Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma
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ABSTRACT:
PROVIDER: PRJNA528817 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Background: Cancer patients with advanced disease exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates sequencing data with functional assay data to develop patient-specific combination cancer treatments. Methods: Tissue taken from a murine model of alveolar rhabdomyosarcoma was used to perform single agent drug screening and DNA/RNA sequencing experiments; results integrated via our computational modeling approach identified a synergistic personalized two-drug combination. Cells derived from the tumor were allografted into mouse models and used to validate the personalized two-drug combination. Computational modeling of single agent drug screening and RNA sequencing of multiple heterogenous sites from a single patient’s epithelioid sarcoma identified a personalized two-drug combination effective across all tumor regions. The heterogeneity-consensus combination was validated in a xenograft model derived from the patient’s primary tumor. Cell cultures derived from human and canine undifferentiated pleomorphic sarcoma were assayed by drug screen; computational modeling identified a resistance-abrogating two-drug combination common to both cell cultures. This combination was validated in vitro via a cell regrowth assay.
Results
Our computational modeling approach addresses three major challenges in personalized cancer therapy: synergistic drug combination predictions (validated in vitro and in vivo in a genetically engineered murine cancer model), identification of unifying therapeutic targets to overcome intra-tumor heterogeneity (validated in vivo in a human cancer xenograft), and mitigation of cancer cell resistance and rewiring mechanisms (validated in vitro in a human and canine cancer model).
Conclusions
These proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy.
| EGAS00001003564 | EGA
Project description:DNA and RNA were isolated from snap frozen primary tumor tissue (human and mouse), and DNA was isolated from matched normal tissue (region matched non-tumor tissue for human, tail section for mouse). A mouse leg was also injected with cardiotoxin to induce tissue damage was taken following sacrifice and was snap frozen, then processed for RNA isolation to serve as a mouse tissue-normal for RNA sequencing. All sequencing was performed at the OHSU MPSSR core.
2019-03-25 | GSE128766 | GEO
Project description:BackgroundCancer patients with advanced disease routinely exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates molecular sequencing data with functional assay data to develop patient-specific combination cancer treatments.MethodsTissue taken from a murine model of alveolar rhabdomyosarcoma was used to perform single agent drug screening and DNA/RNA sequencing experiments; results integrated via our computational modeling approach identified a synergistic personalized two-drug combination. Cells derived from the primary murine tumor were allografted into mouse models and used to validate the personalized two-drug combination. Computational modeling of single agent drug screening and RNA sequencing of multiple heterogenous sites from a single patient's epithelioid sarcoma identified a personalized two-drug combination effective across all tumor regions. The heterogeneity-consensus combination was validated in a xenograft model derived from the patient's primary tumor. Cell cultures derived from human and canine undifferentiated pleomorphic sarcoma were assayed by drug screen; computational modeling identified a resistance-abrogating two-drug combination common to both cell cultures. This combination was validated in vitro via a cell regrowth assay.ResultsOur computational modeling approach addresses three major challenges in personalized cancer therapy: synergistic drug combination predictions (validated in vitro and in vivo in a genetically engineered murine cancer model), identification of unifying therapeutic targets to overcome intra-tumor heterogeneity (validated in vivo in a human cancer xenograft), and mitigation of cancer cell resistance and rewiring mechanisms (validated in vitro in a human and canine cancer model).ConclusionsThese proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy.
| S-EPMC6580486 | biostudies-literature
Project description:Joint probabilistic modeling of paired transcriptome and proteome measurements in single cells [CITE-seq of mouse spleen and lymph nodes]
| PRJNA632888 | ENA
Project description:Structure probing coupled with high-throughput sequencing holds the potential to revolutionize our understanding of the role of RNA structure in regulation of gene expression. Despite major technological advances, intrinsic noise and high coverage requirements greatly limit the applicability of these techniques. Here we describe a probabilistic modeling pipeline which accounts for biological variability and biases in the data, yielding statistically interpretable scores for the probability of nucleotide modification transcriptome-wide. We demonstrate on two yeast data sets that our method has greatly increased sensitivity, enabling the identification of modified regions on many more transcripts compared with existing pipelines. It also provides confident predictions at much lower coverage levels than previously reported. Our results show that statistical modeling greatly extends the scope and potential of transcriptome-wide structure probing experiments.
2016-11-01 | GSE78208 | GEO
Project description:We developed a probabilistic method to simultaneously infer genome-wide heterogeneous evolutionary states. We applied the proposed method to analyze a cross-species DNA replication timing (RT) dataset from the same cell type in five primate species. We demonstrated that the proposed model enables discovery of genomic regions with distinct evolutionary patterns of RT.
2018-06-20 | GSE111733 | GEO
Project description:By obtaining clinical specimens from participants with triple negative breast cancer (TNBC), colorectal cancer (CRC), high grade serous ovarian cancer (HGSOC), and other select tumor types to establish and profile as freshly implanted tumors in mice, the aim of this study is to identify agents with predicted activity in the host patient while also potentially providing them with personalized cancer treatment options
| 2212129 | ecrin-mdr-crc
Project description:The paired measurement of RNA and surface protein abundance in single cells with CITE-seq is a promising approach to connect transcriptional variation with cell phenotypes and functions. However, each data modality exhibits unique technical biases, making it challenging to conduct a joint analysis and combine these two views into a unified representation of cell state. Here we present Total Variational Inference (totalVI), a framework for the joint probabilistic analysis of paired RNA and protein data from single cells. totalVI probabilistically represents the data as a composite of biological and technical factors such as limited sensitivity of the RNA data, background in the protein data, and batch effects. To evaluate totalVI, we performed CITE-seq on immune cells from murine spleen and lymph nodes with biological replicates and with different antibody panels measuring over 100 surface proteins. With this dataset we demonstrate that totalVI provides a cohesive solution for common analysis tasks like the integration of datasets with matched or unmatched protein panels, dimensionality reduction, clustering, evaluation of correlations between molecules, and differential expression testing. totalVI enables scalable, end-to-end analysis of paired RNA and protein data from single cells and is available as open-source software.
2020-05-15 | GSE150599 | GEO
Project description:This SuperSeries is composed of the following subset Series: GSE29996: Deep sequencing of gastric carcinoma reveals somatic mutations relevant to personalized medicine [Affymetrix SNP array data] GSE29998: Deep sequencing of gastric carcinoma reveals somatic mutations relevant to personalized medicine [Illumina mRNA expression array data] Refer to individual Series
2012-10-25 | E-GEOD-29999 | biostudies-arrayexpress