Rosa Falcone1, Federica Conte2,3, Giulia Fiscon2,3, Valeria Pecce1, Marialuisa Sponziello1, Cosimo Durante1, Lorenzo Farina4, Sebastiano Filetti1, Paola Paci5, Antonella Verrienti1. 1. Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy. 2. Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy. 3. ACT Operations Research, Research & Development, Roma, Italy. 4. Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy. 5. Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy. paola.paci@iasi.cnr.it.
Abstract
PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Entities:
Keywords:
BRAF V600E; Network medicine; Prediction of response; Vemurafenib
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