BRAFV600E-mutant cancers display a variety of networks by SWIM analysis: prediction of vemurafenib clinical response

• DOI: 10.1007/s12020-019-01890-4
• Publication Date: 2019-03-08T20:25:27Z
• Authors: Rosa Falcone; Federica Conte; Giulia Fiscon; Valeria Pecce; Marialuisa Sponzi...; Cosimo Durante; Lorenzo Farina; Sebastiano Filetti; Paola Paci; Antonella Verrienti
• Keywords: Proto-Oncogene Proteins B-raf; 0301 basic medicine; Lung Neoplasms; COMPUTATIONAL AND SYSTEMS BIOLOGY; Adenocarcinoma of Lung; Antineoplastic Agents; Models, Theoretical; 3. Good health; 03 medical and health sciences; Vemurafenib; Predictive Value of Tests; BRAF V600E; Network medicine; Prediction of response; Vemurafenib; Endocrinology, Diabetes and Metabolism; Endocrinology; Humans; Thyroid Neoplasms

Several studies have shown that different tumour types sharing a driver gene mutation do not respond uniformly to the same targeted agent. Our aim was to use an unbiased network-based approach to investigate this fundamental issue using BRAFV600E mutant tumours and the BRAF inhibitor vemurafenib.We applied SWIM, a software able to identify putative regulatory (switch) genes involved in drastic changes to the cell phenotype, to gene expression profiles of different BRAFV600E mutant cancers and their normal counterparts in order to identify the switch genes that could potentially explain the heterogeneity of these tumours' responses to vemurafenib.We identified lung adenocarcinoma as the tumour with the highest number of switch genes (298) compared to its normal counterpart. By looking for switch genes encoding for kinases with homology sequences similar to known vemurafenib targets, we found that thyroid cancer and lung adenocarcinoma have a similar number of putative targetable switch gene kinases (5 and 6, respectively) whereas colorectal cancer has just one.We are persuaded that our network analysis may aid in the comprehension of molecular mechanisms underlying the different responses to vemurafenib in BRAFV600E mutant tumours.