1. SIMULTANEOUS SEQUENCING OF TUMOR AND A MATCHED NORMAL SAMPLE
Identifying driver mutations is first of all a matter of identifying and differentiating cancer somatic mutations from patient's germline mutations. Therefore, in the effort of identifying driver mutations it is always necessary to do simultaneous sequencing of both the tumor sample and a matched control of normal tissue from the same patient.
2. ASSESSMENT OF TUMOR PURITY
Each tumor sample is always an admixture of normal tissue sample and different cancer cell lines. Tumor purity is defined by the fraction of cancerous cells in the sample. Therefore to identify putative driver mutations it is necessary to proceed to an estimate of tumor purity. Such an estimate can be done by special algorithms which can be included in a variant calling software. For instance, with VarScan2 the user can enter an estimate manually, whereas software like MuTect and Strelka calculate this automatically based on allele frequencies observed. There are also software which are solely dedicated to the estimation of tumor purity: ABSOLUTE and ASCAT can infer tumor purity and tumor ploidy from SNP array data.
3. ASSESSMENT OF INTRATUMOR HETEROGENEITY
Although cancer development is a clonal process, not all cancer cells are identical. In any cancer it is actually possible to identify different subtypes of cancer cells. Also in this case bioinformatics comes to help. The following methods can be used to infer intratumor heterogeneity: (1) with a software (e.g. THetA: Tumor Heterogeneity Algorithm), (2) by next generation sequencing at ultra high coverage to identify different SNVs (i.e. differentiating clonal mutations, arisen at the beginning, from subclonal mutations, arisen later in tumor development) (3) by sequencing different tumor regions after multiple biopsies.
4. EVALUATION OF ALLELE FREQUENCY
As said in STRATEGIES IN IDENTIFYING DRIVER MUTATIONS IN CANCER, assessing the allele frequency is probably the most accepted strategy in identifying driver mutations, following the concept that the higher the allele frequency of a mutation, the higher the possibility it is a driver mutation.
To do so, it is important to determine if the putative driver mutation has an allele frequency which exceeds the background mutation rate (BMR), i.e. the probability that a given locus a passenger mutation arises (a passenger mutation is a mutation with no significance for cancer development). BMR depends on genomic context, mutation type, gene transcription rate and gene replication timing.
5. VERIFICATION BY FUNCTIONAL STUDIES
Allele frequency and in silico analysis may help the initial phase, but the final evidence that a mutation is a driver mutation must be obtained by functional assays in vitro, which may demonstrate that the mutation has the power to influence cell division, cell dedifferentiation, ecc.
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