Abstract
BackgroundDespite great development in genome and proteome high-throughput methods, treatment failure is a critical point in the management of most solid cancers, including breast cancer (BC). Multiple alternative mechanisms upon drug treatment are involved to offset therapeutic effects, eventually causing drug resistance or treatment failure.MethodsHere, we optimized a computational method to discover novel drug target pathways in cancer subtypes using pathway cross-talk inhibition (PCI). The in silico method is based on the detection and quantification of the pathway cross-talk for distinct cancer subtypes. From a BC data set of The Cancer Genome Atlas, we have identified different networks of cross-talking pathways for different BC subtypes, validated using an independent BC dataset from Gene Expression Omnibus. Then, we predicted in silico the effects of new or approved drugs on different BC subtypes by silencing individual or combined subtype-derived pathways with the aim to find new potential drugs or more effective synergistic combinations of drugs.ResultsOverall, we identified a set of new potential drug target pathways for distinct BC subtypes on which therapeutic agents could synergically act showing antitumour effects and impacting on cross-talk inhibition.ConclusionsWe believe that in silico methods based on PCI could offer valuable approaches to identifying more tailored and effective treatments in particular in heterogeneous cancer diseases.
Highlights
Despite great development in genome and proteome high-throughput methods, treatment failure is a critical point in the management of most solid cancers, including breast cancer (BC)
Here we describe a procedure to build a network of pathways de-regulated for different BC subtypes using gene expression data from The Cancer Genome Atlas (TCGA) and a list of pathways obtained by Ingenuity Pathway Analysis (IPA)
For the second validation task, we evaluated the mechanism of action of 13 Food and Drug Administration (FDA) approved drugs for BC on our BC subtype drug target pathway network (DTPN)
Summary
Despite great development in genome and proteome high-throughput methods, treatment failure is a critical point in the management of most solid cancers, including breast cancer (BC). The advent of genome-wide technologies has made possible the generation of new hypotheses about the role of genomics in the efficiency of drugs developed for cancer and the event of adverse responses to cancer therapy. In this context, several studies examined the effects of drugs considering protein network approaches [2]. The effect of a drug treatment on some proteins, represented by the nodes of a network, is amplified by the interactions of these proteins with other proteins in the networks, being these connections represented by edges [2]. Notwithstanding useful to assess the drug effects on proteins, these approaches have not still
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