Validation of a blood protein signature for non-small cell lung cancer - PubMed (original) (raw)

doi: 10.1186/1559-0275-11-32. eCollection 2014.

Stephen A Williams 1, Jill M Siegfried 2, William L Bigbee 3, Joel L Weissfeld 4, David O Wilson 3, Harvey I Pass 5, William N Rom 6, Thomas Muley 7, Michael Meister 7, Wilbur Franklin 8, York E Miller 9, Edward N Brody 1, Rachel M Ostroff 1

Affiliations

Validation of a blood protein signature for non-small cell lung cancer

Michael R Mehan et al. Clin Proteomics. 2014.

Abstract

Background: CT screening for lung cancer is effective in reducing mortality, but there are areas of concern, including a positive predictive value of 4% and development of interval cancers. A blood test that could manage these limitations would be useful, but development of such tests has been impaired by variations in blood collection that may lead to poor reproducibility across populations.

Results: Blood-based proteomic profiles were generated with SOMAscan technology, which measured 1033 proteins. First, preanalytic variability was evaluated with Sample Mapping Vectors (SMV), which are panels of proteins that detect confounders in protein levels related to sample collection. A subset of well collected serum samples not influenced by preanalytic variability was selected for discovery of lung cancer biomarkers. The impact of sample collection variation on these candidate markers was tested in the subset of samples with higher SMV scores so that the most robust markers could be used to create disease classifiers. The discovery sample set (n = 363) was from a multi-center study of 94 non-small cell lung cancer (NSCLC) cases and 269 long-term smokers and benign pulmonary nodule controls. The analysis resulted in a 7-marker panel with an AUC of 0.85 for all cases (68% adenocarcinoma, 32% squamous) and an AUC of 0.93 for squamous cell carcinoma in particular. This panel was validated by making blinded predictions in two independent cohorts (n = 138 in the first validation and n = 135 in the second). The model was recalibrated for a panel format prior to unblinding the second cohort. The AUCs overall were 0.81 and 0.77, and for squamous cell tumors alone were 0.89 and 0.87. The estimated negative predictive value for a 15% disease prevalence was 93% overall and 99% for squamous lung tumors. The proteins in the classifier function in destruction of the extracellular matrix, metabolic homeostasis and inflammation.

Conclusions: Selecting biomarkers resistant to sample processing variation led to robust lung cancer biomarkers that performed consistently in independent validations. They form a sensitive signature for detection of lung cancer, especially squamous cell histology. This non-invasive test could be used to improve the positive predictive value of CT screening, with the potential to avoid invasive evaluation of nonmalignant pulmonary nodules.

Keywords: Biomarker; Diagnosis; Lung cancer; Preanalytic variability; Proteomic; SOMAmer; Sample bias; Squamous cell carcinoma.

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Figures

Figure 1

Figure 1

Probability Density Function plots of HSP90 and MMP7 distributions for each training site control group. (a) HSP90 is an example of a protein affected by preanalytic variability and the plot demonstrates the bias between control groups. (b) MMP7, selected from the high quality training samples, is consistent between sites.

Figure 2

Figure 2

Training set ROC. Results are plotted for the entire data set and for AD and SQ tumor histologies separately.

Figure 3

Figure 3

UHH validation ROC. Results are plotted for the entire data set and for AD and SQ tumor histologies separately.

Figure 4

Figure 4

EDRN validation ROC. Results are plotted for the entire data set and for AD and SQ tumor histologies separately.

Figure 5

Figure 5

Study flowchart for biomarker discovery and validation studies.

References

    1. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, Abraham J, Adair T, Aggarwal R, Ahn SY, Alvarado M, Anderson HR, Anderson LM, Andrews KG, Atkinson C, Baddour LM, Barker-Collo S, Bartels DH, Bell ML, Benjamin EJ, Bennett D, Bhalla K, Bikbov B, Bin AA, Birbeck G, Blyth F, Bolliger I, Boufous S, Bucello C, Burch M. et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;11:2095–2128. -PMC -PubMed
    1. Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA Cancer J Clin. 2011;11:69–90. -PubMed
    1. She J, Yang P, Hong Q, Bai C. Lung cancer in China: challenges and interventions. Chest. 2013;11:1117–1126. -PubMed
    1. DeAngelis R, Sant M, Coleman MP, Francisci S, Baili P, Pierannunzio D, Trama A, Visser O, Brenner H, Ardanaz E, Bielska-Lasota M, Engholm G, Nennecke A, Siesling S, Berrino F, Capocaccia R. Cancer survival in Europe 1999–2007 by country and age: results of EUROCARE-5-a population-based study. Lancet Oncol. 2013;11:23–34. -PubMed
    1. American Cancer Society. Cancer Facts & Figures 2013. Atlanta: American Cancer Society; 2013.

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