External verification and improvement of the Neo-Bioscore staging system in a Chinese cohort

Geng, Rui; Min, Ningning; Zheng, Yiqiong; Hong, Chenyan; Wu, Rilige; Wu, Huan; Wei, Yufan; Zhang, Yanjun; Li, Xiru

doi:10.21037/atm-21-6738

Cited by 1 publication

(1 citation statement)

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“…Based on the fact that the results of some previous biomarker studies are difficult to be reproduced in other data sets [ 33 ], external validation of results in other independent cohorts is very important [ 34 ]. This being so, we devised an experimental setting where a gene set was selected using TCGA-BRCA data, and the gene set was validated on independent SCAN-B data.…”

Section: Methodsmentioning

confidence: 99%

Risk Stratification for Breast Cancer Patient by Simultaneous Learning of Molecular Subtype and Survival Outcome Using Genetic Algorithm-Based Gene Set Selection

Koo

Lee

et al. 2022

Cancers

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Patient stratification is a clinically important task because it allows us to establish and develop efficient treatment strategies for particular groups of patients. Molecular subtypes have been successfully defined using transcriptomic profiles, and they are used effectively in clinical practice, e.g., PAM50 subtypes of breast cancer. Survival prediction contributed to understanding diseases and also identifying genes related to prognosis. It is desirable to stratify patients considering these two aspects simultaneously. However, there are no methods for patient stratification that consider molecular subtypes and survival outcomes at once. Here, we propose a methodology to deal with the problem. A genetic algorithm is used to select a gene set from transcriptome data, and their expression quantities are utilized to assign a risk score to each patient. The patients are ordered and stratified according to the score. A gene set was selected by our method on a breast cancer cohort (TCGA-BRCA), and we examined its clinical utility using an independent cohort (SCAN-B). In this experiment, our method was successful in stratifying patients with respect to both molecular subtype and survival outcome. We demonstrated that the orders of patients were consistent across repeated experiments, and prognostic genes were successfully nominated. Additionally, it was observed that the risk score can be used to evaluate the molecular aggressiveness of individual patients.

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Section: Methodsmentioning

confidence: 99%