Using this genomic profiling approach coupled to an analytical mutation-calling algorithm and orthogonal validation step, we identified numerous mutations present in genomic DNA from both frozen and FFPE tumor tissue.
Using genomic profiling with OncoMap coupled with an analytical mutation-calling algorithm and orthogonal validation step, numerous mutations have been identified in genomic DNA from both frozen and FFPE tumor tissue with a high degree of specificity and sensitivity[ 21].
For the identification of significantly mutated genes, we applied a novel analytical methodology, mutation significance with covariates (MutSigCV) [ 10], to avoid the false-positive findings detected by the standard significance analysis method (MutSig1.0) [ 55].
Many different methods have been developed with the goal of increasing the analytical sensitivity of mutation testing, however, increased sensitivity often comes with the cost of lower accuracy, increased complexity, and higher costs [ 17].
Previous studies reported a variety of ingeniously designed assay methodologies for the sensitive detection of JAK2 617F and MPL W515L/K mutations, with the analytical sensitivity around 1 5% mutation load [ 4, 16, 33– 33].
However, this test seems to lack analytical sensitivity to detect mutations in small tumors.
We enhanced the analytical specificity of the mutation-specific assay by adding a proprietary co-solvent, Q-Solution, to the PCR reaction.
The study presents the data and analytical results for point mutations and small insertions/deletions from 3,281 tumours across 12 tumour types.
The study of Pan-Cancer analysis presents the data and analytical results for point mutations and small insertions/deletions from 3,281 tumours across 12 tumour types as part of the TCGA Pan-Cancer effort.
Accordingly, analytical criteria to distinguish driver mutations from passenger mutations have been recently suggested.
