The author analyzed data from The Cancer Genome Atlas (TCGA) on 456 tumor samples for which no radiotherapy or additional pharmacotherapy was performed. Subtypes with different survival rates were identified using the k-means method for clustering samples into subgroups with similar characteristics. For gene clustering, Puzanov selected 2,000 genes with highly variable expression patterns in ccRCC.
Gene expression is the process by which a gene is read and copied to produce a messenger RNA (mRNA) which is then used to synthesize proteins.
A bioinformatic algorithm was run 100 times, each time sorting the tumor samples based on similarity of the 2,000 genes’ expression patterns. Three clusters (subtypes) with different survival rates were identified. The cluster with the lowest survival rates was associated with metastases and the worst response to subsequent treatment.
The study was carried out in several stages. In stage one, each cluster’s characteristics were examined for a better understanding of genetic factors which could influence the course of the disease. Then, the study author identified the key genes specific to high and low survival clusters and constructed a network of interactions for proteins whose synthesis is encoded by these genes.
Genetics of Kidney Cancer
Puzanov’s analysis determined which genes encoded proteins with the highest number of network connections. The cluster with the poorest survival rates was found to be associated with the MFI2, CP, APOB, and ENAM genes known to be involved in the transport of insulin-like growth factor (a protein similar in structure to insulin) and in post-translational modification of proteins. In addition, specific to the low-survival subtype were the genes encoding fibrinogen and prothrombin associated with blood clotting (FGA, FGG, and F2).
“Some of these key genes may affect the efficacy of anti-tumor therapies. For example, increased activity of the CP, FGA, and FGG genes is associated with poor response to nivolumab, and high expression of APOB and ENAM predicts a lack of response to sunitinib. This knowledge can help in prescribing the most suitable targeted treatments for patients with malignancies” – Grigory Puzanov, Research Fellow, International Laboratory of Bioinformatics, Faculty of Computer Science, HSE University.
According to the researcher, combined use of conventional anti-tumor agents and anticoagulants (medicines that help prevent blood clots) can increase the effectiveness of cancer treatment.