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• Transcriptome human nasal epithelium

  1. Odors are detected, firstly, by olfactory sensory neurons (OSNs) in the olfactory epithelium of the nose. This neurons then project directly to the olfactory bulb in the brain. Olfaction depends on cellular regeneration of the OE, olfactory bulb and hippocampus, and on their continual re-wiring. The olfactory neural pathway includes regions of the frontal, temporal and limbic brain, which in turn overlap with brain areas involved in brain disorders. OSNs are the only aspect of the human brain exposed to the external environment. This not only makes them vulnerable to environmental changes, but also accessible for biomedical studies. We have already sequenced and developed a protocol for analyzing the transcriptome of mouse main olfactory epithelium and single OSNs. We propose here to perform a similar study for samples from the human olfactory epithelium. We have developed a minimally invasive method for obtaining human OSNs, among other cells from the nasal epithelium. In this experiment, we have obtained cell samples from the olfactory epithelium, including OSN, from healthy volunteers. We would like to further characterize them by RNA sequencing. This will give us valuable insight into human olfaction. It will also provide a first step into a new avenue to study, and find biomarkers for, brain diseases though the analysis of these easily available neurons. 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/

  Dataset EGAD00001002226

• deep-learning-powered tissue deconvolution for cfDNA

  Plasma cell-free DNA (cfDNA) is a noninvasive biomarker for cell death of all organs. Deciphering the tissue origin of cfDNA can reveal abnormal cell death because of diseases, which has great clinical potential in disease detection and monitoring. Despite the great promise, the sensitive and accurate quantification of tissue-derived cfDNA remains challenging to existing methods due to the limited characterization of tissue methylation and the reliance on unsupervised methods. To fully exploit the clinical potential of tissue-derived cfDNA, here we present one of the largest comprehensive and high-resolution methylation atlas based on 521 noncancer tissue samples spanning 29 major types of human tissues. We systematically identified fragment-level tissue-specific methylation patterns and extensively validated them in orthogonal datasets. Based on the rich tissue methylation atlas, we develop the first supervised tissue deconvolution approach, a deep-learning-powered model, cfSort, for sensitive and accurate tissue deconvolution in cfDNA. On the benchmarking data, cfSort showed superior sensitivity and accuracy compared to the existing methods. We further demonstrated the clinical utilities of cfSort with two potential applications: aiding disease diagnosis and monitoring treatment side effects. The tissue-derived cfDNA fraction estimated from cfSort reflected the clinical outcomes of the patients. In summary, the tissue methylation atlas and cfSort enhanced the performance of tissue deconvolution in cfDNA, thus facilitating cfDNA-based disease detection and longitudinal treatment monitoring.

  Study EGAS00001007213

• ctDNA monitoring using patient-specific sequencing and integration of variant reads - Lung cohort

  Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Detection of ctDNA can be challenging in patients with low-volume or residual disease, where plasma contains very few tumor-derived DNA fragments. We show that sensitivity for ctDNA detection in plasma can be improved by analyzing hundreds to thousands of mutations that are first identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) pipeline, which combines custom error-suppression methods and signal-enrichment approaches based on biological features of ctDNA. With this approach, the detection limit in each sample can be estimated independently based on the number of informative reads sequenced across multiple patient-specific loci. We applied INVAR to custom hybrid-capture sequencing data from 176 plasma samples from 105 patients with melanoma, lung, renal, glioma, and breast cancer across both early and advanced disease. By integrating signal across a median of >105 informative reads, ctDNA was routinely quantified to 1 mutant molecule per 100,000, and in some cases with high tumor mutation burden and/or plasma input material, to individual parts per million. This resulted in median Area Under the Curve (AUC) values of 0.98 in advanced cancers, and 0.80 in early stage and challenging settings for ctDNA detection. We generalized this method to whole-exome and whole-genome sequencing, showing that the INVAR may be applied without requiring personalized sequencing panels, so long as a tumor mutation list is available. As tumor sequencing becomes increasingly performed, such methods for personalized cancer monitoring may enhance the sensitivity of cancer liquid biopsies.

  Study EGAS00001004447

• ctDNA monitoring using patient-specific sequencing and integration of variant reads - Breast cohort

  Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Detection of ctDNA can be challenging in patients with low-volume or residual disease, where plasma contains very few tumor-derived DNA fragments. We show that sensitivity for ctDNA detection in plasma can be improved by analyzing hundreds to thousands of mutations that are first identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) pipeline, which combines custom error-suppression methods and signal-enrichment approaches based on biological features of ctDNA. With this approach, the detection limit in each sample can be estimated independently based on the number of informative reads sequenced across multiple patient-specific loci. We applied INVAR to custom hybrid-capture sequencing data from 176 plasma samples from 105 patients with melanoma, lung, renal, glioma, and breast cancer across both early and advanced disease. By integrating signal across a median of >105 informative reads, ctDNA was routinely quantified to 1 mutant molecule per 100,000, and in some cases with high tumor mutation burden and/or plasma input material, to individual parts per million. This resulted in median Area Under the Curve (AUC) values of 0.98 in advanced cancers, and 0.80 in early stage and challenging settings for ctDNA detection. We generalized this method to whole-exome and whole-genome sequencing, showing that the INVAR may be applied without requiring personalized sequencing panels, so long as a tumor mutation list is available. As tumor sequencing becomes increasingly performed, such methods for personalized cancer monitoring may enhance the sensitivity of cancer liquid biopsies.

  Study EGAS00001004446

• Mechanisms of Therapy Driven Clonal Evolution in Oncogenic RAS Mutant Relapsed Acute Lymphoblastic Leukemia

  Although multi-agent combination chemotherapy is curative in a significant fraction of childhood acute lymphoblastic leukemia (ALL) patients, 20% of cases relapse and most die due to chemo-refractory disease. Here we used whole-exome and whole-genome sequencing to analyze the mutational landscape and pattern of clonal evolution at relapse in pediatric ALL cases. These analyses showed that ALL relapses originate from a common ancestral precursor clone of the diagnosis and relapsed populations and frequently harbor mutations implicated in chemotherapy resistance. RAS-MAPK pathway activating mutations in NRAS, KRAS and PTPN11 were present in 24/55 (44%) cases in our series. Notably, while some cases showed emergence of RAS mutant clones at relapse, in others, RAS mutant clones present at diagnosis were replaced by RAS wild type populations. Mechanistically, functional dissection of mouse and human wild type Kras and mutant Kras (Kras G12D) isogenic leukemia cells demonstrated induction of methotrexate resistance, but also improved response to vincristine, in mutant Kras- expressing lymphoblasts. These results identify chemotherapy driven selection as a central mechanism of leukemia clonal evolution and pave the road for the development of tailored personalized therapies for the treatment of relapsed ALL.

  Study phs001072

• Whole-Genome sequencing of hepatocellular carcinomas

  The French ICGC project on liver tumors is coordinated by Pr Jessica Zucman-Rossi and funded by Inca (French Institute for Cancer). The aim of the present project is to identify the catalog of somatic and germline mutations in liver tumors using whole genome and whole exome sequencing together with CGH-SNP, methylome and transcriptomic profiling. For this purpose, a series of 500 liver tumors are collected through the French National Liver Collection and these samples will be analyzed using the different omics technologies. The data will be deposited in the ICGC and EGA database to be publically available for the scientific community. Hepatocellular carcinoma (HCC) accounts for more than 90% of liver cancers, and is a major health problem. It is the 3rd cause of cancer-related mortality. Advances in genomic analyses have formed a comprehensive understanding of different underlying pathobiological layers resulting in hepatocarcinogenesis. Thus, the development of next-generation sequencing technologies has made it possible to generate more comprehensive catalogues of somatic alteration events (single nucleotide substitutions, structural variations, and epigenetic changes) in liver cancer genome than ever before. The dataset will include 50 whole genome sequencing tumor/germline pairs, of which 6 are deposited in February 2014.

  Study EGAS00001000706

• Deciphering molecular mechanisms underlying hematological malignancies and development of novel therapeutic approaches

  We aim to identify epigenomic features dysregulated in hematological malignancies through epigenetic analyses of hematological malignant cells. We also perform RNA sequencing to profile tumor cell-specific transcriptomes and target gene sequencing to profile somatic gene mutations in tumor cells. Regarding identified epigenomic abnormalities, we perform further analysis to determine whether they could be good candidates to translate into therapy development. To this end, we will modulate the activity of targets by using their activators and inhibitors in vitro and establish xenograft mouse models of hematological malignancies and manipulate their activity in vivo. Through these studies, we aim to develop new therapeutic agents or modalities that lead to improved patients��� prognosis.

  Study JGAS000603

• A Joint Linkage/Association Strategy to Interrogate AMD Genetic Susceptibility

  The cohort consists of a collection of large multigenerational families where minimally 4 members have advanced age related macular degeneration (AMD). Retinal photographs were used to determine AMD phenotype and genomic DNA extracted from whole blood was used for sequencing. Gender, age at phenotyping, smoking status, self-declared race and ethnicity, and pedigree relationships were collected. Analyses conducted were designed to elucidate the role of known AMD variants and polymorphisms in heritability within the familial cohort and to discover rare variants that may have large genetic effect on AMD in one or more families.

  Study phs001379

• mRNA-seq of paired Follicular Lymphoma (FL) patient samples before and after high-grade transformation to DLBCL (FL vs. tFL) from FFPE tissues

  The aim of this study was to analyze transcriptional changes in paired samples of follicular lymphoma (FL) and its histological transformation (tFL). Transformation of FL is an adverse event in the course of the disease and is associated with poor outcome. Several genetic aberrations have been linked to tFL pathogenesis, but gene expression changes and microenvironmental interactions have not been well characterized. We performed RNA profiling of FL and paired tFL samples transformed to DLBCL (n=11 pairs). For RNA profiling, we utilized libraries prepared using RNA isolated from archival FFPE tissues.

  Study EGAS50000000941

• miRNA-seq of paired Follicular Lymphoma (FL) patient samples before and after high-grade transformation to DLBCL (FL vs. tFL) from FFPE tissues

  The aim of this study was to analyze transcriptional changes in paired samples of follicular lymphoma (FL) and its histological transformation (tFL). Transformation of FL is an adverse event in the course of the disease and is associated with poor outcome. Several genetic aberrations have been linked to tFL pathogenesis, but gene expression changes and microenvironmental interactions have not been well characterized. We performed miRNA profiling of FL and paired tFL samples transformed to DLBCL (n=10 pairs). For miRNA profiling, we utilized libraries prepared using RNA isolated from archival FFPE tissues.

  Study EGAS50000000942