The applicability of a predictive index for second- and third-line treatment of unselected non-small-cell lung cancer patients.

BACKGROUND

Tyrosine kinase inhibitors of EGFR (TKI-EGFR) induced response in only 10% of Caucasian non-small-cell lung cancer patients in second- or third-line treatment. Independent predictive factors for qualification to TKI-EGFR treatment have not been assessed. In 2008, a prognostic index was reported for patients treated with erlotinib in the BR.21 trial, but its application for real, unselected patients is limited.

OBJECTIVES

Based on clinical and molecular factors of patients treated with erlotinib, we tried to create a predictive index which could be applied in real treatment practice.

METHODS

In a Cox regression model, we established 6 factors which affected overall survival for erlotinib treatment: performance status, erlotinib-induced rash, time from diagnosis to treatment, gender, weight loss and LDH level. We analyzed the risk factors of early progression and survival shorter than 6 months. In addition we included: time from first-line chemotherapy to erlotinib treatment, smoking status, mutation status in EGFR and anemia.

RESULTS

Our model consisted of 10 factors that were assigned points according to HR or χ2 and p value. The score was used to separate patients into 4 risk categories of unfavorable disease course based on 10th, 50th and 90th percentiles: low risk (I), intermediate low risk (II), intermediate high risk (III) and high risk (IV). Survival probability was significantly higher for group I, intermediate for groups II and III, and significantly lower for group IV (χ2 = 49.5, p < 0.0001). Based on the previously reported index we could not qualify our patients for the low risk group.

CONCLUSIONS

Our model could be useful for qualification for erlotinib treatment of patients with numerous adverse factors and limited access to genetic examination.