High-Throughput Mutation Data Now Complement Transcriptomic Profiling: Advances in Molecular Pathway Activation Analysis Approach in Cancer Biology

Abstract

We recently reviewed the current progress in the use of high-throughput molecular "omics" data for the quantitative analysis of molecular pathway activation. These quantitative metrics may be used in many ways, and we focused on their application as tumor biomarkers. Here, we provide an update of the most recent conceptual findings related to pathway analysis in tumor biology, which were not included in the previous review. The major novelties include a method enabling calculation of pathway-scale tumor mutation burden termed "Pathway Instability" and its application for scoring of anticancer target drugs. A new technique termed Shambhala emerged that enables accurate common harmonization of any number of gene expression profiles obtained using any number of experimental platforms. This may be helpful for merging various gene expression data sets and for comparing their pathway activation characteristics. Another recent bioinformatics method, termed FLOating-Window Projective Separator (FloWPS), has the potential to significantly enhance the value of pathway activation profiles as biomarkers of cancer response to treatments. It reduces the minimum required number of training samples needed to construct a machine-learning-based classifier. Finally, several documented clinical cases have been recently published, in which gene-expression-based pathway analysis was successfully used for personalized off-label prescription of target drugs to metastatic cancer patients.

Keywords: bioinformatics; cancer; machine learning; mutation profiling; signaling pathways.

Figure 1.

Dependence of MDS and occurrence of molecular targets in approved cancer drugs. Distribution of MDS values among the potential molecular drug targets. The color scale on the graph indicates densities of clinically approved cancer drugs exploiting the respective molecular targets. MDS indicates Mutation Drug Scoring.

Figure 2.

ERK signaling pathway was hyperactivated in the patient’s tumor tissue. Visualization was provided by Oncobox software. The pathway is shown as an interacting network, where green arrows indicate activation and red arrows indicate inhibition. Color depth of each node of the network corresponds to the logarithms of the case-to-normal (CNR) expression rate for each node, where “normal” is a geometric average between normal tissue samples, and the scale represents extent of up-/down-regulation. The molecular targets of Imatinib are shown by black arrows. ERK indicates extracellular signal-regulated kinase.

Figure 3.

(A) ERK and (B) Ras signaling pathways were hyperactivated in the biopsy CCA tissue. Visualization was provided by Oncobox software. The pathways are shown as an interacting network, where green arrows indicate activation and red arrows indicate inhibition. Color depth of each node of the network corresponds to the logarithms of the case-to-normal (CNR) expression rate for each node, where “normal” is a geometric average between normal tissue samples, and the scale represents extent of up-/down-regulation. The molecular targets of sorafenib and pazopanib are shown by black arrows. ERK indicates extracellular signal-regulated kinase; CCA, cholangiocarcinoma.