MicroRNA classifiers are powerful diagnostic/prognostic tools in ALK-, EGFR-, and KRAS-driven lung cancers

MicroRNAs (miRNAs) can act as oncosuppressors or oncogenes, induce chemoresistance or chemosensitivity, and are major posttranscriptional gene regulators. Anaplastic lymphoma kinase (ALK), EGF receptor (EGFR), and V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) are major drivers of non-small cell lung cancer (NSCLC). The aim of this study was to assess the miRNA profiles of NSCLCs driven by translocated ALK, mutant EGFR, or mutant KRAS to find driver-specific diagnostic and prognostic miRNA signatures. A total of 85 formalin-fixed, paraffin-embedded samples were considered: 67 primary NSCLCs and 18 matched normal lung tissues. Of the 67 primary NSCLCs, 17 were echinoderm microtubule-associated protein-like 4-ALK translocated (ALK+) lung cancers; the remaining 50 were not (ALK-). Of the 50 ALK- primary NSCLCs, 24 were EGFR and KRAS mutation-negative (i.e., WT; triple negative); 11 were mutant EGFR (EGFR+), and 15 were mutant KRAS (KRAS+). We developed a diagnostic classifier that shows how miR-1253, miR-504, and miR-26a-5p expression levels can classify NSCLCs as ALK-translocated, mutant EGFR, or mutant KRAS versus mutation-free. We also generated a prognostic classifier based on miR-769-5p and Let-7d-5p expression levels that can predict overall survival. This classifier showed better performance than the commonly used classifiers based on mutational status. Although it has several limitations, this study shows that miRNA signatures and classifiers have great potential as powerful, cost-effective next-generation tools to improve and complement current genetic tests. Further studies of these miRNAs can help define their roles in NSCLC biology and in identifying best-performing chemotherapy regimens.