Towards Optimization of Precision Oncology in Metastatic Uterine Tumors
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PROVIDER: PRJNA892975 | ENA |
REPOSITORIES: ENA
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Project description:Towards Optimization of Precision Oncology in Metastatic Uterine Tumors
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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
Project description:The incidence of new cancer cases is expected to increase significantly in the future, posing a worldwide problem. In this regard, precision oncology and its diagnostic tools are essential for developing personalized cancer treatments. Nowadays, it is almost impossible to separate technology and digitization from medicine and health sciences, and digital pathology (DP) is emerging as one of the most influential technologies in the transition towards the 4P of new medicine (preventive, participatory, personalized and predictive). DP is a particularly key strategy to study the interactions of tumor cells and the tumor microenvironment (TME), which play a crucial role in tumor initiation, progression and metastasis. 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, with the aim of obtaining key TME biomarkers that can help improve metastatic prediction and shed light on potential therapeutic targets. Automatized algorithms were used to analyze reticulin fiber architecture and immune infiltration in colocalized regions of interest (ROIs) of 133 invasive tumor front (ITF), 89 tumor niches and 70 target tissues in a total of six paired samples of uADC and nine of uLMS. Microdissected tissue from the ITF was employed for transcriptomic and epigenetic studies in primary and metastatic tumors. Reticulin fiber scaffolding was characterized by a large and loose reticular fiber network in uADC, while dense bundles were found in uLMS. Notably, more similarities between reticulin fibers were observed in paired uLMS then paired uADCs. Transcriptomic and multiplex immunofluorescence-based immune profiling showed a higher abundance of T and B cells in primary tumor and in metastatic uADC than uLMS. Moreover, the epigenetic signature of paired samples in uADCs showed more differences than paired samples in uLMS. Some epigenetic variation was also found between the ITF of metastatic uADC and uLMS. Altogether, our data suggest a correlation between morphological and molecular changes at the ITF and the degree of aggressiveness. The use of DP tools for characterizing reticulin scaffolding and immune cell infiltration at the ITF in paired samples together with information provided by omics analyses in a large cohort will hopefully help validate novel biomarkers of tumor aggressiveness, develop new drugs and improve patient quality of life in a much more efficient way.
2022-10-06 | GSE214779 | 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:CPTAC: Microscaled Proteogenomic Methods for Precision Oncology
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Project description:CPTAC: Microscaled Proteogenomic Methods for Precision Oncology
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Project description:The OncoLoop Network-based Precision Oncology Framework
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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: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:Synthetic lethality-mediated precision oncology via the tumor transcriptome
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