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Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines.

ABSTRACT: We demonstrate a method for the prediction of chemotherapeutic response in patients using only before-treatment baseline tumor gene expression data. First, we fitted models for whole-genome gene expression against drug sensitivity in a large panel of cell lines, using a method that allows every gene to influence the prediction. Following data homogenization and filtering, these models were applied to baseline expression levels from primary tumor biopsies, yielding an in vivo drug sensitivity prediction. We validated this approach in three independent clinical trial datasets, and obtained predictions equally good, or better than, gene signatures derived directly from clinical data.

SUBMITTER: Geeleher P

PROVIDER: S-EPMC4054092 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines.

Geeleher Paul P Cox Nancy J NJ Huang R Stephanie RS

Genome biology 20140303 3

We demonstrate a method for the prediction of chemotherapeutic response in patients using only before-treatment baseline tumor gene expression data. First, we fitted models for whole-genome gene expression against drug sensitivity in a large panel of cell lines, using a method that allows every gene to influence the prediction. Following data homogenization and filtering, these models were applied to baseline expression levels from primary tumor biopsies, yielding an in vivo drug sensitivity pre ...[more]

PMID: 24580837

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Project description:BackgroundTrastuzumab is a humanized monoclonal antibody (mAb) currently used for the treatment of breast cancer (BC) patients with HER-2 overexpressing tumor subtype. Previous data reported the involvement of FcγRIIIA/IIA gene polymorphisms and/or antibody-dependent cellular cytotoxicity (ADCC) in the therapeutic efficacy of trastuzumab, although results on these issues are still controversial. This study was aimed to evaluate in vitro the functional relationships among FcγRIIIA/IIA polymorphisms, ADCC intensity and HER-2 expression on tumor target cells and to correlate them with response to trastuzumab.Patients and methodsTwenty-five patients with HER-2 overexpressing BC, receiving trastuzumab in a neoadjuvant (NEO) or metastatic (MTS) setting, were genotyped for the FcγRIIIA 158V>F and FcγRIIA 131H>R polymorphisms by a newly developed pyrosequencing assay and by multiplex Tetra-primer-ARMS PCR, respectively. Trastuzumab-mediated ADCC of patients' peripheral blood mononuclear cells (PBMCs) was evaluated prior to therapy and measured by (51)Chromium release using as targets three human BC cell lines showing different levels of reactivity with trastuzumab.ResultsWe found that the FcγRIIIA 158F and/or the FcγRIIA 131R variants, commonly reported as unfavorable in BC, may actually behave as ADCC favorable genotypes, in both the NEO (P ranging from 0.009 to 0.039 and from 0.007 to 0.047, respectively) and MTS (P ranging from 0.009 to 0.032 and P = 0.034, respectively) patients. The ADCC intensity was affected by different levels of trastuzumab reactivity with BC target cells. In this context, the MCF-7 cell line, showing the lowest reactivity with trastuzumab, resulted the most suitable cell line for evaluating ADCC and response to trastuzumab. Indeed, we found a statistically significant correlation between an increased frequency of patients showing ADCC of MCF-7 and complete response to trastuzumab in the NEO setting (P = 0.006).ConclusionsAlthough this study was performed in a limited number of patients, it would indicate a correlation of FcγR gene polymorphisms to the ADCC extent in combination with the HER-2 expression levels on tumor target cells in BC patients. However, to confirm our findings further experimental evidences obtained from a larger cohort of BC patients are mandatory.

Project description:BackgroundAlthough substantial efforts have been made to build molecular biomarkers to predict radiation sensitivity, the ability to accurately stratify the patients is still limited. In this study, we aim to leverage large-scale radiogenomics datasets to build genomic predictors of radiation response using the integral of the radiation dose-response curve.MethodsTwo radiogenomics datasets consisting of 511 and 60 cancer cell lines were utilized to develop genomic predictors of radiation sensitivity. The intrinsic radiation sensitivity, defined as the integral of the dose-response curve (AUC) was used as the radioresponse variable. The biological determinants driving AUC and SF2 were compared using pathway analysis. To build the predictive model, the largest and smallest datasets consisting of 511 and 60 cancer cell lines were used as the discovery and validation cohorts, respectively, with AUC as the response variable.ResultsUtilizing a compendium of three pathway databases, we illustrated that integral of the radiobiological model provides a more comprehensive characterization of molecular processes underpinning radioresponse compared to SF2. Furthermore, more pathways were found to be unique to AUC than SF2-30, 288 and 38 in KEGG, REACTOME and WIKIPATHWAYS, respectively. Also, the leading-edge genes driving the biological pathways using AUC were unique and different compared to SF2. With regards to radiation sensitivity gene signature, we obtained a concordance index of 0.65 and 0.61 on the discovery and validation cohorts, respectively.ConclusionWe developed an integrated framework that quantifies the impact of physical radiation dose and the biological effect of radiation therapy in interventional pre-clinical model systems. With the availability of more data in the future, the clinical potential of this signature can be assessed, which will eventually provide a framework to integrate genomics into biologically-driven precision radiation oncology.

Dataset Information

Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines.

Publications

Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines.

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