Joint Serum Tumor Markers Serve as survival predictive model of Erlotinib in the treatment of recurrent Non-small Cell Lung Cancer

Abstract

Background and objective Molecular targeting therapy is the direction of individualized treatment of lung cancer, scholars has been established targeted therapy prediction models which provide more guidance for clinical individual therapy. This study investigated the relationship among pulmonary surfactant-associated protein D (SP-D), transforming growth factor α (TGF-α), matrix metalloproteinase 9 (MMP-9), tissue polypeptide specific antigen (TPS), and Krebs von den Lungen-6 (KL-6) and response as well as survival in the patients with recurrent non-small cell lung cancer, which Erlotinib was as second line treatment after failure to chemotherapy. This study also established a predictive prognostic model. Methods Serum levels of SP-D, TGF-α, MMP-9, TPS, and KL-6 in 114 patients before erlotinib treatment were detected by ELISA method. Combined with clinical factors, these levels were used to investigate the relationship with efficacy in erlotinib treatment and construct a predicted prognostic model by Kaplan-Meier curve and Cox proportional hazard model multivariate analysis. Results The objective response rate (ORR) and disease control rate (DCR) in the 114 patients, were 22.8% (26/114) and 72.8% (83/114), to Erlotinib treatment respectively. The median progression-free survival (PFS) and one year survival rate with Erlotinib treatment were 5.13 months and 69.3%, respectively. Patients in the SP-D>110 ng/mL group exhibited more ORR (33.3% vs 13.3%, P=0.011) and DCR (83.3% vs 63.3%, P=0.017) than those in the ≤110 ng/mL group. Patients in the MMP-9≤535 ng/mL group showed more DCR (83.9%) than those in the >535 ng/mL group (62.1%) (P=0.009). Patients in the TPS110 ng/mL (5.95 months vs 3.25 months, P=0.009), MMP-9≤535 ng/mL (5.83 months vs 3.47 months, P=0.046), KL-6<500 U/mL (6.03 months vs 3.40 months, P=0.040), and TPS<80 U/L (6.15 months vs 2.42 months, P=0.014) groups showed better PFS. Multivariate analysis showed that current or ever-smoker, wild style of EGFR status, progression after prior chemotherapy, absence of skin rash, elevated serum LDH level, and TPS≥80 U/L were independent adverse prognostic factors for PFS. These six factors were used in the prognostic model. Patients were categorized into four prognosis risk groups based on the prognostic index from the model, namely, low risk, intermediate low risk, intermediate risk, and high risk groups. The median PFS of good, intermediate, poor, and very poor prognosis groups were 9.12, 6.88, 3.52, and 0.93 months (P<0.001), respectively. Conclusion The prognostic model based on clinical parameters with TPS will be useful in identifying patients who might be most likely to benefit from Erlotinib therapy in the patients with recurrent non-small cell lung cancer.

Keywords

• Lung neoplasms
• Erlotinib
• Serum tumor marker
• Prognostic factor
• Predictive model