Summary We hypothesize that dosage compensation of critical genes arises from systems-level properties for cancer cells to withstand the negative effects of aneuploidy. We identified several candidate genes in cancer multiomics data and developed a biocomputational platform to construct a mathematical model of their interaction network with micro-RNAs and transcription factors, where the property of dosage compensation emerged for MYC and was dependent on the kinetic parameters of its feedback interactions with three micro-RNAs. These circuits were experimentally validated using a genetic tug-of-war technique to overexpress an exogenous MYC , leading to overexpression of the three microRNAs involved and downregulation of endogenous MYC. In addition, MYC overexpression or inhibition of its compensating miRNAs led to dosage-dependent cytotoxicity in MYC -amplified colon cancer cells. Finally, we identified negative correlation of MYC dosage compensation with patient survival in TCGA breast cancer patients, highlighting the potential of this mechanism to prevent aneuploid cancer progression.

Abstract-Cancer complexity and resistance is mediated by cell-to-cell heterogeneity, which is the consequence of the enormous instability of its genetic material. It is unknown how cancer cells are able to withstand the effects of these alterations, while normal cells are typically very sensitive. We hypothesize that cancer requires specific type of stability to survive the enormous chromosomal alterations. This stability may be mediated by a group of genes, whose expression is tightly regulated to maintain viability through a process called gene dosage compensation. This mechanism could be mediated by systems-level properties of complex networks of microRNAs (miRNA) and transcription factors (TF), regulating gene expression despite changes in copy number. Therefore, we designed a biocomputational platform to automatically construct large-scale mathematical models regulating the expression of several candidate genes under dosage compensation. This platform has a broader potential application to other scientific questions involving miRNA and TF networks.

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