the genetic basis of cancer. The long-term goal of our laboratory is to determine the genes and mechanisms responsible for hereditary cancer predisposition associated with UM. BAP1 has been identified as a candidate gene with definitive association with predisposition to UM. However, pathogenic variants in BAP1 are detected in only a small subset of UM patients even those with strong personal and family history of cancer suggesting the existence of other candidate genes. The goal of this project is to identify novel candidate genes contributing to hereditary predisposition to UM in patients with no detectable germline pathogenic variant in BAP1.

  Study phs001943

• Transcriptomics of Liver and PBMCs in Alcohol-Associated Liver Disease

  This study was designed to characterize the transcriptome of alcohol-associated hepatitis using RNA sequencing (RNA-seq) technology for both liver tissue biopsies and peripheral blood mononuclear cells (PBMCs) extracted from blood samples, in order to identify expressed genes that are markers of the disease. To distinguish genes that differentiate alcohol-associated hepatitis from other inflammatory liver diseases, we compared the RNA-seq data generated from participants with alcohol-associated hepatitis, alcohol-associated cirrhosis, chronic hepatitis C infection, non-alcohol-associated fatty liver disease, and healthy controls. We utilized computational approaches to demonstrate that gene expression data generated from either liver tissue or blood samples can be used to identify a small set of gene biomarkers to distinguish between these liver diseases. The raw RNA-seq data will be available through dbGaP.

  Study phs003112

• MYC Drives Progression of Small Cell Lung Cancer to a Variant Neuroendocrine Subtype with Vulnerability to Aurora Kinase Inhibition

  Loss of the tumor suppressors RB1 and TP53 and MYC amplification are frequent oncogenic events in small cell lung cancer (SCLC). We show that Myc expression cooperates with Rb1 and Trp53 loss in the mouse lung to promote aggressive, highly metastatic tumors, that are initially sensitive to chemotherapy followed by relapse, similar to human SCLC. Importantly, MYC drives a neuroendocrine-low “variant” subset of SCLC with high NEUROD1 expression corresponding to transcriptional profiles of human SCLC. Targeted drug screening reveals that SCLC with high MYC expression is vulnerable to Aurora kinase inhibition, which combined with chemotherapy strongly suppresses tumor progression and increases survival. These data identify molecular features for patient stratification and uncover a novel targeted treatment approach for MYC-driven SCLC.

  Study EGAS00001002115

• XClone for analyzing somatic copy number alterations

  Somatic copy number alterations (CNAs) are major mutations that contribute to the development and progression of various cancers. Despite a few computational methods proposed to detect CNAs from single-cell transcriptomic data, the technical sparsity of such data makes it challenging to identify allele-specific CNAs, particularly in complex clonal structures. In this study, we present a statistical method, XClone, that strengthens the signals of read depth and allelic imbalance by effective smoothing on cell neighborhood and gene coordinate graphs to detect haplotype-aware CNAs from scRNA-seq data. By applying XClone to multiple datasets with challenging compositions, we demonstrated its ability to robustly detect different types of allele-specific CNAs and potentially indicate whole genome duplication, therefore enabling the discovery of corresponding subclones and the dissection of their phenotypic impacts.

  Study EGAS00001007854

• Exome_sequencing_of_thyroid_disease_in_Val_Borbera

  The Val Borbera is a region characterized by low iodine and high prevalence of thyroid disorders, the commonest endocrine disorders in the general population. About 30% of the participants of the Val Borbera Project were affected by such disorders and were characterized by several parameters, TSH level, anti TPO antibodies, echography, family origin. Individuals with extreme phenotypes were identified and could be clustered based on family origin and genotype. We propose to exome sequence 6 of them, affected with true goiter, at high dept (40-60x) to obtain information on exonic rare variants. Due to the family structure and to the availability of whole genome sequence information on 110 individuals from the isolated population we expect to be able to identify putative causative variants for thyroid disorders that may be studied in the remaining affected individuals.

  Study EGAS00001000344

• Molecular sub-classification of hormone receptor-positive breast cancer

  Patients with estrogen and/or progesterone receptor-positive breast cancer benefit from hormonal treatment, yet high global death burdens due to high prevalence and long-term recurrence risk call for biomarkers to guide additional treatment approaches. From a prospective, observational study of postmenopausal early breast cancer patients treated with tamoxifen or aromatase inhibitors gene expression analyses of 612 tumors was performed using the NanoString® Breast Cancer 360 panel. We identified novel gene expression signatures, i.e. BRCAness and Tumor Inflammation Signature (associated with high tumor lymphocyte infiltration), that may aid to select high-risk patients in order to improve adjuvant treatment by targeting DNA repair deficiency or immune-checkpoints in addition to standard chemo-endocrine treatment. The overall goal is to foster novel concepts for a stratified early breast cancer management.

  Study EGAS00001004594

• European Hereditary Tumour Group

  This DAC controls access to entries submitted to the European Hereditary Tumour Group datasets according to the EHTG policies.

  Dac EGAC50000000969