Precision Phenomics to Personalized Drug Therapy (P3DT)
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ABSTRACT:
PROVIDER: PRJNA595706 | ENA |
REPOSITORIES: ENA
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Project description:Precision Phenomics to Personalized Drug Therapy (P3DT)
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Project description:Purpose: Advanced high-grade gastroenteropancreatic neuroendocrine neoplasm (GEP-NEN) are highly aggressive and heterogeneous epithelial malignancies with poor clinical outcomes. No therapeutic predictive biomarkers exist and representative preclinical models to study their biology are missing. Patient-derived (PD) tumoroids may enable fast ex vivo pharmacotyping and provide subsidiary biological information for more personalized therapy strategies in individual patients. Experimental Design: PD tumoroids were established from rare biobanked surgical resections of advanced high-grade GEP-NEN patients. Using targeted in vitro pharmacotyping and next-generation sequencing of patient samples and matching PD tumoroids, we profiled individual patients and compared treatment-induced molecular stress response and in vitro drug sensitivity to the clinical therapy response. Results: We demonstrate high success rates in culturing PD tumoroids of high-grade GEP-NENs within clinically meaningful timespans. PD tumoroids recapitulate biological key features of high-grade GEP-NEN and mimic clinical response to cisplatin and temozolomide in vitro. Moreover, investigating treatment-induced molecular stress responses in PD tumoroids in silico, we discovered and functionally validated Lysine demethylase 5A (KDM5A) and interferon-beta (IFNB1) as two vulnerabilities that act synergistically in combination with cisplatin and may present novel therapeutic options in high-grade GEP-NENs. Conclusion: Patient-derived tumoroids from high-grade GEP-NENs represent a relevant model to screen drug sensitivities of individual patients within clinically relevant timespans and provide novel functional insights into drug-induced stress responses. Clinical patient response to standard-of-care chemotherapeutics matches with drug sensitivities of PD tumoroids. Together, our findings provide a functional precision oncology approach for gathering patient-centered subsidiary treatment information that will potentially increase therapeutic opportunities in the framework of personalized medicine.
2024-03-01 | GSE213504 | 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 | 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:Application of machine learning (ML) on cancer-specific pharmacogenomic datasets shows immense promise for identifying predictive response-biomarkers to enable personalized treatment. We introduce CAN-Scan, a precision oncology platform, that applies ML on next-generation pharmacogenomic datasets generated from a freeze-viable biobank of patient-derived primary cell lines (PDCs). These PDCs are screened against 84 FDA-approved drugs at clinically relevant doses (Cmax), focusing on colorectal cancer (CRC) as a model system. CAN-Scan uncovers prognostic biomarkers and alternative treatment strategies, particularly for patients unresponsive to first-line chemotherapy. Specifically, it identifies gene expression signatures linked to resistance against 5-Fluorouracil (5FU)-based drugs and a focal copy number gain on chromosome 7q, harbouring critical resistance-associated genes. CAN-Scan-derived response signatures accurately predict clinical outcomes across four independent, ethnically-diverse CRC cohorts. Notably, drug-specific ML models reveal Regorafenib and Vemurafenib as alternative treatments for BRAF-expressing, 5FU-insensitive CRC. Altogether, this approach demonstrates significant potential in improving biomarker-discovery and guiding personalized treatments.
2025-04-04 | GSE287956 | GEO
Project description:Personalized therapy of rheumatoid arthritis (RA) based on traditional Chinese medicine cold and hot syndromes is selection of the best treatment for an individual patient. Wutou Decoction (WTD) is one of the classic Chinese herbal formulae which achieve favorable therapeutic response in treating RA-cold syndrome. Microarray analysis based on the adjuvant induced arthritis model combined with characteristics of RA and cold/hot syndromes was performed to screen RA-cold and RA-hot-syndrome-related genes, as well as WTD effect genes.
2018-06-13 | GSE115662 | GEO