Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer.
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ABSTRACT:
SUBMITTER: Petralia F
PROVIDER: S-EPMC8143193 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:<p>Francesca Petralia,Nicole Tignor,Boris Reva, et al., (2020) <a href="https://www.cell.com/cell/fulltext/S0092-8674(20)31451-3" target="_blank">Cell 183, 1962-1985</a></p><p>We report a comprehensive proteogenomics analysis, including whole-genome sequencing, RNA sequencing, and proteomics and phosphoproteomics profiling, of 218 tumors across 7 histological types of childhood brain cancer: low-grade glioma (n = 93), ependymoma (32), high-grade glioma (25), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16), and atypical teratoid rhabdoid tumor (12). Proteomics data identify common biological themes that span histological boundaries, suggesting that treatments used for one histological type may be applied effectively to other tumors sharing similar proteomics features. Immune landscape characterization reveals diverse tumor microenvironments across and within diagnoses. Proteomics data further reveal functional effects of somatic mutations and copy number variations (CNVs) not evident in transcriptomics data. Kinase-substrate association and co-expression network analysis identify important biological mechanisms of tumorigenesis. This is the first large-scale proteogenomics analysis across traditional histological boundaries to uncover foundational pediatric brain tumor biology and inform rational treatment selection.</p><p>The Children's Brain Tumor Network (CBTN) and the Pacific Pediatric Neuro-Oncology Consortia (PNOC) are collaborative research consortia focused on identifying therapies for children with brain tumors (<a href="https://cbttc.org" target="_blank">https://cbttc.org</a>). The consortia have contributed a <a href="https://kidsfirstdrc.org/support/studies-and-access/#SD_BHJXBDQK" target="_blank">Pediatric Brain Tumor Atlas (PBTA) dataset</a>, a cohort of 991 brain tumor subject clinical data, with associated whole genome sequencing and RNAseq hosted by the Gabriella Miller Kids First Data Resource (<a href="https://kidsfirstdrc.org/" target="_blank">kidsfirstdrc.org</a>) as part of the Gabriella Miller Kids First Pediatric Research Program (Kids First). Kids First is a Pan NIH Common Fund program dedicated to the development of large-scale data resources to help researchers uncover new insights into the biology of childhood cancer and structural birth defects (<a href="https://commonfund.nih.gov/KidsFirst" target="_blank">https://commonfund.nih.gov/KidsFirst</a>).</p><p>Mass Spectrometry raw data generation along with preliminary analyses were performed at the <a href="https://tcmp.hms.harvard.edu/" target="_blank">Thermo Fisher Scientific Center for Multiplexed Proteomics (TCMP)</a>, Harvard Medical School (HMS) under the direction of <a href="https://cellbio.med.harvard.edu/people/faculty/gygi" target="_blank">Prof. Steven Gygi</a>. Samples were prepared using the streamline (SL)-TMT protocol (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5994137/" target="_blank">Navarrete-Perea et al., 2018</a>) and MS analysis was performed using the SPS-MS3 strategy (<a href="https://www.nature.com/articles/nmeth.1714" target="_blank">Ting et al., 2011</a>) developed in the <a href="https://gygi.med.harvard.edu/" target="_blank">Gygi lab</a>. Additional data analyses from the CPTAC Common Data Analysis Pipeline (<a href="https://www.ncbi.nlm.nih.gov/pubmed/26860878" target="_blank">Rudnick et al., 2016</a>) and from the University of Michigan Proteomics and Integrative Bioinformatics Laboratory (<a href="https://github.com/Nesvilab" target="_blank">https://github.com/Nesvilab</a>) are also provided. All raw and processed genomic data, as well as pathology reports, radiology reports, MRIs, histology slide images are accessible via the <a href="https://portal.kidsfirstdrc.org/dashboard" target="_blank">Kids First DRC (Data Resource Center)</a>.</p>
2021-02-04 | MSV000086810 | MassIVE
Project description:Pituitary neuroendocrine tumor (PitNET) is one of the most common intracranial tumors. Due to its extensive tumor heterogeneity and the lack of high-quality tissues for biomarker discovery, the causative molecular mechanisms are far from being fully defined. Therefore, more studies are needed to improve the current clinicopathological classification system, and advanced treatment strategies such as targeted therapy and immunotherapy are yet to be explored. Here, we performed the largest integrative genomics, transcriptomics, proteomics, and phosphoproteomics analysis reported to date for a cohort of 200 PitNET patients. Genomics data indicate that GNAS copy number gain can serve as a reliable diagnostic marker for hyperproliferation of the PIT1 lineage. Proteomics-based classification of PitNETs identified 7 clusters, among which, tumors overexpressing epithelial-mesenchymal transition (EMT) markers clustered into a more invasive subgroup. Further analysis identified potential therapeutic targets, including CDK6, TWIST1, EGFR, and VEGFR2, for different clusters. Immune subtyping to explore the potential for application of immunotherapy in PitNET identified an association between alterations in the JAK1-STAT1-PDL1 axis and immune exhaustion, and between changes in the JAK3-STAT6-FOS/JUN axis and immune infiltration. These identified molecular markers and alternations in various clusters/subtypes were further confirmed in an independent cohort of 750 PitNET patients. This proteogenomic analysis across traditional histological boundaries improves our current understanding of PitNET pathophysiology and suggests novel therapeutic targets and strategies.
2022-10-17 | PXD031467 |
Project description:To provide a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer, we performed a comprehensive mass-spectrometry-based proteomic characterization of 174 ovarian tumors previously analyzed by The Cancer Genome Atlas (TCGA), of which 169 were high-grade serous carcinomas (HGSCs). Integrating our proteomic measurements with the genomic data yielded a number of insights into disease, such as how different copy-number alternations influence the proteome, the proteins associated with chromosomal instability, the sets of signaling pathways that diverse genome rearrangements converge on, and the ones most associated with short overall survival. Specific protein acetylations associated with homologous recombination deficiency suggest a potential means for stratifying patients for therapy. In addition to providing a valuable resource, these findings provide a view of how the somatic genome drives the cancer proteome and associations between protein and post-translational modification levels and clinical outcomes in HGSC. VIDEO ABSTRACT.
| S-EPMC4967013 | biostudies-literature
Project description:Despite increasing evidence that antitumor immune control exists in the pediatric brain, these findings have yet to be exploited successfully in the clinic. A barrier to development of immunotherapeutic strategies in pediatric brain tumors is that the immunophenotype of these tumors' microenvironment has not been defined. To address this, the current study used multicolor FACS of disaggregated tumor to systematically characterize the frequency and phenotype of infiltrating immune cells in the most common pediatric brain tumor types. The initial study cohort consisted of 7 pilocytic astrocytoma (PA), 19 ependymoma (EPN), 5 glioblastoma (GBM), 6 medulloblastoma (MED), and 5 nontumor brain (NT) control samples obtained from epilepsy surgery. Immune cell types analyzed included both myeloid and T cell lineages and respective markers of activated or suppressed functional phenotypes. Immune parameters that distinguished each of the tumor types were identified. PA and EPN demonstrated significantly higher infiltrating myeloid and lymphoid cells compared with GBM, MED, or NT. Additionally, PA and EPN conveyed a comparatively activated/classically activated myeloid cell-skewed functional phenotype denoted in particular by HLA-DR and CD64 expression. In contrast, GBM and MED contained progressively fewer infiltrating leukocytes and more muted functional phenotypes similar to that of NT. These findings were recapitulated using whole tumor expression of corresponding immune marker genes in a large gene expression microarray cohort of pediatric brain tumors. The results of this cross-tumor comparative analysis demonstrate that different pediatric brain tumor types exhibit distinct immunophenotypes, implying that specific immunotherapeutic approaches may be most effective for each tumor type.
| S-EPMC3827919 | biostudies-literature
Project description:BackgroundUrothelial carcinoma (UC) is the most common pathological type of bladder cancer, a malignant tumor. However, an integrated multi-omics analysis of the Chinese UC patient cohort is lacking.MethodsWe performed an integrated multi-omics analysis, including whole-exome sequencing, RNA-seq, proteomic, and phosphoproteomic analysis of 116 Chinese UC patients, comprising 45 non-muscle-invasive bladder cancer patients (NMIBCs) and 71 muscle-invasive bladder cancer patients (MIBCs).ResultProteogenomic integration analysis indicated that SND1 and CDK5 amplifications on chromosome 7q were associated with the activation of STAT3, which was relevant to tumor proliferation. Chromosome 5p gain in NMIBC patients was a high-risk factor, through modulating actin cytoskeleton implicating in tumor cells invasion. Phosphoproteomic analysis of tumors and morphologically normal human urothelium produced UC-associated activated kinases, including CDK1 and PRKDC. Proteomic analysis identified three groups, U-I, U-II, and U-III, reflecting distinct clinical prognosis and molecular signatures. Immune subtypes of UC tumors revealed a complex immune landscape and suggested the amplification of TRAF2 related to the increased expression of PD-L1. Additionally, increased GARS, related to subtype U-II, was validated to promote pentose phosphate pathway by inhibiting activities of PGK1 and PKM2.ConclusionsThis study provides a valuable resource for researchers and clinicians to further identify molecular pathogenesis and therapeutic opportunities in urothelial carcinoma of the bladder.
| S-EPMC9164575 | biostudies-literature
Project description:To elucidate the deregulated functional modules that drive clear cell renal cell carcinoma (ccRCC), we performed comprehensive genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic characterization of treatment-naive ccRCC and paired normal adjacent tissue samples. Genomic analyses identified a distinct molecular subgroup associated with genomic instability. Integration of proteogenomic measurements uniquely identified protein dysregulation of cellular mechanisms impacted by genomic alterations, including oxidative phosphorylation-related metabolism, protein translation processes, and phospho-signaling modules. To assess the degree of immune infiltration in individual tumors, we identified microenvironment cell signatures that delineated four immune-based ccRCC subtypes characterized by distinct cellular pathways. This study reports a large-scale proteogenomic analysis of ccRCC to discern the functional impact of genomic alterations and provides evidence for rational treatment selection stemming from ccRCC pathobiology.
| S-EPMC7331093 | biostudies-literature
Project description:Chronic inflammation of the colon causes genomic and/or transcriptomic events, which can lead to expression of non-canonical protein sequences contributing to oncogenesis. To better understand these mechanisms, Rag2-/-Il10-/- mice were infected with Helicobacter hepaticus to induce chronic inflammation of the cecum and the colon. Transcriptomic data from harvested proximal colon samples were used to generate a customized FASTA database containing non-canonical protein sequences. Using a proteogenomic approach, mass spectrometry data for proximal colon proteins were searched against this custom FASTA database using the Galaxy for Proteomics (Galaxy-P) platform. In addition to the increased abundance in inflammatory response proteins, we also discovered several non-canonical peptide sequences derived from unique proteoforms. We confirmed the veracity of these novel sequences using an automated bioinformatics verification workflow with targeted MS-based assays for peptide validation. Our bioinformatics discovery workflow identified 235 putative non-canonical peptide sequences, of which 58 were verified with high confidence and 39 were validated in targeted proteomics assays. This study provides insights into challenges faced when identifying non-canonical peptides using a proteogenomics approach and demonstrates an integrated workflow addressing these challenges. Our bioinformatic discovery and verification workflow is publicly available and accessible via the Galaxy platform and should be valuable in non-canonical peptide identification using proteogenomics.
| S-EPMC9036229 | biostudies-literature
Project description:Extensive genomic characterization of human cancers presents the problem of inference from genomic abnormalities to cancer phenotypes. To address this problem, we analysed proteomes of colon and rectal tumours characterized previously by The Cancer Genome Atlas (TCGA) and perform integrated proteogenomic analyses. Somatic variants displayed reduced protein abundance compared to germline variants. Messenger RNA transcript abundance did not reliably predict protein abundance differences between tumours. Proteomics identified five proteomic subtypes in the TCGA cohort, two of which overlapped with the TCGA 'microsatellite instability/CpG island methylation phenotype' transcriptomic subtype, but had distinct mutation, methylation and protein expression patterns associated with different clinical outcomes. Although copy number alterations showed strong cis- and trans-effects on mRNA abundance, relatively few of these extend to the protein level. Thus, proteomics data enabled prioritization of candidate driver genes. The chromosome 20q amplicon was associated with the largest global changes at both mRNA and protein levels; proteomics data highlighted potential 20q candidates, including HNF4A (hepatocyte nuclear factor 4, alpha), TOMM34 (translocase of outer mitochondrial membrane 34) and SRC (SRC proto-oncogene, non-receptor tyrosine kinase). Integrated proteogenomic analysis provides functional context to interpret genomic abnormalities and affords a new paradigm for understanding cancer biology.
| S-EPMC4249766 | biostudies-literature
Project description:BackgroundPatterns of second primary cancers (SPCs) following first primary lung cancers (FPLCs) may provide aetiological insights into FPLC.MethodsCases of FPLCs in 13 cancer registries in Europe, Australia, Canada, and Singapore were followed up from the date of FPLC diagnosis to the date of SPC diagnosis, date of death, or end of follow-up. Standardised incidence ratios (SIRs) were calculated to estimate the magnitude of SPC development following squamous cell carcinoma (SCC), small cell lung carcinoma (SCLC), and adenocarcinoma (ADC).ResultsAmong SCC patients, male SIR=1.58 (95% confidence interval (CI)=1.50-1.66) and female SIR=2.31 (1.94-2.72) for smoking-related SPC. Among SCLC patients, the respective ratios were 1.39 (1.20-1.60) and 2.28 (1.73-2.95), and among ADC patients, they were 1.73 (1.57-1.90) and 2.24 (1.91-2.61). We also observed associations between first primary lung ADC and second primary breast cancer in women (SIR=1.25, 95% CI=1.05-1.48) and prostate cancer (1.56, 1.39-1.79) in men.ConclusionThe FPLC patients carried excess risks of smoking-related SPCs. An association between first primary lung ADC and second primary breast and ovarian cancer in women at younger age and prostate cancers in men may reflect an aetiological role of hormones in lung ADC.
| S-EPMC2853101 | biostudies-literature
Project description:Breast cancer is a heterogeneous disease, comprising multiple entities associated with distinctive histological and biological features, clinical presentations and behaviours and responses to therapy. Microarray-based technologies have unravelled the molecular underpinning of several characteristics of breast cancer, including metastatic propensity and histological grade, and have led to the identification of prognostic and predictive gene expression signatures. Furthermore, a molecular taxonomy of breast cancer based on transcriptomic analysis has been proposed. However, microarray studies have primarily focused on invasive ductal carcinomas of no special type. Owing to the relative rarity of special types of breast cancer, information about the biology and clinical behaviour of breast cancers conveyed by histological type has not been taken into account. Histological special types of breast cancer account for up to 25% of all invasive breast cancers. Recent studies have provided direct evidence of the existence of genotypic-phenotypic correlations. For instance, secretory carcinomas of the breast consistently harbour the t(12;15) translocation that leads to the formation of the ETV6-NTRK3 fusion gene, adenoid cystic carcinomas consistently display the t(6;9) MYB-NFIB translocation and lobular carcinomas consistently show inactivation of the CDH1 gene through multiple molecular mechanisms. Furthermore, histopathological and molecular analysis of tumours from conditional mouse models has provided direct evidence for the causative role of specific genes in the genesis of specific histological special types of breast cancer. Here we review the associations between the molecular taxonomy of breast cancer and histological special types, discuss the possible origins of the heterogeneity of breast cancer and propose an approach for the identification of novel therapeutic targets based on the study of histological special types of breast cancer.
| S-EPMC5527938 | biostudies-other