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Di Mauro F, Arbore G. Spatial Dissection of the Immune Landscape of Solid Tumors to Advance Precision Medicine. Cancer Immunol Res 2024; 12:800-813. [PMID: 38657223 DOI: 10.1158/2326-6066.cir-23-0699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 01/12/2024] [Accepted: 04/19/2024] [Indexed: 04/26/2024]
Abstract
Chemotherapeutics, radiation, targeted therapeutics, and immunotherapeutics each demonstrate clinical benefits for a small subset of patients with solid malignancies. Immune cells infiltrating the tumor and the surrounding stroma play a critical role in shaping cancer progression and modulating therapy response. They do this by interacting with the other cellular and molecular components of the tumor microenvironment. Spatial multi-omics technologies are rapidly evolving. Currently, such technologies allow high-throughput RNA and protein profiling and retain geographical information about the tumor microenvironment cellular architecture and the functional phenotype of tumor, immune, and stromal cells. An in-depth spatial characterization of the heterogeneous tumor immune landscape can improve not only the prognosis but also the prediction of therapy response, directing cancer patients to more tailored and efficacious treatments. This review highlights recent advancements in spatial transcriptomics and proteomics profiling technologies and the ways these technologies are being applied for the dissection of the immune cell composition in solid malignancies in order to further both basic research in oncology and the implementation of precision treatments in the clinic.
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Affiliation(s)
- Francesco Di Mauro
- Vita-Salute San Raffaele University, Milan, Italy
- Experimental Immunology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Giuseppina Arbore
- Vita-Salute San Raffaele University, Milan, Italy
- Experimental Immunology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
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2
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Xie H, Song C, Jian L, Guo Y, Li M, Luo J, Li Q, Tan T. A deep learning-based radiomics model for predicting lymph node status from lung adenocarcinoma. BMC Med Imaging 2024; 24:121. [PMID: 38789936 PMCID: PMC11127329 DOI: 10.1186/s12880-024-01300-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
OBJECTIVES At present, there are many limitations in the evaluation of lymph node metastasis of lung adenocarcinoma. Currently, there is a demand for a safe and accurate method to predict lymph node metastasis of lung cancer. In this study, radiomics was used to accurately predict the lymph node status of lung adenocarcinoma patients based on contrast-enhanced CT. METHODS A total of 503 cases that fulfilled the analysis requirements were gathered from two distinct hospitals. Among these, 287 patients exhibited lymph node metastasis (LNM +) while 216 patients were confirmed to be without lymph node metastasis (LNM-). Using both traditional and deep learning methods, 22,318 features were extracted from the segmented images of each patient's enhanced CT. Then, the spearman test and the least absolute shrinkage and selection operator were used to effectively reduce the dimension of the feature data, enabling us to focus on the most pertinent features and enhance the overall analysis. Finally, the classification model of lung adenocarcinoma lymph node metastasis was constructed by machine learning algorithm. The Accuracy, AUC, Specificity, Precision, Recall and F1 were used to evaluate the efficiency of the model. RESULTS By incorporating a comprehensively selected set of features, the extreme gradient boosting method (XGBoost) effectively distinguished the status of lymph nodes in patients with lung adenocarcinoma. The Accuracy, AUC, Specificity, Precision, Recall and F1 of the prediction model performance on the external test set were 0.765, 0.845, 0.705, 0.784, 0.811 and 0.797, respectively. Moreover, the decision curve analysis, calibration curve and confusion matrix of the model on the external test set all indicated the stability and accuracy of the model. CONCLUSIONS Leveraging enhanced CT images, our study introduces a noninvasive classification prediction model based on the extreme gradient boosting method. This approach exhibits remarkable precision in identifying the lymph node status of lung adenocarcinoma patients, offering a safe and accurate alternative to invasive procedures. By providing clinicians with a reliable tool for diagnosing and assessing disease progression, our method holds the potential to significantly improve patient outcomes and enhance the overall quality of clinical practice.
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Affiliation(s)
- Hui Xie
- Department of Radiation Oncology, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, People's Republic of China
| | - Chaoling Song
- School of Medical Imaging, Laboratory Science and Rehabilitation, Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Lei Jian
- School of Medical Imaging, Laboratory Science and Rehabilitation, Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Yeang Guo
- School of Medical Imaging, Laboratory Science and Rehabilitation, Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Mei Li
- School of Medical Imaging, Laboratory Science and Rehabilitation, Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Jiang Luo
- School of Medical Imaging, Laboratory Science and Rehabilitation, Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Qing Li
- Department of Radiation Oncology, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Tao Tan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, People's Republic of China.
- Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Nijmegen, Netherlands.
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3
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Zhu B, Bai Y, Yeo YY, Lu X, Rovira-Clavé X, Chen H, Yeung J, Gerber GK, Angelo M, Shalek AK, Nolan GP, Jiang S. A Spatial Multi-Modal Dissection of Host-Microbiome Interactions within the Colitis Tissue Microenvironment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.04.583400. [PMID: 38496402 PMCID: PMC10942342 DOI: 10.1101/2024.03.04.583400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The intricate and dynamic interactions between the host immune system and its microbiome constituents undergo dynamic shifts in response to perturbations to the intestinal tissue environment. Our ability to study these events on the systems level is significantly limited by in situ approaches capable of generating simultaneous insights from both host and microbial communities. Here, we introduce Microbiome Cartography (MicroCart), a framework for simultaneous in situ probing of host features and its microbiome across multiple spatial modalities. We demonstrate MicroCart by comprehensively investigating the alterations in both gut host and microbiome components in a murine model of colitis by coupling MicroCart with spatial proteomics, transcriptomics, and glycomics platforms. Our findings reveal a global but systematic transformation in tissue immune responses, encompassing tissue-level remodeling in response to host immune and epithelial cell state perturbations, and bacterial population shifts, localized inflammatory responses, and metabolic process alterations during colitis. MicroCart enables a deep investigation of the intricate interplay between the host tissue and its microbiome with spatial multiomics.
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Affiliation(s)
- Bokai Zhu
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
| | - Yunhao Bai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Xiaowei Lu
- Mass Spectrometry Core Facility, Stanford University, Stanford, CA, United States
| | - Xavier Rovira-Clavé
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
- Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Han Chen
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
- Biological and Medical Informatics program, UCSF, San Francisco, CA, United States
| | - Jason Yeung
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Georg K Gerber
- Division of Computational Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Health Sciences and Technology, Harvard University and MIT, Cambridge, MA, USA
| | - Mike Angelo
- Department of Pathology, Stanford University, Stanford, CA, United States
| | - Alex K Shalek
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA, United States
| | - Sizun Jiang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Department of Microbiology, Harvard Medical School, Boston, MA, USA
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4
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Shaban M, Bai Y, Qiu H, Mao S, Yeung J, Yeo YY, Shanmugam V, Chen H, Zhu B, Weirather JL, Nolan GP, Shipp MA, Rodig SJ, Jiang S, Mahmood F. MAPS: pathologist-level cell type annotation from tissue images through machine learning. Nat Commun 2024; 15:28. [PMID: 38167832 PMCID: PMC10761896 DOI: 10.1038/s41467-023-44188-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024] Open
Abstract
Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for typically challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.
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Affiliation(s)
- Muhammad Shaban
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Yunhao Bai
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Huaying Qiu
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Shulin Mao
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Jason Yeung
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Vignesh Shanmugam
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Han Chen
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Bokai Zhu
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jason L Weirather
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Center for Immuno-oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Margaret A Shipp
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Sizun Jiang
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA, USA.
| | - Faisal Mahmood
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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5
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Gu J, Iyer A, Wesley B, Taglialatela A, Leuzzi G, Hangai S, Decker A, Gu R, Klickstein N, Shuai Y, Jankovic K, Parker-Burns L, Jin Y, Zhang JY, Hong J, Niu S, Chou J, Landau DA, Azizi E, Chan EM, Ciccia A, Gaublomme JT. CRISPRmap: Sequencing-free optical pooled screens mapping multi-omic phenotypes in cells and tissue. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.26.572587. [PMID: 38234835 PMCID: PMC10793456 DOI: 10.1101/2023.12.26.572587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Pooled genetic screens are powerful tools to study gene function in a high-throughput manner. Typically, sequencing-based screens require cell lysis, which limits the examination of critical phenotypes such as cell morphology, protein subcellular localization, and cell-cell/tissue interactions. In contrast, emerging optical pooled screening methods enable the investigation of these spatial phenotypes in response to targeted CRISPR perturbations. In this study, we report a multi-omic optical pooled CRISPR screening method, which we have named CRISPRmap. Our method combines a novel in situ CRISPR guide identifying barcode readout approach with concurrent multiplexed immunofluorescence and in situ RNA detection. CRISPRmap barcodes are detected and read out through combinatorial hybridization of DNA oligos, enhancing barcode detection efficiency, while reducing both dependency on third party proprietary sequencing reagents and assay cost. Notably, we conducted a multi-omic base-editing screen in a breast cancer cell line on core DNA damage repair genes involved in the homologous recombination and Fanconi anemia pathways investigating how nucleotide variants in those genes influence DNA damage signaling and cell cycle regulation following treatment with ionizing radiation or DNA damaging agents commonly used for cancer therapy. Approximately a million cells were profiled with our multi-omic approach, providing a comprehensive phenotypic assessment of the functional consequences of the studied variants. CRISPRmap enabled us to pinpoint likely-pathogenic patient-derived mutations that were previously classified as variants of unknown clinical significance. Furthermore, our approach effectively distinguished barcodes of a pooled library in tumor tissue, and we coupled it with cell-type and molecular phenotyping by cyclic immunofluorescence. Multi-omic spatial analysis of how CRISPR-perturbed cells respond to various environmental cues in the tissue context offers the potential to significantly expand our understanding of tissue biology in both health and disease.
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Affiliation(s)
- Jiacheng Gu
- Department of Biological Sciences, Columbia University, NY, USA
| | - Abhishek Iyer
- Department of Biological Sciences, Columbia University, NY, USA
| | - Ben Wesley
- Department of Biological Sciences, Columbia University, NY, USA
| | - Angelo Taglialatela
- Department of Genetics and Development, Columbia University Irving Medical Center, NY, USA
| | - Giuseppe Leuzzi
- Department of Genetics and Development, Columbia University Irving Medical Center, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, NY, USA
- Institute for Cancer Genetics, Columbia University Irving Medical Center, NY, USA
| | - Sho Hangai
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, NY, USA
| | | | - Ruoyu Gu
- Department of Biological Sciences, Columbia University, NY, USA
| | | | - Yuanlong Shuai
- Department of Biological Sciences, Columbia University, NY, USA
| | - Kristina Jankovic
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, NY, USA
| | - Lucy Parker-Burns
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, NY, USA
| | - Yinuo Jin
- Department of Biomedical Engineering, Columbia University, NY, USA
| | - Jia Yi Zhang
- Department of Biomedical Engineering, Columbia University, NY, USA
| | - Justin Hong
- Department of Computer Science, Columbia University, NY, USA
| | - Steve Niu
- Weill Cornell Medicine, NY, USA
- Genentech Research and Early Development, CA, USA
| | - Jacqueline Chou
- Department of Biological Sciences, Columbia University, NY, USA
- Weill Cornell Medicine, NY, USA
| | - Dan A. Landau
- Weill Cornell Medicine, NY, USA
- New York Genome Center, NY, USA
| | - Elham Azizi
- Department of Biomedical Engineering, Columbia University, NY, USA
- Department of Computer Science, Columbia University, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, NY, USA
| | - Edmond M. Chan
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, NY, USA
- New York Genome Center, NY, USA
- Department of Medicine, Division of Hematology/Oncology, Columbia University Irving Medical Center, NY, USA
| | - Alberto Ciccia
- Department of Genetics and Development, Columbia University Irving Medical Center, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, NY, USA
- Institute for Cancer Genetics, Columbia University Irving Medical Center, NY, USA
| | - Jellert T. Gaublomme
- Department of Biological Sciences, Columbia University, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, NY, USA
- New York Genome Center, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, NY, USA
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6
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Goyette MA, Lipsyc-Sharf M, Polyak K. Clinical and translational relevance of intratumor heterogeneity. Trends Cancer 2023; 9:726-737. [PMID: 37248149 PMCID: PMC10524913 DOI: 10.1016/j.trecan.2023.05.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/03/2023] [Accepted: 05/05/2023] [Indexed: 05/31/2023]
Abstract
Intratumor heterogeneity (ITH) is a driver of tumor evolution and a main cause of therapeutic resistance. Despite its importance, measures of ITH are still not incorporated into clinical practice. Consequently, standard treatment is frequently ineffective for patients with heterogeneous tumors as changes to treatment regimens are made only after recurrence and disease progression. More effective combination therapies require a mechanistic understanding of ITH and ways to assess it in clinical samples. The growth of technologies enabling the spatially intact analysis of tumors at the single-cell level and the development of sophisticated preclinical models give us hope that ITH will not simply be used as a predictor of a poor outcome but will guide treatment decisions from diagnosis through treatment.
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Affiliation(s)
- Marie-Anne Goyette
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Marla Lipsyc-Sharf
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kornelia Polyak
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
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7
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Hammond T, Sage J. Monitoring the Cell Cycle of Tumor Cells in Mouse Models of Human Cancer. Cold Spring Harb Perspect Med 2023; 13:a041383. [PMID: 37460156 PMCID: PMC10691483 DOI: 10.1101/cshperspect.a041383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
Cell division is obligatory to tumor growth. However, both cancer cells and noncancer cells in tumors can be found in distinct stages of the cell cycle, which may inform the growth potential of these tumors, their propensity to metastasize, and their response to therapy. Hence, it is of utmost importance to monitor the cell cycle of tumor cells. Here we discuss well-established methods and new genetic advances to track the cell cycle of tumor cells in mouse models of human cancer. We also review recent genetic studies investigating the role of the cell-cycle machinery in the growth of tumors in vivo, with a focus on the machinery regulating the G1/S transition of the cell cycle.
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Affiliation(s)
- Taylar Hammond
- Department of Pediatrics, Stanford University, Stanford, California 94305, USA
- Department of Biology, and Stanford University, Stanford, California 94305, USA
| | - Julien Sage
- Department of Pediatrics, Stanford University, Stanford, California 94305, USA
- Department of Genetics, Stanford University, Stanford, California 94305, USA
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8
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Bai Y, Zhu B, Oliveria JP, Cannon BJ, Feyaerts D, Bosse M, Vijayaragavan K, Greenwald NF, Phillips D, Schürch CM, Naik SM, Ganio EA, Gaudilliere B, Rodig SJ, Miller MB, Angelo M, Bendall SC, Rovira-Clavé X, Nolan GP, Jiang S. Expanded vacuum-stable gels for multiplexed high-resolution spatial histopathology. Nat Commun 2023; 14:4013. [PMID: 37419873 PMCID: PMC10329015 DOI: 10.1038/s41467-023-39616-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/16/2023] [Indexed: 07/09/2023] Open
Abstract
Cellular organization and functions encompass multiple scales in vivo. Emerging high-plex imaging technologies are limited in resolving subcellular biomolecular features. Expansion Microscopy (ExM) and related techniques physically expand samples for enhanced spatial resolution, but are challenging to be combined with high-plex imaging technologies to enable integrative multiscaled tissue biology insights. Here, we introduce Expand and comPRESS hydrOgels (ExPRESSO), an ExM framework that allows high-plex protein staining, physical expansion, and removal of water, while retaining the lateral tissue expansion. We demonstrate ExPRESSO imaging of archival clinical tissue samples on Multiplexed Ion Beam Imaging and Imaging Mass Cytometry platforms, with detection capabilities of > 40 markers. Application of ExPRESSO on archival human lymphoid and brain tissues resolved tissue architecture at the subcellular level, particularly that of the blood-brain barrier. ExPRESSO hence provides a platform for extending the analysis compatibility of hydrogel-expanded biospecimens to mass spectrometry, with minimal modifications to protocols and instrumentation.
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Affiliation(s)
- Yunhao Bai
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Chemistry, Stanford University, Stanford, CA, USA
| | - Bokai Zhu
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
| | - John-Paul Oliveria
- Department of Translational Medicine, Genentech, Inc., South San Francisco, CA, USA
- Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Bryan J Cannon
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Dorien Feyaerts
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA
| | - Marc Bosse
- Department of Pathology, Stanford University, Stanford, CA, USA
| | | | | | - Darci Phillips
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Christian M Schürch
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Samuel M Naik
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Edward A Ganio
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael B Miller
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics and Genomics, Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Michael Angelo
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Sean C Bendall
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Xavier Rovira-Clavé
- Department of Pathology, Stanford University, Stanford, CA, USA.
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA.
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA, USA.
| | - Sizun Jiang
- Department of Pathology, Stanford University, Stanford, CA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA, USA.
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA, USA.
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9
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Shaban M, Bai Y, Qiu H, Mao S, Yeung J, Yeo YY, Shanmugam V, Chen H, Zhu B, Nolan GP, Shipp MA, Rodig SJ, Jiang S, Mahmood F. MAPS: Pathologist-level cell type annotation from tissue images through machine learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.25.546474. [PMID: 37425872 PMCID: PMC10327211 DOI: 10.1101/2023.06.25.546474] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.
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Affiliation(s)
- Muhammad Shaban
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Data Science Program, Dana-Farber Cancer Institute, Boston, MA, United States
- Broad Institute of Harvard and MIT, Cambridge, MA, United States
| | - Yunhao Bai
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Huaying Qiu
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Shulin Mao
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Jason Yeung
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Vignesh Shanmugam
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- Broad Institute of Harvard and MIT, Cambridge, MA, United States
| | - Han Chen
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Bokai Zhu
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Margaret A Shipp
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - Sizun Jiang
- Broad Institute of Harvard and MIT, Cambridge, MA, United States
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA, United States
| | - Faisal Mahmood
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Data Science Program, Dana-Farber Cancer Institute, Boston, MA, United States
- Broad Institute of Harvard and MIT, Cambridge, MA, United States
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10
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Andhari MD, Antoranz A, De Smet F, Bosisio FM. Recent advancements in tumour microenvironment landscaping for target selection and response prediction in immune checkpoint therapies achieved through spatial protein multiplexing analysis. INTERNATIONAL REVIEW OF CELL AND MOLECULAR BIOLOGY 2023; 382:207-237. [PMID: 38225104 DOI: 10.1016/bs.ircmb.2023.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
Immune checkpoint therapies have significantly advanced cancer treatment. Nevertheless, the high costs and potential adverse effects associated with these therapies highlight the need for better predictive biomarkers to identify patients who are most likely to benefit from treatment. Unfortunately, the existing biomarkers are insufficient to identify such patients. New high-dimensional spatial technologies have emerged as a valuable tool for discovering novel biomarkers by analysing multiple protein markers at a single-cell resolution in tissue samples. These technologies provide a more comprehensive map of tissue composition, cell functionality, and interactions between different cell types in the tumour microenvironment. In this review, we provide an overview of how spatial protein-based multiplexing technologies have fuelled biomarker discovery and advanced the field of immunotherapy. In particular, we will focus on how these technologies contributed to (i) characterise the tumour microenvironment, (ii) understand the role of tumour heterogeneity, (iii) study the interplay of the immune microenvironment and tumour progression, (iv) discover biomarkers for immune checkpoint therapies (v) suggest novel therapeutic strategies.
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Affiliation(s)
- Madhavi Dipak Andhari
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium; The Laboratory for Precision Cancer Medicine, Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Asier Antoranz
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium; The Laboratory for Precision Cancer Medicine, Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Frederik De Smet
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium; The Laboratory for Precision Cancer Medicine, Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Francesca Maria Bosisio
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium.
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11
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Hebert JD, Neal JW, Winslow MM. Dissecting metastasis using preclinical models and methods. Nat Rev Cancer 2023; 23:391-407. [PMID: 37138029 DOI: 10.1038/s41568-023-00568-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/27/2023] [Indexed: 05/05/2023]
Abstract
Metastasis has long been understood to lead to the overwhelming majority of cancer-related deaths. However, our understanding of the metastatic process, and thus our ability to prevent or eliminate metastases, remains frustratingly limited. This is largely due to the complexity of metastasis, which is a multistep process that likely differs across cancer types and is greatly influenced by many aspects of the in vivo microenvironment. In this Review, we discuss the key variables to consider when designing assays to study metastasis: which source of metastatic cancer cells to use and where to introduce them into mice to address different questions of metastasis biology. We also examine methods that are being used to interrogate specific steps of the metastatic cascade in mouse models, as well as emerging techniques that may shed new light on previously inscrutable aspects of metastasis. Finally, we explore approaches for developing and using anti-metastatic therapies, and how mouse models can be used to test them.
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Affiliation(s)
- Jess D Hebert
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Joel W Neal
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Monte M Winslow
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
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12
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Drainas AP. Visualizing intratumoural heterogeneity with EpicMIBI. Nat Rev Cancer 2023; 23:347. [PMID: 37012414 DOI: 10.1038/s41568-023-00569-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
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13
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Lee MC, Cai H, Murray CW, Li C, Shue YT, Andrejka L, He AL, Holzem AME, Drainas AP, Ko JH, Coles GL, Kong C, Zhu S, Zhu C, Wang J, van de Rijn M, Petrov DA, Winslow MM, Sage J. A multiplexed in vivo approach to identify driver genes in small cell lung cancer. Cell Rep 2023; 42:111990. [PMID: 36640300 PMCID: PMC9972901 DOI: 10.1016/j.celrep.2023.111990] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 10/24/2022] [Accepted: 01/04/2023] [Indexed: 01/15/2023] Open
Abstract
Small cell lung cancer (SCLC) is a lethal form of lung cancer. Here, we develop a quantitative multiplexed approach on the basis of lentiviral barcoding with somatic CRISPR-Cas9-mediated genome editing to functionally investigate candidate regulators of tumor initiation and growth in genetically engineered mouse models of SCLC. We found that naphthalene pre-treatment enhances lentiviral vector-mediated SCLC initiation, enabling high multiplicity of tumor clones for analysis through high-throughput sequencing methods. Candidate drivers of SCLC identified from a meta-analysis across multiple human SCLC genomic datasets were tested using this approach, which defines both positive and detrimental impacts of inactivating 40 genes across candidate pathways on SCLC development. This analysis and subsequent validation in human SCLC cells establish TSC1 in the PI3K-AKT-mTOR pathway as a robust tumor suppressor in SCLC. This approach should illuminate drivers of SCLC, facilitate the development of precision therapies for defined SCLC genotypes, and identify therapeutic targets.
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Affiliation(s)
- Myung Chang Lee
- Department of Pediatrics, Stanford University, 265 Campus Drive, SIM1 G2078, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Hongchen Cai
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | | | - Chuan Li
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Yan Ting Shue
- Department of Pediatrics, Stanford University, 265 Campus Drive, SIM1 G2078, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Laura Andrejka
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Andy L He
- Department of Pediatrics, Stanford University, 265 Campus Drive, SIM1 G2078, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Alessandra M E Holzem
- Department of Pediatrics, Stanford University, 265 Campus Drive, SIM1 G2078, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Alexandros P Drainas
- Department of Pediatrics, Stanford University, 265 Campus Drive, SIM1 G2078, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Julie H Ko
- Department of Pediatrics, Stanford University, 265 Campus Drive, SIM1 G2078, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Garry L Coles
- Department of Pediatrics, Stanford University, 265 Campus Drive, SIM1 G2078, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Christina Kong
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Shirley Zhu
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - ChunFang Zhu
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Jason Wang
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Matt van de Rijn
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Dmitri A Petrov
- Department of Biology, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Monte M Winslow
- Department of Genetics, Stanford University, Stanford, CA 94305, USA; Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Julien Sage
- Department of Pediatrics, Stanford University, 265 Campus Drive, SIM1 G2078, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA.
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14
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Proietto M, Crippa M, Damiani C, Pasquale V, Sacco E, Vanoni M, Gilardi M. Tumor heterogeneity: preclinical models, emerging technologies, and future applications. Front Oncol 2023; 13:1164535. [PMID: 37188201 PMCID: PMC10175698 DOI: 10.3389/fonc.2023.1164535] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 04/11/2023] [Indexed: 05/17/2023] Open
Abstract
Heterogeneity describes the differences among cancer cells within and between tumors. It refers to cancer cells describing variations in morphology, transcriptional profiles, metabolism, and metastatic potential. More recently, the field has included the characterization of the tumor immune microenvironment and the depiction of the dynamics underlying the cellular interactions promoting the tumor ecosystem evolution. Heterogeneity has been found in most tumors representing one of the most challenging behaviors in cancer ecosystems. As one of the critical factors impairing the long-term efficacy of solid tumor therapy, heterogeneity leads to tumor resistance, more aggressive metastasizing, and recurrence. We review the role of the main models and the emerging single-cell and spatial genomic technologies in our understanding of tumor heterogeneity, its contribution to lethal cancer outcomes, and the physiological challenges to consider in designing cancer therapies. We highlight how tumor cells dynamically evolve because of the interactions within the tumor immune microenvironment and how to leverage this to unleash immune recognition through immunotherapy. A multidisciplinary approach grounded in novel bioinformatic and computational tools will allow reaching the integrated, multilayered knowledge of tumor heterogeneity required to implement personalized, more efficient therapies urgently required for cancer patients.
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Affiliation(s)
- Marco Proietto
- Next Generation Sequencing Core, The Salk Institute for Biological Studies, La Jolla, CA, United States
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, United States
- NOMIS Center for Immunobiology and Microbial Pathogenesis, The Salk Institute for Biological Studies, La Jolla, CA, United States
| | - Martina Crippa
- Vita-Salute San Raffaele University, Milan, Italy
- Experimental Imaging Center, Istituti di Ricovero e Cura a Carattere Scientifico (IRCCS) Ospedale San Raffaele, Milan, Italy
| | - Chiara Damiani
- Infrastructure Systems Biology Europe /Centre of Systems Biology (ISBE/SYSBIO) Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, School of Sciences, University of Milano-Bicocca, Milan, Italy
| | - Valentina Pasquale
- Infrastructure Systems Biology Europe /Centre of Systems Biology (ISBE/SYSBIO) Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, School of Sciences, University of Milano-Bicocca, Milan, Italy
| | - Elena Sacco
- Infrastructure Systems Biology Europe /Centre of Systems Biology (ISBE/SYSBIO) Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, School of Sciences, University of Milano-Bicocca, Milan, Italy
| | - Marco Vanoni
- Infrastructure Systems Biology Europe /Centre of Systems Biology (ISBE/SYSBIO) Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, School of Sciences, University of Milano-Bicocca, Milan, Italy
- *Correspondence: Marco Vanoni, ; Mara Gilardi,
| | - Mara Gilardi
- NOMIS Center for Immunobiology and Microbial Pathogenesis, The Salk Institute for Biological Studies, La Jolla, CA, United States
- Salk Cancer Center, The Salk Institute for Biological Studies, La Jolla, CA, United States
- *Correspondence: Marco Vanoni, ; Mara Gilardi,
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