Identification of genomics signatures of drug response and tumorigenesis

Recent advances in next-generation sequencing (NGS) have generated a wealth of data of different types whose analysis have helped in the identification of signatures of different cellular sensitivity/resistance responses to hundreds of chemical compounds. Among the different data types, gene expression has proven to be the most successful one for the inference of drug response in cancer cell lines. Although effective, the whole transcriptome can introduce noise in the predictive models, since specific mechanisms are required for different drugs and these realistically involve only part of the proteins encoded in the genome. We analyzed the pharmacogenomics data of 961 cell lines tested with 265 anti-cancer drugs and developed different machine learning approaches for dissecting the genome systematically and inferring the drug responses using both drug-unspecific and drug-specific genes. These methodologies reach better response predictions for the vast majority of the screened drugs using tens to few hundreds genes specific to each drug instead of the whole genome, thus allowing a better understanding and interpretation of drug-specific response mechanisms which are not necessarily restricted to the drug known targets. A growing number of evidences shows the involvement of miRNA-mRNA interactions in tumorigenesis and cancer development. However, the rewiring of the miRNA-mRNA interaction network from healthy to tumoral cells has not been thoroughly investigated yet. With the increasing availability of miRNA and mRNA expression data in healthy and cancer tissues and the development of experimentally validated miRNA-mRNA interaction databases we could build and compare miRNA-mRNA interactions between healthy and tumoral samples. Comparing the landscape of miRNA-mRNA interactions between healthy and tumour samples we highlight that several lost and several new correlations are shared in different cancer types. The genes involved in these dysregulated interactions were enriched in cell proliferation and migration, developmental processes, regulation of cell death and metabolism. These results show how anatomically different cancers can be very close from a molecular point of view, highlighting shared mechanisms of tumorigenesis and cancer progression.