Kreuzaler, P;
Clarke, MA;
Brown, EJ;
Wilson, CH;
Kortlever, RM;
Piterman, N;
Littlewood, T;
... Fisher, J; + view all
(2019)
Heterogeneity of Myc expression in breast cancer exposes pharmacological vulnerabilities revealed through executable mechanistic modeling.
Proceedings of the National Academy of Sciences
, 116
(44)
pp. 22399-22408.
10.1073/pnas.1903485116.
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Abstract
Cells with higher levels of Myc proliferate more rapidly and supercompetitively eliminate neighboring cells. Nonetheless, tumor cells in aggressive breast cancers typically exhibit significant and stable heterogeneity in their Myc levels, which correlates with refractoriness to therapy and poor prognosis. This suggests that Myc heterogeneity confers some selective advantage on breast tumor growth and progression. To investigate this, we created a traceable MMTV-Wnt1-driven in vivo chimeric mammary tumor model comprising an admixture of low-Myc- and reversibly switchable high-Myc-expressing clones. We show that such tumors exhibit interclonal mutualism wherein cells with high-Myc expression facilitate tumor growth by promoting protumorigenic stroma yet concomitantly suppress Wnt expression, which renders them dependent for survival on paracrine Wnt provided by low-Myc-expressing clones. To identify any therapeutic vulnerabilities arising from such interdependency, we modeled Myc/Ras/p53/Wnt signaling cross talk as an executable network for low-Myc, for high-Myc clones, and for the 2 together. This executable mechanistic model replicated the observed interdependence of high-Myc and low-Myc clones and predicted a pharmacological vulnerability to coinhibition of COX2 and MEK. This was confirmed experimentally. Our study illustrates the power of executable models in elucidating mechanisms driving tumor heterogeneity and offers an innovative strategy for identifying combination therapies tailored to the oligoclonal landscape of heterogenous tumors.
| Type: | Article |
|---|---|
| Title: | Heterogeneity of Myc expression in breast cancer exposes pharmacological vulnerabilities revealed through executable mechanistic modeling |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1073/pnas.1903485116 |
| Publisher version: | https://doi.org/10.1073/pnas.1903485116 |
| Language: | English |
| Additional information: | © 2019 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Myc, breast cancer, cancer heterogeneity, computational modeling, oncogenic signaling |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Pathology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10084611 |
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