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Lee ST, Kuboki T, Kidoaki S, Aida Y, Arima Y, Tamada K. A plasmonic metasurface reveals differential motility of breast cancer cell lines at initial phase of adhesion. Colloids Surf B Biointerfaces 2024; 238:113876. [PMID: 38555764 DOI: 10.1016/j.colsurfb.2024.113876] [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: 01/31/2024] [Revised: 03/17/2024] [Accepted: 03/23/2024] [Indexed: 04/02/2024]
Abstract
A plasmonic metasurface composed of a self-assembled monolayer of gold nanoparticles allows for fluorescence imaging with high spatial resolution, owing to the collective excitation of localized surface plasmon resonance. Taking advantage of fluorescence imaging confined to the nano-interface, we examined actin organization in breast cancer cell lines with different metastatic potentials during cell adhesion. Live-cell fluorescence imaging confined within tens of nanometers from the substrate shows a high actin density spanning < 1 μm from the cell edge. Live-cell imaging revealed that the breast cancer cell lines exhibited different actin patterns during the initial phase of cell adhesion (∼ 1 h). Non-tumorous MCF10A cells exhibited symmetric actin localization at the cell edge, whereas highly metastatic MDA-MB-231 cells showed asymmetric actin localization, demonstrating rapid polarization of MDA-MB-231 cells upon adhesion. The rapid actin organization observed by our plasmonic metasurface-based fluorescence imaging provides information on how quickly cancer cells sense the underlying substrate.
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Affiliation(s)
- Shi Ting Lee
- Institute for Materials Chemistry and Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
| | - Thasaneeya Kuboki
- Institute for Materials Chemistry and Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
| | - Satoru Kidoaki
- Institute for Materials Chemistry and Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
| | - Yukiko Aida
- Institute for Materials Chemistry and Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
| | - Yusuke Arima
- Institute for Materials Chemistry and Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan.
| | - Kaoru Tamada
- Institute for Materials Chemistry and Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan.
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Benmelech S, Le T, McKay M, Nam J, Subramaniam K, Tellez D, Vlasak G, Mak M. Biophysical and biochemical aspects of immune cell-tumor microenvironment interactions. APL Bioeng 2024; 8:021502. [PMID: 38572312 PMCID: PMC10990568 DOI: 10.1063/5.0195244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Accepted: 03/19/2024] [Indexed: 04/05/2024] Open
Abstract
The tumor microenvironment (TME), composed of and influenced by a heterogeneous set of cancer cells and an extracellular matrix, plays a crucial role in cancer progression. The biophysical aspects of the TME (namely, its architecture and mechanics) regulate interactions and spatial distributions of cancer cells and immune cells. In this review, we discuss the factors of the TME-notably, the extracellular matrix, as well as tumor and stromal cells-that contribute to a pro-tumor, immunosuppressive response. We then discuss the ways in which cells of the innate and adaptive immune systems respond to tumors from both biochemical and biophysical perspectives, with increased focus on CD8+ and CD4+ T cells. Building upon this information, we turn to immune-based antitumor interventions-specifically, recent biophysical breakthroughs aimed at improving CAR-T cell therapy.
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Affiliation(s)
- Shoham Benmelech
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut 06511, USA
| | - Thien Le
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut 06511, USA
| | - Maggie McKay
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut 06511, USA
| | - Jungmin Nam
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut 06511, USA
| | - Krupakar Subramaniam
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut 06511, USA
| | - Daniela Tellez
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut 06511, USA
| | - Grace Vlasak
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut 06511, USA
| | - Michael Mak
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut 06511, USA
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3
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Pan Y, Xuan Y, Hao P, Chen X, Yan R, Zhang C, Ke X, Qu Y, Zhang X. Time-dependent proteomics and drug response in expanding cancer cells. Pharmacol Res 2024; 204:107208. [PMID: 38729587 DOI: 10.1016/j.phrs.2024.107208] [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: 04/05/2024] [Revised: 04/26/2024] [Accepted: 05/06/2024] [Indexed: 05/12/2024]
Abstract
Cancer cell line is commonly used for discovery and development of anti-cancer drugs. It is generally considered that drug response remains constant for a certain cell line due to the identity of genetics thus protein patterns. Here, we demonstrated that cancer cells continued dividing even after reaching confluence, in that the proteomics was changed continuously and dramatically with strong relevance to cell division, cell adhesion and cell metabolism, indicating time-dependent intrinsically reprogramming of cells during expansion. Of note, the inhibition effect of most anti-cancer drugs was strikingly attenuated in culture cells along with cell expansion, with the strongest change at the third day when cells were still expanding. Profiling of an FDA-approved drug library revealed that attenuation of response with cell expansion is common for most drugs, an exception was TAK165 that was a selective inhibitor of mitochondrial respiratory chain complex I. Finally, we screened a panel of natural products and identified four pentacyclic triterpenes as selective inhibitors of cancer cells under prolonged growth. Taken together, our findings underscore that caution should be taken in evaluation of anti-cancer drugs using culture cells, and provide agents selectively targeting overgrowth cancer cells.
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Affiliation(s)
- Yuting Pan
- Center for Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, PR China
| | - Ying Xuan
- Center for Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, PR China
| | - Piliang Hao
- School of Life Science and Technology, ShanghaiTech University, Shanghai, PR China
| | - Xianzhi Chen
- Center for Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, PR China
| | - Rong Yan
- Center for Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, PR China
| | - Chengqian Zhang
- School of Life Science and Technology, ShanghaiTech University, Shanghai, PR China
| | - Xisong Ke
- Center for Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, PR China.
| | - Yi Qu
- Center for Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, PR China.
| | - Xue Zhang
- Center for Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, PR China.
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4
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Mares-Quiñones MD, Galán-Vásquez E, Pérez-Rueda E, Pérez-Ishiwara DG, Medel-Flores MO, Gómez-García MDC. Identification of modules and key genes associated with breast cancer subtypes through network analysis. Sci Rep 2024; 14:12350. [PMID: 38811600 PMCID: PMC11137066 DOI: 10.1038/s41598-024-61908-4] [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: 07/13/2023] [Accepted: 05/10/2024] [Indexed: 05/31/2024] Open
Abstract
Breast cancer is the most common malignancy in women around the world. Intratumor and intertumoral heterogeneity persist in mammary tumors. Therefore, the identification of biomarkers is essential for the treatment of this malignancy. This study analyzed 28,143 genes expressed in 49 breast cancer cell lines using a Weighted Gene Co-expression Network Analysis to determine specific target proteins for Basal A, Basal B, Luminal A, Luminal B, and HER2 ampl breast cancer subtypes. Sixty-five modules were identified, of which five were characterized as having a high correlation with breast cancer subtypes. Genes overexpressed in the tumor were found to participate in the following mechanisms: regulation of the apoptotic process, transcriptional regulation, angiogenesis, signaling, and cellular survival. In particular, we identified the following genes, considered as hubs: IFIT3, an inhibitor of viral and cellular processes; ETS1, a transcription factor involved in cell death and tumorigenesis; ENSG00000259723 lncRNA, expressed in cancers; AL033519.3, a hypothetical gene; and TMEM86A, important for regulating keratinocyte membrane properties, considered as a key in Basal A, Basal B, Luminal A, Luminal B, and HER2 ampl breast cancer subtypes, respectively. The modules and genes identified in this work can be used to identify possible biomarkers or therapeutic targets in different breast cancer subtypes.
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Affiliation(s)
- María Daniela Mares-Quiñones
- Laboratorio de Biomedicina Molecular, Programa de Doctorado en Biotecnología, Escuela Nacional de Medicina y Homeopatía, Instituto Politécnico Nacional, Ciudad de México, Mexico
| | - Edgardo Galán-Vásquez
- Departamento de Ingeniería de Sistemas Computacionales y Automatización, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Ciudad Universitaria, Ciudad de México, Mexico
| | - Ernesto Pérez-Rueda
- Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Unidad Académica del Estado de Yucatán, Mérida, Mexico
| | - D Guillermo Pérez-Ishiwara
- Laboratorio de Biomedicina Molecular, Programa de Doctorado en Biotecnología, Escuela Nacional de Medicina y Homeopatía, Instituto Politécnico Nacional, Ciudad de México, Mexico
| | - María Olivia Medel-Flores
- Laboratorio de Biomedicina Molecular, Programa de Doctorado en Biotecnología, Escuela Nacional de Medicina y Homeopatía, Instituto Politécnico Nacional, Ciudad de México, Mexico
| | - María Del Consuelo Gómez-García
- Laboratorio de Biomedicina Molecular, Programa de Doctorado en Biotecnología, Escuela Nacional de Medicina y Homeopatía, Instituto Politécnico Nacional, Ciudad de México, Mexico.
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Qin S, Zhang Y, Shi M, Miao D, Lu J, Wen L, Bai Y. In-depth organic mass cytometry reveals differential contents of 3-hydroxybutanoic acid at the single-cell level. Nat Commun 2024; 15:4387. [PMID: 38782922 PMCID: PMC11116506 DOI: 10.1038/s41467-024-48865-2] [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: 06/22/2023] [Accepted: 05/16/2024] [Indexed: 05/25/2024] Open
Abstract
Comprehensive single-cell metabolic profiling is critical for revealing phenotypic heterogeneity and elucidating the molecular mechanisms underlying biological processes. However, single-cell metabolomics remains challenging because of the limited metabolite coverage and inability to discriminate isomers. Herein, we establish a single-cell metabolomics platform for in-depth organic mass cytometry. Extended single-cell analysis time guarantees sufficient MS/MS acquisition for metabolite identification and the isomers discrimination while online sampling ensures the high-throughput of the method. The largest number of identified metabolites (approximately 600) are achieved in single cells and fine subtyping of MCF-7 cells is first demonstrated by an investigation on the differential levels of 3-hydroxybutanoic acid among clusters. Single-cell transcriptome analysis reveals differences in the expression of 3-hydroxybutanoic acid downstream antioxidative stress genes, such as metallothionein 2 (MT2A), while a fluorescence-activated cell sorting assay confirms the positive relationship between 3-hydroxybutanoic acid and target proteins; these results suggest that the heterogeneity of 3-hydroxybutanoic acid provides cancer cells with different ability to resist surrounding oxidative stress. Our method paves the way for deep single-cell metabolome profiling and investigations on the physiological and pathological processes that occur during cancer.
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Affiliation(s)
- Shaojie Qin
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
| | - Yi Zhang
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
| | - Mingying Shi
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
| | - Daiyu Miao
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
| | - Jiansen Lu
- Biomedical Pioneering Innovative Center, School of Life Sciences, Peking University, Beijing, China
| | - Lu Wen
- Biomedical Pioneering Innovative Center, School of Life Sciences, Peking University, Beijing, China
| | - Yu Bai
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing, China.
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6
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Yang L, Kumegawa K, Saeki S, Nakadai T, Maruyama R. Identification of lineage-specific epigenetic regulators FOXA1 and GRHL2 through chromatin accessibility profiling in breast cancer cell lines. Cancer Gene Ther 2024; 31:736-745. [PMID: 38429368 PMCID: PMC11101334 DOI: 10.1038/s41417-024-00745-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: 11/09/2023] [Revised: 02/04/2024] [Accepted: 02/07/2024] [Indexed: 03/03/2024]
Abstract
Breast cancer is a heterogeneous disease, and breast cancer cell lines are invaluable for studying this heterogeneity. However, the epigenetic diversity across these cell lines remains poorly understood. In this study, we performed genome-wide chromatin accessibility analysis on 23 breast cancer cell lines, including 2 estrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative (ER+/HER2-), 3 ER+/HER2+, 3 HER2+, and 15 triple-negative breast cancer (TNBC) lines. These cell lines were classified into three groups based on their chromatin accessibility: the receptor-positive group (Group-P), TNBC basal group (Group-B), and TNBC mesenchymal group (Group-M). Motif enrichment analysis revealed that only Group-P exhibited coenrichment of forkhead box A1 (FOXA1) and grainyhead-like 2 (GRHL2) motifs, whereas Group-B was characterized by the presence of the GRHL2 motif without FOXA1. Notably, Group-M did not show enrichment of either FOXA1 or GRHL2 motifs. Furthermore, gene ontology analysis suggested that group-specific accessible regions were associated with their unique lineage characteristics. To investigate the epigenetic landscape regulatory roles of FOXA1 and GRHL2, we performed knockdown experiments targeting FOXA1 and GRHL2, followed by assay for transposase-accessible chromatin sequencing analysis. The findings revealed that FOXA1 maintains Group-P-specific regions while suppressing Group-B-specific regions in Group-P cells. In contrast, GRHL2 preserves commonly accessible regions shared between Group-P and Group-B in Group-B cells, suggesting that FOXA1 and GRHL2 play a pivotal role in preserving distinct chromatin accessibility patterns for each group. Specifically, FOXA1 distinguishes between receptor-positive and TNBC cell lines, whereas GRHL2 distinguishes between basal-like and mesenchymal subtypes in TNBC lines.
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Affiliation(s)
- Liying Yang
- Project for Cancer Epigenomics, Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Kohei Kumegawa
- Cancer Cell Diversity Project, NEXT-Ganken Program, Japanese Foundation for Cancer Research, Tokyo, Japan.
| | - Sumito Saeki
- Project for Cancer Epigenomics, Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan
- Breast Surgical Oncology, Breast Oncology Center, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Tomoyoshi Nakadai
- Project for Cancer Epigenomics, Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Reo Maruyama
- Project for Cancer Epigenomics, Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan.
- Cancer Cell Diversity Project, NEXT-Ganken Program, Japanese Foundation for Cancer Research, Tokyo, Japan.
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7
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Kinnunen PC, Humphries BA, Luker GD, Luker KE, Linderman JJ. Characterizing heterogeneous single-cell dose responses computationally and experimentally using threshold inhibition surfaces and dose-titration assays. NPJ Syst Biol Appl 2024; 10:42. [PMID: 38637530 PMCID: PMC11026493 DOI: 10.1038/s41540-024-00369-x] [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: 12/16/2023] [Accepted: 04/08/2024] [Indexed: 04/20/2024] Open
Abstract
Single cancer cells within a tumor exhibit variable levels of resistance to drugs, ultimately leading to treatment failures. While tumor heterogeneity is recognized as a major obstacle to cancer therapy, standard dose-response measurements for the potency of targeted kinase inhibitors aggregate populations of cells, obscuring intercellular variations in responses. In this work, we develop an analytical and experimental framework to quantify and model dose responses of individual cancer cells to drugs. We first explore the connection between population and single-cell dose responses using a computational model, revealing that multiple heterogeneous populations can yield nearly identical population dose responses. We demonstrate that a single-cell analysis method, which we term a threshold inhibition surface, can differentiate among these populations. To demonstrate the applicability of this method, we develop a dose-titration assay to measure dose responses in single cells. We apply this assay to breast cancer cells responding to phosphatidylinositol-3-kinase inhibition (PI3Ki), using clinically relevant PI3Kis on breast cancer cell lines expressing fluorescent biosensors for kinase activity. We demonstrate that MCF-7 breast cancer cells exhibit heterogeneous dose responses with some cells requiring over ten-fold higher concentrations than the population average to achieve inhibition. Our work reimagines dose-response relationships for cancer drugs in an emerging paradigm of single-cell tumor heterogeneity.
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Affiliation(s)
- Patrick C Kinnunen
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Brock A Humphries
- Department of Radiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Gary D Luker
- Department of Radiology, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Kathryn E Luker
- Department of Radiology, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jennifer J Linderman
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
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Hsu YC, Chiu YC, Lu TP, Hsiao TH, Chen Y. Predicting drug response through tumor deconvolution by cancer cell lines. PATTERNS (NEW YORK, N.Y.) 2024; 5:100949. [PMID: 38645769 PMCID: PMC11026976 DOI: 10.1016/j.patter.2024.100949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 02/07/2024] [Accepted: 02/12/2024] [Indexed: 04/23/2024]
Abstract
Large-scale cancer drug sensitivity data have become available for a collection of cancer cell lines, but only limited drug response data from patients are available. Bridging the gap in pharmacogenomics knowledge between in vitro and in vivo datasets remains challenging. In this study, we trained a deep learning model, Scaden-CA, for deconvoluting tumor data into proportions of cancer-type-specific cell lines. Then, we developed a drug response prediction method using the deconvoluted proportions and the drug sensitivity data from cell lines. The Scaden-CA model showed excellent performance in terms of concordance correlation coefficients (>0.9 for model testing) and the correctly deconvoluted rate (>70% across most cancers) for model validation using Cancer Cell Line Encyclopedia (CCLE) bulk RNA data. We applied the model to tumors in The Cancer Genome Atlas (TCGA) dataset and examined associations between predicted cell viability and mutation status or gene expression levels to understand underlying mechanisms of potential value for drug repurposing.
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Affiliation(s)
- Yu-Ching Hsu
- Bioinformatics Program, Taiwan International Graduate Program, National Taiwan University, Taipei 115, Taiwan
- Bioinformatics Program, Institute of Statistical Science, Taiwan International Graduate Program, Academia Sinica, Taipei 115, Taiwan
- Institute of Health Data Analytics and Statistics, Department of Public Health, College of Public Health, National Taiwan University, Taipei 100, Taiwan
- Greehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | - Yu-Chiao Chiu
- Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA 15232, USA
| | - Tzu-Pin Lu
- Institute of Health Data Analytics and Statistics, Department of Public Health, College of Public Health, National Taiwan University, Taipei 100, Taiwan
| | - Tzu-Hung Hsiao
- Department of Medical Research, Taichung Veterans General Hospital, Taichung 40705, Taiwan
| | - Yidong Chen
- Greehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA
- Department of Population Health Sciences, University of Texas Health San Antonio, San Antonio, TX 78229, USA
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9
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Pellecchia S, Franchini M, Viscido G, Arnese R, Gambardella G. Single cell lineage tracing reveals clonal dynamics of anti-EGFR therapy resistance in triple negative breast cancer. Genome Med 2024; 16:55. [PMID: 38605363 PMCID: PMC11008053 DOI: 10.1186/s13073-024-01327-2] [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: 05/02/2023] [Accepted: 03/29/2024] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND Most primary Triple Negative Breast Cancers (TNBCs) show amplification of the Epidermal Growth Factor Receptor (EGFR) gene, leading to increased protein expression. However, unlike other EGFR-driven cancers, targeting this receptor in TNBC yields inconsistent therapeutic responses. METHODS To elucidate the underlying mechanisms of this variability, we employ cellular barcoding and single-cell transcriptomics to reconstruct the subclonal dynamics of EGFR-amplified TNBC cells in response to afatinib, a tyrosine kinase inhibitor (TKI) that irreversibly inhibits EGFR. RESULTS Integrated lineage tracing analysis revealed a rare pre-existing subpopulation of cells with distinct biological signature, including elevated expression levels of Insulin-Like Growth Factor Binding Protein 2 (IGFBP2). We show that IGFBP2 overexpression is sufficient to render TNBC cells tolerant to afatinib treatment by activating the compensatory insulin-like growth factor I receptor (IGF1-R) signalling pathway. Finally, based on reconstructed mechanisms of resistance, we employ deep learning techniques to predict the afatinib sensitivity of TNBC cells. CONCLUSIONS Our strategy proved effective in reconstructing the complex signalling network driving EGFR-targeted therapy resistance, offering new insights for the development of individualized treatment strategies in TNBC.
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Affiliation(s)
- Simona Pellecchia
- Telethon Institute of Genetics and Medicine, Naples, Italy
- Scuola Superiore Meridionale, Genomics and Experimental Medicine Program, Naples, Italy
| | - Melania Franchini
- Telethon Institute of Genetics and Medicine, Naples, Italy
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy
| | - Gaetano Viscido
- Telethon Institute of Genetics and Medicine, Naples, Italy
- Department of Chemical, Materials and Industrial Engineering , University of Naples Federico II, Naples, Italy
| | - Riccardo Arnese
- Telethon Institute of Genetics and Medicine, Naples, Italy
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy
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Xie J, Zhu L, Yang X, Yu F, Fan B, Wu Y, Zhou Z, Lin W, Yang Y. Combination of theoretical analysis and experiments: Exploring the role of PLA2G7 in human cancers, including renal cancer. Heliyon 2024; 10:e27906. [PMID: 38509948 PMCID: PMC10950723 DOI: 10.1016/j.heliyon.2024.e27906] [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: 08/02/2023] [Revised: 03/05/2024] [Accepted: 03/07/2024] [Indexed: 03/22/2024] Open
Abstract
Background The pivotal role of phospholipase A2 group VII (PLA2G7) has been identified in specific human cancers, such as prostate cancer, diffuse large B cell lymphoma, and melanoma. Given PLA2G7's significant involvement in established tumors, exploring its role in other cancers is highly relevant. Methods In this study, we acquired and analyzed data from The Cancer Genome Atlas database, the UCSC XENA website, and other online platforms including Gene Set Cancer Analysis, cBioPortal, Tumor Immune Estimation Resource, and TISIDB to investigate PLA2G7's role in human cancers, including renal cancer. Furthermore, in vitro experiments, including immunofluorescence, western blotting, and CCK-8 assays, were conducted to elucidate PLA2G7's role in renal cancer. Finally, the relationship between PLA2G7 and various drug sensitivity was explored. Results Our findings demonstrate that PLA2G7 is highly expressed and may serve as a valuable candidate biomarker in pan-cancer. PLA2G7 exhibits distinct alteration frequencies across human cancers and is correlated with tumor mutation burden, tumor microenvironment, DNA stemness score, RNA stemness score, tumorigenesis, tumor immunity, and microsatellite instability in pan-cancer. Immunofluorescence and western blotting revealed a relative high level of PLA2G7 protein in renal cancer cell lines (ACHN and 786-O), predominantly localized in the cytoplasm. Treatment with a PLA2G7 gene inhibitor (darapladib) significantly decreased the viability of ACHN and 786-O cell lines. Additionally, we observed an association between PLA2G7 mRNA levels and various drug sensitivity. Conclusions Our study suggests that PLA2G7 has the potential to serve as a valuable biomarker and therapeutic target for cancer, particularly in the context of renal cancer.
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Affiliation(s)
- Jun Xie
- Department of Nephrology, Center for Regeneration and Aging Medicine, The Fourth Affiliated Hospital, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
| | - Li Zhu
- Department of Nephrology, Center for Regeneration and Aging Medicine, The Fourth Affiliated Hospital, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
| | - Xutao Yang
- Department of Nephrology, Center for Regeneration and Aging Medicine, The Fourth Affiliated Hospital, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
| | - Fengfei Yu
- Department of Nephrology, Center for Regeneration and Aging Medicine, The Fourth Affiliated Hospital, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
| | - Bingfu Fan
- Department of Hepatobiliary and Pancreatic Surgery, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Yibo Wu
- Department of Orthopedics, Xixi Hospital of Hangzhou, Hangzhou, China
| | - Zonglang Zhou
- Department of Respiratory and Critical Care Medicine, Center for Respiratory Medicine, The Fourth Affiliated Hospital, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
| | - Weiqiang Lin
- Department of Nephrology, Center for Regeneration and Aging Medicine, The Fourth Affiliated Hospital, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
| | - Yi Yang
- Department of Nephrology, Center for Regeneration and Aging Medicine, The Fourth Affiliated Hospital, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
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11
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Zhu Y, Banerjee A, Xie P, Ivanov AA, Uddin A, Jiao Q, Chi JJ, Zeng L, Lee JY, Xue Y, Lu X, Cristofanilli M, Gradishar WJ, Henry CJ, Gillespie TW, Bhave MA, Kalinsky K, Fu H, Bahar I, Zhang B, Wan Y. Pharmacological suppression of the OTUD4/CD73 proteolytic axis revives antitumor immunity against immune-suppressive breast cancers. J Clin Invest 2024; 134:e176390. [PMID: 38530357 PMCID: PMC11093616 DOI: 10.1172/jci176390] [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: 10/04/2023] [Accepted: 03/12/2024] [Indexed: 03/27/2024] Open
Abstract
Despite widespread utilization of immunotherapy, treating immune-cold tumors remains a challenge. Multiomic analyses and experimental validation identified the OTUD4/CD73 proteolytic axis as a promising target in treating immune-suppressive triple negative breast cancer (TNBC). Mechanistically, deubiquitylation of CD73 by OTUD4 counteracted its ubiquitylation by TRIM21, resulting in CD73 stabilization inhibiting tumor immune responses. We further demonstrated the importance of TGF-β signaling for orchestrating the OTUD4/CD73 proteolytic axis within tumor cells. Spatial transcriptomics profiling discovered spatially resolved features of interacting malignant and immune cells pertaining to expression levels of OTUD4 and CD73. In addition, ST80, a newly developed inhibitor, specifically disrupted proteolytic interaction between CD73 and OTUD4, leading to reinvigoration of cytotoxic CD8+ T cell activities. In preclinical models of TNBC, ST80 treatment sensitized refractory tumors to anti-PD-L1 therapy. Collectively, our findings uncover what we believe to be a novel strategy for targeting the immunosuppressive OTUD4/CD73 proteolytic axis in treating immune-suppressive breast cancers with the inhibitor ST80.
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Affiliation(s)
- Yueming Zhu
- Department of Pharmacology and Chemical Biology and
- Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Anupam Banerjee
- Laufer Center for Physical and Quantitative Biology, School of Medicine, Stony Brook University, Stony Brook, New York, USA
| | - Ping Xie
- Department of Medicine, Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Andrey A. Ivanov
- Department of Pharmacology and Chemical Biology and
- Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia, USA
- Emory Chemical Biology Discovery Center, Emory University School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Amad Uddin
- Department of Pharmacology and Chemical Biology and
| | - Qiao Jiao
- Department of Pharmacology and Chemical Biology and
| | - Junlong Jack Chi
- Department of Pharmacology and Chemical Biology and
- Driskill Graduate Program (DPG), Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Lidan Zeng
- Department of Pharmacology and Chemical Biology and
| | - Ji Young Lee
- Laufer Center for Physical and Quantitative Biology, School of Medicine, Stony Brook University, Stony Brook, New York, USA
| | - Yifan Xue
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Xinghua Lu
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | | | - William J. Gradishar
- Department of Medicine, Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Curtis J. Henry
- Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia, USA
- Department of Pediatrics
| | - Theresa W. Gillespie
- Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia, USA
- Department of Surgery, and
- Department of Hematology and Medical Oncology, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Manali Ajay Bhave
- Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia, USA
- Department of Hematology and Medical Oncology, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Kevin Kalinsky
- Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia, USA
- Department of Hematology and Medical Oncology, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Haian Fu
- Department of Pharmacology and Chemical Biology and
- Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia, USA
- Emory Chemical Biology Discovery Center, Emory University School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Ivet Bahar
- Laufer Center for Physical and Quantitative Biology, School of Medicine, Stony Brook University, Stony Brook, New York, USA
- Department of Biochemistry and Cell Biology, School of Medicine, Stony Brook University, Stony Brook, New York, USA
| | - Bin Zhang
- Department of Medicine, Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Yong Wan
- Department of Pharmacology and Chemical Biology and
- Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia, USA
- Department of Hematology and Medical Oncology, Emory University School of Medicine, Atlanta, Georgia, USA
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12
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Pang L, Xiang F, Yang H, Shen X, Fang M, Li R, Long Y, Li J, Yu Y, Pang B. Single-cell integrative analysis reveals consensus cancer cell states and clinical relevance in breast cancer. Sci Data 2024; 11:289. [PMID: 38472225 DOI: 10.1038/s41597-024-03127-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 03/06/2024] [Indexed: 03/14/2024] Open
Abstract
High heterogeneity and complex interactions of malignant cells in breast cancer has been recognized as a driver of cancer progression and therapeutic failure. However, complete understanding of common cancer cell states and their underlying driver factors remain scarce and challenging. Here, we revealed seven consensus cancer cell states recurring cross patients by integrative analysis of single-cell RNA sequencing data of breast cancer. The distinct biological functions, the subtype-specific distribution, the potential cells of origin and the interrelation of consensus cancer cell states were systematically elucidated and validated in multiple independent datasets. We further uncovered the internal regulons and external cell components in tumor microenvironments, which contribute to the consensus cancer cell states. Using the state-specific signature, we also inferred the abundance of cells with each consensus cancer cell state by deconvolution of large breast cancer RNA-seq cohorts, revealing the association of immune-related state with better survival. Our study provides new insights for the cancer cell state composition and potential therapeutic strategies of breast cancer.
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Affiliation(s)
- Lin Pang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| | - Fengyu Xiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Huan Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Xinyue Shen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Ming Fang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Ran Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yongjin Long
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Jiali Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yonghuan Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Bo Pang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
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13
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Grasset EM, Barillé-Nion S, Juin PP. Stress in the metastatic journey - the role of cell communication and clustering in breast cancer progression and treatment resistance. Dis Model Mech 2024; 17:dmm050542. [PMID: 38506114 PMCID: PMC10979546 DOI: 10.1242/dmm.050542] [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] [Indexed: 03/21/2024] Open
Abstract
Breast cancer stands as the most prevalent malignancy afflicting women. Despite significant advancements in its diagnosis and treatment, breast cancer metastasis continues to be a leading cause of mortality among women. To metastasize, cancer cells face numerous challenges: breaking away from the primary tumor, surviving in the circulation, establishing in a distant location, evading immune detection and, finally, thriving to initiate a new tumor. Each of these sequential steps requires cancer cells to adapt to a myriad of stressors and develop survival mechanisms. In addition, most patients with breast cancer undergo surgical removal of their primary tumor and have various therapeutic interventions designed to eradicate cancer cells. Despite this plethora of attacks and stresses, certain cancer cells not only manage to persist but also proliferate robustly, giving rise to substantial tumors that frequently culminate in the patient's demise. To enhance patient outcomes, there is an imperative need for a deeper understanding of the molecular and cellular mechanisms that empower cancer cells to not only survive but also expand. Herein, we delve into the intrinsic stresses that cancer cells encounter throughout the metastatic journey and the additional stresses induced by therapeutic interventions. We focus on elucidating the remarkable strategies adopted by cancer cells, such as cell-cell clustering and intricate cell-cell communication mechanisms, to ensure their survival.
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Affiliation(s)
- Eloïse M. Grasset
- Université de Nantes, INSERM, CNRS, CRCI2NA, 44000 Nantes, France
- Équipe Labellisée LIGUE Contre le Cancer CRCI2NA, 44000 Nantes, France
| | - Sophie Barillé-Nion
- Université de Nantes, INSERM, CNRS, CRCI2NA, 44000 Nantes, France
- Équipe Labellisée LIGUE Contre le Cancer CRCI2NA, 44000 Nantes, France
| | - Philippe P. Juin
- Université de Nantes, INSERM, CNRS, CRCI2NA, 44000 Nantes, France
- Équipe Labellisée LIGUE Contre le Cancer CRCI2NA, 44000 Nantes, France
- Institut de Cancérologie de l'Ouest, 44805 Saint Herblain, France
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14
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Geuenich MJ, Gong DW, Campbell KR. The impacts of active and self-supervised learning on efficient annotation of single-cell expression data. Nat Commun 2024; 15:1014. [PMID: 38307875 PMCID: PMC10837127 DOI: 10.1038/s41467-024-45198-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: 06/22/2023] [Accepted: 01/16/2024] [Indexed: 02/04/2024] Open
Abstract
A crucial step in the analysis of single-cell data is annotating cells to cell types and states. While a myriad of approaches has been proposed, manual labeling of cells to create training datasets remains tedious and time-consuming. In the field of machine learning, active and self-supervised learning methods have been proposed to improve the performance of a classifier while reducing both annotation time and label budget. However, the benefits of such strategies for single-cell annotation have yet to be evaluated in realistic settings. Here, we perform a comprehensive benchmarking of active and self-supervised labeling strategies across a range of single-cell technologies and cell type annotation algorithms. We quantify the benefits of active learning and self-supervised strategies in the presence of cell type imbalance and variable similarity. We introduce adaptive reweighting, a heuristic procedure tailored to single-cell data-including a marker-aware version-that shows competitive performance with existing approaches. In addition, we demonstrate that having prior knowledge of cell type markers improves annotation accuracy. Finally, we summarize our findings into a set of recommendations for those implementing cell type annotation procedures or platforms. An R package implementing the heuristic approaches introduced in this work may be found at https://github.com/camlab-bioml/leader .
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Affiliation(s)
- Michael J Geuenich
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, M5G 1×5, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A8, Canada.
| | - Dae-Won Gong
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, M5G 1×5, Canada
| | - Kieran R Campbell
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, M5G 1×5, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A8, Canada.
- Department of Statistical Sciences, University of Toronto, Toronto, ON, M5S 3G3, Canada.
- Department of Computer Science, University of Toronto, Toronto, ON, M5T 3A1, Canada.
- Ontario Institute of Cancer Research, Toronto, ON, M5G 1M1, Canada.
- Vector Institute, Toronto, ON, M5G 1M1, Canada.
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15
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Sinkala M, Naran K, Ramamurthy D, Mungra N, Dzobo K, Martin D, Barth S. Machine learning and bioinformatic analyses link the cell surface receptor transcript levels to the drug response of breast cancer cells and drug off-target effects. PLoS One 2024; 19:e0296511. [PMID: 38306344 PMCID: PMC10836680 DOI: 10.1371/journal.pone.0296511] [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: 10/01/2023] [Accepted: 12/13/2023] [Indexed: 02/04/2024] Open
Abstract
Breast cancer responds variably to anticancer therapies, often leading to significant off-target effects. This study proposes that the variability in tumour responses and drug-induced adverse events is linked to the transcriptional profiles of cell surface receptors (CSRs) in breast tumours and normal tissues. We analysed multiple datasets to compare CSR expression in breast tumours with that in non-cancerous human tissues. Our findings correlate the drug responses of breast cancer cell lines with the expression levels of their targeted CSRs. Notably, we identified distinct differences in CSR expression between primary breast tumour subtypes and corresponding cell lines, which may influence drug response predictions. Additionally, we used clinical trial data to uncover associations between CSR gene expression in healthy tissues and the incidence of adverse drug reactions. This integrative approach facilitates the selection of optimal CSR targets for therapy, leveraging cell line dose-responses, CSR expression in normal tissues, and patient adverse event profiles.
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Affiliation(s)
- Musalula Sinkala
- Department of Biomedical Sciences, School of Health Sciences, University of Zambia, Lusaka, Zambia
- Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine & Department of Integrative Biomedical Sciences, Computational Biology Division, University of Cape Town, Cape Town, South Africa
| | - Krupa Naran
- Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, Medical Biotechnology & Immunotherapy Research Unit, University of Cape Town, Cape Town, South Africa
| | - Dharanidharan Ramamurthy
- Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, Medical Biotechnology & Immunotherapy Research Unit, University of Cape Town, Cape Town, South Africa
| | - Neelakshi Mungra
- Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, Medical Biotechnology & Immunotherapy Research Unit, University of Cape Town, Cape Town, South Africa
| | - Kevin Dzobo
- Faculty of Health Sciences, Department of Medicine, Division of Dermatology, Medical Research Council-SA Wound Healing Unit, Hair and Skin Research Laboratory, Groote Schuur Hospital, University of Cape Town, Anzio Road, Observatory, Cape Town, South Africa
| | - Darren Martin
- Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine & Department of Integrative Biomedical Sciences, Computational Biology Division, University of Cape Town, Cape Town, South Africa
| | - Stefan Barth
- Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, Medical Biotechnology & Immunotherapy Research Unit, University of Cape Town, Cape Town, South Africa
- Faculty of Health Sciences, Department of Integrative Biomedical Sciences, South African Research Chair in Cancer Biotechnology, University of Cape Town, Cape Town, South Africa
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16
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Maeser D, Zhang W, Huang Y, Huang RS. A review of computational methods for predicting cancer drug response at the single-cell level through integration with bulk RNAseq data. Curr Opin Struct Biol 2024; 84:102745. [PMID: 38109840 PMCID: PMC10922290 DOI: 10.1016/j.sbi.2023.102745] [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: 09/14/2023] [Revised: 11/06/2023] [Accepted: 11/24/2023] [Indexed: 12/20/2023]
Abstract
Cancer treatment failure is often attributed to tumor heterogeneity, where diverse malignant cell clones exist within a patient. Despite a growing understanding of heterogeneous tumor cells depicted by single-cell RNA sequencing (scRNA-seq), there is still a gap in the translation of such knowledge into treatment strategies tackling the pervasive issue of therapy resistance. In this review, we survey methods leveraging large-scale drug screens to generate cellular sensitivities to various therapeutics. These methods enable efficient drug screens in scRNA-seq data and serve as the bedrock of drug discovery for specific cancer cell groups. We envision that they will become an indispensable tool for tailoring patient care in the era of heterogeneity-aware precision medicine.
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Affiliation(s)
- Danielle Maeser
- Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, United States; Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, United States
| | - Weijie Zhang
- Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, United States; Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, United States
| | - Yingbo Huang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, United States
| | - R Stephanie Huang
- Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, United States; Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, United States.
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17
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Lee S, Kim G, Lee J, Lee AC, Kwon S. Mapping cancer biology in space: applications and perspectives on spatial omics for oncology. Mol Cancer 2024; 23:26. [PMID: 38291400 PMCID: PMC10826015 DOI: 10.1186/s12943-024-01941-z] [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: 06/19/2023] [Accepted: 01/12/2024] [Indexed: 02/01/2024] Open
Abstract
Technologies to decipher cellular biology, such as bulk sequencing technologies and single-cell sequencing technologies, have greatly assisted novel findings in tumor biology. Recent findings in tumor biology suggest that tumors construct architectures that influence the underlying cancerous mechanisms. Increasing research has reported novel techniques to map the tissue in a spatial context or targeted sampling-based characterization and has introduced such technologies to solve oncology regarding tumor heterogeneity, tumor microenvironment, and spatially located biomarkers. In this study, we address spatial technologies that can delineate the omics profile in a spatial context, novel findings discovered via spatial technologies in oncology, and suggest perspectives regarding therapeutic approaches and further technological developments.
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Affiliation(s)
- Sumin Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Meteor Biotech,, Co. Ltd, Seoul, 08826, Republic of Korea
| | - Gyeongjun Kim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - JinYoung Lee
- Division of Engineering Science, University of Toronto, Toronto, Ontario, ON, M5S 3H6, Canada
| | - Amos C Lee
- Meteor Biotech,, Co. Ltd, Seoul, 08826, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Sunghoon Kwon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
- Institutes of Entrepreneurial BioConvergence, Seoul National University, Seoul, 08826, Republic of Korea.
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
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18
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Muhammed Y, Lazenby RA. Scanning ion conductance microscopy revealed cisplatin-induced morphological changes related to apoptosis in single adenocarcinoma cells. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2024; 16:503-514. [PMID: 38167666 DOI: 10.1039/d3ay01827j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
The studies of drug-induced apoptosis play a vital role in the identification of potential drugs that could treat diseases such as cancer. Alterations in the native morphology of cancer cells following treatment with anticancer drugs serve as one of the indicators that reveal drug efficacy. Various techniques such as optical microscopy, electron microscopy (EM), and atomic force microscopy (AFM) have been used to map the three dimensional (3D) morphological changes in cells induced with drugs. However, caution should be exercised when interpreting morphological data from techniques that might alter the native morphology of cells, caused by phototoxicity, electron beam invasiveness, intrusive sample preparation, and cell membrane deformation. Herein, we have used scanning ion conductance microscopy (SICM) to study the 3D morphology and roughness of A549 adenocarcinoma cells under physiological conditions before and after cisplatin induced apoptosis, where we observed an increase in height, overall shrinkage of the cells, and irregular features form on the cell membrane. Tracking the morphology of the same single A549 cells exposed to cisplatin unveiled heterogeneity in response to the drug, formation of membrane blebs, and an increase in membrane roughness. We have also demonstrated the use of SICM for studying the effect of cisplatin on the dynamic changes in the volume of A549 cells over days. SICM is demonstrated as a technique for studying the effect of drug induced apoptosis in the same cells over time, and for multiple different single cells.
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Affiliation(s)
- Yusuf Muhammed
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, Florida 32306-4390, USA.
| | - Robert A Lazenby
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, Florida 32306-4390, USA.
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19
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Xiong YX, Zhang XF. scDOT: enhancing single-cell RNA-Seq data annotation and uncovering novel cell types through multi-reference integration. Brief Bioinform 2024; 25:bbae072. [PMID: 38436563 PMCID: PMC10939303 DOI: 10.1093/bib/bbae072] [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: 10/26/2023] [Revised: 01/12/2024] [Accepted: 02/07/2024] [Indexed: 03/05/2024] Open
Abstract
The proliferation of single-cell RNA-seq data has greatly enhanced our ability to comprehend the intricate nature of diverse tissues. However, accurately annotating cell types in such data, especially when handling multiple reference datasets and identifying novel cell types, remains a significant challenge. To address these issues, we introduce Single Cell annotation based on Distance metric learning and Optimal Transport (scDOT), an innovative cell-type annotation method adept at integrating multiple reference datasets and uncovering previously unseen cell types. scDOT introduces two key innovations. First, by incorporating distance metric learning and optimal transport, it presents a novel optimization framework. This framework effectively learns the predictive power of each reference dataset for new query data and simultaneously establishes a probabilistic mapping between cells in the query data and reference-defined cell types. Secondly, scDOT develops an interpretable scoring system based on the acquired probabilistic mapping, enabling the precise identification of previously unseen cell types within the data. To rigorously assess scDOT's capabilities, we systematically evaluate its performance using two diverse collections of benchmark datasets encompassing various tissues, sequencing technologies and diverse cell types. Our experimental results consistently affirm the superior performance of scDOT in cell-type annotation and the identification of previously unseen cell types. These advancements provide researchers with a potent tool for precise cell-type annotation, ultimately enriching our understanding of complex biological tissues.
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Affiliation(s)
- Yi-Xuan Xiong
- School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China
- Key Laboratory of Nonlinear Analysis & Applications (Ministry of Education), Central China Normal University, Wuhan 430079, China
| | - Xiao-Fei Zhang
- School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China
- Key Laboratory of Nonlinear Analysis & Applications (Ministry of Education), Central China Normal University, Wuhan 430079, China
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20
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Copperman J, Mclean IC, Gross SM, Chang YH, Zuckerman DM, Heiser LM. Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.18.576248. [PMID: 38293173 PMCID: PMC10827140 DOI: 10.1101/2024.01.18.576248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Extracellular signals induce changes to molecular programs that modulate multiple cellular phenotypes, including proliferation, motility, and differentiation status. The connection between dynamically adapting phenotypic states and the molecular programs that define them is not well understood. Here we develop data-driven models of single-cell phenotypic responses to extracellular stimuli by linking gene transcription levels to "morphodynamics" - changes in cell morphology and motility observable in time-lapse image data. We adopt a dynamics-first view of cell state by grouping single-cell trajectories into states with shared morphodynamic responses. The single-cell trajectories enable development of a first-of-its-kind computational approach to map live-cell dynamics to snapshot gene transcript levels, which we term MMIST, Molecular and Morphodynamics-Integrated Single-cell Trajectories. The key conceptual advance of MMIST is that cell behavior can be quantified based on dynamically defined states and that extracellular signals alter the overall distribution of cell states by altering rates of switching between states. We find a cell state landscape that is bound by epithelial and mesenchymal endpoints, with distinct sequences of epithelial to mesenchymal transition (EMT) and mesenchymal to epithelial transition (MET) intermediates. The analysis yields predictions for gene expression changes consistent with curated EMT gene sets and provides a prediction of thousands of RNA transcripts through extracellular signal-induced EMT and MET with near-continuous time resolution. The MMIST framework leverages true single-cell dynamical behavior to generate molecular-level omics inferences and is broadly applicable to other biological domains, time-lapse imaging approaches and molecular snapshot data.
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Affiliation(s)
- Jeremy Copperman
- Cancer Early Detection Advanced Research Center, Oregon Health and Science University, Portland OR 97239, U.S.A
| | - Ian C. Mclean
- Department of Biomedical Engineering, Oregon Health and Science University, Portland OR 97239, U.S.A
| | | | - Young Hwan Chang
- Department of Biomedical Engineering, Oregon Health and Science University, Portland OR 97239, U.S.A
- Knight Cancer Institute, Oregon Health and Science University, Portland OR 97239, U.S.A
| | - Daniel M. Zuckerman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland OR 97239, U.S.A
- Knight Cancer Institute, Oregon Health and Science University, Portland OR 97239, U.S.A
| | - Laura M. Heiser
- Department of Biomedical Engineering, Oregon Health and Science University, Portland OR 97239, U.S.A
- Knight Cancer Institute, Oregon Health and Science University, Portland OR 97239, U.S.A
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21
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Hu Y, Li CY, Lu Q, Kuang Y. Multiplex miRNA reporting platform for real-time profiling of living cells. Cell Chem Biol 2024; 31:150-162.e7. [PMID: 38035883 DOI: 10.1016/j.chembiol.2023.11.002] [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: 06/20/2023] [Revised: 09/15/2023] [Accepted: 11/03/2023] [Indexed: 12/02/2023]
Abstract
Accurately characterizing cell types within complex cell structures provides invaluable information for comprehending the cellular status during biological processes. In this study, we have developed an miRNA-switch cocktail platform capable of reporting and tracking the activities of multiple miRNAs (microRNAs) at the single-cell level, while minimizing disruption to the cell culture. Drawing on the principles of traditional miRNA-sensing mRNA switches, our platform incorporates subcellular tags and employs intelligent engineering to segment three subcellular regions using two fluorescent proteins. These designs enable the quantification of multiple miRNAs within the same cell. Through our experiments, we have demonstrated the platform's ability to track marker miRNA levels during cell differentiation and provide spatial information of heterogeneity on outlier cells exhibiting extreme miRNA levels. Importantly, this platform offers real-time and in situ miRNA reporting, allowing for multidimensional evaluation of cell profile and paving the way for a comprehensive understanding of cellular events during biological processes.
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Affiliation(s)
- Yaxin Hu
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China
| | - Cheuk Yin Li
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China
| | - Qiuyu Lu
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China
| | - Yi Kuang
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China.
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22
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Lee CM, Hwang Y, Jeong JW, Kim M, Lee J, Bae SJ, Ahn SG, Fang S. BRCA1 mutation promotes sprouting angiogenesis in inflammatory cancer-associated fibroblast of triple-negative breast cancer. Cell Death Discov 2024; 10:5. [PMID: 38182557 PMCID: PMC10770063 DOI: 10.1038/s41420-023-01768-5] [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: 10/25/2023] [Revised: 12/02/2023] [Accepted: 12/07/2023] [Indexed: 01/07/2024] Open
Abstract
Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with inferior outcomes owing to its low treatment response and high invasiveness. Based on abundant cancer-associated fibroblasts (CAFs) and frequent mutation of breast cancer-associated 1 (BRCA1) in TNBC, the characteristics of CAFs in TNBC patients with BRCA1 mutation compared to wild-type were investigated using single-cell analysis. Intriguingly, we observed that characteristics of inflammatory CAFs (iCAFs) were enriched in patients with BRCA1 mutation compared to the wild-type. iCAFs in patients with BRCA1 mutation exhibited outgoing signals to endothelial cells (ECs) clusters, including chemokine (C-X-C motif) ligand (CXCL) and vascular endothelial growth factor (VEGF). During CXCL signaling, the atypical chemokine receptor 1 (ACKR1) mainly interacts with CXCL family members in tumor endothelial cells (TECs). ACKR1-high TECs also showed high expression levels of angiogenesis-related genes, such as ANGPT2, MMP1, and SELE, which might lead to EC migration. Furthermore, iCAFs showed VEGF signals for FLT1 and KDR in TECs, which showed high co-expression with tip cell marker genes, including ZEB1 and MAFF, involved in sprouting angiogenesis. Moreover, BRCA1 mutation patients with relatively abundant iCAFs and tip cell gene expression exhibited a limited response to neoadjuvant chemotherapy, including cisplatin and bevacizumab. Importantly, our study observed the intricate link between iCAFs-mediated angiogenesis and chemoresistance in TNBC with BRCA1 mutation.
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Affiliation(s)
- Chae Min Lee
- Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea
- Department of Biomedical Sciences, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea
| | - Yeseong Hwang
- Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea
- Department of Biomedical Sciences, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea
| | - Jae Woong Jeong
- Department of Medicine, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea
| | - Minki Kim
- Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea
- Department of Biomedical Sciences, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea
| | - Janghee Lee
- Department of Surgery, Sacred Heart Hospital, Hallym University, Dongtan, 18450, Republic of Korea
- Department of Medicine, Yonsei University Graduate School, Seoul, 03722, Republic of Korea
| | - Soong June Bae
- Department of Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, 06273, Republic of Korea
- Institute for Breast Cancer Precision Medicine, Yonsei University College of Medicine, Seoul, 06273, Republic of Korea
| | - Sung Gwe Ahn
- Department of Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, 06273, Republic of Korea.
- Institute for Breast Cancer Precision Medicine, Yonsei University College of Medicine, Seoul, 06273, Republic of Korea.
| | - Sungsoon Fang
- Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea.
- Department of Biomedical Sciences, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea.
- Chronic Intractable Disease for Systems Medicine Research Center, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea.
- Severance Institute for Vascular and Metabolic Research, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea.
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23
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Feng J, Sun Q, Chen P, Ren K, Zhang Y, Shi Y, Gao S, Song Z, Wang J, Liao F, Han D. Characterization of Cancer Cell Mechanics by Measuring Active Deformation Behavior. SMALL METHODS 2024; 8:e2300520. [PMID: 37775303 DOI: 10.1002/smtd.202300520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 08/17/2023] [Indexed: 10/01/2023]
Abstract
Active deformation behavior reflects cell structural dynamics adapting to varying environmental constraints during malignancy progression. In most cases, cell mechanics is characterized by modeling using static equilibrium systems, which fails to comprehend cell deformation behavior leading to inaccuracies in distinguishing cancer cells from normal cells. Here, a method is introduced to measure the active deformation behavior of cancer cells using atomic force microscopy (AFM) and the newly developed deformation behavior cytometry (DBC). During the measurement, cells are deformed and allows a long timescale relaxation (≈5 s). Two parameters are derived to represent deformation behavior: apparent Poisson's ratio for adherent cells, which is measured with AFM and refers to the ratio of the lateral strain to the longitudinal strain of the cell, and shape recovery for suspended cells, which is measured with DBC. Active deformation behavior defines cancer cell mechanics better than traditional mechanical parameters (e.g., stiffness, diffusion, and viscosity). Additionally, aquaporins are essential for promoting the deformation behavior, while the actin cytoskeleton acts as a downstream effector. Therefore, the potential application of the cancer cell active deformation behavior as a biomechanical marker or therapeutic target in cancer treatment should be evaluated.
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Affiliation(s)
- Jiantao Feng
- Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Quanmei Sun
- CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190, China
| | - Peipei Chen
- CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190, China
| | - Keli Ren
- The Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yuanyuan Zhang
- Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, 100021, China
| | - Yahong Shi
- CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190, China
| | - Songkun Gao
- Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, 100006, China
| | - Zhiwei Song
- CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190, China
| | - Jigang Wang
- Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Fulong Liao
- Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Dong Han
- CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190, China
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24
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Qin M, Xia H, Xu W, Chen B, Wang Y. The spatiotemporal journey of nanomedicines in solid tumors on their therapeutic efficacy. Adv Drug Deliv Rev 2023; 203:115137. [PMID: 37949414 DOI: 10.1016/j.addr.2023.115137] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/19/2023] [Accepted: 11/07/2023] [Indexed: 11/12/2023]
Abstract
The rapid development of nanomedicines is revolutionizing the landscape of cancer treatment, while effectively delivering them into solid tumors remains a formidable challenge. Currently, there is a huge disconnect on therapeutic response between regulatory approved nanomedicines and laboratory reported nanoparticles. The discrepancy is mainly resulted from the failure of using the classic overall pharmacokinetics behaviors of nanomedicines in tumors to predict the antitumor efficacy. Increasing evidence has revealed that the therapeutic efficacy predominantly relies on the intratumoral spatiotemporal distribution of nanomedicines. This review focuses on the spatiotemporal distribution of systemically administered chemotherapeutic nanomedicines in solid tumor. Firstly, the intratumoral biological barriers that regulate the spatiotemporal distribution of nanomedicines are described in detail. Next, the influences on antitumor efficacy caused by the spatial distribution and temporal drug release of nanomedicines are emphatically analyzed. Then, current methodologies for evaluating the spatiotemporal distribution of nanomedicines are summarized. Finally, the advanced strategies to positively modulate the spatiotemporal distribution of nanomedicines for an optimal tumor therapy are comprehensively reviewed.
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Affiliation(s)
- Mengmeng Qin
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, China; Beijing Key Laboratory of Molecular Pharmaceutics and New Drug Delivery System, School of Pharmaceutical Sciences, Peking University, Beijing, China; CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, China
| | - Heming Xia
- Beijing Key Laboratory of Molecular Pharmaceutics and New Drug Delivery System, School of Pharmaceutical Sciences, Peking University, Beijing, China
| | - Wenhao Xu
- Beijing Key Laboratory of Molecular Pharmaceutics and New Drug Delivery System, School of Pharmaceutical Sciences, Peking University, Beijing, China
| | - Binlong Chen
- Beijing Key Laboratory of Molecular Pharmaceutics and New Drug Delivery System, School of Pharmaceutical Sciences, Peking University, Beijing, China.
| | - Yiguang Wang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, China; Beijing Key Laboratory of Molecular Pharmaceutics and New Drug Delivery System, School of Pharmaceutical Sciences, Peking University, Beijing, China; Chemical Biology Center, Peking University, Beijing, China.
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25
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Kim H, Whitman AA, Wisniewska K, Kakati RT, Garcia-Recio S, Calhoun BC, Franco HL, Perou CM, Spanheimer PM. Tamoxifen Response at Single-Cell Resolution in Estrogen Receptor-Positive Primary Human Breast Tumors. Clin Cancer Res 2023; 29:4894-4907. [PMID: 37747807 PMCID: PMC10690085 DOI: 10.1158/1078-0432.ccr-23-1248] [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: 04/25/2023] [Revised: 07/18/2023] [Accepted: 09/21/2023] [Indexed: 09/27/2023]
Abstract
PURPOSE In estrogen receptor-positive (ER+)/HER2- breast cancer, multiple measures of intratumor heterogeneity are associated with a worse response to endocrine therapy. We sought to develop a novel experimental model to measure heterogeneity in response to tamoxifen treatment in primary breast tumors. EXPERIMENTAL DESIGN To investigate heterogeneity in response to treatment, we developed an operating room-to-laboratory pipeline for the collection of live normal breast specimens and human tumors immediately after surgical resection for processing into single-cell workflows for experimentation and genomic analyses. Live primary cell suspensions were treated ex vivo with tamoxifen (10 μmol/L) or control media for 12 hours, and single-cell RNA libraries were generated using the 10X Genomics droplet-based kit. RESULTS In total, we obtained and processed normal breast tissue from two women undergoing reduction mammoplasty and tumor tissue from 10 women with ER+/HER2- invasive breast carcinoma. We demonstrate differences in tamoxifen response by cell type and identify distinctly responsive and resistant subpopulations within the malignant cell compartment of human tumors. Tamoxifen resistance signatures from resistant subpopulations predict poor outcomes in two large cohorts of ER+ breast cancer patients and are enriched in endocrine therapy-resistant tumors. CONCLUSIONS This novel ex vivo model system now provides the foundation to define responsive and resistant subpopulations within heterogeneous human tumors, which can be used to develop precise single cell-based predictors of response to therapy and to identify genes and pathways driving therapeutic resistance.
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Affiliation(s)
- Hyunsoo Kim
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina
| | - Austin A. Whitman
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina
| | - Kamila Wisniewska
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina
| | - Rasha T. Kakati
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina
| | - Susana Garcia-Recio
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina
| | - Benjamin C. Calhoun
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Hector L. Franco
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina
- Computational Medicine Program, University of North Carolina, Chapel Hill, North Carolina
| | - Charles M. Perou
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina
- Computational Medicine Program, University of North Carolina, Chapel Hill, North Carolina
| | - Philip M. Spanheimer
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina
- Department of Surgery, University of North Carolina, Chapel Hill, North Carolina
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26
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Pellecchia S, Viscido G, Franchini M, Gambardella G. Predicting drug response from single-cell expression profiles of tumours. BMC Med 2023; 21:476. [PMID: 38041118 PMCID: PMC10693176 DOI: 10.1186/s12916-023-03182-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 11/20/2023] [Indexed: 12/03/2023] Open
Abstract
BACKGROUND Intra-tumour heterogeneity (ITH) presents a significant obstacle in formulating effective treatment strategies in clinical practice. Single-cell RNA sequencing (scRNA-seq) has evolved as a powerful instrument for probing ITH at the transcriptional level, offering an unparalleled opportunity for therapeutic intervention. RESULTS Drug response prediction at the single-cell level is an emerging field of research that aims to improve the efficacy and precision of cancer treatments. Here, we introduce DREEP (Drug Response Estimation from single-cell Expression Profiles), a computational method that leverages publicly available pharmacogenomic screens from GDSC2, CTRP2, and PRISM and functional enrichment analysis to predict single-cell drug sensitivity from transcriptomic data. We validated DREEP extensively in vitro using several independent single-cell datasets with over 200 cancer cell lines and showed its accuracy and robustness. Additionally, we also applied DREEP to molecularly barcoded breast cancer cells and identified drugs that can selectively target specific cell populations. CONCLUSIONS DREEP provides an in silico framework to prioritize drugs from single-cell transcriptional profiles of tumours and thus helps in designing personalized treatment strategies and accelerating drug repurposing studies. DREEP is available at https://github.com/gambalab/DREEP .
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Affiliation(s)
- Simona Pellecchia
- Telethon Institute of Genetics and Medicine, Naples, Italy
- Genomics and Experimental Medicine Program, Scuola Superiore Meridionale, Naples, Italy
| | - Gaetano Viscido
- Telethon Institute of Genetics and Medicine, Naples, Italy
- Department of Chemical, Materials and Industrial Engineering, University of Naples Federico II, Naples, Italy
| | - Melania Franchini
- Telethon Institute of Genetics and Medicine, Naples, Italy
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy
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27
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Choi H, Gupta M, Hensley C, Lee H, Lu YT, Pantel A, Mankoff D, Zhou R. Disruption of redox balance in glutaminolytic triple negative breast cancer by inhibition of glutamate export and glutaminase. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.19.567663. [PMID: 38014289 PMCID: PMC10680815 DOI: 10.1101/2023.11.19.567663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
In triple-negative breast cancer (TNBC) that relies on catabolism of amino acid glutamine, glutaminase (GLS) converts glutamine to glutamate, which facilitates glutathione synthesis by mediating the enrichment of intracellular cystine via xCT antiporter activity. To overcome chemo resistant TNBC, we have tested a strategy of disrupting cellular redox balance by inhibition of GLS and xCT by CB839 and Erastin, respectively. Key findings of our study include: 1. Dual metabolic inhibition (CB839+Erastin) led to significant increases of cellular superoxide level in both parent and chemo resistant TNBC cells, but superoxide level was distinctly lower in resistant cells. 2. Dual metabolic inhibition combined with doxorubicin or cisplatin induced significant apoptosis in TNBC cells and is associated with high degrees of GSH depletion. In vivo , dual metabolic inhibition plus cisplatin led to significant growth delay of chemo resistant human TNBC xenografts. 3. Ferroptosis is induced by doxorubicin (DOX) but not by cisplatin or paclitaxel. Addition of dual metabolic inhibition to DOX chemotherapy significantly enhanced ferroptotic cell death. 4. Significant changes in cellular metabolites concentration preceded transcriptome changes revealed by single cell RNA sequencing, underscoring the potential of capturing early changes in metabolites as pharmacodynamic markers of metabolic inhibitors. Here we demonstrated that 4-(3-[ 18 F]fluoropropyl)-L-glutamic acid ([ 18 F]FSPG) PET detected xCT blockade by Erastin or its analog in mice bearing human TNBC xenografts. In summary, our study provides compelling evidence for the therapeutic benefit and feasibility of non-invasive monitoring of dual metabolic blockade as a translational strategy to sensitize chemo resistant TNBC to cytotoxic chemotherapy.
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28
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Bahnassy S, Stires H, Jin L, Tam S, Mobin D, Balachandran M, Podar M, McCoy MD, Beckman RA, Riggins RB. Unraveling Vulnerabilities in Endocrine Therapy-Resistant HER2+/ER+ Breast Cancer. Endocrinology 2023; 164:bqad159. [PMID: 37897495 PMCID: PMC10651073 DOI: 10.1210/endocr/bqad159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/01/2023] [Accepted: 10/26/2023] [Indexed: 10/30/2023]
Abstract
Breast tumors overexpressing human epidermal growth factor receptor (HER2) confer intrinsic resistance to endocrine therapy (ET), and patients with HER2/estrogen receptor-positive (HER2+/ER+) breast cancer (BCa) are less responsive to ET than HER2-/ER+. However, real-world evidence reveals that a large subset of patients with HER2+/ER+ receive ET as monotherapy, positioning this treatment pattern as a clinical challenge. In the present study, we developed and characterized 2 in vitro models of ET-resistant (ETR) HER2+/ER+ BCa to identify possible therapeutic vulnerabilities. To mimic ETR to aromatase inhibitors (AIs), we developed 2 long-term estrogen deprivation (LTED) cell lines from BT-474 (BT474) and MDA-MB-361 (MM361). Growth assays, PAM50 subtyping, and genomic and transcriptomic analyses, followed by validation and functional studies, were used to identify targetable differences between ET-responsive parental and ETR-LTED HER2+/ER+ cells. Compared to their parental cells, MM361 LTEDs grew faster, lost ER, and increased HER2 expression, whereas BT474 LTEDs grew slower and maintained ER and HER2 expression. Both LTED variants had reduced responsiveness to fulvestrant. Whole-genome sequencing of aggressive MM361 LTEDs identified mutations in genes encoding transcription factors and chromatin modifiers. Single-cell RNA sequencing demonstrated a shift towards non-luminal phenotypes, and revealed metabolic remodeling of MM361 LTEDs, with upregulated lipid metabolism and ferroptosis-associated antioxidant genes, including GPX4. Combining a GPX4 inhibitor with anti-HER2 agents induced significant cell death in both MM361 and BT474 LTEDs. The BT474 and MM361 AI-resistant models capture distinct phenotypes of HER2+/ER+ BCa and identify altered lipid metabolism and ferroptosis remodeling as vulnerabilities of this type of ETR BCa.
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Affiliation(s)
- Shaymaa Bahnassy
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA
| | | | - Lu Jin
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA
| | - Stanley Tam
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA
| | - Dua Mobin
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA
| | - Manasi Balachandran
- Department of Medicine, University of Tennessee Medical Center, Knoxville, TN 37920, USA
| | - Mircea Podar
- Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Matthew D McCoy
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA
| | - Robert A Beckman
- Department of Oncology and of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC 20007, USA
- Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC 20007, USA
| | - Rebecca B Riggins
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA
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29
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Iglesia MD, Jayasinghe RG, Chen S, Terekhanova NV, Herndon JM, Storrs E, Karpova A, Zhou DC, Al Deen NN, Shinkle AT, Lu RJH, Caravan W, Houston A, Zhao Y, Sato K, Lal P, Street C, Rodrigues FM, Southard-Smith AN, Targino da Costa ALN, Zhu H, Mo CK, Crowson L, Fulton RS, Wyczalkowski MA, Fronick CC, Fulton LA, Sun H, Davies SR, Appelbaum EL, Chasnoff SE, Carmody M, Brooks C, Liu R, Wendl MC, Oh C, Bender D, Cruchaga C, Harari O, Bredemeyer A, Lavine K, Bose R, Margenthaler J, Held JM, Achilefu S, Ademuyiwa F, Aft R, Ma C, Colditz GA, Ju T, Oh ST, Fitzpatrick J, Hwang ES, Shoghi KI, Chheda MG, Veis DJ, Chen F, Fields RC, Gillanders WE, Ding L. Differential chromatin accessibility and transcriptional dynamics define breast cancer subtypes and their lineages. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.31.565031. [PMID: 37961519 PMCID: PMC10634973 DOI: 10.1101/2023.10.31.565031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Breast cancer is a heterogeneous disease, and treatment is guided by biomarker profiles representing distinct molecular subtypes. Breast cancer arises from the breast ductal epithelium, and experimental data suggests breast cancer subtypes have different cells of origin within that lineage. The precise cells of origin for each subtype and the transcriptional networks that characterize these tumor-normal lineages are not established. In this work, we applied bulk, single-cell (sc), and single-nucleus (sn) multi-omic techniques as well as spatial transcriptomics and multiplex imaging on 61 samples from 37 breast cancer patients to show characteristic links in gene expression and chromatin accessibility between breast cancer subtypes and their putative cells of origin. We applied the PAM50 subtyping algorithm in tandem with bulk RNA-seq and snRNA-seq to reliably subtype even low-purity tumor samples and confirm promoter accessibility using snATAC. Trajectory analysis of chromatin accessibility and differentially accessible motifs clearly connected progenitor populations with breast cancer subtypes supporting the cell of origin for basal-like and luminal A and B tumors. Regulatory network analysis of transcription factors underscored the importance of BHLHE40 in luminal breast cancer and luminal mature cells, and KLF5 in basal-like tumors and luminal progenitor cells. Furthermore, we identify key genes defining the basal-like ( PRKCA , SOX6 , RGS6 , KCNQ3 ) and luminal A/B ( FAM155A , LRP1B ) lineages, with expression in both precursor and cancer cells and further upregulation in tumors. Exhausted CTLA4-expressing CD8+ T cells were enriched in basal-like breast cancer, suggesting altered means of immune dysfunction among breast cancer subtypes. We used spatial transcriptomics and multiplex imaging to provide spatial detail for key markers of benign and malignant cell types and immune cell colocation. These findings demonstrate analysis of paired transcription and chromatin accessibility at the single cell level is a powerful tool for investigating breast cancer lineage development and highlight transcriptional networks that define basal and luminal breast cancer lineages.
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30
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Zhang W, Maeser D, Lee A, Huang Y, Gruener RF, Abdelbar IG, Jena S, Patel AG, Huang RS. Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.29.564598. [PMID: 37961545 PMCID: PMC10634928 DOI: 10.1101/2023.10.29.564598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Single-cell RNA sequencing greatly advanced our understanding of intratumoral heterogeneity through identifying tumor subpopulations with distinct biologies. However, translating biological differences into treatment strategies is challenging, as we still lack tools to facilitate efficient drug discovery that tackles heterogeneous tumors. One key component of such approaches tackles accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we present 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 datasets. Our method achieves high accuracy, with predicted sensitivities easily able to separate cells into their true cellular drug resistance status as measured by effect size (Cohen's d > 1.0). More importantly, we examine our method's utility with three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), and in each our predicted results are accurate and mirrored biological expectations. In the first two, we identified drugs for cell subpopulations that are resistant to standard-of-care (SOC) therapies due to intrinsic resistance or effects of tumor microenvironments. Our results showed high consistency with experimental findings from the original studies. In the third test, we generated SOC therapy resistant cell lines, used scIDUC to identify efficacious drugs for the resistant line, and validated the predictions with in-vitro experiments. Together, scIDUC quickly translates scRNA-seq data into drug response for individual cells, displaying the potential as a first-line tool for nuanced and heterogeneity-aware drug discovery.
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Affiliation(s)
- Weijie Zhang
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
| | - Danielle Maeser
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
| | - Adam Lee
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
| | - Yingbo Huang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
| | - Robert F Gruener
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
| | - Israa G Abdelbar
- Department of Experimental and Clinical Pharmacology, 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, 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, University of Minnesota, Minneapolis, MN 55455
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31
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Li S, Guo H, Zhang S, Li Y, Li M. Attention-based deep clustering method for scRNA-seq cell type identification. PLoS Comput Biol 2023; 19:e1011641. [PMID: 37948464 PMCID: PMC10703402 DOI: 10.1371/journal.pcbi.1011641] [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: 06/28/2023] [Revised: 12/07/2023] [Accepted: 10/30/2023] [Indexed: 11/12/2023] Open
Abstract
Single-cell sequencing (scRNA-seq) technology provides higher resolution of cellular differences than bulk RNA sequencing and reveals the heterogeneity in biological research. The analysis of scRNA-seq datasets is premised on the subpopulation assignment. When an appropriate reference is not available, such as specific marker genes and single-cell reference atlas, unsupervised clustering approaches become the predominant option. However, the inherent sparsity and high-dimensionality of scRNA-seq datasets pose specific analytical challenges to traditional clustering methods. Therefore, a various deep learning-based methods have been proposed to address these challenges. As each method improves partially, a comprehensive method needs to be proposed. In this article, we propose a novel scRNA-seq data clustering method named AttentionAE-sc (Attention fusion AutoEncoder for single-cell). Two different scRNA-seq clustering strategies are combined through an attention mechanism, that include zero-inflated negative binomial (ZINB)-based methods dealing with the impact of dropout events and graph autoencoder (GAE)-based methods relying on information from neighbors to guide the dimension reduction. Based on an iterative fusion between denoising and topological embeddings, AttentionAE-sc can easily acquire clustering-friendly cell representations that similar cells are closer in the hidden embedding. Compared with several state-of-art baseline methods, AttentionAE-sc demonstrated excellent clustering performance on 16 real scRNA-seq datasets without the need to specify the number of groups. Additionally, AttentionAE-sc learned improved cell representations and exhibited enhanced stability and robustness. Furthermore, AttentionAE-sc achieved remarkable identification in a breast cancer single-cell atlas dataset and provided valuable insights into the heterogeneity among different cell subtypes.
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Affiliation(s)
- Shenghao Li
- College of Chemistry, Sichuan University, Chengdu, Sichuan, China
| | - Hui Guo
- College of Chemistry, Sichuan University, Chengdu, Sichuan, China
| | - Simai Zhang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Sichuan, China
| | - Yizhou Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Sichuan, China
- School of Cyber Science and Engineering, Sichuan University, Chengdu, Sichuan, China
| | - Menglong Li
- College of Chemistry, Sichuan University, Chengdu, Sichuan, China
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32
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Momin ID, Rigler J, Chitrala KN. Analysis of Potential Biomarkers in Frontal Temporal Dementia: A Bioinformatics Approach. Int J Mol Sci 2023; 24:14910. [PMID: 37834358 PMCID: PMC10573524 DOI: 10.3390/ijms241914910] [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: 08/06/2023] [Revised: 09/29/2023] [Accepted: 10/03/2023] [Indexed: 10/15/2023] Open
Abstract
Frontal temporal dementia (FTD) is a neurological disorder known to have fewer therapeutic options. So far, only a few biomarkers are available for FTD that can be used as potential comorbidity targets. For example, genes such as VCP, which has a role in breast cancer, and WFS1, which has a role in COVID-19, are known to show a role in FTD as well. To this end, in the present study, we aim to identify potential biomarkers or susceptible genes for FTD that show comorbidities with diseases such as COVID-19 and breast cancer. A dataset from Gene Expression Omnibus containing FTD expression profiles from African American and white ethnicity backgrounds was included in our study. In FTD samples of the GSE193391 dataset, we identified 305 DEGs, with 168 genes being up-regulated and 137 genes being down-regulated. We conducted a comorbidity analysis for COVID-19 and breast cancer, followed by an analysis of potential drug interactions, pathogenicity, analysis of genetic variants, and functional enrichment analysis. Our results showed that the genes AKT3, GFAP, ADCYAP1R1, VDAC1, and C4A have significant transcriptomic alterations in FTD along with the comorbidity status with COVID-19 and breast cancer. Functional pathway analysis revealed that these comorbid genes were significantly enriched in the pathways such as glioma, JAK/STAT signaling, systematic lupus erythematosus, neurodegeneration-multiple diseases, and neuroactive ligand-receptor interaction. Overall, from these results, we concluded that these genes could be recommended as potential therapeutic targets for the treatment of comorbidities (breast cancer and COVID-19) in patients with FTD.
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Affiliation(s)
| | | | - Kumaraswamy Naidu Chitrala
- Department of Engineering Technology, University of Houston, Sugar Land, TX 77479, USA; (I.D.M.); (J.R.)
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Sahoo S, Ramu S, Nair MG, Pillai M, San Juan BP, Milioli HZ, Mandal S, Naidu CM, Mavatkar AD, Subramaniam H, Neogi AG, Chaffer CL, Prabhu JS, Somarelli JA, Jolly MK. Multi-modal transcriptomic analysis unravels enrichment of hybrid epithelial/mesenchymal state and enhanced phenotypic heterogeneity in basal breast cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.30.558960. [PMID: 37873432 PMCID: PMC10592858 DOI: 10.1101/2023.09.30.558960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Intra-tumoral phenotypic heterogeneity promotes tumor relapse and therapeutic resistance and remains an unsolved clinical challenge. It manifests along multiple phenotypic axes and decoding the interconnections among these different axes is crucial to understand its molecular origins and to develop novel therapeutic strategies to control it. Here, we use multi-modal transcriptomic data analysis - bulk, single-cell and spatial transcriptomics - from breast cancer cell lines and primary tumor samples, to identify associations between epithelial-mesenchymal transition (EMT) and luminal-basal plasticity - two key processes that enable heterogeneity. We show that luminal breast cancer strongly associates with an epithelial cell state, but basal breast cancer is associated with hybrid epithelial/mesenchymal phenotype(s) and higher phenotypic heterogeneity. These patterns were inherent in methylation profiles, suggesting an epigenetic crosstalk between EMT and lineage plasticity in breast cancer. Mathematical modelling of core underlying gene regulatory networks representative of the crosstalk between the luminal-basal and epithelial-mesenchymal axes recapitulate and thus elucidate mechanistic underpinnings of the observed associations from transcriptomic data. Our systems-based approach integrating multi-modal data analysis with mechanism-based modeling offers a predictive framework to characterize intra-tumor heterogeneity and to identify possible interventions to restrict it.
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Affiliation(s)
- Sarthak Sahoo
- Department of Bioengineering, Indian Institute of Science, Bangalore, 560012, India
| | - Soundharya Ramu
- Department of Bioengineering, Indian Institute of Science, Bangalore, 560012, India
| | - Madhumathy G Nair
- Division of Molecular Medicine, St. John’s Research Institute, St. John’s Medical College, Bangalore, 560012, India
| | - Maalavika Pillai
- Department of Bioengineering, Indian Institute of Science, Bangalore, 560012, India
- Current affiliation: Feinberg School of Medicine, Northwestern University, Chicago, 60611, USA
| | - Beatriz P San Juan
- Garvan Institute of Medical Research, Darlinghurst, NSW, 2010, Australia
| | | | - Susmita Mandal
- Department of Bioengineering, Indian Institute of Science, Bangalore, 560012, India
| | - Chandrakala M Naidu
- Division of Molecular Medicine, St. John’s Research Institute, St. John’s Medical College, Bangalore, 560012, India
| | - Apoorva D Mavatkar
- Division of Molecular Medicine, St. John’s Research Institute, St. John’s Medical College, Bangalore, 560012, India
| | - Harini Subramaniam
- Department of Bioengineering, Indian Institute of Science, Bangalore, 560012, India
| | - Arpita G Neogi
- Department of Bioengineering, Indian Institute of Science, Bangalore, 560012, India
| | - Christine L Chaffer
- Garvan Institute of Medical Research, Darlinghurst, NSW, 2010, Australia
- University of New South Wales, UNSW Medicine, UNSW Sydney, NSW, 2052, Australia
| | - Jyothi S Prabhu
- Division of Molecular Medicine, St. John’s Research Institute, St. John’s Medical College, Bangalore, 560012, India
| | | | - Mohit Kumar Jolly
- Department of Bioengineering, Indian Institute of Science, Bangalore, 560012, India
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Azimnasab-Sorkhabi P, Soltani-Asl M, Yoshinaga TT, Massoco CDO, Kfoury Junior JR. IDO blockade negatively regulates the CTLA-4 signaling in breast cancer cells. Immunol Res 2023; 71:679-686. [PMID: 37014514 DOI: 10.1007/s12026-023-09378-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 03/30/2023] [Indexed: 04/05/2023]
Abstract
Cancer is classified into metabolic and/or genetic disorders; notably, the tryptophan catabolism pathway is vital in different cancer types. Here, we focused on the interaction and molecular connection between the cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) receptor and indoleamine-2,3-dioxygenase (IDO) enzyme. To test the impact of the selected immunotherapies on breast cancer cell migration and cell survival, we used in vitro assays. Also, we test the impact of anti-CTLA-4 antibody on the IDO-positive cells. The results of cell migration and clonogenic assays showed that anti-CTLA-4 antibody reduces cancer cell migration and clonogenic abilities of murine breast cancer cells. In addition, the result of flow cytometry showed that the anti-CTLA-4 antibody did not change the percentage of IDO-positive cancer cells. Notably, administrating an IDO blocker, 1-Methyl-DL-tryptophan (1MT), reduces the efficiency of the antiCTLA-4 antibody. The enzymatic blocking of the IDO reduces the efficiency of the anti-CTLA-4 antibody on cell migration and clonogenic abilities suggesting that there is an inhibitory interaction at the molecular level between functions of CTLA-4 and IDO. It is unclear via which mechanism(s) IDO interacts with CTLA-4 signaling and also why blocking IDO makes disruption in CTLA-4 signaling in cancer cells. Indeed, evaluating the role of IDO in CTLA-4 signaling in cancer cells may assist in clarifying a poor response to CTLA-4 immunotherapies by some patients. Hence, further investigation of the molecular interaction between CTLA-4 and IDO might help to improve the efficiency of CTLA-4 immunotherapy.
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Affiliation(s)
- Parviz Azimnasab-Sorkhabi
- Department of Surgery, School of Veterinary Medicine and Animal Sciences, University of Sao Paulo, Sao Paulo, Brazil.
| | - Maryam Soltani-Asl
- Department of Surgery, School of Veterinary Medicine and Animal Sciences, University of Sao Paulo, Sao Paulo, Brazil
| | - Túlio Teruo Yoshinaga
- Department of Surgery, School of Veterinary Medicine and Animal Sciences, University of Sao Paulo, Sao Paulo, Brazil
| | - Cristina de Oliveira Massoco
- Department of Pathology, School of Veterinary Medicine and Animal Sciences, University of Sao Paulo, Sao Paulo, Brazil
| | - Jose Roberto Kfoury Junior
- Department of Surgery, School of Veterinary Medicine and Animal Sciences, University of Sao Paulo, Sao Paulo, Brazil
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Tang Z, Liu X, Li Z, Zhang T, Yang B, Su J, Song Q. SpaRx: elucidate single-cell spatial heterogeneity of drug responses for personalized treatment. Brief Bioinform 2023; 24:bbad338. [PMID: 37798249 PMCID: PMC10555713 DOI: 10.1093/bib/bbad338] [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: 04/11/2023] [Revised: 08/08/2023] [Accepted: 09/07/2023] [Indexed: 10/07/2023] Open
Abstract
Spatial cellular authors heterogeneity contributes to differential drug responses in a tumor lesion and potential therapeutic resistance. Recent emerging spatial technologies such as CosMx, MERSCOPE and Xenium delineate the spatial gene expression patterns at the single cell resolution. This provides unprecedented opportunities to identify spatially localized cellular resistance and to optimize the treatment for individual patients. In this work, we present a graph-based domain adaptation model, SpaRx, to reveal the heterogeneity of spatial cellular response to drugs. SpaRx transfers the knowledge from pharmacogenomics profiles to single-cell spatial transcriptomics data, through hybrid learning with dynamic adversarial adaption. Comprehensive benchmarking demonstrates the superior and robust performance of SpaRx at different dropout rates, noise levels and transcriptomics coverage. Further application of SpaRx to the state-of-the-art single-cell spatial transcriptomics data reveals that tumor cells in different locations of a tumor lesion present heterogenous sensitivity or resistance to drugs. Moreover, resistant tumor cells interact with themselves or the surrounding constituents to form an ecosystem for drug resistance. Collectively, SpaRx characterizes the spatial therapeutic variability, unveils the molecular mechanisms underpinning drug resistance and identifies personalized drug targets and effective drug combinations.
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Affiliation(s)
- Ziyang Tang
- Department of Computer and Information Technology, Purdue University, Indiana, USA
| | - Xiang Liu
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indiana, USA
| | - Zuotian Li
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indiana, USA
- Department of Computer Graphics Technology, Purdue University, Indiana, USA
| | - Tonglin Zhang
- Department of Statistics, Purdue University, Indiana, USA
| | - Baijian Yang
- Department of Computer and Information Technology, Purdue University, Indiana, USA
| | - Jing Su
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indiana, USA
| | - Qianqian Song
- Department of Cancer Biology, Wake Forest University School of Medicine, North Carolina, USA
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Florida, USA
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Failli M, Demir S, Del Río-Álvarez Á, Carrillo-Reixach J, Royo L, Domingo-Sàbat M, Childs M, Maibach R, Alaggio R, Czauderna P, Morland B, Branchereau S, Cairo S, Kappler R, Armengol C, di Bernardo D. Computational drug prediction in hepatoblastoma by integrating pan-cancer transcriptomics with pharmacological response. Hepatology 2023:01515467-990000000-00573. [PMID: 37729391 DOI: 10.1097/hep.0000000000000601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 08/11/2023] [Indexed: 09/22/2023]
Abstract
BACKGROUND AND AIMS Hepatoblastoma (HB) is the predominant form of pediatric liver cancer, though it remains exceptionally rare. While treatment outcomes for children with HB have improved, patients with advanced tumors face limited therapeutic choices. Additionally, survivors often suffer from long-term adverse effects due to treatment, including ototoxicity, cardiotoxicity, delayed growth, and secondary tumors. Consequently, there is a pressing need to identify new and effective therapeutic strategies for patients with HB. Computational methods to predict drug sensitivity from a tumor's transcriptome have been successfully applied for some common adult malignancies, but specific efforts in pediatric cancers are lacking because of the paucity of data. APPROACH AND RESULTS In this study, we used DrugSense to assess drug efficacy in patients with HB, particularly those with the aggressive C2 subtype associated with poor clinical outcomes. Our method relied on publicly available collections of pan-cancer transcriptional profiles and drug responses across 36 tumor types and 495 compounds. The drugs predicted to be most effective were experimentally validated using patient-derived xenograft models of HB grown in vitro and in vivo. We thus identified 2 cyclin-dependent kinase 9 inhibitors, alvocidib and dinaciclib as potent HB growth inhibitors for the high-risk C2 molecular subtype. We also found that in a cohort of 46 patients with HB, high cyclin-dependent kinase 9 tumor expression was significantly associated with poor prognosis. CONCLUSIONS Our work proves the usefulness of computational methods trained on pan-cancer data sets to reposition drugs in rare pediatric cancers such as HB, and to help clinicians in choosing the best treatment options for their patients.
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Affiliation(s)
- Mario Failli
- Telethon Institute of Genetics and Medicine, Pozzuoli, Naples, Italy
- Department of Chemical, Materials and Industrial Production Engineering, University of Naples "Federico II", Naples, Italy
| | - Salih Demir
- Department of Pediatric Surgery, Dr. von Hauner Children's Hospital, University Hospital, LMU Munich, Germany
| | - Álvaro Del Río-Álvarez
- Childhood Liver Oncology Group (c-LOG), Health Sciences Research Institute Germans Trias i Pujol (IGTP), Badalona, Catalonia, Spain
| | - Juan Carrillo-Reixach
- Childhood Liver Oncology Group (c-LOG), Health Sciences Research Institute Germans Trias i Pujol (IGTP), Badalona, Catalonia, Spain
- Nottingham Clinical Trials Unit, Nottingham, United Kingdom
| | - Laura Royo
- Childhood Liver Oncology Group (c-LOG), Health Sciences Research Institute Germans Trias i Pujol (IGTP), Badalona, Catalonia, Spain
| | - Montserrat Domingo-Sàbat
- Childhood Liver Oncology Group (c-LOG), Health Sciences Research Institute Germans Trias i Pujol (IGTP), Badalona, Catalonia, Spain
| | | | - Rudolf Maibach
- International Breast Cancer Study Group Coordinating Center, Bern, Switzerland
| | - Rita Alaggio
- Pathology Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Piotr Czauderna
- Department of Surgery and Urology for Children and Adolescents, Medical University of Gdansk, Gdansk, Poland
| | - Bruce Morland
- Department of Oncology, Birmingham Women's and Children's Hospital, Birmingham, United Kingdom
| | | | - Stefano Cairo
- XenTech, Evry, France
- Champions Oncology, Rockville, Maryland, USA
| | - Roland Kappler
- Department of Pediatric Surgery, Dr. von Hauner Children's Hospital, University Hospital, LMU Munich, Germany
| | - Carolina Armengol
- Childhood Liver Oncology Group (c-LOG), Health Sciences Research Institute Germans Trias i Pujol (IGTP), Badalona, Catalonia, Spain
- Liver and Digestive Diseases Networking Biomedical Research Centre (CIBEREHD), Madrid, Spain
| | - Diego di Bernardo
- Telethon Institute of Genetics and Medicine, Pozzuoli, Naples, Italy
- Department of Chemical, Materials and Industrial Production Engineering, University of Naples "Federico II", Naples, Italy
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Olsson M, Larsson P, Johansson J, Sah VR, Parris TZ. Cancer stem cells are prevalent in the basal-like 2 and mesenchymal triple-negative breast cancer subtypes in vitro. Front Cell Dev Biol 2023; 11:1237673. [PMID: 37771376 PMCID: PMC10523387 DOI: 10.3389/fcell.2023.1237673] [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: 06/12/2023] [Accepted: 08/14/2023] [Indexed: 09/30/2023] Open
Abstract
Background: Triple-negative breast cancer (TNBC) is an aggressive subtype with the most unfavorable clinical outcomes, in part due to tumor heterogeneity, treatment resistance, and tumor relapse. The TNBC subtypes [basal-like 1 (BL1), basal-like 2 (BL2), mesenchymal (M), and luminal androgen receptor (LAR)] are biologically and clinically distinct entities that respond differently to local and systemic therapies. Therefore, we need to have a better understanding of cancer stemness relating to drug-resistant populations in the TNBC subtypes. Methods: Breast cancer stem cell (BCSC) distribution was investigated using an integrated flow cytometry approach with the ALDEFLUOR™ assay (ALDH) and CD24/CD44 antibodies. In total, 27 commercially available cell lines derived from normal and malignant mammary tissue were characterized into differentiated tumor cells and/or BCSC subpopulations (ALDH-CD44+CD24-/low enriched mesenchymal-like BCSCs, ALDH+non-CD44+CD24-/low enriched epithelial-like BCSCs, and highly purified ALDH+CD44+CD24-/low BCSCs). Results: BCSCs were not only enriched in estrogen receptor (ER) negative (mean, 49.6% versus 6.9% in ER+) and TNBC cell lines (51.3% versus 2.1% in Luminal A), but certain BCSC subpopulations (e.g., enriched mesenchymal-like BCSCs) were also significantly more common in the M (64.0% versus 6.2% in BL1; 64.0% versus 0% in LAR) and BL2 (77.4% versus 6.2% in BL1; 77.4% versus 0% in LAR; 77.4% versus 10.4% in TNBC UNS) TNBC subtypes. In contrast, ALDH status alone was not indicative of ER status or BC subtype. Conclusion: Taken together, these findings demonstrate the enrichment of potentially treatment-resistant BCSC subpopulations in the M and BL2 triple-negative breast cancer subtypes.
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Affiliation(s)
- Maxim Olsson
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
| | - Peter Larsson
- Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Sahlgrenska Center for Cancer Research, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Junko Johansson
- Sahlgrenska Center for Cancer Research, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Vasu R. Sah
- Sahlgrenska Center for Cancer Research, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Toshima Z. Parris
- Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Sahlgrenska Center for Cancer Research, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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Bahnassy S, Stires H, Jin L, Tam S, Mobin D, Balachandran M, Podar M, McCoy MD, Beckman RA, Riggins RB. Unraveling Vulnerabilities in Endocrine Therapy-Resistant HER2+/ER+ Breast Cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.21.554116. [PMID: 37662291 PMCID: PMC10473676 DOI: 10.1101/2023.08.21.554116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Background Breast tumors overexpressing human epidermal growth factor receptor (HER2) confer intrinsic resistance to endocrine therapy (ET), and patients with HER2/ estrogen receptor-positive (HER2+/HR+) breast cancer (BCa) are less responsive to ET than HER2-/ER+. However, real-world evidence reveals that a large subset of HER2+/ER+ patients receive ET as monotherapy, positioning this treatment pattern as a clinical challenge. In the present study, we developed and characterized two distinct in vitro models of ET-resistant (ETR) HER2+/ER+ BCa to identify possible therapeutic vulnerabilities. Methods To mimic ETR to aromatase inhibitors (AI), we developed two long-term estrogen-deprived (LTED) cell lines from BT-474 (BT474) and MDA-MB-361 (MM361). Growth assays, PAM50 molecular subtyping, genomic and transcriptomic analyses, followed by validation and functional studies, were used to identify targetable differences between ET-responsive parental and ETR-LTED HER2+/ER+ cells. Results Compared to their parental cells, MM361 LTEDs grew faster, lost ER, and increased HER2 expression, whereas BT474 LTEDs grew slower and maintained ER and HER2 expression. Both LTED variants had reduced responsiveness to fulvestrant. Whole-genome sequencing of the more aggressive MM361 LTED model system identified exonic mutations in genes encoding transcription factors and chromatin modifiers. Single-cell RNA sequencing demonstrated a shift towards non-luminal phenotypes, and revealed metabolic remodeling of MM361 LTEDs, with upregulated lipid metabolism and antioxidant genes associated with ferroptosis, including GPX4. Combining the GPX4 inhibitor RSL3 with anti-HER2 agents induced significant cell death in both the MM361 and BT474 LTEDs. Conclusions The BT474 and MM361 AI-resistant models capture distinct phenotypes of HER2+/ER+ BCa and identify altered lipid metabolism and ferroptosis remodeling as vulnerabilities of this type of ETR BCa.
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Affiliation(s)
- Shaymaa Bahnassy
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC
| | | | - Lu Jin
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC
| | - Stanley Tam
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC
| | - Dua Mobin
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC
| | - Manasi Balachandran
- Department of Medicine, University of Tennessee Medical Center, Knoxville, TN
| | | | - Matthew D. McCoy
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC
| | - Robert A. Beckman
- Departments of Oncology and of Biostatistics, Bioinformatics, and Biomathematics, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC
| | - Rebecca B. Riggins
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC
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39
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Tang Z, Liu X, Li Z, Zhang T, Yang B, Su J, Song Q. SpaRx: Elucidate single-cell spatial heterogeneity of drug responses for personalized treatment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.03.551911. [PMID: 37577665 PMCID: PMC10418183 DOI: 10.1101/2023.08.03.551911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Spatial cellular heterogeneity contributes to differential drug responses in a tumor lesion and potential therapeutic resistance. Recent emerging spatial technologies such as CosMx SMI, MERSCOPE, and Xenium delineate the spatial gene expression patterns at the single cell resolution. This provides unprecedented opportunities to identify spatially localized cellular resistance and to optimize the treatment for individual patients. In this work, we present a graph-based domain adaptation model, SpaRx, to reveal the heterogeneity of spatial cellular response to drugs. SpaRx transfers the knowledge from pharmacogenomics profiles to single-cell spatial transcriptomics data, through hybrid learning with dynamic adversarial adaption. Comprehensive benchmarking demonstrates the superior and robust performance of SpaRx at different dropout rates, noise levels, and transcriptomics coverage. Further application of SpaRx to the state-of-art single-cell spatial transcriptomics data reveals that tumor cells in different locations of a tumor lesion present heterogenous sensitivity or resistance to drugs. Moreover, resistant tumor cells interact with themselves or the surrounding constituents to form an ecosystem for drug resistance. Collectively, SpaRx characterizes the spatial therapeutic variability, unveils the molecular mechanisms underpinning drug resistance, and identifies personalized drug targets and effective drug combinations. Key Points We have developed a novel graph-based domain adaption model named SpaRx, to reveal the heterogeneity of spatial cellular response to different types of drugs, which bridges the gap between pharmacogenomics knowledgebase and single-cell spatial transcriptomics data.SpaRx is developed tailored for single-cell spatial transcriptomics data and is provided available as a ready-to-use open-source software, which demonstrates high accuracy and robust performance.SpaRx uncovers that tumor cells located in different areas within tumor lesion exhibit varying levels of sensitivity or resistance to drugs. Moreover, SpaRx reveals that tumor cells interact with themselves and the surrounding microenvironment to form an ecosystem capable of drug resistance.
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Affiliation(s)
- Ziyang Tang
- Department of Computer and Information Technology, Purdue University, Indiana, USA
| | - Xiang Liu
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indiana, USA
| | - Zuotian Li
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indiana, USA
- Department of Computer Graphics Technology, Purdue University, Indiana, USA
| | - Tonglin Zhang
- Department of Statistics, Purdue University, Indiana, USA
| | - Baijian Yang
- Department of Computer and Information Technology, Purdue University, Indiana, USA
| | - Jing Su
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indiana, USA
| | - Qianqian Song
- Center for Cancer Genomics and Precision Oncology, Atrium Health Wake Forest Baptist Comprehensive Cancer Center, North Carolina, USA
- Department of Cancer Biology, Wake Forest University School of Medicine, North Carolina, USA
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40
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Bhardwaj PV, Wang Y, Brunk E, Spanheimer PM, Abdou YG. Advances in the Management of Early-Stage Triple-Negative Breast Cancer. Int J Mol Sci 2023; 24:12478. [PMID: 37569851 PMCID: PMC10419523 DOI: 10.3390/ijms241512478] [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: 07/03/2023] [Revised: 08/02/2023] [Accepted: 08/03/2023] [Indexed: 08/13/2023] Open
Abstract
Triple-negative breast cancer (TNBC) is a subtype of breast cancer with both inter- and intratumor heterogeneity, thought to result in a more aggressive course and worse outcomes. Neoadjuvant therapy (NAT) has become the preferred treatment modality of early-stage TNBC as it allows for the downstaging of tumors in the breast and axilla, monitoring early treatment response, and most importantly, provides important prognostic information that is essential to determining post-surgical therapies to improve outcomes. It focuses on combinations of systemic drugs to optimize pathologic complete response (pCR). Excellent response to NAT has allowed surgical de-escalation in ideal candidates. Further, treatment algorithms guide the systemic management of patients based on their pCR status following surgery. The expanding knowledge of molecular pathways, genomic sequencing, and the immunological profile of TNBC has led to the use of immune checkpoint inhibitors and targeted agents, including PARP inhibitors, further revolutionizing the therapeutic landscape of this clinical entity. However, subgroups most likely to benefit from these novel approaches in TNBC remain elusive and are being extensively studied. In this review, we describe current practices and promising therapeutic options on the horizon for TNBC, surgical advances, and future trends in molecular determinants of response to therapy in early-stage TNBC.
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Affiliation(s)
- Prarthna V. Bhardwaj
- Division of Hematology-Oncology, University of Massachusetts Chan Medical School—Baystate, Springfield, MA 01199, USA
| | - Yue Wang
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Curriculum in Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Elizabeth Brunk
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Integrative Program for Biological and Genomic Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Lineberger Comprehensive Cancer Center, UNC Chapel Hill, NC 27599, USA
- Computational Medicine Program, UNC Chapel Hill, NC 27599, USA
| | - Philip M. Spanheimer
- Lineberger Comprehensive Cancer Center, UNC Chapel Hill, NC 27599, USA
- Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yara G. Abdou
- Lineberger Comprehensive Cancer Center, UNC Chapel Hill, NC 27599, USA
- Division of Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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41
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Thakur S, Haider S, Natrajan R. Implications of tumour heterogeneity on cancer evolution and therapy resistance: lessons from breast cancer. J Pathol 2023; 260:621-636. [PMID: 37587096 DOI: 10.1002/path.6158] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/11/2023] [Accepted: 06/14/2023] [Indexed: 08/18/2023]
Abstract
Tumour heterogeneity is pervasive amongst many cancers and leads to disease progression, and therapy resistance. In this review, using breast cancer as an exemplar, we focus on the recent advances in understanding the interplay between tumour cells and their microenvironment using single cell sequencing and digital spatial profiling technologies. Further, we discuss the utility of lineage tracing methodologies in pre-clinical models of breast cancer, and how these are being used to unravel new therapeutic vulnerabilities and reveal biomarkers of breast cancer progression. © 2023 The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Shefali Thakur
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Syed Haider
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Rachael Natrajan
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
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Cobos FA, Panah MJN, Epps J, Long X, Man TK, Chiu HS, Chomsky E, Kiner E, Krueger MJ, di Bernardo D, Voloch L, Molenaar J, van Hooff SR, Westermann F, Jansky S, Redell ML, Mestdagh P, Sumazin P. Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes. Genome Biol 2023; 24:177. [PMID: 37528411 PMCID: PMC10394903 DOI: 10.1186/s13059-023-03016-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 07/17/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq, scnRNA-seq for short), can help characterize the composition of tissues and reveal cells that influence key functions in both healthy and disease tissues. However, the use of these technologies is operationally challenging because of high costs and stringent sample-collection requirements. Computational deconvolution methods that infer the composition of bulk-profiled samples using scnRNA-seq-characterized cell types can broaden scnRNA-seq applications, but their effectiveness remains controversial. RESULTS We produced the first systematic evaluation of deconvolution methods on datasets with either known or scnRNA-seq-estimated compositions. Our analyses revealed biases that are common to scnRNA-seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-seq and scnRNA-seq profiles can help improve the accuracy of both scnRNA-seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), which combines RNA-seq transformation and dampened weighted least-squares deconvolution approaches, consistently outperformed other methods in predicting the composition of cell mixtures and tissue samples. CONCLUSIONS We showed that analysis of concurrent RNA-seq and scnRNA-seq profiles with SQUID can produce accurate cell-type abundance estimates and that this accuracy improvement was necessary for identifying outcomes-predictive cancer cell subclones in pediatric acute myeloid leukemia and neuroblastoma datasets. These results suggest that deconvolution accuracy improvements are vital to enabling its applications in the life sciences.
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Affiliation(s)
- Francisco Avila Cobos
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium; Cancer Research Institute Ghent, Ghent, Belgium
| | - Mohammad Javad Najaf Panah
- Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital Cancer Center, Houston, TX, USA
| | - Jessica Epps
- Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital Cancer Center, Houston, TX, USA
| | - Xiaochen Long
- Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital Cancer Center, Houston, TX, USA
- Department of Statistics, Rice University, Houston, TX, 77251, USA
| | - Tsz-Kwong Man
- Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital Cancer Center, Houston, TX, USA
| | - Hua-Sheng Chiu
- Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital Cancer Center, Houston, TX, USA
| | | | | | - Michael J Krueger
- Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital Cancer Center, Houston, TX, USA
| | - Diego di Bernardo
- Department Chemical, Materials and Industrial Engineering, Telethon Institute of Genetics and Medicine, University of Naples "Federico II", Via Campi Flegrei 34, 80078, Naples, Pozzuoli, Italy
| | | | - Jan Molenaar
- Princess Maxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | | | | | - Selina Jansky
- German Cancer Research Center, DKFZ, Heidelberg, Germany
| | - Michele L Redell
- Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital Cancer Center, Houston, TX, USA
| | - Pieter Mestdagh
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium; Cancer Research Institute Ghent, Ghent, Belgium.
| | - Pavel Sumazin
- Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital Cancer Center, Houston, TX, USA.
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Massimino M, Martorana F, Stella S, Vitale SR, Tomarchio C, Manzella L, Vigneri P. Single-Cell Analysis in the Omics Era: Technologies and Applications in Cancer. Genes (Basel) 2023; 14:1330. [PMID: 37510235 PMCID: PMC10380065 DOI: 10.3390/genes14071330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 07/30/2023] Open
Abstract
Cancer molecular profiling obtained with conventional bulk sequencing describes average alterations obtained from the entire cellular population analyzed. In the era of precision medicine, this approach is unable to track tumor heterogeneity and cannot be exploited to unravel the biological processes behind clonal evolution. In the last few years, functional single-cell omics has improved our understanding of cancer heterogeneity. This approach requires isolation and identification of single cells starting from an entire population. A cell suspension obtained by tumor tissue dissociation or hematological material can be manipulated using different techniques to separate individual cells, employed for single-cell downstream analysis. Single-cell data can then be used to analyze cell-cell diversity, thus mapping evolving cancer biological processes. Despite its unquestionable advantages, single-cell analysis produces massive amounts of data with several potential biases, stemming from cell manipulation and pre-amplification steps. To overcome these limitations, several bioinformatic approaches have been developed and explored. In this work, we provide an overview of this entire process while discussing the most recent advances in the field of functional omics at single-cell resolution.
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Affiliation(s)
- Michele Massimino
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Federica Martorana
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Stefania Stella
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Silvia Rita Vitale
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Cristina Tomarchio
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Livia Manzella
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Paolo Vigneri
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
- Humanitas Istituto Clinico Catanese, University Oncology Department, 95045 Catania, Italy
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Neagu AN, Whitham D, Bruno P, Morrissiey H, Darie CA, Darie CC. Omics-Based Investigations of Breast Cancer. Molecules 2023; 28:4768. [PMID: 37375323 DOI: 10.3390/molecules28124768] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 06/08/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
Breast cancer (BC) is characterized by an extensive genotypic and phenotypic heterogeneity. In-depth investigations into the molecular bases of BC phenotypes, carcinogenesis, progression, and metastasis are necessary for accurate diagnoses, prognoses, and therapy assessments in predictive, precision, and personalized oncology. This review discusses both classic as well as several novel omics fields that are involved or should be used in modern BC investigations, which may be integrated as a holistic term, onco-breastomics. Rapid and recent advances in molecular profiling strategies and analytical techniques based on high-throughput sequencing and mass spectrometry (MS) development have generated large-scale multi-omics datasets, mainly emerging from the three "big omics", based on the central dogma of molecular biology: genomics, transcriptomics, and proteomics. Metabolomics-based approaches also reflect the dynamic response of BC cells to genetic modifications. Interactomics promotes a holistic view in BC research by constructing and characterizing protein-protein interaction (PPI) networks that provide a novel hypothesis for the pathophysiological processes involved in BC progression and subtyping. The emergence of new omics- and epiomics-based multidimensional approaches provide opportunities to gain insights into BC heterogeneity and its underlying mechanisms. The three main epiomics fields (epigenomics, epitranscriptomics, and epiproteomics) are focused on the epigenetic DNA changes, RNAs modifications, and posttranslational modifications (PTMs) affecting protein functions for an in-depth understanding of cancer cell proliferation, migration, and invasion. Novel omics fields, such as epichaperomics or epimetabolomics, could investigate the modifications in the interactome induced by stressors and provide PPI changes, as well as in metabolites, as drivers of BC-causing phenotypes. Over the last years, several proteomics-derived omics, such as matrisomics, exosomics, secretomics, kinomics, phosphoproteomics, or immunomics, provided valuable data for a deep understanding of dysregulated pathways in BC cells and their tumor microenvironment (TME) or tumor immune microenvironment (TIMW). Most of these omics datasets are still assessed individually using distinct approches and do not generate the desired and expected global-integrative knowledge with applications in clinical diagnostics. However, several hyphenated omics approaches, such as proteo-genomics, proteo-transcriptomics, and phosphoproteomics-exosomics are useful for the identification of putative BC biomarkers and therapeutic targets. To develop non-invasive diagnostic tests and to discover new biomarkers for BC, classic and novel omics-based strategies allow for significant advances in blood/plasma-based omics. Salivaomics, urinomics, and milkomics appear as integrative omics that may develop a high potential for early and non-invasive diagnoses in BC. Thus, the analysis of the tumor circulome is considered a novel frontier in liquid biopsy. Omics-based investigations have applications in BC modeling, as well as accurate BC classification and subtype characterization. The future in omics-based investigations of BC may be also focused on multi-omics single-cell analyses.
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Affiliation(s)
- Anca-Narcisa Neagu
- Laboratory of Animal Histology, Faculty of Biology, "Alexandru Ioan Cuza" University of Iasi, Carol I Bvd, No. 20A, 700505 Iasi, Romania
| | - Danielle Whitham
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699, USA
| | - Pathea Bruno
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699, USA
| | - Hailey Morrissiey
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699, USA
| | - Celeste A Darie
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699, USA
| | - Costel C Darie
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699, USA
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45
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Ortiz MMO, Andrechek ER. Molecular Characterization and Landscape of Breast cancer Models from a multi-omics Perspective. J Mammary Gland Biol Neoplasia 2023; 28:12. [PMID: 37269418 DOI: 10.1007/s10911-023-09540-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 05/25/2023] [Indexed: 06/05/2023] Open
Abstract
Breast cancer is well-known to be a highly heterogenous disease. This facet of cancer makes finding a research model that mirrors the disparate intrinsic features challenging. With advances in multi-omics technologies, establishing parallels between the various models and human tumors is increasingly intricate. Here we review the various model systems and their relation to primary breast tumors using available omics data platforms. Among the research models reviewed here, breast cancer cell lines have the least resemblance to human tumors since they have accumulated many mutations and copy number alterations during their long use. Moreover, individual proteomic and metabolomic profiles do not overlap with the molecular landscape of breast cancer. Interestingly, omics analysis revealed that the initial subtype classification of some breast cancer cell lines was inappropriate. In cell lines the major subtypes are all well represented and share some features with primary tumors. In contrast, patient-derived xenografts (PDX) and patient-derived organoids (PDO) are superior in mirroring human breast cancers at many levels, making them suitable models for drug screening and molecular analysis. While patient derived organoids are spread across luminal, basal- and normal-like subtypes, the PDX samples were initially largely basal but other subtypes have been increasingly described. Murine models offer heterogenous tumor landscapes, inter and intra-model heterogeneity, and give rise to tumors of different phenotypes and histology. Murine models have a reduced mutational burden compared to human breast cancer but share some transcriptomic resemblance, and representation of many breast cancer subtypes can be found among the variety subtypes. To date, while mammospheres and three- dimensional cultures lack comprehensive omics data, these are excellent models for the study of stem cells, cell fate decision and differentiation, and have also been used for drug screening. Therefore, this review explores the molecular landscapes and characterization of breast cancer research models by comparing recent published multi-omics data and analysis.
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Affiliation(s)
- Mylena M O Ortiz
- Genetics and Genomics Science Program, Michigan State University, East Lansing, MI, USA
| | - Eran R Andrechek
- Department of Physiology, Michigan State University, 2194 BPS Building 567 Wilson Road, East Lansing, MI, 48824, USA.
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Salemme V, Centonze G, Avalle L, Natalini D, Piccolantonio A, Arina P, Morellato A, Ala U, Taverna D, Turco E, Defilippi P. The role of tumor microenvironment in drug resistance: emerging technologies to unravel breast cancer heterogeneity. Front Oncol 2023; 13:1170264. [PMID: 37265795 PMCID: PMC10229846 DOI: 10.3389/fonc.2023.1170264] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 04/28/2023] [Indexed: 06/03/2023] Open
Abstract
Breast cancer is a highly heterogeneous disease, at both inter- and intra-tumor levels, and this heterogeneity is a crucial determinant of malignant progression and response to treatments. In addition to genetic diversity and plasticity of cancer cells, the tumor microenvironment contributes to tumor heterogeneity shaping the physical and biological surroundings of the tumor. The activity of certain types of immune, endothelial or mesenchymal cells in the microenvironment can change the effectiveness of cancer therapies via a plethora of different mechanisms. Therefore, deciphering the interactions between the distinct cell types, their spatial organization and their specific contribution to tumor growth and drug sensitivity is still a major challenge. Dissecting intra-tumor heterogeneity is currently an urgent need to better define breast cancer biology and to develop therapeutic strategies targeting the microenvironment as helpful tools for combined and personalized treatment. In this review, we analyze the mechanisms by which the tumor microenvironment affects the characteristics of tumor heterogeneity that ultimately result in drug resistance, and we outline state of the art preclinical models and emerging technologies that will be instrumental in unraveling the impact of the tumor microenvironment on resistance to therapies.
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Affiliation(s)
- Vincenzo Salemme
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
- Molecular Biotechnology Center (MBC) “Guido Tarone”, Turin, Italy
| | - Giorgia Centonze
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
- Molecular Biotechnology Center (MBC) “Guido Tarone”, Turin, Italy
| | - Lidia Avalle
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
- Molecular Biotechnology Center (MBC) “Guido Tarone”, Turin, Italy
| | - Dora Natalini
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
- Molecular Biotechnology Center (MBC) “Guido Tarone”, Turin, Italy
| | - Alessio Piccolantonio
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
- Molecular Biotechnology Center (MBC) “Guido Tarone”, Turin, Italy
| | - Pietro Arina
- UCL, Bloomsbury Institute of Intensive Care Medicine, Division of Medicine, University College London, London, United Kingdom
| | - Alessandro Morellato
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
- Molecular Biotechnology Center (MBC) “Guido Tarone”, Turin, Italy
| | - Ugo Ala
- Department of Veterinary Sciences, University of Turin, Grugliasco, TO, Italy
| | - Daniela Taverna
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
- Molecular Biotechnology Center (MBC) “Guido Tarone”, Turin, Italy
| | - Emilia Turco
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
| | - Paola Defilippi
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
- Molecular Biotechnology Center (MBC) “Guido Tarone”, Turin, Italy
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47
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Rimmer N, Liang CY, Coelho R, Lopez MN, Jacob F. Generation of endogenously tagged E-cadherin cells using gene editing via non-homologous end joining. STAR Protoc 2023; 4:102305. [PMID: 37178110 DOI: 10.1016/j.xpro.2023.102305] [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: 02/27/2023] [Revised: 03/31/2023] [Accepted: 04/24/2023] [Indexed: 05/15/2023] Open
Abstract
We provide a protocol using non-homologous end joining to integrate an oligonucleotide sequence of a fluorescence protein at the CDH1 locus encoding for the epithelial glycoprotein E-cadherin. We describe steps for implementing the CRISPR-Cas9-mediated knock-in procedure by transfecting a cancer cell line with a pool of plasmids. The EGFP-tagged cells are traced by fluorescence-activated cell sorting and validated on DNA and protein levels. The protocol is flexible and can be applied in principle to any protein expressed in a cell line. For complete details on the use and execution of this protocol, please refer to Cumin et al. (2022).1.
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Affiliation(s)
- Natalie Rimmer
- Ovarian Cancer Research, Department of Biomedicine, University Hospital Basel and University of Basel, Basel 4031, Switzerland.
| | - Ching-Yeu Liang
- Ovarian Cancer Research, Department of Biomedicine, University Hospital Basel and University of Basel, Basel 4031, Switzerland
| | - Ricardo Coelho
- Ovarian Cancer Research, Department of Biomedicine, University Hospital Basel and University of Basel, Basel 4031, Switzerland
| | - Monica Nunez Lopez
- Ovarian Cancer Research, Department of Biomedicine, University Hospital Basel and University of Basel, Basel 4031, Switzerland
| | - Francis Jacob
- Ovarian Cancer Research, Department of Biomedicine, University Hospital Basel and University of Basel, Basel 4031, Switzerland; Hospital for Women, University Hospital Basel, Basel 4031, Switzerland.
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48
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A rapid and sensitive single-cell proteomic method based on fast liquid-chromatography separation, retention time prediction and MS1-only acquisition. Anal Chim Acta 2023; 1251:341038. [PMID: 36925302 DOI: 10.1016/j.aca.2023.341038] [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: 12/04/2022] [Revised: 02/28/2023] [Accepted: 03/01/2023] [Indexed: 03/06/2023]
Abstract
Single-cell analysis has received much attention in recent years for elucidating the widely existing cellular heterogeneity in biological systems. However, the ability to measure the proteome in single cells is still far behind that of transcriptomics due to the lack of sensitive and high-throughput mass spectrometry methods. Herein, we report an integrated strategy termed "SCP-MS1" that combines fast liquid chromatography (LC) separation, deep learning-based retention time (RT) prediction and MS1-only acquisition for rapid and sensitive single-cell proteome analysis. In SCP-MS1, the peptides were identified via four-dimensional MS1 feature (m/z, RT, charge and FAIMS CV) matching, therefore relieving MS acquisition from the time consuming and information losing MS2 step and making this method particularly compatible with fast LC separation. By completely omitting the MS2 step, all the MS analysis time was utilized for MS1 acquisition in SCP-MS1 and therefore led to 65%-138% increased MS1 feature collection. Unlike "match between run" methods that still needed MS2 information for RT alignment, SCP-MS1 used deep learning-based RT prediction to transfer the measured RTs in long gradient bulk analyses to short gradient single cell analyses, which was the key step to enhance both identification scale and matching accuracy. Using this strategy, more than 2000 proteins were obtained from 0.2 ng of peptides with a 14-min active gradient at a false discovery rate (FDR) of 0.8%. Comparing with the DDA method, improved quantitative performance was also observed for SCP-MS1 with approximately 50% decreased median coefficient of variation of quantified proteins. For single-cell analysis, 1715 ± 204 and 1604 ± 224 proteins were quantified in single 293T and HeLa cells, respectively. Finally, SCP-MS1 was applied to single-cell proteome analysis of sorafenib resistant and non-resistant HepG2 cells and revealed clear cellular heterogeneity in the resistant population that may be masked in bulk studies.
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Kim H, Whitman AA, Wisniewska K, Kakati RT, Garcia-Recio S, Calhoun BC, Franco HL, Perou CM, Spanheimer PM. Tamoxifen Response at Single Cell Resolution in Estrogen Receptor-Positive Primary Human Breast Tumors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.01.535159. [PMID: 37066379 PMCID: PMC10103953 DOI: 10.1101/2023.04.01.535159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
In ER+/HER2- breast cancer, multiple measures of intra-tumor heterogeneity are associated with worse response to endocrine therapy. To investigate heterogeneity in response to treatment, we developed an operating room-to-laboratory pipeline for the collection of live human tumors and normal breast specimens immediately after surgical resection for processing into single-cell workflows for experimentation and genomic analyses. We demonstrate differences in tamoxifen response by cell type and identify distinctly responsive and resistant subpopulations within the malignant cell compartment of human tumors. Tamoxifen resistance signatures from 3 distinct resistant subpopulations are prognostic in large cohorts of ER+ breast cancer patients and enriched in endocrine therapy resistant tumors. This novel ex vivo model system now provides a foundation to define responsive and resistant sub-populations within heterogeneous tumors, to develop precise single cell-based predictors of response to therapy, and to identify genes and pathways driving resistance to therapy.
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Affiliation(s)
- Hyunsoo Kim
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC
| | - Austin A. Whitman
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC
| | - Kamila Wisniewska
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC
| | - Rasha T. Kakati
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC
| | - Susana Garcia-Recio
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC
| | - Benjamin C. Calhoun
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Hector L. Franco
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC
- Department of Genetics, University of North Carolina, Chapel Hill, NC
- Computational Medicine Program, University of North Carolina, Chapel Hill, NC
| | - Charles M. Perou
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Department of Genetics, University of North Carolina, Chapel Hill, NC
- Computational Medicine Program, University of North Carolina, Chapel Hill, NC
| | - Philip M. Spanheimer
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC
- Department of Surgery, University of North Carolina, Chapel Hill, NC
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50
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Bellini D, Milan M, Bordin A, Rizzi R, Rengo M, Vicini S, Onori A, Carbone I, De Falco E. A Focus on the Synergy of Radiomics and RNA Sequencing in Breast Cancer. Int J Mol Sci 2023; 24:ijms24087214. [PMID: 37108377 PMCID: PMC10138689 DOI: 10.3390/ijms24087214] [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: 02/02/2023] [Revised: 04/05/2023] [Accepted: 04/06/2023] [Indexed: 04/29/2023] Open
Abstract
Radiological imaging is currently employed as the most effective technique for screening, diagnosis, and follow up of patients with breast cancer (BC), the most common type of tumor in women worldwide. However, the introduction of the omics sciences such as metabolomics, proteomics, and molecular genomics, have optimized the therapeutic path for patients and implementing novel information parallel to the mutational asset targetable by specific clinical treatments. Parallel to the "omics" clusters, radiological imaging has been gradually employed to generate a specific omics cluster termed "radiomics". Radiomics is a novel advanced approach to imaging, extracting quantitative, and ideally, reproducible data from radiological images using sophisticated mathematical analysis, including disease-specific patterns, that could not be detected by the human eye. Along with radiomics, radiogenomics, defined as the integration of "radiology" and "genomics", is an emerging field exploring the relationship between specific features extracted from radiological images and genetic or molecular traits of a particular disease to construct adequate predictive models. Accordingly, radiological characteristics of the tissue are supposed to mimic a defined genotype and phenotype and to better explore the heterogeneity and the dynamic evolution of the tumor over the time. Despite such improvements, we are still far from achieving approved and standardized protocols in clinical practice. Nevertheless, what can we learn by this emerging multidisciplinary clinical approach? This minireview provides a focused overview on the significance of radiomics integrated by RNA sequencing in BC. We will also discuss advances and future challenges of such radiomics-based approach.
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Affiliation(s)
- Davide Bellini
- Department of Radiological Sciences, Oncology and Pathology, I.C.O.T. Hospital, Sapienza University of Rome, Via Franco Faggiana 1668, 04100 Latina, Italy
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Marika Milan
- UOC Neurology, Fondazione Ca'Granda, Ospedale Maggiore Policlinico, Via F. Sforza, 28, 20122 Milan, Italy
| | - Antonella Bordin
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Roberto Rizzi
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Marco Rengo
- Department of Radiological Sciences, Oncology and Pathology, I.C.O.T. Hospital, Sapienza University of Rome, Via Franco Faggiana 1668, 04100 Latina, Italy
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Simone Vicini
- Department of Radiological Sciences, Oncology and Pathology, I.C.O.T. Hospital, Sapienza University of Rome, Via Franco Faggiana 1668, 04100 Latina, Italy
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Alessandro Onori
- Department of Radiological Sciences, Oncology and Pathology, I.C.O.T. Hospital, Sapienza University of Rome, Via Franco Faggiana 1668, 04100 Latina, Italy
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Iacopo Carbone
- Department of Radiological Sciences, Oncology and Pathology, I.C.O.T. Hospital, Sapienza University of Rome, Via Franco Faggiana 1668, 04100 Latina, Italy
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Elena De Falco
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
- Mediterranea Cardiocentro, 80122 Napoli, Italy
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