Identifying cancer-associated leukocyte profiles using high-resolution flow cytometry screening and machine learning

Frontiers in Immunology

Immunology

Desmond Yip¹  Jason Price²  Naomi Otoo²  Robert O. Slater²  Anna Orlov²  Dillon Hammill²  Jessica Garrett²  Katharine Gosling³  Melissa Ritchie³  David A. Simon Davis³  Ines I. Atmosukarto⁴  Ben J. C. Quah⁵  Kylie Jung⁵  Farhan M. Syed⁵ 

[1] Australian National University, Canberra, ACT, Australia;Department of Medical Oncology, Canberra Hospital & Health Services, Canberra, ACT, Australia;Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia;Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia;Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia;Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia;Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia;Radiation Oncology Department, Canberra Hospital & Health Services, Canberra, ACT, Australia;

关键词: cancer;    immunology;    machine learning;    leukocytes;    flow cytometry;    biomarkers;   

DOI  :  10.3389/fimmu.2023.1211064

 received in 2023-04-24, accepted in 2023-06-26,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundMachine learning (ML) is a valuable tool with the potential to aid clinical decision making. Adoption of ML to this end requires data that reliably correlates with the clinical outcome of interest; the advantage of ML is that it can model these correlations from complex multiparameter data sets that can be difficult to interpret conventionally. While currently available clinical data can be used in ML for this purpose, there exists the potential to discover new “biomarkers” that will enhance the effectiveness of ML in clinical decision making. Since the interaction of the immune system and cancer is a hallmark of tumor establishment and progression, one potential area for cancer biomarker discovery is through the investigation of cancer-related immune cell signatures. Hence, we hypothesize that blood immune cell signatures can act as a biomarker for cancer progression.MethodsTo probe this, we have developed and tested a multiparameter cell-surface marker screening pipeline, using flow cytometry to obtain high-resolution systemic leukocyte population profiles that correlate with detection and characterization of several cancers in murine syngeneic tumor models.ResultsWe discovered a signature of several blood leukocyte subsets, the most notable of which were monocyte subsets, that could be used to train CATboost ML models to predict the presence and type of cancer present in the animals.ConclusionsOur findings highlight the potential utility of a screening approach to identify robust leukocyte biomarkers for cancer detection and characterization. This pipeline can easily be adapted to screen for cancer specific leukocyte markers from the blood of cancer patient.

【 授权许可】

Unknown   
Copyright © 2023 Simon Davis, Ritchie, Hammill, Garrett, Slater, Otoo, Orlov, Gosling, Price, Yip, Jung, Syed, Atmosukarto and Quah