Then, machine learning models are used to cluster the SNVs into subclones and reconstruct the tumor phylogenetic trees 9. First, bulk sequencing provides mixed information about genetic alterations for all subclones in the tumor, including allele frequency for single-nucleotide variations (SNVs) and copy number alteration (CNA). The workflow generally consists of two parts. In recent years, strategies have been developed by combining deep whole-genome sequencing (WGS) or whole-exome sequencing (WES) with novel statistical and computational methods. However, this strategy may not be suitable under all circumstances because of the ethics of carrying out unnecessary invasive procedures as well as the practicality of longitudinal sampling for solid tumors, since the majority of cancer samples are obtained from surgical procedures, and if a tumor has been removed at the one-time point, it will not be available for sampling at a later time point 3. One strategy to deconvolute spatial and temporal tumor heterogeneity is to perform multiregional and longitudinal sampling 2. Hence, dissecting the clonal composition of a tumor not only helps us to understand its biology and evolution but also guides the design of combinatorial therapies 2. Therefore, when targeted therapy exerts its selection pressure, subclones with drug-resistant mutations would gradually dominate due to selective advantage 8. Spatial heterogeneity refers to the phenomenon that a tumor is composed of subclones of different genetic background 5, 6, and temporal heterogeneity refers to the dynamic evolution of the tumor genomes through the disease course 7. This heterogeneity is both spatial and temporal 3, 5, 6, 7. Tumor heterogeneity is a major contributor to the development of drug resistance 1, 2, 3, 4. Targeted therapy, a widely adopted cancer treatment 1, might have the potential to elicit promising initial responses, but many patients develop drug resistance during treatment 2, 3.
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