A novel computational pathology approach for identifying gene signatures prognostic of disease-free survival for papillary thyroid carcinomas.

Autor: Monabbati S; Dept. of Biomedical Engineering, Case Western Reserve University, OH, United States., Khalighi S; Wallace H. Coulter Dept. of Biomedical Engineering, Georgia Institute of Technology and Emory School of Medicine, Atlanta, GA, United States., Fu P; Dept. of Population and Quantitative Health Sciences, Case Western Reserve University, OH, United States., Shi Q; Dept. of Pathology, Emory University Hospital Midtown, Atlanta GA, United States., Asa SL; Dept. of Pathology, School of Medicine, Case Western Reserve University, and University Hospitals Cleveland Medical Center, OH, United States., Madabhushi A; Wallace H. Coulter Dept. of Biomedical Engineering, Georgia Institute of Technology and Emory School of Medicine, Atlanta, GA, United States; Atlanta Veterans Administration Medical Center, GA, United States. Electronic address: anantm@emory.edu.
Jazyk: angličtina
Zdroj: European journal of cancer (Oxford, England : 1990) [Eur J Cancer] 2024 Nov; Vol. 212, pp. 114326. Date of Electronic Publication: 2024 Sep 17.
DOI: 10.1016/j.ejca.2024.114326
Abstrakt: Introduction: Papillary thyroid carcinoma (PTC) is the most prevalent form of thyroid cancer, with the classical and follicular variants representing most cases. Despite generally favorable prognoses, approximately 10% of patients experience recurrence post-surgery and radioactive iodine therapy. Attempts to stratify risk of recurrence have relied on gene expression-based prognostic and predictive signatures with a focus on mutations of well-known driver genes, while hallmarks of tumor morphology have been ignored.
Objectives: We introduce a new computational pathology approach to develop prognostic gene signatures for PTC that is informed by quantitative features of tumor and immune cell morphology.
Methods: We quantified nuclear and immune-related features of tumor morphology to develop a pathomic signature, which was then used to inform an RNA-expression signature model provides a notable advancement in risk stratification compared to both standalone and pathology-informed gene-expression signatures.
Results: There was a 17.8% improvement in the C-index (from 0.605 to 0.783) for 123 cPTCs and 15% (from 0.576 to 0.726) for 38 fvPTCs compared to the standalone gene-expression signature. Hazard ratios also improved for cPTCs from 0.89 (0.67,0.99) to 4.43 (3.65,6.68) and fvPTC from 0.98 (0.76,1.32) to 2.28 (1.87,3.64). We validated the image-based risk model on an independent cohort of 32 cPTCs with hazard ratio 1.8 (1.534,2.167).
Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests. The authors declare the following competing interests: Dr. Madabhushi is an equity holder in Picture Health, Elucid Bioimaging, and Inspirata Inc. Currently he serves on the advisory board of Picture Health, Aiforia Inc, and SimBioSys. He also currently consults for SimBioSys. He also has sponsored research agreements with AstraZeneca, Boehringer-Ingelheim, Eli-Lilly and Bristol Myers-Squibb. His technology has been licensed to Picture Health and Elucid Bioimaging. He is also involved in 3 different R01 grants with Inspirata Inc. He also serves as a member for the Frederick National Laboratory Advisory Committee. Dr Asa is on the Medical Advisory Boards of Leica Biosystems and Ibex Medical Analytics. All remaining authors have declared no conflicts of interest.
(Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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