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Heterogeneity of Myc expression in breast cancer exposes pharmacological vulnerabilities revealed through executable mechanistic modeling

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journal contribution
posted on 2019-10-25, 13:16 authored by N Piterman, P Kreuzaler, MA Clarke, EJ Brown, CH Wilson, RM Kortlever, T Littlewood, GI Evans, J Fisher
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.


We thank Alessandra Perfetto for help with animal husbandry, Ben Hall and David Shorthouse for valuable discussions on analyzing qualitative network models, Victoria Wang for help with modeling the Ras pathway, and Dan Lu for helpful discussions. J.F. is a member of the Mark Foundation Institute for Integrated Cancer Medicine at the University of Cambridge. This work was generously supported by Cancer Research UK (to G.I.E.) (Programme Grant A12077); the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001223), the UK Medical Research Council (FC001223), and the Wellcome Trust (FC001223) (to P.K.); Trinity College Cambridge (to P.K.); the Kay Kendall Leukaemia Fund KKL1045 (to G.I.E.); the Wellcome Trust Mathematical Genomics and Medicine Program (102274/Z/13/Z); the Glover Research Fund hosted at the Department of Biochemistry, University of Cambridge (to M.A.C.); and Microsoft Research (to J.F.).



Proceedings of the National Academy of Sciences, 2019

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