Machine learning models-based on integration of next-generation sequencing testing and tumor cell sizes improve subtype classification of mature B-cell neoplasms

• Published in: Frontiers in oncology Vol. 13; p. 1160383
• Main Authors: Mu, Yafei, Chen, Yuxin, Meng, Yuhuan, Chen, Tao, Fan, Xijie, Yuan, Jiecheng, Lin, Junwei, Pan, Jianhua, Li, Guibin, Feng, Jinghua, Diao, Kaiyuan, Li, Yinghua, Yu, Shihui, Liu, Lingling
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 03.08.2023
• Subjects: machine learning (ML); mature B-cell neoplasms (MBNs); next-generation sequencing (NGS); Oncology; pathological diagnosis; subtype classification
• ISSN: 2234-943X; 2234-943X
• DOI: 10.3389/fonc.2023.1160383

Background Next-generation sequencing (NGS) panels for mature B-cell neoplasms (MBNs) are widely applied clinically but have yet to be routinely used in a manner that is suitable for subtype differential diagnosis. This study retrospectively investigated newly diagnosed cases of MBNs from our laboratory to investigate mutation landscapes in Chinese patients with MBNs and to combine mutational information and machine learning (ML) into clinical applications for MBNs, especially for subtype classification. Methods Samples from the Catalogue Of Somatic Mutations In Cancer (COSMIC) database were collected for ML model construction and cases from our laboratory were used for ML model validation. Five repeats of 10-fold cross-validation Random Forest algorithm was used for ML model construction. Mutation detection was performed by NGS and tumor cell size was confirmed by cell morphology and/or flow cytometry in our laboratory. Results Totally 849 newly diagnosed MBN cases from our laboratory were retrospectively identified and included in mutational landscape analyses. Patterns of gene mutations in a variety of MBN subtypes were found, important to investigate tumorigenesis in MBNs. A long list of novel mutations was revealed, valuable to both functional studies and clinical applications. By combining gene mutation information revealed by NGS and ML, we established ML models that provide valuable information for MBN subtype classification. In total, 8895 cases of 8 subtypes of MBNs in the COSMIC database were collected and utilized for ML model construction, and the models were validated on the 849 MBN cases from our laboratory. A series of ML models was constructed in this study, and the most efficient model, with an accuracy of 0.87, was based on integration of NGS testing and tumor cell sizes. Conclusions The ML models were of great significance in the differential diagnosis of all cases and different MBN subtypes. Additionally, using NGS results to assist in subtype classification of MBNs by method of ML has positive clinical potential.