Although there is a clear molecular basis for patient selection with the BRAF-V600 mutation, which predicts highly effective treatment, the issues of drug resistance and toxicity described above present challenges to curative therapy.
The three-mutation model, which predicted mutation rates more in line with biological data, was therefore somewhat preferable.
A battery of pharmacogenomic biomarkers (other than BCR-ABL mutations) have also been identified, which predict sensitivity to dasatinib in tumor cell lines (Clark et al. 2005).
The functional impact of these non-synonymous coding CPS SNPs was assessed using a combination of four different SNP function prediction tools (SIFT, Polyphen 2, LRT, Mutation taster) which predicted most of these SNPs to have a potential functional impact [ 79– 82].
Tumor-bearing tissue obtained prior to treatment is used to identify tumor-specific alterations (gene mutations, gene amplifications, protein expression-patterns), which predict therapeutic response.
Most notable examples include the V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation in colorectal cancer, which predicts resistance to drugs that target the epidermal growth factor receptor (EGFR), and EGFR-tyrosine kinase (TK) mutations in non-small cell lung cancer (NSCLC), which predict response to EGFR-TK targeting agents such as gefitinib.
The analysis of such double mutations should indicate which predicted inter-helical contacts are most important for stabilizing Rev structure and maintaining full Rev activity.
Pathogenicity of the alteration was evaluated by Mutation Taster software which predicted the change to be disease causing (probability =1; indicating high security of the prediction).
The variants found were further analyzed by mutation prediction software Provean and PolyPhen-2 to filter the mutations, which are predicted to be deleterious/damaging.
