The four most promising biomarkers in precision medicine
Precision medicine relies on how biomarkers can better classify disease risk, prognosis, or treatment response. Biomarkers are biochemical indicators that can label changes in systems, organs, tissues, cells, and subcellular structures or functions, and can be used for disease diagnosis, disease stage staging, or for evaluating new drugs or new therapies in target populations. Safety and effectiveness, etc., have a very wide range of uses.
In this review, the authors outline four of the most promising biomarkers using lung cancer as an example. Lung cancer has traditionally been classified into small cell lung cancer and non-small cell squamous cell carcinoma or adenocarcinoma according to histology. In 1987, researchers identified KRAS mutations in 25% of non-small cell lung cancers and 50% of lung adenocarcinomas; in 2004, researchers found additional mutations in the lung adenocarcinoma, the epidermal growth receptor (EGFR). In 2004, using second-generation sequencing technology, the researchers identified 15 genetic changes associated with lung cancer that are expected to be reclassified for the treatment of lung cancer.
Table: Four most promising biomarkers
Different patients with the same symptoms and signs often have different treatment outcomes. Biomarkers can classify patients with the same cancer into different types, and use the large amount of data collected during the identification of biomarkers to predict the disease. As shown in Figure 1, in patients with class I, for those patients who carry biomarkers associated with low survival (meaning a worse prognosis), a more aggressive treatment regimen (X therapy) can be used. For patients who carry biomarkers associated with better therapeutic effects (meaning a better prognosis), a less aggressive treatment regimen (Y therapy) may be employed. In addition, patients with poor prognosis should be informed of the facts when a less aggressive therapy is not available.
Figure 1: Biomarkers reclassify patients
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