Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA376109 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Stromal-derived intratumoural heterogeneity (ITH) has been shown to undermine molecular stratification of patients into appropriate prognostic/predictive subgroups. Using several clinically relevant colorectal cancer (CRC) gene expression signatures, we assessed the susceptibility of these signatures to the confounding effects of ITH using gene expression microarray data obtained from multiple tumour regions of a cohort of 24 patients, including central tumour, the tumour invasive front and lymph node metastasis.
2017-05-01 | GSE95109 | GEO
Project description:Assessing the impact of genomic alterations on protein networks is fundamental in identifying the mechanisms that shape cancer heterogeneity. We have used isobaric labelling to characterize the proteomic landscapes of 50 colorectal cancer cell lines and to decipher the functional consequences of somatic genomic variants. The robust quantification of over 9,000 proteins and 11,000 phosphopeptides on average, enabled the de novo construction of a functional protein correlation network which ultimately exposed the collateral effects of mutations on protein complexes. CRISPR-cas9 deletion of key chromatin modifiers confirmed that the consequences of genomic alterations can propagate through protein interactions in a transcript-independent manner. Lastly, we leveraged the quantified proteome to perform unsupervised classification of the cell lines and to build predictive models of drug response in colorectal cancer. Overall, we provide a deep integrative view of the functional network and the molecular structure underlying the heterogeneity of colorectal cancer cells.
| MSV000081528 | MassIVE
Project description:Triple-Negative Breast Cancer (TNBC) is a heterogeneous collection of cancers where personalized treatment is difficult and chemotherapy and immunotherapy combinations are the main treatment options. Many attempts to tackle patient heterogeneity have focused on defining cancer-intrinsic subtypes based on differential tumor mRNA-expression across patient cohorts. While these multi-gene diagnostics have shown success in hormone receptor- positive cancers (e.g. OncotypeDX), no TNBC classifiers have shown clinical utility in predicting patient survival or treatment response. We hypothesize that TNBC-infiltrating immune-cells both affect mRNA-based classification and contribute to treatment response variability. To evaluate this hypothesis, we benchmarked the performance of common TNBC-classification (TNBC-type) and infiltrating immune (CIBERSORT) algorithms on the same underlying datasets. Encouragingly, we found that – as with OncotypeDx– highly proliferative TNBC-subtypes (BL1) show the strongest evidence of response to cytotoxic chemotherapies. Interestingly, this cancer-proliferative signature (BL1) is strongly correlated with enrichment in tumor-infiltrating lymphocyte signatures (TIL) which show superior prognostic and predictive power. In addition, Tumor Associated Macrophage (TAM) signatures show independent predictive and prognostic power for both patient survival and response to anthracycline- and taxane-based chemotherapies. These gene signature-based correlations were validated in a new independent cohort of 67 TNBC-patients treated with neoadjuvant chemotherapy. Overall, these results argue for the independent contributions of both cancer-intrinsic and -extrinsic factors in predicting treatment response in the neoadjuvant setting.
2024-03-20 | GSE260693 | GEO
Project description:The heterogeneity and multiple histological categories of soft tissue sarcoma (STS) underlie a need for better classification schemes to improve their management. As none are currently available, we aimed to derive hypoxia and intrinsic molecular subtype mRNA abundance signatures for localized soft tissue sarcoma. RNA sequencing was used to identify genes induced by hypoxia in seven STS cell lines.
2017-11-17 | E-MTAB-6087 | biostudies-arrayexpress
Project description:Assessing the impact of genomic alterations on protein networks is fundamental in identifying the mechanisms that shape cancer heterogeneity. We have used isobaric labelling to characterize the proteomic landscapes of 50 colorectal cancer cell lines and to decipher the functional consequences of somatic genomic variants. The robust quantification of over 9,000 proteins and 11,000 phosphopeptides on average, enabled the de novo construction of a functional protein correlation network which ultimately exposed the collateral effects of mutations on protein complexes. CRISPR-cas9 deletion of key chromatin modifiers confirmed that the consequences of genomic alterations can propagate through protein interactions in a transcript-independent manner. Lastly, we leveraged the quantified proteome to perform unsupervised classification of the cell lines and to build predictive models of drug response in colorectal cancer. Overall, we provide a deep integrative view of the functional network and the molecular structure underlying the heterogeneity of colorectal cancer cells.
2017-08-09 | PXD005235 | Pride
Project description:Immuno-genomic classification of colorectal cancer organoids reveals cancer cells with intrinsic immunogenic properties associated with patient survival
| PRJNA720562 | ENA
Project description:Breast carcinoma (BC) have been extensively profiled by high-throughput technologies for over a decade, and broadly speaking, these studies can be grouped into those that seek to identify patient subtypes (studies of heterogeneity) or those that seek to identify gene signatures with prognostic or predictive capacity. The shear number of reported signatures has led to speculation that everything is prognostic in BC. Here we show that this ubiquity is an apparition caused by a poor understanding of the inter- relatedness between subtype and the molecular determinants of prognosis. Our approach constructively shows how to avoid confounding due to a patient's subtype, clinicopathological or treatment profile. The approach identifies patients who are predicted to have good outcome at time of diagnosis by all available clinical and molecular markers, but who experience a distant metastasis within five years. These inherently difficult patients (~7% of BC) are prioritized for investigations of intra-tumoral heterogeneity. 321 samples from breast cancer patients.
2014-10-08 | E-GEOD-58644 | biostudies-arrayexpress
Project description:While oncogenic mutations shape colorectal cancer biology and therapy response, their prognostic value remains low. Cluster-based classification of patient cancer transcriptomes has shown greater promise for prognosis, yet these systems do not account for the roles of oncogenes in establishing cancer phenotypes. Here, we create and validate a prognostic classifier for colorectal cancer based on transcriptional programs induced by oncogenes. To systematically investigate oncogenic drivers, we employed a barcoded library of colorectal cancer-associated oncogene variants across a panel of genetically diverse colorectal cancer cell lines. We profiled the transcriptomes of over 100,000 transgenic cells and used machine learning to define transcriptional modules capturing key functional traits. Our analysis revealed heterogeneity on the cell-to-cell level and context-dependent gene expression patterns induced by oncogenes. We identified overarching gene expression modules reflecting core oncogenic processes, including cancer cell plasticity, inflammatory response, replicative stress, and epithelial-to-mesenchymal transition. These modules enabled a functional classification that linked oncogenic signalling states to distinct transcriptional profiles. We demonstrated their prognostic value by stratifying clinical colorectal cancer cohorts into high- and low-risk groups. Although partially correlated with established clinical parameters, the modules provided additional prognostic information, improving survival prediction and therapy stratification beyond existing classification systems. In summary, our study establishes a framework that connects oncogenic mutations to core transcriptional modules. By integrating experimental models with clinical data, we provide a resource for investigating colorectal cancer progression and oncogene-specific vulnerabilities, facilitating future research and precision oncology.
2026-01-12 | GSE299651 | GEO
Project description:The main objective of this study was to identify potential protein expression signatures by the SWATH technique of proteomic analysis, to aid in the classification of molecular subtypes of colorectal cancer.
2019-10-04 | PXD007810 | Pride