1
|
Scherr TF, Douglas CE, Schaecher KE, Schoepp RJ, Ricks KM, Shoemaker CJ. Application of a Machine Learning-Based Classification Approach for Developing Host Protein Diagnostic Models for Infectious Disease. Diagnostics (Basel) 2024; 14:1290. [PMID: 38928705 PMCID: PMC11202442 DOI: 10.3390/diagnostics14121290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 06/11/2024] [Accepted: 06/13/2024] [Indexed: 06/28/2024] Open
Abstract
In recent years, infectious disease diagnosis has increasingly turned to host-centered approaches as a complement to pathogen-directed ones. The former, however, typically requires the interpretation of complex multiple biomarker datasets to arrive at an informative diagnostic outcome. This report describes a machine learning (ML)-based classification workflow that is intended as a template for researchers seeking to apply ML approaches for developing host-based infectious disease biomarker classifiers. As an example, we built a classification model that could accurately distinguish between three disease etiology classes: bacterial, viral, and normal in human sera using host protein biomarkers of known diagnostic utility. After collecting protein data from known disease samples, we trained a series of increasingly complex Auto-ML models until arriving at an optimized classifier that could differentiate viral, bacterial, and non-disease samples. Even when limited to a relatively small training set size, the model had robust diagnostic characteristics and performed well when faced with a blinded sample set. We present here a flexible approach for applying an Auto-ML-based workflow for the identification of host biomarker classifiers with diagnostic utility for infectious disease, and which can readily be adapted for multiple biomarker classes and disease states.
Collapse
Affiliation(s)
| | - Christina E. Douglas
- Diagnostic Systems Division, U.S. Army Medical Research Institute of Infectious Diseases, Fort Detrick, MD 21702, USA (R.J.S.); (K.M.R.)
| | - Kurt E. Schaecher
- Virology Division, U.S. Army Medical Research Institute of Infectious Diseases, Fort Detrick, MD 21702, USA
| | - Randal J. Schoepp
- Diagnostic Systems Division, U.S. Army Medical Research Institute of Infectious Diseases, Fort Detrick, MD 21702, USA (R.J.S.); (K.M.R.)
| | - Keersten M. Ricks
- Diagnostic Systems Division, U.S. Army Medical Research Institute of Infectious Diseases, Fort Detrick, MD 21702, USA (R.J.S.); (K.M.R.)
| | - Charles J. Shoemaker
- Diagnostic Systems Division, U.S. Army Medical Research Institute of Infectious Diseases, Fort Detrick, MD 21702, USA (R.J.S.); (K.M.R.)
| |
Collapse
|
2
|
Ren Y, Liang H, Huang Y, Miao Y, Li R, Qiang J, Wu L, Qi J, Li Y, Xia Y, Huang L, Wang S, Kong X, Zhou Y, Zhang Q, Zhu G. Key candidate genes and pathways in T lymphoblastic leukemia/lymphoma identified by bioinformatics and serological analyses. Front Immunol 2024; 15:1341255. [PMID: 38464517 PMCID: PMC10920334 DOI: 10.3389/fimmu.2024.1341255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 02/08/2024] [Indexed: 03/12/2024] Open
Abstract
T-cell acute lymphoblastic leukemia (T-ALL)/T-cell lymphoblastic lymphoma (T-LBL) is an uncommon but highly aggressive hematological malignancy. It has high recurrence and mortality rates and is challenging to treat. This study conducted bioinformatics analyses, compared genetic expression profiles of healthy controls with patients having T-ALL/T-LBL, and verified the results through serological indicators. Data were acquired from the GSE48558 dataset from Gene Expression Omnibus (GEO). T-ALL patients and normal T cells-related differentially expressed genes (DEGs) were investigated using the online analysis tool GEO2R in GEO, identifying 78 upregulated and 130 downregulated genes. Gene Ontology (GO) and protein-protein interaction (PPI) network analyses of the top 10 DEGs showed enrichment in pathways linked to abnormal mitotic cell cycles, chromosomal instability, dysfunction of inflammatory mediators, and functional defects in T-cells, natural killer (NK) cells, and immune checkpoints. The DEGs were then validated by examining blood indices in samples obtained from patients, comparing the T-ALL/T-LBL group with the control group. Significant differences were observed in the levels of various blood components between T-ALL and T-LBL patients. These components include neutrophils, lymphocyte percentage, hemoglobin (HGB), total protein, globulin, erythropoietin (EPO) levels, thrombin time (TT), D-dimer (DD), and C-reactive protein (CRP). Additionally, there were significant differences in peripheral blood leukocyte count, absolute lymphocyte count, creatinine, cholesterol, low-density lipoprotein, folate, and thrombin times. The genes and pathways associated with T-LBL/T-ALL were identified, and peripheral blood HGB, EPO, TT, DD, and CRP were key molecular markers. This will assist the diagnosis of T-ALL/T-LBL, with applications for differential diagnosis, treatment, and prognosis.
Collapse
Affiliation(s)
- Yansong Ren
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Haoyue Liang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Yali Huang
- Clinical Laboratory of Zhengning County People's Hospital, Qingyang, Gansu, China
| | - Yuyang Miao
- Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Geriatrics Institute, Tianjin, China
| | - Ruihua Li
- Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Geriatrics Institute, Tianjin, China
| | - Junlian Qiang
- Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Geriatrics Institute, Tianjin, China
| | - Lihong Wu
- Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Geriatrics Institute, Tianjin, China
| | - Jinfeng Qi
- Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Geriatrics Institute, Tianjin, China
| | - Ying Li
- Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Geriatrics Institute, Tianjin, China
| | - Yonghui Xia
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Lunhui Huang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Shoulei Wang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Xiaodong Kong
- Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Geriatrics Institute, Tianjin, China
| | - Yuan Zhou
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Qiang Zhang
- Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Geriatrics Institute, Tianjin, China
| | - Guoqing Zhu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| |
Collapse
|
3
|
Simon Davis DA, Ritchie M, Hammill D, Garrett J, Slater RO, Otoo N, Orlov A, Gosling K, Price J, Yip D, Jung K, Syed FM, Atmosukarto II, Quah BJC. Identifying cancer-associated leukocyte profiles using high-resolution flow cytometry screening and machine learning. Front Immunol 2023; 14:1211064. [PMID: 37600768 PMCID: PMC10435879 DOI: 10.3389/fimmu.2023.1211064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 06/26/2023] [Indexed: 08/22/2023] Open
Abstract
Background Machine 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. Methods To 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. Results We 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. Conclusions Our 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.
Collapse
Affiliation(s)
- David A. Simon Davis
- Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia
| | - Melissa Ritchie
- Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia
| | - Dillon Hammill
- Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Jessica Garrett
- Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Robert O. Slater
- Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Naomi Otoo
- Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Anna Orlov
- Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Katharine Gosling
- Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia
| | - Jason Price
- Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Desmond Yip
- Australian National University, Canberra, ACT, Australia
- Department of Medical Oncology, Canberra Hospital & Health Services, Canberra, ACT, Australia
| | - Kylie Jung
- Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia
- Radiation Oncology Department, Canberra Hospital & Health Services, Canberra, ACT, Australia
| | - Farhan M. Syed
- Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia
- Radiation Oncology Department, Canberra Hospital & Health Services, Canberra, ACT, Australia
| | - Ines I. Atmosukarto
- 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
| | - Ben J. C. Quah
- Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia
- Radiation Oncology Department, Canberra Hospital & Health Services, Canberra, ACT, Australia
| |
Collapse
|
4
|
Lee JM, Hung YP, Chou KY, Lee CY, Lin SR, Tsai YH, Lai WY, Shao YY, Hsu C, Hsu CH, Chao Y. Artificial intelligence-based immunoprofiling serves as a potentially predictive biomarker of nivolumab treatment for advanced hepatocellular carcinoma. Front Med (Lausanne) 2022; 9:1008855. [PMID: 36425096 PMCID: PMC9679144 DOI: 10.3389/fmed.2022.1008855] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 10/24/2022] [Indexed: 08/30/2023] Open
Abstract
Immune checkpoint inhibitors (ICI) have been applied in treating advanced hepatocellular carcinoma (aHCC) patients, but few patients exhibit stable and lasting responses. Moreover, identifying aHCC patients suitable for ICI treatment is still challenged. This study aimed to evaluate whether dissecting peripheral immune cell subsets by Mann-Whitney U test and artificial intelligence (AI) algorithms could serve as predictive biomarkers of nivolumab treatment for aHCC. Disease control group carried significantly increased percentages of PD-L1+ monocytes, PD-L1+ CD8 T cells, PD-L1+ CD8 NKT cells, and decreased percentages of PD-L1+ CD8 NKT cells via Mann-Whitney U test. By recursive feature elimination method, five featured subsets (CD4 NKTreg, PD-1+ CD8 T cells, PD-1+ CD8 NKT cells, PD-L1+ CD8 T cells and PD-L1+ monocytes) were selected for AI training. The featured subsets were highly overlapping with ones identified via Mann-Whitney U test. Trained AI algorithms committed valuable AUC from 0.8417 to 0.875 to significantly separate disease control group from disease progression group, and SHAP value ranking also revealed PD-L1+ monocytes and PD-L1+ CD8 T cells exclusively and significantly contributed to this discrimination. In summary, the current study demonstrated that integrally analyzing immune cell profiling with AI algorithms could serve as predictive biomarkers of ICI treatment.
Collapse
Affiliation(s)
- Jan-Mou Lee
- FullHope Biomedical Co., Ltd., New Taipei City, Taiwan
| | - Yi-Ping Hung
- Department of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Kai-Yuan Chou
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Cheng-Yun Lee
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Shian-Ren Lin
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ya-Han Tsai
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Wan-Yu Lai
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yu-Yun Shao
- College of Medicine, Graduate Institute of Oncology, National Taiwan University, Taipei, Taiwan
- Department of Medical Oncology, National Taiwan University Cancer Center, Taipei, Taiwan
- Department of Oncology, National Taiwan University Hospital, Taipei, Taiwan
| | - Chiun Hsu
- College of Medicine, Graduate Institute of Oncology, National Taiwan University, Taipei, Taiwan
- Department of Oncology, National Taiwan University Hospital, Taipei, Taiwan
| | - Chih-Hung Hsu
- College of Medicine, Graduate Institute of Oncology, National Taiwan University, Taipei, Taiwan
- Department of Oncology, National Taiwan University Hospital, Taipei, Taiwan
| | - Yee Chao
- Department of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| |
Collapse
|