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Tumour heterogeneity in the clinic

Author

Listed:
  • Philippe L. Bedard

    (Princess Margaret Cancer Centre, University Health Network
    University of Toronto)

  • Aaron R. Hansen

    (Princess Margaret Cancer Centre, University Health Network
    University of Toronto)

  • Mark J. Ratain

    (The University of Chicago)

  • Lillian L. Siu

    (Princess Margaret Cancer Centre, University Health Network
    University of Toronto)

Abstract

Recent therapeutic advances in oncology have been driven by the identification of tumour genotype variations between patients, called interpatient heterogeneity, that predict the response of patients to targeted treatments. Subpopulations of cancer cells with unique genomes in the same patient may exist across different geographical regions of a tumour or evolve over time, called intratumour heterogeneity. Sequencing technologies can be used to characterize intratumour heterogeneity at diagnosis, monitor clonal dynamics during treatment and identify the emergence of clinical resistance during disease progression. Genetic interpatient and intratumour heterogeneity can pose challenges for the design of clinical trials that use these data.

Suggested Citation

  • Philippe L. Bedard & Aaron R. Hansen & Mark J. Ratain & Lillian L. Siu, 2013. "Tumour heterogeneity in the clinic," Nature, Nature, vol. 501(7467), pages 355-364, September.
    • Handle: RePEc:nat:nature:v:501:y:2013:i:7467:d:10.1038_nature12627
    DOI: 10.1038/nature12627
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    Citations

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    Cited by:

    1. Christopher R S Banerji & Simone Severini & Carlos Caldas & Andrew E Teschendorff, 2015. "Intra-Tumour Signalling Entropy Determines Clinical Outcome in Breast and Lung Cancer," PLOS Computational Biology, Public Library of Science, vol. 11(3), pages 1-23, March.
    2. Shiqian Ma & Daniel Johnson & Cody Ashby & Donghai Xiong & Carole L Cramer & Jason H Moore & Shuzhong Zhang & Xiuzhen Huang, 2015. "SPARCoC: A New Framework for Molecular Pattern Discovery and Cancer Gene Identification," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-19, March.
    3. Jae-Woong Min & Woo Jin Kim & Jeong A Han & Yu-Jin Jung & Kyu-Tae Kim & Woong-Yang Park & Hae-Ock Lee & Sun Shim Choi, 2015. "Identification of Distinct Tumor Subpopulations in Lung Adenocarcinoma via Single-Cell RNA-seq," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-17, August.
    4. Li Chen & Peter L Choyke & Niya Wang & Robert Clarke & Zaver M Bhujwalla & Elizabeth M C Hillman & Ge Wang & Yue Wang, 2014. "Unsupervised Deconvolution of Dynamic Imaging Reveals Intratumor Vascular Heterogeneity and Repopulation Dynamics," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-9, November.
    5. Benjamin Wölfl & Hedy te Rietmole & Monica Salvioli & Artem Kaznatcheev & Frank Thuijsman & Joel S. Brown & Boudewijn Burgering & Kateřina Staňková, 2022. "The Contribution of Evolutionary Game Theory to Understanding and Treating Cancer," Dynamic Games and Applications, Springer, vol. 12(2), pages 313-342, June.
    6. Lida Qiu & Deyong Kang & Chuan Wang & Wenhui Guo & Fangmeng Fu & Qingxiang Wu & Gangqin Xi & Jiajia He & Liqin Zheng & Qingyuan Zhang & Xiaoxia Liao & Lianhuang Li & Jianxin Chen & Haohua Tu, 2022. "Intratumor graph neural network recovers hidden prognostic value of multi-biomarker spatial heterogeneity," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    7. Jose M. Ayuso & María Virumbrales-Muñoz & Joshua M. Lang & David J. Beebe, 2022. "A role for microfluidic systems in precision medicine," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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