Ontology highlight
ABSTRACT:
PROVIDER: PRJNA762992 | ENA |
REPOSITORIES: ENA
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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 have generated a collection of patient-derived xenograft (PDX) tumor models and characterized them at the molecular level to facilitate precision oncology.
2014-03-13 | GSE55828 | GEO
Project description:Immunotherapies, including immune checkpoint inhibitors (ICI), have revolutionized the treatment of many cancers, producing significant improvements in survival in patients with many different cancers. However, the intended anti-tumor effects also result in a unique form of autoimmunity, known as immune-related adverse events (irAEs), which have emerged as a limiting factor for many immunotherapies. Cutaneous irAEs (cirAEs), the most frequently occurring ICI–related toxicities, have been associated with improved efficacy and survival but, in their severe forms, require systemic steroids and have in cases led to premature ICI discontinuation and fatality. There is a need for both robust biomarkers and adequate models that effectively predict which patients who develop irAEs may have improved outcomes. Using a microfluidic droplet technology that generates "mini" patient-derived organoids called MicroOrganoSpheres (MOSTM), we successfully generated skin MOS from skin biopsy samples in both healthy skin and tumor-involved skin that sustain the original patient skin immune microenvironment over three weeks. Using this model, we assessed skin cell toxicity and cytokine release in response to ICI and targeted therapies. Clinical responses were largely consistent with the skin and tumor MOS assay readouts, indicating that MOS recapitulates the potential association between skin irAEs and efficacy. Matched pairs of patient melanoma and skin MOS showed good concordance with patient outcome indicating that MOS recapitulates the potential association between skin irAEs and efficacy. Enrichment of genes associated with atopic dermatitis, vitiligo, and psoriatic dermatitis were observed in MOS generated from skin of an ICI-sensitive patient compared to the MOS generated from skin of an ICI-resistant patient. These results indicated that the skin MOS platform can potentially predict the dermatological toxicity of ICI. As the MOS assay can be completed within 12 days after biopsy acquisition, this novel technology may enable personalized medicine approaches by prediction of cirAEs and efficacy for individual patients.
2023-03-31 | GSE226869 | GEO
Project description:We have generated a collection of patient-derived xenograft (PDX) tumor models and characterized them at the molecular level to facilitate precision oncology. Surgically resected HCC specimens were subcutaneously implanted in immunodeficient mice. Resulting xenografts were serially implanted to establish transplantable PDX models, which were sequentially subject to whole exome sequencing (WES), gene expression array, genome-wide human single nucleotide polymorphism (SNP) array 6.0, and serum a–fetoprotein (AFP) detection assay. The feasibility as a preclinical model was validated by efficacy studies using a standard-of-care (SOC) and a targeted agent, respectively.
2014-03-13 | E-GEOD-55828 | biostudies-arrayexpress
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: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: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:Phenotype-driven precision oncology in patient-derived tumor models predict therapeutic response in squamous cell carcinoma
| PRJNA330817 | ENA
Project description:Phenotype-driven precision oncology in patient-derived tumor models predict therapeutic response in squamous cell carcinoma
| PRJNA329106 | ENA
Project description:Phenotype-driven precision oncology in patient-derived tumor models predict therapeutic response in squamous cell carcinoma [expression]
| PRJNA390763 | ENA