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Prasanna CVS, Jolly MK, Bhat R. Spatial heterogeneity in tumor adhesion qualifies collective cell invasion. Biophys J 2024:S0006-3495(24)00319-9. [PMID: 38725244 DOI: 10.1016/j.bpj.2024.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 02/12/2024] [Accepted: 05/03/2024] [Indexed: 05/30/2024] Open
Abstract
Collective cell invasion (CCI), a canon of most invasive solid tumors, is an emergent property of the interactions between cancer cells and their surrounding extracellular matrix (ECM). However, tumor populations invariably consist of cells expressing variable levels of adhesive proteins that mediate such interactions, disallowing an intuitive understanding of how tumor invasiveness at a multicellular scale is influenced by spatial heterogeneity of cell-cell and cell-ECM adhesion. Here, we have used a Cellular Potts model-based multiscale computational framework that is constructed on the histopathological principles of glandular cancers. In earlier efforts on homogenous cancer cell populations, this framework revealed the relative ranges of interactions, including cell-cell and cell-ECM adhesion that drove collective, dispersed, and mixed multimodal invasion. Here, we constitute a tumor core of two separate cell subsets showing distinct intra- and inter-subset cell-cell or cell-ECM adhesion strengths. These two subsets of cells are arranged to varying extents of spatial intermingling, which we call the heterogeneity index (HI). We observe that low and high inter-subset cell adhesion favors invasion of high-HI and low-HI intermingled populations with distinct intra-subset cell-cell adhesion strengths, respectively. In addition, for explored values of cell-ECM adhesion strengths, populations with high HI values collectively invade better than those with lower HI values. We then asked how spatial invasion is regulated by progressively intermingled cellular subsets that are epithelial, i.e., showed high cell-cell but poor cell-ECM adhesion, and mesenchymal, i.e., with reversed adhesion strengths to the former. Here too, inter-subset adhesion plays an important role in contextualizing the proportionate relationship between HI and invasion. An exception to this relationship is seen for cases of heterogeneous cell-ECM adhesion where sub-maximal HI patterns with higher outer localization of cells with stronger ECM adhesion collectively invade better than their relatively higher-HI counterparts. Our simulations also reveal how adhesion heterogeneity qualifies collective invasion, when either cell-cell or cell-ECM adhesion type is varied but results in an invasive dispersion when both adhesion types are simultaneously altered.
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Affiliation(s)
| | - Mohit Kumar Jolly
- Department of Bioengineering, Indian Institute of Science, Bangalore, India.
| | - Ramray Bhat
- Department of Bioengineering, Indian Institute of Science, Bangalore, India; Department of Developmental Biology and Genetics, Indian Institute of Science, Bangalore, India.
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2
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Lê H, Deforges J, Cutolo P, Lamarque A, Hua G, Lindner V, Jain S, Balloul JM, Benkirane-Jessel N, Quéméneur E. Patient-derived tumoroids and proteomic signatures: tools for early drug discovery. Front Immunol 2024; 15:1379613. [PMID: 38698850 PMCID: PMC11063793 DOI: 10.3389/fimmu.2024.1379613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 03/14/2024] [Indexed: 05/05/2024] Open
Abstract
Onco-virotherapy is an emergent treatment for cancer based on viral vectors. The therapeutic activity is based on two different mechanisms including tumor-specific oncolysis and immunostimulatory properties. In this study, we evaluated onco-virotherapy in vitro responses on immunocompetent non-small cell lung cancer (NSCLC) patient-derived tumoroids (PDTs) and healthy organoids. PDTs are accurate tools to predict patient's clinical responses at the in vitro stage. We showed that onco-virotherapy could exert specific antitumoral effects by producing a higher number of viral particles in PDTs than in healthy organoids. In the present work, we used multiplex protein screening, based on proximity extension assay to highlight different response profiles. Our results pointed to the increase of proteins implied in T cell activation, such as IFN-γ following onco-virotherapy treatment. Based on our observation, oncolytic viruses-based therapy responders are dependent on several factors: a high PD-L1 expression, which is a biomarker of greater immune response under immunotherapies, and the number of viral particles present in tumor tissue, which is dependent to the metabolic state of tumoral cells. Herein, we highlight the use of PDTs as an alternative in vitro model to assess patient-specific responses to onco-virotherapy at the early stage of the preclinical phases.
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Affiliation(s)
- Hélène Lê
- Transgene S.A., Illkirch–Graffenstaden, France
- INSERM UMR1260, Regenerative Nanomedicine, Strasbourg, France
| | | | | | | | - Guoqiang Hua
- INSERM UMR1260, Regenerative Nanomedicine, Strasbourg, France
| | - Véronique Lindner
- INSERM UMR1260, Regenerative Nanomedicine, Strasbourg, France
- Department of Pathology, Hopitaux Universitaires de Strasbourg, Strasbourg, France
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Mi H, Varadhan R, Cimino-Mathews AM, Emens LA, Santa-Maria CA, Popel AS. Spatial and Compositional Biomarkers in Tumor Microenvironment Predicts Clinical Outcomes in Triple-Negative Breast Cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.18.572234. [PMID: 38187696 PMCID: PMC10769235 DOI: 10.1101/2023.12.18.572234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited treatment options, which warrants identification of novel therapeutic targets. Deciphering nuances in the tumor microenvironment (TME) may unveil insightful links between anti-tumor immunity and clinical outcomes, yet such connections remain underexplored. Here we employed a dataset derived from imaging mass cytometry of 58 TNBC patient specimens at single-cell resolution and performed in-depth quantifications with a suite of multi-scale computational algorithms. We detected distinct cell distribution patterns among clinical subgroups, potentially stemming from different infiltration related to tumor vasculature and fibroblast heterogeneity. Spatial analysis also identified ten recurrent cellular neighborhoods (CNs) - a collection of local TME characteristics with unique cell components. Coupling of the prevalence of pan-immune and perivasculature immune hotspot CNs, enrichment of inter-CN interactions was associated with improved survival. Using a deep learning model trained on engineered spatial data, we can with high accuracy (mean AUC of 5-fold cross-validation = 0.71) how a separate cohort of patients in the NeoTRIP clinical trial will respond to treatment based on baseline TME features. These data reinforce that the TME architecture is structured in cellular compositions, spatial organizations, vasculature biology, and molecular profiles, and suggest novel imaging-based biomarkers for treatment development in the context of TNBC.
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Affiliation(s)
- Haoyang Mi
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Ravi Varadhan
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Ashley M. Cimino-Mathews
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, MD, United States
| | | | - Cesar A. Santa-Maria
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Aleksander S. Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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4
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Kumar G, Pandurengan RK, Parra ER, Kannan K, Haymaker C. Spatial modelling of the tumor microenvironment from multiplex immunofluorescence images: methods and applications. Front Immunol 2023; 14:1288802. [PMID: 38179056 PMCID: PMC10765501 DOI: 10.3389/fimmu.2023.1288802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 12/07/2023] [Indexed: 01/06/2024] Open
Abstract
Spatial modelling methods have gained prominence with developments in high throughput imaging platforms. Multiplex immunofluorescence (mIF) provides the scope to examine interactions between tumor and immune compartment at single cell resolution using a panel of antibodies that can be chosen based on the cancer type or the clinical interest of the study. The markers can be used to identify the phenotypes and to examine cellular interactions at global and local scales. Several translational studies rely on key understanding of the tumor microenvironment (TME) to identify drivers of immune response in immunotherapy based clinical trials. To improve the success of ongoing trials, a number of retrospective approaches can be adopted to understand differences in response, recurrence and progression by examining the patient's TME from tissue samples obtained at baseline and at various time points along the treatment. The multiplex immunofluorescence (mIF) technique provides insight on patient specific cell populations and their relative spatial distribution as qualitative measures of a favorable treatment outcome. Spatial analysis of these images provides an understanding of the intratumoral heterogeneity and clustering among cell populations in the TME. A number of mathematical models, which establish clustering as a measure of deviation from complete spatial randomness, can be applied to the mIF images represented as spatial point patterns. These mathematical models, developed for landscape ecology and geographic information studies, can be applied to the TME after careful consideration of the tumor type (cold vs. hot) and the tumor immune landscape. The spatial modelling of mIF images can show observable engagement of T cells expressing immune checkpoint molecules and this can then be correlated with single-cell RNA sequencing data.
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Affiliation(s)
| | | | | | - Kasthuri Kannan
- Department of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, United States
| | - Cara Haymaker
- Department of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, United States
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Fick CN, Dunne EG, Lankadasari MB, Mastrogiacomo B, Asao T, Vanstraelen S, Liu Y, Sanchez-Vega F, Jones DR. Genomic profiling and metastatic risk in early-stage non-small cell lung cancer. JTCVS OPEN 2023; 16:9-16. [PMID: 38204702 PMCID: PMC10775106 DOI: 10.1016/j.xjon.2023.10.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/02/2023] [Accepted: 10/11/2023] [Indexed: 01/12/2024]
Affiliation(s)
- Cameron N. Fick
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Elizabeth G. Dunne
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Manendra B. Lankadasari
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
- Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Brooke Mastrogiacomo
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
- Computational Oncology Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Tetsuhiko Asao
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Stijn Vanstraelen
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Yuan Liu
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
- Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Francisco Sanchez-Vega
- Computational Oncology Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - David R. Jones
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
- Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY
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Martinez-Espinosa I, Serrato JA, Ortiz-Quintero B. The Role of Exosome-Derived microRNA on Lung Cancer Metastasis Progression. Biomolecules 2023; 13:1574. [PMID: 38002256 PMCID: PMC10669807 DOI: 10.3390/biom13111574] [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: 09/10/2023] [Revised: 10/16/2023] [Accepted: 10/19/2023] [Indexed: 11/26/2023] Open
Abstract
The high mortality from lung cancer is mainly attributed to the presence of metastases at the time of diagnosis. Despite being the leading cause of lung cancer death, the underlying molecular mechanisms driving metastasis progression are still not fully understood. Recent studies suggest that tumor cell exosomes play a significant role in tumor progression through intercellular communication between tumor cells, the microenvironment, and distant organs. Furthermore, evidence shows that exosomes release biologically active components to distant sites and organs, which direct metastasis by preparing metastatic pre-niche and stimulating tumorigenesis. As a result, identifying the active components of exosome cargo has become a critical area of research in recent years. Among these components are microRNAs, which are associated with tumor progression and metastasis in lung cancer. Although research into exosome-derived microRNA (exosomal miRNAs) is still in its early stages, it holds promise as a potential target for lung cancer therapy. Understanding how exosomal microRNAs promote metastasis will provide evidence for developing new targeted treatments. This review summarizes current research on exosomal miRNAs' role in metastasis progression mechanisms, focusing on lung cancer.
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Affiliation(s)
| | | | - Blanca Ortiz-Quintero
- Department of Molecular Biomedicine and Translational Research, Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Mexico City CP 14080, Mexico; (I.M.-E.); (J.A.S.)
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Vu T, Seal S, Ghosh T, Ahmadian M, Wrobel J, Ghosh D. FunSpace: A functional and spatial analytic approach to cell imaging data using entropy measures. PLoS Comput Biol 2023; 19:e1011490. [PMID: 37756338 PMCID: PMC10561868 DOI: 10.1371/journal.pcbi.1011490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 10/09/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023] Open
Abstract
Spatial heterogeneity in the tumor microenvironment (TME) plays a critical role in gaining insights into tumor development and progression. Conventional metrics typically capture the spatial differential between TME cellular patterns by either exploring the cell distributions in a pairwise fashion or aggregating the heterogeneity across multiple cell distributions without considering the spatial contribution. As such, none of the existing approaches has fully accounted for the simultaneous heterogeneity caused by both cellular diversity and spatial configurations of multiple cell categories. In this article, we propose an approach to leverage spatial entropy measures at multiple distance ranges to account for the spatial heterogeneity across different cellular organizations. Functional principal component analysis (FPCA) is applied to estimate FPC scores which are then served as predictors in a Cox regression model to investigate the impact of spatial heterogeneity in the TME on survival outcome, potentially adjusting for other confounders. Using a non-small cell lung cancer dataset (n = 153) as a case study, we found that the spatial heterogeneity in the TME cellular composition of CD14+ cells, CD19+ B cells, CD4+ and CD8+ T cells, and CK+ tumor cells, had a significant non-zero effect on the overall survival (p = 0.027). Furthermore, using a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer dataset (n = 33), our proposed method identified a significant impact of cellular interactions between tumor and immune cells on the overall survival (p = 0.046). In simulation studies under different spatial configurations, the proposed method demonstrated a high predictive power by accounting for both clinical effect and the impact of spatial heterogeneity.
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Affiliation(s)
- Thao Vu
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Souvik Seal
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Tusharkanti Ghosh
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Mansooreh Ahmadian
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Julia Wrobel
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
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Ottaiano A, Ianniello M, Santorsola M, Ruggiero R, Sirica R, Sabbatino F, Perri F, Cascella M, Di Marzo M, Berretta M, Caraglia M, Nasti G, Savarese G. From Chaos to Opportunity: Decoding Cancer Heterogeneity for Enhanced Treatment Strategies. BIOLOGY 2023; 12:1183. [PMID: 37759584 PMCID: PMC10525472 DOI: 10.3390/biology12091183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 08/24/2023] [Accepted: 08/28/2023] [Indexed: 09/29/2023]
Abstract
Cancer manifests as a multifaceted disease, characterized by aberrant cellular proliferation, survival, migration, and invasion. Tumors exhibit variances across diverse dimensions, encompassing genetic, epigenetic, and transcriptional realms. This heterogeneity poses significant challenges in prognosis and treatment, affording tumors advantages through an increased propensity to accumulate mutations linked to immune system evasion and drug resistance. In this review, we offer insights into tumor heterogeneity as a crucial characteristic of cancer, exploring the difficulties associated with measuring and quantifying such heterogeneity from clinical and biological perspectives. By emphasizing the critical nature of understanding tumor heterogeneity, this work contributes to raising awareness about the importance of developing effective cancer therapies that target this distinct and elusive trait of cancer.
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Affiliation(s)
- Alessandro Ottaiano
- Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale”, Via M. Semmola, 80131 Naples, Italy; (M.S.); (F.P.); (M.C.); (M.D.M.); (G.N.)
| | - Monica Ianniello
- AMES, Centro Polidiagnostico Strumentale srl, Via Padre Carmine Fico 24, 80013 Casalnuovo Di Napoli, Italy; (M.I.); (R.R.); (R.S.); (G.S.)
| | - Mariachiara Santorsola
- Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale”, Via M. Semmola, 80131 Naples, Italy; (M.S.); (F.P.); (M.C.); (M.D.M.); (G.N.)
| | - Raffaella Ruggiero
- AMES, Centro Polidiagnostico Strumentale srl, Via Padre Carmine Fico 24, 80013 Casalnuovo Di Napoli, Italy; (M.I.); (R.R.); (R.S.); (G.S.)
| | - Roberto Sirica
- AMES, Centro Polidiagnostico Strumentale srl, Via Padre Carmine Fico 24, 80013 Casalnuovo Di Napoli, Italy; (M.I.); (R.R.); (R.S.); (G.S.)
| | - Francesco Sabbatino
- Oncology Unit, Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy;
| | - Francesco Perri
- Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale”, Via M. Semmola, 80131 Naples, Italy; (M.S.); (F.P.); (M.C.); (M.D.M.); (G.N.)
| | - Marco Cascella
- Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale”, Via M. Semmola, 80131 Naples, Italy; (M.S.); (F.P.); (M.C.); (M.D.M.); (G.N.)
| | - Massimiliano Di Marzo
- Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale”, Via M. Semmola, 80131 Naples, Italy; (M.S.); (F.P.); (M.C.); (M.D.M.); (G.N.)
| | - Massimiliano Berretta
- Department of Clinical and Experimental Medicine, University of Messina, 98122 Messina, Italy;
| | - Michele Caraglia
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, Via Luigi De Crecchio 7, 80138 Naples, Italy;
| | - Guglielmo Nasti
- Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale”, Via M. Semmola, 80131 Naples, Italy; (M.S.); (F.P.); (M.C.); (M.D.M.); (G.N.)
| | - Giovanni Savarese
- AMES, Centro Polidiagnostico Strumentale srl, Via Padre Carmine Fico 24, 80013 Casalnuovo Di Napoli, Italy; (M.I.); (R.R.); (R.S.); (G.S.)
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Song X, Xiong A, Wu F, Li X, Wang J, Jiang T, Chen P, Zhang X, Zhao Z, Liu H, Cheng L, Zhao C, Wang Z, Pan C, Cui X, Xu T, Luo H, Zhou C. Spatial multi-omics revealed the impact of tumor ecosystem heterogeneity on immunotherapy efficacy in patients with advanced non-small cell lung cancer treated with bispecific antibody. J Immunother Cancer 2023; 11:e006234. [PMID: 36854570 PMCID: PMC9980352 DOI: 10.1136/jitc-2022-006234] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2023] [Indexed: 03/02/2023] Open
Abstract
BACKGROUND Immunotherapy for malignant tumors has made great progress, but many patients do not benefit from it. The complex intratumoral heterogeneity (ITH) hindered the in-depth exploration of immunotherapy. Conventional bulk sequencing has masked intratumor complexity, preventing a more detailed discovery of the impact of ITH on treatment efficacy. Hence, we initiated this study to explore ITH at the multi-omics spatial level and to seek prognostic biomarkers of immunotherapy efficacy considering the presence of ITH. METHODS Using the segmentation strategy of digital spatial profiling (DSP), we obtained differential information on tumor and stromal regions at the proteomic and transcriptomic levels. Based on the consideration of ITH, signatures constructed by candidate proteins in different regions were used to predict the efficacy of immunotherapy. RESULTS Eighteen patients treated with a bispecific antibody (bsAb)-KN046 were enrolled in this study. The tumor and stromal areas of the same samples exhibited distinct features. Signatures consisting of 11 and 18 differentially expressed DSP markers from the tumor and stromal areas, respectively, were associated with treatment response. Furthermore, the spatially resolved signature identified from the stromal areas showed greater predictive power for bsAb immunotherapy response (area under the curve=0.838). Subsequently, our stromal signature was validated in an independent cohort of patients with non-small cell lung cancer undergoing immunotherapy. CONCLUSION We deciphered ITH at the spatial level and demonstrated for the first time that genetic information in the stromal region can better predict the efficacy of bsAb treatment. TRIAL REGISTRATION NUMBER NCT03838848.
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Affiliation(s)
- Xinyu Song
- School of Medicine, Tongji University, Shanghai, China
- Department of Medical Oncology, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
| | - Anwen Xiong
- Department of Medical Oncology, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
| | - Fengying Wu
- Department of Medical Oncology, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
| | - Xuefei Li
- Department of Lung Cancer and Immunology, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
| | - Jing Wang
- Clinical research center, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
| | - Tao Jiang
- Department of Medical Oncology, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
| | - Peixin Chen
- School of Medicine, Tongji University, Shanghai, China
| | | | - Zhikai Zhao
- Department of Pathology, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
| | - Huifang Liu
- Department of Pathology, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
| | - Lei Cheng
- Department of Lung Cancer and Immunology, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
| | - Chao Zhao
- Department of Lung Cancer and Immunology, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
| | - Zhehai Wang
- Department of Medical Oncology, Shandong Cancer Hospital, Jinan, Shandong, China
| | - Chaohu Pan
- Shenzhen Engineering Center for Translational Medicine of Precision Cancer Immunodiagnosis and Therapy, YuceBio Technology Co Ltd, Shenzhen, Guangdong, China
| | - Xiaoli Cui
- Shenzhen Engineering Center for Translational Medicine of Precision Cancer Immunodiagnosis and Therapy, YuceBio Technology Co Ltd, Shenzhen, Guangdong, China
| | - Ting Xu
- Alphamab Biopharmaceuticals, Suzhou, Jiangsu, China
| | - Haitao Luo
- Shenzhen Engineering Center for Translational Medicine of Precision Cancer Immunodiagnosis and Therapy, YuceBio Technology Co Ltd, Shenzhen, Guangdong, China
| | - Caicun Zhou
- School of Medicine, Tongji University, Shanghai, China
- Department of Medical Oncology, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China
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Kuśnierczyk P. Genetic differences between smokers and never-smokers with lung cancer. Front Immunol 2023; 14:1063716. [PMID: 36817482 PMCID: PMC9932279 DOI: 10.3389/fimmu.2023.1063716] [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: 10/07/2022] [Accepted: 01/12/2023] [Indexed: 02/05/2023] Open
Abstract
Smoking is a major risk factor for lung cancer, therefore lung cancer epidemiological trends reflect the past trends of cigarette smoking to a great extent. The geographic patterns in mortality closely follow those in incidence. Although lung cancer is strongly associated with cigarette smoking, only about 15% of smokers get lung cancer, and also some never-smokers develop this malignancy. Although less frequent, lung cancer in never smokers is the seventh leading cause of cancer deaths in both sexes worldwide. Lung cancer in smokers and never-smokers differs in many aspects: in histological types, environmental factors representing a risk, and in genes associated with this disease. In this review, we will focus on the genetic differences between lung cancer in smokers versus never-smokers: gene expression, germ-line polymorphisms, gene mutations, as well as ethnic and gender differences. Finally, treatment options for smokers and never-smokers will be briefly reviewed.
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Affiliation(s)
- Piotr Kuśnierczyk
- Laboratory of Immunogenetics and Tissue Immunology, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wrocław, Poland
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