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

Author

Listed:
  • 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
    Brigham and Women’s Hospital, Harvard Medical School
    Children’s Hospital)

  • Massimo Loda

    (The Broad Institute of The Massachusetts Institute of Technology and Harvard University
    Brigham and Women’s Hospital, Harvard Medical School)

  • Rameen Beroukhim

    (Brigham and Women’s Hospital, Harvard Medical School
    Children’s Hospital)

  • Shivani Agarwal

    (Indian Institute of Science)

  • Shiladitya Sengupta

    (Brigham and Women’s Hospital, Harvard Medical School
    India Innovation Research Center
    Harvard-MIT Division of Health Sciences and Technology)

  • Mallikarjun Sundaram

    (Mitra Biotech)

  • Pradip K. Majumder

    (Mitra Biotech
    India Innovation Research Center)

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.

Suggested Citation

  • Biswanath Majumder & Ulaganathan Baraneedharan & Saravanan Thiyagarajan & Padhma Radhakrishnan & Harikrishna Narasimhan & Muthu Dhandapani & Nilesh Brijwani & Dency D. Pinto & Arun Prasath & Basavaraj, 2015. "Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity," Nature Communications, Nature, vol. 6(1), pages 1-14, May.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms7169
    DOI: 10.1038/ncomms7169
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    Cited by:

    1. JungHo Kong & Doyeon Ha & Juhun Lee & Inhae Kim & Minhyuk Park & Sin-Hyeog Im & Kunyoo Shin & Sanguk Kim, 2022. "Network-based machine learning approach to predict immunotherapy response in cancer patients," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

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