Most cancers occur from nowhere, without being inherited or directly caused by a steady deficient diet (affecting the microbiome), exposure to radiation or carcinogenic toxins, or bad habit (like smoking), although such risk factors increase the chances of the “bad luck”. Tumors are heterogeneous, composed of regions with distinct characteristics, some of them malignant, some others preserving the normal features of the tissue. In spite of a very rich literature, there is not yet a comprehensive explanation of cancer development, nor a perfect therapeutic solution. Moreover, with all similarities, each human is unique and has a unique lifeline, so, although a trained pathologist can recognize the cancer type, the tumors are not identical, nor develop identically or respond identically to treatment. Therefore, instead of targeting the same alleged gene biomarker for all humans with a particular cancer form, we devised a method by which the cancer of the actual patient itself indicates us what genes are now commanding it. We call these commanders “gene master regulators” (GMRs) and identify them by profiling the gene expression in tumor biopsies or blood samples (pending on the suspected cancer type) using RNA sequencing or microarray platforms. Here, we prove that cancer nuclei and surrounding normal tissue are governed by distinct GMRs and that smart manipulation of a GMR’s expression selectively affects cancer cells. The method, consistent with our Genomic Fabric Paradigm, relies on original mathematical algorithm and software that establishes the gene hierarchy from the transcriptomic profiles of tumor biopsies based on their Gene Commanding Height (GCH). GCH is a composite measure of gene expression control and coordination with major functional pathways. We present validation of the approach using microarray data obtained in our previous NYMC laboratory by profiling human kidney, thyroid, blood, lung and prostate cancer samples. The GMR approach provides the most legitimate targets for cancer gene therapy. It is also personalized and time-sensitive because the GMR hierarchy is unique for each patients and changes slowly during cancer development.
Dr. Iacobas, Research Professor and Director of the CCSB Personalized Genomics Laboratory, is an expert of both experimental and computational genomics. Trained as a biophysicist (PhD of the University of Bucharest, Romania), he was on faculty positions at medical schools from Romania (1981-2001) and NY (Albert Einstein College of Medicine-Neuroscience 2001-2013, New York Medical College-Pathology 2013-2017). At NYMC he founded and directed the Systems Biology Core laboratory. Dr. Iacobas was also involved in the technology development, performed analyses of the technical noise of various microarray platforms and took care of transgenic mouse colonies and genetically engineered cell cultures. His lab profiled a wide diversity of tissues and cell cultures from blood and regions of brain, spinal cord, retina, heart, liver, kidney, lung, thyroid and prostate from humans and animal (mouse, rat, rabbit, dog, chicken embryo) models of human diseases. He studied transcriptomic alterations in cancer, neurodegenerative, cardiovascular and infectious diseases, and following hypoxic or low gravity stress. His main contribution to the theoretical genomics is the introduction of the Genomic Fabric Paradigm and the development of the mathematically advanced analyses of the Relative Expression Estimate, Coordination Power, Pair-Wise Relevance and Gene Master Regulators.
Dr. Iacobas’ publications include: 3 patents in microelectrophysiology, 7 books (total 22 editions in Romanian, English, Spanish and Greek), 86 articles in peer-reviewed journals, 27 chapters in books and conference proceedings and 77 genomic experiments included in NIH GDS.