7297 results for "canc*"

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• Genomic Changes in Breast Cancer Among Chinese Women in Hong Kong

  Recently, tumor sequencing and profiling analyses have improved molecular classification and refined existing breast cancer subtypes. However, epidemiologic data are absent in most somatic profiling studies even in extensively studied Western populations. The expansion of genomic studies to understudied populations with the integration of etiologic factors will allow the capture of a much wider range of genetic structure and mutagenic exposures associated with breast cancer risk. We therefore conducted a detailed molecular characterization of paired breast tumor/normal tissues using fresh frozen tissues collected from breast cancer cases in an ongoing breast cancer case-control study in Hong Kong. These analyses included whole genome (WGS)/exome sequencing (WES), targeted DNA panel sequencing, RNA sequencing (RNA-Seq), and genome-wide methylation profiling analyses.

  Study phs001870

• RNA Sequencing Analysis of Patient-Derived Xenograft Tissue PIM-084 Treated with L-NMMA+Alpelisib vs Vehicle Control

  This dataset contains RNA-seq analysis of vehicle control compared to L-NMMA+alpelisib treated metaplastic breast cancer PDX PIM084 tumors (RNA sequencing and analysis performed by Novogene). The submitted data contains the gene ID, gene name, normalized read counts from all samples from control group and combination treatment group, log2-fold change derived from DESeq2 R/EdgeR package, p-value, adjusted p-value, read counts from each group, and gene FPKM from the each sample in the group. Due to patient privacy, we are submitting only the processed and desensitized RNA sequencing analysis to this repository.

  Study phs003814

• Characterization of High-Grade Serous Ovarian Cancer Subtypes via Single-Cell and Spatial-Transcriptomics Profiling

  We measured gene expression in single-cell RNA sequencing samples from patients with high-grade serous ovarian cancer (HGSOC), for a study on improving HGSOC subtype definition by taking into account varying cell type proportions within tumors. The initial dataset (added October, 2020) contains 3 samples (3 individuals). The next dataset (added November, 2022) contains 34 samples from 8 individuals. The samples in the second dataset were sequenced in five different ways: 1) rRNA depletion and bulk RNA sequencing of tumor chunks, 2) rRNA depletion and bulk RNA sequencing of dissociated cells, 3) poly-A capture and bulk RNA sequencing of dissociated cells, 4) poly-A capture and scRNA sequencing of dissociated cells, 5) barcoding and pooling cells and scRNA sequencing (2 pools of 4 samples each). The third dataset (added June, 2024) contains data from one spatially resolved transcriptomics experiment (4 samples, one per Visium capture area, describing one tumor from one individual) and scRNA sequencing on 5 samples (5 individuals).

  Study phs002262

• UCSF Database for the Advancement of JMML - Integration of Metadata with Omic Data

  Juvenile myelomonocytic leukemia (JMML) is a rare and frequently fatal myeloproliferative/myelodysplastic disorder of early childhood with an estimated incidence of 1.2 cases per million. It is associated with a spectrum of diverse outcomes ranging from spontaneous resolution in rare patients to transformation to acute myeloid leukemia in others. The overwhelming majority of JMML patients (~95%) will harbor mutations in canonical Ras pathway genes, including NF1, NRAS, KRAS, PTPN11, and CBL. As Ras proteins are mutated in more than 30% of human cancers, the information gleaned from the study of JMML has provided insights into Ras signaling in cancer as well as a group of congenital diseases with tumor predispositions known as the “Rasopathies”. While oncogenic Ras is one of the most common mutations in human cancer, it remains one of the most vexing targets for efficacious therapy. To better understand this disease, a JMML tissue bank was established at UCSF that allows for consenting of subjects, biobanking of tissue and collection of follow up data from patients both locally and at other institutions. Through the collection and analysis of specimens, knowledge about the genetic, epigenetic, and biochemical basis of JMML has been gained. This integrated database will enable future JMML research, and importantly, additional Ras/MAPK pathway research by depositing linked, processed data to well annotated anonymized patients in an easily accessible online database

  Study phs002504

• Multi-Omics Study of Lung Cancer in Smokers From EAGLE

  EAGLE (Environment And Genetics in Lung cancer Etiology) is a population-based case-control study examining the full spectrum of lung cancer etiology from smoking initiation, smoking dependency to lung cancer development and outcome. It combines a traditional molecular epidemiology design with more integrative approaches by examining epidemiological, molecular, and clinical data together. To comprehensively investigate the effect of tobacco smoking behaviors on genomic or epigenomic alterations, we performed a multi-omics study including whole-genomic, transcriptomic, and epigenomic profiles in a total of 221 individuals from the EAGLE study.

  Study phs002992

• Integrating Circulating Tumor DNA Analysis and Radiomics for Dynamic Risk Assessment in Localized Lung Cancer

  DNA was isolated from pre-treatment and mid-treatment plasma samples and matched germline samples from patients undergoing chemoradiation therapy for stage IIB-IIIB non-small cell lung cancer. Sequencing libraries were hybrid captured using custom selector pools designed to target genes frequently mutated in non-small cell lung cancer prior to ultra-deep next generation sequencing.

  Study phs003947

• Somatic Mutations in 3,929 HPV-Positive Exfoliated Cervical Cell Samples

  We investigated the detection, timing, and HPV type specificity of cervical somatic hotspot mutations (i.e., previously reported cancer driver mutations) in 3,929 HPV-positive exfoliated cervical cell samples from individuals undergoing routine cervical screening in the U.S. using deep-targeted sequencing of 20 candidate genes. Our study included samples from 3,351 women with the following infection outcomes: 1,478 HPV16-, HPV18- and/or HPV45-positive controls (i.e., HPV infections that did not progress to precancer and/or subsequently cleared), 1,712 cervical precancers, and 161 invasive cervical cancers. We utilized residual exfoliated cervical cell samples from women routinely screened for cervical cancer precursors at Kaiser Permanente Northern California (KPNC) as part of the prospective KPNC-NCI HPV Persistence and Progression (PaP) cohort. We included 3,929 samples collected from these 3,351 women, including 974 serial sample from 396 women collected prior to their most recent screening visit to evaluate somatic hotspot mutation detection prior to diagnosis.

  Study phs003691

• 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

• Childhood Cancer Data Initiative (CCDI): Pediatric In Vivo Testing Program – Sarcomas and other Solid Tumors

  The primary goal of this project involves the comprehensive molecular profiling of patient-derived xenograft (PDX) mouse tumor models. Molecular profiling of each PDX model will include whole exome sequencing (WES), RNAseq, MethylEPIC arrays, CytoSNP array, and DNA fingerprint for quality control. Limited clinical demographic data (e.g. diagnosis, disease site, disease status) will be obtained. This data can be utilized to validate PDX models with matched patient tumors and can be used to guide model selection for downstream preclinical drug testing.

  Study phs003161

• Human Tumor Atlas Network (HTAN)

  An NCI-funded Cancer Moonshot initiative to construct 3-dimensional atlases of the dynamic cellular, morphological, and molecular features of human cancers as they evolve from precancerous lesions to advanced disease.

  Study phs002371