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ABSTRACT:
PROVIDER: PRJNA1041452 | ENA |
REPOSITORIES: ENA
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Project description:Background The identification and characterization of somatic cancer driver mutations in the noncoding genome remains challenging.
Objective To broadly characterize noncoding driver mutations for pancreatic ductal adenocarcinoma (PDAC).
Design Using mutation calls from whole-genome sequence (WGS) data in PDACs and genome-scale maps of accessible gene regulatory regions in normal- and tumor-derived pancreatic samples, we analyzed enrichment of noncoding mutations in gene regulatory regions relevant to normal- and tumor-derived pancreatic contexts. Functional follow up of potential driver mutations was performed using chromatin interaction analyses, massively parallel reporter assays (MPRA) and targeted analysis of selected noncoding somatic mutations.
Results We first created genome-scale maps of accessible chromatin regions (ACRs) and histone modification marks (HMMs) in pancreatic cell lines and purified pancreatic acinar and duct cells. Integration with whole-genome mutation calls from 506 PDACs revealed 314 ACRs/HMMs significantly enriched with 3,614 noncoding somatic mutations (NCSMs). Chromatin interaction analysis identified 416 potential target genes and MPRA revealed 178 NCSMs impacting reporter activity (19.45% of those tested). Targeted luciferase validation confirmed negative effects on gene regulatory activity for NCSMs near ZFP36L2 and CDKN2A. For the former, CRISPR interference (CRISPRi) identified ZFP36L2 as a target gene (16.0 - 24.0% reduced expression, P = 0.023-0.0047), and growth inhibition after overexpression of ZFP36L2 (4.1 - 14.1-fold reduction, P = 6.0x10-4 - 3.2x10-3) implicates a possible tumor suppressor function.
Conclusion Our integrative approach provides a catalog of potential noncoding driver mutations and nominates ZFP36L2 as a novel PDAC driver gene with a likely tumor suppressor function.
2025-07-23 | MSV000098624 | MassIVE
Project description:Immunotherapy has shown great therapeutic potential for cancers with high tumor mutational burden (TMB), but much less promise for cancers with low TMB. One primary approach for adoptive lymphocyte transfer-based immunotherapy is to target the somatic mutated peptide neoantigens and cancer testis (CT) antigens recognized by cytotoxic T cells. Here, we employed mass spectrometry (MS)-based proteogenomic large-scale profiling to identify potential immunogenic human leukocyte antigen (HLA) Class ǀ- associated peptides in both melanoma, a “hot tumor”, and EGFR mutant lung adenocarcinoma, a “cold tumor”. We uncovered 19 common driver oncogene-derived peptides and more than 1000 post-translationally modified peptides (PTM) representing 58 different PTMs. We constructed a CT antigen database with 286 antigens by compiling reputed CT antigen resources and “in-house” genomic data and used this to identify 45 CT antigen-derived peptides from the identified HLA peptidome. Using integrated next generation sequencing data, we discovered 12 neopeptides in EGFR mutant lung cancer cell lines. Finally, we report a novel approach for non-canonical peptide discovery, whereby we leveraged a deep learning-based de novo search and a high confidence annotated long noncoding RNA (LncRNA) database to identify 44 lncRNA-derived peptides. Findings of this study, for the first time, provide evidence for a large pool of actionable cancer antigen-derived peptides for use in mutant EGFR lung cancer immunotherapy.
2021-12-07 | PXD019774 | Pride
Project description:Understanding the molecular signatures of cancer is important to apply appropriate targeted therapies. Here we present the first large scale RNA sequencing study of lung adenocarcinoma demonstrating its power to identify somatic point mutations as well as transcriptional variants such as gene fusions, alternative splicing events and expression outliers. Our results reveal the genetic basis of 200 lung adenocarcinomas in Koreans including deep characterization of 87 surgical specimens by transcriptome sequencing. We identified driver somatic mutations in cancer genes including EGFR, KRAS, NRAS, BRAF, PIK3CA, MET and CTNNB1. New cancer genes, such as LMTK2, ARID1A, NOTCH2 and SMARCA4, were also suggested as candidates for novel drivers in lung adenocarcinoma. We found 45 fusion genes, 8 of which were chimeric tyrosine kinases involving ALK, RET, ROS1, FGFR2, AXL and PDGFRA. Of 17 recurrent alternative splicing events, we identified exon 14 skipping in the proto-oncogene MET as highly likely to be a cancer driver. The number of somatic mutations and expression outliers varied markedly between individual cancers and was strongly correlated with smoking history of cancer patients. In addition, we identified genomic blocks where genes were frequently up- or down-regulated together that could be explained by copy number alterations in the cancer tissue. We also found an association between lymph node metastasis and somatic mutations in TP53. Our findings broaden our understanding of lung adenocarcinoma and may also lead to new diagnostic and therapeutic approaches. * Raw data files were submitted to EBI-SRA under accession number ERP001058.
2012-09-06 | GSE40419 | GEO
Project description:Solid-pseudopapillary neoplasm of pancreas (SPN), ductal adenocarcinoma (PCA), neuroendocrine tumor (NET) and non-neoplastic pancreas. comparison with gene expression of tumors and non-tumors To investigate the specific microRNA expression of SPN compared to other types of pancreatic tumor, we analyzed large-scale microRNA expressioin analysis to identify the molecular signature that may affect SPN tumorigenesis with mRNA expression profiles. Differentially expressed microRNAs were analyzed on SPNs, PCAs, NETs and Non-neoplastic tissues.
2013-12-31 | E-GEOD-43796 | biostudies-arrayexpress
Project description:Solid-pseudopapillary neoplasm of pancreas(SPN), ductal adenocarcinoma(PCA), neuroendocrine tumor(NET) and non-neoplastic pancreas. To investigate the specific gene expression of SPN compared to other types of pancreatic tumor, we analyzed large-scale gene expressioin analysis to identify the molecular signature that may affect SPN tumorigenesis. Differentially expressed genes were analyzed on SPNs, PCAs, NETs and Non-neoplastic tissues.
2013-12-31 | GSE43795 | GEO
Project description:Large-scale Multi-omic Analysis of COVID-19 Severity
| PRJNA660067 | ENA
Project description:YAP1 oncogene is a context-specific driver for pancreatic ductal adenocarcinoma
| PRJNA559899 | ENA
Project description:Background: Pancreatic ductal adenocarcinoma (PDAC) is often diagnosed at advanced tumor stages with chemotherapy as the only treatment option. Large-scale gene expression studies have defined two major PDAC subtypes, a classical and basal-like, which differ in their response to chemotherapy. The transcriptional networks, which define the molecular PDAC subtypes, are regulated by epigenetic modifications. Given the reversible nature of the epigenome, we aim to determine if drug-induced epigenetic reprogramming of pancreatic cancer cells affects PDAC subtype identity and chemosensitivity.
2024-01-31 | GSE235027 | GEO
Project description:Expression profiling of 70 Pancreatic Ductal Adenocarcinoma (PDAC) samples was performed on Agilent 44K expression arrays for cancer gene discovery. Additionally, matched aCGH on Agilent 244K arrays was performed. These two datasets were integrated in order to identify driver mutations leading to pancreatic cancer. Promising candidates were interrogated further using functional studies. 68 tumor samples (48 xenografts, 20 cell lines). 2 color arrays hybridized against a common reference pool of RNA from 11 cancer cell lines
2011-12-15 | E-GEOD-26088 | biostudies-arrayexpress