Ex vivo drug sensitivity testing predicts treatment outcomes in advanced ovarian cancer
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE308072 | GEO | 2026/07/22
REPOSITORIES: GEO
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Project description:Background: Understanding individual patient host-response to viruses is key to designing optimal personalized therapy. Indeed, a subjectM-bM-^@M-^Ys epigenetic, transcriptomic and proteomic profiles dynamically respond to environmental challenges according to their intrinsic specific genetics. Unsurprisingly, in vivo human experimentation to understand individualized dynamic response of the transcriptome to viruses are rarely studied because of the obviously limitations stemming from ethical considerations of the clinical risk. In this rhinovirus study, we first establish that ex vivo human cells response to virus can serve as proxy for otherwise controversial in vivo human experimentation of viral infection. We further demonstrate that the N-of-1-pathways framework, that we previously validated for single patient M-bM-^@M-^Xomics analyses in cancer, can be utilized to understand individualized ex vivo response to viral stress and inform clinicians involved in personalized therapeutics. Method: N-of-1-pathways, validated retrospectively in whole tumors and experimentally in cancer cell lines, is a framework designed for identifying deregulated pathways at the single patient level between two M-bM-^@M-^Xomics profiles. It computes a significance score for a list of given genesets, using the M-bM-^@M-^Xomics profiles of a mere two samples as input (e.g. normal/tumoral, pre/post-treatment). We hypothesized that its usage could be extended to virus exposed cells. Additionally, we developed the Similarity Venn Diagram, an efficient and deceptively simple method for comparing results expressed in an ontology organized as a directed acyclic graph. We extracted the peripheral blood of four human subjects, with and without an ex vivo infection to rhinovirus. Their deregulated pathways were compared to those of 9 human subjects prior and after intranasal inoculation M-bM-^@M-^\in vivoM-bM-^@M-^] with rhinovirus. Results: We compared the N-of-1-pathways results using two established cohort-level methodologies: GSEA and enrichment of differentially expressed genes. Results are significantly and biologically similar between in vivo and ex vivo studies, both at the genes and enriched pathways levels. ROC curves demonstrate that deregulated pathways identified by N-of-1-pathways in cells each single subject infected ex vivo recapitulate the biologically relevant pathways observed in vivo in a whole cohort. Conclusion: In the context of less than five published transcriptomes of human viral infections in vivo and one ex vivo, the latter can be supplemented by 2-sample analyses yielding increased insight in individualized response without clinical risks. PBMCs incubated with viruses: The live PBMCs had been isolated from blood samples collected from four human subjects under a protocol approved by The University of Arizona Internal Review Board. Whole blood was obtained from donors and placed in heparin tubes that were centrifuged according to standard protocols to obtain PBMCs, then each aliquoted in two paired samples. Each sample of the pair was subsequently exposed to and incubated with either (i) Human Rhinovirus serotype 16 obtained from the American Type Culture Collection (RV; ATCCM-BM-. VR-283; ex vivo infected sample), or to (ii) sterile medium (control ex vivo non-infected sample) and incubated at 35M-BM-0C. This protocol resulted in 4 ex vivo infected + 4 ex vivo controls = 8 paired samples. RNA was extracted from these samples, amplified, tagged and hybridized on Affymetrix Human Gene 1.0 ST microarrays according to standard operating procedures. Dataset and preprocessing: Robust Multiple-array Average (RMA) normalization was applied on each patient data independently (2 paired samples at a time, to avoid bias in the single-patient experiments) using Affymetrix Power Tools (APT)
2014-08-07 | E-GEOD-60153 | biostudies-arrayexpress
Project description:Acute myeloid leukemia (AML) is characterized by malignant myeloid precursors that span a cellular hierarchy from dedifferentiated leukemic stem cells to mature blasts. While the diagnostic and prognostic importance of AML blast maturation is increasingly recognized, personalized therapies are currently not tailored to a patient’s individual makeup of this cellular hierarchy. In this study, we use multiplexed image-based ex vivo drug screening (pharmacoscopy) to systematically quantify the drug sensitivity across the cellular hierarchy of AML patients. We analyzed 174 prospective and longitudinal patient samples from 44 newly diagnosed AML patients, which indicated that differences in the AML hierarchy significantly identified poor responses to first-line therapy, outperforming European LeukemiaNet (ELN) criteria. Critically, drug response profiling across the AML hierarchy of each patient improved the accuracy of predicting patient response to first-line therapy (AUC 0.91), and revealed alternative individualized treatment options targeting the complete AML hierarchy of non-responding patients. We confirmed these findings in an independent cohort of 26 relapsed/refractory AML patients, for whom pan-hierarchy response profiling improved response predictions post hoc. Overall, our results quantify the clinical importance of therapeutically targeting the complete cellular hierarchy of newly diagnosed AML, and identify multiplexed image-based ex vivo drug screening to enable quantification and targeting of the AML maturation hierarchy for improved personalized treatment.
2024-07-20 | GSE272681 | GEO
Project description:Approximately 30% of chronic myelomonocytic leukemia patients undergo transformation to a chemo-refractory blastic phase (BP-CMML). Seeking novel therapeutic approaches we profiled blast transcriptomes from 42 BP-CMMLs, observing extensive transcriptional heterogeneity and poor alignment to current AML classifications. BP-CMMLs displayed distinctive transcriptomic profiles, including enrichment for quiescence and variability in drug response signatures. Integrating clinical, immunophenotype and transcriptome parameters, RandomForest unsupervised clustering distinguished immature and mature subtypes characterized by differential expression of transcriptional modules, oncogenes, apoptotic regulators and patterns of surface marker expression. Subtypes differed in predicted response to AML drugs, validated ex vivo in primary samples. Iteratively refined stratification resolved a classification structure comprising five subtypes along a maturation spectrum, predictive of response to novel agents including consistent patterns for RTK, CDK, MTOR and MAPK inhibitors. Finally, we generated a clinically-applicable decision tree to stratify BP-CMML with high specificity and sensitivity, and potentially guide personalized drug selection for improved outcomes.
2024-12-20 | GSE251806 | GEO
Project description:BACKGROUND: Understanding individual patient host response to viruses is key to designing optimal personalized therapy. Unsurprisingly, in-vivo human experimentation to understand individualized dynamic response of the transcriptome to viruses are rarely studied because of the obvious limitations stemming from ethical considerations of the clinical risk. OBJECTIVE: In this rhinovirus study, we first hypothesized that ex-vivo human cells response to virus can serve as a proxy for otherwise controversial in-vivo human experimentation. We further hypothesized that the N-of-1-pathways framework, previously validated in cancer, can be effective in understanding the more subtle individual transcriptomic response to viral infection. METHOD: N-of-1-pathways computes a significance score for a given list of gene sets at the patient level, using merely the 'omics profiles of two paired samples as input. We extracted the peripheral blood mononuclear cells (PBMC) of four human subjects, aliquoted in two paired samples, one subjected to ex-vivo rhinovirus infection. Their dysregulated genes and pathways were then compared to those of 9 human subjects prior and after intranasal inoculation in-vivo with rhinovirus. Additionally, we developed the Similarity Venn Diagram, a novel visualization method that goes beyond conventional overlap to show the similarity between two sets of qualitative measures. RESULTS: We evaluated the individual N-of-1-pathways results using two established cohort-based methods: GSEA and enrichment of differentially expressed genes. Similarity Venn Diagrams and individual patient ROC curves illustrate and quantify that the in-vivo dysregulation is recapitulated ex-vivo both at the gene and pathway level (p-values<0.004). CONCLUSION: We established the first evidence that an interpretable dynamic transcriptome metric, conducted as ex-vivo assays for a single subject, has the potential to predict individualized response to infectious disease without the clinical risks otherwise associated to in-vivo challenges. These results serve as a foundational work for personalized "virograms".
2014-08-07 | GSE60153 | GEO
Project description:Predicting Outcomes of Prostate Cancer Immunotherapyby Personalized Mathematical Models
Natalie Kronik1¤, Yuri Kogan1, Moran Elishmereni1, Karin Halevi-Tobias1, Stanimir Vuk-Pavlovic ́2.,Zvia Agur1*.1Institute for Medical BioMathematics, Bene Ataroth, Israel,2College of Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
Abstract
Background:Therapeutic vaccination against disseminated prostate cancer (PCa) is partially effective in some PCa patients.We hypothesized that the efficacy of treatment will be enhanced by individualized vaccination regimens tailored by simplemathematical models.Methodology/Principal Findings:We developed a general mathematical model encompassing the basic interactions of avaccine, immune system and PCa cells, and validated it by the results of a clinical trial testing an allogeneic PCa whole-cellvaccine. For model validation in the absence of any other pertinent marker, we used the clinically measured changes inprostate-specific antigen (PSA) levels as a correlate of tumor burden. Up to 26 PSA levels measured per patient were dividedinto each patient’s training set and his validation set. The training set, used for model personalization, contained thepatient’s initial sequence of PSA levels; the validation set contained his subsequent PSA data points. Personalized modelswere simulated to predict changes in tumor burden and PSA levels and predictions were compared to the validation set.The model accurately predicted PSA levels over the entire measured period in 12 of the 15 vaccination-responsive patients(the coefficient of determination between the predicted and observed PSA values wasR2= 0.972). The model could notaccount for the inconsistent changes in PSA levels in 3 of the 15 responsive patients at the end of treatment. Each validatedpersonalized model was simulated under many hypothetical immunotherapy protocols to suggest alternative vaccinationregimens. Personalized regimens predicted to enhance the effects of therapy differed among the patients.Conclusions/Significance:Using a few initial measurements, we constructed robust patient-specific models of PCaimmunotherapy, which were retrospectively validated by clinical trial results. Our results emphasize the potential value andfeasibility of individualized model-suggested immunotherapy protocols.
2020-01-13 | MODEL2001130003 | BioModels
Project description:Multiple myeloma (MM) is a plasma cell malignancy defined by complex genetics and extensive patient heterogeneity. Despite a growing arsenal of approved therapies, MM remains incurable and in need of guidelines to identify effective personalized treatments. Here, we survey the ex vivo drug and immunotherapy sensitivities across 101 bone marrow (BM) samples from 70 MM patients using multiplexed immunofluorescence, automated microscopy and deep learning-based single-cell phenotyping. Combined with sample-matched genetics, proteotyping, and cytokine profiling, we map the molecular regulatory network of drug sensitivity, implicating the DNA-repair pathway and EYA3 expression in proteasome inhibitor sensitivity, and MHC class II expression in the response to Elotuzumab. Globally, ex vivo drug sensitivity associated with BM microenvironmental signatures reflecting treatment stage, clonality, and inflammation. Furthermore, ex vivo drug sensitivity significantly stratified clinical treatment responses, including to immunotherapy. Taken together, our study provides molecular and actionable insights into diverse treatment strategies for multiple myeloma patients.
2022-03-06 | MSV000088992 | MassIVE
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:<p>The implementation of targeted therapies for acute myeloid leukemia has been challenged by complex mutational patterns within and across patients as well as a dearth of pharmacologic agents for most mutational events. Here, we report initial findings from the Beat AML program on a cohort of 672 tumor specimens collected from 562 patients. We assessed these specimens using whole exome sequencing, RNA-sequencing, and ex vivo drug sensitivity analyses. Our data reveal novel mutational events not previously detected in AML. We show association of drug response with mutational status, including instances of drug sensitivity that are specific to combinatorial mutational events. Integration with RNA-sequencing also revealed gene expression signatures, which predict a role of specific gene networks in drug response. Collectively, this report offers a dataset, accessible by the Beat AML data viewer <a href="http://www.vizome.org/">(www.vizome.org)</a>, that can be leveraged to address clinical, genomic, transcriptomic, and functional inquiries into the biology of AML.</p>
| phs001657 | dbGaP
Project description:Glioblastoma (GBM) is a devastating brain tumor with limited therapeutic success, largely due to the inability to pre-test patient-specific responses to treatment in a reliable and timely manner with diagnostic and analytical methods as current tests fail to provide actionable insights within the critical timeframes necessary for clinical decision-making. To address this gap, we developed a high-throughput 3D culture system utilizing matrix metalloproteinase-responsive starPEG-sulfated glycosaminoglycan hydrogels. This platform enables encapsulation of patient-derived GBM cells, recreating physiologically relevant microenvironments in 384-well plates for rapid, automated drug testing on primary tumor cells from patients. Transcriptomic analyses revealed that 3D cultures significantly resemble primary and recurrent GBM transcriptomic profiles, particularly in pathways critical for tumor growth, invasion, and therapeutic resistance, such as hypoxia, immune modulation, and extracellular matrix regulation. The platform’s versatility extends to drug screening, where single and combinatorial chemotherapeutic treatments elicited robust and reproducible cytoskeletal and transcriptomic responses. Notably, the system effectively characterized synergistic interactions between 5-fluorouracil and carmustine, highlighting its potential for optimizing combinatorial therapies. Our 3D model surpasses traditional 2D cultures in representing tumor-specific molecular programs, offering an invaluable tool for translational research. By delivering reliable, individualized data on therapeutic efficacy within a clinically relevant timeframe, this model holds transformative potential for personalized medicine, enabling optimized, patient-specific treatment strategies for GBM. Its scalability and compatibility with high-throughput workflows position this system as a critical innovation in GBM research, advancing the paradigm of personalized medicine in neuro-oncology.
2026-01-21 | GSE290610 | GEO
Project description:Epithelial ovarian cancer (EOC) is the main subtype of ovarian cancer. In this study, we found that Ependymin-related 1 (EPDR1) was significantly downregulated in EOC tissues and low EPDR1 expression was associated with International Federation of Gynecology and Obstetrics (FIGO) stage, metastasis and poor prognosis. We confirmed that EPDR1 overexpression dramatically inhibited EOC cells proliferation, migration and invasion in vitro and in vivo. Mechanistically, EPDR1 suppressed EOC tumorigenesis and progression, at least in part, through inhibiting PI3K/AKT pathway. Furthermore, the expression and function were regulated by miR-429, as demonstrated by luciferase reporter assays and rescue experiments. In conclusion, our study demonstrated that EPDR1, negatively regulated by miR-429, played an important role in EOC development via PI3K/AKT pathway. The miR-429/EPDR1 axis might provide promising therapeutic targets for individualized treatment of EOC patients in future.
2021-11-18 | GSE188918 | GEO