1
|
Zhu E, Xie Q, Huang X, Zhang Z. Application of spatial omics in gastric cancer. Pathol Res Pract 2024; 262:155503. [PMID: 39128411 DOI: 10.1016/j.prp.2024.155503] [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: 04/27/2024] [Revised: 07/25/2024] [Accepted: 07/27/2024] [Indexed: 08/13/2024]
Abstract
Gastric cancer (GC), a globally prevalent and lethal malignancy, continues to be a key research focus. However, due to its considerable heterogeneity and complex pathogenesis, the treatment and diagnosis of gastric cancer still face significant challenges. With the rapid development of spatial omics technology, which provides insights into the spatial information within tumor tissues, it has emerged as a significant tool in gastric cancer research. This technology affords new insights into the pathology and molecular biology of gastric cancer for scientists. This review discusses recent advances in spatial omics technology for gastric cancer research, highlighting its applications in the tumor microenvironment (TME), tumor heterogeneity, tumor genesis and development mechanisms, and the identification of potential biomarkers and therapeutic targets. Moreover, this article highlights spatial omics' potential in precision medicine and summarizes existing challenges and future directions. It anticipates spatial omics' continuing impact on gastric cancer research, aiming to improve diagnostic and therapeutic approaches for patients. With this review, we aim to offer a comprehensive overview to scientists and clinicians in gastric cancer research, motivating further exploration and utilization of spatial omics technology. Our goal is to improve patient outcomes, including survival rates and quality of life.
Collapse
Affiliation(s)
- Erran Zhu
- Department of Clinical Medicine, Grade 20, Hengyang Medical College, University of South China, Hengyang, Hunan, 421001, China
| | - Qi Xie
- Department of Clinical Medicine, Grade 20, Hengyang Medical College, University of South China, Hengyang, Hunan, 421001, China
| | - Xinqi Huang
- Excellent Class, Clinical Medicine, Grade 20, Hengyang Medical College, University of South China, Hengyang, Hunan, 421001, China
| | - Zhiwei Zhang
- Cancer Research Institute of Hengyang Medical College, University of South China; Key Laboratory of Cancer Cellular and Molecular Pathology of Hunan; Department of Pathology, Department of Pathology of Hengyang Medical College, University of South China; The First Affiliated Hospital of University of South China, China.
| |
Collapse
|
2
|
Prasad R, Peng B, Mendes BB, Kilian HI, Gorain M, Zhang H, Kundu GC, Xia J, Lovell JF, Conde J. Biomimetic bright optotheranostics for metastasis monitoring and multimodal image-guided breast cancer therapeutics. J Control Release 2024; 367:300-315. [PMID: 38281670 DOI: 10.1016/j.jconrel.2024.01.056] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/15/2024] [Accepted: 01/25/2024] [Indexed: 01/30/2024]
Abstract
Nanoparticle formulations blending optical imaging contrast agents and therapeutics have been a cornerstone of preclinical theranostic applications. However, nanoparticle-based theranostics clinical translation faces challenges on reproducibility, brightness, photostability, biocompatibility, and selective tumor targeting and penetration. In this study, we integrate multimodal imaging and therapeutics within cancer cell-derived nanovesicles, leading to biomimetic bright optotheranostics for monitoring cancer metastasis. Upon NIR light irradiation, the engineered optotheranostics enables deep visualization and precise localization of metastatic lung, liver, and solid breast tumors along with solid tumor ablation. Metastatic cell-derived nanovesicles (∼80 ± 5 nm) are engineered to encapsulate imaging (emissive organic dye and gold nanoparticles) and therapeutic agents (anticancer drug doxorubicin and photothermally active organic indocyanine green dye). Systemic administration of biomimetic bright optotheranostic nanoparticles shows escape from mononuclear phagocytic clearance with (i) rapid tumor accumulation (3 h) and retention (up to 168 h), (ii) real-time monitoring of metastatic lung, liver, and solid breast tumors and (iii) 3-fold image-guided solid tumor reduction. These findings are supported by an improvement of X-ray, fluorescence, and photoacoustic signals while demonstrating a tumor reduction (201 mm3) in comparison with single therapies that includes chemotherapy (134 mm3), photodynamic therapy (72 mm3), and photothermal therapy (88mm3). The proposed innovative platform opens new avenues to improve cancer diagnosis and treatment outcomes by allowing the monitorization of cancer metastasis, allowing the precise cancer imaging, and delivering synergistic therapeutic agents at the solid tumor site.
Collapse
Affiliation(s)
- Rajendra Prasad
- School of Biochemical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India; Department of Mechanical Engineering, Tufts University, Medford, MA 02155, USA.
| | - Berney Peng
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, United States
| | - Bárbara B Mendes
- ToxOmics, NOVA Medical School, Faculdade de Ciências Médicas, NMS|FCM, Universidade NOVA de Lisboa, Lisboa, Portugal
| | - Hailey I Kilian
- Department of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo 14260, NY, USA
| | - Mahadeo Gorain
- Laboratory of Tumor Biology, Angiogenesis and Nanomedicine Research, National Center for Cell Science, Pune 411007, India
| | - Huijuan Zhang
- Department of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo 14260, NY, USA
| | - Gopal Chandra Kundu
- Laboratory of Tumor Biology, Angiogenesis and Nanomedicine Research, National Center for Cell Science, Pune 411007, India; School of Biotechnology and Kalinga Institute of Medical Sciences (KIMS), KIIT Deemed to be University, Bhubaneswar 751024, India
| | - Jun Xia
- Department of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo 14260, NY, USA
| | - Jonathan F Lovell
- Department of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo 14260, NY, USA
| | - João Conde
- ToxOmics, NOVA Medical School, Faculdade de Ciências Médicas, NMS|FCM, Universidade NOVA de Lisboa, Lisboa, Portugal.
| |
Collapse
|
3
|
van Driel M, Muñoz A, van Leeuwen JP. Overview of vitamin D actions in cancer. FELDMAN AND PIKE'S VITAMIN D 2024:679-718. [DOI: 10.1016/b978-0-323-91338-6.00034-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
|
4
|
Sajid RS, van Winden LJ, Diamandis P. Towards deciphering glioblastoma intra-tumoral heterogeneity: The importance of integrating multidimensional models. Proteomics 2023; 23:e2200401. [PMID: 37488996 DOI: 10.1002/pmic.202200401] [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: 01/31/2023] [Revised: 07/12/2023] [Accepted: 07/13/2023] [Indexed: 07/26/2023]
Abstract
Glioblastoma (GBM) is the most common and severe form of brain cancer among adults. Its aggressiveness is largely attributed to its complex and heterogeneous biology that despite maximal surgery and multimodal chemoradiation treatment, inevitably recurs. Traditional large-scale profiling approaches have contributed substantially to the understanding of patient-to-patient inter-tumoral differences in GBM. However, it is now clear that biological differences within an individual (intra-tumoral heterogeneity) are also a prominent factor in treatment resistance and recurrence of GBM and will likely require integration of data from multiple recently developed omics platforms to fully unravel. Here we dissect the growing geospatial model of GBM, which layers intra-tumoral heterogeneity on a GBM stem cell (GSC) precursor, single cell, and spatial level. We discuss potential unique and inter-dependant aspects of the model including potential discordances between observed genotypes and phenotypes in GBM.
Collapse
Affiliation(s)
- Rifat Shahriar Sajid
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - Lennart J van Winden
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - Phedias Diamandis
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Laboratory Medicine Program, University Health Network, Toronto, Canada
- Princess Margaret Cancer Center, University Health Network, Toronto, Canada
| |
Collapse
|
5
|
Dent A, Faust K, Lam K, Alhangari N, Leon AJ, Tsang Q, Kamil ZS, Gao A, Pal P, Lheureux S, Oza A, Diamandis P. HAVOC: Small-scale histomic mapping of cancer biodiversity across large tissue distances using deep neural networks. SCIENCE ADVANCES 2023; 9:eadg1894. [PMID: 37774029 PMCID: PMC10541015 DOI: 10.1126/sciadv.adg1894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 08/28/2023] [Indexed: 10/01/2023]
Abstract
Intratumoral heterogeneity can wreak havoc on current precision medicine strategies because of challenges in sufficient sampling of geographically separated areas of biodiversity distributed across centimeter-scale tumor distances. To address this gap, we developed a deep learning pipeline that leverages histomorphologic fingerprints of tissue to create "Histomic Atlases of Variation Of Cancers" (HAVOC). Using a number of objective molecular readouts, we demonstrate that HAVOC can define regional cancer boundaries with distinct biology. Using larger tumor specimens, we show that HAVOC can map biodiversity even across multiple tissue sections. By guiding profiling of 19 partitions across six high-grade gliomas, HAVOC revealed that distinct differentiation states can often coexist and be regionally distributed within these tumors. Last, to highlight generalizability, we benchmark HAVOC on additional tumor types. Together, we establish HAVOC as a versatile tool to generate small-scale maps of tissue heterogeneity and guide regional deployment of molecular resources to relevant biodiverse niches.
Collapse
Affiliation(s)
- Anglin Dent
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Kevin Faust
- Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON M5S 2E4, Canada
- Princess Margaret Cancer Centre, 101 College Street, Toronto, ON M5G 1L7, Canada
| | - K. H. Brian Lam
- Princess Margaret Cancer Centre, 101 College Street, Toronto, ON M5G 1L7, Canada
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Narges Alhangari
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Alberto J. Leon
- Princess Margaret Cancer Centre, 101 College Street, Toronto, ON M5G 1L7, Canada
| | - Queenie Tsang
- Princess Margaret Cancer Centre, 101 College Street, Toronto, ON M5G 1L7, Canada
| | - Zaid Saeed Kamil
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
- Laboratory Medicine Program, Department of Pathology, University Health Network, 200 Elizabeth Street, Toronto, ON M5G 2C4, Canada
| | - Andrew Gao
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
- Laboratory Medicine Program, Department of Pathology, University Health Network, 200 Elizabeth Street, Toronto, ON M5G 2C4, Canada
| | - Prodipto Pal
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
- Laboratory Medicine Program, Department of Pathology, University Health Network, 200 Elizabeth Street, Toronto, ON M5G 2C4, Canada
| | - Stephanie Lheureux
- Princess Margaret Cancer Centre, 101 College Street, Toronto, ON M5G 1L7, Canada
| | - Amit Oza
- Princess Margaret Cancer Centre, 101 College Street, Toronto, ON M5G 1L7, Canada
| | - Phedias Diamandis
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
- Princess Margaret Cancer Centre, 101 College Street, Toronto, ON M5G 1L7, Canada
- Laboratory Medicine Program, Department of Pathology, University Health Network, 200 Elizabeth Street, Toronto, ON M5G 2C4, Canada
- Department of Medical Biophysics, University of Toronto, 101 College St, Toronto, ON M5G 1L7, Canada
| |
Collapse
|
6
|
Paliwal A, Faust K, Alshoumer A, Diamandis P. Standardizing analysis of intra-tumoral heterogeneity with computational pathology. Genes Chromosomes Cancer 2023; 62:526-539. [PMID: 37067005 DOI: 10.1002/gcc.23146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 03/28/2023] [Accepted: 04/04/2023] [Indexed: 04/18/2023] Open
Abstract
Many malignant cancers like glioblastoma are highly adaptive diseases that dynamically change their regional biology to survive and thrive under diverse microenvironmental and therapeutic pressures. While the concept of intra-tumoral heterogeneity has become a major paradigm in cancer research and care, systematic approaches to assess and document bio-variation in cancer are still in their infancy. Here we discuss existing approaches and challenges to documenting intra-tumoral heterogeneity and emerging computational approaches that leverage artificial intelligence to begin to overcome these limitations. We propose how these emerging techniques can be coupled with a diversity of molecular tools to address intra-tumoral heterogeneity more systematically in research and in practice, especially across larger specimens and longitudinal analyses. Systematic documentation and characterization of heterogeneity across entire tumor specimens and their longitudinal evolution has the potential to improve our understanding and treatment of cancer.
Collapse
Affiliation(s)
- Ameesha Paliwal
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Kevin Faust
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Azhar Alshoumer
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Laboratory Medicine Program, Department of Pathology, University Health Network, Toronto, Ontario, Canada
| | - Phedias Diamandis
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- Laboratory Medicine Program, Department of Pathology, University Health Network, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| |
Collapse
|
7
|
Long F, Ma H, Hao Y, Tian L, Li Y, Li B, Chen J, Tang Y, Li J, Deng L, Xie G, Liu M. A novel exosome-derived prognostic signature and risk stratification for breast cancer based on multi-omics and systematic biological heterogeneity. Comput Struct Biotechnol J 2023; 21:3010-3023. [PMID: 37273850 PMCID: PMC10232662 DOI: 10.1016/j.csbj.2023.05.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 04/29/2023] [Accepted: 05/11/2023] [Indexed: 06/06/2023] Open
Abstract
Tumor heterogeneity remains a major challenge for disease subtyping, risk stratification, and accurate clinical management. Exosome-based liquid biopsy can effectively overcome the limitations of tissue biopsy, achieving minimal invasion, multi-point dynamic monitoring, and good prognosis assessment, and has broad clinical prospects. However, there is still lacking comprehensive analysis of tumor-derived exosome (TDE)-based stratification of risk patients and prognostic assessment for breast cancer with systematic dissection of biological heterogeneity. In this study, the robust corroborative analysis for biomarker discovery (RCABD) strategy was used for the identification of exosome molecules, differential expression verification, risk prediction modeling, heterogenous dissection with multi-ome (6101 molecules), our ExoBCD database (306 molecules), and 53 independent studies (481 molecules). Our results showed that a 10-molecule exosome-derived signature (exoSIG) could successfully fulfill breast cancer risk stratification, making it a novel and accurate exosome prognostic indicator (Cox P = 9.9E-04, HR = 3.3, 95% CI 1.6-6.8). Interestingly, HLA-DQB2 and COL17A1, closely related to tumor metastasis, achieved high performance in prognosis prediction (86.35% contribution) and accuracy (Log-rank P = 0.028, AUC = 85.42%). With the combined information of patient age and tumor stage, they formed a bimolecular risk signature (Clinmin-exoSIG) and a convenient nomogram as operable tools for clinical applications. In conclusion, as an extension of ExoBCD, this study conducted systematic analyses to identify prognostic multi-molecular panel and risk signature, stratify patients and dissect biological heterogeneity based on breast cancer exosomes from a multi-omics perspective. Our results provide an important reference for in-depth exploration of the "biological heterogeneity - risk stratification - prognosis prediction".
Collapse
Affiliation(s)
- Fei Long
- Key Laboratory of Clinical Laboratory Diagnostics, College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, PR China
| | - Haodong Ma
- Key Laboratory of Clinical Laboratory Diagnostics, College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, PR China
| | - Youjin Hao
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, PR China
| | - Luyao Tian
- Key Laboratory of Clinical Laboratory Diagnostics, College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, PR China
| | - Yinghong Li
- Key Laboratory on Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, PR China
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, PR China
| | - Juan Chen
- Key Laboratory of Clinical Laboratory Diagnostics, College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, PR China
| | - Ying Tang
- Key Laboratory of Clinical Laboratory Diagnostics, College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, PR China
| | - Jing Li
- Key Laboratory of Clinical Laboratory Diagnostics, College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, PR China
| | - Lili Deng
- Key Laboratory of Clinical Laboratory Diagnostics, College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, PR China
| | - Guoming Xie
- Key Laboratory of Clinical Laboratory Diagnostics, College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, PR China
| | - Mingwei Liu
- Key Laboratory of Clinical Laboratory Diagnostics, College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, PR China
| |
Collapse
|
8
|
Lam KHB, Faust K, Yin R, Fiala C, Diamandis P. The Brain Protein Atlas: A conglomerate of proteomics datasets of human neural tissue. Proteomics 2022; 22:e2200127. [PMID: 35971647 DOI: 10.1002/pmic.202200127] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/09/2022] [Accepted: 08/03/2022] [Indexed: 11/06/2022]
Abstract
The human brain represents one of the most complex biological structures with significant spatiotemporal molecular plasticity occurring through early development, learning, aging, and disease. While much progress has been made in mapping its transcriptional architecture, more downstream phenotypic readouts are relatively scarce due to limitations with tissue heterogeneity and accessibility, as well as an inability to amplify protein species prior to global -OMICS analysis. To address some of these barriers, our group has recently focused on using mass-spectrometry workflows compatible with small amounts of formalin-fixed paraffin-embedded tissue samples. This has enabled exploration into spatiotemporal proteomic signatures of the brain and disease across otherwise inaccessible neurodevelopmental timepoints and anatomical niches. Given the similar theme and approaches, we introduce an integrated online portal, "The Brain Protein Atlas (BPA)" (www.brainproteinatlas.org), representing a public resource that allows users to access and explore these amalgamated datasets. Specifically, this portal contains a growing set of peer-reviewed mass-spectrometry-based proteomic datasets, including spatiotemporal profiles of human cerebral development, diffuse gliomas, clinically aggressive meningiomas, and a detailed anatomic atlas of glioblastoma. One barrier to entry in mass spectrometry-based proteomics data analysis is the steep learning curve required to extract biologically relevant data. BPA, therefore, includes several built-in analytical tools to generate relevant plots (e.g., volcano plots, heatmaps, boxplots, and scatter plots) and evaluate the spatiotemporal patterns of proteins of interest. Future iterations aim to expand available datasets, including those generated by the community at large, and analytical tools for exploration. Ultimately, BPA aims to improve knowledge dissemination of proteomic information across the neuroscience community in hopes of accelerating the biological understanding of the brain and various maladies.
Collapse
Affiliation(s)
- K H Brian Lam
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada.,Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, United States of America
| | - Kevin Faust
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada
| | - Richard Yin
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada
| | - Clare Fiala
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada
| | - Phedias Diamandis
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada.,Laboratory Medicine Program, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| |
Collapse
|
9
|
Herrington CS, Poulsom R, Pillay N, Bankhead P, Coates PJ. Recent Advances in Pathology: the 2022 Annual Review Issue of The Journal of Pathology. J Pathol 2022; 257:379-382. [PMID: 35635736 DOI: 10.1002/path.5972] [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: 05/26/2022] [Accepted: 05/27/2022] [Indexed: 11/10/2022]
Abstract
The 2022 Annual Review Issue of The Journal of Pathology, Recent Advances in Pathology, contains 15 invited reviews on research areas of growing importance in pathology. This year, the articles include those that focus on digital pathology, employing modern imaging techniques and software to enable improved diagnostic and research applications to study human diseases. This subject area includes the ability to identify specific genetic alterations through the morphological changes they induce, as well as integrating digital and computational pathology with 'omics technologies. Other reviews in this issue include an updated evaluation of mutational patterns (mutation signatures) in cancer, the applications of lineage tracing in human tissues, and single cell sequencing technologies to uncover tumour evolution and tumour heterogeneity. The tissue microenvironment is covered in reviews specifically dealing with proteolytic control of epidermal differentiation, cancer-associated fibroblasts, field cancerisation, and host factors that determine tumour immunity. All of the reviews contained in this issue are the work of invited experts selected to discuss the considerable recent progress in their respective fields and are freely available online (https://onlinelibrary.wiley.com/journal/10969896). © 2022 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
Collapse
Affiliation(s)
- C Simon Herrington
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Edinburgh Pathology, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Richard Poulsom
- The Pathological Society of Great Britain and Ireland, London, UK
| | - Nischalan Pillay
- Sarcoma Biology and Genomics Group, UCL Cancer Institute, London, UK
- Department of Histopathology, The Royal National Orthopaedic Hospital NHS Trust, London, UK
| | - Peter Bankhead
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Edinburgh Pathology, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Philip J Coates
- RECAMO, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| |
Collapse
|