Proteomic Biomarkers for Acute Interstitial Lung Disease in Gefitinib-Treated Japanese Lung Cancer Patients
Author Information
Author(s): Fredrik Nyberg, Atsushi Ogiwara, Chris G. Harbron, Takao Kawakami, Keiko Nagasaka, Sachiko Takami, Kazuya Wada, Hsiao-Kun Tu, Makiko Otsuji, Yutaka Kyono, Tae Dobashi, Yasuhiko Komatsu, Makoto Kihara, Shingo Akimoto, Ian S. Peers, Marie C. South, Tim Higenbottam, Masahiro Fukuoka, Koichiro Nakata, Yuichiro Ohe, Shoji Kudoh, Ib Groth Clausen, Toshihide Nishimura, György Marko-Varga, Harubumi Kato
Primary Institution: Global Epidemiology, AstraZeneca R&D, Mölndal, Sweden
Hypothesis
Can proteomic biomarkers improve prediction of acute interstitial lung disease (ILD) in gefitinib-treated lung cancer patients?
Conclusion
The study identified 29 proteins that can predict the risk of acute ILD in patients treated with gefitinib.
Supporting Evidence
- Blood plasma was collected from 43 gefitinib-treated NSCLC patients developing acute ILD and 123 randomly selected controls.
- 41 peptide peaks representing 29 proteins were identified as best predicting ILD.
- Multivariate modeling achieved ILD prediction comparable to previously identified clinical variables.
Takeaway
Researchers looked at blood samples from lung cancer patients to find proteins that could help predict lung problems caused by a cancer drug.
Methodology
Blood plasma was collected from 43 gefitinib-treated NSCLC patients with acute ILD and 123 controls, followed by mass spectrometry analysis.
Potential Biases
Potential biases in patient selection and diagnosis could affect the results.
Limitations
The study was limited to a specific population and may not be generalizable to all lung cancer patients.
Participant Demographics
Japanese patients with advanced or recurrent non-small-cell lung cancer.
Statistical Information
P-Value
1.0×10−25
Statistical Significance
p=1.0×10−25
Digital Object Identifier (DOI)
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