Evidence map›Paper›PMID 24854556›Full record

ArticleZhongguo fei ai za zhi = Chinese journal of lung cancer2014

[Joint serum tumor markers serve as survival predictive model of erlotinib in the treatment of recurrent non-small cell lung cancer].

Lan Shao, Wei Hong, Lei Zheng, Chunxiao He, Beibei Zhang, Fajun Xie, Zhengbo Song, Guangyuan Lou, Yiping Zhang

Open access · greenAbstract readEnglish Abstract
In one paragraph

Article in Zhongguo fei ai za zhi = Chinese journal of lung cancer, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 4 citations in OpenAlex.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

9 authors at 1 institution in 1 country.

Lan ShaoDepartment of Medical Oncology, Zhejiang Cancer Hospital, Hangzhou 310022, China;Key Laboratory Diagnosis and Treatment Technology on Thoracic Oncology, Hangzhou 310022, China.
Wei HongDepartment of Medical Oncology, Zhejiang Cancer Hospital, Hangzhou 310022, China;Key Laboratory Diagnosis and Treatment Technology on Thoracic Oncology, Hangzhou 310022, China.
Lei ZhengZhejiang Chinese Medical University, the 2nd Clinical Medical College, Hangzhou 310053, China.
Chunxiao HeZhejiang Chinese Medical University, the 2nd Clinical Medical College, Hangzhou 310053, China.
Beibei ZhangZhejiang Chinese Medical University, the 2nd Clinical Medical College, Hangzhou 310053, China.
Fajun XieDepartment of Medical Oncology, Zhejiang Cancer Hospital, Hangzhou 310022, China;Key Laboratory Diagnosis and Treatment Technology on Thoracic Oncology, Hangzhou 310022, China.
Zhengbo SongDepartment of Medical Oncology, Zhejiang Cancer Hospital, Hangzhou 310022, China;Key Laboratory Diagnosis and Treatment Technology on Thoracic Oncology, Hangzhou 310022, China.
Guangyuan LouDepartment of Medical Oncology, Zhejiang Cancer Hospital, Hangzhou 310022, China;Key Laboratory Diagnosis and Treatment Technology on Thoracic Oncology, Hangzhou 310022, China.
Yiping ZhangDepartment of Medical Oncology, Zhejiang Cancer Hospital, Hangzhou 310022, China;Key Laboratory Diagnosis and Treatment Technology on Thoracic Oncology, Hangzhou 310022, China.
Zhejiang Chinese Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectiveMolecular 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.

methodsSerum 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.

resultsThe 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 TPS<80 U/L group showed more DCR (82.4%) than those in the ≥80 U/L group (55.0%) (P=0.002). The SP-D>110 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.

conclusionsThe 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.

Indexed as

AdultAgedAged, 80 and overBiomarkers, TumorCarcinoma, Non-Small-Cell LungErlotinib HydrochlorideFemaleHumansLung NeoplasmsMaleMiddle AgedNeoplasm Recurrence, LocalPrognosisQuinazolinesSurvival RateBiomarkers, TumorErlotinib HydrochlorideQuinazolines

Identifiers

PMID24854556
PMCPMC6000447
OpenAlexW1513926940

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.