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• Female Infertility: Primary Ovarian Insufficiency

  Premature ovarian failure, or primary ovarian insufficiency (POI) is a phenotype of diminished or absent ovarian function occurring in 1-2% of reproductive aged women. Most cases occur spontaneously. Evaluation of the gametes in women with POI is difficult and invasive. Practitioners must often rely on indirect biomarkers of ovarian function and oocyte health, making it difficult to identify patients who may benefit from therapies allowing them to achieve pregnancy utilizing their own oocytes. This study will generate exome sequences from POI patients in an effort to elucidate the causes of unexplained POI and to better understand the normal processes of ovarian aging. A better understanding of the genetics of ovarian function may lead to new non-invasive tools for managing women's reproductive health, and direct better use of existing biomarkers in diagnosis, screening and predicting clinical outcomes.

  Study phs001174

• High volume culture initiating in vitro evolution in neuroblastoma cell lines

  Despite the advent of advanced molecular prognostic tools, it is still difficult to predict the course of disease for cancer patients at the individual level. This lack of predictability is also reflected in many experimental cancer model systems, begging the question of whether certain biological aspects of cancer (eg. growth, evolution etc.) can ever be anticipated or if there remains an inherent unpredictability to cancer, similar to other complex biological systems. We demonstrate by a combination of agent-based mathematical modelling, analysis of patient-derived xenograft systems from multiple cancer types, and in-vitro culture that certain conditions may invoke chaotic fluctuations in the clonal landscape of cancer growth. Our findings indicate that under those conditions, the cancer genome may behave as a complex dynamic system, making its long-term evolution inherently unpredictable.

  Study EGAS00001007962

• Clonally heritable gene expression imparts a layer of diversity within cell types

  Cell types can be classified according to shared patterns of transcription. Non-genetic variability among individual cells of the same type has been ascribed to stochastic transcriptional bursting and transient cell states. Using high coverage single cell RNA profiling, we asked whether long-term, heritable differences in gene expression can impart diversity within cells of the same type. Studying clonal human lymphocytes and mouse brain cells, we uncovered a vast diversity of heritable gene expression patterns among different clones of cells of the same type in vivo. We combined chromatin accessibility and RNA profiling on different lymphocyte clones to reveal thousands of regulatory regions exhibiting interclonal variation which could be directly linked to interclonal variation in gene expression. Our findings identify a source of cellular diversity, which may have important implications for how cellular populations are shaped by selective processes in development, aging and disease.

  Study EGAS50000000161

• Analysis of IDHwt-glioblastoma samples from paired primary and recurrent tumor samples

  IDHwt-glioblastomas rapidly recur after initial treatment. To study genetic evolution in glioblastoma, we analyzed whole genomes of 21 pairs of primary and recurrent tumor samples using next generation sequencing. In addition we sequenced RNA from 16 tumor pairs and a panel of 50 glioma-associated genes in tumor pairs from 43 patients (including 14 of the 21 patients included in the WGS discovery set). Glioblastoma subtypes were determined using 450k/EPIC methylation arrays. This study was supported within the e:med program of the German Ministry of Education and Research (BMBF) by the collaborative research project ‘SYS-GLIO - Systems-based prediction of the biological and clinical behavior of gliomas’ (https://www.sys-med.de/de/demonstratoren/sys-glio/) This study is part of the Heidelberg Center for Personalized Oncology - HIPO-043

  Study EGAS00001003184

• The endometrial transcription landscape of MRKH syndrome

  The Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome (OMIM 277000) is characterized by agenesis of the uterus and upper part of the vagina in females with normal ovarian function. While genetic causes have been identified for a small subset of patients and epigenetic mechanisms presumably contribute to the pathogenic unfolding, too, the etiology of the syndrome has remained largely enigmatic. A comprehensive understanding of gene activity in the context of the disease is crucial to identify etiological components and their potential interplay. So far, this understanding is lacking, primarily due to the scarcity of samples and suitable tissue. In order to close this gap, we profiled endometrial tissue of uterus rudiments in a large cohort of MRKH patients using RNA-seq and thereby provide a genome-wide view on the altered transcription landscape of the MRKH syndrome.

  Study EGAS00001004601

• Forty-Five patient-derived xenografts capture the clinical and biological heterogeneity of Wilms tumor

  The lack of model systems has limited the preclinical testing of novel therapies for Wilms tumor (WT) patients who have poor outcomes. Herein, we established 45 heterotopic WT patient-derived xenografts (WTPDX) in CB17 scid-/- mice that capture the biological heterogeneity of Wilms tumor (WT). These WTPDX include 6 from patients with diffuse anaplasia, 9 from patients who later experienced disease relapse, and 13 from patients with bilateral disease. Early passage WTPDX showed enrichment of blastemal gene expression. Favorable histology WTPDX were chemosensitive, whereas unfavorable histology WTPDX were resistant to conventional chemotherapy with vincristine, actinomycin-D, and doxorubicin given singly or in combination. This WTPDX library is a unique scientific resource that retains the spectrum of biological heterogeneity present in WT and provides an essential tool to test novel targeted therapies in the era of precision medicine.

  Study EGAS00001003361

• Genetic origins of the Kiritimati population from central-eastern Micronesia

  The migration of Austronesian-speaking populations through Oceania has intrigued researchers for decades. The Kiribati islands, situated along the boundaries of Micronesia and Polynesia, provide a crucial link in this migration. We analyzed the genome-wide data of the Kiritimati population of Kiribati to uncover their genetic origins and connections with other Oceanian groups. Our study reveals that the Kiritimati population primarily exhibits Remote Oceanian-related ancestry associated with ancient Lapita and present-day Polynesian populations. In addition, our identity-by-descent analysis identifies populations from Philippines as their closest relatives in Island Southeast Asia. The genetic links between Kiritimati, ancient Lapita, and modern Polynesians underscore the shared ancestry and continuous gene flow across these regions. This genetic continuity and ongoing links are supported by linguistic and cultural evidence, illustrating a complex history of migration and admixture in Oceania.

  Study EGAS00001008060

• Comprehensive molecular phenotyping of ARID1A-deficient gastric cancer reveals pervasive epigenomic reprogramming and therapeutic opportunities

  This dataset includes paired tumor-blood whole exome sequencing data for 209 gastric cancer (GC) patients, along with whole transcriptome sequencing data for 125 GC samples. Whole exome sequencing was conducted using Agilent SureSelect Human All Exon V6 kits. For RNA sequencing, total RNA was isolated using the RNeasy Mini Kit, and libraries were prepared with the TruSeq Stranded Total RNA with Ribo-Zero Gold protocol (Illumina). Aligned BAM files for both exome and RNAseq data are included in this dataset.

  Dataset EGAD50000000660

• SGCC GASCAD-II dataset

  The GASCAD-II dataset from the Singapore Gastric Cancer Consortium includes paired tumor-blood whole exome sequencing data for 209 gastric cancer (GC) patients, along with whole transcriptome sequencing data for 125 GC samples. Whole exome sequencing was conducted using Agilent SureSelect Human All Exon V6 kits. For RNA sequencing, total RNA was isolated using the RNeasy Mini Kit, and libraries were prepared with the TruSeq Stranded Total RNA with Ribo-Zero Gold protocol (Illumina). Aligned BAM files for both exome and RNAseq data are included in this dataset

  Dataset EGAD00001015433

• Combined Tumor and Immune Signals From Genomes or Transcriptomes Predict Outcomes of Checkpoint Inhibition in Melanoma

  Cancer immunotherapy with checkpoint blockade (CPB) leads to improved outcomes in melanoma and other tumor types, but a majority of patients do not respond. High tumor mutation burden (TMB) and high levels of tumor-infiltrating T cells have been associated with response to immunotherapy, and integrative models using DNA and RNA have improved predictions of clinical benefit. However, identification of immune and tumor-intrinsic features in models using DNA or RNA alone have not been comprehensively explored in melanoma. In this study, we sequenced DNA and RNA of tumors from 67 melanoma patients receiving CPB, and aggregated this cohort with previously published data, yielding whole exome sequencing (WES) data for 189 patients and bulk RNA sequencing data for 178 patients. Using these datasets, we derived genomic and transcriptomic factors that predict overall survival (OS) and response to immunotherapy. Using whole exome DNA data alone, we calculated T cell burden (TCB) and B cell burden (BCB) based on rearranged TCR/Ig DNA sequences, and found that melanoma patients with high TMB and either high TCB or high BCB survived longer and had higher response rates, as compared to patients with low TMB and either low TCB or low BCB. Next, using bulk RNA-Seq data, differential expression analysis identified 83 genes which are associated with high or low OS. By combining pairs of immune-expressed genes with tumor-expressed genes, we identified three gene pairs which are associated with response and survival (Bonferroni P<0.05). All three gene pair models were validated in an independent cohort (n=180) (Bonferroni P<0.05). The best performing gene pair model included the lymphocyte-expressed MAP4K1 (Gene ID: 11184) and the transcription factor TBX3 (Gene ID: 6926) which is overexpressed in poorly differentiated melanomas. We concluded that RNA-based (MAP4K1 and TBX3) or DNA-based (TCB and TMB) models combining with immune and tumor measures improve predictions of outcome after checkpoint blockade in melanoma.

  Study phs002683