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R. Ma
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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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P3.01-021 - A Multicenter, Non-Interventional Study on Real World EGFR Testing and in Patients with IIIB/IV NSCLC in Northern China (ID 8897)
09:30 - 09:30 | Author(s): R. Ma
- Abstract
Background:
EGFR mutation plays a dominant role in the precise treatment of non-small cell lung cancer (NSCLC), and EGFR-TKIs has been recommended for patients with positive EGFR-sensitive mutation as a standard regimen in clinical practice. In China, application of EGFR-TKIs without knowing EGFR mutation status has been a common phenomenon due to various reasons including the vast territory, uneven distribution of medical resources, differences level of testing technology and others. Therefore, we prospectively conducted a real-world investigation to understand the actual situation of EGFR testing in Northern China, and identify the underlying causes affecting EGFR detection, in order to provide references to improve the standardized treatment.( NCT02620657)
Method:
The patients with IIIB/IV NSCLC who were firstly diagnosed or postoperative recurrence between 2014-1-1 and 2014-12-31 in 28 research centers of Northern China were analyzed. The primary endpoint was testing rate，the secondary endpoints were factors affecting EGFR testing, EGFR mutation status, detection methods and the survival outcomes of patients.
Result:
Among 2809 patients, 2250(90.78%) were adenocarcinoma, 208(7.40%) were squamous carcinoma, 51(1.82%) were other pathologic types. Testing rate was 42.54%(1195/2809) and was significantly related to city level (first-tier cities vs. new first-tier cities vs. second-tier cities vs. third-tier and above cities : 69.04% vs. 38.08% vs. 34.05% vs. 14.11%, P < 0.001), smoking status (never smoking vs. ever smoking vs. smoking: 45.42% vs. 51.10% vs. 33.37%, P<0.001), ECOG PS(0 vs.1vs.2vs.≥3:47.93%vs. 44.48vs.34.89%vs.20.37%, P=0.011), pathological type (adenocarcinoma vs. squamous carcinoma: 44.94% vs.19.23%, P=0.003) and medical insurance situation (social basic medical insurance vs. new rural cooperative medical insurance vs. own expense: 44.98% vs. 36.49% vs. 29.55%, P=0.001). EGFR sensitive mutation rate was 46.44%, the most common subtype was 19Del(42.16%), followed by L858R(40.00%), Exon 20 insertions(1.62%) and other subtypes(16.20%). The most common methodology is ARMS(63.77%), the second common one is DNA sequencing(5.36%). The 1-year and 2-year survival rate in patients receiving EGFR testing was 73.6%and 51.9%, compared with 64.3% and 43.7% respectively in patients without EGFR testing.
Conclusion:
There were regional differences in EGFR testing rates among IIIB/IV NSCLC patients in Northern China. The intention of doctors and patients, medical insurance coverage and differences technical level are major factors affecting the testing rate of EGFR. Approaches should be taken to improve the situation, such as strengthening the training, expanding the coverage of medical insurance, and relying on commercial gene detection companies, and further standardize the molecularly pathological diagnosis and treatment of NSCLC.
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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 | Author(s): R. Ma
- 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.
