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Dang Q, Zuo L, Hu X, Zhou Z, Chen S, Liu S, Ba Y, Zuo A, Xu H, Weng S, Zhang Y, Luo P, Cheng Q, Liu Z, Han X. Molecular subtypes of colorectal cancer in the era of precision oncotherapy: Current inspirations and future challenges. Cancer Med 2024; 13:e70041. [PMID: 39054866 PMCID: PMC11272957 DOI: 10.1002/cam4.70041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 07/07/2024] [Accepted: 07/12/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND Colorectal cancer (CRC) is among the most hackneyed malignancies. Even patients with identical clinical symptoms and the same TNM stage still exhibit radically different clinical outcomes after receiving equivalent treatment regimens, indicating extensive heterogeneity of CRC. Myriad molecular subtypes of CRC have been exploited for decades, including the most compelling consensus molecular subtype (CMS) classification that has been broadly applied for patient stratification and biomarker-drug combination formulation. Encountering barriers to clinical translation, however, CMS classification fails to fully reflect inter- or intra-tumor heterogeneity of CRC. As a consequence, addressing heterogeneity and precisely managing CRC patients with unique characteristics remain arduous tasks for clinicians. REVIEW In this review, we systematically summarize molecular subtypes of CRC and further elaborate on their clinical applications, limitations, and future orientations. CONCLUSION In recent years, exploration of subtypes through cell lines, animal models, patient-derived xenografts (PDXs), organoids, and clinical trials contributes to refining biological insights and unraveling subtype-specific therapies in CRC. Therapeutic interventions including nanotechnology, clustered regulatory interspaced short palindromic repeat/CRISPR-associated nuclease 9 (CRISPR/Cas9), gut microbiome, and liquid biopsy are powerful tools with the possibility to shift the immunologic landscape and outlook for CRC precise medicine.
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Affiliation(s)
- Qin Dang
- Department of Interventional RadiologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
- Department of Colorectal SurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Lulu Zuo
- Center for Reproductive MedicineThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Xinru Hu
- Department of Cardiology, West China HospitalSichuan UniversityChengduSichuanChina
| | - Zhaokai Zhou
- Department of UrologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Shuang Chen
- Center for Reproductive MedicineThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Shutong Liu
- Department of Interventional RadiologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Yuhao Ba
- Department of Interventional RadiologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Anning Zuo
- Department of Interventional RadiologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Hui Xu
- Department of Interventional RadiologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Siyuan Weng
- Department of Interventional RadiologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Yuyuan Zhang
- Department of Interventional RadiologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Peng Luo
- Department of Oncology, Zhujiang HospitalSouthern Medical UniversityGuangzhouGuangdongChina
| | - Quan Cheng
- Department of Neurosurgery, Xiangya HospitalCentral South UniversityChangshaHunanChina
| | - Zaoqu Liu
- Department of Interventional RadiologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
- Interventional Treatment and Clinical Research Center of Henan ProvinceZhengzhouHenanChina
- Interventional Institute of Zhengzhou UniversityZhengzhouHenanChina
- Institute of Basic Medical SciencesChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Xinwei Han
- Department of Interventional RadiologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
- Interventional Treatment and Clinical Research Center of Henan ProvinceZhengzhouHenanChina
- Interventional Institute of Zhengzhou UniversityZhengzhouHenanChina
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2
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Yu L, Huang Z, Xiao Z, Tang X, Zeng Z, Tang X, Ouyang W. Unveiling the best predictive models for early‑onset metastatic cancer: Insights and innovations (Review). Oncol Rep 2024; 51:60. [PMID: 38456540 PMCID: PMC10940877 DOI: 10.3892/or.2024.8719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 01/22/2024] [Indexed: 03/09/2024] Open
Abstract
Cancer metastasis is the primary cause of cancer deaths. Metastasis involves the spread of cancer cells from the primary tumors to other body parts, commonly through lymphatic and vascular pathways. Key aspects include the high mutation rate and the capability of metastatic cells to form invasive tumors even without a large initial tumor mass. Particular emphasis is given to early metastasis, occurring in initial cancer stages and often leading to misdiagnosis, which adversely affects survival and prognosis. The present review highlighted the need for improved understanding and detection methods for early metastasis, which has not been effectively identified clinically. The present review demonstrated the clinicopathological and molecular characteristics of early‑onset metastatic types of cancer, noting factors such as age, race, tumor size and location as well as the histological and pathological grade as significant predictors. In conclusion, the present review underscored the importance of early detection and management of metastatic types of cancer and called for improved predictive models, including advanced techniques such as nomograms and machine learning, so as to enhance patient outcomes, acknowledging the challenges and limitations of the current research as well as the necessity for further studies.
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Affiliation(s)
- Liqing Yu
- Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510120, P.R. China
- The Second Clinical Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
| | - Zhenjun Huang
- Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510120, P.R. China
| | - Ziqi Xiao
- The Second Clinical Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
| | - Xiaofu Tang
- The Second Clinical Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
| | - Ziqiang Zeng
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
- School of Public Health, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
| | - Xiaoli Tang
- School of Basic Medicine, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
| | - Wenhao Ouyang
- Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510120, P.R. China
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3
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YADOLLAHVANDMIANDOAB REZA, JALALIZADEH MEHRSA, DIONATO FRANCIELEAPARECIDAVECHIA, BUOSI KEINI, LEME PATRÍCIAAF, COL LUCIANASBDAL, GIACOMELLI CRISTIANEF, ASSIS ALEXDIAS, BASHIRICHELKASARI NASIM, REIS LEONARDOOLIVEIRA. Clinical implications of single cell sequencing for bladder cancer. Oncol Res 2024; 32:597-605. [PMID: 38560564 PMCID: PMC10972735 DOI: 10.32604/or.2024.045442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 01/08/2024] [Indexed: 04/04/2024] Open
Abstract
Bladder cancer (BC) is the 10th most common cancer worldwide, with about 0.5 million reported new cases and about 0.2 million deaths per year. In this scoping review, we summarize the current evidence regarding the clinical implications of single-cell sequencing for bladder cancer based on PRISMA guidelines. We searched PubMed, CENTRAL, Embase, and supplemented with manual searches through the Scopus, and Web of Science for published studies until February 2023. We included original studies that used at least one single-cell technology to study bladder cancer. Forty-one publications were included in the review. Twenty-nine studies showed that this technology can identify cell subtypes in the tumor microenvironment that may predict prognosis or response to immune checkpoint inhibition therapy. Two studies were able to diagnose BC by identifying neoplastic cells through single-cell sequencing urine samples. The remaining studies were mainly a preclinical exploration of tumor microenvironment at single cell level. Single-cell sequencing technology can discriminate heterogeneity in bladder tumor cells and determine the key molecular properties that can lead to the discovery of novel perspectives on cancer management. This nascent tool can advance the early diagnosis, prognosis judgment, and targeted therapy of bladder cancer.
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Affiliation(s)
- REZA YADOLLAHVANDMIANDOAB
- UroScience, School of Medical Sciences, University of Campinas, UNICAMP, Campinas, Sao Paulo, 13083-872, Brazil
| | - MEHRSA JALALIZADEH
- UroScience, School of Medical Sciences, University of Campinas, UNICAMP, Campinas, Sao Paulo, 13083-872, Brazil
| | | | - KEINI BUOSI
- UroScience, School of Medical Sciences, University of Campinas, UNICAMP, Campinas, Sao Paulo, 13083-872, Brazil
| | - PATRÍCIA A. F. LEME
- UroScience, School of Medical Sciences, University of Campinas, UNICAMP, Campinas, Sao Paulo, 13083-872, Brazil
| | - LUCIANA S. B. DAL COL
- UroScience, School of Medical Sciences, University of Campinas, UNICAMP, Campinas, Sao Paulo, 13083-872, Brazil
| | - CRISTIANE F. GIACOMELLI
- UroScience, School of Medical Sciences, University of Campinas, UNICAMP, Campinas, Sao Paulo, 13083-872, Brazil
| | - ALEX DIAS ASSIS
- UroScience, School of Medical Sciences, University of Campinas, UNICAMP, Campinas, Sao Paulo, 13083-872, Brazil
| | - NASIM BASHIRICHELKASARI
- UroScience, School of Medical Sciences, University of Campinas, UNICAMP, Campinas, Sao Paulo, 13083-872, Brazil
| | - LEONARDO OLIVEIRA REIS
- UroScience, School of Medical Sciences, University of Campinas, UNICAMP, Campinas, Sao Paulo, 13083-872, Brazil
- ImmunOncology, Pontifical Catholic University of Campinas, PUC-Campinas, Campinas, Sao Paulo, 13087-571, Brazil
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Ali A, Davidson S, Fraenkel E, Gilmore I, Hankemeier T, Kirwan JA, Lane AN, Lanekoff I, Larion M, McCall LI, Murphy M, Sweedler JV, Zhu C. Single cell metabolism: current and future trends. Metabolomics 2022; 18:77. [PMID: 36181583 PMCID: PMC10063251 DOI: 10.1007/s11306-022-01934-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 09/05/2022] [Indexed: 11/29/2022]
Abstract
Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification. In this review we discuss both significant technical issues of measurement and interpretation, and progress toward addressing them, with recent examples from diverse biological systems. We provide a framework for further directions aimed at improving workflow and robustness so that such analyses may become commonly applied, especially in combination with metabolic imaging and single cell transcriptomics and proteomics.
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Affiliation(s)
- Ahmed Ali
- Leiden Academic Centre for Drug Research, University of Leiden, Gorlaeus Building Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Shawn Davidson
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Ernest Fraenkel
- Department of Biological Engineering and the Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ian Gilmore
- National Physical Laboratory, Teddington, TW11 0LW, Middlesex, UK
| | - Thomas Hankemeier
- Leiden Academic Centre for Drug Research, University of Leiden, Room number GW4.07, Gorlaeus Building, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Jennifer A Kirwan
- Berlin Institute of Health, Metabolomics Platform, Translational Research Unit of the Charite-Universitätsmedizin Berlin, Anna-Louisa-Karsch-Str 2, 10178, Berlin, Germany
| | - Andrew N Lane
- Department of Toxicology and Cancer Biology, and Center for Environmental and Systems Biochemistry, University of Kentucky, 789 S. Limestone St, Lexington, KY, 40536, USA.
| | - Ingela Lanekoff
- Department of Chemistry-BMC, Uppsala University, Husargatan 3 (576), 751 23, Uppsala, Sweden
| | - Mioara Larion
- Center for Cancer Research, National Cancer Institute, Building 37, Room 1136A, Bethesda, MD, 20892, USA
| | - Laura-Isobel McCall
- Department of Chemistry & Biochemistry, Department of Microbiology and Plant Biology, Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma, 101 Stephenson Parkway, room 3750, Norman, OK, 73019-5251, USA
| | - Michael Murphy
- Departments of Biological Engineering, Department of Electrical Engineering, and Computer Science and the Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, USA
| | - Jonathan V Sweedler
- Department of Chemistry, and the Beckman Institute, University of Illinois Urbana-Champaign, 505 South Mathews Avenue, Urbana, IL, 61801, USA
| | - Caigang Zhu
- Department of Biomedical Engineering, University of Kentucky, Lexington, KY, 40536, USA
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5
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Capturing tumour heterogeneity in pre- and post-chemotherapy colorectal cancer ascites-derived cells using single-cell RNA-sequencing. Biosci Rep 2021; 41:230018. [PMID: 34708245 PMCID: PMC8655500 DOI: 10.1042/bsr20212093] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 10/16/2021] [Accepted: 10/22/2021] [Indexed: 12/18/2022] Open
Abstract
Malignant ascites is an abnormal accumulation of fluid within the peritoneal cavity, caused by metastasis of several types of cancers, including colorectal cancer (CRC). Cancer cells in ascites reflect poor prognosis and serve as a good specimen to study tumour heterogeneity, as they represent a collection of multiple metastatic sites in the peritoneum. In the present study, we have employed single-cell RNA-sequencing (scRNA-seq) to explore and characterise ascites-derived cells from a CRC patient. The samples were prepared using mechanical and enzymatic dissociations, and obtained before and after a chemotherapy treatment. Unbiased clustering of 19,653 cells from four samples reveals 14 subclusters with unique transcriptomic patterns in four major cell types: epithelial cells, myeloid cells, fibroblasts, and lymphocytes. Interestingly, the percentages of cells recovered from different cell types appeared to be influenced by the preparation protocols, with more than 90% reduction in the number of myeloid cells recovered by enzymatic preparation. Analysis of epithelial cell subpopulations unveiled only three out of eleven subpopulations with clear contraction after the treatment, suggesting that the majority of the heterogeneous ascites-derived cells were resistant to the treatment, potentially reflecting the poor treatment outcome observed in the patient. Overall, our study showcases highly heterogeneous cancer subpopulations at single-cell resolution, which respond differently to a particular chemotherapy treatment. All in all, this work highlights the potential benefit of single-cell analyses in planning appropriate treatments and real-time monitoring of therapeutic response in cancer patients through routinely discarded ascites samples.
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6
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Rosati D, Giordano A. Single-cell RNA sequencing and bioinformatics as tools to decipher cancer heterogenicity and mechanisms of drug resistance. Biochem Pharmacol 2021; 195:114811. [PMID: 34673017 DOI: 10.1016/j.bcp.2021.114811] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 09/28/2021] [Accepted: 09/29/2021] [Indexed: 12/12/2022]
Abstract
It is well known that cancer is an aggressive disease, often associated with relapse, in many cases due to drug resistance. Cancer stem cell and clonal evolution are frequently causes of innate or acquired drug resistance. Current RNA sequencing technologies do not distinguish gene expression of different cell lineages because they are based on bulk cell studies. Single-cell RNA sequencing technologies and related bioinformatics clustering and differential expression analysis represent a turning point in cancer research. They are emerging as essential tools for dissecting tumors at single-cell resolution and represent novel tools to understand carcinogenesis and drug response. In this review, we will outline the role of these new technologies in addressing cancer heterogeneity and cell lineage-dependent drug resistance.
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Affiliation(s)
- Diletta Rosati
- Department of Medical Biotechnology, University of Siena, 53100 Siena, Italy
| | - Antonio Giordano
- Department of Medical Biotechnology, University of Siena, 53100 Siena, Italy; Sbarro Institute for Cancer Research and Molecular Medicine, Center for Biotechnology, College of Science and Technology, Temple University, Philadelphia, PA 19122, USA.
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7
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Markopoulos GS, Glantzounis GK, Goussia AC, Lianos GD, Karampa A, Alexiou GA, Vartholomatos G. Touch Imprint Intraoperative Flow Cytometry as a Complementary Tool for Detailed Assessment of Resection Margins and Tumor Biology in Liver Surgery for Primary and Metastatic Liver Neoplasms. Methods Protoc 2021; 4:mps4030066. [PMID: 34564312 PMCID: PMC8482241 DOI: 10.3390/mps4030066] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 09/09/2021] [Accepted: 09/10/2021] [Indexed: 12/14/2022] Open
Abstract
Liver resection is the main treatment for primary and metastatic liver tumors in order to achieve long-term survival with good quality of life. The ultimate goal of surgical oncology is to achieve complete tumor removal with adequate clear surgical margins. Flow cytometry is a powerful analytical technique with applications such as phenotypic analysis and quantification of DNA content. Intraoperative flow cytometry (iFC) is the application of flow cytometry for DNA content/ploidy and cell cycle distribution analysis during surgery for tumor cell analysis and margin evaluation. It has been used for cell analysis of intracranial tumors and recently of head and neck carcinomas and breast carcinomas, as well as for tumor margin evaluation. Herein, we present a novel touch imprint iFC protocol for the detailed assessment of tumor margins during excision of malignant hepatic lesions. The protocol aims to offer information on surgical margins after removal of malignant liver tumors based on DNA content of cancer cells and to corroborate the results of iFC with that of histopathological analysis. Based on the established role of iFC in other types of malignancies, our specialized protocol has the potential, through characterization of cells in liver transection surface post hepatectomy, to offer significant information on the type of resection and tumor biology. This information can be used to effectively guide intra- and postoperative patient management.
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Affiliation(s)
- Georgios S. Markopoulos
- Neurosurgical Institute, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (G.S.M.); (G.A.A.)
- Haematology Laboratory-Unit of Molecular Biology, University Hospital of Ioannina, 45110 Ioannina, Greece
| | - Georgios K. Glantzounis
- Department of Surgery, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (G.K.G.); (G.D.L.); (A.K.)
| | - Anna C. Goussia
- Department of Pathology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece;
| | - Georgios D. Lianos
- Department of Surgery, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (G.K.G.); (G.D.L.); (A.K.)
| | - Anastasia Karampa
- Department of Surgery, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (G.K.G.); (G.D.L.); (A.K.)
| | - George A. Alexiou
- Neurosurgical Institute, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (G.S.M.); (G.A.A.)
- Department of Neurosurgery, University of Ioannina, 45110 Ioannina, Greece
| | - George Vartholomatos
- Haematology Laboratory-Unit of Molecular Biology, University Hospital of Ioannina, 45110 Ioannina, Greece
- Correspondence:
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He S, Schein A, Sarsani V, Flaherty P. A BAYESIAN NONPARAMETRIC MODEL FOR INFERRING SUBCLONAL POPULATIONS FROM STRUCTURED DNA SEQUENCING DATA. Ann Appl Stat 2021; 15:925-951. [PMID: 34262633 DOI: 10.1214/20-aoas1434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
There are distinguishing features or "hallmarks" of cancer that are found across tumors, individuals, and types of cancer, and these hallmarks can be driven by specific genetic mutations. Yet, within a single tumor there is often extensive genetic heterogeneity as evidenced by single-cell and bulk DNA sequencing data. The goal of this work is to jointly infer the underlying genotypes of tumor subpopulations and the distribution of those subpopulations in individual tumors by integrating single-cell and bulk sequencing data. Understanding the genetic composition of the tumor at the time of treatment is important in the personalized design of targeted therapeutic combinations and monitoring for possible recurrence after treatment. We propose a hierarchical Dirichlet process mixture model that incorporates the correlation structure induced by a structured sampling arrangement and we show that this model improves the quality of inference. We develop a representation of the hierarchical Dirichlet process prior as a Gamma-Poisson hierarchy and we use this representation to derive a fast Gibbs sampling inference algorithm using the augment-and-marginalize method. Experiments with simulation data show that our model outperforms standard numerical and statistical methods for decomposing admixed count data. Analyses of real acute lymphoblastic leukemia cancer sequencing dataset shows that our model improves upon state-of-the-art bioinformatic methods. An interpretation of the results of our model on this real dataset reveals co-mutated loci across samples.
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Affiliation(s)
- Shai He
- Department of Mathematics and Statistics, University of Massachusetts Amherst
| | | | - Vishal Sarsani
- Department of Mathematics and Statistics, University of Massachusetts Amherst
| | - Patrick Flaherty
- Department of Mathematics and Statistics, University of Massachusetts Amherst
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9
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Zhang Y, Zhong Z, Li M, Chen J, Lin T, Sun J, Wang D, Mu Q, Su H, Wu N, Liu A, Yu Y, Zhang M, Liu Y, Guo J, Yu W. The roles and prognostic significance of ABI1-TSV-11 expression in patients with left-sided colorectal cancer. Sci Rep 2021; 11:10734. [PMID: 34031495 PMCID: PMC8144562 DOI: 10.1038/s41598-021-90220-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 05/07/2021] [Indexed: 12/24/2022] Open
Abstract
Abnormally expressed and/or phosphorylated Abelson interactor 1 (ABI1) participates in the metastasis and progression of colorectal cancer (CRC). ABI1 presents as at least 12 transcript variants (TSVs) by mRNA alternative splicing, but it is unknown which of them is involved in CRC metastasis and prognosis. Here, we firstly identified ABI1-TSV-11 as a key TSV affecting the metastasis and prognosis of left-sided colorectal cancer (LsCC) and its elevated expression is related to lymph node metastasis and shorter overall survival (OS) in LsCC by analyzing data from The Cancer Genome Atlas and TSVdb. Secondly, ABI1-TSV-11 overexpression promoted LoVo and SW480 cells adhesion and migration in vitro, and accelerated LoVo and SW480 cells lung metastasis in vivo. Finally, mechanism investigations revealed that ABI1-isoform-11 interacted with epidermal growth factor receptor pathway substrate 8 (ESP8) and regulated actin dynamics to affect LoVo and SW480 cells biological behaviors. Taken together, our data demonstrated that ABI1-TSV-11 plays an oncogenic role in LsCC, it is an independent risk factor of prognosis and may be a potential molecular marker and therapeutic target in LsCC.
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Affiliation(s)
- Yu Zhang
- Department of Central Laboratory and Institute of Clinical Molecular Biology, Peking University People's Hospital, Beijing, China
- Department of Gastroenterology, Peking University People's Hospital, Beijing, China
| | - Zhaohui Zhong
- Department of General Surgery, Peking University People's Hospital, Beijing, China
| | - Mei Li
- Department of Central Laboratory and Institute of Clinical Molecular Biology, Peking University People's Hospital, Beijing, China
| | - Jingyi Chen
- Department of Central Laboratory and Institute of Clinical Molecular Biology, Peking University People's Hospital, Beijing, China
- Department of Gastroenterology, Peking University People's Hospital, Beijing, China
| | - Tingru Lin
- Department of Central Laboratory and Institute of Clinical Molecular Biology, Peking University People's Hospital, Beijing, China
- Department of Gastroenterology, Peking University People's Hospital, Beijing, China
| | - Jie Sun
- Department of Central Laboratory and Institute of Clinical Molecular Biology, Peking University People's Hospital, Beijing, China
| | - Di Wang
- Department of Central Laboratory and Institute of Clinical Molecular Biology, Peking University People's Hospital, Beijing, China
| | - Qing Mu
- Department of Central Laboratory and Institute of Clinical Molecular Biology, Peking University People's Hospital, Beijing, China
| | - Huiting Su
- Department of Central Laboratory and Institute of Clinical Molecular Biology, Peking University People's Hospital, Beijing, China
| | - Na Wu
- Department of Central Laboratory and Institute of Clinical Molecular Biology, Peking University People's Hospital, Beijing, China
| | - Aiyu Liu
- Department of Central Laboratory and Institute of Clinical Molecular Biology, Peking University People's Hospital, Beijing, China
| | - Yimeng Yu
- Department of Central Laboratory and Institute of Clinical Molecular Biology, Peking University People's Hospital, Beijing, China
| | - Menglei Zhang
- Department of Animal Laboratory, Peking University People's Hospital, Beijing, China
| | - Yulan Liu
- Department of Gastroenterology, Peking University People's Hospital, Beijing, China
| | - Jingzhu Guo
- Department of Pediatric, Peking University People's Hospital, Beijing, China.
| | - Weidong Yu
- Department of Central Laboratory and Institute of Clinical Molecular Biology, Peking University People's Hospital, Beijing, China.
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10
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Li J, Zeng Z, Chen J, Liu X, Jiang X, Sun W, Luo Y, Ren J, Gong Y, Xie C. Pathologic evolution-related Gene Analysis based on both single-cell and bulk transcriptomics in Colorectal Cancer. J Cancer 2020; 11:6861-6873. [PMID: 33123277 PMCID: PMC7591993 DOI: 10.7150/jca.49262] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 10/11/2020] [Indexed: 02/06/2023] Open
Abstract
Purpose: The patients diagnosed with colorectal cancer (CRC) are likely to undergo differential outcomes in clinical survival owing to different pathologic stages. However, signatures in association with pathologic evolution and CRC prognosis are not clearly defined. This study aimed to identify pathologic evolution-related genes in CRC based on both single-cell and bulk transcriptomics. Patients and methods: The CRC single-cell transcriptomic dataset (GSE81861, n=590) with clinical information and tumor microenvironmental tissues was collected to identify the pathologic evolution-related genes. The colonic adenocarcinoma and rectum adenocarcinoma transcriptomics from The Cancer Genome Atlas were obtained as the training dataset (n=363) and 5 other CRC transcriptomics cohorts from Gene Expression Omnibus (n=1031) were acquired as validation data. Graph-based clustering analysis algorithm was applied to identify pathologic evolution-related cell populations. Pseudotime analysis was performed to construct the trajectory plot of pathologic evolution and to define hub genes in the evolution process. Cell-type identification by estimating relative subsets of RNA transcripts was then executed to build a novel cell infiltration classifier. The prediction efficacy of this classifier was validated in bulk transcriptomic datasets. Results: Epithelial and T cells were elucidated to be related to the pathologic stages in CRC tissues. Pseudotime analysis and survival analysis indicated that HOXC5, HOXC8 and BMP5 were the marker genes in pathologic evolution process. Our cell infiltration classifier exhibited excellent forecast efficacy in predicting pathologic stages and prognosis of CRC patients. Conclusion: We identified pathologic evolution-related genes in single-cell transcriptomic and proposed a novel specific cell infiltration classifier to forecast the prognosis of CRC patients based on pathologic stage-related hub genes HOXC6, HOXC8 and BMP5.
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Affiliation(s)
- Jiali Li
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Zihang Zeng
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Jiarui Chen
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xingyu Liu
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xueping Jiang
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Wenjie Sun
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yuan Luo
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Jiangbo Ren
- Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yan Gong
- Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, China.,Human Genetics Resource Preservation Center of Hubei Province, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Conghua Xie
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China.,Hubei Key Laboratory of Tumor Biological Behaviors, Zhongnan Hospital of Wuhan University, Wuhan, China.,Hubei Cancer Clinical Study Center, Zhongnan Hospital of Wuhan University, Wuhan, China
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11
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Xu ZY, Zhao M, Chen W, Li K, Qin F, Xiang WW, Sun Y, Wei J, Yuan LQ, Li SK, Lin SH. Analysis of prognostic genes in the tumor microenvironment of lung adenocarcinoma. PeerJ 2020; 8:e9530. [PMID: 32775050 PMCID: PMC7382940 DOI: 10.7717/peerj.9530] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 06/22/2020] [Indexed: 12/11/2022] Open
Abstract
Background Prognostic genes in the tumor microenvironment play an important role in immune biological processes and the response of cancer to immunotherapy. Thus, we aimed to assess new biomarkers that are associated with immune/stromal cells in lung adenocarcinomas (LUAD) using the ESTIMATE algorithm, which also significantly affects the prognosis of cancer. Methods The RNA sequencing (RNA-Seq) and clinical data of LUAD were downloaded from the the Cancer Genome Atlas (TCGA ). The immune and stromal scores were calculated for each sample using the ESTIMATE algorithm. The LUAD gene chip expression profile data and the clinical data (GSE37745, GSE11969, and GSE50081) were downloaded from the Gene Expression Omnibus (GEO) for subsequent validation analysis. Differentially expressed genes were calculated between high and low score groups. Univariate Cox regression analysis was performed on differentially expressed genes (DEGs) between the two groups to obtain initial prognosis genes. These were verified by three independent LUAD cohorts from the GEO database. Multivariate Cox regression was used to identify overall survival-related DEGs. UALCAN and the Human Protein Atlas were used to analyze the mRNA /protein expression levels of the target genes. Immune cell infiltration was evaluated using the Tumor Immune Estimation Resource (TIMER) and CIBERSORT methods, and stromal cell infiltration was assessed using xCell. Results In this study, immune scores and stromal scores are significantly associated with the clinical characteristics of LUAD, including T stage, M stage, pathological stage, and overall survival time. 530 DEGs (18 upregulated and 512 downregulated) were found to coexist in the difference analysis with the immune scores and stromal scores subgroup. Univariate Cox regression analysis showed that 286 of the 530 DEGs were survival-related genes (p < 0.05). Of the 286 genes initially identified, nine prognosis-related genes (CSF2RB, ITK, FLT3, CD79A, CCR4, CCR6, DOK2, AMPD1, and IGJ) were validated from three separate LUAD cohorts. In addition, functional analysis of DEGs also showed that various immunoregulatory molecular pathways, including regulation of immune response and the chemokine signaling pathways, were involved. Five genes (CCR6, ITK, CCR4, DOK2, and AMPD1) were identified as independent prognostic indicators of LUAD in specific data sets. The relationship between the expression levels of these genes and immune genes was assessed. We found that CCR6 mRNA and protein expression levels of LUAD were greater than in normal tissues. We evaluated the infiltration of immune cells and stromal cells in groups with high and low levels of expression of CCR6 in the TCGA LUAD cohort. In summary, we found a series of prognosis-related genes that were associated with the LUAD tumor microenvironment.
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Affiliation(s)
- Zhan-Yu Xu
- Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Mengli Zhao
- Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Wenjie Chen
- Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Kun Li
- Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Fanglu Qin
- Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Wei-Wei Xiang
- Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yu Sun
- Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jiangbo Wei
- Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Li-Qiang Yuan
- Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Shi-Kang Li
- Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Sheng-Hua Lin
- Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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12
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Baltagiannis EG, Kyrochristos ID, Ziogas DE, Goussia A, Mitsis M, Roukos DH. From personalized to precision cancer medicine. Per Med 2020; 17:245-250. [PMID: 32589113 DOI: 10.2217/pme-2020-0056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Evangelos G Baltagiannis
- Centre for Biosystems & Genome Network Medicine, Ioannina University, Ioannina, Greece.,Department of Surgery, University Hospital of Ioannina, Ioannina, Greece
| | - Ioannis D Kyrochristos
- Centre for Biosystems & Genome Network Medicine, Ioannina University, Ioannina, Greece.,Department of Surgery, University Hospital of Ioannina, Ioannina, Greece
| | - Demosthenes E Ziogas
- Centre for Biosystems & Genome Network Medicine, Ioannina University, Ioannina, Greece.,Department of Surgery, 'G. Hatzikosta' General Hospital, Ioannina, Greece
| | - Anna Goussia
- Department of Pathology, University Hospital of Ioannina, Ioannina, Greece
| | - Michail Mitsis
- Department of Surgery, University Hospital of Ioannina, Ioannina, Greece.,Cancer Biobank Centre, Ioannina University, Ioannina, Greece
| | - Dimitrios H Roukos
- Centre for Biosystems & Genome Network Medicine, Ioannina University, Ioannina, Greece.,Department of Surgery, University Hospital of Ioannina, Ioannina, Greece.,Department of Systems Biology, Biomedical Research Foundation of the Academy of Athens (BRFAA), Athens, Greece
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13
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Resolving Metabolic Heterogeneity in Experimental Models of the Tumor Microenvironment from a Stable Isotope Resolved Metabolomics Perspective. Metabolites 2020; 10:metabo10060249. [PMID: 32549391 PMCID: PMC7345423 DOI: 10.3390/metabo10060249] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 06/02/2020] [Accepted: 06/04/2020] [Indexed: 12/11/2022] Open
Abstract
The tumor microenvironment (TME) comprises complex interactions of multiple cell types that determines cell behavior and metabolism such as nutrient competition and immune suppression. We discuss the various types of heterogeneity that exist in solid tumors, and the complications this invokes for studies of TME. As human subjects and in vivo model systems are complex and difficult to manipulate, simpler 3D model systems that are compatible with flexible experimental control are necessary for studying metabolic regulation in TME. Stable Isotope Resolved Metabolomics (SIRM) is a valuable tool for tracing metabolic networks in complex systems, but at present does not directly address heterogeneous metabolism at the individual cell level. We compare the advantages and disadvantages of different model systems for SIRM experiments, with a focus on lung cancer cells, their interactions with macrophages and T cells, and their response to modulators in the immune microenvironment. We describe the experimental set up, illustrate results from 3D cultures and co-cultures of lung cancer cells with human macrophages, and outline strategies to address the heterogeneous TME.
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14
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Pappas-Gogos G, Baltagiannis EG, Kyrochristos ID, Ziogas DE, Goussia A, Mitsis M, Roukos DH. Predictive and patient-monitoring biomarkers: precision in the management of colorectal cancer. Biomark Med 2020; 14:335-339. [PMID: 32250157 DOI: 10.2217/bmm-2020-0025] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Affiliation(s)
- Georgios Pappas-Gogos
- Centre for Biosystems and Genome Network Medicine, Ioannina University, Ioannina, Greece.,Department of Surgery, University Hospital of Ioannina, Ioannina, Greece
| | - Evangelos G Baltagiannis
- Centre for Biosystems and Genome Network Medicine, Ioannina University, Ioannina, Greece.,Department of Surgery, University Hospital of Ioannina, Ioannina, Greece
| | - Ioannis D Kyrochristos
- Centre for Biosystems and Genome Network Medicine, Ioannina University, Ioannina, Greece.,Department of Surgery, University Hospital of Ioannina, Ioannina, Greece
| | - Demosthenes E Ziogas
- Centre for Biosystems and Genome Network Medicine, Ioannina University, Ioannina, Greece.,Department of Surgery, 'G Hatzikosta' General Hospital, Ioannina, Greece
| | - Anna Goussia
- Department of Pathology, University Hospital of Ioannina, Ioannina, Greece
| | - Michail Mitsis
- Department of Surgery, University Hospital of Ioannina, Ioannina, Greece
| | - Dimitrios H Roukos
- Centre for Biosystems and Genome Network Medicine, Ioannina University, Ioannina, Greece.,Department of Surgery, University Hospital of Ioannina, Ioannina, Greece.,Department of Systems Biology, Biomedical Research Foundation of the Academy of Athens (BRFAA), Athens, Greece
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15
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Kyrochristos ID, Baltagiannis EG, Mitsis M, Roukos DH. Precision in cancer pharmacogenomics. Pharmacogenomics 2020; 21:311-316. [PMID: 32242500 DOI: 10.2217/pgs-2020-0011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Affiliation(s)
- Ioannis D Kyrochristos
- Centre for Biosystems & Genome Network Medicine, Ioannina University, Ioannina, Greece
- Department of Surgery, University Hospital of Ioannina, Ioannina, Greece
| | - Evangelos G Baltagiannis
- Centre for Biosystems & Genome Network Medicine, Ioannina University, Ioannina, Greece
- Department of Surgery, University Hospital of Ioannina, Ioannina, Greece
| | - Michail Mitsis
- Department of Surgery, University Hospital of Ioannina, Ioannina, Greece
- Cancer Biobank Centre, Ioannina University, Ioannina, Greece
| | - Dimitrios H Roukos
- Centre for Biosystems & Genome Network Medicine, Ioannina University, Ioannina, Greece
- Department of Surgery, University Hospital of Ioannina, Ioannina, Greece
- Department of Systems Biology, Biomedical Research Foundation of the Academy of Athens (BRFAA), Athens, Greece
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