OncoMark Researchers Shed Light on the Discovery and Validation of Master Transcriptional Regulators in Cancer
OncoMark researchers have recently had a review published in the American Association for Cancer Research (AACR) journal, Cancer Research, describing how the reverse engineering of transcriptional networks using gene expression data enables the identification of genes that underpin the development and progression of different cancers.
This review describes how Master Transcriptional Regulators (MTRs) are identified and focuses on providing an overview of how MTRs can and have been validated for use in clinical decision making in malignant disease.
This review is particularly relevant to the OncoMasTR signature, which was discovered through the identification of upstream drivers of breast cancer progression. In this case, the MTRs were originally identified using ARACNe to infer the transcriptional interactions of two independent breast cancer signatures and were found to share a common role in cell proliferation and other hallmarks of cancer. These MTRs, along with the cellular senescence biomarker p16, comprise the OncoMasTR signature which is currently being developed into a test by OncoMark and is due for launch in July 2018. The test will determine the risk of recurrence for early-stage breast cancer patients and will aid clinicians in determining the best treatment options for their patients.
Cancer Research has an Impact Factor of 8.556 and is ranked 2nd in terms of citation frequency in the category of Oncology Journals. This journal has an audience that carries out a wide variety of research, from basic and preclinical to clinical and prevention.
The review can be found here: http://cancerres.aacrjournals.org/content/early/2017/04/20/0008-5472.CAN-16-1813
This work was funded by BREAST-PREDICT, the Irish Cancer Society Collaborative Cancer Research Centre (CCRC13GAL), SYS-MEL (FP7 project, reference 611107), RATHER (FP7 project, reference 258967), MEL-PLEX (H2020 Marie Curie Actions, reference 642295) and OPTi-PREDICT (Science Foundation Ireland Investigator Programme, grant code 15/IA/3104).