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

Biswanath Majumder, Ulaganathan Baraneedharan, Saravanan Thiyagarajan, Padhma Radhakrishnan, Harikrishna Narasimhan, Muthu Dhandapani, Nilesh Brijwani, Dency D. Pinto, Arun Prasath, Basavaraja U. Shanthappa, Allen Thayakumar, Rajagopalan Surendran, Govind K. Babu, Ashok M. Shenoy, Moni A. Kuriakose, Guillaume Bergthold, Peleg Horowitz, Massimo Loda, Rameen Beroukhim, Shivani Agarwal, Shiladitya Sengupta, Mallikarjun Sundaram and Pradip K. Majumder ()
Additional contact information
Biswanath Majumder: Mitra Biotech
Ulaganathan Baraneedharan: Mitra Biotech
Saravanan Thiyagarajan: Mitra Biotech
Padhma Radhakrishnan: Mitra Biotech
Harikrishna Narasimhan: Indian Institute of Science
Muthu Dhandapani: Mitra Biotech
Nilesh Brijwani: Mitra Biotech
Dency D. Pinto: Mitra Biotech
Arun Prasath: Mitra Biotech
Basavaraja U. Shanthappa: Mitra Biotech
Allen Thayakumar: Mitra Biotech
Rajagopalan Surendran: Government Stanley Medical College
Govind K. Babu: Kidwai Memorial Institute of Oncology
Ashok M. Shenoy: Kidwai Memorial Institute of Oncology
Moni A. Kuriakose: Mazumdar-Shaw Cancer Center
Guillaume Bergthold: The Broad Institute of The Massachusetts Institute of Technology and Harvard University
Peleg Horowitz: The Broad Institute of The Massachusetts Institute of Technology and Harvard University
Massimo Loda: The Broad Institute of The Massachusetts Institute of Technology and Harvard University
Rameen Beroukhim: Brigham and Women’s Hospital, Harvard Medical School
Shivani Agarwal: Indian Institute of Science
Shiladitya Sengupta: Brigham and Women’s Hospital, Harvard Medical School
Mallikarjun Sundaram: Mitra Biotech
Pradip K. Majumder: Mitra Biotech

Nature Communications, 2015, vol. 6, issue 1, 1-14

Abstract: 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.

Date: 2015
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DOI: 10.1038/ncomms7169

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