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  • Author or Editor: Lisa M. McLennan x
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Abstract

OBJECTIVE

The purpose of this study was to evaluate the performance of a next-generation sequencing-based liquid biopsy test for cancer monitoring in dogs.

SAMPLES

Pre- and postoperative blood samples were collected from dogs with confirmed cancer diagnoses originally enrolled in the CANcer Detection in Dogs (CANDiD) study. A subset of dogs also had longitudinal blood samples collected for recurrence monitoring.

METHODS

All cancer-diagnosed patients had a preoperative blood sample in which a cancer signal was detected and had at least 1 postoperative sample collected. Clinical data were used to assign a clinical disease status for each follow-up visit.

RESULTS

Following excisional surgery, in the absence of clinical residual disease at the postoperative visit, patients with Cancer Signal Detected results at that visit were 1.94 times as likely (95% CI, 1.21 to 3.12; P = .013) to have clinical recurrence within 6 months compared to patients with Cancer Signal Not Detected results. In the subset of patients with longitudinal liquid biopsy samples that had clinical recurrence documented during the study period, 82% (9/11; 95% CI, 48% to 97%) had Cancer Signal Detected in blood prior to or concomitant with clinical recurrence; in the 6 patients where molecular recurrence was detected prior to clinical recurrence, the median lead time was 168 days (range, 47 to 238).

CLINICAL RELEVANCE

Next-generation sequencing-based liquid biopsy is a noninvasive tool that may offer utility as an adjunct to current standard-of-care clinical assessment for cancer monitoring; further studies are needed to confirm diagnostic accuracy in a larger population.

Open access
in American Journal of Veterinary Research

Abstract

OBJECTIVE

To validate the performance of a novel, integrated test for canine cancer screening that combines cell-free DNA quantification with next-generation sequencing (NGS) analysis.

SAMPLE

Retrospective data from a total of 1,947 cancer-diagnosed and presumably cancer-free dogs were used to validate test performance for the detection of 7 predefined cancer types (lymphoma, hemangiosarcoma, osteosarcoma, leukemia, histiocytic sarcoma, primary lung tumors, and urothelial carcinoma), using independent training and testing sets.

METHODS

Cell-free DNA quantification data from all samples were analyzed using a proprietary machine learning algorithm to determine a Cancer Probability Index (High, Moderate, or Low). High and Low Probability of Cancer were final result classifications. Moderate cases were additionally analyzed by NGS to arrive at a final classification of High Probability of Cancer (Cancer Signal Detected) or Low Probability of Cancer (Cancer Signal Not Detected).

RESULTS

Of the 595 dogs in the testing set, 89% (n = 530) received a High or Low Probability result based on the machine learning algorithm; 11% (65) were Moderate Probability, and NGS results were used to assign a final classification. Overall, 87 of 122 dogs with the 7 predefined cancer types were classified as High Probability and 467 of 473 presumably cancer-free dogs were classified as Low Probability, corresponding to a sensitivity of 71.3% for the predefined cancer types at a specificity of 98.7%.

CLINICAL RELEVANCE

This integrated test offers a novel option to screen for cancer types that may be difficult to detect by physical examination at a dog’s wellness visit.

Open access
in Journal of the American Veterinary Medical Association