Combined circulating tumor DNA and protein biomarker-based liquid biopsy for the earlier detection of pancreatic cancers

Abstract

The earlier diagnosis of cancer is one of the keys to reducing cancer deaths in the future. Here we describe our efforts to develop a noninvasive blood test for the detection of pancreatic ductal adenocarcinoma. We combined blood tests for KRAS gene mutations with carefully thresholded protein biomarkers to determine whether the combination of these markers was superior to any single marker. The cohort tested included 221 patients with resectable pancreatic ductal adenocarcinomas and 182 control patients without known cancer. KRAS mutations were detected in the plasma of 66 patients (30%), and every mutation found in the plasma was identical to that subsequently found in the patient's primary tumor (100% concordance). The use of KRAS in conjunction with four thresholded protein biomarkers increased the sensitivity to 64%. Only one of the 182 plasma samples from the control cohort was positive for any of the DNA or protein biomarkers (99.5% specificity). This combinatorial approach may prove useful for the earlier detection of many cancer types.

Keywords: circulating tumor DNA; early cancer detection; liquid biopsy; pancreatic cancer; protein biomarkers.

Fig. S1.

Kaplan–Meier survival plot of the 221 PDAC patients included in this study stratified by AJCC stage (stage IA or IB: blue curve; stage IIA or IIB: red curve).

Fig. 1.

Combining ctDNA KRAS mutations with protein biomarkers increases sensitivity for early detection of PDAC. (A) Sensitivities of ctDNA KRAS mutations alone, ctDNA KRAS mutations plus CA19-9, and ctDNA KRAS mutations with CA19-9 and other proteins (combination assay) with respect to AJCC stage. (B) Sensitivities of ctDNA KRAS mutations alone, ctDNA KRAS mutations plus CA19-9, and ctDNA KRAS mutations with CA19-9 and other proteins (combination assay) with respect to tumor size. Error bars represent 95% CIs.

Fig. S2.

Correlation between triplex assay markers and tumor size. (A) KRAS mutations were found more frequently in larger tumors than smaller tumors, but the MAF did not correlate with tumor size (Pearson’s r = 0.039). (B) In patients with elevated CA19-9, the CA19-9 plasma concentration weakly correlates with tumor size (Pearson’s r = 0.287). (C–E) Plasma levels of CEA (C), HGF (D), and OPN (E) were less dependent on tumor size than KRAS mutations or CA19-9 (CEA: Pearson’s r = 0.153; HGF: Pearson’s r = 0.037; OPN: Pearson’s r = 0.018). Shaded regions represent 95% CIs.

Fig. 2.

(A) Combining ctDNA and protein markers increases sensitivity because a large proportion of patients are detected by only one marker. The Venn diagram shows the number of patients detected by ctDNA KRAS mutations (red circle), CA19-9 (green circle), the three other protein biomarkers (blue circle), and by combinations thereof (overlapping regions). Eighty patients (36% of the total) were not detectable by any of the three makers. (B) MAF of KRAS and TP53 mutations are strongly correlated (Pearson’s r = 0.885) in the plasma of the 12 patients whose plasma contained detectable amounts of both mutations, providing validation of the reliability of the ctDNA assay and its quantitative nature. The shaded region represents the 95% CI.

Fig. S3.

Levels of prolactin (G) and midkine (E) were significantly elevated in samples that were collected after the administration of anesthesia but before surgical excision. In contrast, no difference in the proportion of samples with mutant KRAS ctDNA (A), CA19-9 plasma concentration (B), CEA plasma concentration (C), HGF plasma concentration (D), and OPN plasma concentration (F) was observed between samples collected before or after the administration of anesthesia. P > 0.05 (exact permutation t test), NS., not significant.

Fig. S4.

Fold change in protein biomarker levels from 29 pairs plasma samples collected before and immediately after the administration of anesthesia. Of the six markers evaluated, only prolactin and midkine were found to be elevated by anesthesia, in perfect agreement with the correlation between collection site and protein levels.

Fig. S5.

Receiver operator characteristic (ROC) curves for KRAS mutations (A), CA19-9 (B), CEA (C), HGF (D), OPN (E), and the combination assay (F). (A–E) ROC curves demonstrate the performance of each combination assay biomarker individually. The red points on the curves indicate the marker performance at the thresholds used in the combination assay. Error bars represent 95% CIs for sensitivity and specificity at the particular threshold (red font). (F) ROC curves demonstrating the performance of the combination assay when the KRAS threshold was varied and CA19-9, CEA, HGF, and OPN thresholds were fixed at the levels used in the combination assay (black curve), the CA19-9 threshold was varied and KRAS, CEA, HGF, and OPN thresholds were fixed at the levels used in the combination assay (red curve), the CEA threshold was varied and KRAS, CA19-9, HGF, and OPN thresholds were fixed at the levels used in the combination assay (blue curve), the HGF threshold was varied and KRAS, CA19-9, CEA, and OPN thresholds were fixed at the levels used in the combination assay (green curve), and the OPN threshold was varied and KRAS, CA19-9, CEA, and HGF thresholds were fixed at the levels used in the combination assay (orange curve). The intersection of these three curves designates the overall performance of the triplex assay (64% sensitivity, 99.5% sensitivity).

Fig. S6.

Kaplan–Meier survival plots stratified by independent predictors of overall survival identified by multivariate analysis. (A) Combination assay status (HR = 1.76, 95% CI 1.10–2.84, P = 0.018). (B) Grade of differentiation (poorly differentiated, HR = 1.72, 95% CI 1.11–2.66, P = 0.015). (C) Lymphovascular invasion (present, HR = 1.81, 95% CI 1.06–3.09, P = 0.028). (D) Nodal disease (present, HR = 2.35, 95% CI 1.20–4.61, P = 0.013). (E) Margin status (HR = 1.59, 95% CI 1.01–2.55, P = 0.050).