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Towards a PBMC "virogram assay" for precision medicine: concordance between ex vivo and in vivo viral infection transcriptomes

ABSTRACT: 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".

ORGANISM(S): Homo sapiens

PROVIDER: GSE60153 | GEO | 2014/08/07

SECONDARY ACCESSION(S): PRJNA257639

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)

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.

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.

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Towards a PBMC "virogram assay" for precision medicine: concordance between ex vivo and in vivo viral infection transcriptomes

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