Lead-Time Trajectory of CA19-9 as an Anchor Marker for Pancreatic Cancer Early Detection

Abstract

Background & aims: There is substantial interest in liquid biopsy approaches for cancer early detection among subjects at risk, using multi-marker panels. CA19-9 is an established circulating biomarker for pancreatic cancer; however, its relevance for pancreatic cancer early detection or for monitoring subjects at risk has not been established.

Methods: CA19-9 levels were assessed in blinded sera from 175 subjects collected up to 5 years before diagnosis of pancreatic cancer and from 875 matched controls from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. For comparison of performance, CA19-9 was assayed in blinded independent sets of samples collected at diagnosis from 129 subjects with resectable pancreatic cancer and 275 controls (100 healthy subjects; 50 with chronic pancreatitis; and 125 with noncancerous pancreatic cysts). The complementary value of 2 additional protein markers, TIMP1 and LRG1, was determined.

Results: In the PLCO cohort, levels of CA19-9 increased exponentially starting at 2 years before diagnosis with sensitivities reaching 60% at 99% specificity within 0 to 6 months before diagnosis for all cases and 50% at 99% specificity for cases diagnosed with early-stage disease. Performance was comparable for distinguishing newly diagnosed cases with resectable pancreatic cancer from healthy controls (64% sensitivity at 99% specificity). Comparison of resectable pancreatic cancer cases to subjects with chronic pancreatitis yielded 46% sensitivity at 99% specificity and for subjects with noncancerous cysts, 30% sensitivity at 99% specificity. For prediagnostic cases below cutoff value for CA19-9, the combination with LRG1 and TIMP1 yielded an increment of 13.2% in sensitivity at 99% specificity (P = .031) in identifying cases diagnosed within 1 year of blood collection.

Conclusion: CA19-9 can serve as an anchor marker for pancreatic cancer early detection applications.

Keywords: Biomarker; Detection; Pancreatic Cancer.

Figure 1.. Time-dependent classifier performance of CA19-9 in the PLCO Cohort.

A) AUC point estimates (95% CI) of CA19-9 for distinguishing cases stratified by time to diagnosis from baseline blood draw from matched controls. B) Sensitivity (95% CI) of CA19-9 at 99% specificity at various lead times. A 4th order fitted spline curve was used to illustrate the trajectory of the classifier performance or sensitivity at 99% specificity of CA19-9 in relation to time to diagnosis from baseline blood draw. Tabulated values are shown in the table beneath the figures.

Figure 2.. Predicted probability of pancreatic cancer within 1 year according to CA19-9 values.

The rug plot shows the observed distribution of biomarker scores. The vertical broken lines correspond to the quartiles threshold for CA19-9 values amongst controls (Q1, Q2, Q3, and Q4).

Figure 3.. Time-dependent classifier performance of CA19-9 for cases stratified based on presentation of localized, regional or distant disease at time of diagnosis and that were diagnosed within 3 years of baseline blood draw in the PLCO Cohort.

Sensitivity of CA19-9 at 99% specificity for distinguishing cases stratified based on presentation of localized, regional or distant disease at time of diagnosis from time-interval matched controls. A 4th order fitted spline curve was used to illustrate the trajectory of the sensitivity at 99% specificity of CA19-9 in relation to time to diagnosis from baseline blood draw.

Figure 4.. Classifier performance of CA19-9 in Test Set #1 and Test Set #2.

A-B) AUC (95% CI) and sensitivity (95% CI) at 99% specificity of CA19-9 for distinguishing resectable PDAC cases (n=99) from matched healthy controls (n=100) (A) or subjects with chronic pancreatitis (n=50) (B). C) AUC (95% CI) and sensitivity at 99% specificity of CA19-9 for distinguishing resectable PDAC cases (8 PDAC and 22 IPMN with an associated invasive ductal adenocarcinoma) from subjects harboring benign IPMN (n=125). D-E) AUC (95% CI) and sensitivity (95% CI) at 99% specificity of CA19-9 for distinguishing PDAC (n=8) (D) or Invasive IPMN (n=22) (E) from subjects harboring benign IPMN (n=125).

Figure 5.. Classifier performance of a 3-marker panel consisting of LRG1, TIMP1 and CA19-9 for identifying cases diagnosed within 1 year and that were ‘negative’ for CA19-9 alone based on a 99% specificity cutoff.

A) Scatter plot illustrating the distribution of the 3-marker panel scores (Y-axis) and log10 CA19-9 values (X-axis). Broken lines represent >99% specificity cutoffs. The 3-marker panel was derived using fixed beta-coefficients from the logistic regression model previously developed elsewhere. B) Confusion matrix describing the performance of the classification model corresponding to the 3-marker panel and CA19-9 alone at >99% specificity. Statistical significance was determined by 1-sided McNemar exact test.