A multi-axis biomarker model integrating KIM-1 and MAdCAM-1 in metastatic renal cell carcinoma.

M Marc Machaalani R Renee Maria Saliby (Center of Molecular and Cellular Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT) C Carolina Alves Costa Silva E Eddy Saad C Clara Steiner (University Hospital Leipzig, Leipzig, Germany) C Caiwei Zhong (Dana-Farber Cancer Institute, Boston, MA) E Emre Yekedüz X Xiaowen Liu L Liliana Ascione (Dana-Farber Cancer Institute, Boston, MA) P Pablo Barrios (Dana-Farber Cancer Institute, Boston, MA) T Ti Cai (EMD Serono Research and Development Institute, Billerica, MA) G Gwo-Shu Mary Lee (Dana-Farber Cancer Institute, Boston, MA) W Wanling Xie (Department of Medical Oncology Dana‐Farber Cancer Institute Boston Massachusetts USA) S Sabina Signoretti M Maxine Sun (Dana-Farber Cancer Institute, Boston, MA) L Laurence Zitvogel L Laurence Albiges (Department of Medical Oncology Gustave Roussy Villejuif France) D David F. McDermott (Division of Medical Oncology, Department of Medicine Beth Israel Deaconess Medical Center Boston Massachusetts USA) W Wenxin Xu (Department of Medical Oncology Dana‐Farber Cancer Institute Boston Massachusetts USA) T Toni K. Choueiri (Department of Medical Oncology Dana‐Farber Cancer Institute Boston Massachusetts USA)

Abstract

4554 Background: Kidney injury molecule-1 (KIM-1) and mucosal vascular addressin cell adhesion molecule-1 (MAdCAM-1) have each emerged as prognostic circulating biomarkers in metastatic renal cell carcinoma (mRCC). Whether these biomarkers capture overlapping or complementary aspects of disease biology, and whether their integration improves risk stratification, remains unclear. Methods: Baseline plasma KIM-1 and MAdCAM-1 levels were measured in 612 patients with previously untreated mRCC from the JAVELIN Renal 101 trial, including 323 patients treated with avelumab + axitinib and 289 with sunitinib. Biomarker concentrations were analyzed as continuous variables after log10 transformation. Associations with overall survival (OS) and progression-free survival (PFS) were assessed using multivariable Cox models adjusted for IMDC risk, age, and sex. A composite KIM-1–MAdCAM-1 risk score was developed to assess the incremental prognostic value of integrating these biomarkers. Results: Both KIM-1 and MAdCAM-1 levels were independently associated with OS and PFS in univariable analyses. In multivariable models including both biomarkers and adjusted for age, sex, and IMDC risk, higher KIM-1 levels remained associated with worse survival (OS: HR 1.57, 95% CI 1.28–1.93), whereas higher MAdCAM-1 levels were associated with improved survival (OS: HR 0.37, 95% CI 0.15–0.92). No significant interaction was observed between KIM-1 and MAdCAM-1, nor between either biomarker and treatment arm for OS or PFS. A composite KIM-1–MAdCAM-1 risk score was strongly associated with clinical outcomes. Patients in the highest-risk quartile had significantly worse PFS (median: 8.3 [5.7–11.1] vs 19.4 [13.8–NR] months) and OS rate (at 18 months: 65.0% [57.3–73.8] vs 90.0% [85.0–95.2]) compared to the lowest-risk quartile, across both treatment arms (Table). A joint KIM-1–MAdCAM-1 model demonstrated improved discrimination compared with either biomarker alone (p < 0.001), and the addition of both biomarkers to IMDC significantly improved OS discrimination (C-index 0.73 vs 0.67; p < 0.001). Conclusions: KIM-1 and MAdCAM-1 provide complementary and non-redundant prognostic information in mRCC. Their integration into a composite risk score significantly improves risk stratification beyond either biomarker alone and beyond IMDC criteria. These findings support a multi-axis circulating biomarker approach to prognostication in mRCC. Association of KIM-1–MAdCAM-1 composite score quartiles with progression-free and overall survival in the overall study cohort and by treatment arm. KIM-1–MAdCAM-1 Composite Score Entire CohortHR (95% CI) Avelumab + AxitinibHR (95% CI) SunitinibHR (95% CI) Progression-Free Survival (Q4 vs Q1) 2.12 (1.56–2.87) 2.00 (1.31–3.05) 2.20 (1.41–3.43) Overall Survival (Q4 vs Q1) 4.67 (2.71–8.05) 3.61 (1.75–7.44) 5.89 (2.54–13.7) HR, hazard ratio; CI, confidence interval; Q, quartile.

Article Details

Volume / Issue Vol. 44, Issue 16_suppl
Published June 01, 2026
Pages 4554-4554
ISSN 0732-183X
Publisher Lippincott Williams & Wilkins

Journal Info

Journal of Clinical Oncology

Lippincott Williams & Wilkins

ISSN: 0732-183X Health Sciences

Authors (20)

M

Marc Machaalani

R

Renee Maria Saliby

Center of Molecular and Cellular Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT

C

Carolina Alves Costa Silva

E

Eddy Saad

C

Clara Steiner

University Hospital Leipzig, Leipzig, Germany

C

Caiwei Zhong

Dana-Farber Cancer Institute, Boston, MA

E

Emre Yekedüz

X

Xiaowen Liu

L

Liliana Ascione

Dana-Farber Cancer Institute, Boston, MA

P

Pablo Barrios

Dana-Farber Cancer Institute, Boston, MA

T

Ti Cai

EMD Serono Research and Development Institute, Billerica, MA

G

Gwo-Shu Mary Lee

Dana-Farber Cancer Institute, Boston, MA

W

Wanling Xie

Department of Medical Oncology Dana‐Farber Cancer Institute Boston Massachusetts USA

S

Sabina Signoretti

M

Maxine Sun

Dana-Farber Cancer Institute, Boston, MA

L

Laurence Zitvogel

L

Laurence Albiges

Department of Medical Oncology Gustave Roussy Villejuif France

D

David F. McDermott

Division of Medical Oncology, Department of Medicine Beth Israel Deaconess Medical Center Boston Massachusetts USA

W

Wenxin Xu

Department of Medical Oncology Dana‐Farber Cancer Institute Boston Massachusetts USA

T

Toni K. Choueiri

Department of Medical Oncology Dana‐Farber Cancer Institute Boston Massachusetts USA