Multi-cohort validation of a multi-analyte liquid biopsy test for early-stage pancreatic cancer detection.

A Anna Bergamaschi (ClearNote Health, San Diego, CA) V Verena Friedl D David Haan G Glenn Oliviera (ClearNote Health, San Diego, CA) Y Yuan Xue M Micah Collins (ClearNote Health, San Diego, CA) V Vanessa Lopez (ClearNote Health, San Diego, CA) M Melissa Peters (ClearNote Health, San Diego, CA) S Shimul Chowdhury (ClearNote Health, San Diego, CA) W Wayne Volkmuth (ClearNote Health, San Mateo, CA) P Philip Hart D Darwin Conwell (University of Kentucky College of Medicine, Lexington, KY) Z Ziding Feng (Fred Hutch Cancer Center, Seattle, WA) C Camden Lopez (Fred Hutch Cancer Center, Seattle, WA) A Anirban Maitra S Samuel Levy (ClearNote Health, San Diego, CA) S Suresh Chari (The University of Texas MD Anderson Cancer Center, Houston, TX)

Abstract

4139 Background: Pancreatic ductal adenocarcinoma (PDAC) has a 5-year survival rate below 12%, largely due to diagnosis at advanced, noncurative stages. Earlier detection could significantly improve survival outcomes; however, current approaches, including imaging and blood based assays lack sufficient sensitivity and specificity. Liquid biopsy methods that combine genomic, epigenomic, and glycan-based biomarkers may improve the detection accuracy by integrating a multi-analyte approach. We developed an improved prediction model for the Avantect Pancreatic Cancer Test (Avantect) using 5-hydroxymethylcytosine (5hmC) profiling, whole-genome based fragmentomics, together with genotyping and haplotype data, and CA19-9 biomarker levels. Methods: We employed a training cohort consisting of 162 PDAC cases and 983 noncancer controls. Cell-free DNA was analyzed using 5hmC profiling, low-pass whole-genome sequencing (WGS), and genotyping, alongside matched plasma CA19-9 measurements. A logistic regression model integrating 5hmC features, fragment size metrics, haplotype information, and CA19-9 levels was constructed and locked at a specificity of 97.75%. Performance was evaluated in two independent validation cohorts consisting of 1,445 individuals (259 PDAC; 1,186 noncancer participants) and 173 individuals (67 PDAC and 106 non-cancers). Sensitivity, specificity, and 95% confidence intervals (CIs) were computed. Results: In an independent validation cohort of 1,445 individuals with various high-risk features including type 2 diabetes, family history, and genetic predisposition, Avantect achieved an overall sensitivity of 82.6% (95% CI: 77.45%-87.04%) and an early stage (stage I-II) sensitivity of 76.8% (n=138; 95% CI: 68.87%-83.57%). A second validation cohort of 173 individuals, enriched for new onset type 2 diabetes, was evaluated and showed a sensitivity of 74.6% (95% CI: 62.51%-84.47%). Specificity in both cohorts remained high at 97.5% (95% CI: 96.41%-98.29%) and 98.1% (95% CI: 93.33%-99.77%) respectively, consistent with the pre-specified rate of 97.75%. Test specificity was further evaluated in a cohort comprising other cancer types, including breast, colorectal, lung, liver, prostate, ovarian, bladder, and kidney cancers, revealing a consistent high specificity of 97.70 (0.05% reduction). Conclusions: The multi-analyte model shows strong and robust performance across multiple independent cohorts detecting PDAC at high specificity. By integrating orthogonal biological signals, through the incorporation of epigenomic, genomic, and glycan biomarkers, the Avantect Pancreatic Cancer Test showed an improved PDAC detection to provide a tool that could significantly improve survival for patients with pancreatic cancer.

Article Details

Volume / Issue Vol. 44, Issue 16_suppl
Published June 01, 2026
Pages 4139-4139
ISSN 0732-183X
Publisher Lippincott Williams & Wilkins

Journal Info

Journal of Clinical Oncology

Lippincott Williams & Wilkins

ISSN: 0732-183X Health Sciences

Authors (17)

A

Anna Bergamaschi

ClearNote Health, San Diego, CA

V

Verena Friedl

D

David Haan

G

Glenn Oliviera

ClearNote Health, San Diego, CA

Y

Yuan Xue

M

Micah Collins

ClearNote Health, San Diego, CA

V

Vanessa Lopez

ClearNote Health, San Diego, CA

M

Melissa Peters

ClearNote Health, San Diego, CA

S

Shimul Chowdhury

ClearNote Health, San Diego, CA

W

Wayne Volkmuth

ClearNote Health, San Mateo, CA

P

Philip Hart

D

Darwin Conwell

University of Kentucky College of Medicine, Lexington, KY

Z

Ziding Feng

Fred Hutch Cancer Center, Seattle, WA

C

Camden Lopez

Fred Hutch Cancer Center, Seattle, WA

A

Anirban Maitra

S

Samuel Levy

ClearNote Health, San Diego, CA

S

Suresh Chari

The University of Texas MD Anderson Cancer Center, Houston, TX