Powerful quantifiers for cancer transcriptomics

Abstract

Every day, investigators find a new link between a form of cancer and a particular alteration in the sequence or/and expression level of a key gene, awarding this gene the title of “biomarker”. The clinician may choose from numerous available panels to assess the type of cancer based on the mutation or expression regulation (“transcriptomic signature”) of “driver” genes. However, cancer is not a “one-gene show” and, together with the alleged biomarker, hundreds other genes are found as mutated or/and regulated in cancer samples. Regardless of the platform, a well-designed transcriptomic study produces three independent features for each gene: Average expression level, expression variability and coordination with expression of each other gene. While the average expression level is used in all studies to identify what genes were up-/down-regulated or turn on/off, the other two features are unfairly ignored. We use all three features to quantify the transcriptomic change during the progression of the disease and recovery in response to a treatment. Data from our published microarray experiments on cancer nodules and surrounding normal tissue from surgically removed tumors prove that the transcriptomic topologies are not only different in histopathologically distinct regions of a tumor but also dynamic and unique for each human being. We show also that the most influential genes in cancer nodules [the Gene Master Regulators (GMRs)] are significantly less influential in the normal tissue. As such, “smart” manipulation of the cancer GMRs expression may selectively kill cancer cells with little consequences on the normal ones. Therefore, we strongly recommend a really personalized approach of cancer medicine and present the experimental procedure and the mathematical algorithm to identify the most legitimate targets (GMRs) for gene therapy.

Genomic Fabric Remodeling in Metastatic Clear Cell Renal Cell Carcinoma (ccRCC): A New Paradigm and Proposal for a Personalized Gene Therapy Approach

“…In this manner, instead of a compilation of expression levels of N genes, we analyzed a multi-dimensional transcriptome subjected to differential expression control and coordination of individual genes, i.e., N + N + N(N-1)/2 values in each region. In addition to the examples from Figure 1, we proved the independence of the three characteristics in several previous genomic studies on other samples and selections of genes (e.g., [38,54,55]. The analysis of expression variability was possible because the biological replicas collected from the same region can be considered as practically identical systems subjected to slightly different (non-regulatory) local conditions.…”

“…Assuming normal distributions of REV values in each region, we found these reductions in the cancer nodules to be highly statistically significant with the respective p-values: 3.9 × 10 −17 (PTA), 5.5 × 10 −4 (PTB), and 4.5 × 10 −36 (CWM). REV reduction indicates significant increase of the expression control of CS genes in the cancer cells by the homeostatic mechanisms to confine the fluctuations of the expression levels within narrow intervals, as reported by us in other cancer studies [40,54,55]. The higher median expression variability indicates that PTB region was less altered than the other two cancer regions.…”

“…We ranked the genes in each profiled region with respect to their gene commanding height (GCH) scores [55,58]. As illustrated in Figure 6, the gene hierarchy was strongly perturbed by the cancer.…”

A Personalized Genomics Approach of the Prostate Cancer

“…Owing to the astronomic number of potential combinations, it is impossible to find two individuals with the same combination of regulatory factors and identical influences of these factors. Thus, the same cancer phenotype may be associated with distinct transcriptomes in different individuals as we reported for two cases of metastatic prostate cancers [ 30 ]. Although histologically similar, the unrepeatable combination of the “other” regulated/mutated genes makes the cancer of each individual unique.…”

“…The separately profiled biological replicas are like instances of the same system subjected to slightly different environmental conditions. Thus, the expression variability among biological replicas provided an indirect estimate of how much the stability of transcripts’ abundances are controlled by the cellular homeostatic mechanisms [ 30 ]. Genes whose right expression is critical for the cell’s normal behavior are kept under stricter control and protected against environmental fluctuations, while genes empowering the cell adaptation to the environmental irregular changes are left to adjust.…”