The Swedish researchers of the Human Pathology Atlas launched a new Pathology Atlas subsequently by analyzing all human genes in all major cancers which affected the protein levels and related patients’ survival. In the study, the difference in expression patterns of individual cancers compelled to personalized cancer treatment based on precision medicine.
According to the study published in Science1, the Pathology Atlas is based on the analysis of 17 main cancer types using data from 8,000 patients, along with 5 million pathology-based images generated by the Human Protein Atlas consortium. Besides, Interactive Survival Scatter plots were also introduced, and the atlas includes more than 400,000 such plots.
Over 2.5 petabytes of data from the Cancer Genome Atlas (TCGA) were analyzed with the aid of a national supercomputer center to generate more than 900,000 survival plots describing the consequence of RNA and protein levels on clinical survival.
The present research differs from earlier cancer study, as it was not focused on mutations in cancers, but the downstream consequence of such mutations across all protein-coding genes, says Professor Mathias Uhlen, Director of the Human Protein Atlas consortium. “We show, for the first time, the influence of the gene expression levels demonstrating the power of "big data" to change how medical research is performed. It also shows the advantage of open access policies in science in which researchers share data with each other to allow integration of huge amounts of data from different sources."
The current research outcomes were reported to be related to cancer biology and treatment. It was noted that in cancer, the differentially expressed genes can also effect overall patient survival. In addition, varied gene expression pattern of individual tumors including variations among the different cancer types was also observed.
Shorter survival of patients was related to up-regulation (mitosis and cell growth) and down-regulation (cellular differentiation) of genes. Furthermore, investigators were able to generate personalized genome-scale metabolic models for cancer patients to identify key genes contributing to tumor growth.
"We are now in possession of incredibly powerful systems biology tools for medical research, allowing, for the first time, genome-wide analysis of individual patients with regards to the consequence of their expression profiles for clinical survival," said Dr. Adil Mardinoglu, SciLife Lab Fellow and leader of the systems biology effort in the project.
The researchers discovered the application of the new tool in two particular cancers. Fredrik Ponten from Uppsala University reported that to validate the gene expression patterns at the protein level in lung and colorectal cancer, several prognostic genes identified in the Atlas were studied in independent, prospective cancer cohorts using immunohistochemistry.
"We are pleased to provide a stand-alone open-access resource for cancer researchers worldwide, which we hope will help accelerate their efforts to find the biomarkers needed to develop personalized cancer treatments," Ponten concluded.