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Predicting biomaterial property-dendritic cell phenotype relationships from the multivariate analysis of responses to polymethacrylates.

ABSTRACT: Dendritic cells (DCs) play a critical role in orchestrating the host responses to a wide variety of foreign antigens and are essential in maintaining immune tolerance. Distinct biomaterials have been shown to differentially affect the phenotype of DCs, which suggested that biomaterials may be used to modulate immune response toward the biologic component in combination products. The elucidation of biomaterial property-DC phenotype relationships is expected to inform rational design of immuno-modulatory biomaterials. In this study, DC response to a set of 12 polymethacrylates (pMAs) was assessed in terms of surface marker expression and cytokine profile. Principal component analysis (PCA) determined that surface carbon correlated with enhanced DC maturation, while surface oxygen was associated with an immature DC phenotype. Partial square linear regression, a multivariate modeling approach, was implemented and successfully predicted biomaterial-induced DC phenotype in terms of surface marker expression from biomaterial properties with R(prediction)(2) = 0.76. Furthermore, prediction of DC phenotype was effective based on only theoretical chemical composition of the bulk polymers with R(prediction)(2) = 0.80. These results demonstrated that immune cell response can be predicted from biomaterial properties, and computational models will expedite future biomaterial design and selection.

SUBMITTER: Kou PM

PROVIDER: S-EPMC3399429 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature

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Predicting biomaterial property-dendritic cell phenotype relationships from the multivariate analysis of responses to polymethacrylates.

Kou Peng Meng PM Pallassana Narayanan N Bowden Rebeca R Cunningham Barry B Joy Abraham A Kohn Joachim J Babensee Julia E JE

Biomaterials 20111201 6

Dendritic cells (DCs) play a critical role in orchestrating the host responses to a wide variety of foreign antigens and are essential in maintaining immune tolerance. Distinct biomaterials have been shown to differentially affect the phenotype of DCs, which suggested that biomaterials may be used to modulate immune response toward the biologic component in combination products. The elucidation of biomaterial property-DC phenotype relationships is expected to inform rational design of immuno-mod ...[more]

PMID: 22136715

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Project description:Purpose: Despite advances in radical surgery and chemotherapy delivery, ovarian cancer is the most lethal gynecologic malignancy. Most of these patients are treated with platinum-based chemotherapies, but there is no biomarker model to guide their responses to these therapeutic agents. We have developed and independently tested our novel multivariate molecular predictors for forecasting patients' responses to individual drugs on a cohort of 58 ovarian cancer patients. Experimental Design: We adapted and applied the previously-published COXEN algorithm to develop molecular predictors for therapeutic responses of patients' tumors based on expression signatures derived from the NCI-60 in vitro drug activities and genomic expression data. Genome-wide candidate biomarkers were first triaged by examining expression patterns of frozen and formalin-fixed paraffin embedded (FFPE) tissue samples. We then identify initial drug sensitivity biomarkers for carboplatin and paclitaxel, respectively. These biomarkers were further narrowed by examining concordant expression patterns between cell lines and a historical set of ovarian cancer patients. Multivariate predictors were obtained from the NCI-60 cell lines and refined using historical patient cohorts. To independent validate these molecular predictors, we performed genome-wide profiling on FFPE samples of 58 ovarian cancer patients obtained prior to adjuvant chemotherapy. Results: Carboplatin predictor significantly stratified platinum sensitive and resistant patients (p = 0.019) with sensitivity = 93%, specificity = 33%, PPV = 65%, and NPV = 78%. Paclitaxel predictor also significantly stratified patients' responses (p = 0.033) with sensitivity = 96%, specificity = 26%, PPV = 61%, and NPV = 86%. The combination predictor for platinum-taxane combination demonstrated a significant survival difference between the predicted responders and nonresponders with median survival of 12.9 months vs. 8.1 months (p = 0.045). Conclusions: COXEN predictors successfully stratified platinum resistance and taxane response in this retrospective cohort, especially based on their FFPE tumor samples. Accurate prediction of chemotherapeutic response, especially to platinum agents is highly clinically relevant and could alter primary management of ovarian cancer.

Dataset Information

Predicting biomaterial property-dendritic cell phenotype relationships from the multivariate analysis of responses to polymethacrylates.

Publications

Predicting biomaterial property-dendritic cell phenotype relationships from the multivariate analysis of responses to polymethacrylates.

Similar Datasets

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