Circulating tumor microemboli diagnostics for patients with non-small-cell lung cancer

Abstract

Introduction: Circulating tumor microemboli (CTM) are potentially important cancer biomarkers, but using them for cancer detection in early-stage disease has been assay limited. We examined CTM test performance using a sensitive detection platform to identify stage I non-small-cell lung cancer (NSCLC) patients undergoing imaging evaluation.

Methods: First, we prospectively enrolled patients during 18F-FDG PET-CT imaging evaluation for lung cancer that underwent routine phlebotomy where CTM and circulating tumor cells (CTCs) were identified in blood using nuclear (DAPI), cytokeratin (CK), and CD45 immune-fluorescent antibodies followed by morphologic identification. Second, CTM and CTC data were integrated with patient (age, gender, smoking, and cancer history) and imaging (tumor diameter, location in lung, and maximum standard uptake value [SUVmax]) data to develop and test multiple logistic regression models using a case-control design in a training and test cohort followed by cross-validation in the entire group.

Results: We examined 104 patients with NSCLC, and the subgroup of 80 with stage I disease, and compared them to 25 patients with benign disease. Clinical and imaging data alone were moderately discriminating for all comers (Area under the Curve [AUC] = 0.77) and by stage I disease only (AUC = 0.77). However, the presence of CTM combined with clinical and imaging data was significantly discriminating for diagnostic accuracy in all NSCLC patients (AUC = 0.88, p value = 0.001) and for stage I patients alone (AUC = 0.87, p value = 0.002).

Conclusion: CTM may add utility for lung cancer diagnosis during imaging evaluation using a sensitive detection platform.

Figure 1. Circulating Tumor Cells and Circulating Tumor Microemboli Used for Modeling

Panel (A) shows the composite image for an HD-CTC from a patient with stage I adenocarcinoma followed by the individual DAPI positive (Blue, B), Cytokeratin positive (Red, C), and CD45 negative (Green, D) channels defining the cell. A doublet (Panels E–H), triplet (Panels I–L) and “mega” cluster of more than 8 HD-CTCs (Panels M–P) are shown as composites and by individual channels. HD-CTMs were defined as more than one HD-CTC with touching cytoplasm (see methods) for further modeling.

Figure 2. Patient Flow

This was a prospective, observational study of patients undergoing evaluation for lung cancer by FDG PET-CT that had blood drawn for CTC analysis. We excluded patients with blood processing errors, those without a definitive benign or non-small cell lung cancer diagnosis, and those with concurrent cancers. Clinical, imaging and CTC variables of interest were explored in a training set (n = 88) and validated in a test set (n = 41) for all patients and for the stage I subgroup only.

Figure 3. HD-CTM Integrated with Clinical and Imaging Data Improve Diagnostic Accuracy for NSCLC

Receiver operating characteristic (ROC) curves for diagnostic models calibrated using the training set and carried forward to the test set are shown. Models A–C were trained and tested using all comers, while D–F used stage I cancers only. Each plot displays the performance of two models (clinical alone and with CTM) and a p-value estimating the significance of the difference between the ROC curves. The ROC AUCs and their confidence intervals are given in the bottom right corner of each plot.