Abstract

EGFR-TKIs achieved excellent efficacy in EGFR-mutated patients. Unfortunately, most patients would inevitably develop progressive disease within a median of 10 to 14 months. Predicting the resistance probability remains a challenge. Therefore, we created an R-index model trained by single-cell RNA data with the OCLR algorithm. This model can be applied to estimate the level of EGFR-TKI resistance in cell line and xenograft mice models and predict prognosis in multiple cohorts. Comparing the high and the low R-index group, we found that the glycolysis pathway and KRAS up-regulation pathway were related to resistance, and MDSC was the leading cause of immunosuppression in the tumor microenvironment. These results are consistent with previous studies indicating that the R-index provides an insight into resistance status and a new way to explore resistance mechanisms and clinical treatment by the combination of Glucose metabolism-targeted or MDSC-targeted therapies. This is the first quantification method of EGFR-TKI resistance based on single-cell sequencing data solving the problem of the mixed resistance state of tumor cells and helping explore transcriptome characteristics of drug-resistant cell populations.