Prediction of the efficacy and clinical prognosis of first-line EGFR-tyrosine kinase inhibitors in non-small cell lung cancer patients based on ΔCt values derived from the super-amplification refractory mutation system (ARMS): a real-world retrospective study

Abstract

Background: Lung cancer, especially non-small cell lung cancer (NSCLC), is a leading cause of cancer mortality. Epidermal growth factor receptor (EGFR) mutations drive NSCLC progression but also sensitize tumors to EGFR-tyrosine kinase inhibitors (TKIs). However, the response rate to targeted therapy is only 70%, and most patients experience disease progression 9 to 14 months after first- or second-generation EGFR-TKI treatment. This study aims to examine the association between super-amplification refractory mutation system (ARMS)-derived ΔCt values [mutant DNA cycle threshold (Ct) value relative to the endogenous reference gene (Ct) value] and EGFR mutation (EGFRm) abundance in predicting the efficacy and prognosis of EGFR-TKIs in NSCLC patients.

Methods: The present retrospective research encompassed 139 patients with stage IIIB-IV NSCLC treated with EGFR-TKIs. Patients were categorized based on super-ARMS ΔCt values and Kaplan-Meier, and Cox regression models were used to evaluate the outcomes in survival and independent influencing factors, thus establishing the optimal ΔCt value for EGFR-TKIs response.

Results: High mutation abundance, defined by ΔCt ≤3.76, was correlated with increased objective response rate (ORR) (61.2% vs. 36.8%, P=0.003) and longer median progression-free survival (mPFS) (20.9 vs. 15.8 months, log-rank P=0.005) compared to low abundance. The optimal ΔCt cut-off predictive of EGFR-TKIs response was 4.335. Patients with ΔCt ≤4.335 demonstrated superior ORR (64.6% vs. 28.1%, P<0.001) and mPFS (20.9 vs. 13.5 months, log-rank P<0.001) compared to those with ΔCt >4.335. Multivariate Cox analysis identified median ΔCt value group (ΔCt ≤3.76 or ΔCt >3.76), the optimal ΔCt cut-off value group (ΔCt ≤4.335 or ΔCt >4.335), brain metastasis, liver metastasis, EGFRm status, performance status (PS) score, and the generation of EGFR-TKIs as independent predictors of PFS in first-line EGFR-TKIs-treated patients.

Conclusions: Stratification based on ΔCt values derived from the super-ARMS system can predict the efficacy and clinical prognosis of first-line EGFR-TKI treatment in NSCLC patients. Additionally, higher mutation abundance may contribute to the superior efficacy and prognosis of EGFR-TKIs in patients with exon 19 deletions compared to those with the 21L858R mutation.

Keywords: Non-small cell lung cancer (NSCLC); circulating tumor DNA (ctDNA); epidermal growth factor receptor (EGFR); super-amplification refractory mutation system (super-ARMS); tyrosine kinase inhibitors (TKIs).

Figure 1

Flowchart of patient enrollment in this study. A total of 139 patients met the inclusion and exclusion criteria, of whom 65 patients received first- or second-generation EGFR-TKIs and 74 patients received third-generation EGFR-TKIs. DCR, disease control rate; EGFR, epidermal growth factor receptor; mPFS, median progression-free survival; NSCLC, non-small cell lung cancer; ORR, objective response rate; qPCR, quantitative polymerase chain reaction; ΔCt, mutant DNA cycle threshold (Ct) value relative to the endogenous reference gene (Ct) value; super-ARMS, super-Amplification Refractory Mutation System; TKIs, tyrosine kinase inhibitors.

Figure 2

Analysis of PFS curves for NSCLC patients treated with EGFR-TKIs, categorized by performance status (A), EGFR mutation status (B), and the presence of liver (C) and brain (D) metastases. EGFR, epidermal growth factor receptor; NSCLC, non-small cell lung cancer; PFS, progression-free survival; PS, performance status; TKIs, tyrosine kinase inhibitors.

Figure 3

Analysis of PFS curves for NSCLC patients receiving EGFR-TKI therapy, categorized by smoking history (A), clinical stage (B), gender (C), and age (D). EGFR, epidermal growth factor receptor; NSCLC, non-small cell lung cancer; PFS, progression-free survival; TKIs, tyrosine kinase inhibitors.

Figure 4

Analysis of PFS curves for NSCLC patients receiving first- and second-generation EGFR-TKIs versus third-generation EGFR-TKIs (A), with further stratification of the PFS curves based on the presence or absence of brain metastases between treatment groups of first- or second-generation EGFR-TKIs (B) and third-generation EGFR-TKIs (C). The analysis of PFS curves is also stratified based on the median ΔCt value (ΔCt ≤3.76 or ΔCt >3.76) (D) and the optimal ΔCt cut-off value (ΔCt ≤4.335 or ΔCt >4.335) (E). ΔCt, mutant DNA cycle threshold (Ct) value relative to the endogenous reference gene (Ct) value; EGFR, epidermal growth factor receptor; NSCLC, non-small cell lung cancer; PFS, progression-free survival; TKIs, tyrosine kinase inhibitors.

Figure 5

Multivariate Cox regression analysis examining the association of various stratification methods, including ΔCt median value group (ΔCt ≤3.76 or ΔCt >3.76), the optimal ΔCt cut-off value group (ΔCt ≤4.335 or ΔCt >4.335), brain metastases (yes or no), liver metastases (yes or no), EGFR mutation status (exon 19 deletion or 21L858R), performance status score (>1 or 0–1), generation of EGFR-TKIs (1^st and 2^nd, or 3^rd), median age at diagnosis (≥65 or <65 years), and smoking history (yes or no), with PFS in patients treated with EGFR-TKIs. HR exceeding 1 signifies a positive factor for PFS, whereas an HR below 1 indicates an unfavorable factor for PFS. ΔCt, mutant DNA cycle threshold (Ct) value relative to the endogenous reference gene (Ct) value; CI, confidence interval; EGFR, epidermal growth factor receptor; PFS, progression-free survival; TKIs, tyrosine kinase inhibitors.