Detection of Lung Cancer through Metabolic Changes Measured in Blood Plasma Evelyne Louis, PhD, Peter Adriaensens, PhD, Wanda Guedens, PhD, Theophile.

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Detection of Lung Cancer through Metabolic Changes Measured in Blood Plasma Evelyne Louis, PhD, Peter Adriaensens, PhD, Wanda Guedens, PhD, Theophile Bigirumurame, Kurt Baeten, PhD, Karolien Vanhove, MD, Kurt Vandeurzen, MD, Karen Darquennes, MD, Johan Vansteenkiste, MD, Christophe Dooms, MD, Ziv Shkedy, PhD, Liesbet Mesotten, MD, Michiel Thomeer, MD Journal of Thoracic Oncology Volume 11, Issue 4, Pages 516-523 (April 2016) DOI: 10.1016/j.jtho.2016.01.011 Copyright © 2016 International Association for the Study of Lung Cancer Terms and Conditions

Figure 1 CONSORT diagram of the study. Journal of Thoracic Oncology 2016 11, 516-523DOI: (10.1016/j.jtho.2016.01.011) Copyright © 2016 International Association for the Study of Lung Cancer Terms and Conditions

Figure 2 (A) Orthogonal partial least squares discriminant analysis (OPLS-DA) score plot derived from the training cohort of 226 controls and 233 patients with lung cancer, (B) Receiver operating curves showing the high predictive accuracy of the OPLS-DA model for the cross-validation of the training cohort as well as for the independent validation, (C) OPLS-DA score plot for the classification of the independent cohort of 89 controls and 98 patients with lung cancer by means of the trained classifier. AUC, area under the curve; C, controls; CV, cross-validation; LC: patients with lung cancer; PS, predicted scores. Journal of Thoracic Oncology 2016 11, 516-523DOI: (10.1016/j.jtho.2016.01.011) Copyright © 2016 International Association for the Study of Lung Cancer Terms and Conditions

Figure 3 Discrimination according to histological subtype. Orthogonal partial least squares discriminant analysis score plot of patients with an adenocarcinoma (n = 91) and a squamous carcinoma (n = 66). Adeno, adenocarcinoma; Sq, squamous carcinoma. Journal of Thoracic Oncology 2016 11, 516-523DOI: (10.1016/j.jtho.2016.01.011) Copyright © 2016 International Association for the Study of Lung Cancer Terms and Conditions

Figure 4 Discrimination according to tumor stage. (A) Orthogonal partial least squares discriminant analysis score plot of patients with early-stage disease (n = 76) and patients with metastatic disease (n = 63) and (B) Orthogonal partial least squares discriminant analysis score plot of patients with early-stage disease (n = 76) and a randomly chosen but equally populated group of controls (n = 76). C, controls. Journal of Thoracic Oncology 2016 11, 516-523DOI: (10.1016/j.jtho.2016.01.011) Copyright © 2016 International Association for the Study of Lung Cancer Terms and Conditions

Supplementary Figure 1 Figure demonstrating the PCA score plot of all subjects (357 lung cancer patients and 347 controls) stained for (A) disease, (B) gender, (C) smoking habits, and (D) COPD. Abbreviations: C: controls, COPD: chronic obstructive pulmonary disease, F: females, LC: lung cancer patients, M: males, PC: principal component. Journal of Thoracic Oncology 2016 11, 516-523DOI: (10.1016/j.jtho.2016.01.011) Copyright © 2016 International Association for the Study of Lung Cancer Terms and Conditions

Supplementary Figure 2 Figure showing the OPLS-DA score plot derived from the training cohort when the statistical outliers were not excluded (250 controls and 250 lung cancer patients. Abbreviations: C: controls, LC: lung cancer patients. Journal of Thoracic Oncology 2016 11, 516-523DOI: (10.1016/j.jtho.2016.01.011) Copyright © 2016 International Association for the Study of Lung Cancer Terms and Conditions

Supplementary Figure 3 Figure showing the result of the permutation test demonstrating that the obtained classification model is not the result of overfitting. All resulting R2 and Q2 values (at the left) are lower than these of the model (at the right), indicative for a valid model. Abbreviations: R2: explained variation, Q2: predicted variation. Journal of Thoracic Oncology 2016 11, 516-523DOI: (10.1016/j.jtho.2016.01.011) Copyright © 2016 International Association for the Study of Lung Cancer Terms and Conditions

Supplementary Figure 4 Figure showing the OPLS-DA score plot of patients with and without COPD. Abbreviations: COPD: chronic obstructive pulmonary disease. Journal of Thoracic Oncology 2016 11, 516-523DOI: (10.1016/j.jtho.2016.01.011) Copyright © 2016 International Association for the Study of Lung Cancer Terms and Conditions

Supplementary Figure 5 Figure demonstrating the S-plot of the OPLS-DA model showing the variables contributing most to group discrimination. Variables situated at the right end are increased in the plasma of controls, whereas those situated at the left end are increased for the lung cancer patients. The 45 most differentiating variables used to explain the disturbed biochemical pathways (VIP > 0.5) in lung cancer are marked (●). Abbreviations: Var: variable, VIP: variable importance for the projection. Journal of Thoracic Oncology 2016 11, 516-523DOI: (10.1016/j.jtho.2016.01.011) Copyright © 2016 International Association for the Study of Lung Cancer Terms and Conditions

Supplementary Figure 6 Figure showing the discrimination according to histological subtype. (A) PCA score plot, (B) OPLS-DA score plot. Abbreviations: Adeno: adenocarcinoma, Adenosq: adenosquamous carcinoma, C: controls, NOS: not otherwise specified, NSCLC: non-small cell lung cancer, PC: principal component, Sq: squamous carcinoma. Journal of Thoracic Oncology 2016 11, 516-523DOI: (10.1016/j.jtho.2016.01.011) Copyright © 2016 International Association for the Study of Lung Cancer Terms and Conditions

Supplementary Figure 7 Figure demonstrating the discrimination according to tumor stage. Abbreviations: C: controls, PC: principal component. Journal of Thoracic Oncology 2016 11, 516-523DOI: (10.1016/j.jtho.2016.01.011) Copyright © 2016 International Association for the Study of Lung Cancer Terms and Conditions