The dynamic and never exactly repeatable tumor transcriptomic profile of people affected by the same form of cancer requires a personalized and time-sensitive approach of the gene therapy. The Gene Master Regulators (GMRs) were defined as genes whose highly controlled expression by the homeostatic mechanisms commands the cell phenotype by modulating major functional pathways through expression correlation with their genes. The Gene Commanding Height (GCH), a measure that combines the expression control and expression correlation with all other genes, is used to establish the gene hierarchy in each cell phenotype. We developed the experimental protocol, the mathematical algorithm and the computer software to identify the GMRs from transcriptomic data in surgically removed tumors, biopsies or blood from cancer patients. The GMR approach is illustrated with applications to our microarray data on human kidney, thyroid and prostate cancer samples, and on thyroid, prostate and blood cancer cell lines. We proved experimentally that each patient has his/her own GMRs, that cancer nuclei and surrounding normal tissue are governed by different GMRs, and that manipulating the expression has larger consequences for genes with higher GCH. Therefore, we launch the hypothesis that silencing the GMR may selectively kill the cancer cells from a tissue.

The dynamic and never exactly repeatable tumor transcriptomic profile of people 10 affected by the same form of cancer requires a personalized and time-sensitive approach of the 11 gene therapy. The Gene Master Regulators (GMRs) were defined as genes whose highly controlled 12 expression by the homeostatic mechanisms commands the cell phenotype by modulating major 13 functional pathways through expression correlation with their genes. The Gene Commanding 14Height (GCH), a measure that combines the expression control and expression correlation with all 15 other genes, is used to establish the gene hierarchy in each cell phenotype. We developed the 16 experimental protocol, the mathematical algorithm and the computer software to identify the 17 GMRs from transcriptomic data in surgically removed tumors, biopsies or blood from cancer 18 patients. The GMR approach is illustrated with applications to our microarray data on human 19 kidney, thyroid and prostate cancer samples, and on thyroid, prostate and blood cancer cell lines. 20We proved experimentally that each patient has his/her own GMRs, that cancer nuclei and 21 surrounding normal tissue are governed by different GMRs, and that manipulating the expression 22 has larger consequences for genes with higher GCH. Therefore, we launch the hypothesis that 23 silencing the GMR may selectively kill the cancer cells from a tissue. 24

Objective Advancements in human genomics have generated a surge of available data, fueling the growth and accessibility of databases for more comprehensive, in-depth genetic studies. Methods We provide a straightforward and innovative methodology to optimize cloud configuration in order to conduct genome-wide association studies. We utilized Spark clusters on both Google Cloud Platform and Amazon Web Services, as well as Hail (http://doi.org/10.5281/zenodo.2646680) for analysis and exploration of genomic variants dataset. Results Comparative evaluation of numerous cloud-based cluster configurations demonstrate a successful and unprecedented compromise between speed and cost for performing genome-wide association studies on 4 distinct whole-genome sequencing datasets. Results are consistent across the 2 cloud providers and could be highly useful for accelerating research in genetics. Conclusions We present a timely piece for one of the most frequently asked questions when moving to the cloud: what is the trade-off between speed and cost?