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• National Institutes of Health H3Africa African Collaborative Center for Microbiome and Genomics Research (ACCME)

  The African Collaborative Center for Microbiome and Genomics Research is a multi-institutional collaborative research project. The objectives of the project are to collaborate and implement high impact integrative epidemiology and genomics research into discovery of biomarkers associated with cervical carcinogenesis. Specifically ACCME links and leverages existing funded research and program activities at the collaborating institutions to study the interaction between vaginal microbiome, host genetic factors and molecular variants of Human Papilloma Virus (HPV) to determine correlates of viral persistence in the causal pathway of cervical cancer, a major cause of preventable mortality in Nigeria and other parts of sub-Saharan Africa. This research offers several opportunities to advance understanding of cervical carcinogenesis, viral oncogenesis, new biomarker discovery and risk stratification by genotype in a cohort of African women. Sponsorship: This project is sponsored by the National Institutes of Health (NIH)

  Study phs001945

• This study explored and validated the clinical application of targeted NGS of circulating tumor DNA in identifying tumor-specific mutations and uncovering clinical actionable targets in a variety of solid tumors in large patient cohorts.

  Cancer is a disease of genetic alterations. Comprehensive genetic diagnosis is needed to match each patient to appropriate cancer therapy. However, acquisition of representative tumor samples is invasive and often infeasible. Circulating tumor DNA (ctDNA) is a promising non-invasive biomarker for cancer mutational profiling. Here we implemented targeted next generation sequencing (NGS) with a customized pan-cancer gene panel on 605 clinical ctDNA samples in multiple cancer types. Overall, tumor-specific mutations were identified in 87% of ctDNA samples, with mutation spectra highly concordant with their matched tumor tissues. 71% of patients have at least one clinical actionable mutation with 76% of which have suggested drugs approved or in clinical trials. In particular, our study reveals a unique mutation spectrum in Chinese lung cancer patients, which could be used to guide treatment decision and monitor the appearance of drug-resistant mutations. Taken together, our study demonstrated the clinical utility of target NGS-based ctDNA mutational profiling to guide cancer treatment decision.

  Study EGAS00001002251

• DAC for Cornell-NCI DLBCL NGS Genomic Project

  Genome-wide sequencing experiments were conducted on 103 DLBCL biopsy samples, of which the datasets (called Cornell-NCI DLBCL cohort) are available for authorized scientists. The Center of Technology Licensing at Cornell University is in charge of this data access policy and approval. Particularly, Cornell University will email a DAA to each requestor to have their institution sign before access is granted

  Dac EGAC50000000704

• Orphan_Tumour_Study_NB

  The aim of this study is to investigate the transcriptional landscape of human cancer. 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/

  Study EGAS00001003445

• Exome sequencing of DNA from pituitary neuroendocrine tumor (PitNET) and germline DNA from the same patient

  The goal of the project is to study tumor - normal DNA pairs from the PitNET patients. Total of 15 PitNET patients were recruited for the study to investigate sometic changes in the tumor compared to normal tissue. Illumina technology were used for sequencing and two data analysis pipelines employed for data analysis. Recurrent somatic variations were investigated and genomic disruption analysis from exome sequencing data attempted.

  Study EGAS00001004654

• Demographic History and Local Adaptation in Asian Population

  Due to harboring high genetic and cultural diversity of populations, Central Asia is one key area for understanding the recent evolutionary history of humans. In this project, our aims include: (1) to resequence genome sequences with high sequencing depth for Central Asian populations Balti; (2) to discern the genomic diversity, populations structure and demographic history of Balti; (3) to investigate the genetic mechanisms for local adaptations in Balti.

  Study JGAS000238

• SARS‐CoV‐2 receptor ACE2 and TMPRSS2 are primarily expressed in bronchial transient secretory cells

  The SARS‐CoV‐2 pandemic affecting the human respiratory system severely challenges public health and urgently demands for increasing our understanding of COVID‐19 pathogenesis, especially host factors facilitating virus infection and replication. SARS‐CoV‐2 was reported to enter cells via binding to ACE2, followed by its priming by TMPRSS2. Here, we investigate ACE2 and TMPRSS2 expression levels and their distribution across cell types in lung tissue (twelve donors, 39,778 cells) and in cells derived from subsegmental bronchial branches (four donors, 17,521 cells) by single nuclei and single cell RNA sequencing, respectively. While TMPRSS2 is strongly expressed in both tissues, in the subsegmental bronchial branches ACE2 is predominantly expressed in a transient secretory cell type. Interestingly, these transiently differentiating cells show an enrichment for pathways related to RHO GTPase function and viral processes suggesting increased vulnerability for SARS‐CoV‐2 infection. Our data provide a rich resource for future investigations of COVID‐19 infection and pathogenesis.

  Study EGAS00001004419

• Genomic characterization of retinoblastoma (targeted sequencing)

  Retinoblastoma is the most frequent intraocular malignancy in children, originating from a maturing cone precursor in the developing retina. Little is known on the molecular basis underlying the biological and clinical behavior of this cancer. Here, using multi-omics data, we demonstrate the existence of two retinoblastoma subtypes. Subtype 1, of earlier onset, includes most of the heritable forms. It harbors few genetic alterations other than the initiating RB1 inactivation and corresponds to differentiated tumors expressing mature cone markers. By contrast, subtype 2 tumors harbor frequent recurrent genetic alterations including MYCN-amplification. They express markers of less differentiated cone together with neuronal/ganglion cell markers with marked inter- and intra-tumor heterogeneity. The cone dedifferentiation in subtype 2 is associated with stemness features including low immune and interferon response, E2F and MYC/MYCN activation and a higher propensity for metastasis. The recognition of these two subtypes, one maintaining a cone-differentiated state, and the other, more aggressive, associated with cone dedifferentiation and expression of neuronal markers, opens up important biological and clinical perspectives for retinoblastomas. In this study, we performed targeted sequencing of the exonic regions of RB1, BCOR and ARID1A on 37 samples from the series of 102 retinoblastomas. Among them, 23 samples were not subjected to whole-exome sequencing, and 14 samples have the whole-exome sequencing data (deposited in EGAS00001005248).

  Study EGAS00001005550

• Testicular large B-cell lymphoma is genetically similar to PCNSL and distinct from nodal DLBCL

  Next generation sequencing (NGS) data analyzed in Rivas-Delgado, López, et al. HemaSphere 2024. Single nucleotide variants (SNVs) and short insertions /deletions (indels) were evaluated in 42 samples using a B-cell malignancy-oriented targeted panel covering 115 genes (SureSelectXT, Agilent Technologies and sequenced in a MiSeq instrument (Illumina; 2x150bp; mean coverage of 599x). The NGS data of 39 samples is provided here. The 3 additional samples were previously published. Please, see the original publication for additional details.

  Study EGAS50000000521

• Efficient prediction of a spatial transcriptomics profile better characterizes breast cancer tissue sections without costly experimentation

  Spatial transcriptomics is an emerging technology requiring costly reagents and considerable skills, limiting the identification of transcriptional markers related to histology. Here, we show that predicted spatial gene-expressions in unmeasured regions and tissues can enhance biologists��� histological interpretations. We developed the Deep learning model for Spatial gene Clusters and Expression, DeepSpaCE and confirmed its performance using the spatial-transcriptome profiles and immunohistochemistry images of consecutive human breast cancer tissue sections. For example, the predicted expression patterns of SPARC, an invasion marker, highlighted a small tumor-invasion region that is difficult to identify using raw data of spatial transcriptome alone because of a lack of measurements. We further developed semi-supervised DeepSpaCE using unlabeled histology images and increased the imputation accuracy of consecutive sections, enhancing applicability for a small sample size. Our method enables users to derive hidden histological characters via spatial transcriptome and gene annotations, leading to accelerated biological discoveries without additional experiments.

  Study JGAS000290