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Li Y, Wu X, Fang D, Luo Y. Informing immunotherapy with multi-omics driven machine learning. NPJ Digit Med 2024; 7:67. [PMID: 38486092 PMCID: PMC10940614 DOI: 10.1038/s41746-024-01043-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 02/14/2024] [Indexed: 03/18/2024] Open
Abstract
Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.
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Affiliation(s)
- Yawei Li
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA
- Center for Collaborative AI in Healthcare, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Xin Wu
- Department of Medicine, University of Illinois at Chicago, Chicago, IL, 60612, USA
| | - Deyu Fang
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA.
- Center for Collaborative AI in Healthcare, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA.
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Prelaj A, Miskovic V, Zanitti M, Trovo F, Genova C, Viscardi G, Rebuzzi SE, Mazzeo L, Provenzano L, Kosta S, Favali M, Spagnoletti A, Castelo-Branco L, Dolezal J, Pearson AT, Lo Russo G, Proto C, Ganzinelli M, Giani C, Ambrosini E, Turajlic S, Au L, Koopman M, Delaloge S, Kather JN, de Braud F, Garassino MC, Pentheroudakis G, Spencer C, Pedrocchi ALG. Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review. Ann Oncol 2024; 35:29-65. [PMID: 37879443 DOI: 10.1016/j.annonc.2023.10.125] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/31/2023] [Accepted: 10/08/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. MATERIALS AND METHODS We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. RESULTS A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. CONCLUSION AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice.
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Affiliation(s)
- A Prelaj
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan; Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy; ESMO Real World Data and Digital Health Working Group, ESMO, Lugano, Switzerland.
| | - V Miskovic
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan; Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - M Zanitti
- Department of Electronic Systems, Aalborg University Copenhagen, Denmark
| | - F Trovo
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - C Genova
- UO Clinica di Oncologia Medica, IRCCS Ospedale Policlinico San Martino, Genoa; Department of Internal Medicine and Medical Specialties (Di.M.I.), University of Genoa, Genoa
| | - G Viscardi
- Precision Medicine Department, Università degli Studi della Campania Luigi Vanvitelli, Naples
| | - S E Rebuzzi
- Department of Internal Medicine and Medical Specialties (Di.M.I.), University of Genoa, Genoa; Medical Oncology Unit, Ospedale San Paolo, Savona, Italy
| | - L Mazzeo
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan; Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - L Provenzano
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - S Kosta
- Department of Electronic Systems, Aalborg University Copenhagen, Denmark
| | - M Favali
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - A Spagnoletti
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - L Castelo-Branco
- ESMO European Society for Medical Oncology, Lugano, Switzerland; NOVA National School of Public Health, Lisboa, Portugal
| | - J Dolezal
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, USA
| | - A T Pearson
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, USA
| | - G Lo Russo
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - C Proto
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - M Ganzinelli
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - C Giani
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - E Ambrosini
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - S Turajlic
- Cancer Dynamics Laboratory, The Francis Crick Institute, London
| | - L Au
- Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London, UK; Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne; Sir Peter MacCallum Department of Medical Oncology, The University of Melbourne, Melbourne, Australia
| | - M Koopman
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands; ESMO Real World Data and Digital Health Working Group, ESMO, Lugano, Switzerland
| | - S Delaloge
- Department of Cancer Medicine, Gustave Roussy, Villejuif, France; ESMO Real World Data and Digital Health Working Group, ESMO, Lugano, Switzerland
| | - J N Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - F de Braud
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - M C Garassino
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, USA
| | | | - C Spencer
- Cancer Dynamics Laboratory, The Francis Crick Institute, London.
| | - A L G Pedrocchi
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
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Zhang F, Zhang X, Zhang H, Lin D, Fan H, Guo S, An F, Zhao Y, Li J, Schrodi SJ, Zhang D. Pan-precancer and cancer DNA methylation profiles revealed significant tissue specificity of interrupted biological processes in tumorigenesis. Epigenetics 2023; 18:2231222. [PMID: 37393582 DOI: 10.1080/15592294.2023.2231222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 06/13/2023] [Accepted: 06/21/2023] [Indexed: 07/04/2023] Open
Abstract
DNA methylation (DNAme) alterations are known to initiate from the precancerous stage of tumorigenesis. Herein, we investigated the global and local patterns of DNAme perturbations in tumorigenesis by analysing the genome-wide DNAme profiles of the cervix, colorectum, stomach, prostate, and liver at precancerous and cancer stages. We observed global hypomethylation in tissues of both two stages, except for the cervix, whose global DNAme level in normal tissue was lower than that of the other four tumour types. For alterations shared by both stages, there were common hyper-methylation (sHyperMethyl) and hypo-methylation (sHypoMethyl) changes, of which the latter type was more frequently identified in all tissues. Biological pathways interrupted by sHyperMethyl and sHypoMethyl alterations demonstrated significant tissue specificity. DNAme bidirectional chaos indicated by the enrichment of both sHyperMethyl and sHypoMethyl changes in the same pathway was observed in most tissues and was a common phenomenon, particularly in liver lesions. Moreover, for the same enriched pathways, different tissues may be affected by distinct DNAme types. For the PI3K-Akt signalling pathway, sHyperMethyl enrichment was observed in the prostate dataset, but sHypoMethyl enrichment was observed in the colorectum and liver datasets. Nevertheless, they did not show an increased possibility in survival prediction of patients in comparison with other DNAme types. Additionally, our study demonstrated that gene-body DNAme changes of tumour suppressor genes and oncogenes may persist from precancerous lesions to the tumour. Overall, we demonstrate the tissue specificity and commonality of cross-stage alterations in DNA methylation profiles in multi-tissue tumorigenesis.
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Affiliation(s)
- Feifan Zhang
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Engineering Medicine, Beihang University, Beijing, China
| | - Xin Zhang
- Department of Urology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Haikun Zhang
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Engineering Medicine, Beihang University, Beijing, China
| | - Dongdong Lin
- Department of Urology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Hailang Fan
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Engineering Medicine, Beihang University, Beijing, China
| | - Shicheng Guo
- Department of Medical Genetics, University of Wisconsin-Madison, Madison, WI, USA
| | - Fang An
- Department of Obstetrics and Gynecology, Peking University People's Hospital, Beijing, China
| | - Yaqian Zhao
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Engineering Medicine, Beihang University, Beijing, China
| | - Jun Li
- Department of Gastroenterology, Peking University Third Hospital, Beijing, China
| | - Steven J Schrodi
- Department of Medical Genetics, University of Wisconsin-Madison, Madison, WI, USA
- Computation and Informatics in Biology and Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | - Dake Zhang
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Engineering Medicine, Beihang University, Beijing, China
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Jiang W, Hu K, Liu X, Gao J, Zhu L. Single-cell transcriptome analysis reveals the clinical implications of myeloid-derived suppressor cells in head and neck squamous cell carcinoma. Pathol Oncol Res 2023; 29:1611210. [PMID: 37475874 PMCID: PMC10354270 DOI: 10.3389/pore.2023.1611210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 06/28/2023] [Indexed: 07/22/2023]
Abstract
Head and neck squamous cell carcinoma (HNSC) is the most common malignant tumor that arises in the epithelium of the head and neck regions. Myeloid-derived suppressor cells (MDSCs) are one of the tumor-infiltrating immune cell populations, which play a powerful role in inhibiting anti-tumor immune response. Herein, we employed a single-cell RNA sequencing (scRNA-seq) dataset to dissect the heterogeneity of myeloid cells. We found that SPP1 + tumor-associated macrophages (TAMs) and MDSCs were the most abundant myeloid cells in the microenvironment. By cell cluster deconvolution from bulk RNA-seq datasets of larger patient groups, we observed that highly-infiltrated MDSC was a poor prognostic marker for patients' overall survival (OS) probabilities. To better apply the MDSC OS prediction values, we identified a set of six MDSC-related genes (ALDOA, CD52, FTH1, RTN4, SLC2A3, and TNFAIP6) as the prognostic signature. In both training and test cohorts, MDSC-related prognostic signature showed a promising value for predicting patients' prognosis outcomes. Further parsing the ligand-receptor pairs of intercellular communications by CellChat, we found that MDSCs could frequently interact with cytotoxic CD8 + T cells, SPP1 + TAMs, and endothelial cells. These interactions likely contributed to the establishment of an immunosuppressive microenvironment and the promotion of tumor angiogenesis. Our findings suggest that targeting MDSCs may serve as an alternative and promising target for the immunotherapy of HNSC.
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Affiliation(s)
- Wenru Jiang
- Department of Implant and Prosthodontics, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Kangyao Hu
- Department of Implant and Prosthodontics, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xiaofei Liu
- Department of Implant and Prosthodontics, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Jili Gao
- Department of Implant and Prosthodontics, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Liping Zhu
- Department of Implant and Prosthodontics, Harbin First Hospital, Harbin, China
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Wang X, Zhang H, Zhang M, Zhang X, Mao W, Gao M. Proteogenomic characterization of ferroptosis regulators reveals therapeutic potential in glioblastoma. BMC Cancer 2023; 23:415. [PMID: 37158834 PMCID: PMC10165763 DOI: 10.1186/s12885-023-10894-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 04/27/2023] [Indexed: 05/10/2023] Open
Abstract
BACKGROUND Ferroptosis is iron-dependent non-apoptotic cell death, that is characterized by the excessive accumulation of lipid peroxides. Ferroptosis-inducing therapy also shows promise in the treatment of cancers. However, ferroptosis-inducing therapy for glioblastoma multiforme (GBM) is still in the exploratory stage. METHODS We identified the differentially expressed ferroptosis regulators using Mann-Whitney U test in the proteome data from Clinical Proteomic Tumor Analysis Consortium (CPTAC). We next analyzed the effect of mutation on protein abundance. A multivariate Cox model was constructed to identify the prognostic signature. RESULTS In this study, we systemically portrayed the proteogenomic landscape of ferroptosis regulators in GBM. We observed that some mutation-specific ferroptosis regulators, such as down-regulated ACSL4 in EGFR-mutated patients and up-regulated FADS2 in IDH1-mutated patients, were linked to the inhibited ferroptosis activity in GBM. To interrogate the valuable treatment targets, we performed the survival analysis and identified five ferroptosis regulators (ACSL3, HSPB1, ELAVL1, IL33, and GPX4) as the prognostic biomarkers. We also validated their efficiency in external validation cohorts. Notably, we found overexpressed protein and phosphorylation abundances of HSPB1 were poor prognosis markers for overall survival of GBM to inhibit ferroptosis activity. Alternatively, HSPB1 showed a significant association with macrophage infiltration levels. Macrophage-secreted SPP1 could be a potential activator for HSPB1 in glioma cells. Finally, we recognized that ipatasertib, a novel pan-Akt inhibitor, could be a potential drug for suppressing HSPB1 phosphorylation, inducing ferroptosis of glioma cells. CONCLUSION In summary, our study characterized the proteogenomic landscape of ferroptosis regulators and identified that HSPB1 could be a candidate target for ferroptosis-inducing therapy strategy for GBM.
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Affiliation(s)
- Xinzhuang Wang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Hong Zhang
- Department of Hematology, Liaocheng People's Hospital, Liaocheng, China
| | - Mingchu Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xuezhi Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Wenbin Mao
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ming Gao
- Department of Neurosurgery, First Affiliated Hospital of Harbin Medical University, Harbin, China
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Zhu S, Wang Y, Tang J, Cao M. Radiotherapy induced immunogenic cell death by remodeling tumor immune microenvironment. Front Immunol 2022; 13:1074477. [PMID: 36532071 PMCID: PMC9753984 DOI: 10.3389/fimmu.2022.1074477] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 11/15/2022] [Indexed: 12/04/2022] Open
Abstract
Emerging evidence indicates that the induction of radiotherapy(RT) on the immunogenic cell death (ICD) is not only dependent on its direct cytotoxic effect, changes in the tumor immune microenvironment also play an important role in it. Tumor immune microenvironment (TIME) refers to the immune microenvironment that tumor cells exist, including tumor cells, inflammatory cells, immune cells, various signaling molecules and extracellular matrix. TIME has a barrier effect on the anti-tumor function of immune cells, which can inhibit all stages of anti-tumor immune response. The remodeling of TIME caused by RT may affect the degree of immunogenicity, and make it change from immunosuppressive phenotype to immunostimulatory phenotype. It is of great significance to reveal the causes of immune escape of tumor cells, especially for the treatment of drug-resistant tumor. In this review, we focus on the effect of RT on the TIME, the mechanism of RT in reversing the TIME to suppress intrinsic immunity, and the sensitization effect of the remodeling of TIME caused by RT on the effectiveness of immunotherapy.
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Bi XA, Mao Y, Luo S, Wu H, Zhang L, Luo X, Xu L. A novel generation adversarial network framework with characteristics aggregation and diffusion for brain disease classification and feature selection. Brief Bioinform 2022; 23:6762742. [PMID: 36259367 DOI: 10.1093/bib/bbac454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 09/01/2022] [Accepted: 09/23/2022] [Indexed: 12/14/2022] Open
Abstract
Imaging genetics provides unique insights into the pathological studies of complex brain diseases by integrating the characteristics of multi-level medical data. However, most current imaging genetics research performs incomplete data fusion. Also, there is a lack of effective deep learning methods to analyze neuroimaging and genetic data jointly. Therefore, this paper first constructs the brain region-gene networks to intuitively represent the association pattern of pathogenetic factors. Second, a novel feature information aggregation model is constructed to accurately describe the information aggregation process among brain region nodes and gene nodes. Finally, a deep learning method called feature information aggregation and diffusion generative adversarial network (FIAD-GAN) is proposed to efficiently classify samples and select features. We focus on improving the generator with the proposed convolution and deconvolution operations, with which the interpretability of the deep learning framework has been dramatically improved. The experimental results indicate that FIAD-GAN can not only achieve superior results in various disease classification tasks but also extract brain regions and genes closely related to AD. This work provides a novel method for intelligent clinical decisions. The relevant biomedical discoveries provide a reliable reference and technical basis for the clinical diagnosis, treatment and pathological analysis of disease.
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Affiliation(s)
- Xia-An Bi
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, and College of Information Science and Engineering in Hunan Normal University, Changsha, P.R. China
| | - Yuhua Mao
- Department of Computing, School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Sheng Luo
- Department of Computing, School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Hao Wu
- Department of Computing, School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Lixia Zhang
- School of Information Science and Engineering, Hunan Normal University, Changsha, P.R. China
| | - Xun Luo
- College of Information Science and Engineering in Hunan Normal University, Changsha, P.R. China
| | - Luyun Xu
- College of Business in Hunan Normal University, Changsha, P.R. China
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Liu Y, Fang Y, Bao L, Wu F, Wang S, Hao S. Intercellular Communication Reveals Therapeutic Potential of Epithelial-Mesenchymal Transition in Triple-Negative Breast Cancer. Biomolecules 2022; 12:biom12101478. [PMID: 36291687 PMCID: PMC9599658 DOI: 10.3390/biom12101478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/06/2022] [Accepted: 10/11/2022] [Indexed: 12/07/2022] Open
Abstract
(1) Background: Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with high intra-tumoral heterogeneity. The epithelial-mesenchymal transition (EMT) is one of the inducers of cancer metastasis and migration. However, the description of the EMT process in TNBC using single-cell RNA sequencing (scRNA-seq) remains unclear. (2) Methods: In this study, we analyzed 8938 cellular gene expression profiles from five TNBC patients. We first scored each malignant cell based on functional pathways to determine its EMT characteristics. Then, a pseudo-time trajectory analysis was employed to characterize the cell trajectories. Furthermore, CellChat was used to identify the cellular communications. (3) Results: We identified 888 epithelium-like and 846 mesenchyme-like malignant cells, respectively. A further pseudo-time trajectory analysis indicated the transition trends from epithelium-like to mesenchyme-like in malignant cells. To characterize the potential regulators of the EMT process, we identified 10 dysregulated transcription factors (TFs) between epithelium-like and mesenchyme-like malignant cells, in which overexpressed forkhead box protein A1 (FOXA1) was recognized as a poor prognosis marker of TNBC. Furthermore, we dissected the cell-cell communications via ligand-receptor (L-R) interactions. We observed that tumor-associated macrophages (TAMs) may support the invasion of malignant epithelial cells, based on CXCL-CXCR2 signaling. The tumor necrosis factor (TNF) signaling pathway secreted by TAMs was identified as an outgoing communication pattern, mediating the communications between monocytes/TAMs and malignant epithelial cells. Alternatively, the TNF-related ligand-receptor (L-R) pairs showed promising clinical implications. Some immunotherapy and anti-neoplastic drugs could interact with the L-R pairs as a potential strategy for the treatment of TNBC. In summary, this study enhances the understanding of the EMT process in the TNBC microenvironment, and dissections of EMT-related cell communications also provided us with potential treatment targets.
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Affiliation(s)
- Yang Liu
- Pharmacy Intravenous Admixture Services, The Second Affiliated Hospital of Harbin Medical University, Harbin 150081, China
| | - Yu Fang
- Department of Phase I Clinical Trial Ward, Harbin Medical University Cancer Hospital, Harbin 150081, China
| | - Lili Bao
- Pharmacy Intravenous Admixture Services, The Second Affiliated Hospital of Harbin Medical University, Harbin 150081, China
| | - Feng Wu
- Department of Gastroenterology, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Shilong Wang
- Pharmacy Intravenous Admixture Services, The Second Affiliated Hospital of Harbin Medical University, Harbin 150081, China
- Correspondence: (S.W.); (S.H.)
| | - Siyu Hao
- Department of Dermatology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150081, China
- Correspondence: (S.W.); (S.H.)
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