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• Exploring_the_landscape_of_somatic_mutations_in_normal_tissue___BD___WGS

  The aim of this project is to investiagte somatic mutations, including rates of mutations and signatures, and clonal dynamics in normal (non-cancerous) human tissues from the biliary tree. The project will involve the studyof normal tissues from healthy individuals and those with specific candidate diseases. We are utilising laser capture microscopy to idnetifiy and isolate specific cell populations which are then subjected to whole genome sequencing to estimate mutational burdens and begin to understand clonal structures across the normal tissues.

  Study EGAS00001003014

• Exploring_the_landscape_of_somatic_mutations_in_normal_tissue___GI___TPS___WGS

  The aim of this project is to investigate somatic mutations, including rates of mutations and signatures, and clonal dynamics in normal (non-cancerous) human tissues from the gastrointestinal tract. The project will involve the study of normal tissues from healthy individuals and those with specific candidate diseases. We are utilising laser capture microscopy to identify and isolate specific cell populations which are then subjected to whole genome sequencing to estimate mutational burdens and begin to understand clonal structures across the normal tissues.

  Study EGAS00001003089

• Exome sequencing of samples taken at multipl time points to monitor therapy response in AML

  As part of our individualized systems medicine program, personalized treatment options are identified and administered to chemorefractory AML patients based on exome sequencing and ex-vivo drug sensitivity and resistance testing data. In this study, we developed this approach further to take into account clonal heterogeneity and analyzed responses of 13 AML patients to chemotherapy and six patients to targeted treatments by using exome and ultra-deep amplicon resequencing. The data submitted includes fastq files from exome sequencing of multiple time-point samples collected from the 13 patients. In this study we identified rare subclonal variants present at diagnosis or later relapse samples and developed a correlation-based method to compare subclonal responses in serial samples across multiple time-points. Significant subclone-specific responses were observed subsequent to chemotherapy and specifically to targeted therapy in five patients.

  Study EGAS00001001948

• NHLBI GO-ESP: Family Studies (Thoracic aortic aneurysms leading to acute aortic dissections)

  The NHLBI "Grand Opportunity" Exome Sequencing Project (GO-ESP), a signature project of the NHLBI Recovery Act investment, was designed to identify genetic variants in coding regions (exons) of the human genome (the "exome") that are associated with heart, lung and blood diseases. These and related diseases that are of high impact to public health and individuals from diverse racial and ethnic groups will be studied. These data may help researchers understand the causes of disease, contributing to better ways to prevent, diagnose, and treat diseases, as well as determine whether to tailor prevention and treatments to specific populations. This could lead to more effective treatments and reduce the likelihood of side effects. GO-ESP is comprised of five collaborative components: 3 cohort consortia - HeartGO, LungGO, and WHISP - and 2 sequencing centers - BroadGO and SeattleGO. Acute aortic dissections are a common cause of premature death in the United States, ranking as high as the 15th leading cause of death. The goal of the Familial Thoracic Aortic Aneurysm and Dissection (TAAD) study at the University of Texas Health Science Center at Houston is to identify the genes causing thoracic aortic disease, and prevent premature deaths due to aortic dissections by identifying individuals who are genetically predisposed to the disease and initiating medical and surgical therapies to prevent dissections. In this study ("NHLBI GO-ESP Family Studies: Aortic Disease"), we have selected a series of families from the TAAD study, and are performing exome sequencing of 2 to 3 individuals per family.

  Study phs000347

• Identifying transcriptional programs underlying anti-EGFR small molecule response and resistance with TraCe-seq

  Genetic and non-genetic heterogeneity within cancer cell populations represents a major challenge to anti-cancer therapies. We currently lack robust methods to determine how pre-existing and adaptive features affect cellular responses to therapies. Here, by conducting clonal fitness mapping and transcriptional characterization using expressed barcodes and single-cell RNA-sequencing, we have developed TraCe-seq, a method that captures at clonal resolution the origin, fate, and differential early adaptive transcriptional programs of cells in a complex population in response to distinct treatments. We used TraCe-seq to benchmark how next-generation dual EGFR inhibitors-degraders compare to standard EGFR kinase inhibitors in EGFR-mutant lung cancer cells. To QC the TraCe-seq strategy, single-cell RNA-seq libraries were generated from a variety of human cancer cell lines transduced with the TraCe-seq library to validate the TraCe-seq strategy. Specifically, 5 different cell lines (PC9, MCF-10A, MDA-MB-231, NCI-H358, and NCI-H1373) were each transduced with a unique TraCe-seq barcode. The transduced cells were selected with puromycin only, dissociated to single cell suspensions, and then mixed together. The complex mixture of the 5 cell lines was profiled by 10X scRNA-seq. Furthermore, transduced NCI-H1373 cells were sorted by FACS to enrich for the top 50% of eGFP positive cells, and sorted cells were cultured briefly and used to construct scRNA-seq libraries and profiled by 10x scRNA-seq. To carry out the full TraCe-seq experiment, ~600 PC9 cells carrying unique TraCe-seq barcodes were expanded over 12 doublings to establish the barcoded population. A subset of the barcoded PC9 population was used to generate scRNA-seq libraries and profiled by 10x scRNA-seq prior to treatment to establish a baseline transcription profile for each barcoded clone. The rest of the cells were then treated for four days with 1 µM erlotinib, 1 µM GNE-069, or 1 µM GNE-104 respectively. scRNA-seq libraries were then generated form the treated cells and profiled by 10x scRNA-seq.

  Study EGAS00001005405

• SCLC

  The aim of this study is to use sequence data generated from 500 bp paired end reads using the Illumina platform to characterise the breakpoints of chromosomal translocations, amplifications and deletions at high resolution (to the bp).

  Study EGAS00001000009

• TEST_STUDY for submitter testing

  This is a test study for the purpose of submitters wishing to test the EGA submission procedure. All objects, such as DAC and datasets, linked to this study can also be considered to be for testing purposes.

  Study EGAS00001000889

• Y_chromosome_mis_segregation_in_the_DLD_1_cell_line

  Study of the well-established DLD-1 colorectal cancer cell line, engineered with an inactivatable Y centromere that leads to mis-segregation. The goal is to gain insights into the mechanism of chromothripsis due to chromosome mis-segration.

  Study EGAS00001002551

• Machine Learning Signal Enrichment for Ultrasensitive Plasma Tumor Burden Monitoring

  In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole genome sequencing (WGS). We now introduce MRD-EDGE, a composite machine learning-guided WGS ctDNA single nucleotide variant (SNV) and copy number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGESNV uses deep learning and a ctDNA-specific feature space to increase SNV signal-to-noise enrichment in WGS by 300X compared to previous WGS error suppression. MRD-EDGECNV also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1 Gb to 200 Mb, thereby expanding its applicability to a wider range of solid tumors. We harness the improved performance to identify MRD following surgery in multiple cancer types, track changes in tumor burden in response to neoadjuvant immunotherapy in non-small cell lung cancer (NSCLC) and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal-to-noise enrichment in MRD-EDGESNV enables de novo mutation calling in melanoma and small cell lung cancer (SCLC) without matched tumor, yielding clinically informative TF monitoring for patients on immune checkpoint inhibition (ICI).

  Study EGAS00001007451

• Machine learning guided signal enrichment for ultrasensitive plasma tumor burden monitoring

  In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole genome sequencing (WGS). We now introduce MRD-EDGE, a composite machine learning-guided WGS ctDNA single nucleotide variant (SNV) and copy number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGESNV uses deep learning and a ctDNA-specific feature space to increase SNV signal-to-noise enrichment in WGS by 300X compared to previous WGS error suppression. MRD-EDGECNV also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1 Gb to 200 Mb, thereby expanding its applicability to a wider range of solid tumors. We harness the improved performance to identify MRD following surgery in multiple cancer types, track changes in tumor burden in response to neoadjuvant immunotherapy in non-small cell lung cancer (NSCLC) and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal-to-noise enrichment in MRD-EDGESNV enables de novo mutation calling in melanoma and small cell lung cancer (SCLC) without matched tumor, yielding clinically informative TF monitoring for patients on immune checkpoint inhibition (ICI).

  Study EGAS00001007545