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Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity.

ABSTRACT: Predicting clinical response to anticancer drugs remains a major challenge in cancer treatment. Emerging reports indicate that the tumour microenvironment and heterogeneity can limit the predictive power of current biomarker-guided strategies for chemotherapy. Here we report the engineering of personalized tumour ecosystems that contextually conserve the tumour heterogeneity, and phenocopy the tumour microenvironment using tumour explants maintained in defined tumour grade-matched matrix support and autologous patient serum. The functional response of tumour ecosystems, engineered from 109 patients, to anticancer drugs, together with the corresponding clinical outcomes, is used to train a machine learning algorithm; the learned model is then applied to predict the clinical response in an independent validation group of 55 patients, where we achieve 100% sensitivity in predictions while keeping specificity in a desired high range. The tumour ecosystem and algorithm, together termed the CANScript technology, can emerge as a powerful platform for enabling personalized medicine.

SUBMITTER: Majumder B

PROVIDER: S-EPMC4351621 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity.

Majumder Biswanath B Baraneedharan Ulaganathan U Thiyagarajan Saravanan S Radhakrishnan Padhma P Narasimhan Harikrishna H Dhandapani Muthu M Brijwani Nilesh N Pinto Dency D DD Prasath Arun A Shanthappa Basavaraja U BU Thayakumar Allen A Surendran Rajagopalan R Babu Govind K GK Shenoy Ashok M AM Kuriakose Moni A MA Bergthold Guillaume G Horowitz Peleg P Loda Massimo M Beroukhim Rameen R Agarwal Shivani S Sengupta Shiladitya S Sundaram Mallikarjun M Majumder Pradip K PK

Nature communications 20150227

Predicting clinical response to anticancer drugs remains a major challenge in cancer treatment. Emerging reports indicate that the tumour microenvironment and heterogeneity can limit the predictive power of current biomarker-guided strategies for chemotherapy. Here we report the engineering of personalized tumour ecosystems that contextually conserve the tumour heterogeneity, and phenocopy the tumour microenvironment using tumour explants maintained in defined tumour grade-matched matrix support ...[more]

PMID: 25721094

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Project description:Multiple myeloma remains treatable but incurable. Despite a growing armamentarium of effective agents, choice of therapy, especially in relapse, still relies almost exclusively on clinical acumen. We have developed a system, Ex vivo Mathematical Myeloma Advisor (EMMA), consisting of patient-specific mathematical models parameterized by an ex vivo assay that reverse engineers the intensity and heterogeneity of chemosensitivity of primary cells from multiple myeloma patients, allowing us to predict clinical response to up to 31 drugs within 5 days after bone marrow biopsy. From a cohort of 52 multiple myeloma patients, EMMA correctly classified 96% as responders/nonresponders and correctly classified 79% according to International Myeloma Working Group stratification of level of response. We also observed a significant correlation between predicted and actual tumor burden measurements (Pearson r = 0.5658, P < 0.0001). Preliminary estimates indicate that, among the patients enrolled in this study, 60% were treated with at least one ineffective agent from their therapy combination regimen, whereas 30% would have responded better if treated with another available drug or combination. Two in silico clinical trials with experimental agents ricolinostat and venetoclax, in a cohort of 19 multiple myeloma patient samples, yielded consistent results with recent phase I/II trials, suggesting that EMMA is a feasible platform for estimating clinical efficacy of drugs and inclusion criteria screening. This unique platform, specifically designed to predict therapeutic response in multiple myeloma patients within a clinically actionable time frame, has shown high predictive accuracy in patients treated with combinations of different classes of drugs. The accuracy, reproducibility, short turnaround time, and high-throughput potential of this platform demonstrate EMMA's promise as a decision support system for therapeutic management of multiple myeloma. Cancer Res; 77(12); 3336-51. ©2017 AACR.

Dataset Information

Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity.

Publications

Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity.

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