| Literature DB >> 27234388 |
Abstract
Lung cancer is a heterogeneous and complex disease. Genomic and transcriptomic profiling of lung cancer not only further our knowledge about cancer initiation and progression, but could also provide guidance on treatment decisions. The fact that targeted treatment is most successful in a subset of tumors indicates the need for better classification of clinically related molecular tumor phenotypes based on better understanding of the mutations in relevant genes, especially in those oncogenic driver mutations. EGFR gene mutations, KRAS gene mutations, EML4-ALK rearrangements and altered MET signaling are widely recognized alterations that play important roles in both the biological mechanisms and the clinical sensitivity to treatment in lung cancer. In this article, we reviewed the discovery of the clinical values of these oncogenic driver mutations and the clinical studies revealing the prognostic and predictive values of these biomarkers for clinical sensitivity and resistance to anti-EGFR therapy or other targeted therapies. These form the basis of personalized treatment in lung cancer based on biomarker profiles of individual tumor, leading to therapeutic advancement and betterment.Entities:
Keywords: ALK; Driver mutations; EGFR; Lung cancer
Year: 2013 PMID: 27234388 PMCID: PMC6733434 DOI: 10.1186/2213-0802-1-6
Source DB: PubMed Journal: Transl Respir Med ISSN: 2213-0802
Figure 1Analysis of exon 19 mutation by direct sequencing. (A) Wild-type EGFR exon 19; (B) An example of inframe deletion in EGFR exon 19 (2481_2495del). The arrows indicate the span of deletion in each amplified sequence of exon 19.
Figure 2exon 21 mutation analysis via direct sequencing. (A) Wild-type EGFR exon 21; (B) One missense mutation (L858R) in EGFR exon 21. The arrow indicates the position of the mutant nucleotide. Only forward sequences are shown.
Summary of common oncogenic driver mutations, their corresponding testing methods and their respective inhibitors
| Target | Detection method | Inhibitor |
|---|---|---|
|
| Direct sequencing | Gefitinib, Erlotinib |
| Real-time PCR | BIBW2992 (Afatinib) | |
| Single-strand conformational polymorphism | PF00299804 (Dacomitinib) | |
| High-resolution melting amplicon analysis | HKI-272 (Neratinib) | |
| BPI-2009 (Icotinib) | ||
| EKB-569 (Pelitinib) | ||
| CI-1033 (Canertinib) | ||
| GW572016 (Lapatinib) | ||
|
| Direct sequencing | Not available |
| Real-time PCR | ||
| Amplification refractory mutation system (ARMS) | ||
| Restriction fragment length polymorphism (RFLP) | ||
| Co-amplification at lower denaturation temperature-polymerase chain reaction (COLD-PCR) | ||
|
| Fluorescence | PF-02341066 (Crizotinib) |
| Immunohistochemistry (IHC) | CH5424802 (AF802) | |
| Real-time Reverse Transcription-PCR | ||
|
| Quantitative PCR | PF-02341066 (Crizotinib) |
| Fluorescence | ARQ197 (Tivantinib) | |
| PCR-based sequencing | GSK1363089 (Foretinib) | |
| XL184 (Cabozantinib) | ||
| PF-04217903 | ||
| SGX523 |
Figure 3A suggested schema for guidance of clinical testing for oncogenic driver mutations which aid in personalized treatment in lung cancer.