Pathological Bases for a Robust Application of Cancer Molecular Classification
AbstractAny robust classification system depends on its purpose and must refer to accepted standards, its strength relying on predictive values and a careful consideration of known factors that can affect its reliability. In this context, a molecular classification of human cancer must refer to the current gold standard (histological classification) and try to improve it with key prognosticators for metastatic potential, staging and grading. Although organ-specific examples have been published based on proteomics, transcriptomics and genomics evaluations, the most popular approach uses gene expression analysis as a direct correlate of cellular differentiation, which represents the key feature of the histological classification. RNA is a labile molecule that varies significantly according with the preservation protocol, its transcription reflect the adaptation of the tumor cells to the microenvironment, it can be passed through mechanisms of intercellular transference of genetic information (exosomes), and it is exposed to epigenetic modifications. More robust classifications should be based on stable molecules, at the genetic level represented by DNA to improve reliability, and its analysis must deal with the concept of intratumoral heterogeneity, which is at the origin of tumor progression and is the byproduct of the selection process during the clonal expansion and progression of neoplasms. The simultaneous analysis of multiple DNA targets and next generation sequencing offer the best practical approach for an analytical genomic classification of tumors. View Full-Text
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Diaz-Cano, S.J. Pathological Bases for a Robust Application of Cancer Molecular Classification. Int. J. Mol. Sci. 2015, 16, 8655-8675.
Diaz-Cano SJ. Pathological Bases for a Robust Application of Cancer Molecular Classification. International Journal of Molecular Sciences. 2015; 16(4):8655-8675.Chicago/Turabian Style
Diaz-Cano, Salvador J. 2015. "Pathological Bases for a Robust Application of Cancer Molecular Classification." Int. J. Mol. Sci. 16, no. 4: 8655-8675.