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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Systems Immunology
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1390298

Etiological stratification and prognostic assessment of haemophagocytic lymphohistiocytosis by machine learning on Onco-mNGS data and clinical data

Provisionally accepted
Lin Wu Lin Wu 1Xuefang Cao Xuefang Cao 2Jingshi Wang Jingshi Wang 1Qi Kong Qi Kong 1Junxia Hu Junxia Hu 1Lin Shi Lin Shi 1Liurui Dou Liurui Dou 1Deli Song Deli Song 1Leilei Chen Leilei Chen 1Mengyuan Zhou Mengyuan Zhou 1Huan Liu Huan Liu 1Ruotong Ren Ruotong Ren 3*Zhao Wang Zhao Wang 1
  • 1 Department of Haematology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
  • 2 Matridx Biotechnology Co., Ltd, Hangzhou, Zhejiang, China
  • 3 Independent researcher, Beijing, China

The final, formatted version of the article will be published soon.

    Hemophagocytic lymphohistiocytosis (HLH) is a rare, complicated and lifethreatening hyperinflammatory syndrome that maybe triggered by various infectious agents, malignancies and rheumatologic disorders. Early diagnosis and identification of the cause is essential to initiate appropriate treatment and improve the quality of life and survival of patients. The recently developed Onco-mNGS technology can be successfully used for simultaneous detection of infections and tumors. In the present study, 99 patients with clinically confirmed HLH were etiologically subtyped for infection, tumor and autoimmunity based on CNV and microbial data generated by Onco-mNGS technology, and a predictive model was developed and validated for the differential diagnosis of the underlying disease leading to secondary HLH. Furthermore, the treatment outcomes of patients with HLH triggered by EBV infection and non-EBV infection were evaluated, respectively. The current study demonstrated that the novel Onco-mNGS can identify the infection and malignancyrelated triggers among patients with secondary HLH. A random forest classification model based on CNV profile, infectious pathogen spectrum and blood microbial community was developed to better identify the different HLH subtypes anddetermine the underlying triggers. The prognosis for treatment of HLH patients is not only associated with CNV, but also with the presence of pathogens and nonpathogens in peripheral blood. Higher CNV burden along with frequent deletions on chromosome 19, higher pathogen burden and lower non-pathogenic microbes were prognosis factors that significantly related with unfavorable treatment outcomes. Our study provided comprehensive knowledge in the triggers and prognostic predictors of patients with secondary HLH, which may help early diagnosis and appropriate targeted therapy, thus improving the survival and prognosis of the patients.

    Keywords: hemophagocytic lymphohistiocytosis, Etiological stratification, Prognostic assessment, Onco-mNGS, machine learning

    Received: 23 Feb 2024; Accepted: 16 Aug 2024.

    Copyright: © 2024 Wu, Cao, Wang, Kong, Hu, Shi, Dou, Song, Chen, Zhou, Liu, Ren and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ruotong Ren, Independent researcher, Beijing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.