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

Majumder, Biswanath and Baraneedharan, Ulaganathan and Thiyagarajan, Saravanan and Radhakrishnan, Padhma and Narasimhan, Harikrishna and Dhandapani, Muthu and Brijwani, Nilesh and Pinto, Dency D and Prasath, Arun and Shanthappa, Basavaraja U and Thayakumar, Allen and Surendran, Rajagopalan and Babu, Govind K and Shenoy, Ashok M and Kuriakose, Moni A and Bergthold, Guillaume and Horowitz, Peleg and Loda, Massimo and Beroukhim, Rameen and Agarwal, Shivani and Sengupta, Shiladitya and Sundaram, Mallikarjun and Majumder, Pradip K (2015) Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity. In: NATURE COMMUNICATIONS, 6 .

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Official URL: http://dx.doi.org/10.1038/ncomms7169

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.

Item Type: Journal Article
Additional Information: Copy right for this article belongs to the NATURE PUBLISHING GROUP, MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
Keywords: METASTATIC COLORECTAL-CANCER; SQUAMOUS-CELL CARCINOMA; BREAST-CANCER; GROWTH-FACTOR; LUNG-CANCER; EXTRACELLULAR-MATRIX; SIGNALING PATHWAYS; MASS-SPECTROMETRY; PROGENITOR CELLS; AKT ACTIVATION
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Depositing User: Id for Latest eprints
Date Deposited: 20 Apr 2015 07:32
Last Modified: 20 Apr 2015 07:32
URI: http://eprints.iisc.ac.in/id/eprint/51255

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