Mathematical deconvolution uncovers the genetic regulatory signal of cancer cellular heterogeneity on resistance to paclitaxel.

Drug resistance remains a major problem in combating malignancies, resulting critical the resistance to paclitaxel used in the treatment of many different cancers. Elucidating the cellular heterogeneity composition of tumours may be relevant to designing more effective treatment strategies on drug resistance. In particular, such heterogeneity correlates with the measurement of gene expression below the population level. However, experimental assays capturing differential response are limited and cannot discern the variation in gene expression specific to different cellular types in tumour populations. These limitations led us to consider a mathematical modelling approach, in which the gene expression of cellular subpopulations is recovered by deconvolution. Mathematically, the deconvolution is a multi-linear regression-based problem. We combined herein data on cellular subpopulation frequency composition with gene expression values from 16 tumour lines (8 resistant and 8 sensitive to paclitaxel treatment) to find genes that are differentially expressed between paclitaxel resistant and paclitaxel sensitive tumour lines in different cellular subpopulations. The results indicate that many genes differentially expressed between paclitaxel resistant and sensitive cancer lines are only detected when considering their heterogeneous cellular composition. Overall, our methodology is thought to keep in mind phenotypic heterogeneity improving our resolution in the identification of biomarkers on resistance to chemo-therapeutic agents.