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. 2019 Jul-Aug;16(4):229-244.
doi: 10.21873/cgp.20128.

Diagnosis of Non-small Cell Lung Cancer for Early Stage Asymptomatic Patients

Cherylle Goebel  1 , Christopher L Louden  2 , Robert McKenna Jr  3 , Osita Onugha  3 , Andrew Wachtel  4 , Thomas Long  5
Affiliations

Diagnosis of Non-small Cell Lung Cancer for Early Stage Asymptomatic Patients

Cherylle Goebel et al. Cancer Genomics Proteomics. 2019 Jul-Aug.

Abstract

Background/aim: In 2016 in the United States, 7 of 10 patients were estimated to die following lung cancer diagnosis. This is due to a lack of a reliable screening method that detects early-stage lung cancer. Our aim is to accurately detect early stage lung cancer using algorithms and protein biomarkers.

Patients and methods: A total of 1,479 human plasma samples were processed using a multiplex immunoassay platform. 82 biomarkers and 6 algorithms were explored. There were 351 NSCLC samples (90.3% Stage I, 2.3% Stage II, and 7.4% Stage III/IV).

Results: We identified 33 protein biomarkers and developed a classifier using Random Forest. Our test detected early-stage Non-Small Cell Lung Cancer (NSCLC) with a 90% accuracy, 80% sensitivity, and 95% specificity in the validation set using the 33 markers.

Conclusion: A specific, non-invasive, early-detection test, in combination with low-dose computed tomography, could increase survival rates and reduce false positives from screenings.

Keywords: Early stage lung cancer; biomarkers; detection; diagnosis; immunoassay; non-small cell lung cancer; proteomics.

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Conflict of interest statement

Thomas Long serves as the Chief Executive Officer and Chief Financial Officer for Lung Cancer Proteomics LLC. Cherylle Goebel and Chris Louden are consultants for Lung Cancer Proteomics LLC.

Figures

Figure 1
Figure 1. Box plots for each of the 33 analytes. These plots show the distribution of each pathology cohort’s biomarker concentration. Each box represents the interquartile range (from the 25th percentile to the 75th). The bar in the middle of the box is the median. The tails on either end of the box (if present) reach out to the minimum and maximum. If there are values greater than 1.5 times the interquartile range plus the 75th percentile (or less than the 25th percentile minus 1.5 times the interquartile range), they are represented by individual points.
Figure 1
Figure 1. Box plots for each of the 33 analytes. These plots show the distribution of each pathology cohort’s biomarker concentration. Each box represents the interquartile range (from the 25th percentile to the 75th). The bar in the middle of the box is the median. The tails on either end of the box (if present) reach out to the minimum and maximum. If there are values greater than 1.5 times the interquartile range plus the 75th percentile (or less than the 25th percentile minus 1.5 times the interquartile range), they are represented by individual points.
Figure 1
Figure 1. Box plots for each of the 33 analytes. These plots show the distribution of each pathology cohort’s biomarker concentration. Each box represents the interquartile range (from the 25th percentile to the 75th). The bar in the middle of the box is the median. The tails on either end of the box (if present) reach out to the minimum and maximum. If there are values greater than 1.5 times the interquartile range plus the 75th percentile (or less than the 25th percentile minus 1.5 times the interquartile range), they are represented by individual points.
Figure 1
Figure 1. Box plots for each of the 33 analytes. These plots show the distribution of each pathology cohort’s biomarker concentration. Each box represents the interquartile range (from the 25th percentile to the 75th). The bar in the middle of the box is the median. The tails on either end of the box (if present) reach out to the minimum and maximum. If there are values greater than 1.5 times the interquartile range plus the 75th percentile (or less than the 25th percentile minus 1.5 times the interquartile range), they are represented by individual points.
Figure 1
Figure 1. Box plots for each of the 33 analytes. These plots show the distribution of each pathology cohort’s biomarker concentration. Each box represents the interquartile range (from the 25th percentile to the 75th). The bar in the middle of the box is the median. The tails on either end of the box (if present) reach out to the minimum and maximum. If there are values greater than 1.5 times the interquartile range plus the 75th percentile (or less than the 25th percentile minus 1.5 times the interquartile range), they are represented by individual points.
Figure 1
Figure 1. Box plots for each of the 33 analytes. These plots show the distribution of each pathology cohort’s biomarker concentration. Each box represents the interquartile range (from the 25th percentile to the 75th). The bar in the middle of the box is the median. The tails on either end of the box (if present) reach out to the minimum and maximum. If there are values greater than 1.5 times the interquartile range plus the 75th percentile (or less than the 25th percentile minus 1.5 times the interquartile range), they are represented by individual points.
Figure 2
Figure 2. AUC/ROC graphs.
See this image and copyright information in PMC

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