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Lung cancer is the leading cause of cancer- related mortality worldwide. For most patients with lung cancer, excluding those with stage I disease, systemic chemotherapy is applied to prolong survival and improve quality of life, or as adjuvant/neoadjuvant therapy to improve the outcome of surgical treatment. However, some patients not only fail to obtain any benefit from these drugs, but also suffer from adverse events because of their toxicity. Because cancer cells are originally derived from host cells, compared with exogenous microbial infection, effective dose levels of these anticancer drugs are often close to or overlap with the toxic dose levels.
To solve this problem, it is very important to discover specific markers for tumors that predict higher responsiveness to anticancer drugs. So far, many classes of biomarkers and biomarker candidates (such as clinical features, serum markers, pathological distinction, polymorphisms, levels of gene or protein expression, somatic mutations, and gene or protein signatures), have been reported in the literature. Among these biomarkers, qualitative ones (such as oncogenic driver mutations, which we will focus on in this article) give "yes or no" results when analyzing whether a specific treatment is likely to be effective, and thus yield fewer inter-observer variations and are reproducible. On the other hand, quantitative biomarkers (such as levels of expression of mRNA or protein) are more subjective and less reliable in general.
To apply biomarkers for the correct selection of drugs, we have to clarify whether the biomarker is predictive (identifies patients who will or will not respond to a certain drug) or prognostic (identifies patients who have a favorable or poor prognosis irrespective of treatment). For example, in the situation that a subgroup of patients defined by "biomarker A" has favorable prognosis compared with a control group after treatment with "drug B", it is not clear if biomarker A is a predictive biomarker for drug ? or the biomarker just defines patients with a favorable prognosis.
Regarding the treatment of lung cancer, a number of predictive biomarkers have recently been evaluated to select patients who will benefit from treatment with specific drugs, and some of these markers have already found use in the clinic. In particular, oncogenic driver mutations are now regarded not only as key molecules for lung carcinogenesis...