The Pan-Cancer Proteome Atlas, a mass spectrometry-based landscape for understanding tumor biology and discovering biomarkers and therapeutic targets
Ontology highlight
ABSTRACT:
INSTRUMENT(S):
ORGANISM(S): Homo Sapiens (human)
SUBMITTER: 
Thang Pham
LAB HEAD: Connie Jimenez
PROVIDER: PXD054790 | Pride | 2025-05-30
REPOSITORIES: Pride
Dataset's files
Items per page: 5 1 - 5 of 1242 |
Similar Datasets
Project description:There are no widely-accepted prognostic markers currently available to predict outcomes in patients with triple-negative breast cancer (TNBC), and no targeted therapies with confirmed benefit. We have used MALDI mass spectrometry imaging (MSI) of tryptic peptides to compare regions of cancer and benign tissue in 10 formalin-fixed, paraffin-embedded sections of TNBC tumors. Proteins were identified by reference to a peptide library constructed by LC-MALDI-MS/MS analyses of the same tissues. The prognostic significance of proteins that distinguished between cancer and benign regions was estimated by Kaplan-Meier analysis of their gene expression from public databases. Among peptides that distinguished between cancer and benign tissue in at least 3 tissues with a ROC area under the curve >0.7, 14 represented proteins identified from the reference library, including proteins not previously associated with breast cancer. Initial network analysis using the STRING database showed no obvious functional relationships except among collagen subunits COL1A1, COL1A2, and COL63A, but manual curation, including the addition of EGFR to the analysis, revealed a unique network connecting 10 of the 14 proteins. Kaplan-Meier survival analysis to examine the relationship between tumor expression of genes encoding the 14 proteins, and recurrence-free survival (RFS) in patients with basal-like TNBC showed that, compared to low expression, high expression of nine of the genes was associated with significantly worse RFS, most with hazard ratios >2. In contrast, in estrogen receptor-positive tumors, high expression of these genes showed only low, or no, association with worse RFS. These proteins are proposed as putative markers of RFS in TNBC, and some may also be considered as possible targets for future therapies.
2019-05-06 | PXD013397 | Pride
Project description:Artificial intelligence (AI) applications in biomedical settings face challenges such as data privacy and regulatory compliance. Federated Deep Learning (FDL) effectively addresses these issues. We developed ProCanFDL, where local models were trained on simulated sites using proteomic data drawn from a pan-cancer cohort (n = 1,260) and 29 other cohorts (n = 6,265), representing 4,956 patients and 19,930 mass spectrometry (MS) runs, all held behind private firewalls. Local parameter updates were aggregated to build the global model, achieving a 43% performance gain over local models on the hold-out test set (n = 625) in 14 cancer subtyping tasks. Additionally, ProCanFDL preserved data privacy while matching centralized model performance. External validation assessed generalization by retraining the global model with data from two external cohorts (n = 55) and eight (n = 832) using a different MS technology. ProCanFDL presents a solution for internationally collaborative machine learning initiatives using proteomic data while maintaining data privacy.
2025-05-29 | PXD056810 | Pride
Project description:Purpose: We generated extensive transcriptional and proteomic profiles from a Her2-driven mouse model of breast cancer that closely recapitulates human breast cancer. This report makes these data publicly available in raw and processed forms, as a resource to the community. Importantly, we previously made biospecimens from this same mouse model freely available through a sample repository, so researchers can obtain samples to test biological hypotheses without the need of breeding animals and collecting biospecimens. Experimental design: Twelve datasets are available, encompassing 841 LC-MS/MS experiments (plasma and tissues) and 255 microarray analyses of multiple tissues (thymus, spleen, liver, blood cells, and breast). Cases and controls were rigorously paired to avoid bias. Results: In total, 18,880 unique peptides were identified (PeptideProphet peptide error rate â¤1%), with 3884 and 1659 non-redundant protein groups identified in plasma and tissue datasets, respectively. Sixty-one of these protein groups overlapped between cancer plasma and cancer tissue. Conclusions and clinical relevance: These data are of use for advancing our understanding of cancer biology, for software and quality control tool development, investigations of analytical variation in MS/MS data, and selection of proteotypic peptides for MRM-MS. The availability of these datasets will contribute positively to clinical proteomics. Affymetrix GeneChip Mouse Genome 430 2.0 microarrays were used to profile whole tissues from 5 different tissue types of 25 tumor-bearing and 25 control mice of the Her2/Neu breast cancer mouse model. The 5 tissues tested were from breast, liver, spleen, blood cell, and thymus. The tumor-bearing mice were bitransgenic for MMTV-rtTA/TetO-NeuNT, and the control mice were transgenic for TetO-NeuNT only. The control mice were age- and cage-matched to the tumor-bearing mice. All samples were lysed and total RNA isolated and amplified prior to hybridization.
2010-10-22 | E-GEOD-20465 | biostudies-arrayexpress
Project description:Alterations in glycosylation are seen in many types of cancer, including colorectal cancer (CRC). CRC is known to display glycosylation alterations. Glycans, the sugar moieties of glycoconjugates, are involved in many important functions relevant to cancer, such as cell signaling and adhesion, and may can be of value as biomarkers. In this study, we have used mass spectrometry to analyze the N-glycan profiles of 35 CRC tissue samples from patients with tumors in the right or left colon and 10 healthy tissue samples from non-CRC patients who underwent operations for other reasons. The tumor samples were divided into groups depending on tumor location (right or left colon) and stage (II or III), while the healthy samples were divided into right or left side of the colon. The levels of neutral and acidic N-glycan compositions and glycan classes were analyzed in a total of ten different groups. Surprisingly, there were no significant differences in glycan levels when all right- and left-sided CRC samples were compared, and few differences (such as in the abundance of the neutral N-glycan H3N5) were seen when the samples were divided according to both location and stage. Multiple significant differences were found in the levels of glycans and glycan classes when stage II and III samples were compared, and these glycans could be of value as candidates for new markers of cancer progression. In order to validate our findings, we analyzed healthy tissue samples from the right and left colon and found no significant differences in the levels of any of the glycans analyzed, confirming that our findings when comparing CRC samples from the right and left colon are not due to normal variations in the levels of glycans between the healthy right and left colon. Additionally, the levels of the acidic glycans H4N3F1P1, H5N4F1P1, and S1H5N4F1 were found to change in a cancer-specific but colon location-nonspecific manner, indicating that CRC affects glycan levels in similar ways, regardless of tumor location.
2020-06-10 | PXD018673 | Pride
Project description:Molecular characterization of tumors has been critical for identifying important genes in cancer biology and for improving tumor classification and diagnosis. Long non-coding RNAs (lncRNAs), as a new, relatively unstudied class of transcripts, provide a rich opportunity to identify both functional drivers and cancer-type specific biomarkers. However, despite the potential importance of lncRNAs to the cancer field, no comprehensive survey of lncRNA expression across various cancers has been reported. We used 3'-End Sequencing for Expression Quantification (3SEQ) to quantify transcript abundance across 64 solid tumors representing 17 diagnostic subtypes of adenocarcinomas, squamous cell carcinomas, and sarcomas. We identified hundreds of transcripts from among the known 1,065 lncRNAs surveyed that show variability in transcript levels between the tumor types, and therefore, make potential biomarker candidates. We discovered 1,071 novel intergenic transcribed regions and demonstrate that these show similar patterns of variability between tumor types. We find that many of these differentially expressed cancer transcripts are also expressed in normal tissues. One such novel transcript specifically expressed in breast tissue was further evaluated using RNA in situ hybridization on a panel of breast tumors and shown to correlate with low tumor grade and estrogen receptor expression, thereby representing a potentially important new breast cancer biomarker. This study provides the first large survey of lncRNA expression within a panel of solid cancers and also identifies a number of novel transcribed regions differentially expressed across distinct cancer types that represent candidate biomarkers for future research. 3SEQ was performed on 64 formalin-fixed, paraffin-embedded (FFPE) human tumors representing 17 diagnostic cancer subtypes. Duplicate libraries were prepared for two of the tumors (ESS STT5520 and LMS STT516). 3SEQ was also performed on 27 normal human tissue samples. RNA-Seq was performed on 6 breast cancer cell lines for the examination of the breast-specific transcript on chr10. Series supplementary files: 'GSE28866_raw_counts_54511_peaks_cancer_and_normal.txt': Raw_counts: Total 3SEQ reads in each peak for each sample. File includes read counts for 54,511 peaks for the 66 cancer libraries and the 27 normal libraries. 'GSE28866_36048_normalized_peaks_cancer_and_normal.txt': Normalized_peaks: Normalized 3SEQ expression data for the 36,048 filtered peaks for the 66 cancer libraries and the 27 normal libraries. Peaks were classified as coding, lncRNA (Rinn, Bartel, Hughes, Gencode_lnc), other known transcripts (ref_known_Gencode_nc, all_mrna, other known), downstream, intron, promoter, or novel_intergenic. Peaks determined to be differentially expressed in one of the 17 cancer subtypes using a series of 2-Class SAM analyses are noted along with the type of cancer showing upregulation. Expression data was normalized using the sequencing depth of each sample by scaling the data using the mean value of each sample. Data was further compressed to reduce outliers by taking the square root of each value.
2012-08-24 | E-GEOD-28866 | biostudies-arrayexpress
Project description:Colorectal adenomas are cancer precursor lesions of the large bowel. In these preinvasive lesions, a vast array of genomic and epigenomic changes have been detailed, but the consequence of these molecular alterations on the effectors of biological function (proteins) has not been comprehensively explored.
2014-04-03 | PXD000445 | Pride
Project description:microRNA signatures with diagnosis, distant metastasis and prognosis for nasopharyngeal carcinoma 62 nasopharyngeal carcinoma and 6 non-cancer nasopharyngitis fresh tissues were detected by microRNA microarray
2012-05-09 | E-GEOD-36682 | biostudies-arrayexpress
Project description:Next generation sequencing of 28 thymic epithelial tumors (TETs) revealed a high frequency of GTF2I missense mutation (chr7:74146970T/A) in A thymomas, a relatively indolent subtype. The GTF2I mutation was confirmed in 82% of A and 74% of AB thymomas in a series of 274 TETs but was rare in aggressive subtypes, where recurrent mutations of known cancer genes were identified. Therefore, GTF2I mutation correlated with a better survival. GTF2I Beta and Delta isoforms were expressed in TETs and both mutant isoforms were able to stimulate cell proliferation in vitro. Thymic carcinomas presented a higher number of mutations than thymomas (average 43.5 and 18.4, respectively). Recurrent mutations of known cancer genes, including TP53, CYLD, CDKN2A, BAP1 and PBRM1 were identified in thymic carcinomas. These findings will complement the diagnostic work up of these rare tumors, and also help the development of a molecular classification, and assessment of prognosis and treatment strategies. Tumor samples of 286 patients were collected from 4 different institutions: National Cancer Institute (Bethesda MD), Pisa University Hospital (Pisa, Italy), Padua University Hospital (Padua, Italy) and IRCCS Istituto Clinico Humanitas (Rozzano, Italy).
2014-06-24 | E-GEOD-57892 | biostudies-arrayexpress
Project description:We prepared small RNA libraries from 29 tumor/normal pairs of human cervical tissue samples. Analysis of the resulting sequences (42 million in total) defined 64 new human microRNA (miRNA) genes. Both arms of the hairpin precursor were observed in twenty-three of the newly identified miRNA candidates. We tested several computational approaches for analysis of class differences between high throughput sequencing datasets, and describe a novel application of log linear model that has provided the most datasets, and describe a novel application of log linear model that has provided the most effective analysis for this data. This method resulted in the identification of 67 miRNAs that were differentially-expressed between the tumor and normal samples at a false discovery rate less than 0.001. A total of 29 tumor/normal pairs of human cervical tissue samples were analyzed. Two samples (G699N_2 and G761T_2) were performed in duplicates. No Fastq files for GSM532871 to GSM532889, GSM532929, and GSM532930. Sequence files are provided as text files for these 22 Sample records in GSE20592_RAW.tar. 38 samples with quality scores are available from SRA as SRP002/SRP002326 (see Supplementary file below).
2010-04-28 | E-GEOD-20592 | biostudies-arrayexpress
Project description:The choice for adjuvant chemotherapy in stage II colorectal cancer (CRC) is controversial as many patients are cured by surgery alone and it is difficult to identify patients with high-risk of recurrence of the disease. There is a need for better stratification of this group of patients. Mass spectrometry imaging could identify patients at risk. We report here the N-glycosylation signatures of the different cell populations in a group of stage II CRC tissue samples.
2020-10-21 | PXD021682 | Pride