Nowadays, the human genome is already sequenced, which means that about three billion base pairs of our DNA have already been identified, such as its order. This sequence, however, is not exactly the same in each of us. About 99.5% of a person's DNA is similar to anyone else's DNA, even if not relatives. Nonetheless, the small, but distinct, 0.5% are responsible for the genetic variability. Such differences include changes in a single nucleotide, changes in one or more genes, or even changes in chromosome number or structure. Some differences that occur in DNA sequences have little or no effect on protein function, while other differences are directly responsible for causing disease.

A genetic test consists of analyzing our DNA, through molecular biology techniques, such as Sanger sequencing or NGS, where it is possible to compare the analyzed DNA with the reference genome and identify the changes. Assessing the clinical significance of a genetic change (genetic variant) in an individual's DNA helps the clinician to make decisions about the patient. For example, in some cases, the identification of a certain genetic variant may inform about treatment options for certain cancers (personalized medicine); in other cases, it may allow decisions about offspring (prenatal testing) or may help inform at-risk family members about possible preventive measures. We can find many variants during the sequencing of an individual's DNA, so one of the important steps in sequencing interpretation is to establish which variant is most relevant to explain a given phenotype by looking at various existing sources of evidence. Thus, the American College of Medical Genetics (ACMG) elaborated guidelines and standard terminology to classify identified variants in genes that cause Mendelian Diseases. This classification system defines five classes: 1.Benign, 2.Probably benign, 3.Variant of uncertain clinical significance (VUS), 4.Probably pathogenic and 5.Pathogenic. This recommendation describes the process of classifying variants into these categories based on criteria that use different types of evidence (population, computational, functional, segregation data, among others).

In conclusion, this workshop explores the ACMG criteria and use practical examples to reach the various classifications of variants, identified in cancer patients.