The OncoLoop Network-based Precision Oncology Framework
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA774549 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:We introduce OncoLoop, a highly-generalizable, precision medicine framework to triangulate between available mouse models, human tumors, and large-scale drug perturbational assays with in vivo validation to predict personalized treatment
2022-10-31 | GSE186566 | GEO
Project description:Patient-derived xenografts (PDX) and organoids (PDO) have been shown to model clinical response to cancer therapy. However, it remains challenging to use these models to guide timely clinical decisions for cancer patients. Here we used droplet emulsion microfluidics with temperature control and dead-volume minimization to rapidly generate thousands of Micro- Organospheres (MOS) from low-volume patient tissues, which serve as an ideal patient-derived model for clinical precision oncology. A clinical study of newly diagnosed metastatic colorectal cancer (CRC) patients using a MOS-based precision oncology pipeline reliably predicted patient treatment outcome within 14 days, a timeline suitable for guiding treatment decisions in clinic. Furthermore, MOS capture original stromal cells and allow T cell penetration, providing a clinical assay for testing immuno-oncology (IO) therapies such as PD-1 blockade, bispecific antibodies, and T cell therapies on patient tumors.
2022-09-01 | GSE184242 | GEO
Project description:We use a Master Regular-based precision cancer medicine framework to predict drug sensitivity. The network based analysis uses RNASeq profiles of patient tumors, in conjunction with de novo drug mechanism analysis from high throughput drug screens with post-perturbation RNASeq profiles performed in cognate cell line models. The analysis aims to predict available antineoplastic drugs most likely to invert the aberrant protein activity state of individual tumors. Validation of predictions was performed in corresponding patient-derived xenograft (PDX) mice. Pharmacodynamic samples were obtained from PDX mice after the third dose for RNASeq and downstream analysis.
2022-09-10 | GSE212854 | GEO
Project description:The tumor immune microenvironment is a main contributor to cancer progression and a promising therapeutic target for oncology. However, immune microenvironments vary profoundly between patients and biomarkers for prognosis and treatment response lack precision. A comprehensive compendium of tumor immune cells is required to pinpoint predictive cellular states and their spatial localization. We generated a single-cell resolved tumor immune cell atlas, jointly analyzing >500,000 cells from 217 patients and 13 cancer types, providing the basis for a patient stratification based on immune cell compositions. Projecting immune cells from external tumors onto the atlas facilitated an automated cell annotation system for a harmonized interpretation. To enable in situ mapping of immune populations for digital pathology, we developed SPOTlight, a computational tool that identified striking spatial immune cell patterns in tumor sections. We expect the atlas, together with our versatile toolbox for precision oncology, to advance currently applied stratification strategies for prognosis and immuno-therapy response.
2020-10-27 | GSE158803 | GEO
Project description:CPTAC: Microscaled Proteogenomic Methods for Precision Oncology
| PRJNA574688 | ENA
Project description:CPTAC: Microscaled Proteogenomic Methods for Precision Oncology
| PRJNA574689 | ENA
Project description:Personalized treatment for patients with advanced solid tumors critically depends on the deep characterization of tumor cells. These patients frequently develop malignant serous effusions (MSE). The value of MSE-based tumor cell characterization for guiding precision oncology is, however, currently unclear. Here, we present a comprehensive characterization of a pan-cancer cohort of 150 MSE samples at the cellular, molecular, and functional level. Our integrative analysis reveals dynamic cellular heterogeneity in MSE, and uncovers links between tumor driver mutations and ex vivo growth patterns. Strong concordance between genomic and transcriptional profiles of MSE and their corresponding solid tumors validates their use as a model system for solid tumor biology. We link baseline gene expression patterns to global ex vivo drug sensitivity, and demonstrate that drug-induced transcriptional changes in MSE are highly indicative of compound mode of action. Two case studies exemplify the utility of our approach in investigating acquired resistance to targeted therapy and identifying treatment options for relapsed solid tumors. In summary, our study provides a functional multi-omics view on a pan-cancer MSE cohort and underlines the utility of MSE-based precision oncology.
2024-08-26 | GSE240952 | GEO
Project description:Personalized treatment for patients with advanced solid tumors critically depends on the deep characterization of tumor cells. These patients frequently develop malignant serous effusions (MSE). The value of MSE-based tumor cell characterization for guiding precision oncology is, however, currently unclear. Here, we present a comprehensive characterization of a pan-cancer cohort of 150 MSE samples at the cellular, molecular, and functional level. Our integrative analysis reveals dynamic cellular heterogeneity in MSE, and uncovers links between tumor driver mutations and ex vivo growth patterns. Strong concordance between genomic and transcriptional profiles of MSE and their corresponding solid tumors validates their use as a model system for solid tumor biology. We link baseline gene expression patterns to global ex vivo drug sensitivity, and demonstrate that drug-induced transcriptional changes in MSE are highly indicative of compound mode of action. Two case studies exemplify the utility of our approach in investigating acquired resistance to targeted therapy and identifying treatment options for relapsed solid tumors. In summary, our study provides a functional multi-omics view on a pan-cancer MSE cohort and underlines the utility of MSE-based precision oncology.
2024-08-26 | GSE240951 | GEO
Project description:Precision oncology has made significant advances in the last few years, mainly by targeting actionable mutations in cancer driver genes. However, the proportion of patients whose tumors can be targeted therapeutically remains limited. Recent studies have begun to explore the benefit of analyzing tumor transcriptomics data to guide patient treatment, raising the need for new approaches for systematically accomplishing that. Here we show that computationally derived genetic interactions can successfully predict patient response.
2021-02-11 | GSE166449 | GEO
Project description:Precision oncology and its diagnostic tools are essential for developing personalized cancer treatments. The purpose of this study was to integrate data on the digital patterns of reticulin fiber scaffolding and the immune cell infiltrate, transcriptomic and epigenetic profiles in aggressive uterine adenocarcinoma (uADC), uterine leiomyosarcoma (uLMS) and their respective lung metastases (LM-uADC and LM-uLMS), with the aim of obtaining key tumor microenvironment (TME) biomarkers that can help improve metastatic prediction and shed light on potential therapeutic targets.
2022-12-16 | GSE216313 | GEO