Detection and localization of surgically resectable cancers with a multi-analyte blood test

Abstract

Earlier detection is key to reducing cancer deaths. Here, we describe a blood test that can detect eight common cancer types through assessment of the levels of circulating proteins and mutations in cell-free DNA. We applied this test, called CancerSEEK, to 1005 patients with nonmetastatic, clinically detected cancers of the ovary, liver, stomach, pancreas, esophagus, colorectum, lung, or breast. CancerSEEK tests were positive in a median of 70% of the eight cancer types. The sensitivities ranged from 69 to 98% for the detection of five cancer types (ovary, liver, stomach, pancreas, and esophagus) for which there are no screening tests available for average-risk individuals. The specificity of CancerSEEK was greater than 99%: only 7 of 812 healthy controls scored positive. In addition, CancerSEEK localized the cancer to a small number of anatomic sites in a median of 83% of the patients.

Fig. 1. Development of a PCR-based assay to identify tumor-specific mutations in plasma samples

Colored curves indicate the proportion of cancers of the eight types evaluated in this study that can be detected with an increasing number of short (<40 bp) amplicons. The sensitivity of detection increases with the number of amplicons but plateaus at ~60 amplicons. Colored dots indicate the fraction of cancers detected by using the 61-amplicon panel used in 805 cancers evaluated in our study, which averaged 82%. Publicly available sequencing data were obtained from the COSMIC repository.

Fig. 2. Performance of CancerSEEK

(A) ROC curve for CancerSEEK. The red point on the curve indicates the test’s average performance (62%) at >99% specificity. Error bars represent 95% confidence intervals for sensitivity and specificity at this particular point. The median performance among the eight cancer types assessed was 70%. (B) Sensitivity of CancerSEEK by stage. Bars represent the median sensitivity of the eight cancer types, and error bars represent standard errors of the median. (C) Sensitivity of CancerSEEK by tumor type. Error bars represent 95% confidence intervals.

Fig. 3. Identification of cancer type by supervised machine learning for patients classified as positive by CancerSEEK

Percentages correspond to the proportion of patients correctly classified by one of the two most likely types (sum of light and dark blue bars) or the most likely type (light blue bar). Predictions for all patients for all cancer types are provided in table S8. Error bars represent 95% confidence intervals.