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

Artikel i vetenskaplig tidskrift
Författare P. Kreuzaler
M. A. Clarke
E. J. Brown
C. H. Wilson
R. M. Kortlever
Nir Piterman
T. Littlewood
G. I. Evan
J. Fisher
Publicerad i Proceedings of the National Academy of Sciences of the United States of America
Volym 116
Nummer/häfte 44
Sidor 22399-22408
ISSN 0027-8424
Publiceringsår 2019
Publicerad vid Institutionen för data- och informationsteknik, datorteknik (GU)
Sidor 22399-22408
Språk en
Länkar dx.doi.org/10.1073/pnas.1903485116
Ämneskategorier Cancer och onkologi

Sammanfattning

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.

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