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Chin KW, Khoo SC, Paul RPM, Luang-In V, Lam SD, Ma NL. Potential of Synbiotics and Probiotics as Chemopreventive Agent. Probiotics Antimicrob Proteins 2024:10.1007/s12602-024-10299-z. [PMID: 38896220 DOI: 10.1007/s12602-024-10299-z] [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] [Accepted: 05/22/2024] [Indexed: 06/21/2024]
Abstract
Cancer remains a global problem, with millions of new cases diagnosed yearly and countless lives lost. The financial burden of cancer therapy, along with worries about the long-term safety of existing medicines, necessitates the investigation of alternative approaches to cancer prevention. Probiotics generate chemopreventive compounds such as bacteriocins, short-chain fatty acids (SCFA), and extracellular polymeric substances (EPS), which have demonstrated the ability to impede cancer cell proliferation, induce apoptosis, and bolster the expression of pro-apoptotic genes. On the other hand, prebiotics, classified as non-digestible food ingredients, promote the proliferation of probiotics within the colon, thereby ensuring sustained functionality of the gut microbiota. Consequently, the synergistic effect of combining prebiotics with probiotics, known as the synbiotic effect, in dietary interventions holds promise for potentially mitigating cancer risk and augmenting preventive measures. The utilization of gut microbiota in cancer treatment has shown promise in alleviating adverse health effects. This review explored the potential and the role of probiotics and synbiotics in enhancing health and contributing to cancer prevention efforts. In this review, the applications of functional probiotics and synbiotics, the mechanisms of action of probiotics in cancer, and the relationship of probiotics with various drugs were discussed, shedding light on the potential of probiotics and synbiotics to alleviate the burdens of cancer treatment.
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Affiliation(s)
- Kah Wei Chin
- Bioses Research Interest Group (BIOSES), Faculty of Science and Marine Environment, Universiti Malaysia Terengganu, Kuala Nerus, 21030, Terengganu, Malaysia
| | - Shing Ching Khoo
- Bioses Research Interest Group (BIOSES), Faculty of Science and Marine Environment, Universiti Malaysia Terengganu, Kuala Nerus, 21030, Terengganu, Malaysia
| | - Richard Paul Merisha Paul
- Bioses Research Interest Group (BIOSES), Faculty of Science and Marine Environment, Universiti Malaysia Terengganu, Kuala Nerus, 21030, Terengganu, Malaysia
| | - Vijitra Luang-In
- Natural Antioxidant Innovation Research Unit, Department of Biotechnology, Faculty of Technology, Mahasarakham University, Khamriang, 44150, Kantarawichai, Maha Sarakham, Thailand
| | - Su Datt Lam
- School of Biosciences and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, 43600, Selangor, Malaysia
| | - Nyuk Ling Ma
- Bioses Research Interest Group (BIOSES), Faculty of Science and Marine Environment, Universiti Malaysia Terengganu, Kuala Nerus, 21030, Terengganu, Malaysia.
- Department of Sustainable Engineering, Institute of Biotechnology, Saveetha School of Engineering, SIMATS, Chennai, 602105, India.
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2
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Shin D, Kim Y, Park J, Kim Y. High-throughput Proteomics-Guided Biomarker Discovery of Hepatocellular Carcinoma. Biomed J 2024:100752. [PMID: 38901798 DOI: 10.1016/j.bj.2024.100752] [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: 01/28/2024] [Revised: 06/07/2024] [Accepted: 06/12/2024] [Indexed: 06/22/2024] Open
Abstract
Liver cancer stands as the fifth leading cause of cancer-related deaths globally. Hepatocellular carcinoma (HCC) comprises approximately 85%-90% of all primary liver malignancies. However, only 20-30% of HCC patients qualify for curative therapy, primarily due to the absence of reliable tools for early detection and prognosis of HCC. This underscores the critical need for molecular biomarkers for HCC management. Since proteins reflect disease status directly, proteomics has been utilized in biomarker developments for HCC. In particular, proteomics coupled with liquid chromatography-mass spectrometer (LC-MS) methods facilitate the process of discovering biomarker candidates for diagnosis, prognosis, and therapeutic strategies. In this work, we investigated LC-MS-based proteomics methods through recent reference reviews, with a particular focus on sample preparation and LC-MS methods appropriate for the discovery of HCC biomarkers and their clinical applications. We classified proteomics studies of HCC according to sample types, and we examined the coverage of protein biomarker candidates based on LC-MS methods in relation to study scales and goals. Comprehensively, we proposed protein biomarker candidates categorized by sample types and biomarker types for appropriate clinical use. In this review, we summarized recent LC-MS-based proteomics studies on HCC and proposed potential protein biomarkers. Our findings are expected to expand the understanding of HCC pathogenesis and enhance the efficiency of HCC diagnosis and prognosis, thereby contributing to improved patient outcomes.
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Affiliation(s)
- Dongyoon Shin
- Proteomics Research Team, CHA Institute of Future Medicine, Seongnam, Republic of Korea
| | - Yeongshin Kim
- Proteomics Research Team, CHA Institute of Future Medicine, Seongnam, Republic of Korea; Department of Medical Science, School of Medicine, CHA University, Seongnam, Republic of Korea
| | - Junho Park
- Proteomics Research Team, CHA Institute of Future Medicine, Seongnam, Republic of Korea; Department of Pharmacology, School of Medicine, CHA University, Seongnam, Republic of Korea.
| | - Youngsoo Kim
- Proteomics Research Team, CHA Institute of Future Medicine, Seongnam, Republic of Korea; Department of Medical Science, School of Medicine, CHA University, Seongnam, Republic of Korea.
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3
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Xi NM, Huang DP. Drug safety assessment by machine learning models. J Biopharm Stat 2024:1-12. [PMID: 38888177 DOI: 10.1080/10543406.2024.2365976] [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: 06/15/2023] [Accepted: 06/05/2024] [Indexed: 06/20/2024]
Abstract
The evaluation of drug-induced Torsades de pointes (TdP) risks is crucial in drug safety assessment. In this study, we discuss machine learning approaches in the prediction of drug-induced TdP risks using preclinical data. Specifically, a random forest model was trained on the dataset generated by the rabbit ventricular wedge assay. The model prediction performance was measured on 28 drugs from the Comprehensive In Vitro Proarrhythmia Assay initiative. Leave-one-drug-out cross-validation provided an unbiased estimation of model performance. Stratified bootstrap revealed the uncertainty in the asymptotic model prediction. Our study validated the utility of machine learning approaches in predicting drug-induced TdP risks from preclinical data. Our methods can be extended to other preclinical protocols and serve as a supplementary evaluation in drug safety assessment.
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Affiliation(s)
- Nan Miles Xi
- Department of Mathematics and Statistics, Loyola University Chicago, Chicago, IL, USA
| | - Dalong Patrick Huang
- Office of Biostatistics, Office of Translational Science, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
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4
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Balan D, Kampan NC, Plebanski M, Abd Aziz NH. Unlocking ovarian cancer heterogeneity: advancing immunotherapy through single-cell transcriptomics. Front Oncol 2024; 14:1388663. [PMID: 38873253 PMCID: PMC11169633 DOI: 10.3389/fonc.2024.1388663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 05/15/2024] [Indexed: 06/15/2024] Open
Abstract
Ovarian cancer, a highly fatal gynecological cancer, warrants the need for understanding its heterogeneity. The disease's prevalence and impact are underscored with statistics on mortality rates. Ovarian cancer is categorized into distinct morphological groups, each with its characteristics and prognosis. Despite standard treatments, survival rates remain low due to relapses and chemoresistance. Immune system involvement is evident in ovarian cancer's progression, although the tumor employs immune evasion mechanisms. Immunotherapy, particularly immune checkpoint blockade therapy, is promising, but ovarian cancer's heterogeneity limits its efficacy. Single-cell sequencing technology could be explored as a solution to dissect the heterogeneity within tumor-associated immune cell populations and tumor microenvironments. This cutting-edge technology has the potential to enhance diagnosis, prognosis, and personalized immunotherapy in ovarian cancer, reflecting its broader application in cancer research. The present review focuses on recent advancements and the challenges in applying single-cell transcriptomics to ovarian cancer.
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Affiliation(s)
- Dharvind Balan
- Department of Obstetrics and Gynaecology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Nirmala Chandralega Kampan
- Department of Obstetrics and Gynaecology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Magdalena Plebanski
- School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC, Australia
| | - Nor Haslinda Abd Aziz
- Department of Obstetrics and Gynaecology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
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Dyikanov D, Zaitsev A, Vasileva T, Wang I, Sokolov AA, Bolshakov ES, Frank A, Turova P, Golubeva O, Gantseva A, Kamysheva A, Shpudeiko P, Krauz I, Abdou M, Chasse M, Conroy T, Merriam NR, Alesse JE, English N, Shpak B, Shchetsova A, Tikhonov E, Filatov I, Radko A, Bolshakova A, Kachalova A, Lugovykh N, Bulahov A, Kilina A, Asanbekov S, Zheleznyak I, Skoptsov P, Alekseeva E, Johnson JM, Curry JM, Linnenbach AJ, South AP, Yang E, Morozov K, Terenteva A, Nigmatullina L, Fastovetz D, Bobe A, Balabanian L, Nomie K, Yong ST, Davitt CJH, Ryabykh A, Kudryashova O, Tazearslan C, Bagaev A, Fowler N, Luginbuhl AJ, Ataullakhanov RI, Goldberg MF. Comprehensive peripheral blood immunoprofiling reveals five immunotypes with immunotherapy response characteristics in patients with cancer. Cancer Cell 2024; 42:759-779.e12. [PMID: 38744245 DOI: 10.1016/j.ccell.2024.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 02/20/2024] [Accepted: 04/15/2024] [Indexed: 05/16/2024]
Abstract
The lack of comprehensive diagnostics and consensus analytical models for evaluating the status of a patient's immune system has hindered a wider adoption of immunoprofiling for treatment monitoring and response prediction in cancer patients. To address this unmet need, we developed an immunoprofiling platform that uses multiparameter flow cytometry to characterize immune cell heterogeneity in the peripheral blood of healthy donors and patients with advanced cancers. Using unsupervised clustering, we identified five immunotypes with unique distributions of different cell types and gene expression profiles. An independent analysis of 17,800 open-source transcriptomes with the same approach corroborated these findings. Continuous immunotype-based signature scores were developed to correlate systemic immunity with patient responses to different cancer treatments, including immunotherapy, prognostically and predictively. Our approach and findings illustrate the potential utility of a simple blood test as a flexible tool for stratifying cancer patients into therapy response groups based on systemic immunoprofiling.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Jennifer M Johnson
- Department of Medical Oncology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Joseph M Curry
- Department of Otolaryngology Head and Neck Surgery, Thomas Jefferson University, Philadelphia, PA, USA
| | - Alban J Linnenbach
- Department of Otolaryngology Head and Neck Surgery, Thomas Jefferson University, Philadelphia, PA, USA
| | - Andrew P South
- Department of Pharmacology, Physiology, and Cancer Biology, Thomas Jefferson University, Philadelphia, PA, USA
| | - EnJun Yang
- The Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Adam J Luginbuhl
- Department of Otolaryngology Head and Neck Surgery, Thomas Jefferson University, Philadelphia, PA, USA
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Zhang X, Xiao Q, Zhang C, Zhou Q, Xu T. Construction of a prognostic model with CAFs for predicting the prognosis and immunotherapeutic response of lung squamous cell carcinoma. J Cell Mol Med 2024; 28:e18262. [PMID: 38520221 PMCID: PMC10960179 DOI: 10.1111/jcmm.18262] [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: 12/18/2023] [Revised: 02/29/2024] [Accepted: 03/04/2024] [Indexed: 03/25/2024] Open
Abstract
Lung squamous cell carcinoma (LUSC) is one of the subtypes of lung cancer (LC) that contributes to approximately 25%-30% of its prevalence. Cancer-associated fibroblasts (CAFs) are key cellular components of the TME, and the large number of CAFs in tumour tissues creates a favourable environment for tumour development. However, the function of CAFs in the LUSC is complex and uncertain. First, we processed the scRNA-seq data and classified distinct types of CAFs. We also identified prognostic CAFRGs using univariate Cox analysis and conducted survival analysis. Additionally, we assessed immune cell infiltration in CAF clusters using ssGSEA. We developed a model with a significant prognostic correlation and verified the prognostic model. Furthermore, we explored the immune landscape of LUSC and further investigated the correlation between malignant features and LUSC. We identified CAFs and classified them into three categories: iCAFs, mCAFs and apCAFs. The survival analysis showed a significant correlation between apCAFs and iCAFs and LUSC patient prognosis. Kaplan-Meier analysis showed that patients in CAF cluster C showed a better survival probability compared to clusters A and B. In addition, we identified nine significant prognostic CAFRGs (CLDN1, TMX4, ALPL, PTX3, BHLHE40, TNFRSF12A, VKORC1, CST3 and ADD3) and subsequently employed multivariate Cox analysis to develop a signature and validate the model. Lastly, the correlation between CAFRG and malignant features indicates the potential role of CAFRG in promoting tumour angiogenesis, EMT and cell cycle alterations. We constructed a CAF prognostic signature for identifying potential prognostic CAFRGs and predicting the prognosis and immunotherapeutic response for LUSC. Our study may provide a more accurate prognostic assessment and immunotherapy targeting strategies for LUSC.
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Affiliation(s)
- Xiang Zhang
- Lung cancer center, West China hospitalSichuan universityChengduChina
| | - Qingqing Xiao
- Division of Abdominal Tumor Multimodality Treatment, Cancer Center, West China HospitalSichuan UniversityChengduChina
| | - Cong Zhang
- Department of Thoracic surgeryChengdu Seventh People's Hospital (Affiliated Cancer Hospital of Chengdu Medical College)ChengduChina
| | - Qinghua Zhou
- Lung cancer center, West China hospitalSichuan universityChengduChina
| | - Tao Xu
- Department of Thoracic SurgeryThe Affiliated Hospital, Southwest Medical UniversityLuzhouChina
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Tian G, Zhang J, Bao Y, Li Q, Hou J. A prognostic model based on Scissor + cancer associated fibroblasts identified from bulk and single cell RNA sequencing data in head and neck squamous cell carcinoma. Cell Signal 2024; 114:110984. [PMID: 38029947 DOI: 10.1016/j.cellsig.2023.110984] [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: 08/31/2023] [Revised: 10/23/2023] [Accepted: 11/15/2023] [Indexed: 12/01/2023]
Abstract
Head and neck squamous cell carcinoma (HNSCC) is one of the most lethal diseases in the world, which often recur after multimodality treatment approaches, leading to a poor prognosis. Fibroblasts, a heterogeneous component of the tumor microenvironment, can modulate numerous aspects of tumor biology and have been increasingly acknowledged in dictating the clinical outcome of patients with HNSCC. However, the subpopulation of fibroblasts that are related to the prognosis of HNSCC has not yet been fully explored. To do so, we combined a single-cell RNA sequencing (scRNA-seq) dataset and bulk RNA-sequencing dataset with clinical information, identifying the fibroblast population that are related to poor prognosis of HNSCC. We found these specific population of fibroblasts are less differentiated. In addition, to identify the prognostic signatures of HNSCC, bioinformatics analysis included least absolute shrinkage and selection operator (LASSO) analyses and univariate cox and were performed. We selected 12 prognosis-related genes for constructing a risk model using The Cancer Genome Atlas (TCGA). The AUC values and calibration plots of this model indicated good prognostic prediction efficacy. This model also was validated in two Gene Expression Omnibus (GEO) datasets. In conclusion, we constructed an optimal model that was derived from single cell RNA-seq and bulk RNA-seq to predict the survival probability of HNSCC patients. Among this model, AKR1C3 higher expression in cancer associated fibroblasts (CAFs) of HNSCC has been confirmed by preliminary experiments.
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Affiliation(s)
- Guoli Tian
- Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China; Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Jiaqiang Zhang
- Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China; Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Yong Bao
- Department of Medical Oncology; Institute of Precision Medicine; Center for Translational Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China.
| | - Qiuli Li
- Department of Head and Neck Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
| | - Jinsong Hou
- Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China; Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Guangzhou, China.
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He D, Mount SM, Patro R. scCensus: Off-target scRNA-seq reads reveal meaningful biology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.29.577807. [PMID: 38352549 PMCID: PMC10862729 DOI: 10.1101/2024.01.29.577807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Single-cell RNA-sequencing (scRNA-seq) provides unprecedented insights into cellular heterogeneity. Although scRNA-seq reads from most prevalent and popular tagged-end protocols are expected to arise from the 3' end of polyadenylated RNAs, recent studies have shown that "off-target" reads can constitute a substantial portion of the read population. In this work, we introduced scCensus, a comprehensive analysis workflow for systematically evaluating and categorizing off-target reads in scRNA-seq. We applied scCensus to seven scRNA-seq datasets. Our analysis of intergenic reads shows that these off-target reads contain information about chromatin structure and can be used to identify similar cells across modalities. Our analysis of antisense reads suggests that these reads can be used to improve gene detection and capture interesting transcriptional activities like antisense transcription. Furthermore, using splice-aware quantification, we find that spliced and unspliced reads provide distinct information about cell clusters and biomarkers, suggesting the utility of integrating signals from reads with different splicing statuses. Overall, our results suggest that off-target scRNA-seq reads contain underappreciated information about various transcriptional activities. These observations about yet-unexploited information in existing scRNA-seq data will help guide and motivate the community to improve current algorithms and analysis methods, and to develop novel approaches that utilize off-target reads to extend the reach and accuracy of single-cell data analysis pipelines.
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Affiliation(s)
- Dongze He
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA
- Program in Computational Biology, Bioinformatics and Genomices, University of Maryland, College Park, MD 20742, USA
| | - Stephen M. Mount
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
| | - Rob Patro
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA
- Department of Computer Science, University of Maryland, College Park, MD 20742, USA
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Tang J, Liu Y, Zhang Z, Ren Y, Ma Y, Wang Y, Li J, Gao Y, Li C, Cheng C, Su S, Chen S, Zhang P, Lu R. Heterogeneous Expression Patterns of the Minichromosome Maintenance Complex Members in Retinoblastoma Unveil Its Clinical Significance. Invest Ophthalmol Vis Sci 2024; 65:31. [PMID: 38231525 PMCID: PMC10795548 DOI: 10.1167/iovs.65.1.31] [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: 10/10/2023] [Accepted: 12/28/2023] [Indexed: 01/18/2024] Open
Abstract
Purpose To explore the expression patterns and clinical significance of minichromosome maintenance (MCM) complex members in retinoblastoma (RB). Methods Single-cell RNA sequencing datasets from five normal retina, six intraocular, and five extraocular RB samples were integrated to characterize the expression patterns of MCM complex members at the single-cell level. Western blot and quantitative PCR were used to detect the expression of MCM complex members in RB cell lines. Immunohistochemistry was conducted to validate the expression of MCM complex members in RB patient samples and a RB mouse model. Results The expression of MCM2-7 is increased in RB tissue, with MCM2/3/7 showing particularly higher levels in extraocular RB. MCM3/7 are abundantly detected in cell types associated with oncogenesis. Both mRNA and protein levels of MCM3/4/6/7 are increased in RB cell lines. Immunohistochemistry further confirmed the elevated expression of MCM3 in extraocular RB, with MCM6 being the most abundantly expressed MCM in RB. Conclusions The distinct MCM expression patterns across various RB cell types suggest diverse functional roles, offering valuable insights for targeted therapeutic strategies. The upregulation of MCM3, MCM4, MCM6, and MCM7 in RB, with a specific emphasis on MCM6 as a notable marker, highlights their potential significance.
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Affiliation(s)
- Junjie Tang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Yaoming Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Zhihui Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Yi Ren
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Yujun Ma
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Yinghao Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Jinmiao Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Yang Gao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Cheng Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Chao Cheng
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Shicai Su
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Shuxia Chen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Ping Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Rong Lu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
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Grunert M, Dorn C, Dopazo A, Sánchez-Cabo F, Vázquez J, Rickert-Sperling S, Lara-Pezzi E. Technologies to Study Genetics and Molecular Pathways. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1441:435-458. [PMID: 38884724 DOI: 10.1007/978-3-031-44087-8_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
Over the last few decades, the study of congenital heart disease (CHD) has benefited from various model systems and the development of molecular biological techniques enabling the analysis of single gene as well as global effects. In this chapter, we first describe different models including CHD patients and their families, animal models ranging from invertebrates to mammals, and various cell culture systems. Moreover, techniques to experimentally manipulate these models are discussed. Second, we introduce cardiac phenotyping technologies comprising the analysis of mouse and cell culture models, live imaging of cardiogenesis, and histological methods for fixed hearts. Finally, the most important and latest molecular biotechniques are described. These include genotyping technologies, different applications of next-generation sequencing, and the analysis of transcriptome, epigenome, proteome, and metabolome. In summary, the models and technologies presented in this chapter are essential to study the function and development of the heart and to understand the molecular pathways underlying CHD.
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Affiliation(s)
- Marcel Grunert
- Cardiovascular Genetics, Charité - Universitätsmedizin Berlin, Berlin, Germany
- DiNAQOR AG, Schlieren, Switzerland
| | - Cornelia Dorn
- Cardiovascular Genetics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ana Dopazo
- Genomics Unit, Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
| | - Fátima Sánchez-Cabo
- Bioinformatics Unit, Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
| | - Jésus Vázquez
- Proteomics Unit, Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
| | | | - Enrique Lara-Pezzi
- Myocardial Homeostasis and Cardiac Injury Programme, Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain.
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Madaci L, Gard C, Nin S, Sarrabay A, Baier C, Venton G, Rihet P, Puthier D, Loriod B, Costello R. Single-Cell Transcriptome Analysis of Acute Myeloid Leukemia Cells Using Methanol Fixation and Cryopreservation. Diseases 2023; 12:1. [PMID: 38275564 PMCID: PMC10814800 DOI: 10.3390/diseases12010001] [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: 11/16/2023] [Revised: 12/08/2023] [Accepted: 12/18/2023] [Indexed: 01/27/2024] Open
Abstract
INTRODUCTION The application of single-cell RNA sequencing has greatly improved our understanding of various cellular and molecular mechanisms involved in physiological and pathophysiological processes. However, obtaining living cells for this technique can be difficult under certain conditions. To solve this problem, the methanol fixation method appeared as a promising alternative for routine clinical use. MATERIALS AND METHODS In this study, we selected two AML samples that had been fixed in methanol for 12-18 months. Once the cells were rehydrated, these samples were subjected to single-cell RNA sequencing. We then compared the results obtained from these samples with those obtained from the same samples cryopreserved in DMSO. RESULTS We used a previously validated methanol fixation protocol to perform scRNA-seq on DMSO cryopreserved cells and cells fixed in methanol for more than one year. Preliminary results show that methanol fixation induces some genetic and transcriptional modification compared with DMSO cryopreservation but remains a valuable method for single-cell analysis of primary human leukemia cells. CONCLUSIONS The initial findings from this study highlight certain resemblances in methanol fixation over a 12-month period and cryopreservation with DMSO, along with associated transcriptional level modifications. However, we observed genetic degradation in the fixation condition when extending beyond one year. Despite certain study limitations, it is evident that short-term methanol fixation can be effectively used for leukemia blast samples. Its ease of implementation holds the potential to simplify the integration of this technique into routine clinical practice.
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Affiliation(s)
- Lamia Madaci
- TAGC, TGML, INSERM, UMR1090, Aix-Marseille University, Parc Scientifique de Luminy, 13009 Marseille, France; (L.M.); (C.G.); (S.N.); (G.V.); (P.R.); (D.P.); (B.L.)
| | - Charlyne Gard
- TAGC, TGML, INSERM, UMR1090, Aix-Marseille University, Parc Scientifique de Luminy, 13009 Marseille, France; (L.M.); (C.G.); (S.N.); (G.V.); (P.R.); (D.P.); (B.L.)
| | - Sébastien Nin
- TAGC, TGML, INSERM, UMR1090, Aix-Marseille University, Parc Scientifique de Luminy, 13009 Marseille, France; (L.M.); (C.G.); (S.N.); (G.V.); (P.R.); (D.P.); (B.L.)
| | - Alexandre Sarrabay
- TAGC, TGML, INSERM, UMR1090, Aix-Marseille University, Parc Scientifique de Luminy, 13009 Marseille, France; (L.M.); (C.G.); (S.N.); (G.V.); (P.R.); (D.P.); (B.L.)
| | - Céline Baier
- Advanced BioDesign, Parc Technologique de Lyon, 655 Allée des Parcs, 69800 Saint Priest, France
| | - Geoffroy Venton
- TAGC, TGML, INSERM, UMR1090, Aix-Marseille University, Parc Scientifique de Luminy, 13009 Marseille, France; (L.M.); (C.G.); (S.N.); (G.V.); (P.R.); (D.P.); (B.L.)
- Hematology and Cellular Therapy Department, Conception University Hospital, 13005 Marseille, France
| | - Pascal Rihet
- TAGC, TGML, INSERM, UMR1090, Aix-Marseille University, Parc Scientifique de Luminy, 13009 Marseille, France; (L.M.); (C.G.); (S.N.); (G.V.); (P.R.); (D.P.); (B.L.)
| | - Denis Puthier
- TAGC, TGML, INSERM, UMR1090, Aix-Marseille University, Parc Scientifique de Luminy, 13009 Marseille, France; (L.M.); (C.G.); (S.N.); (G.V.); (P.R.); (D.P.); (B.L.)
| | - Béatrice Loriod
- TAGC, TGML, INSERM, UMR1090, Aix-Marseille University, Parc Scientifique de Luminy, 13009 Marseille, France; (L.M.); (C.G.); (S.N.); (G.V.); (P.R.); (D.P.); (B.L.)
| | - Régis Costello
- TAGC, TGML, INSERM, UMR1090, Aix-Marseille University, Parc Scientifique de Luminy, 13009 Marseille, France; (L.M.); (C.G.); (S.N.); (G.V.); (P.R.); (D.P.); (B.L.)
- Hematology and Cellular Therapy Department, Conception University Hospital, 13005 Marseille, France
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12
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Awuah WA, Ahluwalia A, Ghosh S, Roy S, Tan JK, Adebusoye FT, Ferreira T, Bharadwaj HR, Shet V, Kundu M, Yee ALW, Abdul-Rahman T, Atallah O. The molecular landscape of neurological disorders: insights from single-cell RNA sequencing in neurology and neurosurgery. Eur J Med Res 2023; 28:529. [PMID: 37974227 PMCID: PMC10652629 DOI: 10.1186/s40001-023-01504-w] [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: 08/10/2023] [Accepted: 11/03/2023] [Indexed: 11/19/2023] Open
Abstract
Single-cell ribonucleic acid sequencing (scRNA-seq) has emerged as a transformative technology in neurological and neurosurgical research, revolutionising our comprehension of complex neurological disorders. In brain tumours, scRNA-seq has provided valuable insights into cancer heterogeneity, the tumour microenvironment, treatment resistance, and invasion patterns. It has also elucidated the brain tri-lineage cancer hierarchy and addressed limitations of current models. Neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis have been molecularly subtyped, dysregulated pathways have been identified, and potential therapeutic targets have been revealed using scRNA-seq. In epilepsy, scRNA-seq has explored the cellular and molecular heterogeneity underlying the condition, uncovering unique glial subpopulations and dysregulation of the immune system. ScRNA-seq has characterised distinct cellular constituents and responses to spinal cord injury in spinal cord diseases, as well as provided molecular signatures of various cell types and identified interactions involved in vascular remodelling. Furthermore, scRNA-seq has shed light on the molecular complexities of cerebrovascular diseases, such as stroke, providing insights into specific genes, cell-specific expression patterns, and potential therapeutic interventions. This review highlights the potential of scRNA-seq in guiding precision medicine approaches, identifying clinical biomarkers, and facilitating therapeutic discovery. However, challenges related to data analysis, standardisation, sample acquisition, scalability, and cost-effectiveness need to be addressed. Despite these challenges, scRNA-seq has the potential to transform clinical practice in neurological and neurosurgical research by providing personalised insights and improving patient outcomes.
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Affiliation(s)
- Wireko Andrew Awuah
- Faculty of Medicine, Sumy State University, Zamonstanksya 7, Sumy, 40007, Ukraine
| | | | - Shankaneel Ghosh
- Institute of Medical Sciences and SUM Hospital, Bhubaneswar, India
| | - Sakshi Roy
- School of Medicine, Queen's University Belfast, Belfast, UK
| | | | | | - Tomas Ferreira
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | | | - Vallabh Shet
- Faculty of Medicine, Bangalore Medical College and Research Institute, Bangalore, Karnataka, India
| | - Mrinmoy Kundu
- Institute of Medical Sciences and SUM Hospital, Bhubaneswar, India
| | | | - Toufik Abdul-Rahman
- Faculty of Medicine, Sumy State University, Zamonstanksya 7, Sumy, 40007, Ukraine
| | - Oday Atallah
- Department of Neurosurgery, Hannover Medical School, Carl-Neuberg-Strasse 1, 30625, Hannover, Germany
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13
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Yue S, Wang Q, Zhang J, Hu Q, Liu C. Understanding cervical cancer at single-cell resolution. Cancer Lett 2023; 576:216408. [PMID: 37769795 DOI: 10.1016/j.canlet.2023.216408] [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: 05/24/2023] [Revised: 09/20/2023] [Accepted: 09/20/2023] [Indexed: 10/03/2023]
Abstract
Cervical cancer is now the fourth most prevalent malignancy in women worldwide, representing a tremendous burden of cancer. The heterogeneity of complex tumor ecosystem impacts tumorigenesis, malignant progression, and response to treatment; thus, a thorough understanding of the tumor ecosystem is vital for enhancing the prognosis of patients with cervical cancer. The rapid development and widespread use of single-cell sequencing have generated a new paradigm of cancer research, providing a comprehensive and in-depth understanding of cancers. In this review, we give an overview of the recent advances made by leveraging single-cell sequencing studies in the dissection of cervical cancer ecosystem heterogeneity. We highlight the evolution of the cervical cancer ecosystem during tumor initiation, progression, and treatment. High-resolution dissection of cervical cancer at the single-cell level has the potential to drive the development of targeted therapies and enable the realization of personalized medicine.
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Affiliation(s)
- Shengqin Yue
- Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Qian Wang
- Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Jiajun Zhang
- Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Qinyong Hu
- Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
| | - Chao Liu
- Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
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14
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Moratalla-Navarro F, Moreno V, Sanz-Pamplona R. TALKIEN: crossTALK IntEraction Network. A web-based tool for deciphering molecular communication through ligand-receptor interactions. Mol Omics 2023; 19:688-696. [PMID: 37403821 DOI: 10.1039/d3mo00049d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/06/2023]
Abstract
Molecular crosstalk, the dialogue between different cell types, is attracting more attention in cancer research. On the one hand, the communication between tumor and non-tumor cells in the microenvironment or between different tumor clones has influential consequences for the progression and spread of tumors and response to treatment. On the other hand, novel techniques such as single-cell sequencing or spatial transcriptomics provide detailed information that needs to be interpreted. TALKIEN: crossTALK IntEraction Network is a simple and intuitive online R/shiny application to visualize molecular crosstalk information through the construction and analysis of a protein-protein interaction network. Taking two or more lists of genes or proteins as input, which are representative of cell lineages, TALKIEN extracts information about ligand-receptor interactions, builds a network and analyzes it using systems biology techniques such as centrality measures and component analysis, among others. Moreover, it expands the network displaying pathways downstream receptors. The application allows users to select different graphical layouts, performs functional analysis and gives information about drugs targeting receptors. In conclusion, TALKIEN allows users to detect ligand-receptor interactions generating new in silico predictions of cell-cell communication thus providing a translational rationale for future experiments. It is freely available at https://www.odap-ico.org/talkien.
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Affiliation(s)
- Ferran Moratalla-Navarro
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Epidemiologia y Salud Pública (CIBERESP), Spain
- Department of Clinical Sciences, Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Víctor Moreno
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Epidemiologia y Salud Pública (CIBERESP), Spain
- Department of Clinical Sciences, Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Rebeca Sanz-Pamplona
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Epidemiologia y Salud Pública (CIBERESP), Spain
- University Hospital Lozano Blesa, Aragon Health Research Institute (IISA), ARAID Foundation, Aragon Government, Zaragoza, Spain.
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15
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Shen K, Chen B, Gao W. Integrated single-cell RNA sequencing analysis reveals a mesenchymal stem cell-associated signature for estimating prognosis and drug sensitivity in gastric cancer. J Cancer Res Clin Oncol 2023; 149:11829-11847. [PMID: 37410142 DOI: 10.1007/s00432-023-05058-6] [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: 05/20/2023] [Accepted: 06/28/2023] [Indexed: 07/07/2023]
Abstract
BACKGROUND Mesenchymal stem cells (MSCs) play an important role in regulating all stages of the immune response, angiogenesis, and transformation of matrix components in the tumor microenvironment. The aim of this study was to identify the prognostic value of MSC-related signatures in patients with gastric cancer (GC). METHODS MSC marker genes were identified by analyzing single-cell RNA sequencing (scRNA-seq) data for GC from the Gene Expression Omnibus (GEO) database. Using bulk sequencing data from the Cancer Genome Atlas-Stomach adenocarcinoma (TCGA-STAD), as a training cohort, and data from GEO, as a validation cohort, we developed a risk model consisting of MSC prognostic signature genes, and classified GC patients into high- and low-MSC risk subgroups. Multifactorial Cox regression was used to evaluate whether MSC prognostic signature was an independent prognostic factor. An MSC nomogram was constructed combining clinical information and risk grouping. Subsequently, we evaluated the effect of MSC prognostic signature on immune cell infiltration, antitumor drugs and immune checkpoints and verified the expression of MSC prognostic signature by in vitro cellular assays. RESULTS In this study, 174 MSC marker genes were identified by analyzing scRNA-seq data. We identified seven genes (POSTN, PLOD2, ITGAV, MMP11, SDC2, MARCKS, ANXA5) to construct MSC prognostic signature. MSC prognostic signature was an independent risk factor in the TCGA and GEO cohorts. GC patients in the high-MSC risk group had worse prognoses. In addition, the MSC nomogram has a high clinical application value. Notably, the MSC signature can induce the development of a poor immune microenvironment. GC patients in the high MSC-risk group were more sensitive to anticancer drugs and tended to have higher levels of immune checkpoint markers. In qRT-PCR assays, the MSC signature was more highly expressed in GC cell lines. CONCLUSIONS The MSC marker gene-based risk signature developed in this study can not only be used to predict the prognosis of GC patients, but also has the potential to reflect the efficacy of antitumor therapies.
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Affiliation(s)
- Kaiyu Shen
- The Second Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Binyu Chen
- The Second Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Wencang Gao
- Department of Oncology, The Second Affiliated Hospital, Zhejiang Chinese Medical University, Hangzhou, 310005, China.
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Yaghoubi Naei V, Bordhan P, Mirakhorli F, Khorrami M, Shrestha J, Nazari H, Kulasinghe A, Ebrahimi Warkiani M. Advances in novel strategies for isolation, characterization, and analysis of CTCs and ctDNA. Ther Adv Med Oncol 2023; 15:17588359231192401. [PMID: 37692363 PMCID: PMC10486235 DOI: 10.1177/17588359231192401] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 07/19/2023] [Indexed: 09/12/2023] Open
Abstract
Over the past decade, the detection and analysis of liquid biopsy biomarkers such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) have advanced significantly. They have received recognition for their clinical usefulness in detecting cancer at an early stage, monitoring disease, and evaluating treatment response. The emergence of liquid biopsy has been a helpful development, as it offers a minimally invasive, rapid, real-time monitoring, and possible alternative to traditional tissue biopsies. In resource-limited settings, the ideal platform for liquid biopsy should not only extract more CTCs or ctDNA from a minimal sample volume but also accurately represent the molecular heterogeneity of the patient's disease. This review covers novel strategies and advancements in CTC and ctDNA-based liquid biopsy platforms, including microfluidic applications and comprehensive analysis of molecular complexity. We discuss these systems' operational principles and performance efficiencies, as well as future opportunities and challenges for their implementation in clinical settings. In addition, we emphasize the importance of integrated platforms that incorporate machine learning and artificial intelligence in accurate liquid biopsy detection systems, which can greatly improve cancer management and enable precision diagnostics.
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Affiliation(s)
- Vahid Yaghoubi Naei
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
- Faculty of Medicine, Frazer Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Pritam Bordhan
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
- Faculty of Science, Institute for Biomedical Materials & Devices, University of Technology Sydney, Australia
| | - Fatemeh Mirakhorli
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
| | - Motahare Khorrami
- Immunology Research Center, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Jesus Shrestha
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
| | - Hojjatollah Nazari
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
| | - Arutha Kulasinghe
- Faculty of Medicine, Frazer Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Majid Ebrahimi Warkiani
- School of Biomedical Engineering, University of Technology Sydney, 1, Broadway, Ultimo New South Wales 2007, Australia
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17
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Shen K, Ke S, Chen B, Zhang T, Wang H, Lv J, Gao W. Identification and validation of biomarkers for epithelial-mesenchymal transition-related cells to estimate the prognosis and immune microenvironment in primary gastric cancer by the integrated analysis of single-cell and bulk RNA sequencing data. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:13798-13823. [PMID: 37679111 DOI: 10.3934/mbe.2023614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
BACKGROUND The epithelial-mesenchymal transition (EMT) is associated with gastric cancer (GC) progression and immune microenvironment. To better understand the heterogeneity underlying EMT, we integrated single-cell RNA-sequencing (scRNA-seq) data and bulk sequencing data from GC patients to evaluate the prognostic utility of biomarkers for EMT-related cells (ERCs), namely, cancer-associated fibroblasts (CAFs) and epithelial cells (ECs). METHODS scRNA-seq data from primary GC tumor samples were obtained from the Gene Expression Omnibus (GEO) database to identify ERC marker genes. Bulk GC datasets from the Cancer Genome Atlas (TCGA) and GEO were used as training and validation sets, respectively. Differentially expressed markers were identified from the TCGA database. Univariate Cox, least-absolute shrinkage, and selection operator regression analyses were performed to identify EMT-related cell-prognostic genes (ERCPGs). Kaplan-Meier, Cox regression, and receiver-operating characteristic (ROC) curve analyses were adopted to evaluate the prognostic utility of the ERCPG signature. An ERCPG-based nomogram was constructed by integrating independent prognostic factors. Finally, we evaluated the correlations between the ERCPG signature and immune-cell infiltration and verified the expression of ERCPG prognostic signature genes by in vitro cellular assays. RESULTS The ERCPG signature was comprised of seven genes (COL4A1, F2R, MMP11, CAV1, VCAN, FKBP10, and APOD). Patients were divided into high- and low-risk groups based on the ERCPG risk scores. Patients in the high-risk group showed a poor prognosis. ROC and calibration curves suggested that the ERCPG signature and nomogram had a good prognostic utility. An immune cell-infiltration analysis suggested that the abnormal expression of ERCPGs induced the formation of an unfavorable tumor immune microenvironment. In vitro cellular assays showed that ERCPGs were more abundantly expressed in GC cell lines compared to normal gastric tissue cell lines. CONCLUSIONS We constructed and validated an ERCPG signature using scRNA-seq and bulk sequencing data from ERCs of GC patients. Our findings support the estimation of patient prognosis and tumor treatment in future clinical practice.
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Affiliation(s)
- Kaiyu Shen
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Shuaiyi Ke
- Department of Internal Medicine, XianJu People's Hospital, XianJu 317399, China
| | - Binyu Chen
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Tiantian Zhang
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Hongtai Wang
- Department of General Surgery, XianJu People' Hospital, XianJu 317399, China
| | - Jianhui Lv
- Department of General Surgery, XianJu People' Hospital, XianJu 317399, China
| | - Wencang Gao
- Department of Oncology, Zhejiang Chinese Medical University, Hangzhou 310005, China
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18
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Cao Y, Ghazanfar S, Yang P, Yang J. Benchmarking of analytical combinations for COVID-19 outcome prediction using single-cell RNA sequencing data. Brief Bioinform 2023; 24:7140296. [PMID: 37096588 DOI: 10.1093/bib/bbad159] [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: 01/17/2023] [Revised: 03/30/2023] [Accepted: 04/03/2023] [Indexed: 04/26/2023] Open
Abstract
The advances of single-cell transcriptomic technologies have led to increasing use of single-cell RNA sequencing (scRNA-seq) data in large-scale patient cohort studies. The resulting high-dimensional data can be summarized and incorporated into patient outcome prediction models in several ways; however, there is a pressing need to understand the impact of analytical decisions on such model quality. In this study, we evaluate the impact of analytical choices on model choices, ensemble learning strategies and integrate approaches on patient outcome prediction using five scRNA-seq COVID-19 datasets. First, we examine the difference in performance between using single-view feature space versus multi-view feature space. Next, we survey multiple learning platforms from classical machine learning to modern deep learning methods. Lastly, we compare different integration approaches when combining datasets is necessary. Through benchmarking such analytical combinations, our study highlights the power of ensemble learning, consistency among different learning methods and robustness to dataset normalization when using multiple datasets as the model input.
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Affiliation(s)
- Yue Cao
- School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, NSW 2006, Australia
- Sydney Precision Data Science Centre, University of Sydney, NSW 2006, Australia
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
| | - Shila Ghazanfar
- School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, NSW 2006, Australia
- Sydney Precision Data Science Centre, University of Sydney, NSW 2006, Australia
| | - Pengyi Yang
- School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, NSW 2006, Australia
- Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, NSW 2145, Australia
- Sydney Precision Data Science Centre, University of Sydney, NSW 2006, Australia
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
| | - Jean Yang
- School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, NSW 2006, Australia
- Sydney Precision Data Science Centre, University of Sydney, NSW 2006, Australia
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
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Zang Z, Xu Y, Lu L, Geng Y, Yang S, Li SZ. UDRN: Unified Dimensional Reduction Neural Network for feature selection and feature projection. Neural Netw 2023; 161:626-637. [PMID: 36827960 DOI: 10.1016/j.neunet.2023.02.018] [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: 04/12/2022] [Revised: 11/22/2022] [Accepted: 02/11/2023] [Indexed: 02/17/2023]
Abstract
Dimensional reduction (DR) maps high-dimensional data into a lower dimensions latent space with minimized defined optimization objectives. The two independent branches of DR are feature selection (FS) and feature projection (FP). FS focuses on selecting a critical subset of dimensions but risks destroying the data distribution (structure). On the other hand, FP combines all the input features into lower dimensions space, aiming to maintain the data structure, but lacks interpretability and sparsity. Moreover, FS and FP are traditionally incompatible categories and have not been unified into an amicable framework. Therefore, we consider that the ideal DR approach combines both FS and FP into a unified end-to-end manifold learning framework, simultaneously performing fundamental feature discovery while maintaining the intrinsic relationships between data samples in the latent space. This paper proposes a unified framework named Unified Dimensional Reduction Network (UDRN) to integrate FS and FP in an end-to-end way. Furthermore, a novel network framework is designed to implement FS and FP tasks separately using a stacked feature selection network and feature projection network. In addition, a stronger manifold assumption and a novel loss function are proposed. Furthermore, the loss function can leverage the priors of data augmentation to enhance the generalization ability of the proposed UDRN. Finally, comprehensive experimental results on four image and four biological datasets, including very high-dimensional data, demonstrate the advantages of DRN over existing methods (FS, FP, and FS&FP pipeline), especially in downstream tasks such as classification and visualization.
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Affiliation(s)
- Zelin Zang
- Zhejiang University, Hangzhou, 310000, China; Westlake University, AI Lab, School of Engineering, Hangzhou, 310000, China; Westlake Institute for Advanced Study, Institute of Advanced Technology, Hangzhou, 310000, China.
| | - Yongjie Xu
- Zhejiang University, Hangzhou, 310000, China; Westlake University, AI Lab, School of Engineering, Hangzhou, 310000, China; Westlake Institute for Advanced Study, Institute of Advanced Technology, Hangzhou, 310000, China
| | - Linyan Lu
- China Telecom Corporation Limited, Hangzhou Branch, Hangzhou, 310000, China
| | - Yulan Geng
- Westlake University, AI Lab, School of Engineering, Hangzhou, 310000, China
| | - Senqiao Yang
- Westlake University, AI Lab, School of Engineering, Hangzhou, 310000, China
| | - Stan Z Li
- Westlake University, AI Lab, School of Engineering, Hangzhou, 310000, China; Westlake Institute for Advanced Study, Institute of Advanced Technology, Hangzhou, 310000, China.
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20
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Bärthel S, Falcomatà C, Rad R, Theis FJ, Saur D. Single-cell profiling to explore pancreatic cancer heterogeneity, plasticity and response to therapy. NATURE CANCER 2023; 4:454-467. [PMID: 36959420 DOI: 10.1038/s43018-023-00526-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 02/08/2023] [Indexed: 03/25/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer entity characterized by a heterogeneous genetic landscape and an immunosuppressive tumor microenvironment. Recent advances in high-resolution single-cell sequencing and spatial transcriptomics technologies have enabled an in-depth characterization of both malignant and host cell types and increased our understanding of the heterogeneity and plasticity of PDAC in the steady state and under therapeutic perturbation. In this Review we outline single-cell analyses in PDAC, discuss their implications on our understanding of the disease and present future perspectives of multimodal approaches to elucidate its biology and response to therapy at the single-cell level.
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Affiliation(s)
- Stefanie Bärthel
- Division of Translational Cancer Research, German Cancer Research Center and German Cancer Consortium, Heidelberg, Germany
- Institute of Experimental Cancer Therapy, Klinikum Rechts der Isar, School of Medicine, Technische Universität München, Munich, Germany
- Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technische Universität München, Munich, Germany
| | - Chiara Falcomatà
- Division of Translational Cancer Research, German Cancer Research Center and German Cancer Consortium, Heidelberg, Germany
- Institute of Experimental Cancer Therapy, Klinikum Rechts der Isar, School of Medicine, Technische Universität München, Munich, Germany
- Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technische Universität München, Munich, Germany
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Roland Rad
- Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technische Universität München, Munich, Germany
- Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technische Universität München, Munich, Germany
- German Cancer Consortium Partner Site Munich, Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- School of Computation, Information and Technology (CIT), Technische Universität München, Munich, Germany
| | - Dieter Saur
- Division of Translational Cancer Research, German Cancer Research Center and German Cancer Consortium, Heidelberg, Germany.
- Institute of Experimental Cancer Therapy, Klinikum Rechts der Isar, School of Medicine, Technische Universität München, Munich, Germany.
- Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technische Universität München, Munich, Germany.
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Amini P, Hajihosseini M, Pyne S, Dinu I. Geographically weighted linear combination test for gene-set analysis of a continuous spatial phenotype as applied to intratumor heterogeneity. Front Cell Dev Biol 2023; 11:1065586. [PMID: 36998245 PMCID: PMC10044624 DOI: 10.3389/fcell.2023.1065586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 02/22/2023] [Indexed: 03/11/2023] Open
Abstract
Background: The impact of gene-sets on a spatial phenotype is not necessarily uniform across different locations of cancer tissue. This study introduces a computational platform, GWLCT, for combining gene set analysis with spatial data modeling to provide a new statistical test for location-specific association of phenotypes and molecular pathways in spatial single-cell RNA-seq data collected from an input tumor sample.Methods: The main advantage of GWLCT consists of an analysis beyond global significance, allowing the association between the gene-set and the phenotype to vary across the tumor space. At each location, the most significant linear combination is found using a geographically weighted shrunken covariance matrix and kernel function. Whether a fixed or adaptive bandwidth is determined based on a cross-validation cross procedure. Our proposed method is compared to the global version of linear combination test (LCT), bulk and random-forest based gene-set enrichment analyses using data created by the Visium Spatial Gene Expression technique on an invasive breast cancer tissue sample, as well as 144 different simulation scenarios.Results: In an illustrative example, the new geographically weighted linear combination test, GWLCT, identifies the cancer hallmark gene-sets that are significantly associated at each location with the five spatially continuous phenotypic contexts in the tumors defined by different well-known markers of cancer-associated fibroblasts. Scan statistics revealed clustering in the number of significant gene-sets. A spatial heatmap of combined significance over all selected gene-sets is also produced. Extensive simulation studies demonstrate that our proposed approach outperforms other methods in the considered scenarios, especially when the spatial association increases.Conclusion: Our proposed approach considers the spatial covariance of gene expression to detect the most significant gene-sets affecting a continuous phenotype. It reveals spatially detailed information in tissue space and can thus play a key role in understanding the contextual heterogeneity of cancer cells.
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Affiliation(s)
- Payam Amini
- Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
- School of Medicine, Keele University, Keele, Staffordshire, United Kingdom
| | - Morteza Hajihosseini
- School of Public Health, University of Alberta, Edmonton, AB, Canada
- Stanford Department of Urology, Center for Academic Medicine, Palo Alto, CA, United States
| | - Saumyadipta Pyne
- Health Analytics Network, Pittsburgh, PA, United States
- University of California, Santa Barbara, Santa Barbara, CA, United States
- *Correspondence: Saumyadipta Pyne, ; Irina Dinu,
| | - Irina Dinu
- School of Public Health, University of Alberta, Edmonton, AB, Canada
- *Correspondence: Saumyadipta Pyne, ; Irina Dinu,
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22
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From the Catastrophic Objective Irreproducibility of Cancer Research and Unavoidable Failures of Molecular Targeted Therapies to the Sparkling Hope of Supramolecular Targeted Strategies. Int J Mol Sci 2023; 24:ijms24032796. [PMID: 36769134 PMCID: PMC9917659 DOI: 10.3390/ijms24032796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/22/2023] [Accepted: 01/23/2023] [Indexed: 02/05/2023] Open
Abstract
The unprecedented non-reproducibility of the results published in the field of cancer research has recently come under the spotlight. In this short review, we try to highlight some general principles in the organization and evolution of cancerous tumors, which objectively lead to their enormous variability and, consequently, the irreproducibility of the results of their investigation. This heterogeneity is also extremely unfavorable for the effective use of molecularly targeted medicine. Against the seemingly comprehensive background of this heterogeneity, we single out two supramolecular characteristics common to all tumors: the clustered nature of tumor interactions with their microenvironment and the formation of biomolecular condensates with tumor-specific distinctive features. We suggest that these features can form the basis of strategies for tumor-specific supramolecular targeted therapies.
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23
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Nettersheim FS, Armstrong SS, Durant C, Blanco-Dominguez R, Roy P, Orecchioni M, Suryawanshi V, Ley K. Titration of 124 antibodies using CITE-Seq on human PBMCs. Sci Rep 2022; 12:20817. [PMID: 36460735 PMCID: PMC9718773 DOI: 10.1038/s41598-022-24371-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 11/14/2022] [Indexed: 12/04/2022] Open
Abstract
Single-cell RNA-sequencing (scRNA-Seq) is widely used to characterize immune cell populations. However, mRNA levels correlate poorly with expression of surface proteins, which are well established to define immune cell types. CITE-Seq (cellular indexing of transcriptomes and epitopes by sequencing) utilizes oligonucleotide-tagged antibodies to simultaneously analyze surface phenotypes and transcriptomes. Considering the high costs of adding surface phenotyping to scRNA-Seq, we aimed to determine which of 188 tested CITE-Seq antibodies can detect their antigens on human peripheral blood mononuclear cells (PBMCs), a commonly interrogated cell population in immunology, and find the optimal concentration for staining. The recommended concentration was optimal for 76 antibodies, whereas staining quality of 7 antibodies improved when the concentration was doubled. 33 and 8 antibodies still worked well when the concentration was reduced to 1/5 or 1/25, respectively. 64 antigens were not detected at any antibody concentration. Optimizing the antibody panel by removing antibodies not able to detect their target antigens and adjusting concentrations of the remaining antibodies will improve the analysis and may reduce costs. In conclusion, our data are a resource for building an informative and cost-effective panel of CITE-Seq antibodies and use them at their optimal concentrations in future CITE-seq experiments on human PBMCs.
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Affiliation(s)
- Felix Sebastian Nettersheim
- La Jolla Institute for Immunology, La Jolla, CA, 92037, USA
- Department of Cardiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937, Cologne, Germany
| | | | | | - Rafael Blanco-Dominguez
- La Jolla Institute for Immunology, La Jolla, CA, 92037, USA
- Centro Nacional de Investigaciones Cardiovasculares, 28029, Madrid, Spain
| | - Payel Roy
- La Jolla Institute for Immunology, La Jolla, CA, 92037, USA
| | | | | | - Klaus Ley
- La Jolla Institute for Immunology, La Jolla, CA, 92037, USA.
- Department of Bioengineering, University of California, San Diego, San Diego, CA, 92093, USA.
- Immunology Center of Georgia (IMMCG), Augusta University, Augusta, GA, 30912, USA.
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24
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PANAGOPOULOS IOANNIS, HEIM SVERRE. Neoplasia-associated Chromosome Translocations Resulting in Gene Truncation. Cancer Genomics Proteomics 2022; 19:647-672. [PMID: 36316036 PMCID: PMC9620447 DOI: 10.21873/cgp.20349] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/19/2022] [Accepted: 08/23/2022] [Indexed: 11/27/2022] Open
Abstract
Chromosomal translocations in cancer as well as benign neoplasias typically lead to the formation of fusion genes. Such genes may encode chimeric proteins when two protein-coding regions fuse in-frame, or they may result in deregulation of genes via promoter swapping or translocation of the gene into the vicinity of a highly active regulatory element. A less studied consequence of chromosomal translocations is the fusion of two breakpoint genes resulting in an out-of-frame chimera. The breaks then occur in one or both protein-coding regions forming a stop codon in the chimeric transcript shortly after the fusion point. Though the latter genetic events and mechanisms at first awoke little research interest, careful investigations have established them as neither rare nor inconsequential. In the present work, we review and discuss the truncation of genes in neoplastic cells resulting from chromosomal rearrangements, especially from seemingly balanced translocations.
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Affiliation(s)
- IOANNIS PANAGOPOULOS
- Section for Cancer Cytogenetics, Institute for Cancer Genetics and Informatics, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - SVERRE HEIM
- Section for Cancer Cytogenetics, Institute for Cancer Genetics and Informatics, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
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25
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ID1 marks the tumorigenesis of pancreatic ductal adenocarcinoma in mouse and human. Sci Rep 2022; 12:13555. [PMID: 35941362 PMCID: PMC9359991 DOI: 10.1038/s41598-022-17827-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 08/01/2022] [Indexed: 11/26/2022] Open
Abstract
Pancreatic Ductal Adenocarcinoma (PDAC) is a deadly disease that has an increasing death rate but no effective treatment to now. Although biological and immunological hallmarks of PDAC have been frequently reported recently, early detection and the particularly aggressive biological features are the major challenges remaining unclear. In the current study, we retrieved multiple scRNA-seq datasets and illustrated the genetic programs of PDAC development in genetically modified mouse models. Notably, the transcription levels of Id1 were elevated specifically along with the PDAC development. Pseudotime trajectory analysis revealed that Id1 was closely correlated with the malignancy of PDAC. The gene expression patterns of human PDAC cells were determined by the comparative analysis of the scRNA-seq data on human PDAC and normal pancreas tissues. ID1 levels in human PDAC cancer cells were dramatically increased compared to normal epithelial cells. ID1 deficiency in vitro significantly blunt the invasive tumor-formation related phenotypes. IPA analysis on the differentially expressed genes suggested that EIF2 signaling was the core pathway regulating the development of PDAC. Blocking EFI2 signaling remarkably decreased the expression of ID1 and attenuated the tumor-formation related phenotypes. These observations confirmed that ID1 was regulated by EIF2 signaling and was the critical determinator of PDAC development and progression. This study suggests that ID1 is a potential malignant biomarker of PDAC in both mouse models and human and detecting and targeting ID1 may be a promising strategy to treat or even rescue PDAC.
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26
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Xi NM, Wang L, Yang C. Improving the diagnosis of thyroid cancer by machine learning and clinical data. Sci Rep 2022; 12:11143. [PMID: 35778428 PMCID: PMC9249901 DOI: 10.1038/s41598-022-15342-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 06/22/2022] [Indexed: 12/13/2022] Open
Abstract
Thyroid cancer is a common endocrine carcinoma that occurs in the thyroid gland. Much effort has been invested in improving its diagnosis, and thyroidectomy remains the primary treatment method. A successful operation without unnecessary side injuries relies on an accurate preoperative diagnosis. Current human assessment of thyroid nodule malignancy is prone to errors and may not guarantee an accurate preoperative diagnosis. This study proposed a machine learning framework to predict thyroid nodule malignancy based on our collected novel clinical dataset. The ten-fold cross-validation, bootstrap analysis, and permutation predictor importance were applied to estimate and interpret the model performance under uncertainty. The comparison between model prediction and expert assessment shows the advantage of our framework over human judgment in predicting thyroid nodule malignancy. Our method is accurate, interpretable, and thus useable as additional evidence in the preoperative diagnosis of thyroid cancer.
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Affiliation(s)
- Nan Miles Xi
- Department of Mathematics and Statistics, Loyola University Chicago, Chicago, IL, 60660, USA
| | - Lin Wang
- Department of Statistics, Purdue University, West Lafayette, IN, 47907, USA
| | - Chuanjia Yang
- Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, China.
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27
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Cao YH, Ding J, Tang QH, Zhang J, Huang ZY, Tang XM, Liu JB, Ma YS, Fu D. Deciphering cell-cell interactions and communication in the tumor microenvironment and unraveling intratumoral genetic heterogeneity via single-cell genomic sequencing. Bioengineered 2022; 13:14974-14986. [PMID: 37105769 DOI: 10.1080/21655979.2023.2185434] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023] Open
Abstract
A tumor's heterogeneity has important implications in terms of its clonal origin, progression, stemness, and drug resistance. Therefore, because of its significance in treatment, it is important to understand the gene expression pattern of a single cell, track gene expression or mutation in heterogeneous cells, evaluate the clonal origin of cancer cells, and determine the selective evolution of different subpopulations of cancer cells. Researchers are able to trace a cell's mutation and identify different types of tumor cells by measuring the whole transcriptome with single-cell sequencing (scRNA-seq). This technology provides a better understanding of the molecular mechanisms driving tumor growth than that offered by traditional RNA sequencing methods. In addition, it has revealed changes in the mutations and functions of somatic cells as a tumor evolves; it has also clarified immune cell infiltration and activation. Research on scRNA-seq technology has recently advanced significantly, suggesting new strategies for the treatment of cancer. In short, cancer researchers have become increasingly dependent on scRNA-seq. This paper reviews the development, detection principles, and processes of scRNA-seq technology and their application in tumor research. It also considers potential clinical applications.
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Affiliation(s)
- Ya-Hong Cao
- Department of Respiratory, Nantong Traditional Chinese Medicine Hospital, Affiliated Nantong Traditional Chinese Medicine Hospital of Nantong University, Nantong, Jiangsu, China
| | - Jie Ding
- Department of Clinical Laboratory, Jingjiang Traditional Chinese Medicine Hospital, Jingjiang, Jiangsu, China
| | - Qing-Hai Tang
- Hunan Key Laboratory for Conservation and Utilization of Biological Resources in the Nanyue Mountainous Region and College of Life Sciences and Environment, Hengyang Normal University, Hengyang, Hunan, China
| | - Jie Zhang
- Department of Immunology, School of Medicine, Nantong University, Nantong, Jiangsu, China
| | - Zhong-Yan Huang
- Institute of Pancreatic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, Huangpu, China
| | - Xiao-Mei Tang
- Institute of Pancreatic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, Huangpu, China
| | - Ji-Bin Liu
- Institute of Oncology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, China
| | - Yu-Shui Ma
- Cancer Institute, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, Xuhui, China
| | - Da Fu
- Institute of Pancreatic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, Huangpu, China
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28
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Xi NM, Hsu YY, Dang Q, Huang DP. Statistical learning in preclinical drug proarrhythmic assessment. J Biopharm Stat 2022; 32:450-473. [PMID: 35771997 DOI: 10.1080/10543406.2022.2065505] [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] [Indexed: 10/17/2022]
Abstract
Torsades de pointes (TdP) is an irregular heart rhythm characterized by faster beat rates and potentially could lead to sudden cardiac death. Much effort has been invested in understanding the drug-induced TdP in preclinical studies. However, a comprehensive statistical learning framework that can accurately predict the drug-induced TdP risk from preclinical data is still lacking. We proposed ordinal logistic regression and ordinal random forest models to predict low-, intermediate-, and high-risk drugs based on datasets generated from two experimental protocols. Leave-one-drug-out cross-validation, stratified bootstrap, and permutation predictor importance were applied to estimate and interpret the model performance under uncertainty. The potential outlier drugs identified by our models are consistent with their descriptions in the literature. Our method is accurate, interpretable, and thus useable as supplemental evidence in the drug safety assessment.
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Affiliation(s)
- Nan Miles Xi
- Department of Mathematics and Statistics, Loyola University Chicago, Chicago, Illinois, USA
| | - Yu-Yi Hsu
- Office of Biostatistics, Office of Translational Science, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Qianyu Dang
- Office of Biostatistics, Office of Translational Science, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Dalong Patrick Huang
- Office of Biostatistics, Office of Translational Science, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
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29
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Pan Y, Cao W, Mu Y, Zhu Q. Microfluidics Facilitates the Development of Single-Cell RNA Sequencing. BIOSENSORS 2022; 12:bios12070450. [PMID: 35884253 PMCID: PMC9312765 DOI: 10.3390/bios12070450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 06/20/2022] [Accepted: 06/21/2022] [Indexed: 12/12/2022]
Abstract
Single-cell RNA sequencing (scRNA-seq) technology provides a powerful tool for understanding complex biosystems at the single-cell and single-molecule level. The past decade has been a golden period for the development of single-cell sequencing, with scRNA-seq undergoing a tremendous leap in sensitivity and throughput. The application of droplet- and microwell-based microfluidics in scRNA-seq has contributed greatly to improving sequencing throughput. This review introduces the history of development and important technical factors of scRNA-seq. We mainly focus on the role of microfluidics in facilitating the development of scRNA-seq technology. To end, we discuss the future directions for scRNA-seq.
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Affiliation(s)
- Yating Pan
- Research Center for Analytical Instrumentation, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; (Y.P.); (W.C.)
- College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Wenjian Cao
- Research Center for Analytical Instrumentation, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; (Y.P.); (W.C.)
| | - Ying Mu
- Research Center for Analytical Instrumentation, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; (Y.P.); (W.C.)
- Correspondence: (Y.M.); (Q.Z.); Tel.: +86-88208383 (Y.M.); +86-88208383 (Q.Z.)
| | - Qiangyuan Zhu
- Research Center for Analytical Instrumentation, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; (Y.P.); (W.C.)
- Huzhou Institute of Zhejiang University, Huzhou 313002, China
- Correspondence: (Y.M.); (Q.Z.); Tel.: +86-88208383 (Y.M.); +86-88208383 (Q.Z.)
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30
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Sharifi-Azad M, Fathi M, Cho WC, Barzegari A, Dadashi H, Dadashpour M, Jahanban-Esfahlan R. Recent advances in targeted drug delivery systems for resistant colorectal cancer. Cancer Cell Int 2022; 22:196. [PMID: 35590367 PMCID: PMC9117978 DOI: 10.1186/s12935-022-02605-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 05/02/2022] [Indexed: 01/05/2023] Open
Abstract
Colorectal cancer (CRC) is one of the deadliest cancers in the world, the incidences and morality rate are rising and poses an important threat to the public health. It is known that multiple drug resistance (MDR) is one of the major obstacles in CRC treatment. Tumor microenvironment plus genomic instability, tumor derived exosomes (TDE), cancer stem cells (CSCs), circulating tumor cells (CTCs), cell-free DNA (cfDNA), as well as cellular signaling pathways are important issues regarding resistance. Since non-targeted therapy causes toxicity, diverse side effects, and undesired efficacy, targeted therapy with contribution of various carriers has been developed to address the mentioned shortcomings. In this paper the underlying causes of MDR and then various targeting strategies including exosomes, liposomes, hydrogels, cell-based carriers and theranostics which are utilized to overcome therapeutic resistance will be described. We also discuss implication of emerging approaches involving single cell approaches and computer-aided drug delivery with high potential for meeting CRC medical needs.
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Affiliation(s)
- Masoumeh Sharifi-Azad
- Department of Medical Biotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Marziyeh Fathi
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - William C Cho
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, China
| | - Abolfazl Barzegari
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Hamed Dadashi
- Department of Medical Biotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mehdi Dadashpour
- Department of Medical Biotechnology, Faculty of Medicine, Semnan University of Medical Sciences, Semnan, Iran. .,Cancer Research Center, Semnan University of Medical Sciences, Semnan, Iran.
| | - Rana Jahanban-Esfahlan
- Department of Medical Biotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran.
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31
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Zhang QY, Ho DWH, Tsui YM, Ng IOL. Single-Cell Transcriptomics of Liver Cancer: Hype or Insights? Cell Mol Gastroenterol Hepatol 2022; 14:513-525. [PMID: 35577269 PMCID: PMC9294331 DOI: 10.1016/j.jcmgh.2022.04.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 04/24/2022] [Accepted: 04/29/2022] [Indexed: 12/13/2022]
Abstract
Hepatocellular carcinoma (HCC) is characterized by its high degrees of both inter- and intratumoral heterogeneity. Its complex tumor microenvironment is also crucial in promoting tumor progression. Recent advances in single-cell RNA sequencing provide an important highway to characterize the underlying pathogenesis and heterogeneity of HCC in an unprecedented degree of resolution. This review discusses the up-to-date discoveries from the latest studies of HCC with respect to the strength of single-cell RNA sequencing. We discuss its use in the dissection of the landscape of the intricate HCC ecosystem and highlight the major features at cellular levels, including the malignant cells, different immune cell types, and the various cell-cell interactions, which are crucial for developing effective immunotherapies. Finally, its translational applications will be discussed. Altogether, these explorations may give us some hints at the tumor growth and progression and drug resistance and recurrence, particularly in this era of personalized medicine.
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Affiliation(s)
- Qing-Yang Zhang
- Department of Pathology and State Key Laboratory of Liver Research, The University of Hong Kong, Hong Kong
| | - Daniel Wai-Hung Ho
- Department of Pathology and State Key Laboratory of Liver Research, The University of Hong Kong, Hong Kong
| | - Yu-Man Tsui
- Department of Pathology and State Key Laboratory of Liver Research, The University of Hong Kong, Hong Kong
| | - Irene Oi-Lin Ng
- Department of Pathology and State Key Laboratory of Liver Research, The University of Hong Kong, Hong Kong.
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32
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Eraslan G, Drokhlyansky E, Anand S, Fiskin E, Subramanian A, Slyper M, Wang J, Van Wittenberghe N, Rouhana JM, Waldman J, Ashenberg O, Lek M, Dionne D, Win TS, Cuoco MS, Kuksenko O, Tsankov AM, Branton PA, Marshall JL, Greka A, Getz G, Segrè AV, Aguet F, Rozenblatt-Rosen O, Ardlie KG, Regev A. Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function. Science 2022; 376:eabl4290. [PMID: 35549429 PMCID: PMC9383269 DOI: 10.1126/science.abl4290] [Citation(s) in RCA: 156] [Impact Index Per Article: 78.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Understanding gene function and regulation in homeostasis and disease requires knowledge of the cellular and tissue contexts in which genes are expressed. Here, we applied four single-nucleus RNA sequencing methods to eight diverse, archived, frozen tissue types from 16 donors and 25 samples, generating a cross-tissue atlas of 209,126 nuclei profiles, which we integrated across tissues, donors, and laboratory methods with a conditional variational autoencoder. Using the resulting cross-tissue atlas, we highlight shared and tissue-specific features of tissue-resident cell populations; identify cell types that might contribute to neuromuscular, metabolic, and immune components of monogenic diseases and the biological processes involved in their pathology; and determine cell types and gene modules that might underlie disease mechanisms for complex traits analyzed by genome-wide association studies.
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Affiliation(s)
- Gökcen Eraslan
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Eugene Drokhlyansky
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Shankara Anand
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Evgenij Fiskin
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ayshwarya Subramanian
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Michal Slyper
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jiali Wang
- Department of Ophthalmology, Harvard Medical School, Boston, MA 02115, USA
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
- Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - John M. Rouhana
- Department of Ophthalmology, Harvard Medical School, Boston, MA 02115, USA
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
- Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Julia Waldman
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Orr Ashenberg
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Monkol Lek
- Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA
| | - Danielle Dionne
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Thet Su Win
- Department of Dermatology, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Michael S. Cuoco
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Olena Kuksenko
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Philip A. Branton
- The Joint Pathology Center Gynecologic/Breast Pathology, Silver Spring, MD 20910, USA
| | | | - Anna Greka
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Gad Getz
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Center for Cancer Research and Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Ayellet V. Segrè
- Department of Ophthalmology, Harvard Medical School, Boston, MA 02115, USA
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
- Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - François Aguet
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Orit Rozenblatt-Rosen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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33
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Bowes A, Tarabichi M, Pillay N, Van Loo P. Leveraging single cell sequencing to unravel intra-tumour heterogeneity and tumour evolution in human cancers. J Pathol 2022; 257:466-478. [PMID: 35438189 PMCID: PMC9322001 DOI: 10.1002/path.5914] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/12/2022] [Accepted: 04/13/2022] [Indexed: 11/11/2022]
Abstract
Intra-tumour heterogeneity and tumour evolution are well-documented phenomena in human cancers. While the advent of next-generation sequencing technologies has facilitated the large-scale capture of genomic data, the field of single cell genomics is nascent but rapidly advancing and generating many new insights into the complex molecular mechanisms of tumour biology. In this review, we provide an overview of current single cell DNA sequencing technologies, exploring how recent methodological advancements have enumerated new insights into intra-tumour heterogeneity and tumour evolution. Areas highlighted include the potential power of single cell genome sequencing studies to explore evolutionary dynamics contributing to tumourigenesis through to progression, metastasis and therapy resistance. We also explore the use of in-situ sequencing technologies to study intra-tumour heterogeneity in a spatial context, as well as examining the use of single cell genomics to perform lineage tracing in both normal and malignant tissues. Finally, we consider the use of multi-modal single cell sequencing technologies. Taken together, it is hoped that these many facets of single cell genome sequencing will improve our understanding of tumourigenesis, progression and lethality in cancer leading to the development of novel therapies. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Amy Bowes
- Cancer Genomics Group, The Francis Crick Institute, London, UK.,Sarcoma Biology and Genomics Group, UCL Cancer Institute, London, UK
| | - Maxime Tarabichi
- Cancer Genomics Group, The Francis Crick Institute, London, UK.,Institute for Interdisciplinary Research, Université Libre de Bruxelles, Brussels, Belgium
| | - Nischalan Pillay
- Sarcoma Biology and Genomics Group, UCL Cancer Institute, London, UK.,Department of Histopathology, The Royal National Orthopaedic Hospital NHS Trust, London, UK
| | - Peter Van Loo
- Cancer Genomics Group, The Francis Crick Institute, London, UK.,Department of Genetics, The University of Texas MD Anderson Cancer Centre, Houston, USA.,Department of Genomic Medicine, The University of Texas MD Anderson Cancer Centre, Houston, USA
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Radfar P, Aboulkheyr Es H, Salomon R, Kulasinghe A, Ramalingam N, Sarafraz-Yazdi E, Thiery JP, Warkiani ME. Single-cell analysis of circulating tumour cells: enabling technologies and clinical applications. Trends Biotechnol 2022; 40:1041-1060. [DOI: 10.1016/j.tibtech.2022.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 02/15/2022] [Accepted: 02/16/2022] [Indexed: 11/26/2022]
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Kim MC, Jin Z, Kolb R, Borcherding N, Chatzkel JA, Falzarano SM, Zhang W. Updates on Immunotherapy and Immune Landscape in Renal Clear Cell Carcinoma. Cancers (Basel) 2021; 13:5856. [PMID: 34831009 PMCID: PMC8616149 DOI: 10.3390/cancers13225856] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/15/2021] [Accepted: 11/19/2021] [Indexed: 12/24/2022] Open
Abstract
Several clinicopathological features of clear cell renal cell carcinomas (ccRCC) contribute to make an "atypical" cancer, including resistance to chemotherapy, sensitivity to anti-angiogenesis therapy and ICIs despite a low mutational burden, and CD8+ T cell infiltration being the predictor for poor prognosis-normally CD8+ T cell infiltration is a good prognostic factor in cancer patients. These "atypical" features have brought researchers to investigate the molecular and immunological mechanisms that lead to the increased T cell infiltrates despite relatively low molecular burdens, as well as to decipher the immune landscape that leads to better response to ICIs. In the present study, we summarize the past and ongoing pivotal clinical trials of immunotherapies for ccRCC, emphasizing the potential molecular and cellular mechanisms that lead to the success or failure of ICI therapy. Single-cell analysis of ccRCC has provided a more thorough and detailed understanding of the tumor immune microenvironment and has facilitated the discovery of molecular biomarkers from the tumor-infiltrating immune cells. We herein will focus on the discussion of some major immune cells, including T cells and tumor-associated macrophages (TAM) in ccRCC. We will further provide some perspectives of using molecular and cellular biomarkers derived from these immune cell types to potentially improve the response rate to ICIs in ccRCC patients.
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Affiliation(s)
- Myung-Chul Kim
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, USA; (M.-C.K.); (Z.J.); (R.K.); (S.M.F.)
- UF Health Cancer Center, University of Florida, Gainesville, FL 32610, USA
| | - Zeng Jin
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, USA; (M.-C.K.); (Z.J.); (R.K.); (S.M.F.)
- UF Health Cancer Center, University of Florida, Gainesville, FL 32610, USA
| | - Ryan Kolb
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, USA; (M.-C.K.); (Z.J.); (R.K.); (S.M.F.)
- UF Health Cancer Center, University of Florida, Gainesville, FL 32610, USA
| | - Nicholas Borcherding
- Department of Pathology and Immunology, Washington University, St. Louis, MO 63110, USA;
| | | | - Sara Moscovita Falzarano
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, USA; (M.-C.K.); (Z.J.); (R.K.); (S.M.F.)
- UF Health Cancer Center, University of Florida, Gainesville, FL 32610, USA
| | - Weizhou Zhang
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, USA; (M.-C.K.); (Z.J.); (R.K.); (S.M.F.)
- UF Health Cancer Center, University of Florida, Gainesville, FL 32610, USA
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Roma S, Carpen L, Raveane A, Bertolini F. The Dual Role of Innate Lymphoid and Natural Killer Cells in Cancer. from Phenotype to Single-Cell Transcriptomics, Functions and Clinical Uses. Cancers (Basel) 2021; 13:cancers13205042. [PMID: 34680190 PMCID: PMC8533946 DOI: 10.3390/cancers13205042] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 10/05/2021] [Accepted: 10/06/2021] [Indexed: 02/07/2023] Open
Abstract
Simple Summary Innate lymphoid cells (ILCs), a family of innate immune cells including natural killers (NKs), play a multitude of roles in first-line cancer control, in escape from immunity and in cancer progression. In this review, we summarize preclinical and clinical data on ILCs and NK cells concerning their phenotype, function and clinical applications in cellular therapy trials. We also describe how single-cell transcriptome sequencing has been used and forecast how it will be used to better understand ILC and NK involvement in cancer control and progression as well as their therapeutic potential. Abstract The role of innate lymphoid cells (ILCs), including natural killer (NK) cells, is pivotal in inflammatory modulation and cancer. Natural killer cell activity and count have been demonstrated to be regulated by the expression of activating and inhibitory receptors together with and as a consequence of different stimuli. The great majority of NK cell populations have an anti-tumor activity due to their cytotoxicity, and for this reason have been used for cellular therapies in cancer patients. On the other hand, the recently classified helper ILCs are fundamentally involved in inflammation and they can be either helpful or harmful in cancer development and progression. Tissue niche seems to play an important role in modulating ILC function and conversion, as observed at the transcriptional level. In the past, these cell populations have been classified by the presence of specific cellular receptor markers; more recently, due to the advent of single-cell RNA sequencing (scRNA-seq), it has been possible to also explore them at the transcriptomic level. In this article we review studies on ILC (and NK cell) classification, function and their involvement in cancer. We also summarize the potential application of NK cells in cancer therapy and give an overview of the most recent studies involving ILCs and NKs at scRNA-seq, focusing on cancer. Finally, we provide a resource for those who wish to start single-cell transcriptomic analysis on the context of these innate lymphoid cell populations.
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