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Capp JP, Catania F, Thomas F. From genetic mosaicism to tumorigenesis through indirect genetic effects. Bioessays 2024; 46:e2300238. [PMID: 38736323 DOI: 10.1002/bies.202300238] [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: 12/13/2023] [Revised: 04/19/2024] [Accepted: 04/22/2024] [Indexed: 05/14/2024]
Abstract
Genetic mosaicism has long been linked to aging, and several hypotheses have been proposed to explain the potential connections between mosaicism and susceptibility to cancer. It has been proposed that mosaicism may disrupt tissue homeostasis by affecting intercellular communications and releasing microenvironmental constraints within tissues. The underlying mechanisms driving these tissue-level influences remain unidentified, however. Here, we present an evolutionary perspective on the interplay between mosaicism and cancer, suggesting that the tissue-level impacts of genetic mosaicism can be attributed to Indirect Genetic Effects (IGEs). IGEs can increase the level of cellular stochasticity and phenotypic instability among adjacent cells, thereby elevating the risk of cancer development within the tissue. Moreover, as cells experience phenotypic changes in response to challenging microenvironmental conditions, these changes can initiate a cascade of nongenetic alterations, referred to as Indirect non-Genetic Effects (InGEs), which in turn catalyze IGEs among surrounding cells. We argue that incorporating both InGEs and IGEs into our understanding of the process of oncogenic transformation could trigger a major paradigm shift in cancer research with far-reaching implications for practical applications.
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Affiliation(s)
- Jean-Pascal Capp
- Toulouse Biotechnology Institute, INSA/University of Toulouse, CNRS, INRAE, Toulouse, France
| | - Francesco Catania
- Institute of Environmental Radioactivity, Fukushima University, Kanayagawa, Fukushima, Japan
| | - Frédéric Thomas
- CREEC, UMR IRD 224-CNRS 5290-University of Montpellier, Montpellier, France
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2
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Wang L, Mao X, Yu X, Su J, Li Z, Chen Z, Ren Y, Huang H, Wang W, Zhao C, Hu Y. FPR3 reprograms glycolytic metabolism and stemness in gastric cancer via calcium-NFATc1 pathway. Cancer Lett 2024; 593:216841. [PMID: 38614385 DOI: 10.1016/j.canlet.2024.216841] [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: 01/14/2024] [Revised: 03/19/2024] [Accepted: 04/01/2024] [Indexed: 04/15/2024]
Abstract
Aerobic glycolysis accelerates tumor proliferation and progression, and inhibitors or drugs targeting abnormal cancer metabolism have been developing. Cancer stem-like cells (CSCs) significantly contribute to tumor initiation, metastasis, therapy resistance, and recurrence. Formyl peptide receptor 3 (FPR3), a member of FPR family, involves in inflammation, tissue repair, and angiogenesis. However, studies in exploring the regulatory mechanisms of aerobic glycolysis and CSCs by FPR3 in gastric cancer (GC) remain unknown. Here, we demonstrated that overexpressed FPR3 suppressed glycolytic capacity and stemness of tumor cells, then inhibited GC cells proliferation. Mechanistically, FPR3 impeded cytoplasmic calcium ion flux and hindered nuclear factor of activated T cells 1 (NFATc1) nuclear translocation, leading to the transcriptional inactivation of NFATc1-binding neurogenic locus notch homolog protein 3 (NOTCH3) promoter, subsequently obstructing NOTCH3 expression and the AKT/mTORC1 signaling pathway, and ultimately downregulating glycolysis. Additionally, NFATc1 directly binds to the sex determining region Y-box 2 (SOX2) promoter and modifies stemness in GC. In conclusion, our work illustrated that FPR3 played a negative role in GC progression by modulating NFATc1-mediated glycolysis and stemness in a calcium-dependent manner, providing potential insights into cancer therapy.
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Affiliation(s)
- Lingzhi Wang
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Xinyuan Mao
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Xiang Yu
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Jin Su
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China; Department of General Surgery, Zhuzhou Hospital affiliated to Xiangya School of Medicine, Central South University, Zhuzhou, 412000, China
| | - Zhenyuan Li
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Zhian Chen
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Yingxin Ren
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Huilin Huang
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Weisheng Wang
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Cuiyin Zhao
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Yanfeng Hu
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
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Donati M, Kazakov DV. Beyond typical histology of BAP1-inactivated melanocytoma. Pathol Res Pract 2024; 259:155162. [PMID: 38326181 DOI: 10.1016/j.prp.2024.155162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/05/2024] [Accepted: 01/20/2024] [Indexed: 02/09/2024]
Abstract
BAP1-inactivated melanocytoma (BIM) is a novel subgroup of melanocytic neoplasm listed in the 5th edition of WHO classification of skin tumor. BIM is characterized by two molecular alterations, including a mitogenic driver mutation (usually BRAF gene) and the loss of function of BAP1, a tumor suppressor gene located on chromosome 3p21, which encodes for BRCA1-associated protein (BAP1). The latter represents a nuclear-localized deubiquitinase involved in several cellular processes including cell cycle regulation, chromatin remodeling, DNA damage response, differentiation, senescence and cell death. BIMs are histologically characterized by a population of large epithelioid melanocytes with well-demarcated cytoplasmic borders and copious eosinophilic cytoplasm, demonstrating loss of BAP1 nuclear expression by immunohistochemistry. Recently, we have published a series of 50 cases, extending the morphological spectrum of the neoplasm and highlighting some new microscopic features. In the current article, we focus on some new histological features, attempting to explain and link them to certain mechanisms of tumor development, including senescence, endoreplication, endocycling, asymmetric cytokinesis, entosis and others. In light of the morphological and molecular findings observed in BIM, we postulated that this entity unmasks a fine mechanism of tumor in which both clonal/stochastic and hierarchical model can be unified.
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Affiliation(s)
- Michele Donati
- Department of Pathology, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy; Department of Pathology, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21 - 00128 Roma, Italy.
| | - Dmitry V Kazakov
- IDP Dermatohistopathologie Institut, Pathologie Institut Enge, Zurich, Switzerland
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Omidiran O, Patel A, Usman S, Mhatre I, Abdelhalim H, DeGroat W, Narayanan R, Singh K, Mendhe D, Ahmed Z. GWAS advancements to investigate disease associations and biological mechanisms. CLINICAL AND TRANSLATIONAL DISCOVERY 2024; 4:e296. [PMID: 38737752 PMCID: PMC11086745 DOI: 10.1002/ctd2.296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Accepted: 04/16/2024] [Indexed: 05/14/2024]
Abstract
Genome-wide association studies (GWAS) have been instrumental in elucidating the genetic architecture of various traits and diseases. Despite the success of GWAS, inherent limitations such as identifying rare and ultra-rare variants, the potential for spurious associations, and in pinpointing causative agents can undermine diagnostic capabilities. This review provides an overview of GWAS and highlights recent advances in genetics that employ a range of methodologies, including Whole Genome Sequencing (WGS), Mendelian Randomization (MR), the Pangenome's high-quality T2T-CHM13 panel, and the Human BioMolecular Atlas Program (HuBMAP), as potential enablers of current and future GWAS research. State of the literature demonstrate the capabilities of these techniques in enhancing the statistical power of GWAS. WGS, with its comprehensive approach, captures the entire genome, surpassing the capabilities of the traditional GWAS technique focused on predefined Single Nucleotide Polymorphism (SNP) sites. The Pangenome's T2T-CHM13 panel, with its holistic approach, aids in the analysis of regions with high sequence identity, such as segmental duplications (SDs). Mendelian Randomization has advanced causative inference, improving clinical diagnostics and facilitating definitive conclusions. Furthermore, spatial biology techniques like HuBMAP, enable 3D molecular mapping of tissues at single-cell resolution, offering insights into pathology of complex traits. This study aims to elucidate and advocate for the increased application of these technologies, highlighting their potential to shape the future of GWAS research.
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Affiliation(s)
- Oluwaferanmi Omidiran
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Aashna Patel
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Sarah Usman
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Ishani Mhatre
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Habiba Abdelhalim
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - William DeGroat
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Rishabh Narayanan
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Kritika Singh
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Dinesh Mendhe
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Zeeshan Ahmed
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
- Department of Medicine, Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, 125 Paterson St, New Brunswick, NJ, USA
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Yang X, Niu W, Wu K, Li X, Hou H, Tan Y, Wang X, Yang G, Wang L, Zhang H. Diffusion kurtosis imaging-based habitat analysis identifies high-risk molecular subtypes and heterogeneity matching in diffuse gliomas. Ann Clin Transl Neurol 2024. [PMID: 38887966 DOI: 10.1002/acn3.52128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 05/14/2024] [Accepted: 06/02/2024] [Indexed: 06/20/2024] Open
Abstract
OBJECTIVE High-risk types of diffuse gliomas in adults include isocitrate dehydrogenase (IDH) wild-type glioblastomas and grade 4 astrocytomas. Achieving noninvasive prediction of high-risk molecular subtypes of gliomas is important for personalized and precise diagnosis and treatment. METHODS We retrospectively collected data from 116 patients diagnosed with adult diffuse gliomas. Multiple high-risk molecular markers were tested, and various habitat models and whole-tumor models were constructed based on preoperative routine and diffusion kurtosis imaging (DKI) sequences to predict high-risk molecular subtypes of gliomas. Feature selection and model construction utilized Least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM). Finally, the Wilcoxon rank-sum test was employed to explore the correlation between habitat quantitative features (intra-tumor heterogeneity score,ITH score) and heterogeneity, as well as high-risk molecular subtypes. RESULTS The results showed that the habitat analysis model based on DKI performed remarkably well (with AUC values reaching 0.977 and 0.902 in the training and test sets, respectively). The model's performance was further enhanced when combined with clinical variables. (The AUC values were 0.994 and 0.920, respectively.) Additionally, we found a close correlation between ITH score and heterogeneity, with statistically significant differences observed between high-risk and non-high-risk molecular subtypes. INTERPRETATION The habitat model based on DKI is an ideal means for preoperatively predicting high-risk molecular subtypes of gliomas, holding significant value for noninvasively alerting malignant gliomas and those with malignant transformation potential.
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Affiliation(s)
- Xiangli Yang
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, 030001, China
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, 030032, China
- College of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, China
| | - Wenju Niu
- College of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, China
| | - Kai Wu
- Department of Information Management, First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Xiang Li
- College of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, China
| | - Heng Hou
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Yan Tan
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Xiaochun Wang
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Guoqiang Yang
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, 030001, China
- College of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, China
- Shanxi Key Laboratory of Intelligent Imaging and Nanomedicine, First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Lei Wang
- Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, China
| | - Hui Zhang
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, 030001, China
- College of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, China
- Shanxi Key Laboratory of Intelligent Imaging and Nanomedicine, First Hospital of Shanxi Medical University, Taiyuan, 030001, China
- Intelligent Imaging Big Data and Functional Nano-imaging Engineering Research Center of Shanxi Province, First Hospital of Shanxi Medical University, Taiyuan, 030001, China
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6
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Desai P, Takahashi N, Kumar R, Nichols S, Malin J, Hunt A, Schultz C, Cao Y, Tillo D, Nousome D, Chauhan L, Sciuto L, Jordan K, Rajapakse V, Tandon M, Lissa D, Zhang Y, Kumar S, Pongor L, Singh A, Schroder B, Sharma AK, Chang T, Vilimas R, Pinkiert D, Graham C, Butcher D, Warner A, Sebastian R, Mahon M, Baker K, Cheng J, Berger A, Lake R, Abel M, Krishnamurthy M, Chrisafis G, Fitzgerald P, Nirula M, Goyal S, Atkinson D, Bateman NW, Abulez T, Nair G, Apolo A, Guha U, Karim B, El Meskini R, Ohler ZW, Jolly MK, Schaffer A, Ruppin E, Kleiner D, Miettinen M, Brown GT, Hewitt S, Conrads T, Thomas A. Microenvironment shapes small-cell lung cancer neuroendocrine states and presents therapeutic opportunities. Cell Rep Med 2024; 5:101610. [PMID: 38897168 DOI: 10.1016/j.xcrm.2024.101610] [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: 03/02/2023] [Revised: 08/04/2023] [Accepted: 05/17/2024] [Indexed: 06/21/2024]
Abstract
Small-cell lung cancer (SCLC) is the most fatal form of lung cancer. Intratumoral heterogeneity, marked by neuroendocrine (NE) and non-neuroendocrine (non-NE) cell states, defines SCLC, but the cell-extrinsic drivers of SCLC plasticity are poorly understood. To map the landscape of SCLC tumor microenvironment (TME), we apply spatially resolved transcriptomics and quantitative mass spectrometry-based proteomics to metastatic SCLC tumors obtained via rapid autopsy. The phenotype and overall composition of non-malignant cells in the TME exhibit substantial variability, closely mirroring the tumor phenotype, suggesting TME-driven reprogramming of NE cell states. We identify cancer-associated fibroblasts (CAFs) as a crucial element of SCLC TME heterogeneity, contributing to immune exclusion, and predicting exceptionally poor prognosis. Our work provides a comprehensive map of SCLC tumor and TME ecosystems, emphasizing their pivotal role in SCLC's adaptable nature, opening possibilities for reprogramming the TME-tumor communications that shape SCLC tumor states.
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Affiliation(s)
- Parth Desai
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Department of Medical Oncology, Fox Chase Cancer Center, Temple University Hospital and Lewis Katz School of Medicine, Philadelphia, PA, USA
| | - Nobuyuki Takahashi
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Department of Medical Oncology, National Cancer Center Hospital East, Kashiwa, Japan
| | - Rajesh Kumar
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Samantha Nichols
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Justin Malin
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Allison Hunt
- Women's Health Integrated Research Center, Inova Health System, Falls Church, VA, USA
| | - Christopher Schultz
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yingying Cao
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Desiree Tillo
- CCR Collaborative Bioinformatics, Resource, Office of Science and Technology Resources, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Darryl Nousome
- CCR Collaborative Bioinformatics, Resource, Office of Science and Technology Resources, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lakshya Chauhan
- Center for Biosystems Science and Engineering, Indian Institute of Science, Bangalore, India
| | - Linda Sciuto
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Kimberly Jordan
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Vinodh Rajapakse
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mayank Tandon
- CCR Collaborative Bioinformatics, Resource, Office of Science and Technology Resources, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Delphine Lissa
- Laboratory of Human Carcinogenesis, Center for Cancer Research National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yang Zhang
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Suresh Kumar
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lorinc Pongor
- HCEMM Cancer Genomics and Epigenetics Research Group, Szeged, Hungary
| | - Abhay Singh
- Center for Biosystems Science and Engineering, Indian Institute of Science, Bangalore, India
| | - Brett Schroder
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ajit Kumar Sharma
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Tiangen Chang
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Rasa Vilimas
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Danielle Pinkiert
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Chante Graham
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Donna Butcher
- Molecular Histopathology Laboratory, Laboratory Animal Sciences Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - Andrew Warner
- Molecular Histopathology Laboratory, Laboratory Animal Sciences Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - Robin Sebastian
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mimi Mahon
- Pain and Palliative care services, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Karen Baker
- Pain and Palliative care services, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Jennifer Cheng
- Pain and Palliative care services, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Ann Berger
- Pain and Palliative care services, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Ross Lake
- Laboratory of Genitourinary cancer Pathogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Melissa Abel
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Manan Krishnamurthy
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - George Chrisafis
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter Fitzgerald
- CCR Collaborative Bioinformatics, Resource, Office of Science and Technology Resources, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Micheal Nirula
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Shubhank Goyal
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Devon Atkinson
- Center for Advanced Preclinical Research, Frederick National Laboratory for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - Nicholas W Bateman
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD, USA
| | - Tamara Abulez
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD, USA
| | - Govind Nair
- National Institute of Neurological Disorders and Stroke, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Andrea Apolo
- Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Udayan Guha
- Thoracic and GI Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Baktiar Karim
- Molecular Histopathology Laboratory, Laboratory Animal Sciences Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - Rajaa El Meskini
- Center for Advanced Preclinical Research, Frederick National Laboratory for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - Zoe Weaver Ohler
- Center for Advanced Preclinical Research, Frederick National Laboratory for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - Mohit Kumar Jolly
- Center for Biosystems Science and Engineering, Indian Institute of Science, Bangalore, India
| | - Alejandro Schaffer
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - David Kleiner
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Markku Miettinen
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - G Tom Brown
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephen Hewitt
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Thomas Conrads
- Women's Health Integrated Research Center, Inova Health System, Falls Church, VA, USA
| | - Anish Thomas
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
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7
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Xin S, Zhang Y, Zhang Z, Li Z, Sun X, Liu X, Jin L, Li W, Tang C, Mei W, Cao Q, Wang H, Wei Z, Zhou Z, Li R, Wen X, Yang G, Chen W, Zheng J, Ye L. ScRNA-seq revealed the tumor microenvironment heterogeneity related to the occurrence and metastasis in upper urinary tract urothelial carcinoma. Cancer Gene Ther 2024:10.1038/s41417-024-00779-3. [PMID: 38877164 DOI: 10.1038/s41417-024-00779-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 04/15/2024] [Accepted: 04/22/2024] [Indexed: 06/16/2024]
Abstract
Metastasis is the greatest clinical challenge for UTUCs, which may have distinct molecular and cellular characteristics from earlier cancers. Herein, we provide single-cell transcriptome profiles of UTUC para cancer normal tissue, primary tumor lesions, and lymphatic metastases to explore possible mechanisms associated with UTUC occurrence and metastasis. From 28,315 cells obtained from normal and tumor tissues of 3 high-grade UTUC patients, we revealed the origin of UTUC tumor cells and the homology between metastatic and primary tumor cells. Unlike the immunomicroenvironment suppression of other tumors, we found no immunosuppression in the tumor microenvironment of UTUC. Moreover, it is imperative to note that stromal cells are pivotal in the advancement of UTUC. This comprehensive single-cell exploration enhances our comprehension of the molecular and cellular dynamics of metastatic UTUCs and discloses promising diagnostic and therapeutic targets in cancer-microenvironment interactions.
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Affiliation(s)
- Shiyong Xin
- Department of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
- Department of Urology, The First Affiliated Hospital and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, 471000, China
| | - Yanwei Zhang
- Department of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Zhenhua Zhang
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, 510006, China
| | - Ziyao Li
- Department of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Xianchao Sun
- Department of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Xiang Liu
- Department of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Liang Jin
- Department of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Weiyi Li
- Department of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Chaozhi Tang
- Department of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Wangli Mei
- Department of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Qiong Cao
- Department of Pathology, The Third Affiliated Hospital of Henan University of Science and Technology, Luoyang, 471003, China
| | - Haojie Wang
- Department of Central Laboratory, Zhengzhou University, Luoyang Central Hospital, Luoyang, 471003, China
| | - Zhihao Wei
- Department of Pathology, Yiluo Hospital of Luoyang, The Teaching Hospital of Henan University of Science and Technology, Luoyang, China
| | - Zhen Zhou
- Department of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Rongbing Li
- Department of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Xiaofei Wen
- Department of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Guosheng Yang
- Department of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Weihua Chen
- Department of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China.
| | - Junhua Zheng
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
| | - Lin Ye
- Department of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China.
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Zhang W, Maeser D, Lee A, Huang Y, Gruener RF, Abdelbar IG, Jena S, Patel AG, Huang RS. Integration of Pan-Cancer Cell Line and Single-Cell Transcriptomic Profiles Enables Inference of Therapeutic Vulnerabilities in Heterogeneous Tumors. Cancer Res 2024; 84:2021-2033. [PMID: 38581448 PMCID: PMC11178452 DOI: 10.1158/0008-5472.can-23-3005] [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] [Received: 09/29/2023] [Revised: 10/18/2023] [Accepted: 04/01/2024] [Indexed: 04/08/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) greatly advanced the understanding of intratumoral heterogeneity by identifying distinct cancer cell subpopulations. However, translating biological differences into treatment strategies is challenging due to a lack of tools to facilitate efficient drug discovery that tackles heterogeneous tumors. Developing such approaches requires accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we developed a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening data sets. This method achieved high accuracy in separating cells into their correct cellular drug response statuses. In three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), the predicted results using scIDUC were accurate and mirrored biological expectations. In the first two tests, the framework identified drugs for cell subpopulations that were resistant to standard-of-care (SOC) therapies due to intrinsic resistance or tumor microenvironmental effects, and the results showed high consistency with experimental findings from the original studies. In the third test using newly generated SOC therapy-resistant cell lines, scIDUC identified efficacious drugs for the resistant line, and the predictions were validated with in vitro experiments. Together, this study demonstrates the potential of scIDUC to quickly translate scRNA-seq data into drug responses for individual cells, displaying the potential as a tool to improve the treatment of heterogenous tumors. SIGNIFICANCE A versatile method that infers cell-level drug response in scRNA-seq data facilitates the development of therapeutic strategies to target heterogeneous subpopulations within a tumor and address issues such as treatment failure and resistance.
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Affiliation(s)
- Weijie Zhang
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Danielle Maeser
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Adam Lee
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Yingbo Huang
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Robert F. Gruener
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Israa G. Abdelbar
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
- Clinical Pharmacy Practice Department, The British University in Egypt, El Sherouk, 11837, Egypt
| | - Sampreeti Jena
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Anand G. Patel
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN 38105
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105
| | - R. Stephanie Huang
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
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9
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Wang W, Dong G, Yang Z, Li S, Li J, Wang L, Zhu Q, Wang Y. Single-cell analysis of tumor microenvironment and cell adhesion reveals that interleukin-1 beta promotes cancer cell proliferation in breast cancer. Animal Model Exp Med 2024. [PMID: 38860503 DOI: 10.1002/ame2.12445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 05/20/2024] [Indexed: 06/12/2024] Open
Abstract
BACKGROUND Triple-negative breast cancer (TNBC), which is so called because of the lack of estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptor 2 (HER2) receptors on the cancer cells, accounts for 10%-15% of all breast cancers. The heterogeneity of the tumor microenvironment is high. However, the role of plasma cells controlling the tumor migration progression in TNBC is still not fully understood. METHODS We analyzed single-cell RNA sequencing data from five HER2 positive, 12 ER positive/PR positive, and nine TNBC samples. The potential targets were validated by immunohistochemistry. RESULTS Plasma cells were enriched in TNBC samples, which was consistent with validation using data from The Cancer Genome Atlas. Cell communication analysis revealed that plasma cells interact with T cells through the intercellular adhesion molecule 2-integrin-aLb2 complex, and then release interleukin 1 beta (IL1B), as verified by immunohistochemistry, ultimately promoting tumor growth. CONCLUSION Our results revealed the role of plasma cells in TNBC and identified IL1B as a new prognostic marker for TNBC.
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Affiliation(s)
- Wenyan Wang
- Department of General Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Gehong Dong
- Department of Pathology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ziguo Yang
- Department of General Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shaoxiang Li
- Department of Pathology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jia Li
- Department of Pathology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lin Wang
- Department of General Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Qiang Zhu
- Department of General Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yuchen Wang
- Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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10
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Liu J, Ren Q, Xiao H, Li S, Zheng L, Yang X, Feng L, Zhou Z, Wang H, Yang J, Wang W. Whole-tumoral metabolic heterogeneity in 18F-FDG PET/CT is a novel prognostic marker for neuroblastoma. Cancer Imaging 2024; 24:72. [PMID: 38863073 PMCID: PMC11167917 DOI: 10.1186/s40644-024-00718-3] [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: 02/22/2024] [Accepted: 06/05/2024] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND Neuroblastoma (NB) is a highly heterogeneous tumor, and more than half of newly diagnosed NB are associated with extensive metastases. Accurately characterizing the heterogeneity of whole-body tumor lesions remains clinical challenge. This study aims to quantify whole-tumoral metabolic heterogeneity (WMH) derived from whole-body tumor lesions, and investigate the prognostic value of WMH in NB. METHODS We retrospectively enrolled 95 newly diagnosed pediatric NB patients in our department. Traditional semi-quantitative PET/CT parameters including the maximum standardized uptake value (SUVmax), the mean standardized uptake value (SUVmean), the peak standardized uptake value (SUVpeak), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were measured. These PET/CT parameters were expressed as PSUVmax, PSUVmean, PSUVpeak, PMTV, PTLG for primary tumor, WSUVmax, WSUVmean, WSUVpeak, WMTV, WTLG for whole-body tumor lesions. The metabolic heterogeneity was quantified using the areas under the curve of the cumulative SUV-volume histogram index (AUC-CSH index). Intra-tumoral metabolic heterogeneity (IMH) and WMH were extracted from primary tumor and whole-body tumor lesions, respectively. The outcome endpoints were overall survival (OS) and progression-free survival (PFS). Survival analysis was performed utilizing the univariate and multivariate Cox proportional hazards regression. The optimal cut-off values for metabolic parameters were obtained by receiver operating characteristic curve (ROC). RESULTS During follow up, 27 (28.4%) patients died, 21 (22.1%) patients relapsed and 47 (49.5%) patients remained progression-free survival, with a median follow-up of 35.0 months. In survival analysis, WMTV and WTLG were independent indicators of PFS, and WMH was an independent risk factor of PFS and OS. However, IMH only showed association with PFS and OS. In addition to metabolic parameters, the International Neuroblastoma Staging System (INSS) was identified as an independent risk factor for PFS, and neuron-specific enolase (NSE) served as an independent predictor of OS. CONCLUSION WMH was an independent risk factor for PFS and OS, suggesting its potential as a novel prognostic marker for newly diagnosed NB patients.
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Affiliation(s)
- Jun Liu
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Qinghua Ren
- Department of Surgical Oncology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Haonan Xiao
- Department of Radiation Oncology and Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, No.440, Jiyan Road, 250117, Jinan, Shandong Province, China
| | - Siqi Li
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Lingling Zheng
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Xu Yang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Lijuan Feng
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Ziang Zhou
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Huanmin Wang
- Department of Surgical Oncology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Jigang Yang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Wei Wang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China.
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11
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Liu H, Gao J, Feng M, Cheng J, Tang Y, Cao Q, Zhao Z, Meng Z, Zhang J, Zhang G, Zhang C, Zhao M, Yan Y, Wang Y, Xue R, Zhang N, Li H. Integrative molecular and spatial analysis reveals evolutionary dynamics and tumor-immune interplay of in situ and invasive acral melanoma. Cancer Cell 2024; 42:1067-1085.e11. [PMID: 38759655 DOI: 10.1016/j.ccell.2024.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 03/21/2024] [Accepted: 04/26/2024] [Indexed: 05/19/2024]
Abstract
In acral melanoma (AM), progression from in situ (AMis) to invasive AM (iAM) leads to significantly reduced survival. However, evolutionary dynamics during this process remain elusive. Here, we report integrative molecular and spatial characterization of 147 AMs using genomics, bulk and single-cell transcriptomics, and spatial transcriptomics and proteomics. Vertical invasion from AMis to iAM displays an early and monoclonal seeding pattern. The subsequent regional expansion of iAM exhibits two distinct patterns, clonal expansion and subclonal diversification. Notably, molecular subtyping reveals an aggressive iAM subset featured with subclonal diversification, increased epithelial-mesenchymal transition (EMT), and spatial enrichment of APOE+/CD163+ macrophages. In vitro and ex vivo experiments further demonstrate that APOE+CD163+ macrophages promote tumor EMT via IGF1-IGF1R interaction. Adnexal involvement can predict AMis with higher invasive potential whereas APOE and CD163 serve as prognostic biomarkers for iAM. Altogether, our results provide implications for the early detection and treatment of AM.
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MESH Headings
- Humans
- Melanoma/genetics
- Melanoma/immunology
- Melanoma/pathology
- Epithelial-Mesenchymal Transition/genetics
- Skin Neoplasms/genetics
- Skin Neoplasms/immunology
- Skin Neoplasms/pathology
- Antigens, Differentiation, Myelomonocytic/metabolism
- Antigens, Differentiation, Myelomonocytic/genetics
- Antigens, CD/metabolism
- Antigens, CD/genetics
- Neoplasm Invasiveness
- Apolipoproteins E/genetics
- Macrophages/immunology
- Macrophages/metabolism
- Male
- Female
- Receptor, IGF Type 1/genetics
- Receptor, IGF Type 1/metabolism
- Tumor Microenvironment/immunology
- Tumor Microenvironment/genetics
- Biomarkers, Tumor/genetics
- Biomarkers, Tumor/metabolism
- Gene Expression Regulation, Neoplastic
- Spatial Analysis
- Middle Aged
- Prognosis
- Disease Progression
- Aged
- Receptors, Cell Surface
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Affiliation(s)
- Hengkang Liu
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University First Hospital, Beijing 100191, China; School of Basic Medical Sciences, International Cancer Institute, Peking University, Beijing 100191, China
| | - Jiawen Gao
- National Clinical Research Center for Skin and Immune Diseases, NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Peking University First Hospital, Beijing 100034, China; Institute of Photomedicine and Department of Phototherapy at Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai 200443, China
| | - Mei Feng
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University First Hospital, Beijing 100191, China
| | - Jinghui Cheng
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University First Hospital, Beijing 100191, China
| | - Yuchen Tang
- National Clinical Research Center for Skin and Immune Diseases, NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Peking University First Hospital, Beijing 100034, China
| | - Qi Cao
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University First Hospital, Beijing 100191, China
| | - Ziji Zhao
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University First Hospital, Beijing 100191, China
| | - Ziqiao Meng
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University First Hospital, Beijing 100191, China
| | - Jiarui Zhang
- National Clinical Research Center for Skin and Immune Diseases, NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Peking University First Hospital, Beijing 100034, China
| | - Guohong Zhang
- National Clinical Research Center for Skin and Immune Diseases, NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Peking University First Hospital, Beijing 100034, China
| | - Chong Zhang
- National Clinical Research Center for Skin and Immune Diseases, NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Peking University First Hospital, Beijing 100034, China
| | - Mingming Zhao
- National Clinical Research Center for Skin and Immune Diseases, NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Peking University First Hospital, Beijing 100034, China
| | - Yicen Yan
- National Clinical Research Center for Skin and Immune Diseases, NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Peking University First Hospital, Beijing 100034, China
| | - Yang Wang
- National Clinical Research Center for Skin and Immune Diseases, NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Peking University First Hospital, Beijing 100034, China
| | - Ruidong Xue
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University First Hospital, Beijing 100191, China; School of Basic Medical Sciences, International Cancer Institute, Peking University, Beijing 100191, China.
| | - Ning Zhang
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University First Hospital, Beijing 100191, China; School of Basic Medical Sciences, International Cancer Institute, Peking University, Beijing 100191, China; Yunnan Baiyao Group, Kunming 650500, China.
| | - Hang Li
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University First Hospital, Beijing 100191, China; National Clinical Research Center for Skin and Immune Diseases, NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Peking University First Hospital, Beijing 100034, China; Yunnan Baiyao Group, Kunming 650500, China.
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12
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Wang FA, Zhuang Z, Gao F, He R, Zhang S, Wang L, Liu J, Li Y. TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology. Genome Biol 2024; 25:149. [PMID: 38845006 PMCID: PMC11157742 DOI: 10.1186/s13059-024-03293-9] [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: 12/14/2023] [Accepted: 05/29/2024] [Indexed: 06/09/2024] Open
Abstract
Cancer is a complex disease composing systemic alterations in multiple scales. In this study, we develop the Tumor Multi-Omics pre-trained Network (TMO-Net) that integrates multi-omics pan-cancer datasets for model pre-training, facilitating cross-omics interactions and enabling joint representation learning and incomplete omics inference. This model enhances multi-omics sample representation and empowers various downstream oncology tasks with incomplete multi-omics datasets. By employing interpretable learning, we characterize the contributions of distinct omics features to clinical outcomes. The TMO-Net model serves as a versatile framework for cross-modal multi-omics learning in oncology, paving the way for tumor omics-specific foundation models.
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Affiliation(s)
- Feng-Ao Wang
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China
- Guangzhou National Laboratory, Guangzhou, 510005, China
| | - Zhenfeng Zhuang
- Department of Computer Science at the School of Informatics, Xiamen University, Xiamen, 361005, China
| | - Feng Gao
- Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655, China
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200433, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655, China
| | - Ruikun He
- BYHEALTH Institute of Nutrition & Health, Guangzhou, 510000, China
| | - Shaoting Zhang
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200433, China
| | - Liansheng Wang
- Department of Computer Science at the School of Informatics, Xiamen University, Xiamen, 361005, China.
| | - Junwei Liu
- Guangzhou National Laboratory, Guangzhou, 510005, China.
| | - Yixue Li
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China.
- Guangzhou National Laboratory, Guangzhou, 510005, China.
- Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200030, China.
- GZMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Medical University, Guangzhou, 511436, China.
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
- Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, 200433, China.
- Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, 200032, China.
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13
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Liu L, Zhao Q, Xiong D, Li D, Du J, Huang Y, Yang Y, Chen R. Suppressing mitochondrial inner membrane protein (IMMT) inhibits the proliferation of breast cancer cells through mitochondrial remodeling and metabolic regulation. Sci Rep 2024; 14:12766. [PMID: 38834715 PMCID: PMC11150385 DOI: 10.1038/s41598-024-63427-8] [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: 02/28/2024] [Accepted: 05/29/2024] [Indexed: 06/06/2024] Open
Abstract
Metabolic reprogramming is widely recognized as a hallmark of malignant tumors, and the targeting of metabolism has emerged as an appealing approach for cancer treatment. Mitochondria, as pivotal organelles, play a crucial role in the metabolic regulation of tumor cells, and their morphological and functional alterations are intricately linked to the biological characteristics of tumors. As a key regulatory subunit of mitochondria, mitochondrial inner membrane protein (IMMT), plays a vital role in degenerative diseases, but its role in tumor is almost unknown. The objective of this research was to investigate the roles that IMMT play in the development and progression of breast cancer (BC), as well as to elucidate the underlying biological mechanisms that drive these effects. In this study, it was confirmed that the expression of IMMT in BC tissues was significantly higher than that in normal tissues. The analysis of The Cancer Genome Atlas (TCGA) database revealed that IMMT can serve as an independent prognostic factor for BC patients. Additionally, verification in clinical specimens of BC demonstrated a positive association between high IMMT expression and larger tumor size (> 2 cm), Ki-67 expression (> 15%), and HER-2 status. Furthermore, in vitro experiments have substantiated that the suppression of IMMT expression resulted in a reduction in cell proliferation and alterations in mitochondrial cristae, concomitant with the liberation of cytochrome c, but it did not elicit mitochondrial apoptosis. Through Gene Set Enrichment Analysis (GSEA) analysis, we have predicted the associated metabolic genes and discovered that IMMT potentially modulates the advancement of BC through its interaction with 16 metabolic-related genes, and the changes in glycolysis related pathways have been validated in BC cell lines after IMMT inhibition. Consequently, this investigation furnishes compelling evidence supporting the classification of IMMT as prognostic marker in BC, and underscoring its prospective utility as a novel target for metabolic therapy.
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Affiliation(s)
- Li Liu
- Clinical Medical College, Zunyi Medical University, Zunyi, China
| | - Qingqing Zhao
- Clinical Medical College, Zunyi Medical University, Zunyi, China
| | - Daigang Xiong
- Department of General Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- Department of Thyroid and Breast Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Dan Li
- Clinical Medical College, Zunyi Medical University, Zunyi, China
| | - Jie Du
- Department of Laboratory Medicine, Affiliated Hospital of ZunYi Medical University, Zunyi, China
- School of Laboratory Medicine, Zunyi Medical University, Zunyi, China
| | - Yunfei Huang
- Department of Laboratory Medicine, Affiliated Hospital of ZunYi Medical University, Zunyi, China
- School of Laboratory Medicine, Zunyi Medical University, Zunyi, China
| | - Yan Yang
- Department of Laboratory Medicine, Affiliated Hospital of ZunYi Medical University, Zunyi, China.
- School of Laboratory Medicine, Zunyi Medical University, Zunyi, China.
| | - Rui Chen
- Department of General Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
- Department of Thyroid and Breast Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
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14
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Ghiandai V, Grassi ES, Gazzano G, Fugazzola L, Persani L. Characterization of EpCAM in thyroid cancer biology by three-dimensional spheroids in vitro model. Cancer Cell Int 2024; 24:196. [PMID: 38835027 DOI: 10.1186/s12935-024-03378-2] [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: 02/09/2024] [Accepted: 05/19/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND Thyroid cancer (TC) is the most common endocrine malignancy. Nowadays, undifferentiated thyroid cancers (UTCs) are still lethal, mostly due to the insurgence of therapy resistance and disease relapse. These events are believed to be caused by a subpopulation of cancer cells with stem-like phenotype and specific tumor-initiating abilities, known as tumor-initiating cells (TICs). A comprehensive understanding of how to isolate and target these cells is necessary. Here we provide insights into the role that the protein Epithelial Cell Adhesion Molecule (EpCAM), a known TICs marker for other solid tumors, may have in TC biology, thus considering EpCAM a potential marker of thyroid TICs in UTCs. METHODS The characterization of EpCAM was accomplished through Western Blot and Immunofluorescence on patient-derived tissue samples, adherent cell cultures, and 3D sphere cultures of poorly differentiated thyroid cancer (PDTC) and anaplastic thyroid cancer (ATC) cell lines. The frequency of tumor cells with putative tumor-initiating ability within the 3D cultures was assessed through extreme limiting dilution analysis (ELDA). EpCAM proteolytic cleavages were studied through treatments with different cleavages' inhibitors. To evaluate the involvement of EpCAM in inducing drug resistance, Vemurafenib (PLX-4032) treatments were assessed through MTT assay. RESULTS Variable EpCAM expression pattern was observed in TC tissue samples, with increased cleavage in the more UTC. We demonstrated that EpCAM is subjected to an intense cleavage process in ATC-derived 3D tumor spheres and that the 3D model faithfully mimics what was observed in patient's samples. We also proved that the integrity of the protein appears to be crucial for the generation of 3D spheres, and its expression and cleavage in a 3D system could contribute to drug resistance in thyroid TICs. CONCLUSIONS Our data provide novel information on the role of EpCAM expression and cleavage in the biology of thyroid TICs, and our 3D model reflects the variability of EpCAM cleavage observed in tissue samples. EpCAM evaluation could play a role in clinical decisions regarding patient therapy since its expression and cleavage may have a fundamental role in the switch to a drug-resistant phenotype of UTC cells.
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Affiliation(s)
- Viola Ghiandai
- Department of Endocrine and Metabolic Diseases, Istituto Auxologico Italiano IRCCS, Milan, Italy
| | - Elisa Stellaria Grassi
- Department of Medical Biotechnology and Translational Medicine (BIOMETRA), Università degli Studi di Milano, Milan, Italy
| | - Giacomo Gazzano
- Pathology Unit, Istituto Auxologico Italiano IRCCS, Milan, Italy
| | - Laura Fugazzola
- Department of Endocrine and Metabolic Diseases, Istituto Auxologico Italiano IRCCS, Milan, Italy
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milan, Italy
| | - Luca Persani
- Department of Endocrine and Metabolic Diseases, Istituto Auxologico Italiano IRCCS, Milan, Italy.
- Department of Medical Biotechnology and Translational Medicine (BIOMETRA), Università degli Studi di Milano, Milan, Italy.
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15
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Yoon SB, Chen L, Robinson IE, Khatib TO, Arthur RA, Claussen H, Zohbi NM, Wu H, Mouw JK, Marcus AI. Subpopulation commensalism promotes Rac1-dependent invasion of single cells via laminin-332. J Cell Biol 2024; 223:e202308080. [PMID: 38551497 PMCID: PMC10982113 DOI: 10.1083/jcb.202308080] [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] [Received: 08/28/2023] [Revised: 02/02/2024] [Accepted: 03/11/2024] [Indexed: 04/02/2024] Open
Abstract
Phenotypic heterogeneity poses a significant hurdle for cancer treatment but is under-characterized in the context of tumor invasion. Amidst the range of phenotypic heterogeneity across solid tumor types, collectively invading cells and single cells have been extensively characterized as independent modes of invasion, but their intercellular interactions have rarely been explored. Here, we isolated collectively invading cells and single cells from the heterogeneous 4T1 cell line and observed extensive transcriptional and epigenetic diversity across these subpopulations. By integrating these datasets, we identified laminin-332 as a protein complex exclusively secreted by collectively invading cells. Live-cell imaging revealed that laminin-332 derived from collectively invading cells increased the velocity and directionality of single cells. Despite collectively invading and single cells having similar expression of the integrin α6β4 dimer, single cells demonstrated higher Rac1 activation upon laminin-332 binding to integrin α6β4. This mechanism suggests a novel commensal relationship between collectively invading and single cells, wherein collectively invading cells promote the invasive potential of single cells through a laminin-332/Rac1 axis.
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Affiliation(s)
- Sung Bo Yoon
- Department of Hematology and Medical Oncology, Emory University, Atlanta, GA, USA
- Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Luxiao Chen
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA
| | - Isaac E. Robinson
- Department of Hematology and Medical Oncology, Emory University, Atlanta, GA, USA
- Winship Cancer Institute, Emory University, Atlanta, GA, USA
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Tala O. Khatib
- Department of Hematology and Medical Oncology, Emory University, Atlanta, GA, USA
- Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Robert A. Arthur
- Emory Integrated Computational Core, Emory University, Atlanta, GA, USA
| | - Henry Claussen
- Emory Integrated Computational Core, Emory University, Atlanta, GA, USA
| | - Najdat M. Zohbi
- Graduate Medical Education, Piedmont Macon Medical, Macon, GA, USA
| | - Hao Wu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Janna K. Mouw
- Department of Hematology and Medical Oncology, Emory University, Atlanta, GA, USA
- Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Adam I. Marcus
- Department of Hematology and Medical Oncology, Emory University, Atlanta, GA, USA
- Winship Cancer Institute, Emory University, Atlanta, GA, USA
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16
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Gorlov IP, Gorlova OY, Tsavachidis S, Amos CI. Strength of selection in lung tumors correlates with clinical features better than tumor mutation burden. Sci Rep 2024; 14:12732. [PMID: 38831004 PMCID: PMC11148192 DOI: 10.1038/s41598-024-63468-z] [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: 12/08/2023] [Accepted: 05/29/2024] [Indexed: 06/05/2024] Open
Abstract
Single nucleotide substitutions are the most common type of somatic mutations in cancer genome. The goal of this study was to use publicly available somatic mutation data to quantify negative and positive selection in individual lung tumors and test how strength of directional and absolute selection is associated with clinical features. The analysis found a significant variation in strength of selection (both negative and positive) among tumors, with median selection tending to be negative even though tumors with strong positive selection also exist. Strength of selection estimated as the density of missense mutations relative to the density of silent mutations showed only a weak correlation with tumor mutation burden. In the "all histology together" analysis we found that absolute strength of selection was strongly correlated with all clinically relevant features analyzed. In histology-stratified analysis selection was strongest in small cell lung cancer. Selection in adenocarcinoma was somewhat higher compared to squamous cell carcinoma. The study suggests that somatic mutation- based quantifying of directional and absolute selection in individual tumors can be a useful biomarker of tumor aggressiveness.
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Affiliation(s)
- Ivan P Gorlov
- Institute for Clinical and Translational Research, Baylor College of Medicine, One Baylor Plaza, Mailstop: BCM451, Houston, TX, 77030, USA.
| | - Olga Y Gorlova
- Institute for Clinical and Translational Research, Baylor College of Medicine, One Baylor Plaza, Mailstop: BCM451, Houston, TX, 77030, USA
| | - Spyridon Tsavachidis
- Institute for Clinical and Translational Research, Baylor College of Medicine, One Baylor Plaza, Mailstop: BCM451, Houston, TX, 77030, USA
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, One Baylor Plaza, Mailstop: BCM451, Houston, TX, 77030, USA
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17
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Geukens T, Maetens M, Hooper JE, Oesterreich S, Lee AV, Miller L, Atkinson JM, Rosenzweig M, Puhalla S, Thorne H, Devereux L, Bowtell D, Loi S, Bacon ER, Ihle K, Song M, Rodriguez-Rodriguez L, Welm AL, Gauchay L, Murali R, Chanda P, Karacay A, Naceur-Lombardelli C, Bridger H, Swanton C, Jamal-Hanjani M, Kollath L, True L, Morrissey C, Chambers M, Chinnaiyan AM, Wilson A, Mehra R, Reichert Z, Carey LA, Perou CM, Kelly E, Maeda D, Goto A, Kulka J, Székely B, Szasz AM, Tőkés AM, Van Den Bogaert W, Floris G, Desmedt C. Research autopsy programmes in oncology: shared experience from 14 centres across the world. J Pathol 2024; 263:150-165. [PMID: 38551513 DOI: 10.1002/path.6271] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 12/22/2023] [Accepted: 02/09/2024] [Indexed: 05/12/2024]
Abstract
While there is a great clinical need to understand the biology of metastatic cancer in order to treat it more effectively, research is hampered by limited sample availability. Research autopsy programmes can crucially advance the field through synchronous, extensive, and high-volume sample collection. However, it remains an underused strategy in translational research. Via an extensive questionnaire, we collected information on the study design, enrolment strategy, study conduct, sample and data management, and challenges and opportunities of research autopsy programmes in oncology worldwide. Fourteen programmes participated in this study. Eight programmes operated 24 h/7 days, resulting in a lower median postmortem interval (time between death and start of the autopsy, 4 h) compared with those operating during working hours (9 h). Most programmes (n = 10) succeeded in collecting all samples within a median of 12 h after death. A large number of tumour sites were sampled during each autopsy (median 15.5 per patient). The median number of samples collected per patient was 58, including different processing methods for tumour samples but also non-tumour tissues and liquid biopsies. Unique biological insights derived from these samples included metastatic progression, treatment resistance, disease heterogeneity, tumour dormancy, interactions with the tumour micro-environment, and tumour representation in liquid biopsies. Tumour patient-derived xenograft (PDX) or organoid (PDO) models were additionally established, allowing for drug discovery and treatment sensitivity assays. Apart from the opportunities and achievements, we also present the challenges related with postmortem sample collections and strategies to overcome them, based on the shared experience of these 14 programmes. Through this work, we hope to increase the transparency of postmortem tissue donation, to encourage and aid the creation of new programmes, and to foster collaborations on these unique sample collections. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Tatjana Geukens
- Laboratory for Translational Breast Cancer Research, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Marion Maetens
- Laboratory for Translational Breast Cancer Research, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Jody E Hooper
- Stanford University School of Medicine, Palo Alto, CA, USA
| | - Steffi Oesterreich
- University of Pittsburgh UPMC Hillman Cancer Center, and Magee Womens Research Institute, Pittsburgh, PA, USA
| | - Adrian V Lee
- University of Pittsburgh UPMC Hillman Cancer Center, and Magee Womens Research Institute, Pittsburgh, PA, USA
| | - Lori Miller
- University of Pittsburgh UPMC Hillman Cancer Center, and Magee Womens Research Institute, Pittsburgh, PA, USA
| | - Jenny M Atkinson
- University of Pittsburgh UPMC Hillman Cancer Center, and Magee Womens Research Institute, Pittsburgh, PA, USA
| | - Margaret Rosenzweig
- University of Pittsburgh UPMC Hillman Cancer Center, and Magee Womens Research Institute, Pittsburgh, PA, USA
| | - Shannon Puhalla
- University of Pittsburgh UPMC Hillman Cancer Center, and Magee Womens Research Institute, Pittsburgh, PA, USA
| | - Heather Thorne
- Peter MacCallum Cancer Centre, Melbourne, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Australia
| | - Lisa Devereux
- Peter MacCallum Cancer Centre, Melbourne, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Australia
| | | | - Sherene Loi
- Peter MacCallum Cancer Centre, Melbourne, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Australia
| | - Eliza R Bacon
- Center for Precision Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Kena Ihle
- Center for Precision Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Mihae Song
- Center for Precision Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | | | - Alana L Welm
- University of Utah Huntsman Cancer Institute, Salt Lake City, UT, USA
| | - Lisa Gauchay
- University of Utah Huntsman Cancer Institute, Salt Lake City, UT, USA
| | | | - Pharto Chanda
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ali Karacay
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Hayley Bridger
- Cancer Research UK, and UCL Cancer Trials Centre, University College London, London, UK
| | - Charles Swanton
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, UCL Cancer Institute, London, UK
- Department of Medical Oncology, University College London Hospitals, London, UK
| | - Mariam Jamal-Hanjani
- Cancer Research UK Lung Cancer Centre of Excellence, UCL Cancer Institute, London, UK
- Department of Medical Oncology, University College London Hospitals, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
| | | | | | | | | | | | | | | | | | - Lisa A Carey
- University of North Carolina, Lineberger Comprehensive Cancer Center, Chapel Hill, NC, USA
| | - Charles M Perou
- University of North Carolina, Lineberger Comprehensive Cancer Center, Chapel Hill, NC, USA
| | - Erin Kelly
- University of North Carolina, Lineberger Comprehensive Cancer Center, Chapel Hill, NC, USA
| | - Daichi Maeda
- Department of Molecular and Cellular Pathology, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan
| | - Akiteru Goto
- Department of Cellular and Organ Pathology, Graduate School of Medicine, Akita University, Akita, Japan
| | - Janina Kulka
- Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Budapest, Hungary
| | - Borbála Székely
- Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Budapest, Hungary
- National Institute of Oncology, Budapest, Hungary
| | - A Marcell Szasz
- Division of Oncology, Department of Internal Medicine and Oncology, Semmelweis University, Budapest, Hungary
| | - Anna-Mária Tőkés
- Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Budapest, Hungary
| | | | - Giuseppe Floris
- Department of Pathology, University Hospitals Leuven, Leuven, Belgium
| | - Christine Desmedt
- Laboratory for Translational Breast Cancer Research, Department of Oncology, KU Leuven, Leuven, Belgium
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18
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Thakur C, Qiu Y, Pawar A, Chen F. Epigenetic regulation of breast cancer metastasis. Cancer Metastasis Rev 2024; 43:597-619. [PMID: 37857941 DOI: 10.1007/s10555-023-10146-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 10/02/2023] [Indexed: 10/21/2023]
Abstract
Breast cancer is the most frequently diagnosed malignancy and the second leading cause of cancer-related mortality among women worldwide. Recurrent metastasis is associated with poor patient outcomes and poses a significant challenge in breast cancer therapies. Cancer cells adapting to a new tissue microenvironment is the key event in distant metastasis development, where the disseminating tumor cells are likely to acquire genetic and epigenetic alterations during the process of metastatic colonization. Despite several decades of research in this field, the exact mechanisms governing metastasis are not fully understood. However, emerging body of evidence indicates that in addition to genetic changes, epigenetic reprogramming of cancer cells and the metastatic niche are paramount toward successful metastasis. Here, we review and discuss the latest knowledge about the salient attributes of metastasis and epigenetic regulation in breast cancer and crucial research domains that need further investigation.
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Affiliation(s)
- Chitra Thakur
- Stony Brook Cancer Center, Renaissance School of Medicine, Stony Brook University, Lauterbur Drive, Stony Brook, NY, 11794, USA.
| | - Yiran Qiu
- Stony Brook Cancer Center, Renaissance School of Medicine, Stony Brook University, Lauterbur Drive, Stony Brook, NY, 11794, USA
| | - Aashna Pawar
- Stony Brook Cancer Center, Renaissance School of Medicine, Stony Brook University, Lauterbur Drive, Stony Brook, NY, 11794, USA
| | - Fei Chen
- Stony Brook Cancer Center, Renaissance School of Medicine, Stony Brook University, Lauterbur Drive, Stony Brook, NY, 11794, USA.
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19
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Neefjes J, Gurova K, Sarthy J, Szabó G, Henikoff S. Chromatin as an old and new anticancer target. Trends Cancer 2024:S2405-8033(24)00095-5. [PMID: 38825423 DOI: 10.1016/j.trecan.2024.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Revised: 05/08/2024] [Accepted: 05/13/2024] [Indexed: 06/04/2024]
Abstract
Recent genome-wide analyses identified chromatin modifiers as one of the most frequently mutated classes of genes across all cancers. However, chemotherapies developed for cancers involving DNA damage remain the standard of care for chromatin-deranged malignancies. In this review we address this conundrum by establishing the concept of 'chromatin damage': the non-genetic damage to protein-DNA interactions induced by certain small molecules. We highlight anthracyclines, a class of chemotherapeutic agents ubiquitously applied in oncology, as an example of overlooked chromatin-targeting agents. We discuss our current understanding of this phenomenon and explore emerging chromatin-damaging agents as a basis for further studies to maximize their impact in modern cancer treatment.
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Affiliation(s)
- Jacques Neefjes
- Department of Cell and Chemical Biology and Oncode Institute, LUMC, Einthovenweg 20, 2333, ZC, Leiden, The Netherlands
| | - Katerina Gurova
- Department of Cell Stress Biology, Roswell Park Cancer Institute, Elm and Carlton Streets, Buffalo, NY, 14263, USA.
| | - Jay Sarthy
- Department of Pediatrics, University of Washington School of Medicine and Seattle Children's Research Institute, 1920 Terry Ave, Seattle, WA 98109, USA
| | - Gábor Szabó
- Faculty of Medicine, Department of Biophysics and Cell Biology, University of Debrecen, Debrecen, Egyetem tér 1, 4032, Hungary
| | - Steven Henikoff
- Basic Sciences Division, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
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20
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Aalam SMM, Nguyen LV, Ritting ML, Kannan N. Clonal tracking in cancer and metastasis. Cancer Metastasis Rev 2024; 43:639-656. [PMID: 37910295 DOI: 10.1007/s10555-023-10149-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 10/16/2023] [Indexed: 11/03/2023]
Abstract
The eradication of many cancers has proven challenging due to the presence of functionally and genetically heterogeneous clones maintained by rare cancer stem cells (CSCs), which contribute to disease progression, treatment refractoriness, and late relapse. The characterization of functional CSC activity has necessitated the development of modern clonal tracking strategies. This review describes viral-based and CRISPR-Cas9-based cellular barcoding, lineage tracing, and imaging-based approaches. DNA-based cellular barcoding technology is emerging as a powerful and robust strategy that has been widely applied to in vitro and in vivo model systems, including patient-derived xenograft models. This review also highlights the potential of these methods for use in the clinical and drug discovery contexts and discusses the important insights gained from such approaches.
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Affiliation(s)
| | - Long Viet Nguyen
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Megan L Ritting
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Nagarajan Kannan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
- Mayo Clinic Comprehensive Cancer Center, Mayo Clinic, Rochester, MN, USA.
- Center for Regenerative Biotherapeutics, Mayo Clinic, Rochester, MN, USA.
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21
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Chen K, He Y, Wang W, Yuan X, Carbone DP, Yang F. Development of new techniques and clinical applications of liquid biopsy in lung cancer management. Sci Bull (Beijing) 2024; 69:1556-1568. [PMID: 38641511 DOI: 10.1016/j.scib.2024.03.062] [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: 09/25/2023] [Revised: 12/12/2023] [Accepted: 01/17/2024] [Indexed: 04/21/2024]
Abstract
Lung cancer is an exceedingly malignant tumor reported as having the highest morbidity and mortality of any cancer worldwide, thus posing a great threat to global health. Despite the growing demand for precision medicine, current methods for early clinical detection, treatment and prognosis monitoring in lung cancer are hampered by certain bottlenecks. Studies have found that during the formation and development of a tumor, molecular substances carrying tumor-related genetic information can be released into body fluids. Liquid biopsy (LB), a method for detecting these tumor-related markers in body fluids, maybe a way to make progress in these bottlenecks. In recent years, LB technology has undergone rapid advancements. Therefore, this review will provide information on technical updates to LB and its potential clinical applications, evaluate its effectiveness for specific applications, discuss the existing limitations of LB, and present a look forward to possible future clinical applications. Specifically, this paper will introduce technical updates from the prospectives of engineering breakthroughs in the detection of membrane-based LB biomarkers and other improvements in sequencing technology. Additionally, it will summarize the latest applications of liquid biopsy for the early detection, diagnosis, treatment, and prognosis of lung cancer. We will present the interconnectedness of clinical and laboratory issues and the interplay of technology and application in LB today.
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Affiliation(s)
- Kezhong Chen
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing 100044, China; Peking University People's Hospital Thoracic Oncology Institute & Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Beijing 100044, China
| | - Yue He
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing 100044, China; Peking University People's Hospital Thoracic Oncology Institute & Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Beijing 100044, China
| | - Wenxiang Wang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing 100044, China; Peking University People's Hospital Thoracic Oncology Institute & Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Beijing 100044, China
| | - Xiaoqiu Yuan
- Peking University Health Science Center, Beijing 100191, China
| | - David P Carbone
- Thoracic Oncology Center, Ohio State University, Columbus 43026, USA.
| | - Fan Yang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing 100044, China; Peking University People's Hospital Thoracic Oncology Institute & Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Beijing 100044, China.
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22
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Luque LM, Carlevaro CM, Rodriguez-Lomba E, Lomba E. In silico study of heterogeneous tumour-derived organoid response to CAR T-cell therapy. Sci Rep 2024; 14:12307. [PMID: 38811838 PMCID: PMC11137006 DOI: 10.1038/s41598-024-63125-5] [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: 02/19/2024] [Accepted: 05/24/2024] [Indexed: 05/31/2024] Open
Abstract
Chimeric antigen receptor (CAR) T-cell therapy is a promising immunotherapy for treating cancers. This method consists in modifying the patients' T-cells to directly target antigen-presenting cancer cells. One of the barriers to the development of this type of therapies, is target antigen heterogeneity. It is thought that intratumour heterogeneity is one of the leading determinants of therapeutic resistance and treatment failure. While understanding antigen heterogeneity is important for effective therapeutics, a good therapy strategy could enhance the therapy efficiency. In this work we introduce an agent-based model (ABM), built upon a previous ABM, to rationalise the outcomes of different CAR T-cells therapies strategies over heterogeneous tumour-derived organoids. We found that one dose of CAR T-cell therapy should be expected to reduce the tumour size as well as its growth rate, however it may not be enough to completely eliminate it. Moreover, the amount of free CAR T-cells (i.e. CAR T-cells that did not kill any cancer cell) increases as we increase the dosage, and so does the risk of side effects. We tested different strategies to enhance smaller dosages, such as enhancing the CAR T-cells long-term persistence and multiple dosing. For both approaches an appropriate dosimetry strategy is necessary to produce "effective yet safe" therapeutic results. Moreover, an interesting emergent phenomenon results from the simulations, namely the formation of a shield-like structure of cells with low antigen expression. This shield turns out to protect cells with high antigen expression. Finally we tested a multi-antigen recognition therapy to overcome antigen escape and heterogeneity. Our studies suggest that larger dosages can completely eliminate the organoid, however the multi-antigen recognition increases the risk of side effects. Therefore, an appropriate small dosages dosimetry strategy is necessary to improve the outcomes. Based on our results, it is clear that a proper therapeutic strategy could enhance the therapies outcomes. In that direction, our computational approach provides a framework to model treatment combinations in different scenarios and to explore the characteristics of successful and unsuccessful treatments.
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Affiliation(s)
- Luciana Melina Luque
- Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, EH16 4UU, UK.
| | - Carlos Manuel Carlevaro
- Instituto de Física de Líquidos y Sistemas Biológicos, Consejo Nacional de Investigaciones Científicas y Técnicas, 1900, La Plata, Argentina
- Departamento de Ingeniería Mecánica, Universidad Tecnológica Nacional, Facultad Regional La Plata, 1900, La Plata, Argentina
| | | | - Enrique Lomba
- Instituto de Química Física Blas Cabrera, Consejo Superior de Investigaciones Científicas, 28006, Madrid, Spain
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23
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Xi Y, Zheng K, Deng F, Liu Y, Sun H, Zheng Y, Tong HHY, Ji Y, Zhang Y, Chen W, Zhang Y, Zou X, Hao J. Themis: advancing precision oncology through comprehensive molecular subtyping and optimization. Brief Bioinform 2024; 25:bbae261. [PMID: 38833322 PMCID: PMC11149663 DOI: 10.1093/bib/bbae261] [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: 01/30/2024] [Revised: 04/30/2024] [Accepted: 05/21/2024] [Indexed: 06/06/2024] Open
Abstract
Recent advances in tumor molecular subtyping have revolutionized precision oncology, offering novel avenues for patient-specific treatment strategies. However, a comprehensive and independent comparison of these subtyping methodologies remains unexplored. This study introduces 'Themis' (Tumor HEterogeneity analysis on Molecular subtypIng System), an evaluation platform that encapsulates a few representative tumor molecular subtyping methods, including Stemness, Anoikis, Metabolism, and pathway-based classifications, utilizing 38 test datasets curated from The Cancer Genome Atlas (TCGA) and significant studies. Our self-designed quantitative analysis uncovers the relative strengths, limitations, and applicability of each method in different clinical contexts. Crucially, Themis serves as a vital tool in identifying the most appropriate subtyping methods for specific clinical scenarios. It also guides fine-tuning existing subtyping methods to achieve more accurate phenotype-associated results. To demonstrate the practical utility, we apply Themis to a breast cancer dataset, showcasing its efficacy in selecting the most suitable subtyping methods for personalized medicine in various clinical scenarios. This study bridges a crucial gap in cancer research and lays a foundation for future advancements in individualized cancer therapy and patient management.
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Affiliation(s)
- Yue Xi
- Department of Reproductive Medicine, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250013, China
- Institute of Clinical Science, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Kun Zheng
- Department of Urology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Fulan Deng
- School of Materials Science and Engineering, Shanghai Institute of Technology, Shanghai 201418, China
| | - Yujun Liu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Hourong Sun
- Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Department of Cardiovascular Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Yingxia Zheng
- Department of Laboratory Medicine, Xin Hua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Henry H Y Tong
- Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China
| | - Yuan Ji
- Molecular Pathology center, Dept. Pathology, Zhongshan Hospital, Fudan University
| | - Yingchun Zhang
- Department of Reproductive Medicine, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250013, China
| | - Wantao Chen
- Ninth People's Hospital, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, National Clinical Research Center of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
| | - Yiming Zhang
- Department of Reproductive Medicine, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250013, China
| | - Xin Zou
- National Engineering Center for Biochip at Shanghai, China
| | - Jie Hao
- Institute of Clinical Science, Zhongshan Hospital, Fudan University, Shanghai, China
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24
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Gao S, Li X, Hu Z, Wang Z, Hao X. Dual targeting negative enrichment strategy for highly sensitive and purity detection of CTCs. Front Chem 2024; 12:1400988. [PMID: 38831912 PMCID: PMC11144890 DOI: 10.3389/fchem.2024.1400988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 04/15/2024] [Indexed: 06/05/2024] Open
Abstract
Circulating tumor cells (CTCs) have significant clinical value in early tumor detection, dynamic monitoring and immunotherapy. CTC detection stands out as a leading non-invasive approach for tumor diagnostics and therapeutics. However, the high heterogeneity of CTCs and the occurrence of epithelial-mesenchymal transition (EMT) during metastasis pose challenges to methods relying on EpCAM-positive enrichment. To address these limitations, a method based on negative enrichment of CTCs using specific leukocyte targets has been developed. In this study, aiming to overcome the low purity associated with immunomagnetic beads targeting solely the leukocyte common antigen CD45, we introduced CD66b-modified immunomagnetic beads. CD66b, a specific target for neutrophils with abundant residues, was chosen as a complementary approach. The process involved initial collection of nucleated cells from whole blood samples using density gradient centrifugation. Subsequently, magnetically labeled leukocytes were removed by magnetic field, enabling the capture of CTCs with higher sensitivity and purity while retaining their activity. Finally, we selected 20 clinical blood samples from patients with various cancers to validate the effectiveness of this strategy, providing a new generalized tool for the clinical detection of CTCs.
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Affiliation(s)
- Siying Gao
- School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan, China
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, CAS Key Laboratory of Standardization and Measurement for Nanotechnology, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, China
| | - Xuejie Li
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, CAS Key Laboratory of Standardization and Measurement for Nanotechnology, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, China
| | - Zhiyuan Hu
- School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan, China
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, CAS Key Laboratory of Standardization and Measurement for Nanotechnology, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, China
- Fujian Provincial Key Laboratory of Brain Aging and Neurodegenerative Diseases, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
- School of Nanoscience and Technology, SinoDanish College, University of Chinese Academy of Sciences, Beijing, China
| | - Zihua Wang
- Fujian Provincial Key Laboratory of Brain Aging and Neurodegenerative Diseases, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Xiaopeng Hao
- Department of General Surgery, First Medical Center of Chinese PLA General Hospital, Beijing, China
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Küçükosmanoglu A, van der Borden CL, de Boer LEA, Verhaak R, Noske D, Wurdinger T, Radonic T, Westerman BA. Oncogenic composite mutations can be predicted by co-mutations and their chromosomal location. Mol Oncol 2024. [PMID: 38757376 DOI: 10.1002/1878-0261.13636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 06/23/2023] [Accepted: 03/12/2024] [Indexed: 05/18/2024] Open
Abstract
Genetic heterogeneity in tumors can show a remarkable selectivity when two or more independent genetic events occur in the same gene. This phenomenon, called composite mutation, points toward a selective pressure, which frequently causes therapy resistance to mutation-specific drugs. Since composite mutations have been described to occur in sub-clonal populations, they are not always captured through biopsy sampling. Here, we provide a proof of concept to predict composite mutations to anticipate which patients might be at risk for sub-clonally driven therapy resistance. We found that composite mutations occur in 5% of cancer patients, mostly affecting the PIK3CA, EGFR, BRAF, and KRAS genes, which are common precision medicine targets. Furthermore, we found a strong and significant relationship between the frequencies of composite mutations with commonly co-occurring mutations in a non-composite context. We also found that co-mutations are significantly enriched on the same chromosome. These observations were independently confirmed using cell line data. Finally, we show the feasibility of predicting compositive mutations based on their co-mutations (AUC 0.62, 0.81, 0.82, and 0.91 for EGFR, PIK3CA, KRAS, and BRAF, respectively). This prediction model could help to stratify patients who are at risk of developing therapy resistance-causing mutations.
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Affiliation(s)
- Asli Küçükosmanoglu
- Department of Neurosurgery, Brain Tumor Center Amsterdam, Amsterdam University Medical Center, Cancer Center Amsterdam, The Netherlands
| | - Carolien L van der Borden
- Department of Neurosurgery, Brain Tumor Center Amsterdam, Amsterdam University Medical Center, Cancer Center Amsterdam, The Netherlands
| | - Lisanne E A de Boer
- Department of Neurosurgery, Brain Tumor Center Amsterdam, Amsterdam University Medical Center, Cancer Center Amsterdam, The Netherlands
| | - Roel Verhaak
- Department of Neurosurgery, Brain Tumor Center Amsterdam, Amsterdam University Medical Center, Cancer Center Amsterdam, The Netherlands
- Department of Computational Biology, The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - David Noske
- Department of Neurosurgery, Brain Tumor Center Amsterdam, Amsterdam University Medical Center, Cancer Center Amsterdam, The Netherlands
| | - Tom Wurdinger
- Department of Neurosurgery, Brain Tumor Center Amsterdam, Amsterdam University Medical Center, Cancer Center Amsterdam, The Netherlands
| | - Teodora Radonic
- Department of Pathology, Amsterdam University Medical Center, Cancer Center Amsterdam, The Netherlands
| | - Bart A Westerman
- Department of Neurosurgery, Brain Tumor Center Amsterdam, Amsterdam University Medical Center, Cancer Center Amsterdam, The Netherlands
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Li K, Qiu L, Zhao Y, Sun X, Shao J, He C, Qin B, Jiao S. Nomograms Predict PFS and OS for SCLC Patients After Standardized Treatment: A Real-World Study. Int J Gen Med 2024; 17:1949-1965. [PMID: 38736664 PMCID: PMC11088392 DOI: 10.2147/ijgm.s457329] [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: 01/02/2024] [Accepted: 04/22/2024] [Indexed: 05/14/2024] Open
Abstract
Purpose This study aims to investigate the process of small cell lung cancer (SCLC) patients from achieving optimal efficacy to experiencing disease progression until death. It examines the predictive value of the treatment response on progression free survival (PFS) and overall survival (OS) of SCLC patients. Patients and Methods We conducted a retrospective analysis on 136 SCLC patients diagnosed from 1992 to 2018. Important prognostic factors were identified to construct nomogram models. The predictive performance of the models was evaluated using the receiver operating characteristic curves and calibration curves. Survival differences between groups were compared using Kaplan-Meier survival curves. Subsequently, an independent cohort consisting of 106 SCLC patients diagnosed from 2014 to 2021 was used for validation. Results We constructed two nomograms to predict first-line PFS (PFS1) and OS of SCLC. The area under the receiver operating characteristic curves for the PFS1 nomogram predicting PFS at 3-, 6-, and 12-months were 0.919 (95% CI: 0.867-0.970), 0.908 (95% CI: 0.860-0.956) and 0.878 (95% CI: 0.798-0.958), and for the OS nomogram predicting OS at 6-, 12-, and 24-months were 0.814 (95% CI: 0.736-0.892), 0.819 (95% CI: 0.749-0.889) and 0.809 (95% CI: 0.678-0.941), indicating those two models with a high discriminative ability. The calibration curves demonstrated the models had a high degree of consistency between predicted and observed values. According to the risk scores, patients were divided into high-risk and low-risk groups, showing a significant difference in survival rate. And these findings were validated in another independent validation cohort. Conclusion Based on the patients' treatment response after standardized treatment, we developed and validated two nomogram models to predict PFS1 and OS of SCLC. The models demonstrated good accuracy, reliability and clinical applicability by validating in an independent cohort.
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Affiliation(s)
- Ke Li
- Medical School of Chinese PLA, Beijing, 100853, People’s Republic of China
- Department of Oncology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China
| | - Lupeng Qiu
- Medical School of Chinese PLA, Beijing, 100853, People’s Republic of China
- Department of Oncology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China
| | - Yang Zhao
- Department of Vascular Intervention, Special Medical Center for Strategic Support Forces, Beijing, 100101, People’s Republic of China
| | - Xiaohui Sun
- Medical School of Chinese PLA, Beijing, 100853, People’s Republic of China
- Department of Oncology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China
| | - Jiakang Shao
- Medical School of Chinese PLA, Beijing, 100853, People’s Republic of China
| | - Chang He
- Medical School of Chinese PLA, Beijing, 100853, People’s Republic of China
- Department of Oncology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China
| | - Boyu Qin
- Department of Oncology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100071, People’s Republic of China
| | - Shunchang Jiao
- Department of Oncology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100071, People’s Republic of China
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Ma R, Feng D, Chen J, Zhou J, Xia K, Kong X, Hu G, Lu P. Targeting Tumor Heterogeneity by Breaking a Stem Cell and Epithelial Niche Interaction Loop. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2307452. [PMID: 38708713 DOI: 10.1002/advs.202307452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 03/20/2024] [Indexed: 05/07/2024]
Abstract
Tumor heterogeneity, the presence of multiple distinct subpopulations of cancer cells between patients or among the same tumors, poses a major challenge to current targeted therapies. The way these different subpopulations interact among themselves and the stromal niche environment, and how such interactions affect cancer stem cell behavior has remained largely unknown. Here, it is shown that an FGF-BMP7-INHBA signaling positive feedback loop integrates interactions among different cell populations, including mammary gland stem cells, luminal epithelial and stromal fibroblast niche components not only in organ regeneration but also, with certain modifications, in cancer progression. The reciprocal dependence of basal stem cells and luminal epithelium is based on basal-derived BMP7 and luminal-derived INHBA, which promote their respective expansion, and is regulated by stromal-epithelial FGF signaling. Targeting this interaction loop, for example, by reducing the function of one or more of its components, inhibits organ regeneration and breast cancer progression. The results have profound implications for overcoming drug resistance because of tumor heterogeneity in future targeted therapies.
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Affiliation(s)
- Rongze Ma
- MOE Key Lab of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics of the School of Life Sciences, Hengyang, Hunan, 421001, China
- Institute of Cell Biology, University of South China, Hengyang, Hunan, 421001, China
- Institute for Future Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Deyi Feng
- MOE Key Lab of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics of the School of Life Sciences, Hengyang, Hunan, 421001, China
- Institute of Cell Biology, University of South China, Hengyang, Hunan, 421001, China
| | - Jing Chen
- School of Life Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China
| | - Jiecan Zhou
- MOE Key Lab of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics of the School of Life Sciences, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Pharmacy Department, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Kun Xia
- MOE Key Lab of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics of the School of Life Sciences, Hengyang, Hunan, 421001, China
- Institute of Cell Biology, University of South China, Hengyang, Hunan, 421001, China
| | - Xiangyin Kong
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Guohong Hu
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Pengfei Lu
- MOE Key Lab of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics of the School of Life Sciences, Hengyang, Hunan, 421001, China
- Institute of Cell Biology, University of South China, Hengyang, Hunan, 421001, China
- Institute for Future Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
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Zhao Y, Zhang B, Ma Y, Guo M, Zhao F, Chen J, Wang B, Jin H, Zhou F, Guan J, Zhao Q, Liu Q, Wang H, Zhao F, Wang X. Distinct molecular profiles drive multifaceted characteristics of colorectal cancer metastatic seeds. J Exp Med 2024; 221:e20231359. [PMID: 38502057 PMCID: PMC10949939 DOI: 10.1084/jem.20231359] [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] [Received: 08/02/2023] [Revised: 10/10/2023] [Accepted: 02/08/2024] [Indexed: 03/20/2024] Open
Abstract
Metastasis of primary tumors remains a challenge for early diagnosis and prevention. The cellular properties and molecular drivers of metastatically competent clones within primary tumors remain unclear. Here, we generated 10-16 single cell-derived lines from each of three colorectal cancer (CRC) tumors to identify and characterize metastatic seeds. We found that intrinsic factors conferred clones with distinct metastatic potential and cellular communication capabilities, determining organ-specific metastasis. Poorly differentiated or highly metastatic clones, rather than drug-resistant clones, exhibited poor clinical prognostic impact. Personalized genetic alterations, instead of mutation burden, determined the occurrence of metastatic potential during clonal evolution. Additionally, we developed a gene signature for capturing metastatic potential of primary CRC tumors and demonstrated a strategy for identifying metastatic drivers using isogenic clones with distinct metastatic potential in primary tumors. This study provides insight into the origin and mechanisms of metastasis and will help develop potential anti-metastatic therapeutic targets for CRC patients.
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Affiliation(s)
- Yuanyuan Zhao
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
- Institute for Intelligent Healthcare, Tsinghua University, Beijing, China
| | - Bing Zhang
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yiming Ma
- State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mengmeng Guo
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Fuqiang Zhao
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianan Chen
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bingzhi Wang
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hua Jin
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Fulai Zhou
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Jiawei Guan
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Qian Zhao
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Qian Liu
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongying Wang
- State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China
| | - Xia Wang
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
- Institute for Intelligent Healthcare, Tsinghua University, Beijing, China
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Liu X, Wu L, Wang L, Li Y. Identification and classification of glioma subtypes based on RNA-binding proteins. Comput Biol Med 2024; 174:108404. [PMID: 38582000 DOI: 10.1016/j.compbiomed.2024.108404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 03/23/2024] [Accepted: 04/01/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Glioma is a common and aggressive primary malignant cancer known for its high morbidity, mortality, and recurrence rates. Despite this, treatment options for glioma are currently restricted. The dysregulation of RBPs has been linked to the advancement of several types of cancer, but their precise role in glioma evolution is still not fully understood. This study sought to investigate how RBPs may impact the development and prognosis of glioma, with potential implications for prognosis and therapy. METHODS RNA-seq profiles of glioma and corresponding clinical data from the CGGA database were initially collected for analysis. Unsupervised clustering was utilized to identify crucial tumor subtypes in glioma development. Subsequent time-series analysis and MS model were employed to track the progression of these identified subtypes. RBPs playing a significant role in glioma progression were then pinpointed using WGCNA and Lasso Cox regression models. Functional analysis of these key RBP-related genes was conducted through GSEA. Additionally, the CIBERSORT algorithm was utilized to estimate immune infiltrating cells, while the STRING database was consulted to uncover potential mechanisms of the identified biomarkers. RESULTS Six tumor subgroups were identified and found to be highly homogeneous within each subgroup. The progression stages of these tumor subgroups were determined using time-series analysis and a MS model. Through WGCNA, Lasso Cox, and multivariate Cox regression analysis, it was confirmed that BCLAF1 is correlated with survival in glioma patients and is closely linked to glioma progression. Functional annotation suggests that BCLAF1 may impact glioma progression by influencing RNA splicing, which in turn affects the cell cycle, Wnt signaling pathway, and other cancer development pathways. CONCLUSIONS The study initially identified six subtypes of glioma progression and assessed their malignancy ranking. Furthermore, it was determined that BCLAF1 could serve as an RBP-related prognostic marker, offering significant implications for the clinical diagnosis and personalized treatment of glioma.
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Affiliation(s)
- Xudong Liu
- School of Medicine, Chongqing University, Chongqing, 400044, China; Department of Medical Oncology, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Lei Wu
- Department of Medical Oncology, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Lei Wang
- College of Life Sciences, Xinyang Normal University, Xinyang, 464000, China.
| | - Yongsheng Li
- Department of Medical Oncology, Chongqing University Cancer Hospital, Chongqing, 400030, China.
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30
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Karras P, Black JRM, McGranahan N, Marine JC. Decoding the interplay between genetic and non-genetic drivers of metastasis. Nature 2024; 629:543-554. [PMID: 38750233 DOI: 10.1038/s41586-024-07302-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 03/12/2024] [Indexed: 05/18/2024]
Abstract
Metastasis is a multistep process by which cancer cells break away from their original location and spread to distant organs, and is responsible for the vast majority of cancer-related deaths. Preventing early metastatic dissemination would revolutionize the ability to fight cancer. Unfortunately, the relatively poor understanding of the molecular underpinnings of metastasis has hampered the development of effective anti-metastatic drugs. Although it is now accepted that disseminating tumour cells need to acquire multiple competencies to face the many obstacles they encounter before reaching their metastatic site(s), whether these competencies are acquired through an accumulation of metastasis-specific genetic alterations and/or non-genetic events is often debated. Here we review a growing body of literature highlighting the importance of both genetic and non-genetic reprogramming events during the metastatic cascade, and discuss how genetic and non-genetic processes act in concert to confer metastatic competencies. We also describe how recent technological advances, and in particular the advent of single-cell multi-omics and barcoding approaches, will help to better elucidate the cross-talk between genetic and non-genetic mechanisms of metastasis and ultimately inform innovative paths for the early detection and interception of this lethal process.
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Affiliation(s)
- Panagiotis Karras
- Laboratory for Molecular Cancer Biology, VIB Center for Cancer Biology, KU Leuven, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | - James R M Black
- Cancer Genome Evolution Research Group, UCL Cancer Institute, London, UK
| | | | - Jean-Christophe Marine
- Laboratory for Molecular Cancer Biology, VIB Center for Cancer Biology, KU Leuven, Leuven, Belgium.
- Department of Oncology, KU Leuven, Leuven, Belgium.
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Gao W, Liu J, Shtylla B, Venkatakrishnan K, Yin D, Shah M, Nicholas T, Cao Y. Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development. CPT Pharmacometrics Syst Pharmacol 2024; 13:691-709. [PMID: 37969061 PMCID: PMC11098159 DOI: 10.1002/psp4.13079] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 10/20/2023] [Accepted: 10/30/2023] [Indexed: 11/17/2023] Open
Abstract
Project Optimus is a US Food and Drug Administration Oncology Center of Excellence initiative aimed at reforming the dose selection and optimization paradigm in oncology drug development. This project seeks to bring together pharmaceutical companies, international regulatory agencies, academic institutions, patient advocates, and other stakeholders. Although there is much promise in this initiative, there are several challenges that need to be addressed, including multidimensionality of the dose optimization problem in oncology, the heterogeneity of cancer and patients, importance of evaluating long-term tolerability beyond dose-limiting toxicities, and the lack of reliable biomarkers for long-term efficacy. Through the lens of Totality of Evidence and with the mindset of model-informed drug development, we offer insights into dose optimization by building a quantitative knowledge base integrating diverse sources of data and leveraging quantitative modeling tools to build evidence for drug dosage considering exposure, disease biology, efficacy, toxicity, and patient factors. We believe that rational dose optimization can be achieved in oncology drug development, improving patient outcomes by maximizing therapeutic benefit while minimizing toxicity.
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Affiliation(s)
- Wei Gao
- Quantitative PharmacologyEMD Serono Research & Development Institute, Inc.BillericaMassachusettsUSA
| | - Jiang Liu
- Food and Drug AdministrationSilver SpringMarylandUSA
| | - Blerta Shtylla
- Quantitative Systems PharmacologyPfizerSan DiegoCaliforniaUSA
| | - Karthik Venkatakrishnan
- Quantitative PharmacologyEMD Serono Research & Development Institute, Inc.BillericaMassachusettsUSA
| | - Donghua Yin
- Clinical PharmacologyPfizerSan DiegoCaliforniaUSA
| | - Mirat Shah
- Food and Drug AdministrationSilver SpringMarylandUSA
| | | | - Yanguang Cao
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of PharmacyUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
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Liu W, Zhou H, Lai W, Hu C, Xu R, Gu P, Luo M, Zhang R, Li G. The immunosuppressive landscape in tumor microenvironment. Immunol Res 2024:10.1007/s12026-024-09483-8. [PMID: 38691319 DOI: 10.1007/s12026-024-09483-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 04/16/2024] [Indexed: 05/03/2024]
Abstract
Recent advances in cancer immunotherapy, especially immune checkpoint inhibitors (ICIs), have revolutionized the clinical outcome of many cancer patients. Despite the fact that impressive progress has been made in recent decades, the response rate remains unsatisfactory, and many patients do not benefit from ICIs. Herein, we summarized advanced studies and the latest insights on immune inhibitory factors in the tumor microenvironment. Our in-depth discussion and updated landscape of tumor immunosuppressive microenvironment may provide new strategies for reversing tumor immune evasion, enhancing the efficacy of ICIs therapy, and ultimately achieving a better clinical outcome.
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Affiliation(s)
- Wuyi Liu
- Department of Pharmacy, The Second Affiliated Hospital of Army Medical University, 83 Xinqiao Road, Shapingba, Chongqing, China
| | - Huyue Zhou
- Department of Pharmacy, The Second Affiliated Hospital of Army Medical University, 83 Xinqiao Road, Shapingba, Chongqing, China
| | - Wenjing Lai
- Department of Pharmacy, The Second Affiliated Hospital of Army Medical University, 83 Xinqiao Road, Shapingba, Chongqing, China
| | - Changpeng Hu
- Department of Pharmacy, The Second Affiliated Hospital of Army Medical University, 83 Xinqiao Road, Shapingba, Chongqing, China
| | - Rufu Xu
- Department of Pharmacy, The Second Affiliated Hospital of Army Medical University, 83 Xinqiao Road, Shapingba, Chongqing, China
| | - Peng Gu
- Department of Pharmacy, The Second Affiliated Hospital of Army Medical University, 83 Xinqiao Road, Shapingba, Chongqing, China
| | - Menglin Luo
- Department of Pharmacy, The Second Affiliated Hospital of Army Medical University, 83 Xinqiao Road, Shapingba, Chongqing, China
| | - Rong Zhang
- Department of Pharmacy, The Second Affiliated Hospital of Army Medical University, 83 Xinqiao Road, Shapingba, Chongqing, China.
| | - Guobing Li
- Department of Pharmacy, The Second Affiliated Hospital of Army Medical University, 83 Xinqiao Road, Shapingba, Chongqing, China.
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Khosravi G, Mostafavi S, Bastan S, Ebrahimi N, Gharibvand RS, Eskandari N. Immunologic tumor microenvironment modulators for turning cold tumors hot. Cancer Commun (Lond) 2024; 44:521-553. [PMID: 38551889 PMCID: PMC11110955 DOI: 10.1002/cac2.12539] [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: 09/21/2023] [Revised: 03/03/2024] [Accepted: 03/12/2024] [Indexed: 05/23/2024] Open
Abstract
Tumors can be classified into distinct immunophenotypes based on the presence and arrangement of cytotoxic immune cells within the tumor microenvironment (TME). Hot tumors, characterized by heightened immune activity and responsiveness to immune checkpoint inhibitors (ICIs), stand in stark contrast to cold tumors, which lack immune infiltration and remain resistant to therapy. To overcome immune evasion mechanisms employed by tumor cells, novel immunologic modulators have emerged, particularly ICIs targeting cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and programmed cell death protein 1/programmed death-ligand 1(PD-1/PD-L1). These agents disrupt inhibitory signals and reactivate the immune system, transforming cold tumors into hot ones and promoting effective antitumor responses. However, challenges persist, including primary resistance to immunotherapy, autoimmune side effects, and tumor response heterogeneity. Addressing these challenges requires innovative strategies, deeper mechanistic insights, and a combination of immune interventions to enhance the effectiveness of immunotherapies. In the landscape of cancer medicine, where immune cold tumors represent a formidable hurdle, understanding the TME and harnessing its potential to reprogram the immune response is paramount. This review sheds light on current advancements and future directions in the quest for more effective and safer cancer treatment strategies, offering hope for patients with immune-resistant tumors.
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Affiliation(s)
- Gholam‐Reza Khosravi
- Department of Medical ImmunologySchool of MedicineIsfahan University of Medical SciencesIsfahanIran
| | - Samaneh Mostafavi
- Department of ImmunologyFaculty of Medical SciencesTarbiat Modares UniversityTehranIran
| | - Sanaz Bastan
- Department of Medical ImmunologySchool of MedicineIsfahan University of Medical SciencesIsfahanIran
| | - Narges Ebrahimi
- Department of Medical ImmunologySchool of MedicineIsfahan University of Medical SciencesIsfahanIran
| | - Roya Safari Gharibvand
- Department of ImmunologySchool of MedicineAhvaz Jundishapur University of Medical SciencesAhvazIran
| | - Nahid Eskandari
- Department of Medical ImmunologySchool of MedicineIsfahan University of Medical SciencesIsfahanIran
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Nissar I, Alam S, Masood S, Kashif M. MOB-CBAM: A dual-channel attention-based deep learning generalizable model for breast cancer molecular subtypes prediction using mammograms. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 248:108121. [PMID: 38531147 DOI: 10.1016/j.cmpb.2024.108121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 02/15/2024] [Accepted: 03/06/2024] [Indexed: 03/28/2024]
Abstract
BACKGROUND AND OBJECTIVE Deep Learning models have emerged as a significant tool in generating efficient solutions for complex problems including cancer detection, as they can analyze large amounts of data with high efficiency and performance. Recent medical studies highlight the significance of molecular subtype detection in breast cancer, aiding the development of personalized treatment plans as different subtypes of cancer respond better to different therapies. METHODS In this work, we propose a novel lightweight dual-channel attention-based deep learning model MOB-CBAM that utilizes the backbone of MobileNet-V3 architecture with a Convolutional Block Attention Module to make highly accurate and precise predictions about breast cancer. We used the CMMD mammogram dataset to evaluate the proposed model in our study. Nine distinct data subsets were created from the original dataset to perform coarse and fine-grained predictions, enabling it to identify masses, calcifications, benign, malignant tumors and molecular subtypes of cancer, including Luminal A, Luminal B, HER-2 Positive, and Triple Negative. The pipeline incorporates several image pre-processing techniques, including filtering, enhancement, and normalization, for enhancing the model's generalization ability. RESULTS While identifying benign versus malignant tumors, i.e., coarse-grained classification, the MOB-CBAM model produced exceptional results with 99 % accuracy, precision, recall, and F1-score values of 0.99 and MCC of 0.98. In terms of fine-grained classification, the MOB-CBAM model has proven to be highly efficient in accurately identifying mass with (benign/malignant) and calcification with (benign/malignant) classification tasks with an impressive accuracy rate of 98 %. We have also cross-validated the efficiency of the proposed MOB-CBAM deep learning architecture on two datasets: MIAS and CBIS-DDSM. On the MIAS dataset, an accuracy of 97 % was reported for the task of classifying benign, malignant, and normal images, while on the CBIS-DDSM dataset, an accuracy of 98 % was achieved for the classification of mass with either benign or malignant, and calcification with benign and malignant tumors. CONCLUSION This study presents lightweight MOB-CBAM, a novel deep learning framework, to address breast cancer diagnosis and subtype prediction. The model's innovative incorporation of the CBAM enhances precise predictions. The extensive evaluation of the CMMD dataset and cross-validation on other datasets affirm the model's efficacy.
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Affiliation(s)
- Iqra Nissar
- Department of Computer Engineering, Jamia Millia Islamia (A Central University), New Delhi, 110025, India.
| | - Shahzad Alam
- Department of Computer Engineering, Jamia Millia Islamia (A Central University), New Delhi, 110025, India
| | - Sarfaraz Masood
- Department of Computer Engineering, Jamia Millia Islamia (A Central University), New Delhi, 110025, India
| | - Mohammad Kashif
- Department of Computer Engineering, Jamia Millia Islamia (A Central University), New Delhi, 110025, India
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Suh YS, Lee J, George J, Seol D, Jeong K, Oh SY, Bang C, Jun Y, Kong SH, Lee HJ, Kim JI, Kim WH, Yang HK, Lee C. RNA expression of 6 genes from metastatic mucosal gastric cancer serves as the global prognostic marker for gastric cancer with functional validation. Br J Cancer 2024; 130:1571-1584. [PMID: 38467827 PMCID: PMC11059174 DOI: 10.1038/s41416-024-02642-6] [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: 08/16/2023] [Revised: 02/08/2024] [Accepted: 02/26/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Molecular analysis of advanced tumors can increase tumor heterogeneity and selection bias. We developed a robust prognostic signature for gastric cancer by comparing RNA expression between very rare early gastric cancers invading only mucosal layer (mEGCs) with lymph node metastasis (Npos) and those without metastasis (Nneg). METHODS Out of 1003 mEGCs, all Npos were matched to Nneg using propensity scores. Machine learning approach comparing Npos and Nneg was used to develop prognostic signature. The function and robustness of prognostic signature was validated using cell lines and external datasets. RESULTS Extensive machine learning with cross-validation identified the prognostic classifier consisting of four overexpressed genes (HDAC5, NPM1, DTX3, and PPP3R1) and two downregulated genes (MED12 and TP53), and enabled us to develop the risk score predicting poor prognosis. Cell lines engineered to high-risk score showed increased invasion, migration, and resistance to 5-FU and Oxaliplatin but maintained sensitivity to an HDAC inhibitor. Mouse models after tail vein injection of cell lines with high-risk score revealed increased metastasis. In three external cohorts, our risk score was identified as the independent prognostic factor for overall and recurrence-free survival. CONCLUSION The risk score from the 6-gene classifier can successfully predict the prognosis of gastric cancer.
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Affiliation(s)
- Yun-Suhk Suh
- Department of Surgery, Seoul National University College of Medicine, Seoul, South Korea
- Department of Surgery, Seoul National University Hospital, Seoul, South Korea
- Department of Surgery, Seoul National University Bundang Hospital, Seongnam, South Korea
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Jieun Lee
- Department of Surgery, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Joshy George
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Donghyeok Seol
- Department of Surgery, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Kyoungyun Jeong
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, South Korea
| | - Seung-Young Oh
- Department of Surgery, Seoul National University College of Medicine, Seoul, South Korea
- Department of Surgery, Seoul National University Hospital, Seoul, South Korea
| | - Chanmi Bang
- Department of Surgery, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Yukyung Jun
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
- Center for Supercomputing Applications, Division of National Supercomputing, Korea Institute of Science and Technology Information, Daejeon, South Korea
| | - Seong-Ho Kong
- Department of Surgery, Seoul National University College of Medicine, Seoul, South Korea
- Department of Surgery, Seoul National University Hospital, Seoul, South Korea
| | - Hyuk-Joon Lee
- Department of Surgery, Seoul National University College of Medicine, Seoul, South Korea
- Department of Surgery, Seoul National University Hospital, Seoul, South Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, South Korea
| | - Jong-Il Kim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
- Genomic Medicine Institute, Medical Research Center, Seoul National University College of Medicine, Seoul, South Korea
| | - Woo Ho Kim
- Department of Pathology, Seoul National University College of Medicine, Seoul, South Korea
| | - Han-Kwang Yang
- Department of Surgery, Seoul National University College of Medicine, Seoul, South Korea.
- Department of Surgery, Seoul National University Hospital, Seoul, South Korea.
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, South Korea.
| | - Charles Lee
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA.
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Chang JC, Rekhtman N. Pathologic Assessment and Staging of Multiple Non-Small Cell Lung Carcinomas: A Paradigm Shift with the Emerging Role of Molecular Methods. Mod Pathol 2024; 37:100453. [PMID: 38387831 PMCID: PMC11102290 DOI: 10.1016/j.modpat.2024.100453] [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: 12/05/2023] [Revised: 01/29/2024] [Accepted: 02/13/2024] [Indexed: 02/24/2024]
Abstract
Non-small cell lung carcinomas (NSCLCs) commonly present as 2 or more separate tumors. Biologically, this encompasses 2 distinct processes: separate primary lung carcinomas (SPLCs), representing independently arising tumors, and intrapulmonary metastases (IPMs), representing intrapulmonary spread of a single tumor. The advent of computed tomography imaging has substantially increased the detection of multifocal NSCLCs. The strategies and approaches for distinguishing between SPLCs and IPMs have evolved significantly over the years. Recently, genomic sequencing of somatic mutations has been widely adopted to identify targetable alterations in NSCLC. These molecular techniques have enabled pathologists to reliably discern clonal relationships among multiple NSCLCs in clinical practice. However, a standardized approach to evaluating and staging multiple NSCLCs using molecular methods is still lacking. Here, we reviewed the historical context and provided an update on the growing applications of genomic testing as a clinically relevant benchmark for determining clonal relationships in multiple NSCLCs, a practice we have designated "comparative molecular profiling." We examined the strengths and limitations of the morphology-based distinction of SPLCs vs IPMs and highlighted pivotal clinical and pathologic insights that have emerged from studying multiple NSCLCs using genomic approaches as a gold standard. Lastly, we suggest a practical approach for evaluating multiple NSCLCs in the clinical setting, considering the varying availability of molecular techniques.
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Affiliation(s)
- Jason C Chang
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Natasha Rekhtman
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.
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37
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Junior MGV, Côrtes AMDA, Carneiro FRG, Carels N, da Silva FAB. Unveiling the Dynamics behind Glioblastoma Multiforme Single-Cell Data Heterogeneity. Int J Mol Sci 2024; 25:4894. [PMID: 38732140 PMCID: PMC11084314 DOI: 10.3390/ijms25094894] [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: 03/08/2024] [Revised: 04/02/2024] [Accepted: 04/03/2024] [Indexed: 05/13/2024] Open
Abstract
Glioblastoma Multiforme is a brain tumor distinguished by its aggressiveness. We suggested that this aggressiveness leads single-cell RNA-sequence data (scRNA-seq) to span a representative portion of the cancer attractors domain. This conjecture allowed us to interpret the scRNA-seq heterogeneity as reflecting a representative trajectory within the attractor's domain. We considered factors such as genomic instability to characterize the cancer dynamics through stochastic fixed points. The fixed points were derived from centroids obtained through various clustering methods to verify our method sensitivity. This methodological foundation is based upon sample and time average equivalence, assigning an interpretative value to the data cluster centroids and supporting parameters estimation. We used stochastic simulations to reproduce the dynamics, and our results showed an alignment between experimental and simulated dataset centroids. We also computed the Waddington landscape, which provided a visual framework for validating the centroids and standard deviations as characterizations of cancer attractors. Additionally, we examined the stability and transitions between attractors and revealed a potential interplay between subtypes. These transitions might be related to cancer recurrence and progression, connecting the molecular mechanisms of cancer heterogeneity with statistical properties of gene expression dynamics. Our work advances the modeling of gene expression dynamics and paves the way for personalized therapeutic interventions.
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Affiliation(s)
- Marcos Guilherme Vieira Junior
- Graduate Program in Computational and Systems Biology, Oswaldo Cruz Institute (IOC), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040-900, Brazil;
| | - Adriano Maurício de Almeida Côrtes
- Department of Applied Mathematics, Institute of Mathematics, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-909, Brazil;
- Systems Engineering and Computer Science Program, Coordination of Postgraduate Programs in Engineering (COPPE), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-972, Brazil
| | - Flávia Raquel Gonçalves Carneiro
- Center of Technological Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040-361, Brazil;
- Laboratório Interdisciplinar de Pesquisas Médicas, Oswaldo Cruz Institute (IOC), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040-900, Brazil
- Program of Immunology and Tumor Biology, Brazilian National Cancer Institute (INCA), Rio de Janeiro 20231-050, Brazil
| | - Nicolas Carels
- Laboratory of Biological System Modeling, Center of Technological Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040-361, Brazil
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38
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Liu Y, Edrisi M, Yan Z, A Ogilvie H, Nakhleh L. NestedBD: Bayesian inference of phylogenetic trees from single-cell copy number profiles under a birth-death model. Algorithms Mol Biol 2024; 19:18. [PMID: 38685065 PMCID: PMC11059640 DOI: 10.1186/s13015-024-00264-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 03/27/2024] [Indexed: 05/02/2024] Open
Abstract
Copy number aberrations (CNAs) are ubiquitous in many types of cancer. Inferring CNAs from cancer genomic data could help shed light on the initiation, progression, and potential treatment of cancer. While such data have traditionally been available via "bulk sequencing," the more recently introduced techniques for single-cell DNA sequencing (scDNAseq) provide the type of data that makes CNA inference possible at the single-cell resolution. We introduce a new birth-death evolutionary model of CNAs and a Bayesian method, NestedBD, for the inference of evolutionary trees (topologies and branch lengths with relative mutation rates) from single-cell data. We evaluated NestedBD's performance using simulated data sets, benchmarking its accuracy against traditional phylogenetic tools as well as state-of-the-art methods. The results show that NestedBD infers more accurate topologies and branch lengths, and that the birth-death model can improve the accuracy of copy number estimation. And when applied to biological data sets, NestedBD infers plausible evolutionary histories of two colorectal cancer samples. NestedBD is available at https://github.com/Androstane/NestedBD .
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Affiliation(s)
- Yushu Liu
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, USA.
| | - Mohammadamin Edrisi
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, USA
| | - Zhi Yan
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, USA
| | - Huw A Ogilvie
- Department of Genetics, University of Texas MD Anderson Cancer Center, TX, 77030, Houston, USA
| | - Luay Nakhleh
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, USA
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39
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Aguadé-Gorgorió G, Anderson AR, Solé R. Modeling tumors as species-rich ecological communities. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590504. [PMID: 38712062 PMCID: PMC11071393 DOI: 10.1101/2024.04.22.590504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Many advanced cancers resist therapeutic intervention. This process is fundamentally related to intra-tumor heterogeneity: multiple cell populations, each with different mutational and phenotypic signatures, coexist within a tumor and its metastatic nodes. Like species in an ecosystem, many cancer cell populations are intertwined in a complex network of ecological interactions. Most mathematical models of tumor ecology, however, cannot account for such phenotypic diversity nor are able to predict its consequences. Here we propose that the Generalized Lotka-Volterra model (GLV), a standard tool to describe complex, species-rich ecological communities, provides a suitable framework to describe the ecology of heterogeneous tumors. We develop a GLV model of tumor growth and discuss how its emerging properties, such as outgrowth and multistability, provide a new understanding of the disease. Additionally, we discuss potential extensions of the model and their application to three active areas of cancer research, namely phenotypic plasticity, the cancer-immune interplay and the resistance of metastatic tumors to treatment. Our work outlines a set of questions and a tentative road map for further research in cancer ecology.
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Affiliation(s)
| | - Alexander R.A. Anderson
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, USA
| | - Ricard Solé
- ICREA-Complex Systems Lab, UPF-PRBB, Dr. Aiguader 80, 08003 Barcelona, Spain
- Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA
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40
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Payne KFB, Brotherwood P, Suriyanarayanan H, Brooks JM, Batis N, Beggs AD, Gendoo DMA, Mehanna H, Nankivell P. Circulating tumour DNA detects somatic variants contributing to spatial and temporal intra-tumoural heterogeneity in head and neck squamous cell carcinoma. Front Oncol 2024; 14:1374816. [PMID: 38846976 PMCID: PMC11154907 DOI: 10.3389/fonc.2024.1374816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 04/09/2024] [Indexed: 06/09/2024] Open
Abstract
Background As circulating tumour DNA (ctDNA) liquid biopsy analysis is increasingly incorporated into modern oncological practice, establishing the impact of genomic intra-tumoural heterogeneity (ITH) upon data output is paramount. Despite advances in other cancer types the evidence base in head and neck squamous cell carcinoma (HNSCC) remains poor. We sought to investigate the utility of ctDNA to detect ITH in HNSCC. Methods In a pilot cohort of 9 treatment-naïve HNSCC patients, DNA from two intra-tumoural sites (core and margin) was whole-exome sequenced. A 9-gene panel was designed to perform targeted sequencing on pre-treatment plasma cell-free DNA and selected post-treatment samples. Results Rates of genomic ITH among the 9 patients was high. COSMIC variants from 19 TCGA HNSCC genes demonstrated an 86.9% heterogeneity rate (present in one tumour sub-site only). Across all patients, cell-free DNA (ctDNA) identified 12.9% (range 7.5-19.8%) of tumour-specific variants, of which 55.6% were specific to a single tumour sub-site only. CtDNA identified 79.0% (range: 55.6-90.9%) of high-frequency variants (tumour VAF>5%). Analysis of ctDNA in serial post-treatment blood samples in patients who suffered recurrence demonstrated dynamic changes in both tumour-specific and acquired variants that predicted recurrence ahead of clinical detection. Conclusion We demonstrate that a ctDNA liquid biopsy identified spatial genomic ITH in HNSCC and reliably detected high-frequency driver mutations. Serial sampling allowed post-treatment surveillance and early identification of treatment failure.
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Affiliation(s)
- Karl F. B. Payne
- Institute of Head and Neck Studies and Education, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Peter Brotherwood
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Harini Suriyanarayanan
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Jill M. Brooks
- Institute of Head and Neck Studies and Education, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Nikolaos Batis
- School of Biomedical Sciences, Institute of Clinical Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Andrew D. Beggs
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Deena M. A. Gendoo
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
- Institute for Interdisciplinary Data Science and AI, University of Birmingham, Birmingham, United Kingdom
| | - Hisham Mehanna
- Institute of Head and Neck Studies and Education, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Paul Nankivell
- Institute of Head and Neck Studies and Education, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
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41
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Li R, Shi F, Song L, Yu Z. scGAL: unmask tumor clonal substructure by jointly analyzing independent single-cell copy number and scRNA-seq data. BMC Genomics 2024; 25:393. [PMID: 38649804 PMCID: PMC11034052 DOI: 10.1186/s12864-024-10319-w] [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: 11/09/2023] [Accepted: 04/17/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Accurately deciphering clonal copy number substructure can provide insights into the evolutionary mechanism of cancer, and clustering single-cell copy number profiles has become an effective means to unmask intra-tumor heterogeneity (ITH). However, copy numbers inferred from single-cell DNA sequencing (scDNA-seq) data are error-prone due to technically confounding factors such as amplification bias and allele-dropout, and this makes it difficult to precisely identify the ITH. RESULTS We introduce a hybrid model called scGAL to infer clonal copy number substructure. It combines an autoencoder with a generative adversarial network to jointly analyze independent single-cell copy number profiles and gene expression data from same cell line. Under an adversarial learning framework, scGAL exploits complementary information from gene expression data to relieve the effects of noise in copy number data, and learns latent representations of scDNA-seq cells for accurate inference of the ITH. Evaluation results on three real cancer datasets suggest scGAL is able to accurately infer clonal architecture and surpasses other similar methods. In addition, assessment of scGAL on various simulated datasets demonstrates its high robustness against the changes of data size and distribution. scGAL can be accessed at: https://github.com/zhyu-lab/scgal . CONCLUSIONS Joint analysis of independent single-cell copy number and gene expression data from a same cell line can effectively exploit complementary information from individual omics, and thus gives more refined indication of clonal copy number substructure.
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Affiliation(s)
- Ruixiang Li
- School of Information Engineering, Ningxia University, Yinchuan, 750021, China
| | - Fangyuan Shi
- School of Information Engineering, Ningxia University, Yinchuan, 750021, China
- Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, Yinchuan, 750021, China
| | - Lijuan Song
- School of Information Engineering, Ningxia University, Yinchuan, 750021, China
- Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, Yinchuan, 750021, China
| | - Zhenhua Yu
- School of Information Engineering, Ningxia University, Yinchuan, 750021, China.
- Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, Yinchuan, 750021, China.
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42
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Tang H, Miao X, Yu C, Chai C, Su Y, Li L, Yi J, Ye Z, Miao L, Wang Z, Zhang H, Xu H, Zhou W. A novel multidrug-resistant cell line from a Chinese patient with pancreatic ductal adenocarcinoma. Sci Rep 2024; 14:9259. [PMID: 38649719 PMCID: PMC11035558 DOI: 10.1038/s41598-024-56464-w] [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: 01/06/2024] [Accepted: 03/06/2024] [Indexed: 04/25/2024] Open
Abstract
Chemotherapy resistance poses clinical challenges in pancreatic cancer treatment. Developing cell lines resistant to chemotherapy is crucial for investigating drug resistance mechanisms and identifying alternative treatment pathways. The genetic and biological attributes of pancreatic cancer depend on its aetiology, racial demographics and anatomical origin, underscoring the need for models that comprehensively represent these characteristics. Here, we introduce PDAC-X2, a pancreatic cancer cell line derived from Chinese patients. We conducted a comprehensive analysis encompassing the immune phenotype, biology, genetics, molecular characteristics and tumorigenicity of the cell line. PDAC-X2 cells displayed epithelial morphology and expressed cell markers (CK7 and CK19) alongside other markers (E-cadherin, Vimentin, Ki-67, CEA and CA19-9). The population doubling time averaged around 69 h. In vivo, PDAC-X2 cells consistently maintained their tumorigenicity, achieving a 100% tumour formation rate. Characterised by a predominantly tetraploid karyotype, this cell line exhibited a complex genetic markup. Notably, PDAC-X2 cells demonstrated resistance to multiple drugs, including gemcitabine, paclitaxel, 5-fluorouracil and oxaliplatin. In conclusion, PDAC-X2 presents an invaluable preclinical model. Its utility lies in facilitating the study of drug resistance mechanisms and the exploration of alternative therapeutic approaches aimed at enhancing the prognosis of this tumour type.
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Affiliation(s)
- Huan Tang
- The Second Clinical Medical School of Lanzhou University, Lanzhou, 730000, China
| | - Xin Miao
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310006, China
| | - Cheng Yu
- The Second Clinical Medical School of Lanzhou University, Lanzhou, 730000, China
- Department of Anesthesiology, Lanzhou University Second Hospital, Lanzhou, 730000, China
| | - Changpeng Chai
- The Second Clinical Medical School of Lanzhou University, Lanzhou, 730000, China
- The Fourth Department of General Surgery, the First Hospital of Lanzhou University, Lanzhou, 730000, China
| | - Yuanhui Su
- The Second Clinical Medical School of Lanzhou University, Lanzhou, 730000, China
| | - Lu Li
- The Fourth Department of General Surgery, the First Hospital of Lanzhou University, Lanzhou, 730000, China
| | - Jianfeng Yi
- The First Clinical Medical School of Lanzhou University, Lanzhou, 730000, China
- The First School of Clinical Medicine of Gansu University of Chinese Medicine, Lanzhou, 730000, China
| | - Zhenzhen Ye
- The Second Clinical Medical School of Lanzhou University, Lanzhou, 730000, China
- The First School of Clinical Medicine of Gansu University of Chinese Medicine, Lanzhou, 730000, China
| | - Long Miao
- The Second Clinical Medical School of Lanzhou University, Lanzhou, 730000, China
- The Fourth Department of General Surgery, the First Hospital of Lanzhou University, Lanzhou, 730000, China
| | - Zhengfeng Wang
- The Second Clinical Medical School of Lanzhou University, Lanzhou, 730000, China
- The Fourth Department of General Surgery, the First Hospital of Lanzhou University, Lanzhou, 730000, China
| | - Hui Zhang
- The Second Clinical Medical School of Lanzhou University, Lanzhou, 730000, China.
- Department of General Surgery, Lanzhou University Second Hospital, No. 82 Cuiyingmen, Chengguan District, Lanzhou, 730000, Gansu, China.
| | - Hao Xu
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310006, China.
- Department of Hepatobiliary Surgery, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, 310006, China.
| | - Wence Zhou
- The Second Clinical Medical School of Lanzhou University, Lanzhou, 730000, China.
- Department of General Surgery, Lanzhou University Second Hospital, No. 82 Cuiyingmen, Chengguan District, Lanzhou, 730000, Gansu, China.
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Zhang Y, Hu Q, Pei Y, Luo H, Wang Z, Xu X, Zhang Q, Dai J, Wang Q, Fan Z, Fang Y, Ye M, Li B, Chen M, Xue Q, Zheng Q, Zhang S, Huang M, Zhang T, Gu J, Xiong Z. A patient-specific lung cancer assembloid model with heterogeneous tumor microenvironments. Nat Commun 2024; 15:3382. [PMID: 38643164 PMCID: PMC11032376 DOI: 10.1038/s41467-024-47737-z] [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: 11/10/2022] [Accepted: 04/08/2024] [Indexed: 04/22/2024] Open
Abstract
Cancer models play critical roles in basic cancer research and precision medicine. However, current in vitro cancer models are limited by their inability to mimic the three-dimensional architecture and heterogeneous tumor microenvironments (TME) of in vivo tumors. Here, we develop an innovative patient-specific lung cancer assembloid (LCA) model by using droplet microfluidic technology based on a microinjection strategy. This method enables precise manipulation of clinical microsamples and rapid generation of LCAs with good intra-batch consistency in size and cell composition by evenly encapsulating patient tumor-derived TME cells and lung cancer organoids inside microgels. LCAs recapitulate the inter- and intratumoral heterogeneity, TME cellular diversity, and genomic and transcriptomic landscape of their parental tumors. LCA model could reconstruct the functional heterogeneity of cancer-associated fibroblasts and reflect the influence of TME on drug responses compared to cancer organoids. Notably, LCAs accurately replicate the clinical outcomes of patients, suggesting the potential of the LCA model to predict personalized treatments. Collectively, our studies provide a valuable method for precisely fabricating cancer assembloids and a promising LCA model for cancer research and personalized medicine.
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Affiliation(s)
- Yanmei Zhang
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, 100084, China
- Biomanufacturing and Engineering Living Systems Innovation International Talents Base (111 Base), Beijing, 100084, China
- Institute of New Materials and Advanced Manufacturing, Beijing Academy of Science and Technology, Beijing, 100089, China
| | - Qifan Hu
- MOE Key Laboratory of Bioinformatics, BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Yuquan Pei
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Thoracic Surgery II, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Hao Luo
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, 100084, China
- Biomanufacturing and Engineering Living Systems Innovation International Talents Base (111 Base), Beijing, 100084, China
| | - Zixuan Wang
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, 100084, China
- Biomanufacturing and Engineering Living Systems Innovation International Talents Base (111 Base), Beijing, 100084, China
| | - Xinxin Xu
- Medical School of Chinese PLA, Beijing, 100853, China
| | - Qing Zhang
- Institute of New Materials and Advanced Manufacturing, Beijing Academy of Science and Technology, Beijing, 100089, China
| | - Jianli Dai
- Institute of New Materials and Advanced Manufacturing, Beijing Academy of Science and Technology, Beijing, 100089, China
| | - Qianqian Wang
- Institute of New Materials and Advanced Manufacturing, Beijing Academy of Science and Technology, Beijing, 100089, China
| | - Zilian Fan
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, 100084, China
- Biomanufacturing and Engineering Living Systems Innovation International Talents Base (111 Base), Beijing, 100084, China
| | - Yongcong Fang
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, 100084, China
- Biomanufacturing and Engineering Living Systems Innovation International Talents Base (111 Base), Beijing, 100084, China
| | - Min Ye
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, 100084, China
- Biomanufacturing and Engineering Living Systems Innovation International Talents Base (111 Base), Beijing, 100084, China
| | - Binhan Li
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, 100084, China
- Biomanufacturing and Engineering Living Systems Innovation International Talents Base (111 Base), Beijing, 100084, China
| | - Mailin Chen
- Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Qi Xue
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Qingfeng Zheng
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Shulin Zhang
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Miao Huang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Thoracic Surgery II, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Ting Zhang
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, 100084, China
- Biomanufacturing and Engineering Living Systems Innovation International Talents Base (111 Base), Beijing, 100084, China
| | - Jin Gu
- MOE Key Laboratory of Bioinformatics, BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Zhuo Xiong
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China.
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, 100084, China.
- Biomanufacturing and Engineering Living Systems Innovation International Talents Base (111 Base), Beijing, 100084, China.
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44
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Liu X, Zhang K, Kaya NA, Jia Z, Wu D, Chen T, Liu Z, Zhu S, Hillmer AM, Wuestefeld T, Liu J, Chan YS, Hu Z, Ma L, Jiang L, Zhai W. Tumor phylogeography reveals block-shaped spatial heterogeneity and the mode of evolution in Hepatocellular Carcinoma. Nat Commun 2024; 15:3169. [PMID: 38609353 PMCID: PMC11015015 DOI: 10.1038/s41467-024-47541-9] [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: 08/04/2022] [Accepted: 03/26/2024] [Indexed: 04/14/2024] Open
Abstract
Solid tumors are complex ecosystems with heterogeneous 3D structures, but the spatial intra-tumor heterogeneity (sITH) at the macroscopic (i.e., whole tumor) level is under-explored. Using a phylogeographic approach, we sequence genomes and transcriptomes from 235 spatially informed sectors across 13 hepatocellular carcinomas (HCC), generating one of the largest datasets for studying sITH. We find that tumor heterogeneity in HCC segregates into spatially variegated blocks with large genotypic and phenotypic differences. By dissecting the transcriptomic heterogeneity, we discover that 30% of patients had a "spatially competing distribution" (SCD), where different spatial blocks have distinct transcriptomic subtypes co-existing within a tumor, capturing the critical transition period in disease progression. Interestingly, the tumor regions with more advanced transcriptomic subtypes (e.g., higher cell cycle) often take clonal dominance with a wider geographic range, rejecting neutral evolution for SCD patients. Extending the statistical tests for detecting natural selection to many non-SCD patients reveal varying levels of selective signal across different tumors, implying that many evolutionary forces including natural selection and geographic isolation can influence the overall pattern of sITH. Taken together, tumor phylogeography unravels a dynamic landscape of sITH, pinpointing important evolutionary and clinical consequences of spatial heterogeneity in cancer.
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Affiliation(s)
- Xiaodong Liu
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Ke Zhang
- Department of General Surgery, Beijing Ditan Hospital, Capital Medical University, No. 8, Jingshun East Street, Chaoyang District, Beijing, P.R. China
| | - Neslihan A Kaya
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Zhe Jia
- Department of General Surgery, Beijing Ditan Hospital, Capital Medical University, No. 8, Jingshun East Street, Chaoyang District, Beijing, P.R. China
| | - Dafei Wu
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Tingting Chen
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- University of the Chinese Academy of Sciences, Beijing, China
| | - Zhiyuan Liu
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- University of the Chinese Academy of Sciences, Beijing, China
| | - Sinan Zhu
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- Centre for Quantitative Medicine, Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
| | - Axel M Hillmer
- Institute of Pathology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Torsten Wuestefeld
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Jin Liu
- Centre for Quantitative Medicine, Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
| | - Yun Shen Chan
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Zheng Hu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Liang Ma
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.
| | - Li Jiang
- Department of General Surgery, Beijing Ditan Hospital, Capital Medical University, No. 8, Jingshun East Street, Chaoyang District, Beijing, P.R. China.
| | - Weiwei Zhai
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China.
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45
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Brandlmaier M, Hoellwerth M, Koelblinger P, Lang R, Harrer A. Adjuvant PD-1 Checkpoint Inhibition in Early Cutaneous Melanoma: Immunological Mode of Action and the Role of Ultraviolet Radiation. Cancers (Basel) 2024; 16:1461. [PMID: 38672543 PMCID: PMC11047851 DOI: 10.3390/cancers16081461] [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: 02/09/2024] [Revised: 04/03/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
Melanoma ranks as the fifth most common solid cancer in adults worldwide and is responsible for a significant proportion of skin-tumor-related deaths. The advent of immune checkpoint inhibition with anti-programmed death protein-1 (PD-1) antibodies has revolutionized the adjuvant treatment of high-risk, completely resected stage III/IV melanoma. However, not all patients benefit equally. Current strategies for improving outcomes involve adjuvant treatment in earlier disease stages (IIB/C) as well as perioperative treatment approaches. Interfering with T-cell exhaustion to counteract cancer immune evasion and the immunogenic nature of melanoma is key for anti-PD-1 effectiveness. Yet, the biological rationale for the efficacy of adjuvant treatment in clinically tumor-free patients remains to be fully elucidated. High-dose intermittent sun exposure (sunburn) is a well-known primary risk factor for melanomagenesis. Also, ultraviolet radiation (UVR)-induced immunosuppression may impair anti-cancer immune surveillance. In this review, we summarize the current knowledge about adjuvant anti-PD-1 blockade, including a characterization of the main cell types most likely responsible for its efficacy. In conclusion, we propose that local and systemic immunosuppression, to some extent UVR-mediated, can be restored by adjuvant anti-PD-1 therapy, consequently boosting anti-melanoma immune surveillance and the elimination of residual melanoma cell clones.
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Affiliation(s)
- Matthias Brandlmaier
- Department of Dermatology and Allergology, Paracelsus Medical University, 5020 Salzburg, Austria; (M.B.); (M.H.); (P.K.)
| | - Magdalena Hoellwerth
- Department of Dermatology and Allergology, Paracelsus Medical University, 5020 Salzburg, Austria; (M.B.); (M.H.); (P.K.)
| | - Peter Koelblinger
- Department of Dermatology and Allergology, Paracelsus Medical University, 5020 Salzburg, Austria; (M.B.); (M.H.); (P.K.)
| | - Roland Lang
- Department of Dermatology and Allergology, Paracelsus Medical University, 5020 Salzburg, Austria; (M.B.); (M.H.); (P.K.)
| | - Andrea Harrer
- Department of Dermatology and Allergology, Paracelsus Medical University, 5020 Salzburg, Austria; (M.B.); (M.H.); (P.K.)
- Department of Neurology, Christian Doppler University Hospital, Paracelsus Medical University and Center for Cognitive Neuroscience, 5020 Salzburg, Austria
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46
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Shen W, Liu C, Hu Y, Lei Y, Wong HS, Wu S, Zhou XM. Leveraging cross-source heterogeneity to improve the performance of bulk gene expression deconvolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.07.588458. [PMID: 38645128 PMCID: PMC11030304 DOI: 10.1101/2024.04.07.588458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
A main limitation of bulk transcriptomic technologies is that individual measurements normally contain contributions from multiple cell populations, impeding the identification of cellular heterogeneity within diseased tissues. To extract cellular insights from existing large cohorts of bulk transcriptomic data, we present CSsingle, a novel method designed to accurately deconvolve bulk data into a predefined set of cell types using a scRNA-seq reference. Through comprehensive benchmark evaluations and analyses using diverse real data sets, we reveal the systematic bias inherent in existing methods, stemming from differences in cell size or library size. Our extensive experiments demonstrate that CSsingle exhibits superior accuracy and robustness compared to leading methods, particularly when dealing with bulk mixtures originating from cell types of markedly different cell sizes, as well as when handling bulk and single-cell reference data obtained from diverse sources. Our work provides an efficient and robust methodology for the integrated analysis of bulk and scRNA-seq data, facilitating various biological and clinical studies.
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Affiliation(s)
- Wenjun Shen
- Department of Bioinformatics, Shantou University Medical College, Shantou, Guangdong 515041, China
| | - Cheng Liu
- Department of Computer Science, Shantou University, Shantou, Guangdong 515041, China
| | - Yunfei Hu
- Department of Computer Science, Vanderbilt University, Nashville, TN 37235, USA
| | - Yuanfang Lei
- Department of Bioinformatics, Shantou University Medical College, Shantou, Guangdong 515041, China
| | - Hau-San Wong
- Department of Computer Sciences, City University of Hong Kong, Kowloon, Hong Kong
| | - Si Wu
- Department of Computer Science, South China University of Technology, Guangzhou, Guangdong 510006, China
| | - Xin Maizie Zhou
- Department of Computer Science, Vanderbilt University, Nashville, TN 37235, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
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47
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Yang H, Cheng J, Zhuang H, Xu H, Wang Y, Zhang T, Yang Y, Qian H, Lu Y, Han F, Cao L, Yang N, Liu R, Yang X, Zhang J, Wu J, Zhang N. Pharmacogenomic profiling of intra-tumor heterogeneity using a large organoid biobank of liver cancer. Cancer Cell 2024; 42:535-551.e8. [PMID: 38593780 DOI: 10.1016/j.ccell.2024.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 12/27/2023] [Accepted: 03/11/2024] [Indexed: 04/11/2024]
Abstract
Inter- and intra-tumor heterogeneity is a major hurdle in primary liver cancer (PLC) precision therapy. Here, we establish a PLC biobank, consisting of 399 tumor organoids derived from 144 patients, which recapitulates histopathology and genomic landscape of parental tumors, and is reliable for drug sensitivity screening, as evidenced by both in vivo models and patient response. Integrative analysis dissects PLC heterogeneity, regarding genomic/transcriptomic characteristics and sensitivity to seven clinically relevant drugs, as well as clinical associations. Pharmacogenomic analysis identifies and validates multi-gene expression signatures predicting drug response for better patient stratification. Furthermore, we reveal c-Jun as a major mediator of lenvatinib resistance through JNK and β-catenin signaling. A compound (PKUF-01) comprising moieties of lenvatinib and veratramine (c-Jun inhibitor) is synthesized and screened, exhibiting a marked synergistic effect. Together, our study characterizes the landscape of PLC heterogeneity, develops predictive biomarker panels, and identifies a lenvatinib-resistant mechanism for combination therapy.
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Affiliation(s)
- Hui Yang
- Translational Cancer Research Center, Peking University First Hospital, Beijing, China
| | - Jinghui Cheng
- Translational Cancer Research Center, Peking University First Hospital, Beijing, China
| | - Hao Zhuang
- Department of Hepatobiliopancreatic Surgery, Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Hongchuang Xu
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Yinuo Wang
- Translational Cancer Research Center, Peking University First Hospital, Beijing, China
| | - Tingting Zhang
- Department of Hepatobiliopancreatic Surgery, Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Yinmo Yang
- Department of Hepatobiliary and Pancreatic Surgery, Peking University First Hospital, Beijing, China
| | - Honggang Qian
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepato-Pancreato-Biliary Surgery, Peking University Cancer Hospital & Institute, Beijing, China
| | - Yinying Lu
- Comprehensive Liver Cancer Department, The Fifth Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Feng Han
- Department of Hepatobiliopancreatic Surgery, Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Lihua Cao
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Center for Cancer Bioinformatics, Peking University Cancer Hospital & Institute, Beijing, China; International Cancer Institute, Peking University Health Science Center, Beijing, China
| | - Nanmu Yang
- Department of Hepatobiliopancreatic Surgery, Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Rong Liu
- Translational Cancer Research Center, Peking University First Hospital, Beijing, China
| | - Xing Yang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Jiangong Zhang
- Department of Cancer Epidemiology, Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China.
| | - Jianmin Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Center for Cancer Bioinformatics, Peking University Cancer Hospital & Institute, Beijing, China; International Cancer Institute, Peking University Health Science Center, Beijing, China.
| | - Ning Zhang
- Translational Cancer Research Center, Peking University First Hospital, Beijing, China; International Cancer Institute, Peking University Health Science Center, Beijing, China; Yunnan Baiyao Group, Kunming, China.
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48
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Sali R, Jiang Y, Attaranzadeh A, Holmes B, Li R. Morphological diversity of cancer cells predicts prognosis across tumor types. J Natl Cancer Inst 2024; 116:555-564. [PMID: 37982756 PMCID: PMC10995848 DOI: 10.1093/jnci/djad243] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/23/2023] [Accepted: 11/13/2023] [Indexed: 11/21/2023] Open
Abstract
BACKGROUND Intratumor heterogeneity drives disease progression and treatment resistance, which can lead to poor patient outcomes. Here, we present a computational approach for quantification of cancer cell diversity in routine hematoxylin-eosin-stained histopathology images. METHODS We analyzed publicly available digitized whole-slide hematoxylin-eosin images for 2000 patients. Four tumor types were included: lung, head and neck, colon, and rectal cancers, representing major histology subtypes (adenocarcinomas and squamous cell carcinomas). We performed single-cell analysis on hematoxylin-eosin images and trained a deep convolutional autoencoder to automatically learn feature representations of individual cancer nuclei. We then computed features of intranuclear variability and internuclear diversity to quantify tumor heterogeneity. Finally, we used these features to build a machine-learning model to predict patient prognosis. RESULTS A total of 68 million cancer cells were segmented and analyzed for nuclear image features. We discovered multiple morphological subtypes of cancer cells (range = 15-20) that co-exist within the same tumor, each with distinct phenotypic characteristics. Moreover, we showed that a higher morphological diversity is associated with chromosome instability and genomic aneuploidy. A machine-learning model based on morphological diversity demonstrated independent prognostic values across tumor types (hazard ratio range = 1.62-3.23, P < .035) in validation cohorts and further improved prognostication when combined with clinical risk factors. CONCLUSIONS Our study provides a practical approach for quantifying intratumor heterogeneity based on routine histopathology images. The cancer cell diversity score can be used to refine risk stratification and inform personalized treatment strategies.
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Affiliation(s)
- Rasoul Sali
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Yuming Jiang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Radiation Oncology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Armin Attaranzadeh
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Brittany Holmes
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
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49
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Denisenko E, de Kock L, Tan A, Beasley AB, Beilin M, Jones ME, Hou R, Muirí DÓ, Bilic S, Mohan GRKA, Salfinger S, Fox S, Hmon KPW, Yeow Y, Kim Y, John R, Gilderman TS, Killingbeck E, Gray ES, Cohen PA, Yu Y, Forrest ARR. Spatial transcriptomics reveals discrete tumour microenvironments and autocrine loops within ovarian cancer subclones. Nat Commun 2024; 15:2860. [PMID: 38570491 PMCID: PMC10991508 DOI: 10.1038/s41467-024-47271-y] [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: 10/05/2022] [Accepted: 03/26/2024] [Indexed: 04/05/2024] Open
Abstract
High-grade serous ovarian carcinoma (HGSOC) is genetically unstable and characterised by the presence of subclones with distinct genotypes. Intratumoural heterogeneity is linked to recurrence, chemotherapy resistance, and poor prognosis. Here, we use spatial transcriptomics to identify HGSOC subclones and study their association with infiltrating cell populations. Visium spatial transcriptomics reveals multiple tumour subclones with different copy number alterations present within individual tumour sections. These subclones differentially express various ligands and receptors and are predicted to differentially associate with different stromal and immune cell populations. In one sample, CosMx single molecule imaging reveals subclones differentially associating with immune cell populations, fibroblasts, and endothelial cells. Cell-to-cell communication analysis identifies subclone-specific signalling to stromal and immune cells and multiple subclone-specific autocrine loops. Our study highlights the high degree of subclonal heterogeneity in HGSOC and suggests that subclone-specific ligand and receptor expression patterns likely modulate how HGSOC cells interact with their local microenvironment.
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Affiliation(s)
- Elena Denisenko
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Nedlands, Perth, WA, 6009, Australia.
| | - Leanne de Kock
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Nedlands, Perth, WA, 6009, Australia
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada
| | - Adeline Tan
- Anatomical Pathology Department, Clinipath, Sonic Healthcare, Perth, WA, 6017, Australia
| | - Aaron B Beasley
- Centre for Precision Health, Edith Cowan University, Joondalup, WA, 6027, Australia
| | - Maria Beilin
- Department of Gynaecological Oncology, Bendat Family Comprehensive Cancer Centre, St John of God Subiaco Hospital, 12 Salvado Rd, Subiaco, WA, 6008, Australia
| | - Matthew E Jones
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Nedlands, Perth, WA, 6009, Australia
| | - Rui Hou
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Nedlands, Perth, WA, 6009, Australia
| | - Dáithí Ó Muirí
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Nedlands, Perth, WA, 6009, Australia
| | - Sanela Bilic
- Department of Gynaecological Oncology, Bendat Family Comprehensive Cancer Centre, St John of God Subiaco Hospital, 12 Salvado Rd, Subiaco, WA, 6008, Australia
| | - G Raj K A Mohan
- Department of Gynaecological Oncology, Bendat Family Comprehensive Cancer Centre, St John of God Subiaco Hospital, 12 Salvado Rd, Subiaco, WA, 6008, Australia
- School of Medicine, University of Notre Dame, Fremantle, WA, 6160, Australia
| | | | - Simon Fox
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Nedlands, Perth, WA, 6009, Australia
| | - Khaing P W Hmon
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Nedlands, Perth, WA, 6009, Australia
| | - Yen Yeow
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Nedlands, Perth, WA, 6009, Australia
| | | | - Rhea John
- NanoString Technologies, Seattle, WA, USA
| | | | | | - Elin S Gray
- Centre for Precision Health, Edith Cowan University, Joondalup, WA, 6027, Australia
| | - Paul A Cohen
- Division of Obstetrics and Gynaecology, Medical School, University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia.
- Institute for Health Research, The University of Notre Dame Australia, 32 Mouat Street Fremantle, Fremantle, WA, 6160, Australia.
| | - Yu Yu
- Division of Obstetrics and Gynaecology, Medical School, University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia.
- Curtin Medical School, Curtin University, 410 Koorliny Way, Bentley, WA, 6102, Australia.
- Curtin Health Innovation Research Institute, Curtin University B305, Bentley, WA, 6102, Australia.
| | - Alistair R R Forrest
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Nedlands, Perth, WA, 6009, Australia.
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50
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Xiang Y, Liu J, Chen J, Xiao M, Pei H, Li L. MoS 2-Based Sensor Array for Accurate Identification of Cancer Cells with Ensemble-Modified Aptamers. ACS APPLIED MATERIALS & INTERFACES 2024; 16:15861-15869. [PMID: 38508220 DOI: 10.1021/acsami.3c19159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
In this work, we present an array-based chemical nose sensor that utilizes a set of ensemble-modified aptamer (EMAmer) probes to sense subtle physicochemical changes on the cell surface for cancer cell identification. The EMAmer probes are engineered by domain-selective incorporation of different types and/or copies of positively charged functional groups into DNA scaffolds, and their differential interactions with cancer cells can be transduced through competitive adsorption of fluorophore-labeled EMAmer probes loaded on MoS2 nanosheets. We demonstrate that this MoS2-EMAmer-based sensor array enables rapid and effective discrimination among six types of cancer cells and their mixtures with a concentration of 104 cells within 60 min, achieving a 94.4% accuracy in identifying blinded unknown cell samples. The established MoS2-EMAmer sensing platform is anticipated to show significant promise in the advancement of cancer diagnostics.
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Affiliation(s)
- Ying Xiang
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, School of Chemistry and Molecular Engineering, Shanghai Frontiers Science Center of Genome Editing and Cell Therapy, East China Normal University, 500 Dongchuan Road, Shanghai 200241, P. R. China
| | - Jingjing Liu
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, School of Chemistry and Molecular Engineering, Shanghai Frontiers Science Center of Genome Editing and Cell Therapy, East China Normal University, 500 Dongchuan Road, Shanghai 200241, P. R. China
| | - Jing Chen
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, School of Chemistry and Molecular Engineering, Shanghai Frontiers Science Center of Genome Editing and Cell Therapy, East China Normal University, 500 Dongchuan Road, Shanghai 200241, P. R. China
| | - Mingshu Xiao
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, School of Chemistry and Molecular Engineering, Shanghai Frontiers Science Center of Genome Editing and Cell Therapy, East China Normal University, 500 Dongchuan Road, Shanghai 200241, P. R. China
| | - Hao Pei
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, School of Chemistry and Molecular Engineering, Shanghai Frontiers Science Center of Genome Editing and Cell Therapy, East China Normal University, 500 Dongchuan Road, Shanghai 200241, P. R. China
| | - Li Li
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, School of Chemistry and Molecular Engineering, Shanghai Frontiers Science Center of Genome Editing and Cell Therapy, East China Normal University, 500 Dongchuan Road, Shanghai 200241, P. R. China
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