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Kai Li
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P3.01 - Advanced NSCLC (ID 621)
- Event: WCLC 2017
- Type: Poster Session with Presenters Present
- Track: Advanced NSCLC
- Presentations: 2
- Moderators:
- Coordinates: 10/18/2017, 09:30 - 16:00, Exhibit Hall (Hall B + C)
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P3.01-084 - Analysis on ALTER0303 Trial: aCECs Level May Correlate with Metastases Burden and Predict PFS of Anlotinib in Advanced NSCLC (ID 8866)
09:30 - 09:30 | Presenting Author(s): Kai Li
- Abstract
Background:
Activated circulating endothelial cells (aCECs) have been indicated as a potential biomarker for cancerous angiogenesis in varieties of malignancies. Furthermore, several studies have exhibited aCECs were related with progression-free survival (PFS) and overall survival (OS) in anti-angiogenesis therapy. Anlotinib is a TKI of VEGFR1/2/3, FGFR1-4, PDGFR α/β, and c-Kit. As a third-line and above treatment on advanced NSCLC, Anlotinib has shown an affirmatory efficacy in ALTER0303 controlled trial. Herein we investigated the connection between aCECs and PFS, OS and metastases burden in the trial.
Method:
Blood samples were collected at baseline (pre-therapeutically), the 7[th], 15[th], 21[th], 42[th,] 63[th] day of Anlotinib or placebo. aCECs was measured by Flow Cytometry. Receiver-operating characteristics (ROC) analysis was used to determine a cutoff value of baseline aCECs counts to divide them into high and low groups. The predicting value of aCECs for PFS was investigated by univariate survival analysis. Chi-square test for baseline aCECs counts and patients’ clinical characteristics before Anlotinib or placebo treatment was performed.
Result:
aCECs were obtained in 78 patients (Anlotinib n=49). No significant difference in baseline characteristics was found between two arms (P﹥0.05). High baseline aCECs count was statistically in connection with more metastatic lesions (﹥3) (c[2]= 4.905,P=0.027). 49 Anlotinib treated patients were divided into 35 and 14 according to the ratio of minimal aCECs counts at every time point and baseline (aCECs min/baseline), as <1 and ≥1. Median follow-up was 8.6 months. Patients with min/baseline<1 had longer median PFS than ones with min/baseline>1 (193 vs.124 days, HR=0.439, 95%CI 0.211-0.912, P=0.023. shown in Table1). However, no significant relation between PFS and aCECs min/baseline was found in control arm.Table 1.Comparison of Progression Rate in Various aCECs min/baseline N Progression Rate % Log Rank χ2 P-value 3 months 6 months 9 months aCECs min/baseline≥1 14 36.5 52.4 84.1 5.149 0.023 aCECs min/baseline<1 35 20.4 26.7 47.3
Conclusion:
Decreased aCECs during an initial period of Anlotinib therapy may predict longer PFS and baseline aCECs count may be related with the number of metastatic lesions.
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P3.01-087 - Impact Factor Analysis for Efficacy and Prognosis of Anlotinib in NSCLC as Third-Line Treatment: Data from Trial ALTER 0303 (ID 9129)
09:30 - 09:30 | Presenting Author(s): Kai Li
- Abstract
Background:
Anlotinib hydrochloride is a novel TKI targeting the VEGFR, FGFR, PDGFR and c-Kit. With the capability of inhibiting the tumor angiogenesis and tumor cell itself, anlotinib had showed significantly improvement in OS (9.63 vs. 6.30 months, HR=0.68, 95%CI 0.54-0.87, p=0.0018) and PFS (5.37 vs. 1.40 months, HR=0.26, 95%CI 0.21-0.33, p<0.0001) in ALTER 0303 study for refractory cancer, a randomized, double-blind, placebo-controlled Phase Ⅲ trial in China. Here, we report the main impact factors affecting the efficacy and prognosis of anlotinib based on the data from ALTER0303 to elucidate the most benefit population.
Method:
Analyzed data were collected from 294 patients that were enrolled in ALTER0303 trial and received anlotinib treatment between 4[th] March 2015 and 15[th] August 2016. The statistical analysis was conducted using SPSS19.0 software, in which the measuring and enumeration materials were described with Mean±SD and frequency/percentage respectively, Kaplan-Meier method was used for survival curves in survival analysis. Independent impact factors of OS and PFS were identified by univariate and multivariate analysis in Cox proportional hazards regression model (Significant level, α=0.05).
Result:
Several factors were discovered to be associated with the efficacy of Anlotinib treatment. The impact factors were presented in Tab1.Tab1. Impact factors for PFS and OS analyzed by Cox proportional hazards regression model Independent risk factor Independent protective factor PFS Ratio of granulocytes to lymphocytes at PD (HR=1.07, 95%CI 1.041-1.100, p<0.0001) Elevated ALP level (HR=1.553, 95%CI 1.142-2.112, p=0.005) Baseline sum of longest diameters of target lesions (HR=1.004, 95%CI 1.001-1.006, p=0.007) Elevated TSH level (HR= 0.555, 95%CI 0.422-0.730, p<0.0001) Hypercholesteremia (HR=0.720, 95%CI 0.534-0.971, p=0.031) Hypertension (HR=0.482, 95%CI 0.370-0.628, p<0.0001) Hand-foot skin reaction (HR=0.489, 95%CI 0.373- 0.643, p<0.0001) Elevated LDL level (HR=0.630, 95%CI 0.437-0.909, p=0.014) Age (HR=0.987, 95%CI 0.975-0.999, p=0.039) OS Ratio of granulocytes to lymphocytes at PD (HR=1.116, 95%CI 1.081-1.151, p<0.0001) Baseline sum of longest diameters of target lesions (HR=1.006, 95%CI 1.003-1.008, p<0.0001) ECOG PS≥2 at PD (HR=2.245, 95%CI 1.704- 3.508, p<0.0001) Elevated TSH level (HR=0.725, 95%CI 0.524- 1.005, p=0.053) Hypertriglyceridemia (HR=0.601, 95%CI 0.440-0.821, p<0.0001) Rash (HR=0.581, 95%CI 0.369-0.916, p=0.019) Female (HR=0.713, 95%CI 0.533-0.953, p=0.022)
Conclusion:
This analysis explored the possible impact factors of PFS and OS in Anlotinib treatment. Moreover, we provide real data for the prediction of Anlotinib efficacy and most benefit population through the baseline characteristics and variety of clinical index. However, further analysis in the larger scale study is still looking forward.
