Optimal timing of circulating tumor DNA (ctDNA) dynamics to prognosticate immune checkpoint inhibitors (ICI) efficacy in patients with advanced stage melanoma.

V Vincent T. Ma (Division of Hematology, Medical Oncology, and Palliative Care, Department of Medicine, University of Wisconsin, Madison, WI) A Amir W. Forati (University of Wisconsin Madison, Madison, WI) A Alice Yao Zhou (Washington University in St. Louis, St. Louis, MO) T Trevor McCracken (University of Kansas, Kansas City, KS) M Meena Hosny (University of Rochester, Rochester, NY) A Alyssa K. Steimle G Golbarg Rahimi (University of Southern California Keck School of Medicine, Los Angeles, CA) J Janmesh D. Patel (University of Wisconsin Madison, Madison, WI) C Caroline Burkey (University of Wisconsin Hospitals and Clinics, Madison, WI) J Jessica Caraway (University of Kansas, Kansas City, KS) A Andrew Wood M Madison S. Harris (University of Wisconsin Madison, Madison, WI) A Alexander Birbrair (Department of Dermatology, University of Wisconsin-Madison) D Deepak M. Sahasrabudhe (University of Rochester Medical Center Department of Neurobiology and Anatomy, Rochester, NY) F Fauzia Hollnagel G Gary C. Doolittle (University of Kansas Medical Center, Westwood, KS) N Nina S. Mathew (University of Kansas Medical Center, Westwood, KS) G Gino Kim In (Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA) A Adrienne I. Victor (University of Rochester, Rochester, NY)

Abstract

9534 Background: ctDNA predicts relapse in resected tumors, but its role in advanced disease is less defined. We previously reported that early ctDNA changes at 3-4 weeks after ICI initiation can inform treatment response and survival. In this study, we attempt to identify the optimal timing of ctDNA dynamics within 90 days at various windows to better associate with ORR, PFS, and OS outcomes in patients (pts) with advanced stage melanoma treated with ICI. Methods: We performed a multicenter retrospective analysis using a personalized tumor-informed ctDNA assay (Natera) on prospectively collected plasma from pts with unresectable stage III/IV melanoma treated with anti-PD-1-based therapy. ctDNA was assessed at baseline and within 90 days of ICI initiation, stratified by ≤30, 31-60, and 61-90 days. Logistic regression evaluated associations between ctDNA change (decrease/stable vs increase) and ORR. Cox proportional hazards models assessed PFS and OS. Pearson (ORR) and Spearman (PFS, OS) correlations were used for continuous ctDNA analyses. Results: Among 182 pts, 80% had cutaneous, 11% mucosal, and 9% unknown primary melanoma; 23% had unresectable stage III and 77% stage IV disease. Treatments included ipilimumab/nivolumab (55%), nivolumab/relatlimab (24%), and anti–PD-1 monotherapy (20%). Median follow-up was 12.1 months. Decreasing/stable ctDNA at all timepoints was strongly associated with ORR (p<0.0001), with the greatest effect at 31-60 days (OR 25.38, 95% CI 7.12-90.45). Similar patterns were observed for survival, with improved PFS across all windows and strongest separation at 61-90 days (HR 0.170, 0.101-0.288). OS was likewise improved, most notably at 61-90 days (HR 0.137, 0.058-0.321). Continuous ctDNA change correlated with ORR (r -0.51 to -0.63) and PFS (ρ -0.35 to -0.47); OS correlations were weaker but significant at 61-90 days (ρ -0.21). See Table 1. Conclusions: While ctDNA dynamics within 30 days showed prognostic value, associations with ORR, PFS, and OS were strongest and most consistent at 31–60 and 61–90 days. Prospective studies are needed to validate ctDNA for ICI treatment monitoring. Association of ctDNA dynamics with outcomes by time interval. ≤30 days 31-60 days 61-90 days ORR (OR, 95% CI) 15.28 (5.58–41.78), p<0.0001 25.38 (7.12–90.45), p<0.0001 17.82 (5.66–56.08), p<0.0001 PFS (HR, 95% CI) 0.373 (0.224-0.621), p<0.0001 0.233 (95% CI 0.139-0.388), p<0.0001 0.170 (0.101-0.288), p<0.0001 OS (HR, 95% CI) 0.477 (0.237-0.961), p=0.038 0.392 (95% CI 0.185-0.831), p<0.015 0.137 (0.058-0.321), p<0.0001 ORR (Pearson, r) -0.553, p<0.0001 -0.628, p<0.0001 -0.513, p<0.0001 PFS (Spearman, ρ) -0.352, p<0.0001 -0.438, p<0.0001 -0.473, p<0.0001 OS (Spearman, ρ) -0.128, p=0.168 -0.104, p=0.240 -0.210, p=0.0171

Article Details

Volume / Issue Vol. 44, Issue 16_suppl
Published June 01, 2026
Pages 9534-9534
ISSN 0732-183X
Publisher Lippincott Williams & Wilkins

Journal Info

Journal of Clinical Oncology

Lippincott Williams & Wilkins

ISSN: 0732-183X Health Sciences

Authors (19)

V

Vincent T. Ma

Division of Hematology, Medical Oncology, and Palliative Care, Department of Medicine, University of Wisconsin, Madison, WI

A

Amir W. Forati

University of Wisconsin Madison, Madison, WI

A

Alice Yao Zhou

Washington University in St. Louis, St. Louis, MO

T

Trevor McCracken

University of Kansas, Kansas City, KS

M

Meena Hosny

University of Rochester, Rochester, NY

A

Alyssa K. Steimle

G

Golbarg Rahimi

University of Southern California Keck School of Medicine, Los Angeles, CA

J

Janmesh D. Patel

University of Wisconsin Madison, Madison, WI

C

Caroline Burkey

University of Wisconsin Hospitals and Clinics, Madison, WI

J

Jessica Caraway

University of Kansas, Kansas City, KS

A

Andrew Wood

M

Madison S. Harris

University of Wisconsin Madison, Madison, WI

A

Alexander Birbrair

Department of Dermatology, University of Wisconsin-Madison

D

Deepak M. Sahasrabudhe

University of Rochester Medical Center Department of Neurobiology and Anatomy, Rochester, NY

F

Fauzia Hollnagel

G

Gary C. Doolittle

University of Kansas Medical Center, Westwood, KS

N

Nina S. Mathew

University of Kansas Medical Center, Westwood, KS

G

Gino Kim In

Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA

A

Adrienne I. Victor

University of Rochester, Rochester, NY