101
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Zhou H, Luo Q, Wu W, Li N, Yang C, Zou L. Radiomics-guided checkpoint inhibitor immunotherapy for precision medicine in cancer: A review for clinicians. Front Immunol 2023; 14:1088874. [PMID: 36936913 PMCID: PMC10014595 DOI: 10.3389/fimmu.2023.1088874] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/16/2023] [Indexed: 03/05/2023] Open
Abstract
Immunotherapy using immune checkpoint inhibitors (ICIs) is a breakthrough in oncology development and has been applied to multiple solid tumors. However, unlike traditional cancer treatment approaches, immune checkpoint inhibitors (ICIs) initiate indirect cytotoxicity by generating inflammation, which causes enlargement of the lesion in some cases. Therefore, rather than declaring progressive disease (PD) immediately, confirmation upon follow-up radiological evaluation after four-eight weeks is suggested according to immune-related Response Evaluation Criteria in Solid Tumors (ir-RECIST). Given the difficulty for clinicians to immediately distinguish pseudoprogression from true disease progression, we need novel tools to assist in this field. Radiomics, an innovative data analysis technique that quantifies tumor characteristics through high-throughput extraction of quantitative features from images, can enable the detection of additional information from early imaging. This review will summarize the recent advances in radiomics concerning immunotherapy. Notably, we will discuss the potential of applying radiomics to differentiate pseudoprogression from PD to avoid condition exacerbation during confirmatory periods. We also review the applications of radiomics in hyperprogression, immune-related biomarkers, efficacy, and immune-related adverse events (irAEs). We found that radiomics has shown promising results in precision cancer immunotherapy with early detection in noninvasive ways.
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Affiliation(s)
- Huijie Zhou
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Qian Luo
- Department of Hematology, the Second Affiliated Hospital Zhejiang University School of Medicine, Zhejiang, China
| | - Wanchun Wu
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Na Li
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Chunli Yang
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Liqun Zou
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
- *Correspondence: Liqun Zou,
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102
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Jiang Z, Shi H, Tang X, Qin J. Recent advances in droplet microfluidics for single-cell analysis. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2023.116932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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103
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DasGupta R, Yap A, Yaqing EY, Chia S. Evolution of precision oncology-guided treatment paradigms. WIREs Mech Dis 2023; 15:e1585. [PMID: 36168283 DOI: 10.1002/wsbm.1585] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 06/30/2022] [Accepted: 07/11/2022] [Indexed: 01/31/2023]
Abstract
Cancer treatment is gradually evolving from the classical use of nonspecific cytotoxic drugs targeting generic mechanisms of cell growth and proliferation. Instead, new "patient-specific treatment paradigms" that are based on an individual patient's tumor-specific molecular features are emerging, and these include "druggable" genomic alterations such as oncogenic driver mutations, downstream activities of cancer-signaling pathways, and the expression of specific genes involved in tumorigenesis and cancer progression. This evolving landscape of making evidence-based treatment decisions forms the foundation of precision oncology, which aims to deliver "the right drug, to the right patient and at the right time". The long-term vision for this approach is to maximize the treatment efficacy while minimizing exposure to ineffective therapy and reducing co-morbidity-related side effects. Successful clinical translation and implementation of this vision have the potential to revolutionize treatment paradigms from predominantly reactive, to more evidence-based, proactive and predictive care. In this article, we review the past and current approaches in precision oncology, and describe their remarkable power and limitations. We also speculate on the evolution of newly emerging methodologies of the future that can be used to address some of the key challenges associated with the existing paradigms. This article is categorized under: Cancer > Genetics/Genomics/Epigenetics Cancer > Molecular and Cellular Physiology Cancer > Computational Models.
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Affiliation(s)
- Ramanuj DasGupta
- Laboratory of Precision Oncology and Cancer Evolution, Genome Institute of Singapore, A*STAR, Singapore, Singapore.,Cancer Science Institute, National University of Singapore, Singapore, Singapore
| | - Aixin Yap
- Laboratory of Precision Oncology and Cancer Evolution, Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Elena Yong Yaqing
- Laboratory of Precision Oncology and Cancer Evolution, Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Shumei Chia
- Laboratory of Precision Oncology and Cancer Evolution, Genome Institute of Singapore, A*STAR, Singapore, Singapore
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104
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Hart A, Nguyen LK. Meta-Dynamic Network Modelling for Biochemical Networks. Methods Mol Biol 2023; 2634:167-189. [PMID: 37074579 DOI: 10.1007/978-1-0716-3008-2_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
ODE modelling requires accurate knowledge of parameter and state variable values to deliver accurate and robust predictions. Parameters and state variables, however, are rarely static and immutable entities, especially in a biological context. This observation undermines the predictions made by ODE models that rely on specific parameter and state variable values and limits the contexts in which their predictions remain accurate and useful. Meta-dynamic network (MDN) modelling is a technique that can be synergistically integrated into an ODE modelling pipeline to assist in overcoming these limitations. The core mechanic of MDN modelling is the generation of a large number of model instances, each with a unique set of parameters and/or state variable values, followed by the simulation of each to determine how parameter and state variable variation affects protein dynamics. This process reveals the range of possible protein dynamics for a given network topology. Since MDN modelling is integrated with traditional ODE modelling, it can also be used to investigate the underlying causal mechanics. This technique is particularly suited to the investigation of network behaviors in systems that are highly heterogenous or systems wherein the network properties can change over time. MDN is a collection of principles rather than a strict protocol, so in this chapter, we have introduced the core principles using an example, the Hippo-ERK crosstalk signalling network.
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Affiliation(s)
- Anthony Hart
- Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Monash University, Clayton, VIC, Australia
| | - Lan K Nguyen
- Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Monash University, Clayton, VIC, Australia.
- Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia.
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105
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Cha J, Lavi M, Kim J, Shomron N, Lee I. Imputation of single-cell transcriptome data enables the reconstruction of networks predictive of breast cancer metastasis. Comput Struct Biotechnol J 2023; 21:2296-2304. [PMID: 37035549 PMCID: PMC10073994 DOI: 10.1016/j.csbj.2023.03.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 03/21/2023] [Accepted: 03/21/2023] [Indexed: 03/30/2023] Open
Abstract
Single-cell transcriptome data provide a unique opportunity to explore the gene networks of a particular cell type. However, insufficient capture rate and high dimensionality of single-cell RNA sequencing (scRNA-seq) data challenge cell-type-specific gene network (CGN) reconstruction. Here, we demonstrated that the imputation of scRNA-seq data enables reconstruction of CGNs by effective retrieval of gene functional associations. We reconstructed CGNs for seven primary and nine metastatic breast cancer cell lines using scRNA-seq data with imputation. Key genes for primary or metastatic cell lines were prioritized based on network centrality measures and CGN hub genes that were presumed to be the major determinant of cell type characteristics. To identify novel genes in breast cancer metastasis, we used the average rank difference of centrality between the primary and metastatic cell lines. Genes predicted using CGN centrality analysis were more enriched for known breast cancer metastatic genes than those predicted using differential expression. The molecular chaperone CCT2 was identified as a novel gene for breast metastasis during knockdown assays of several candidate genes. Overall, our study demonstrated an effective CGN reconstruction technique with imputation of scRNA-seq data and the feasibility of identifying key genes for particular cell subsets using single-cell network analysis.
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Affiliation(s)
- Junha Cha
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
| | - Michael Lavi
- Faculty of Medicine and Edmond J Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv 69978, Israel
| | - Junhan Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
| | - Noam Shomron
- Faculty of Medicine and Edmond J Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv 69978, Israel
- Corresponding author.
| | - Insuk Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
- POSTECH Biotech Center, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea
- Corresponding author at: Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of Korea.
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106
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Saeki S, Kumegawa K, Takahashi Y, Yang L, Osako T, Yasen M, Otsuji K, Miyata K, Yamakawa K, Suzuka J, Sakimoto Y, Ozaki Y, Takano T, Sano T, Noda T, Ohno S, Yao R, Ueno T, Maruyama R. Transcriptomic intratumor heterogeneity of breast cancer patient-derived organoids may reflect the unique biological features of the tumor of origin. Breast Cancer Res 2023; 25:21. [PMID: 36810117 PMCID: PMC9942352 DOI: 10.1186/s13058-023-01617-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 02/10/2023] [Indexed: 02/23/2023] Open
Abstract
BACKGROUND The intratumor heterogeneity (ITH) of cancer cells plays an important role in breast cancer resistance and recurrence. To develop better therapeutic strategies, it is necessary to understand the molecular mechanisms underlying ITH and their functional significance. Patient-derived organoids (PDOs) have recently been utilized in cancer research. They can also be used to study ITH as cancer cell diversity is thought to be maintained within the organoid line. However, no reports investigated intratumor transcriptomic heterogeneity in organoids derived from patients with breast cancer. This study aimed to investigate transcriptomic ITH in breast cancer PDOs. METHODS We established PDO lines from ten patients with breast cancer and performed single-cell transcriptomic analysis. First, we clustered cancer cells for each PDO using the Seurat package. Then, we defined and compared the cluster-specific gene signature (ClustGS) corresponding to each cell cluster in each PDO. RESULTS Cancer cells were clustered into 3-6 cell populations with distinct cellular states in each PDO line. We identified 38 clusters with ClustGS in 10 PDO lines and used Jaccard similarity index to compare the similarity of these signatures. We found that 29 signatures could be categorized into 7 shared meta-ClustGSs, such as those related to the cell cycle or epithelial-mesenchymal transition, and 9 signatures were unique to single PDO lines. These unique cell populations appeared to represent the characteristics of the original tumors derived from patients. CONCLUSIONS We confirmed the existence of transcriptomic ITH in breast cancer PDOs. Some cellular states were commonly observed in multiple PDOs, whereas others were specific to single PDO lines. The combination of these shared and unique cellular states formed the ITH of each PDO.
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Affiliation(s)
- Sumito Saeki
- grid.410807.a0000 0001 0037 4131Project for Cancer Epigenomics, Cancer Institute, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto-ku, Tokyo, 135-8550 Japan ,grid.410807.a0000 0001 0037 4131Breast Surgical Oncology, Breast Oncology Center, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Kohei Kumegawa
- grid.410807.a0000 0001 0037 4131Cancer Cell Diversity Project, NEXT-Ganken Program, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Yoko Takahashi
- grid.410807.a0000 0001 0037 4131Breast Surgical Oncology, Breast Oncology Center, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Liying Yang
- grid.410807.a0000 0001 0037 4131Project for Cancer Epigenomics, Cancer Institute, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto-ku, Tokyo, 135-8550 Japan
| | - Tomo Osako
- grid.410807.a0000 0001 0037 4131Division of Pathology, Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Mahmut Yasen
- grid.410807.a0000 0001 0037 4131Cancer Informatics and Biobanking Platform Project, NEXT-Ganken Program, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Kazutaka Otsuji
- grid.410807.a0000 0001 0037 4131Cancer Cell Diversity Project, NEXT-Ganken Program, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Kenichi Miyata
- grid.410807.a0000 0001 0037 4131Project for Cancer Epigenomics, Cancer Institute, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto-ku, Tokyo, 135-8550 Japan
| | - Kaoru Yamakawa
- grid.410807.a0000 0001 0037 4131Cancer Informatics and Biobanking Platform Project, NEXT-Ganken Program, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Jun Suzuka
- grid.410807.a0000 0001 0037 4131Cancer Cell Diversity Project, NEXT-Ganken Program, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Yuri Sakimoto
- grid.410807.a0000 0001 0037 4131Project for Cancer Epigenomics, Cancer Institute, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto-ku, Tokyo, 135-8550 Japan
| | - Yukinori Ozaki
- grid.410807.a0000 0001 0037 4131Breast Medical Oncology, Breast Oncology Center, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Toshimi Takano
- grid.410807.a0000 0001 0037 4131Breast Medical Oncology, Breast Oncology Center, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Takeshi Sano
- grid.410807.a0000 0001 0037 4131Department of Gastroenterological Surgery, Gastroenterological Center, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Tetsuo Noda
- grid.410807.a0000 0001 0037 4131Director’s Room, Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Shinji Ohno
- grid.410807.a0000 0001 0037 4131Breast Oncology Center, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Ryoji Yao
- grid.410807.a0000 0001 0037 4131Department of Cell Biology, Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Takayuki Ueno
- grid.410807.a0000 0001 0037 4131Breast Surgical Oncology, Breast Oncology Center, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Reo Maruyama
- Project for Cancer Epigenomics, Cancer Institute, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto-ku, Tokyo, 135-8550, Japan. .,Cancer Cell Diversity Project, NEXT-Ganken Program, Japanese Foundation for Cancer Research, Tokyo, Japan.
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107
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Li K, Raveendran A, Xie G, Zhang Y, Wu H, Huang Z, Jia Z, Yang J. Prediction for recurrent non-muscle invasive bladder cancer. Cancer Biomark 2023; 38:275-285. [PMID: 37661872 DOI: 10.3233/cbm-220373] [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] [Indexed: 09/05/2023]
Abstract
Non-muscle invasive bladder cancer (NMIBC) has a high recurrence rate, which places a significant burden on both patients and the healthcare system. Hence, it holds significant importance to predict the recurrence risk following treatment for individuals diagnosed with non-muscle invasive bladder cancer (NMIBC). As new generation technologies continue to emerge, an increasing number of recurrence risk prediction tools are being developed and discovered. This article provides an overview of the primary recurrence risk prediction tools currently available, including the liquid biopsy, tissue biopsy, and risk prediction tables. Each of these tools is described in detail and illustrated with relevant examples. Furthermore, we conduct an analysis of the advantages and disadvantages of these tools. This article aims to enhance the reader's understanding of the current progress in recurrence prediction tools and encourage their practical utilization in the fields of precision medicine and public health.
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Affiliation(s)
- Keqiang Li
- Laboratory Urology, Department of Urology, The First Affiliated Hospital of Zhengzhou University, Henan, China
- Academy of Medical Sciences, Zhengzhou University, Henan, China
| | - Aravind Raveendran
- Laboratory Urology, Department of Urology, The First Affiliated Hospital of Zhengzhou University, Henan, China
| | - Guoqing Xie
- Laboratory Urology, Department of Urology, The First Affiliated Hospital of Zhengzhou University, Henan, China
- Academy of Medical Sciences, Zhengzhou University, Henan, China
| | - Yu Zhang
- Laboratory Urology, Department of Urology, The First Affiliated Hospital of Zhengzhou University, Henan, China
- Academy of Medical Sciences, Zhengzhou University, Henan, China
| | - Haofan Wu
- Department of Gastrointestinal Surgery, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China
| | - Zhenlin Huang
- Laboratory Urology, Department of Urology, The First Affiliated Hospital of Zhengzhou University, Henan, China
| | - Zhankui Jia
- Laboratory Urology, Department of Urology, The First Affiliated Hospital of Zhengzhou University, Henan, China
| | - Jinjian Yang
- Laboratory Urology, Department of Urology, The First Affiliated Hospital of Zhengzhou University, Henan, China
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108
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Vegliante R, Pastushenko I, Blanpain C. Deciphering functional tumor states at single-cell resolution. EMBO J 2022; 41:e109221. [PMID: 34918370 PMCID: PMC8762559 DOI: 10.15252/embj.2021109221] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 10/07/2021] [Accepted: 11/10/2021] [Indexed: 01/19/2023] Open
Abstract
Within a tumor, cancer cells exist in different states that are associated with distinct tumor functions, including proliferation, differentiation, invasion, metastasis, and resistance to anti-cancer therapy. The identification of the gene regulatory networks underpinning each state is essential for better understanding functional tumor heterogeneity and revealing tumor vulnerabilities. Here, we review the different studies identifying tumor states by single-cell sequencing approaches and the mechanisms that promote and sustain these functional states and regulate their transitions. We also describe how different tumor states are spatially distributed and interact with the specific stromal cells that compose the tumor microenvironment. Finally, we discuss how the understanding of tumor plasticity and transition states can be used to develop new strategies to improve cancer therapy.
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Affiliation(s)
- Rolando Vegliante
- Laboratory of Stem Cells and CancerUniversité Libre de BruxellesBrusselsBelgium
| | | | - Cédric Blanpain
- Laboratory of Stem Cells and CancerUniversité Libre de BruxellesBrusselsBelgium
- WELBIOUniversité Libre de BruxellesBrusselsBelgium
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109
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Pancancer Analysis of the Prognostic and Immunotherapeutic Value of Progestin and AdipoQ Receptor 4. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:2528164. [PMID: 36573110 PMCID: PMC9789910 DOI: 10.1155/2022/2528164] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/09/2022] [Accepted: 11/10/2022] [Indexed: 12/23/2022]
Abstract
AdipoQ receptor 4 (PAQR4) belongs to the family of progestin and AdipoQ receptors. PAQR4 plays an oncogenic role in lung and breast cancer. However, systematic pancancer analyses of PAQR4 have not been performed. The purpose was to investigate the prognostic and immunological significance of PAQR4 across 31 tumor types. Data were obtained from the following sources: TCGA, GEO, UALCAN, TIMER, GEPIA2, KM plotter, and TISIDB databases. The results proved that PAQR4 expression was significantly elevatory in most cancer types. We then explored the utility of PAQR4 as a prognostic indicator across all cancers. Using Cox proportional risk regression models, it has been demonstrated that PAQR4 is an independent risk factor in. High PAQR4 expression was not associated with other prognostic indicators, including overall survival, disease-free interval, disease-specific survival, and progression-free period. Subsequently, we explored the immunological value of PAQR4 and found that PAQR4 expression significantly correlated with tumor mutational burden, microsatellite instability, neoantigen, and immune checkpoint genes in tumors. It also significantly negatively correlated with most tumors' ESTIMATE scores, indicating that PAQR4 can influence the cellular composition of the tumor microenvironment. Our findings suggest the immunotherapeutic potential of PAQR4 in tumors. Finally, we explored the role of PAQR4 in tumor drug resistance and found that PAQR4 expression affected the sensitivity to multiple chemotherapeutic agents. A significant role for PAQR4 in tumor immunity is evident in these studies, as well as its potential role in cancer diagnosis, prognosis, and treatment precision.
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110
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Su M, Pan T, Chen QZ, Zhou WW, Gong Y, Xu G, Yan HY, Li S, Shi QZ, Zhang Y, He X, Jiang CJ, Fan SC, Li X, Cairns MJ, Wang X, Li YS. Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications. Mil Med Res 2022; 9:68. [PMID: 36461064 PMCID: PMC9716519 DOI: 10.1186/s40779-022-00434-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 11/18/2022] [Indexed: 12/03/2022] Open
Abstract
The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.
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Affiliation(s)
- Min Su
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Tao Pan
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199, Hainan, China
| | - Qiu-Zhen Chen
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Wei-Wei Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Yi Gong
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China.,Department of Immunology, Nanjing Medical University, Nanjing, 211166, China
| | - Gang Xu
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199, Hainan, China
| | - Huan-Yu Yan
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Si Li
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199, Hainan, China
| | - Qiao-Zhen Shi
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Ya Zhang
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199, Hainan, China
| | - Xiao He
- Department of Laboratory Medicine, Women and Children's Hospital of Chongqing Medical University, Chongqing, 401174, China
| | | | - Shi-Cai Fan
- Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, 518110, Guangdong, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, Faculty of Health and Medicine, the University of Newcastle, University Drive, Callaghan, NSW, 2308, Australia. .,Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW, 2305, Australia.
| | - Xi Wang
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China.
| | - Yong-Sheng Li
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199, Hainan, China.
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111
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High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging. Nat Biotechnol 2022; 40:1794-1806. [PMID: 36203011 DOI: 10.1038/s41587-022-01483-z] [Citation(s) in RCA: 166] [Impact Index Per Article: 83.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 08/19/2022] [Indexed: 02/07/2023]
Abstract
Resolving the spatial distribution of RNA and protein in tissues at subcellular resolution is a challenge in the field of spatial biology. We describe spatial molecular imaging, a system that measures RNAs and proteins in intact biological samples at subcellular resolution by performing multiple cycles of nucleic acid hybridization of fluorescent molecular barcodes. We demonstrate that spatial molecular imaging has high sensitivity (one or two copies per cell) and very low error rate (0.0092 false calls per cell) and background (~0.04 counts per cell). The imaging system generates three-dimensional, super-resolution localization of analytes at ~2 million cells per sample. Cell segmentation is morphology based using antibodies, compatible with formalin-fixed, paraffin-embedded samples. We measured multiomic data (980 RNAs and 108 proteins) at subcellular resolution in formalin-fixed, paraffin-embedded tissues (nonsmall cell lung and breast cancer) and identified >18 distinct cell types, ten unique tumor microenvironments and 100 pairwise ligand-receptor interactions. Data on >800,000 single cells and ~260 million transcripts can be accessed at http://nanostring.com/CosMx-dataset .
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112
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K27M in canonical and noncanonical H3 variants occurs in distinct oligodendroglial cell lineages in brain midline gliomas. Nat Genet 2022; 54:1865-1880. [PMID: 36471070 PMCID: PMC9742294 DOI: 10.1038/s41588-022-01205-w] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 09/16/2022] [Indexed: 12/12/2022]
Abstract
Canonical (H3.1/H3.2) and noncanonical (H3.3) histone 3 K27M-mutant gliomas have unique spatiotemporal distributions, partner alterations and molecular profiles. The contribution of the cell of origin to these differences has been challenging to uncouple from the oncogenic reprogramming induced by the mutation. Here, we perform an integrated analysis of 116 tumors, including single-cell transcriptome and chromatin accessibility, 3D chromatin architecture and epigenomic profiles, and show that K27M-mutant gliomas faithfully maintain chromatin configuration at developmental genes consistent with anatomically distinct oligodendrocyte precursor cells (OPCs). H3.3K27M thalamic gliomas map to prosomere 2-derived lineages. In turn, H3.1K27M ACVR1-mutant pontine gliomas uniformly mirror early ventral NKX6-1+/SHH-dependent brainstem OPCs, whereas H3.3K27M gliomas frequently resemble dorsal PAX3+/BMP-dependent progenitors. Our data suggest a context-specific vulnerability in H3.1K27M-mutant SHH-dependent ventral OPCs, which rely on acquisition of ACVR1 mutations to drive aberrant BMP signaling required for oncogenesis. The unifying action of K27M mutations is to restrict H3K27me3 at PRC2 landing sites, whereas other epigenetic changes are mainly contingent on the cell of origin chromatin state and cycling rate.
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Sun Q, Wang L, Zhang C, Hong Z, Han Z. Cervical cancer heterogeneity: a constant battle against viruses and drugs. Biomark Res 2022; 10:85. [PMCID: PMC9670454 DOI: 10.1186/s40364-022-00428-7] [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: 08/16/2022] [Accepted: 10/30/2022] [Indexed: 11/19/2022] Open
Abstract
Cervical cancer is the first identified human papillomavirus (HPV) associated cancer and the most promising malignancy to be eliminated. However, the ever-changing virus subtypes and acquired multiple drug resistance continue to induce failure of tumor prevention and treatment. The exploration of cervical cancer heterogeneity is the crucial way to achieve effective prevention and precise treatment. Tumor heterogeneity exists in various aspects including the immune clearance of viruses, tumorigenesis, neoplasm recurrence, metastasis and drug resistance. Tumor development and drug resistance are often driven by potential gene amplification and deletion, not only somatic genomic alterations, but also copy number amplifications, histone modification and DNA methylation. Genomic rearrangements may occur by selection effects from chemotherapy or radiotherapy which exhibits genetic intra-tumor heterogeneity in advanced cervical cancers. The combined application of cervical cancer therapeutic vaccine and immune checkpoint inhibitors has become an effective strategy to address the heterogeneity of treatment. In this review, we will integrate classic and recently updated epidemiological data on vaccination rates, screening rates, incidence and mortality of cervical cancer patients worldwide aiming to understand the current situation of disease prevention and control and identify the direction of urgent efforts. Additionally, we will focus on the tumor environment to summarize the conditions of immune clearance and gene integration after different HPV infections and to explore the genomic factors of tumor heterogeneity. Finally, we will make a thorough inquiry into completed and ongoing phase III clinical trials in cervical cancer and summarize molecular mechanisms of drug resistance among chemotherapy, radiotherapy, biotherapy, and immunotherapy.
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Affiliation(s)
- Qian Sun
- grid.33199.310000 0004 0368 7223Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
| | - Liangliang Wang
- grid.33199.310000 0004 0368 7223Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
| | - Cong Zhang
- grid.33199.310000 0004 0368 7223Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
| | - Zhenya Hong
- grid.33199.310000 0004 0368 7223Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
| | - Zhiqiang Han
- grid.33199.310000 0004 0368 7223Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
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Samal BR, Loers JU, Vermeirssen V, De Preter K. Opportunities and challenges in interpretable deep learning for drug sensitivity prediction of cancer cells. FRONTIERS IN BIOINFORMATICS 2022; 2:1036963. [PMID: 36466148 PMCID: PMC9714662 DOI: 10.3389/fbinf.2022.1036963] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 11/03/2022] [Indexed: 01/02/2024] Open
Abstract
In precision oncology, therapy stratification is done based on the patients' tumor molecular profile. Modeling and prediction of the drug response for a given tumor molecular type will further improve therapeutic decision-making for cancer patients. Indeed, deep learning methods hold great potential for drug sensitivity prediction, but a major problem is that these models are black box algorithms and do not clarify the mechanisms of action. This puts a limitation on their clinical implementation. To address this concern, many recent studies attempt to overcome these issues by developing interpretable deep learning methods that facilitate the understanding of the logic behind the drug response prediction. In this review, we discuss strengths and limitations of recent approaches, and suggest future directions that could guide further improvement of interpretable deep learning in drug sensitivity prediction in cancer research.
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Affiliation(s)
- Bikash Ranjan Samal
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Center for Medical Genetics Ghent (CMGG), Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Jens Uwe Loers
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Center for Medical Genetics Ghent (CMGG), Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
| | - Vanessa Vermeirssen
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Center for Medical Genetics Ghent (CMGG), Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
| | - Katleen De Preter
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Center for Medical Genetics Ghent (CMGG), Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
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Xu BL, Wang XM, Chen GY, Yuan P, Han L, Qin P, Li TP, You HQ, Zhang CJ, Fu XM, Yuan L, Wang ZB, Gao QL. In vivo growth of subclones derived from Lewis lung carcinoma is determined by the tumor microenvironment. Am J Cancer Res 2022; 12:5255-5270. [PMID: 36504888 PMCID: PMC9729899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 11/02/2022] [Indexed: 12/15/2022] Open
Abstract
Heterogeneity is a fundamental feature of human tumors and plays a major role in drug resistance and disease progression. In the present study, we selected single-cell-derived cell lines (SCDCLs) derived from Lewis lung carcinoma (LLC1) cells to investigate tumorigenesis and heterogeneity. SCDCLs were generated using limiting dilution. Five SCDCLs were subcutaneously injected into wild-type C57BL/6N mice; however, they displayed significant differences in tumor growth. Subclone SCC1 grew the fastest in vivo, whereas it grew slower in vitro. The growth pattern of SCC2 was the opposite to that of SCC1. Genetic differences in these two subclones showed marked differences in cell adhesion and proliferation. Pathway enrichment results indicate that signal transduction and immune system responses were the most significantly altered functional categories in SCC2 cells compared to those in SCC1 cells in vitro. The number and activation of CD3+ and CD8+ T cells and NK cells in the tumor tissue of tumor-bearing mice inoculated with SCC2 were significantly higher, whereas those of myeloid cells were significantly lower, than those in the SCC1 and LLC1 groups. Our results suggest that the in vivo growth of two subclones derived from LLC1 was determined by the tumor microenvironment rather than their intrinsic proliferative cell characteristics.
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Affiliation(s)
- Ben-Ling Xu
- Department of Immunotherapy, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Xiao-Ming Wang
- Department of General Surgery, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Guang-Yu Chen
- Department of Immunotherapy, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Peng Yuan
- Department of Breast Surgery, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Lu Han
- Department of Immunotherapy, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Peng Qin
- Department of Immunotherapy, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Tie-Peng Li
- Department of Immunotherapy, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Hong-Qin You
- Department of Immunotherapy, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Cheng-Juan Zhang
- Center of Bio Repository, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Xiao-Min Fu
- Department of Immunotherapy, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Long Yuan
- Department of General Surgery, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Zi-Bing Wang
- Department of Immunotherapy, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
| | - Quan-Li Gao
- Department of Immunotherapy, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhou 450008, Henan, P. R. China
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Xu Z, Luo J, Xiong Z. scSemiGAN: a single-cell semi-supervised annotation and dimensionality reduction framework based on generative adversarial network. Bioinformatics 2022; 38:5042-5048. [PMID: 36193998 DOI: 10.1093/bioinformatics/btac652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 09/05/2022] [Accepted: 10/02/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Cell-type annotation plays a crucial role in single-cell RNA-seq (scRNA-seq) data analysis. As more and more well-annotated scRNA-seq reference data are publicly available, automatical label transference algorithms are gaining popularity over manual marker gene-based annotation methods. However, most existing methods fail to unify cell-type annotation with dimensionality reduction and are unable to generate deep latent representation from the perspective of data generation. RESULTS In this article, we propose scSemiGAN, a single-cell semi-supervised cell-type annotation and dimensionality reduction framework based on a generative adversarial network, to overcome these challenges, modeling scRNA-seq data from the aspect of data generation. Our proposed scSemiGAN is capable of performing deep latent representation learning and cell-type label prediction simultaneously. Through extensive comparison with four state-of-the-art annotation methods on diverse simulated and real scRNA-seq datasets, scSemiGAN achieves competitive or superior performance in multiple downstream tasks including cell-type annotation, latent representation visualization, confounding factor removal and enrichment analysis. AVAILABILITY AND IMPLEMENTATION The code and data of scSemiGAN are available on GitHub: https://github.com/rafa-nadal/scSemiGAN. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zhongyuan Xu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Zehao Xiong
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
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Sawicki CM, Janal MN, Nicholson SJ, Wu AK, Schmidt BL, Albertson DG. Oral cancer patients experience mechanical and chemical sensitivity at the site of the cancer. BMC Cancer 2022; 22:1165. [PMID: 36368973 PMCID: PMC9650819 DOI: 10.1186/s12885-022-10282-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 11/04/2022] [Indexed: 11/13/2022] Open
Abstract
INTRODUCTION Oral cancer patients suffer severe chronic and mechanically-induced pain at the site of the cancer. Our clinical experience is that oral cancer patients report new sensitivity to spicy foods. We hypothesized that in cancer patients, mechanical and chemical sensitivity would be greater when measured at the cancer site compared to a contralateral matched normal site. METHODS We determined mechanical pain thresholds (MPT) on the right and left sides of the tongue of 11 healthy subjects, and at the cancer and contralateral matched normal site in 11 oral cancer patients in response to von Frey filaments in the range of 0.008 to 300 g (normally not reported as painful). We evaluated chemical sensitivity in 13 healthy subjects and seven cancer patients, who rated spiciness/pain on a visual analog scale in response to exposure to six paper strips impregnated with capsaicin (0-10 mM). RESULTS Mechanical detection thresholds (MDT) were recorded for healthy subjects, but not MPTs. By contrast, MPTs were measured at the site of the cancer in oral cancer patients (7/11 patients). No MPTs were measured at the cancer patients' contralateral matched normal sites. Measured MPTs were correlated with patients' responses to the University of California Oral Cancer Pain Questionnaire. Capsaicin sensitivity at the site of the cancer was evident in cancer patients by a leftward shift of the cancer site capsaicin dose-response curve compared to that of the patient's contralateral matched normal site. We detected no difference in capsaicin sensitivity on the right and left sides of tongues of healthy subjects. CONCLUSIONS Mechanical and chemical sensitivity testing was well tolerated by the majority of oral cancer patients. Sensitivity is greater at the site of the cancer than at a contralateral matched normal site.
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Affiliation(s)
- Caroline M. Sawicki
- grid.137628.90000 0004 1936 8753Department of Pediatric Dentistry, New York University College of Dentistry, 421 First Avenue, Room 233W, New York, NY 10010 USA
| | - Malvin N. Janal
- grid.137628.90000 0004 1936 8753Department of Epidemiology & Health Promotion, New York University College of Dentistry, Room 301, 433 First Avenue, New York, NY 10010 USA
| | - Samuel J. Nicholson
- grid.137628.90000 0004 1936 8753Department of Oral and Maxillofacial Surgery, New York University College of Dentistry, 421 First Avenue, Room 233W, New York, NY 10010 USA
| | - Angie K. Wu
- grid.137628.90000 0004 1936 8753Bluestone Center for Clinical Research, New York University College of Dentistry, 421 First Avenue, Room 233W, New York, NY 10010 USA
| | - Brian L. Schmidt
- grid.137628.90000 0004 1936 8753Department of Oral and Maxillofacial Surgery, New York University College of Dentistry, 421 First Avenue, Room 233W, New York, NY 10010 USA ,grid.137628.90000 0004 1936 8753Bluestone Center for Clinical Research, New York University College of Dentistry, 421 First Avenue, Room 233W, New York, NY 10010 USA ,grid.137628.90000 0004 1936 8753NYU Oral Cancer Center, New York University College of Dentistry, 421 First Avenue, Room 233W, New York, NY 10010 USA
| | - Donna G. Albertson
- grid.137628.90000 0004 1936 8753Department of Oral and Maxillofacial Surgery, New York University College of Dentistry, 421 First Avenue, Room 233W, New York, NY 10010 USA ,grid.137628.90000 0004 1936 8753Bluestone Center for Clinical Research, New York University College of Dentistry, 421 First Avenue, Room 233W, New York, NY 10010 USA ,grid.137628.90000 0004 1936 8753NYU Oral Cancer Center, New York University College of Dentistry, 421 First Avenue, Room 233W, New York, NY 10010 USA
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Shan Y, Yang J, Li X, Zhong X, Chang Y. GLAE: A Graph-learnable Auto-encoder for Single-cell RNA-seq Analysis. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.11.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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The role of transcription factors in the acquisition of the four latest proposed hallmarks of cancer and corresponding enabling characteristics. Semin Cancer Biol 2022; 86:1203-1215. [PMID: 36244529 DOI: 10.1016/j.semcancer.2022.10.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 10/10/2022] [Accepted: 10/12/2022] [Indexed: 01/27/2023]
Abstract
With the recent description of the molecular and cellular characteristics that enable acquisition of both core and new hallmarks of cancer, the consequences of transcription factor dysregulation in the hallmarks scheme has become increasingly evident. Dysregulation or mutation of transcription factors has long been recognized in the development of cancer where alterations in these key regulatory molecules can result in aberrant gene expression and consequential blockade of normal cellular differentiation. Here, we provide an up-to-date review of involvement of dysregulated transcription factor networks with the most recently reported cancer hallmarks and enabling characteristic properties. We present some illustrative examples of the impact of dysregulated transcription factors, specifically focusing on the characteristics of phenotypic plasticity, non-mutational epigenetic reprogramming, polymorphic microbiomes, and senescence. We also discuss how new insights into transcription factor dysregulation in cancer is contributing to addressing current therapeutic challenges.
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120
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Allard P, Papazotos F, Potvin-Trottier L. Microfluidics for long-term single-cell time-lapse microscopy: Advances and applications. Front Bioeng Biotechnol 2022; 10:968342. [PMID: 36312536 PMCID: PMC9597311 DOI: 10.3389/fbioe.2022.968342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 09/21/2022] [Indexed: 11/13/2022] Open
Abstract
Cells are inherently dynamic, whether they are responding to environmental conditions or simply at equilibrium, with biomolecules constantly being made and destroyed. Due to their small volumes, the chemical reactions inside cells are stochastic, such that genetically identical cells display heterogeneous behaviors and gene expression profiles. Studying these dynamic processes is challenging, but the development of microfluidic methods enabling the tracking of individual prokaryotic cells with microscopy over long time periods under controlled growth conditions has led to many discoveries. This review focuses on the recent developments of one such microfluidic device nicknamed the mother machine. We overview the original device design, experimental setup, and challenges associated with this platform. We then describe recent methods for analyzing experiments using automated image segmentation and tracking. We further discuss modifications to the experimental setup that allow for time-varying environmental control, replicating batch culture conditions, cell screening based on their dynamic behaviors, and to accommodate a variety of microbial species. Finally, this review highlights the discoveries enabled by this technology in diverse fields, such as cell-size control, genetic mutations, cellular aging, and synthetic biology.
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Affiliation(s)
- Paige Allard
- Department of Biology, Concordia University, Montréal, QC, Canada
| | - Fotini Papazotos
- Department of Biology, Concordia University, Montréal, QC, Canada
| | - Laurent Potvin-Trottier
- Department of Biology, Concordia University, Montréal, QC, Canada
- Department of Physics, Concordia University, Montréal, QC, Canada
- Centre for Applied Synthetic Biology, Concordia University, Montréal, QC, Canada
- *Correspondence: Laurent Potvin-Trottier,
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Junaid M, Lee A, Kim J, Park TJ, Lim SB. Transcriptional Heterogeneity of Cellular Senescence in Cancer. Mol Cells 2022; 45:610-619. [PMID: 35983702 PMCID: PMC9448649 DOI: 10.14348/molcells.2022.0036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 06/02/2022] [Accepted: 06/11/2022] [Indexed: 11/27/2022] Open
Abstract
Cellular senescence plays a paradoxical role in tumorigenesis through the expression of diverse senescence-associated (SA) secretory phenotypes (SASPs). The heterogeneity of SA gene expression in cancer cells not only promotes cancer stemness but also protects these cells from chemotherapy. Despite the potential correlation between cancer and SA biomarkers, many transcriptional changes across distinct cell populations remain largely unknown. During the past decade, single-cell RNA sequencing (scRNA-seq) technologies have emerged as powerful experimental and analytical tools to dissect such diverse senescence-derived transcriptional changes. Here, we review the recent sequencing efforts that successfully characterized scRNA-seq data obtained from diverse cancer cells and elucidated the role of senescent cells in tumor malignancy. We further highlight the functional implications of SA genes expressed specifically in cancer and stromal cell populations in the tumor microenvironment. Translational research leveraging scRNA-seq profiling of SA genes will facilitate the identification of novel expression patterns underlying cancer susceptibility, providing new therapeutic opportunities in the era of precision medicine.
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Affiliation(s)
- Muhammad Junaid
- Department of Biochemistry and Molecular Biology, Ajou University School of Medicine, Suwon 16499, Korea
- Department of Biomedical Sciences, Ajou University Graduate School, Suwon 16499, Korea
| | - Aejin Lee
- Department of Biochemistry and Molecular Biology, Ajou University School of Medicine, Suwon 16499, Korea
| | - Jaehyung Kim
- Department of Biochemistry and Molecular Biology, Ajou University School of Medicine, Suwon 16499, Korea
| | - Tae Jun Park
- Department of Biochemistry and Molecular Biology, Ajou University School of Medicine, Suwon 16499, Korea
- Department of Biomedical Sciences, Ajou University Graduate School, Suwon 16499, Korea
| | - Su Bin Lim
- Department of Biochemistry and Molecular Biology, Ajou University School of Medicine, Suwon 16499, Korea
- Department of Biomedical Sciences, Ajou University Graduate School, Suwon 16499, Korea
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Yu X, Zhang Q, Zhang S, He Y, Guo W. Single-cell sequencing and establishment of an 8-gene prognostic model for pancreatic cancer patients. Front Oncol 2022; 12:1000447. [PMID: 36237305 PMCID: PMC9552769 DOI: 10.3389/fonc.2022.1000447] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 09/05/2022] [Indexed: 11/15/2022] Open
Abstract
Background Single-cell sequencing (SCS) technologies enable analysis of gene structure and expression data at single-cell resolution. However, SCS analysis in pancreatic cancer remains largely unexplored. Methods We downloaded pancreatic cancer SCS data from different databases and applied appropriate dimensionality reduction algorithms. We identified 10 cell types and subsequently screened differentially expressed marker genes of these 10 cell types using FindAllMarkers analysis. Also, we evaluated the tumor immune microenvironment based on ESTIMATE and MCP-counter. Statistical enrichment was evaluated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis. We used all candidate gene sets in KEGG database to perform gene set enrichment analysis. We used LASSO regression to reduce the number of genes in the pancreatic risk model by R package glmnet, followed by rtPCR to validate the expression of the signature genes in different pancreatic cancer cell lines. Results We identified 15 cell subpopulations by dimension reduction and data clustering. We divided the 15 subpopulations into 10 distinct cell types based on marker gene expression. Then, we performed functional enrichment analysis for the 352 marker genes in pancreatic cancer cells. Based on RNA expression data and prognostic information from TCGA and GEO datasets, we identified 42 prognosis-related genes, including 5 protective genes and 37 high-risk genes, which we used to identified two molecular subtypes. C1 subtype was associated with a better prognosis, whereas C2 subtype was associated with a worse prognosis. Moreover, chemokine and chemokine receptor genes were differentially expressed between C1 and C2 subtypes. Functional and pathway enrichment uncovered functional differences between C1 and C2 subtype. We identified eight genes that could serve as potential biomarkers for prognosis prediction in pancreatic cancer patients. These genes were used to establish an 8-gene pancreatic cancer prognostic model. Conclusions We established an 8-gene pancreatic cancer prognostic model. This model can meaningfully predict prognosis and treatment response in pancreatic cancer patients.
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Affiliation(s)
- Xiao Yu
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory of Hepatobiliary and Pancreatic Surgery and Digestive Organ Transplantation of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Open and Key Laboratory of Hepatobiliary & Pancreatic Surgery and Digestive Organ Transplantation at Henan Universities, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Digestive Organ Transplantation, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Qiyao Zhang
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory of Hepatobiliary and Pancreatic Surgery and Digestive Organ Transplantation of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Open and Key Laboratory of Hepatobiliary & Pancreatic Surgery and Digestive Organ Transplantation at Henan Universities, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Digestive Organ Transplantation, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shuijun Zhang
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory of Hepatobiliary and Pancreatic Surgery and Digestive Organ Transplantation of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Open and Key Laboratory of Hepatobiliary & Pancreatic Surgery and Digestive Organ Transplantation at Henan Universities, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Digestive Organ Transplantation, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuting He
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory of Hepatobiliary and Pancreatic Surgery and Digestive Organ Transplantation of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Open and Key Laboratory of Hepatobiliary & Pancreatic Surgery and Digestive Organ Transplantation at Henan Universities, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Digestive Organ Transplantation, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Wenzhi Guo, ; Yuting He,
| | - Wenzhi Guo
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory of Hepatobiliary and Pancreatic Surgery and Digestive Organ Transplantation of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Open and Key Laboratory of Hepatobiliary & Pancreatic Surgery and Digestive Organ Transplantation at Henan Universities, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Digestive Organ Transplantation, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Wenzhi Guo, ; Yuting He,
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Gene expression based inference of cancer drug sensitivity. Nat Commun 2022; 13:5680. [PMID: 36167836 PMCID: PMC9515171 DOI: 10.1038/s41467-022-33291-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 09/12/2022] [Indexed: 11/09/2022] Open
Abstract
Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer and are responsible for imparting differential drug responses in cancer patients. Recently, the availability of high-throughput screening datasets has paved the way for machine learning based personalized therapy recommendations using the molecular profiles of cancer specimens. In this study, we introduce Precily, a predictive modeling approach to infer treatment response in cancers using gene expression data. In this context, we demonstrate the benefits of considering pathway activity estimates in tandem with drug descriptors as features. We apply Precily on single-cell and bulk RNA sequencing data associated with hundreds of cancer cell lines. We then assess the predictability of treatment outcomes using our in-house prostate cancer cell line and xenografts datasets exposed to differential treatment conditions. Further, we demonstrate the applicability of our approach on patient drug response data from The Cancer Genome Atlas and an independent clinical study describing the treatment journey of three melanoma patients. Our findings highlight the importance of chemo-transcriptomics approaches in cancer treatment selection. Predicting treatment response in cancer remains a highly complex task. Here, the authors develop Precily, a deep neural network framework to predict treatment response in cancer by considering gene expression, pathway activity estimates and drug features, and test this method in multiple datasets and preclinical models.
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Di Z, Zhou S, Xu G, Ren L, Li C, Ding Z, Huang K, Liang L, Yuan Y. Single-cell and WGCNA uncover a prognostic model and potential oncogenes in colorectal cancer. Biol Proced Online 2022; 24:13. [PMID: 36117173 PMCID: PMC9484253 DOI: 10.1186/s12575-022-00175-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 08/30/2022] [Indexed: 11/19/2022] Open
Abstract
Background Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide. Single-cell transcriptome sequencing (scRNA-seq) can provide accurate gene expression data for individual cells. In this study, a new prognostic model was constructed by scRNA-seq and bulk transcriptome sequencing (bulk RNA-seq) data of CRC samples to develop a new understanding of CRC. Methods CRC scRNA-seq data were downloaded from the GSE161277 database, and CRC bulk RNA-seq data were downloaded from the TCGA and GSE17537 databases. The cells were clustered by the FindNeighbors and FindClusters functions in scRNA-seq data. CIBERSORTx was applied to detect the abundance of cell clusters in the bulk RNA-seq expression matrix. WGCNA was performed with the expression profiles to construct the gene coexpression networks of TCGA-CRC. Next, we used a tenfold cross test to construct the model and a nomogram to assess the independence of the model for clinical application. Finally, we examined the expression of the unreported model genes by qPCR and immunohistochemistry. A clone formation assay and orthotopic colorectal tumour model were applied to detect the regulatory roles of unreported model genes. Results A total of 43,851 cells were included after quality control, and 20 cell clusters were classified by the FindCluster () function. We found that the abundances of C1, C2, C4, C5, C15, C16 and C19 were high and the abundances of C7, C10, C11, C13, C14 and C17 were low in CRC tumour tissues. Meanwhile, the results of survival analysis showed that high abundances of C4, C11 and C13 and low abundances of C5 and C14 were associated with better survival. The WGCNA results showed that the red module was most related to the tumour and the C14 cluster, which contains 615 genes. Lasso Cox regression analysis revealed 8 genes (PBXIP1, MPMZ, SCARA3, INA, ILK, MPP2, L1CAM and FLNA), which were chosen to construct a risk model. In the model, the risk score features had the greatest impact on survival prediction, indicating that the 8-gene risk model can better predict prognosis. qPCR and immunohistochemistry analysis showed that the expression levels of MPZ, SCARA3, MPP2 and PBXIP1 were high in CRC tissues. The functional experiment results indicated that MPZ, SCARA3, MPP2 and PBXIP1 could promote the colony formation ability of CRC cells in vitro and tumorigenicity in vivo. Conclusions We constructed a risk model to predict the prognosis of CRC patients based on scRNA-seq and bulk RNA-seq data, which could be used for clinical application. We also identified 4 previously unreported model genes (MPZ, SCARA3, MPP2 and PBXIP1) as novel oncogenes in CRC. These results suggest that this model could potentially be used to evaluate the prognostic risk and provide potential therapeutic targets for CRC patients. Supplementary Information The online version contains supplementary material available at 10.1186/s12575-022-00175-x.
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Affiliation(s)
- Ziyang Di
- Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Sicheng Zhou
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Gaoran Xu
- Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Lian Ren
- Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Chengxin Li
- Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Zheyu Ding
- Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Kaixin Huang
- Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Leilei Liang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Yihang Yuan
- Department of General Surgery, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, 1111 XianXia Road, Shanghai, 200336, China.
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125
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Genetic and microenvironmental intra-tumor heterogeneity impacts colorectal cancer evolution and metastatic development. Commun Biol 2022; 5:937. [PMID: 36085309 PMCID: PMC9463147 DOI: 10.1038/s42003-022-03884-x] [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: 03/08/2022] [Accepted: 08/23/2022] [Indexed: 11/08/2022] Open
Abstract
AbstractColorectal cancer (CRC) is a highly diverse disease, where different genomic instability pathways shape genetic clonal diversity and tumor microenvironment. Although intra-tumor heterogeneity has been characterized in primary tumors, its origin and consequences in CRC outcome is not fully understood. Therefore, we assessed intra- and inter-tumor heterogeneity of a prospective cohort of 136 CRC samples. We demonstrate that CRC diversity is forged by asynchronous forms of molecular alterations, where mutational and chromosomal instability collectively boost CRC genetic and microenvironment intra-tumor heterogeneity. We were able to depict predictor signatures of cancer-related genes that can foresee heterogeneity levels across the different tumor consensus molecular subtypes (CMS) and primary tumor location. Finally, we show that high genetic and microenvironment heterogeneity are associated with lower metastatic potential, whereas late-emerging copy number variations favor metastasis development and polyclonal seeding. This study provides an exhaustive portrait of the interplay between genetic and microenvironment intra-tumor heterogeneity across CMS subtypes, depicting molecular events with predictive value of CRC progression and metastasis development.
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126
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Li Z, Wang Y, Ganan-Gomez I, Colla S, Do KA. A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq data. Bioinformatics 2022; 38:4885-4892. [PMID: 36083008 PMCID: PMC9801963 DOI: 10.1093/bioinformatics/btac617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 09/06/2022] [Accepted: 09/08/2022] [Indexed: 01/07/2023] Open
Abstract
MOTIVATION Single-cell RNA sequencing (scRNA-seq) has been widely used to decompose complex tissues into functionally distinct cell types. The first and usually the most important step of scRNA-seq data analysis is to accurately annotate the cell labels. In recent years, many supervised annotation methods have been developed and shown to be more convenient and accurate than unsupervised cell clustering. One challenge faced by all the supervised annotation methods is the identification of the novel cell type, which is defined as the cell type that is not present in the training data, only exists in the testing data. Existing methods usually label the cells simply based on the correlation coefficients or confidence scores, which sometimes results in an excessive number of unlabeled cells. RESULTS We developed a straightforward yet effective method combining autoencoder with iterative feature selection to automatically identify novel cells from scRNA-seq data. Our method trains an autoencoder with the labeled training data and applies the autoencoder to the testing data to obtain reconstruction errors. By iteratively selecting features that demonstrate a bi-modal pattern and reclustering the cells using the selected feature, our method can accurately identify novel cells that are not present in the training data. We further combined this approach with a support vector machine to provide a complete solution for annotating the full range of cell types. Extensive numerical experiments using five real scRNA-seq datasets demonstrated favorable performance of the proposed method over existing methods serving similar purposes. AVAILABILITY AND IMPLEMENTATION Our R software package CAMLU is publicly available through the Zenodo repository (https://doi.org/10.5281/zenodo.7054422) or GitHub repository (https://github.com/ziyili20/CAMLU). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ziyi Li
- To whom correspondence should be addressed. or
| | - Yizhuo Wang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Irene Ganan-Gomez
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Simona Colla
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Kim-Anh Do
- To whom correspondence should be addressed. or
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127
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Ke M, Elshenawy B, Sheldon H, Arora A, Buffa FM. Single cell RNA-sequencing: A powerful yet still challenging technology to study cellular heterogeneity. Bioessays 2022; 44:e2200084. [PMID: 36068142 DOI: 10.1002/bies.202200084] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 08/18/2022] [Accepted: 08/19/2022] [Indexed: 11/11/2022]
Abstract
Almost all biomedical research to date has relied upon mean measurements from cell populations, however it is well established that what it is observed at this macroscopic level can be the result of many interactions of several different single cells. Thus, the observable macroscopic 'average' cannot outright be used as representative of the 'average cell'. Rather, it is the resulting emerging behaviour of the actions and interactions of many different cells. Single-cell RNA sequencing (scRNA-Seq) enables the comparison of the transcriptomes of individual cells. This provides high-resolution maps of the dynamic cellular programmes allowing us to answer fundamental biological questions on their function and evolution. It also allows to address medical questions such as the role of rare cell populations contributing to disease progression and therapeutic resistance. Furthermore, it provides an understanding of context-specific dependencies, namely the behaviour and function that a cell has in a specific context, which can be crucial to understand some complex diseases, such as diabetes, cardiovascular disease and cancer. Here, we provide an overview of scRNA-Seq, including a comparative review of emerging technologies and computational pipelines. We discuss the current and emerging applications and focus on tumour heterogeneity a clear example of how scRNA-Seq can provide new understanding of a complex disease. Additionally, we review the limitations and highlight the need of powerful computational pipelines and reproducible protocols for the broader acceptance of this technique in basic and clinical research.
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Affiliation(s)
- May Ke
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Badran Elshenawy
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Helen Sheldon
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Anjali Arora
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Francesca M Buffa
- Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK.,Department of Computing Sciences, Bocconi University, Milano, Italy.,Institute for Data Science and Analytics, Bocconi University, Milano, Italy
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128
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Chen G, Xie L, Zhao F, Kreil DP. Editorial: The application of sequencing technologies and bioinformatics methods in cancer biology. Front Cell Dev Biol 2022; 10:1002813. [PMID: 36147744 PMCID: PMC9486313 DOI: 10.3389/fcell.2022.1002813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 08/11/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Geng Chen
- Stemirna Therapeutics Co., Ltd., Shanghai, China
- *Correspondence: Geng Chen,
| | - Lu Xie
- Institute for Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Fangqing Zhao
- Computational Genomics Laboratory, Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, China
| | - David P. Kreil
- Department of Biotechnology, Boku University Vienna, Vienna, Austria
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129
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Gupta A, Martin-Rufino JD, Jones TR, Subramanian V, Qiu X, Grody EI, Bloemendal A, Weng C, Niu SY, Min KH, Mehta A, Zhang K, Siraj L, Al' Khafaji A, Sankaran VG, Raychaudhuri S, Cleary B, Grossman S, Lander ES. Inferring gene regulation from stochastic transcriptional variation across single cells at steady state. Proc Natl Acad Sci U S A 2022; 119:e2207392119. [PMID: 35969771 PMCID: PMC9407670 DOI: 10.1073/pnas.2207392119] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 07/20/2022] [Indexed: 12/24/2022] Open
Abstract
Regulatory relationships between transcription factors (TFs) and their target genes lie at the heart of cellular identity and function; however, uncovering these relationships is often labor-intensive and requires perturbations. Here, we propose a principled framework to systematically infer gene regulation for all TFs simultaneously in cells at steady state by leveraging the intrinsic variation in the transcriptional abundance across single cells. Through modeling and simulations, we characterize how transcriptional bursts of a TF gene are propagated to its target genes, including the expected ranges of time delay and magnitude of maximum covariation. We distinguish these temporal trends from the time-invariant covariation arising from cell states, and we delineate the experimental and technical requirements for leveraging these small but meaningful cofluctuations in the presence of measurement noise. While current technology does not yet allow adequate power for definitively detecting regulatory relationships for all TFs simultaneously in cells at steady state, we investigate a small-scale dataset to inform future experimental design. This study supports the potential value of mapping regulatory connections through stochastic variation, and it motivates further technological development to achieve its full potential.
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Affiliation(s)
- Anika Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115
| | - Jorge D. Martin-Rufino
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA 02115
- Dana-Farber Cancer Institute, Boston, MA 02215
| | | | | | - Xiaojie Qiu
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142
- HHMI, Massachusetts Institute of Technology, Cambridge, MA 02139
| | | | | | - Chen Weng
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA 02115
- Dana-Farber Cancer Institute, Boston, MA 02215
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142
| | | | - Kyung Hoi Min
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Arnav Mehta
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Dana-Farber Cancer Institute, Boston, MA 02215
- Department of Medicine, Massachusetts General Hospital, Boston, MA 02114
| | - Kaite Zhang
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | - Layla Siraj
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | | | - Vijay G. Sankaran
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA 02115
- Dana-Farber Cancer Institute, Boston, MA 02215
| | - Soumya Raychaudhuri
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA 02115
| | - Brian Cleary
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | | | - Eric S. Lander
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115
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130
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Glycosphingolipids are mediators of cancer plasticity through independent signaling pathways. Cell Rep 2022; 40:111181. [PMID: 35977490 DOI: 10.1016/j.celrep.2022.111181] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 06/01/2022] [Accepted: 07/19/2022] [Indexed: 11/23/2022] Open
Abstract
The molecular repertoire promoting cancer cell plasticity is not fully elucidated. Here, we propose that glycosphingolipids (GSLs), specifically the globo and ganglio series, correlate and promote the transition between epithelial and mesenchymal cells. The epithelial character of ovarian cancer remains stable throughout disease progression, and spatial glycosphingolipidomics reveals elevated globosides in the tumor compartment compared with the ganglioside-rich stroma. CRISPR-Cas9 knockin mediated truncation of endogenous E-cadherin induces epithelial-to-mesenchymal transition (EMT) and decreases globosides. The transcriptomics analysis identifies the ganglioside-synthesizing enzyme ST8SIA1 to be consistently elevated in mesenchymal-like samples, predicting poor outcome. Subsequent deletion of ST8SIA1 induces epithelial cell features through mTORS2448 phosphorylation, whereas loss of globosides in ΔA4GALT cells, resulting in EMT, is accompanied by increased ERKY202/T204 and AKTS124. The GSL composition dynamics corroborate cancer cell plasticity, and further evidence suggests that mesenchymal cells are maintained through ganglioside-dependent, calcium-mediated mechanisms.
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131
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Pivotal models and biomarkers related to the prognosis of breast cancer based on the immune cell interaction network. Sci Rep 2022; 12:13673. [PMID: 35953532 PMCID: PMC9372165 DOI: 10.1038/s41598-022-17857-x] [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: 09/30/2021] [Accepted: 08/02/2022] [Indexed: 12/03/2022] Open
Abstract
The effect of breast cancer heterogeneity on prognosis of patients is still unclear, especially the role of immune cells in prognosis of breast cancer. In this study, single cell transcriptome sequencing data of breast cancer were used to analyze the relationship between breast cancer heterogeneity and prognosis. In this study, 14 cell clusters were identified in two single-cell datasets (GSE75688 and G118389). Proportion analysis of immune cells showed that NK cells were significantly aggregated in triple negative breast cancer, and the proportion of macrophages was significantly increased in primary breast cancer, while B cells, T cells, and neutrophils may be involved in the metastasis of breast cancer. The results of ligand receptor interaction network revealed that macrophages and DC cells were the most frequently interacting cells with other cells in breast cancer. The results of WGCNA analysis suggested that the MEblue module is most relevant to the overall survival time of triple negative breast cancer. Twenty-four prognostic genes in the blue module were identified by univariate Cox regression analysis and KM survival analysis. Multivariate regression analysis combined with risk analysis was used to analyze 24 prognostic genes to construct a prognostic model. The verification result of our prognostic model showed that there were significant differences in the expression of PCDH12, SLIT3, ACVRL1, and DLL4 genes between the high-risk group and the low-risk group, which can be used as prognostic biomarkers.
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132
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Chen Y, He J, Chen R, Wang Z, Dai Z, Liang X, Wu W, Luo P, Zhang J, Peng Y, Zhang N, Liu Z, Zhang L, Zhang H, Cheng Q. Pan-Cancer Analysis of the Immunological Role of PDIA5: A Potential Target for Immunotherapy. Front Immunol 2022; 13:881722. [PMID: 36003400 PMCID: PMC9393377 DOI: 10.3389/fimmu.2022.881722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 05/23/2022] [Indexed: 01/27/2023] Open
Abstract
The aberrant protein disulfide isomerase A5 (PDIA5) expression was relevant to the poor prognosis of patients with human cancers. However, its relationship with the epigenetic and genetic alterations and its effect on tumor immunity is still lacking. In the present study, we comprehensively analyzed the immune infiltration role of PDIA5 in human cancers based on large-scale bioinformatics analyses and in vitro experiments. Obvious DNA methylation and moderate alteration frequency of PDIA5 were observed in human cancers. The expression level of PDIA5 was significantly correlated with infiltrated immune cells, immune pathways, and other immune signatures. We found that cancer cells and macrophages exhibited high PDIA5 expression in human cancers using the single-cell RNA sequencing analysis. We also demonstrated the interaction between PDIA5 and immune cells in glioblastoma multiforme (GBM). Multiplex immunofluorescence staining showed the upregulated expression level of PDIA5 and the increased number of M2 macrophage markers-CD163 positive cells in pan-cancer samples. Notably, PDIA5 silencing resulted in upregulated expression of PD-L1 and SPP1 in U251 cells. Silencing of PDIA5 in hepG2 cells, U251 cells, and PC3 cells contributed to a decline in their ability of proliferation, clone formation, and invasion and inhibited the migration of cocultured M2 macrophages. Additionally, PDIA5 also displayed predictive value in the immunotherapy response of both murine and human cancer cohorts. Overall, our findings indicated that PDIA5 might be a potential target for immunotherapies in cancers.
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Affiliation(s)
- Yu Chen
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jialin He
- Department of Neurology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Rui Chen
- Department of Neurosurgery, Affiliated Nanhua Hospital, University of South China, Changsha, China
| | - Zeyu Wang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Ziyu Dai
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Xisong Liang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Wantao Wu
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Department of Oncology, Xiangya Hospital, Central South University, Guangzhou, China
| | - Peng Luo
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Changsha, China
| | - Jian Zhang
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Changsha, China
| | - Yun Peng
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China
- Teaching and Research Section of Clinical Nursing, Xiangya Hospital of Central South University, Changsha, China
| | - Nan Zhang
- One-third Lab, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zaoqu Liu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou, Changsha, China
| | - Liyang Zhang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Quan Cheng, ; Hao Zhang, ; Liyang Zhang,
| | - Hao Zhang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
- *Correspondence: Quan Cheng, ; Hao Zhang, ; Liyang Zhang,
| | - Quan Cheng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Quan Cheng, ; Hao Zhang, ; Liyang Zhang,
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133
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Roden D, Swarbrick A. Mapping the cancer cell states conserved across solid tumors. Nat Genet 2022; 54:1066-1067. [PMID: 35931862 DOI: 10.1038/s41588-022-01151-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Daniel Roden
- Cancer Ecosystems Program, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia.,School of Clinical Medicine, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia
| | - Alexander Swarbrick
- Cancer Ecosystems Program, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia. .,School of Clinical Medicine, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia.
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134
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Barkley D, Moncada R, Pour M, Liberman DA, Dryg I, Werba G, Wang W, Baron M, Rao A, Xia B, França GS, Weil A, Delair DF, Hajdu C, Lund AW, Osman I, Yanai I. Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment. Nat Genet 2022; 54:1192-1201. [PMID: 35931863 PMCID: PMC9886402 DOI: 10.1038/s41588-022-01141-9] [Citation(s) in RCA: 141] [Impact Index Per Article: 70.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 06/22/2022] [Indexed: 02/01/2023]
Abstract
Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including 'stress', 'interferon response', 'epithelial-mesenchymal transition', 'metal response', 'basal' and 'ciliated'. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.
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Affiliation(s)
- Dalia Barkley
- Institute for Computational Medicine, New York, NY, USA
| | | | - Maayan Pour
- Institute for Computational Medicine, New York, NY, USA
| | | | - Ian Dryg
- Department of Dermatology, NYU School of Medicine, New York, NY, USA
| | - Gregor Werba
- Department of Surgery, NYU School of Medicine, New York, NY, USA,Department of Pathology, NYU School of Medicine, New York, NY, USA
| | - Wei Wang
- Department of Surgery, NYU School of Medicine, New York, NY, USA,Department of Pathology, NYU School of Medicine, New York, NY, USA
| | - Maayan Baron
- Institute for Computational Medicine, New York, NY, USA
| | - Anjali Rao
- Institute for Computational Medicine, New York, NY, USA
| | - Bo Xia
- Institute for Computational Medicine, New York, NY, USA
| | | | - Alejandro Weil
- Department of Pathology, NYU School of Medicine, New York, NY, USA
| | | | - Cristina Hajdu
- Department of Pathology, NYU School of Medicine, New York, NY, USA
| | - Amanda W. Lund
- Department of Dermatology, NYU School of Medicine, New York, NY, USA,Department of Surgery, NYU School of Medicine, New York, NY, USA,Perlmutter Cancer Center NYU School of Medicine, New York, NY, USA
| | - Iman Osman
- Department of Dermatology, NYU School of Medicine, New York, NY, USA,Department of Pathology, NYU School of Medicine, New York, NY, USA,Perlmutter Cancer Center NYU School of Medicine, New York, NY, USA
| | - Itai Yanai
- Institute for Computational Medicine, New York, NY, USA,Perlmutter Cancer Center NYU School of Medicine, New York, NY, USA,Corresponding author:
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135
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Pan L, Ku WL, Tang Q, Cao Y, Zhao K. scPCOR-seq enables co-profiling of chromatin occupancy and RNAs in single cells. Commun Biol 2022; 5:678. [PMID: 35804086 PMCID: PMC9270334 DOI: 10.1038/s42003-022-03584-6] [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: 01/25/2022] [Accepted: 06/14/2022] [Indexed: 11/30/2022] Open
Abstract
Cell-to-cell variation in gene expression is a widespread phenomenon, which may play important roles in cellular differentiation, function, and disease development1–9. Chromatin is implicated in contributing to the cellular heterogeneity in gene expression10–16. Fully understanding the mechanisms of cellular heterogeneity requires simultaneous measurement of RNA and occupancy of histone modifications and transcription factors on chromatin due to their critical roles in transcriptional regulation17,18. We generally term the occupancy of histone modifications and transcription factors as Chromatin occupancy. Here, we report a technique, termed scPCOR-seq (single-cell Profiling of Chromatin Occupancy and RNAs Sequencing), for simultaneously profiling genome-wide chromatin protein binding or histone modification marks and RNA expression in the same cell. We demonstrated that scPCOR-seq can profile either H3K4me3 or RNAPII and RNAs in a mixture of human H1, GM12878 and 293 T cells at a single-cell resolution and either H3K4me3, RNAPII, or RNA profile can correctly separate the cells. Application of scPCOR-seq to the in vitro differentiation of the erythrocyte precursor CD36 cells from human CD34 stem or progenitor cells revealed that H3K4me3 and RNA exhibit distinct properties in clustering cells during differentiation. Overall, our work provides a promising approach to understand the relationships among different omics layers. scPCOR-seq is a single-cell sequencing technique that enables simultaneous profiling of genome-wide chromatin protein binding or histone modification marks and RNA expression in the same cell.
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Affiliation(s)
- Lixia Pan
- Laboratory of Epigenome Biology, Systems Biology Center, Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Wai Lim Ku
- Laboratory of Epigenome Biology, Systems Biology Center, Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Qingsong Tang
- Laboratory of Epigenome Biology, Systems Biology Center, Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yaqiang Cao
- Laboratory of Epigenome Biology, Systems Biology Center, Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Keji Zhao
- Laboratory of Epigenome Biology, Systems Biology Center, Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
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136
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Mullins R, Pal A, Barrett TF, Neal MEH, Puram SV. Epithelial-Mesenchymal Plasticity in Tumor Immune Evasion. Cancer Res 2022; 82:2329-2343. [PMID: 35363853 PMCID: PMC9256788 DOI: 10.1158/0008-5472.can-21-4370] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 03/05/2022] [Accepted: 03/29/2022] [Indexed: 01/07/2023]
Abstract
Epithelial-mesenchymal transition (EMT) is a fundamental process that occurs during embryogenesis and tissue repair. However, EMT can be hijacked by malignant cells, where it may promote immune evasion and metastasis. Classically considered a dichotomous transition, EMT in cancer has recently been considered a plastic process whereby malignant cells display and interconvert among hybrid epithelial/mesenchymal (E/M) states. Epithelial-mesenchymal plasticity (EMP) and associated hybrid E/M states are divergent from classical EMT, with unique immunomodulatory effects. Here, we review recent insights into the EMP-immune cross-talk, highlighting possible mechanisms of immune evasion conferred by hybrid E/M states and roles of immune cells in EMP.
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Affiliation(s)
- Riley Mullins
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, U.S.A.,Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis, Missouri, U.S.A
| | - Ananya Pal
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, U.S.A.,Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis, Missouri, U.S.A
| | - Thomas F Barrett
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, U.S.A.,Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis, Missouri, U.S.A
| | - Molly E Heft Neal
- Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis, Missouri, U.S.A
| | - Sidharth V Puram
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, U.S.A.,Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis, Missouri, U.S.A.,Corresponding author: Sidharth V. Puram, MD PhD, Washington University School of Medicine, 660 S. Euclid Ave., Campus Box 8115, St. Louis, MO 63110, (314) 362-7509,
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137
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Trastulla L, Noorbakhsh J, Vazquez F, McFarland J, Iorio F. Computational estimation of quality and clinical relevance of cancer cell lines. Mol Syst Biol 2022; 18:e11017. [PMID: 35822563 PMCID: PMC9277610 DOI: 10.15252/msb.202211017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 06/10/2022] [Accepted: 06/13/2022] [Indexed: 12/12/2022] Open
Abstract
Immortal cancer cell lines (CCLs) are the most widely used system for investigating cancer biology and for the preclinical development of oncology therapies. Pharmacogenomic and genome-wide editing screenings have facilitated the discovery of clinically relevant gene-drug interactions and novel therapeutic targets via large panels of extensively characterised CCLs. However, tailoring pharmacological strategies in a precision medicine context requires bridging the existing gaps between tumours and in vitro models. Indeed, intrinsic limitations of CCLs such as misidentification, the absence of tumour microenvironment and genetic drift have highlighted the need to identify the most faithful CCLs for each primary tumour while addressing their heterogeneity, with the development of new models where necessary. Here, we discuss the most significant limitations of CCLs in representing patient features, and we review computational methods aiming at systematically evaluating the suitability of CCLs as tumour proxies and identifying the best patient representative in vitro models. Additionally, we provide an overview of the applications of these methods to more complex models and discuss future machine-learning-based directions that could resolve some of the arising discrepancies.
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Affiliation(s)
| | - Javad Noorbakhsh
- Broad Institute of MIT and HarvardCambridgeMAUSA
- Present address:
Kojin TherapeuticsBostonMAUSA
| | - Francisca Vazquez
- Broad Institute of MIT and HarvardCambridgeMAUSA
- Department of Medical OncologyDana‐Farber Cancer InstituteBostonMAUSA
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138
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Zhang L, Qiu Z, Zheng H, Yang X, Ye J, He J, Li Y, Chen L. Single Cell RNA Sequencing Reveals the Pathogenesis of Aortic Dissection Caused by Hypertension and Marfan Syndrome. Front Cell Dev Biol 2022; 10:880320. [PMID: 35800890 PMCID: PMC9253298 DOI: 10.3389/fcell.2022.880320] [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: 02/21/2022] [Accepted: 04/14/2022] [Indexed: 11/13/2022] Open
Abstract
Aortic dissection (AD) is mainly caused by hypertension and Marfan syndrome. However, it is unclear whether the cellular components and functions are different between the two causes. A total of 11 aortic samples were collected for single-cell RNA analysis and 20 clusters were disclosed, including VSMCs, fibroblasts, endothelial cells, T cells, B cells, monocytes, macrophages, mast cells, and neutrophils components. There were differences in cell subclusters and function between hypertension and Marfan patients. The cells also had different differentiations. Cellchat identified cell ligand–receptor interactions that were associated with hypertension and Marfan-induced AD involving SMC, fibroblast, mo-macrophages, and T-cell subsets. This study revealed the heterogeneity of cellular components and gene changes in hypertension and Marfan-induced AD. Through functional analysis and the changes in intercellular communication, the possible mechanisms of different causes of AD were explained from a new perspective, so we can better understand the occurrence and development of diseases.
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Affiliation(s)
- Li Zhang
- Department of Cardiac Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- The Key Laboratory of Fujian Province Universities on Ion Channel and Signal Transduction in Cardiovascular Diseases, The School of Basic Medical Sciences, Fuzhou, China
| | - Zhihuang Qiu
- Department of Cardiac Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Hui Zheng
- Department of Cardiac Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Xi Yang
- Department of Cardiac Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Jianqiang Ye
- Fujian Center for Safety Evaluation of New Drug, Fujian Medical University, Fuzhou, China
| | - Jian He
- Department of Cardiac Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Yumei Li
- Department of Cardiac Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Fujian Center for Safety Evaluation of New Drug, Fujian Medical University, Fuzhou, China
- *Correspondence: Yumei Li, ; Liangwan Chen,
| | - Liangwan Chen
- Department of Cardiac Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- *Correspondence: Yumei Li, ; Liangwan Chen,
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139
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From single-omics to interactomics: How can ligand-induced perturbations modulate single-cell phenotypes? ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2022; 131:45-83. [PMID: 35871896 DOI: 10.1016/bs.apcsb.2022.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Cells suffer from perturbations by different stimuli, which, consequently, rise to individual alterations in their profile and function that may end up affecting the tissue as a whole. This is no different if we consider the effect of a therapeutic agent on a biological system. As cells are exposed to external ligands their profile can change at different single-omics levels. Detecting how these changes take place through different sequencing technologies is key to a better understanding of the effects of therapeutic agents. Single-cell RNA-sequencing stands out as one of the most common approaches for cell profiling and perturbation analysis. As a result, single-cell transcriptomics data can be integrated with other omics data sources, such as proteomics and epigenomics data, to clarify the perturbation effects and mechanism at the cell level. Appropriate computational tools are key to process and integrate the available information. This chapter focuses on the recent advances on ligand-induced perturbation and single-cell omics computational tools and algorithms, their current limitations, and how the deluge of data can be used to improve the current process of drug research and development.
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140
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Abstract
Microfluidics has enabled a new era of cellular and molecular assays due to the small length scales, parallelization, and the modularity of various analysis and actuation functions. Droplet microfluidics, in particular, has been instrumental in providing new tools for biology with its ability to quickly and reproducibly generate drops that act as individual reactors. A notable beneficiary of this technology has been single-cell RNA sequencing, which has revealed new heterogeneities and interactions for the fundamental unit of life. However, viruses far surpass the diversity of cellular life, affect the dynamics of all ecosystems, and are a chronic source of global health crises. Despite their impact on the world, high-throughput and high-resolution viral profiling has been difficult, with conventional methods being limited to population-level averaging, large sample volumes, and few cultivable hosts. Consequently, most viruses have not been identified and studied. Droplet microfluidics holds the potential to address many of these limitations and offers new levels of sensitivity and throughput for virology. This Feature highlights recent efforts that have applied droplet microfluidics to the detection and study of viruses, including for diagnostics, virus-host interactions, and cell-independent virus assays. In combination with traditional virology methods, droplet microfluidics should prove a potent tool toward achieving a better understanding of the most abundant biological species on Earth.
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Affiliation(s)
- Wenyang Jing
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Hee-Sun Han
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, 1206 West Gregory Drive, Urbana, Illinois 61801, United States
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141
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de Assis LVM, Lacerda JT, Moraes MN, Domínguez-Amorocho OA, Kinker GS, Mendes D, Silva MM, Menck CFM, Câmara NOS, Castrucci AMDL. Melanopsin (Opn4) is an oncogene in cutaneous melanoma. Commun Biol 2022; 5:461. [PMID: 35562405 PMCID: PMC9106662 DOI: 10.1038/s42003-022-03425-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 04/27/2022] [Indexed: 02/08/2023] Open
Abstract
The search for new therapeutical targets for cutaneous melanoma and other cancers is an ongoing task. We expanded this knowledge by evaluating whether opsins, light- and thermo-sensing proteins, could display tumor-modulatory effects on melanoma cancer. Using different experimental approaches, we show that melanoma cell proliferation is slower in the absence of Opn4, compared to Opn4WT due to an impaired cell cycle progression and reduced melanocyte inducing transcription factor (Mitf) expression. In vivo tumor progression of Opn4KO cells is remarkably reduced due to slower proliferation, and higher immune system response in Opn4KO tumors. Using pharmacological assays, we demonstrate that guanylyl cyclase activity is impaired in Opn4KO cells. Evaluation of Tumor Cancer Genome Atlas (TCGA) database confirms our experimental data as reduced MITF and OPN4 expression in human melanoma correlates with slower cell cycle progression and presence of immune cells in the tumor microenvironment (TME). Proteomic analyses of tumor bulk show that the reduced growth of Opn4KO tumors is associated with reduced Mitf signaling, higher translation of G2/M proteins, and impaired guanylyl cyclase activity. Conversely, in Opn4WT tumors increased small GTPase and an immune-suppressive TME are found. Such evidence points to OPN4 as an oncogene in melanoma, which could be pharmacologically targeted.
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Affiliation(s)
- Leonardo Vinícius Monteiro de Assis
- Laboratory of Comparative Physiology of Pigmentation, Department of Physiology, Institute of Biosciences, University of São Paulo, São Paulo, Brazil.
- Institute of Neurobiology, Center for Brain, Behavior, and Metabolism, University of Lübeck, Lübeck, Germany.
| | - José Thalles Lacerda
- Laboratory of Comparative Physiology of Pigmentation, Department of Physiology, Institute of Biosciences, University of São Paulo, São Paulo, Brazil
| | - Maria Nathália Moraes
- Laboratory of Neurobiology, Department of Physiology and Biophysics, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | | | - Gabriela Sarti Kinker
- Laboratory of Translational Immuno-Oncology A. C. Camargo Cancer Center - International Research Center, São Paulo, Brazil
| | - Davi Mendes
- DNA Repair Lab, Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo (USP), São Paulo, Brazil
| | - Matheus Molina Silva
- DNA Repair Lab, Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo (USP), São Paulo, Brazil
| | - Carlos Frederico Martins Menck
- DNA Repair Lab, Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo (USP), São Paulo, Brazil
| | - Niels Olsen Saraiva Câmara
- Laboratory of Transplantation Immunobiology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Ana Maria de Lauro Castrucci
- Laboratory of Comparative Physiology of Pigmentation, Department of Physiology, Institute of Biosciences, University of São Paulo, São Paulo, Brazil
- Department of Biology, University of Virginia, Charlottesville, VA, USA
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142
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Lai W, Li D, Kuang J, Deng L, Lu Q. Integrated analysis of single-cell RNA-seq dataset and bulk RNA-seq dataset constructs a prognostic model for predicting survival in human glioblastoma. Brain Behav 2022; 12:e2575. [PMID: 35429411 PMCID: PMC9120724 DOI: 10.1002/brb3.2575] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 02/20/2022] [Accepted: 03/20/2022] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. For patients with GBM, the median overall survival (OS) is 14.6 months and the 5-year survival rate is 7.2%. It is imperative to develop a reliable model to predict the survival probability in new GBM patients. To date, most prognostic models for predicting survival in GBM were constructed based on bulk RNA-seq dataset, which failed to accurately reflect the difference between tumor cores and peripheral regions, and thus show low predictive capability. An effective prognostic model is desperately needed in clinical practice. METHODS We studied single-cell RNA-seq dataset and The Cancer Genome Atlas-glioblastoma multiforme (TCGA-GBM) dataset to identify differentially expressed genes (DEGs) that impact the OS of GBM patients. We then applied the least absolute shrinkage and selection operator (LASSO) Cox penalized regression analysis to determine the optimal genes to be included in our risk score prognostic model. Then, we used another dataset to test the accuracy of our risk score prognostic model. RESULTS We identified 2128 DEGs from the single-cell RNA-seq dataset and 6461 DEGs from the bulk RNA-seq dataset. In addition, 896 DEGs associated with the OS of GBM patients were obtained. Five of these genes (LITAF, MTHFD2, NRXN3, OSMR, and RUFY2) were selected to generate a risk score prognostic model. Using training and validation datasets, we found that patients in the low-risk group showed better OS than those in the high-risk group. We validated our risk score model with the training and validating datasets and demonstrated that it can effectively predict the OS of GBM patients. CONCLUSION We constructed a novel prognostic model to predict survival in GBM patients by integrating a scRNA-seq dataset and a bulk RNA-seq dataset. Our findings may advance the development of new therapeutic targets and improve clinical outcomes for GBM patients.
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Affiliation(s)
- Wenwen Lai
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China.,Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, China
| | - Defu Li
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China.,Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, China
| | - Jie Kuang
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Libin Deng
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China.,Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, China
| | - Quqin Lu
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China.,Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, China
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143
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Hodis E, Triglia ET, Kwon JYH, Biancalani T, Zakka LR, Parkar S, Hütter JC, Buffoni L, Delorey TM, Phillips D, Dionne D, Nguyen LT, Schapiro D, Maliga Z, Jacobson CA, Hendel A, Rozenblatt-Rosen O, Mihm MC, Garraway LA, Regev A. Stepwise-edited, human melanoma models reveal mutations' effect on tumor and microenvironment. Science 2022; 376:eabi8175. [PMID: 35482859 PMCID: PMC9427199 DOI: 10.1126/science.abi8175] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Establishing causal relationships between genetic alterations of human cancers and specific phenotypes of malignancy remains a challenge. We sequentially introduced mutations into healthy human melanocytes in up to five genes spanning six commonly disrupted melanoma pathways, forming nine genetically distinct cellular models of melanoma. We connected mutant melanocyte genotypes to malignant cell expression programs in vitro and in vivo, replicative immortality, malignancy, rapid tumor growth, pigmentation, metastasis, and histopathology. Mutations in malignant cells also affected tumor microenvironment composition and cell states. Our melanoma models shared genotype-associated expression programs with patient melanomas, and a deep learning model showed that these models partially recapitulated genotype-associated histopathological features as well. Thus, a progressive series of genome-edited human cancer models can causally connect genotypes carrying multiple mutations to phenotype.
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Affiliation(s)
- Eran Hodis
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
- Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA 02115, USA
| | | | - John Y. H. Kwon
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | | | - Labib R. Zakka
- Department of Dermatology, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Saurabh Parkar
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Lorenzo Buffoni
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Toni M. Delorey
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Devan Phillips
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Danielle Dionne
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Lan T. Nguyen
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Denis Schapiro
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Zoltan Maliga
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Connor A. Jacobson
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Ayal Hendel
- The Mina and Everard Goodman Faculty of Life Sciences and Advanced Materials and Nanotechnology Institute, Bar-Ilan University, Ramat-Gan 52900, Israel
| | | | - Martin C. Mihm
- Department of Dermatology, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Levi A. Garraway
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
- Koch Institute for Integrative Cancer Research, Department of Biology, MIT, Cambridge, MA 02139, USA
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144
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Kumar-Sinha C, Chinnaiyan AM. Defining cancer growth beyond the mitotic index. Nat Cell Biol 2022; 24:285-287. [PMID: 35292782 DOI: 10.1038/s41556-022-00862-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Chandan Kumar-Sinha
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA.,Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Arul M Chinnaiyan
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA. .,Department of Pathology, University of Michigan, Ann Arbor, MI, USA. .,Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA. .,Howard Hughes Medical Institute, Chevy Chase, MD, USA.
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145
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Computational Analyses of YY1 and Its Target RKIP Reveal Their Diagnostic and Prognostic Roles in Lung Cancer. Cancers (Basel) 2022; 14:cancers14040922. [PMID: 35205667 PMCID: PMC8869872 DOI: 10.3390/cancers14040922] [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: 11/11/2021] [Revised: 01/18/2022] [Accepted: 02/08/2022] [Indexed: 11/17/2022] Open
Abstract
Simple Summary Lung cancer (LC) is the tumor with the highest global mortality rate. Novel personalized therapies are currently being tested (e.g., targeted inhibitors, the immune-checkpoint inhibitors), but they cannot yet prevent the very frequent relapse and generalized metastases observed in a large population of LC patients. Currently, there is an urgent need for novel reliable biomarkers for the prognosis and diagnosis of LC. Through the systematic analysis of multiple deposited expression datasets, this report aims to explore the role of the Yin-Yang 1 (YY1) transcription factor and its target the Raf Kinase Inhibitory Protein (RKIP) in LC. The computational analysis suggested the predictive diagnostic and prognostic roles for both YY1 and RKIP stimulating further studies for proving their implication as novel biomarkers, as well as therapeutically druggable targets in LC. Abstract Lung cancer (LC) represents a global threat, being the tumor with the highest mortality rate. Despite the introduction of novel therapies (e.g., targeted inhibitors, immune-checkpoint inhibitors), relapses are still very frequent. Accordingly, there is an urgent need for reliable predictive biomarkers and therapeutically druggable targets. Yin-Yang 1 (YY1) is a transcription factor that may work either as an oncogene or a tumor suppressor, depending on the genotype and the phenotype of the tumor. The Raf Kinase Inhibitory Protein (RKIP), is a tumor suppressor and immune enhancer often found downregulated in the majority of the examined cancers. In the present report, the role of both YY1 and RKIP in LC is thoroughly explored through the analysis of several deposited RNA and protein expression datasets. The computational analyses revealed that YY1 negatively regulates RKIP expression in LC, as corroborated by the deposited YY1-ChIP-Seq experiments and validated by their robust negative correlation. Additionally, YY1 expression is significantly higher in LC samples compared to normal matching ones, whereas RKIP expression is lower in LC and high in normal matching tissues. These observed differences, unlike many current biomarkers, bear a diagnostic significance, as proven by the ROC analyses. Finally, the survival data support the notion that both YY1 and RKIP might represent strong prognostic biomarkers. Overall, the reported findings indicate that YY1 and RKIP expression levels may play a role in LC as potential biomarkers and therapeutic targets. However, further studies will be necessary to validate the in silico results.
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146
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Hu H, Srinivas KP, Wang S, Chao MV, Lionnet T, Mohr I, Wilson AC, Depledge DP, Huang TT. Single-cell transcriptomics identifies Gadd45b as a regulator of herpesvirus-reactivating neurons. EMBO Rep 2022; 23:e53543. [PMID: 34842321 PMCID: PMC8811635 DOI: 10.15252/embr.202153543] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 11/10/2021] [Accepted: 11/11/2021] [Indexed: 02/05/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) is a powerful technique for dissecting the complexity of normal and diseased tissues, enabling characterization of cell diversity and heterogeneous phenotypic states in unprecedented detail. However, this technology has been underutilized for exploring the interactions between the host cell and viral pathogens in latently infected cells. Herein, we use scRNA-seq and single-molecule sensitivity fluorescent in situ hybridization (smFISH) technologies to investigate host single-cell transcriptome changes upon the reactivation of a human neurotropic virus, herpes simplex virus-1 (HSV-1). We identify the stress sensor growth arrest and DNA damage-inducible 45 beta (Gadd45b) as a critical antiviral host factor that regulates HSV-1 reactivation events in a subpopulation of latently infected primary neurons. We show that distinct subcellular localization of Gadd45b correlates with the viral late gene expression program, as well as the expression of the viral transcription factor, ICP4. We propose that a hallmark of a "successful" or "aborted" HSV-1 reactivation state in primary neurons is determined by a unique subcellular localization signature of the stress sensor Gadd45b.
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Affiliation(s)
- Hui‐Lan Hu
- Department of Biochemistry & Molecular PharmacologyNew York University School of MedicineNew YorkNYUSA
| | | | - Shuoshuo Wang
- Department of Cell BiologyInstitute for Systems GeneticsNew York University School of MedicineNew YorkNYUSA
| | - Moses V Chao
- Departments of Cell Biology, Physiology & Neuroscience, and PsychiatrySkirball Institute of Biomolecular MedicineNew York University School of MedicineNew YorkNYUSA
| | - Timothee Lionnet
- Department of Cell BiologyInstitute for Systems GeneticsNew York University School of MedicineNew YorkNYUSA
| | - Ian Mohr
- Department of MicrobiologyNew York University School of MedicineNew YorkNYUSA
| | - Angus C Wilson
- Department of MicrobiologyNew York University School of MedicineNew YorkNYUSA
| | - Daniel P Depledge
- Department of MedicineNew York University School of MedicineNew YorkNYUSA
- Present address:
Institute of VirologyHannover Medical SchoolHannoverGermany
| | - Tony T Huang
- Department of Biochemistry & Molecular PharmacologyNew York University School of MedicineNew YorkNYUSA
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147
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Li Y, Li X, Chen H, Sun K, Li H, Zhou Y, Wang J, Bai F, Yang F. Single-cell RNA sequencing reveals the multi-cellular ecosystem in different radiological components of pulmonary part-solid nodules. Clin Transl Med 2022; 12:e723. [PMID: 35184398 PMCID: PMC8858630 DOI: 10.1002/ctm2.723] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 01/12/2022] [Accepted: 01/17/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Early-stage lung adenocarcinoma that radiologically manifests as part-solid nodules, consisting of both ground-glass and solid components, has distinctive growth patterns and prognosis. The characteristics of the tumour microenvironment and transcriptional features of the malignant cells of different radiological phenotypes remain poorly understood. METHODS Twelve treatment-naive patients with radiological part-solid nodules were enrolled. After frozen pathology was confirmed as lung adenocarcinoma, two regions (ground-glass and solid) from each of the 12 part-solid nodules and 5 normal lung tissues from 5 of the12 patients were subjected to single-cell sequencing by 10x Genomics. We used Seurat v3.1.5 for data integration and analysis. RESULTS We comprehensively dissected the multicellular ecosystem of the ground-glass and solid components of part-solid nodules at the single-cell resolution. In tumours, these components had comparable proportions of malignant cells. However, the angiogenesis, epithelial-to-mesenchymal transition, KRAS, p53, and cell-cycle signalling pathways were significantly up-regulated in malignant cells within solid components compared to those within ground-glass components. For the tumour microenvironment, the relative abundance of myeloid and NK cells tended to be higher in solid components than in ground-glass components. Slight subtype composition differences existed between the ground-glass and solid components. The T/NK cell subsets' cytotoxic function and the macrophages' pro-inflammation function were suppressed in solid components. Moreover, pericytes in solid components had a stronger communication related to angiogenesis promotion with endothelial cells and tumour cells. CONCLUSION The cellular landscape of ground-glass components is significantly different from that of normal tissue and similar to that of solid components. However, transcriptional differences exist in the vital signalling pathways of malignant and immune cells within these components.
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Affiliation(s)
- Yanmeng Li
- Biomedical Pioneering Innovation Center (BIOPIC)School of Life Sciences & Department of Thoracic SurgeryPeople's Hospital, Peking UniversityBeijingChina
| | - Xiao Li
- Biomedical Pioneering Innovation Center (BIOPIC)School of Life Sciences & Department of Thoracic SurgeryPeople's Hospital, Peking UniversityBeijingChina
| | - Haiming Chen
- Biomedical Pioneering Innovation Center (BIOPIC)School of Life Sciences & Department of Thoracic SurgeryPeople's Hospital, Peking UniversityBeijingChina
| | - Kunkun Sun
- Department of PathologyPeking University People's HospitalBeijingChina
| | - Hao Li
- Biomedical Pioneering Innovation Center (BIOPIC)School of Life Sciences & Department of Thoracic SurgeryPeople's Hospital, Peking UniversityBeijingChina
| | - Ying Zhou
- Department of PathologyPeking University People's HospitalBeijingChina
| | - Jun Wang
- Biomedical Pioneering Innovation Center (BIOPIC)School of Life Sciences & Department of Thoracic SurgeryPeople's Hospital, Peking UniversityBeijingChina
| | - Fan Bai
- Biomedical Pioneering Innovation Center (BIOPIC)School of Life Sciences & Department of Thoracic SurgeryPeople's Hospital, Peking UniversityBeijingChina
- Beijing Advanced Innovation Center for Genomics (ICG)Peking UniversityBeijingChina
| | - Fan Yang
- Biomedical Pioneering Innovation Center (BIOPIC)School of Life Sciences & Department of Thoracic SurgeryPeople's Hospital, Peking UniversityBeijingChina
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148
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Cai L, Xiao G, Gerber D, D Minna J, Xie Y. Lung Cancer Computational Biology and Resources. Cold Spring Harb Perspect Med 2022; 12:a038273. [PMID: 34751162 PMCID: PMC8805643 DOI: 10.1101/cshperspect.a038273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Comprehensive clinical, pathological, and molecular data, when appropriately integrated with advanced computational approaches, are transforming the way we characterize and study lung cancer. Clinically, cancer registry and publicly available historical clinical trial data enable retrospective analyses to examine how socioeconomic factors, patient demographics, and cancer characteristics affect treatment and outcome. Pathologically, digital pathology and artificial intelligence are revolutionizing histopathological image analyses, not only with improved efficiency and accuracy, but also by extracting additional information for prognostication and tumor microenvironment characterization. Genetically and molecularly, individual patient tumors and preclinical models of lung cancer are profiled by various high-throughput platforms to characterize the molecular properties and functional liabilities. The resulting multi-omics data sets and their interrogation facilitate both basic research mechanistic studies and translation of the findings into the clinic. In this review, we provide a list of resources and tools potentially valuable for lung cancer basic and translational research. Importantly, we point out pitfalls and caveats when performing computational analyses of these data sets and provide a vision of future computational biology developments that will aid lung cancer translational research.
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Affiliation(s)
- Ling Cai
- Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
- Children's Medical Center Research Institute, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
- Harrold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas 75390, USA
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
- Harrold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas 75390, USA
| | - David Gerber
- Harrold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Hamon Center for Therapeutic Oncology Research, UT Southwestern Medical Center, Dallas, Texas 75390, USA
| | - John D Minna
- Harrold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Hamon Center for Therapeutic Oncology Research, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
| | - Yang Xie
- Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
- Harrold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas 75390, USA
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149
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Zinovyev A, Sadovsky M, Calzone L, Fouché A, Groeneveld CS, Chervov A, Barillot E, Gorban AN. Modeling Progression of Single Cell Populations Through the Cell Cycle as a Sequence of Switches. Front Mol Biosci 2022; 8:793912. [PMID: 35178429 PMCID: PMC8846220 DOI: 10.3389/fmolb.2021.793912] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/15/2021] [Indexed: 11/13/2022] Open
Abstract
Cell cycle is a biological process underlying the existence and propagation of life in time and space. It has been an object for mathematical modeling for long, with several alternative mechanistic modeling principles suggested, describing in more or less details the known molecular mechanisms. Recently, cell cycle has been investigated at single cell level in snapshots of unsynchronized cell populations, exploiting the new methods for transcriptomic and proteomic molecular profiling. This raises a need for simplified semi-phenomenological cell cycle models, in order to formalize the processes underlying the cell cycle, at a higher abstracted level. Here we suggest a modeling framework, recapitulating the most important properties of the cell cycle as a limit trajectory of a dynamical process characterized by several internal states with switches between them. In the simplest form, this leads to a limit cycle trajectory, composed by linear segments in logarithmic coordinates describing some extensive (depending on system size) cell properties. We prove a theorem connecting the effective embedding dimensionality of the cell cycle trajectory with the number of its linear segments. We also develop a simplified kinetic model with piecewise-constant kinetic rates describing the dynamics of lumps of genes involved in S-phase and G2/M phases. We show how the developed cell cycle models can be applied to analyze the available single cell datasets and simulate certain properties of the observed cell cycle trajectories. Based on our model, we can predict with good accuracy the cell line doubling time from the length of cell cycle trajectory.
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Affiliation(s)
- Andrei Zinovyev
- Institut Curie, PSL Research University, Paris, France
- INSERM, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
- *Correspondence: Andrei Zinovyev,
| | - Michail Sadovsky
- Institute of Computational Modeling (RAS), Krasnoyarsk, Russia
- Laboratory of Medical Cybernetics, V.F.Voino-Yasenetsky Krasnoyarsk State Medical University, Krasnoyarsk, Russia
- Federal Research and Clinic Center of FMBA of Russia, Krasnoyarsk, Russia
- Laboratory of Advanced Methods for High-Dimensional Data Analysis, Lobachevsky University, Nizhniy Novgorod, Russia
| | - Laurence Calzone
- Institut Curie, PSL Research University, Paris, France
- INSERM, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Aziz Fouché
- Institut Curie, PSL Research University, Paris, France
- INSERM, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Clarice S. Groeneveld
- Cartes d’Identité des Tumeurs (CIT) Program, Ligue Nationale Contre le Cancer, Paris, France
- Oncologie Moleculaire, UMR144, Institut Curie, Paris, France
| | - Alexander Chervov
- Institut Curie, PSL Research University, Paris, France
- INSERM, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, Paris, France
- INSERM, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Alexander N. Gorban
- Laboratory of Advanced Methods for High-Dimensional Data Analysis, Lobachevsky University, Nizhniy Novgorod, Russia
- Department of Mathematics, University of Leicester, Leicester, United Kingdom
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150
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Li J, Sheng Q, Shyr Y, Liu Q. scMRMA: single cell multiresolution marker-based annotation. Nucleic Acids Res 2022; 50:e7. [PMID: 34648021 PMCID: PMC8789072 DOI: 10.1093/nar/gkab931] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 09/10/2021] [Accepted: 09/28/2021] [Indexed: 01/22/2023] Open
Abstract
Single-cell RNA sequencing has become a powerful tool for identifying and characterizing cellular heterogeneity. One essential step to understanding cellular heterogeneity is determining cell identities. The widely used strategy predicts identities by projecting cells or cell clusters unidirectionally against a reference to find the best match. Here, we develop a bidirectional method, scMRMA, where a hierarchical reference guides iterative clustering and deep annotation with enhanced resolutions. Taking full advantage of the reference, scMRMA greatly improves the annotation accuracy. scMRMA achieved better performance than existing methods in four benchmark datasets and successfully revealed the expansion of CD8 T cell populations in squamous cell carcinoma after anti-PD-1 treatment.
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Affiliation(s)
- Jia Li
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Quanhu Sheng
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Yu Shyr
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Qi Liu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
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