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• Clinical Proteomic Tumor Analysis Consortium (CPTAC) Proteogenomic Confirmatory Study of Breast, Colon, Lung, and Ovarian Tumors

  Recently, significant progress has been made in characterizing and sequencing the genomic alterations in statistically robust numbers of samples from several types of cancer. For example, The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and other similar efforts are identifying genomic alterations associated with specific cancers (e.g., copy number aberrations, rearrangements, point mutations, epigenomic changes, etc.). The availability of these multi-dimensional data to the scientific community sets the stage for the development of new molecularly targeted cancer interventions. Understanding the comprehensive functional changes in cancer proteomes arising from genomic alterations and other factors is the next logical step in the development of high-value candidate protein biomarkers. Hence, proteomics can greatly advance the understanding of molecular mechanisms of disease pathology via the analysis of changes in protein expression, their modifications and variations, as well as protein-protein interaction, signaling pathways and networks responsible for cellular functions such as apoptosis and oncogenesis. Realizing this great potential, the NCI launched the second phase of the CPTC initiative in September 2011. Renamed the Clinical Proteomic Tumor Analysis Consortium, CPTAC is beginning to leverage its analytical outputs from Phase I to define cancer proteomes on genomically-characterized biospecimens. The purpose of this integrative approach is to provide the broad scientific community with knowledge that links genotype to proteotype and ultimately phenotype. The data contained in this dataset are derived from samples designed to confirm CPTAC findings from the TCGA samples. These confirmatory samples contain breast, ovarian, colon, and lung tumors collected via a protocol optimized for proteomics. Specifically, ischemic time of the sample was controlled and restricted to less than 30 minutes. ACGT, Inc. produced whole exome, mRNAseq, and miRNAseq for these samples. Corresponding proteomic data are available at: https://cptac-data-portal.georgetown.edu/cptacPublic/ The study design was to profile colon, breast, ovarian, and lung tumors both genomically and proteomically. Germline DNA was obtained from blood. Normal control samples for proteomics varied by organ site: adjacent colon tissue for colon cases, contralateral breast tissue for some breast cases, and Fallopian tube fimbria for some ovarian cases. Lung cases had no normal control for proteomic analysis. All cancer samples were derived from primary and untreated tumors.

  Study phs000892

• A body map of somatic mutagenesis in morphologically normal human tissues

  This dataset contained raw sequencing fastq data of the article "A body map of somatic mutagenesis in morphologically normal human tissues". We sampled morphologically normal tissue biopsies from 5 donors. We performed low-depth WGS on 1,764 samples, high-depth WGS on 48 samples and WES on 1,772 samples.

  Dataset EGAD00001007859

• Plasma MicroRNA Signatures of Aging

  Background. MicroRNAs are small non-coding RNAs that regulate gene expression post-transcriptionally and show differential expression in various tissues with aging phenotypes. Detectable in circulation, extracellular microRNAs reflect (patho)physiological processes and hold promise as biomarkers for healthy aging and age-related diseases. This study aimed to explore plasma extracellular microRNAs as biological aging indicator and their associations with health outcomes using population-level data. Methods. We quantified plasma expression levels of 2,083 extracellular microRNAs using targeted RNA-sequencing in 2,684 participants from the population-based Rotterdam Study cohort. The training and test sets included 1,930 participants from the advanced-aged initial and second subcohort (RS-I/RS-II; median age: 70.6), while the validation set comprised 754 participants from the middle-aged fourth subcohort (RS-IV; median age: 53.5). Based on 591 microRNAs well-expressed in plasma, we examined differential expression of microRNAs with chronological age, PhenoAge –a composite score of age and nine multi-system blood biomarkers–, the frailty index, and mortality. Next, elastic net models were employed to construct composite microRNA-based aging biomarkers predicting chronological age (mirAge), PhenoAge (mirPA), frailty index (mirFI), and mortality (mirMort). The association of these aging biomarkers with different age-related health outcomes was assessed using Cox Proportional Hazard, linear regression, and logistic regression models in the test and validation sets. Results. We identified 188 microRNAs differentially expressed with chronological age within the RS-I/RS-II advanced-aged population (ntraining=1158, ntest=772), of which 177 microRNAs (94.1%) were replicated in the middle-aged RS-IV subcohort (nvalidation=754). Moreover, 227 miRNAs showed robust associations with PhenoAge, 61 with FI, and 16 with ten-year mortality independent of chronological age. Subsequently, we constructed four plasma microRNA-based aging biomarkers: mirAge with 108, mirPA with 153, mirFI with 81, and mirMort with 50 miRNAs. Elevated scores on these microRNA-based aging biomarkers were associated with unfavorable health outcomes, including lower subjective physical functioning and self-reported health and increased mortality and frailty risk, but not with first- or multi-morbidity. Overall, larger effect estimates were observed for mirPA, mirFI, and mirMort compared to mirAge. Conclusions. This study describes distinct plasma microRNA-aging signatures and introduces four microRNA-based aging biomarkers with potential to identify accelerated aging and age-related decline, providing insights into the intricate process of human aging.

  Study EGAS00001008117

• Proteogenomic Analysis of CALGB40601 (ALLIANCE) a Neoadjuvant Phase III HER2-Positive Breast Cancer Trial

  80 frozen core needle biopsies from 54 HER2+ breast cancer patients from the CALGB 40601 trial (NCT00770809) were processed for proteogenomic analysis to identify genes, proteins, and phosphosites associated with pathological complete response to neodjuvant anti-HER2 therapies. The final set included 22 patients treated with trastuzumab plus lapatinib in combination with pacletaxel (THL arm), 24 patients treated with trastuzumab in combination with pacletaxel (TH arm), and 8 patients treated lapatinib in combination with pacletaxel (TL arm). Principal findings of this study included identifying negative cases misdiagnosed as HER2+ that did not respond to targeted therapy and observing higher levels of two biomarker candidates in non-pCR tumors. Mass spec-based proteomics and phosphoproteomics data generated from 75 biopsies (all 54 patients) will be made available via the Proteomic Data Commons. Whole exome- (67 biopsies from 49 patients) and RNA-sequencing (52 biopsies from 39 patients) data for the subset profiled with proteogenomics will be made available through dbGaP, but genomic and transcriptomic profiling data for the whole cohort has previously been deposited (phs001570).

  Study phs003576

• Timing and trajectory of BCR-ABL1 driven chronic myeloid leukaemia

  Mutation of some genes provide fitness advantages that drive uncontrolled cell proliferation, leading to clinical manifestations of cancer. The discovery of the Philadelphia chromosome in chronic myeloid leukaemia (CML) provided the very first such genetic link to cancer and the hypothesis that this lesion provided an abnormal growth advantage to cells. However, little is known about the trajectory to CML, the rate of BCR::ABL1 driven clonal expansion, and how this impacts on disease presentation. Here, using whole-genome sequencing of 1013 single-cell-derived colonies from patients with CML, we reconstruct phylogenetic trees of haematopoiesis in 9 patients aged 22 to 81 years at diagnosis. The expected intronic breaks in BCR and ABL1 were not always observed, and out-of-frame exonic breakpoints in BCR, requiring exon skipping to derive BCR::ABL1 fusion transcripts, were noted in several patients. Apart from ASXL1 and RUNX1 mutations, additional somatic mutations in myeloid genes were generally present only in wild-type cells and did not contribute to the CML clone. Using phylogenetic analysis, we infer sustained explosive growth attributed to BCR::ABL1 fusion, with clonal expansions beginning 3-14 years (Cl 2-16 years) before diagnosis and annual growth rates exceeding 70,000% per year in some young patients with the most recent and rapid clonal expansions. Mutation rate was higher in BCR::ABL1-positive cells than in wild-type counterparts, together with markedly shorter telomere lengths, reflecting their excessive cell division history. Clonal expansion rates inferred from phylogenetic trees were in line with mutational signature patterns and telomere attrition, and inversely correlated with the time to diagnosis. Whole genome sequencing of advanced phase CML was characterised by genomic evolution within BCR::ABL1-positive cells. Our findings highlight the oncogenic potency of a single BCR::ABL1 fusion and its capacity to drive fast clonal outgrowth to cancer. These data contrast with the early-in-life, slow, and sequential clonal trajectories typical of most cancers, such as Philadelphia-negative myeloproliferative neoplasms, solid organ malignancies, and patterns of clonal expansion in healthy tissue ageing.

  Dataset EGAD00001015473

• Whole exome sequencing data from non-small-cell lung cancer patients receiving immunotherapy prognosis

  The plasma samples and white blood cell samples were collected from 30 non-small-cell lung cancer patients and 3 healthy individuals. The solid tumor biopsy samples from 14 patients (a subset of the 30 patients) were collected. The cfDNA was extracted from their plasma samples using the QIAamp circulating nucleic acid kit from QIAGEN (Germantown, MD). The cfDNA WES library was constructed with the SureSelect XT HS kit from Agilent Technologies (Santa Clara, CA) according to the manufacturer’s protocol. In brief, 10ng of cfDNA was used as input material. After end repair/dA-tailing of cfDNA, the adaptor was ligated. The ligation product was purified with Ampure XP beads (Beckman-Coulter, Atlanta, GA) and the adaptor-ligated library was amplified with index primer in 10-cycle PCR. The amplified library was purified again with Ampure XP beads, and the amount of amplified DNA was measured using the Qubit 1xdsDNA HS assay kit (ThermoFisher, Waltham, MA). 700-1000 ng of DNA sample was hybridized to the Agilent SureSelect Human All Exon V6 (Agilent) capture library and pulled down by streptavidin-coated beads. After washing the beads, the DNA library captured on the beads was re-amplified with 10-cycle PCR. The final libraries were purified by Ampure XP beads. The library concentration was measured by Qubit, and the quality was further examined with Agilent Bioanalyzer before the final step of 2x150bp paired-end sequencing on the Illumina HiSeq X10 platform (Illumina) at an average coverage of 200. Whole-exome capture libraries of genomic DNA of the 30 non-small cell lung cancer patients were constructed via Roche SeqCap EZ Exome V3 (Roche); whole-exome capture libraries of genomic DNA of the 3 healthy individuals were constructed via Agilent SureSelect Human All Exon V6 (Agilent). Enriched exome libraries were sequenced on the Illumina HiSeq 3000 platform (Illumina) to generate 2x100bp paired-end reads at an average coverage of 200.

  Dataset EGAD00001006096

• Deep whole genome ctDNA chronology of treatment-resistant prostate cancer

  Circulating tumor DNA (ctDNA) in blood plasma is an emerging tool for clinical cancer genotyping and longitudinal disease monitoring. We performed deep whole-genome sequencing of serial plasma and synchronous metastases in patients with aggressive prostate cancer. We comprehensively assess all classes of genomic alterations and demonstrate that ctDNA harbors multiple dominant populations whose evolutionary histories frequently indicate whole-genome doubling and shifts in mutational processes. Although tissue and ctDNA showed concordant clonally-expanded cancer driver alterations, each individual metastasis contributed only a minor share of total ctDNA. By comparing serial ctDNA before and after clinical progression on potent androgen receptor (AR) pathway inhibitors, we reveal population restructuring converging solely on AR augmentation as the dominant genomic driver of acquired treatment-resistance. Finally we leverage nucleosome footprints in ctDNA to infer mRNA abundance in synchronously biopsied metastases, including treatment-induced changes in AR pathway transcriptional activity.

  Dataset EGAD00001008460

• AML Proteogenomic Landscape Whole-transcriptome RNA-Sequencing

  Whole transcriptome RNA-sequencing of purified bone marrow blasts of 136 de novo, treatment naive AML patients. For further details, we refer to the manuscript "The Proteogenomic Landscape of AML" by Jayavelu, Wolf, Buettner et al. mRNA extraction and whole transcriptome sequencing For transcriptome analysis the TruSeq Total Stranded RNA kit was used, starting with 250ng of total RNA, to generate RNA libraries following the manufacturer’s recommendations (Illumina, San Diego, CA, USA). 100bp paired-end reads were sequenced on the NovaSeq 6000 (Illumina) with a median of 57 mio. reads per sample. RNA Data Analysis Data quality control was performed with FastQC v0.11.9. Reads were aligned to the human reference genome (Ensembl GRCh38 release 82) using STAR v2.6.1. Gene count tables were generated while mapping, using Gencode v31 annotations. All downstream analyses were carried out using R v4.0 and BioConductor v3.12 (Huber et al., 2015; R Core Team, 2020). Size-factor based normalization was performed using DESeq2 v1.28.1(Love et al., 2014).

  Dataset EGAD00001008484

• The cellular immune and airway epithelial profile throughout childhood and in response to COVID-19 at multi-omic single cell level.

  Single-cell data gene expression data set (5’Chromium 10X) of healthy paediatric volunteers, and paediatric and adult COVID-19 patients. Gene expression was determined from samples of nasal, tracheal and bronchial brushings and blood (PBMCs). In addition to gene expression, PBMC’s were assayed by CITE-seq. A subset of samples have VDJ sequencing data for T cell receptors (TCR) and B cell receptors (BCR).

  Dataset EGAD00001007718

• Clonal dynamics after allogeneic haematopoietic cell transplantation using genome-wide somatic mutations - TGS

  Allogeneic haematopoietic cell transplantation (HCT) replaces the stem cells responsible for blood production with those harvested from a donor, and is received by 40,000 patients worldwide each year. To quantify dynamics of long-term stem cell engraftment, we sequenced whole genomes of 2,824 single-cell-derived haematopoietic colonies from blood samples of 10 donor-recipient pairs taken 9-31 years after HLA-matched sibling HCT. With younger donors, 10,000-50,000 stem cells had engrafted and were still contributing to haematopoiesis at time of sampling, but estimates were 10-fold lower with older donors. Engrafted stem cells made multilineage contributions to myeloid, B-lymphoid and T-lymphoid populations, although individual clones often showed biases towards one or other mature cell type. Recipients had lower clonal diversity than matched donors, equivalent to ~10-15 years of additional ageing, arising from up to 25-fold greater expansion of stem cell clones. An HCT-related population bottleneck alone could not explain these differences: instead, phylogenetic trees evinced two distinct modes of HCT-specific selection. In 'pruning selection', cell divisions underpinning recipient-enriched clonal expansions had occurred in the donor, preceding transplant - their selective advantage derived from preferential mobilisation, harvest, survival ex vivo or initial homing. In 'growth selection', cell divisions underpinning clonal expansion occurred through proliferative advantage in the recipient's marrow after homing - clones with multiple driver mutations especially demonstrated this pattern. Uprooting stem cells from their native environment and transplanting them to foreign soil exaggerates selective pressures, distorting and accelerating the loss of clonal diversity compared to the unperturbed haematopoiesis of donors.

  Dataset EGAD00001010874