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Development and Validation of a Prognostic Risk Model for Patients with Advanced Melanoma Treated with Immune Checkpoint Inhibitors

Authors :
Igor Stukalin
Vishal Navani
Mehul Gupta
Yibing Ruan
Devon J Boyne
Dylan E O’Sullivan
Daniel E Meyers
Siddhartha Goutam
Michael Sander
Benjamin W Ewanchuk
Darren R Brenner
Aleksi Suo
Winson Y Cheung
Daniel Y C Heng
Jose G Monzon
Tina Cheng
Source :
The Oncologist.
Publication Year :
2023
Publisher :
Oxford University Press (OUP), 2023.

Abstract

Background Risk stratification tools for patients with advanced melanoma (AM) treated with immune checkpoint inhibitors (ICI) are lacking. We identified a new prognostic model associated with overall survival (OS). Patients and Methods A total of 318 treatment naïve patients with AM receiving ICI were collected from a multi-centre retrospective cohort study. LASSO Cox regression identified independent prognostic factors associated with OS. Model validation was carried out on 500 iterations of bootstrapped samples. Harrel’s C-index was calculated and internally validated to outline the model’s discriminatory performance. External validation was carried out in 142 advanced melanoma patients receiving ICI in later lines. Results High white blood cell count (WBC), high lactate dehydrogenase (LDH), low albumin, Eastern Cooperative Oncology Group (ECOG) performance status ≥1, and the presence of liver metastases were included in the model. Patients were parsed into 3 risk groups: favorable (0-1 factors) OS of 52.9 months, intermediate (2-3 factors) OS 13.0 months, and poor (≥4 factors) OS 2.7 months. The C-index of the model from the discovery cohort was 0.69. External validation in later-lines (N = 142) of therapy demonstrated a c-index of 0.65. Conclusions Liver metastases, low albumin, high LDH, high WBC, and ECOG≥1 can be combined into a prognostic model for AM patients treated with ICI.

Subjects

Subjects :
Cancer Research
Oncology

Details

ISSN :
1549490X and 10837159
Database :
OpenAIRE
Journal :
The Oncologist
Accession number :
edsair.doi...........8fbc6aaee499972aadbfb347e9f60ae7