1
|
Guo Y, Ren C, He Y, Wu Y, Yang X. Deciphering the spatiotemporal transcriptional landscape of intestinal diseases (Review). Mol Med Rep 2024; 30:157. [PMID: 38994768 PMCID: PMC11258600 DOI: 10.3892/mmr.2024.13281] [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: 11/21/2023] [Accepted: 04/19/2024] [Indexed: 07/13/2024] Open
Abstract
The intestines are the largest barrier organ in the human body. The intestinal barrier plays a crucial role in maintaining the balance of the intestinal environment and protecting the intestines from harmful bacterial invasion. Single‑cell RNA sequencing technology allows the detection of the different cell types in the intestine in two dimensions and the exploration of cell types that have not been fully characterized. The intestinal mucosa is highly complex in structure, and its proper functioning is linked to multiple structures in the proximal‑distal intestinal and luminal‑mucosal axes. Spatial localization is at the core of the efforts to explore the interactions between the complex structures. Spatial transcriptomics (ST) is a method that allows for comprehensive tissue analysis and the acquisition of spatially separated genetic information from individual cells, while preserving their spatial location and interactions. This approach also prevents the loss of fragile cells during tissue disaggregation. The emergence of ST technology allows us to spatially dissect enzymatic processes and interactions between multiple cells, genes, proteins and signals in the intestine. This includes the exchange of oxygen and nutrients in the intestine, different gradients of microbial populations and the role of extracellular matrix proteins. This regionally precise approach to tissue studies is gaining more acceptance and is increasingly applied in the investigation of disease mechanisms related to the gastrointestinal tract. Therefore, this review summarized the application of ST in gastrointestinal diseases.
Collapse
Affiliation(s)
- Yajing Guo
- School of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, P.R. China
| | - Chao Ren
- Graduate School, Hunan University of Traditional Chinese Medicine, Changsha, Hunan 410208, P.R. China
| | - Yuxi He
- Department of Digestive Medicine, Chongqing City Hospital of Traditional Chinese Medicine, Chongqing 400021, P.R. China
| | - Yue Wu
- Department of Digestive Medicine, Chongqing City Hospital of Traditional Chinese Medicine, Chongqing 400021, P.R. China
| | - Xiaojun Yang
- Department of Digestive Medicine, Chongqing City Hospital of Traditional Chinese Medicine, Chongqing 400021, P.R. China
| |
Collapse
|
2
|
Chen J, Larsson L, Swarbrick A, Lundeberg J. Spatial landscapes of cancers: insights and opportunities. Nat Rev Clin Oncol 2024:10.1038/s41571-024-00926-7. [PMID: 39043872 DOI: 10.1038/s41571-024-00926-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/28/2024] [Indexed: 07/25/2024]
Abstract
Solid tumours comprise many different cell types organized in spatially structured arrangements, with substantial intratumour and intertumour heterogeneity. Advances in spatial profiling technologies over the past decade hold promise to capture the complexity of these cellular architectures to build a holistic view of the intricate molecular mechanisms that shape the tumour ecosystem. Some of these mechanisms act at the cellular scale and are controlled by cell-autonomous programmes or communication between nearby cells, whereas other mechanisms result from coordinated efforts between large networks of cells and extracellular molecules organized into tissues and organs. In this Review we provide insights into the application of single-cell and spatial profiling tools, with a focus on spatially resolved transcriptomic tools developed to understand the cellular architecture of the tumour microenvironment and identify opportunities to use them to improve clinical management of cancers.
Collapse
Affiliation(s)
- Julia Chen
- Cancer Ecosystems Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
- Department of Medical Oncology, St George Hospital, Sydney, New South Wales, Australia
| | - Ludvig Larsson
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden
| | - Alexander Swarbrick
- Cancer Ecosystems Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
- School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.
| | - Joakim Lundeberg
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden.
| |
Collapse
|
3
|
Zhang X, Venkatachalapathy S, Paysan D, Schaerer P, Tripodo C, Uhler C, Shivashankar GV. Unsupervised representation learning of chromatin images identifies changes in cell state and tissue organization in DCIS. Nat Commun 2024; 15:6112. [PMID: 39030176 PMCID: PMC11271523 DOI: 10.1038/s41467-024-50285-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 07/05/2024] [Indexed: 07/21/2024] Open
Abstract
Ductal carcinoma in situ (DCIS) is a pre-invasive tumor that can progress to invasive breast cancer, a leading cause of cancer death. We generate a large-scale tissue microarray dataset of chromatin images, from 560 samples from 122 female patients in 3 disease stages and 11 phenotypic categories. Using representation learning on chromatin images alone, without multiplexed staining or high-throughput sequencing, we identify eight morphological cell states and tissue features marking DCIS. All cell states are observed in all disease stages with different proportions, indicating that cell states enriched in invasive cancer exist in small fractions in normal breast tissue. Tissue-level analysis reveals significant changes in the spatial organization of cell states across disease stages, which is predictive of disease stage and phenotypic category. Taken together, we show that chromatin imaging represents a powerful measure of cell state and disease stage of DCIS, providing a simple and effective tumor biomarker.
Collapse
Affiliation(s)
- Xinyi Zhang
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, USA
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, USA
| | - Saradha Venkatachalapathy
- Department of Health Sciences and Technology, ETH Zurich, Switzerland
- Laboratory of Nanoscale Biology, Paul Scherrer Institute, Villigen, Switzerland
| | - Daniel Paysan
- Department of Health Sciences and Technology, ETH Zurich, Switzerland
- Laboratory of Nanoscale Biology, Paul Scherrer Institute, Villigen, Switzerland
| | - Paulina Schaerer
- Department of Health Sciences and Technology, ETH Zurich, Switzerland
- Laboratory of Nanoscale Biology, Paul Scherrer Institute, Villigen, Switzerland
| | - Claudio Tripodo
- Tumor Immunology Unit, University of Palermo, Palermo, Italy
- IFOM, FIRC Institute of Molecular Oncology, Milan, Italy
| | - Caroline Uhler
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, USA.
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, USA.
| | - G V Shivashankar
- Department of Health Sciences and Technology, ETH Zurich, Switzerland.
- Laboratory of Nanoscale Biology, Paul Scherrer Institute, Villigen, Switzerland.
| |
Collapse
|
4
|
Grimm LJ. Radiology for Ductal Carcinoma In Situ of the Breast: Updates on Invasive Cancer Progression and Active Monitoring. Korean J Radiol 2024; 25:25.e48. [PMID: 39028009 DOI: 10.3348/kjr.2024.0117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/17/2024] [Accepted: 04/30/2024] [Indexed: 07/20/2024] Open
Abstract
Ductal carcinoma in situ (DCIS) accounts for approximately 30% of new breast cancer diagnoses. However, our understanding of how normal breast tissue evolves into DCIS and invasive cancers remains insufficient. Further, conclusions regarding the mechanisms of disease progression in terms of histopathology, genetics, and radiology are often conflicting and have implications for treatment planning. Moreover, the increase in DCIS diagnoses since the adoption of organized breast cancer screening programs has raised concerns about overdiagnosis and subsequent overtreatment. Active monitoring, a nonsurgical management strategy for DCIS, avoids surgery in favor of close imaging follow-up to de-escalate therapy and provides more treatment options. However, the two major challenges in active monitoring are identifying occult invasive cancer and patients at risk of invasive cancer progression. Subsequently, four prospective active monitoring trials are ongoing to determine the feasibility of active monitoring and refine the patient eligibility criteria and follow-up intervals. Radiologists play a major role in determining eligibility for active monitoring and reviewing surveillance images for disease progression. Trial results published over the next few years would support a new era of multidisciplinary DCIS care.
Collapse
Affiliation(s)
- Lars J Grimm
- Department of Radiology, Duke University, Duke University Medical Center, Durham, NC, USA.
| |
Collapse
|
5
|
Gao Y, Feder AF. Detecting branching rate heterogeneity in multifurcating trees with applications in lineage tracing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.27.601073. [PMID: 39005367 PMCID: PMC11244928 DOI: 10.1101/2024.06.27.601073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Understanding cellular birth rate differences is crucial for predicting cancer progression and interpreting tumor-derived genetic data. Lineage tracing experiments enable detailed reconstruction of cellular genealogies, offering new opportunities to measure branching rate heterogeneity. However, the lineage tracing process can introduce complex tree features that complicate this effort. Here, we examine tree characteristics in lineage tracing-derived genealogies and find that editing window placement leads to multifurcations at a tree's root or tips. We propose several ways in which existing tree topology-based metrics can be extended to test for rate heterogeneity on trees even in the presence of lineage-tracing associated distortions. Although these methods vary in power and robustness, a test based on the J 1 statistic effectively detects branching rate heterogeneity in simulated lineage tracing data. Tests based on other common statistics ( ŝ and the Sackin index) show interior performance to J 1 . We apply our validated methods to xenograft experimental data and find widespread rate heterogeneity across multiple study systems. Our results demonstrate the potential of tree topology statistics in analyzing lineage tracing data, and highlight the challenges associated with adapting phylogenetic methods to these systems.
Collapse
|
6
|
Hutten SJ, Messal HA, Lips EH, Sheinman M, Ciwinska M, Braams E, van der Borden C, Kristel P, Stoffers S, Wessels LF, Jonkers J, van Rheenen J, Wesseling J, Scheele CL. Ductal carcinoma in situ develops within clonal fields of mutant cells in morphologically normal ducts. J Pathol 2024; 263:360-371. [PMID: 38779852 DOI: 10.1002/path.6289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/28/2024] [Accepted: 03/30/2024] [Indexed: 05/25/2024]
Abstract
Mutations are abundantly present in tissues of healthy individuals, including the breast epithelium. Yet it remains unknown whether mutant cells directly induce lesion formation or first spread, leading to a field of mutant cells that is predisposed towards lesion formation. To study the clonal and spatial relationships between morphologically normal breast epithelium adjacent to pre-cancerous lesions, we developed a three-dimensional (3D) imaging pipeline combined with spatially resolved genomics on archival, formalin-fixed breast tissue with the non-obligate breast cancer precursor ductal carcinoma in situ (DCIS). Using this 3D image-guided characterization method, we built high-resolution spatial maps of DNA copy number aberration (CNA) profiles within the DCIS lesion and the surrounding normal mammary ducts. We show that the local heterogeneity within a DCIS lesion is limited. However, by mapping the CNA profiles back onto the 3D reconstructed ductal subtree, we find that in eight out of 16 cases the healthy epithelium adjacent to the DCIS lesions has overlapping structural variations with the CNA profile of the DCIS. Together, our study indicates that pre-malignant breast transformations frequently develop within mutant clonal fields of morphologically normal-looking ducts. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Collapse
Affiliation(s)
- Stefan J Hutten
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Oncode Institute, Amsterdam, The Netherlands
| | - Hendrik A Messal
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Oncode Institute, Amsterdam, The Netherlands
| | - Esther H Lips
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Michael Sheinman
- Oncode Institute, Amsterdam, The Netherlands
- Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Marta Ciwinska
- Department of Oncology, VIB Center for Cancer Biology, KU Leuven, Leuven, Belgium
| | - Esmee Braams
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Carolien van der Borden
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Petra Kristel
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Saskia Stoffers
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Lodewyk Fa Wessels
- Oncode Institute, Amsterdam, The Netherlands
- Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jos Jonkers
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Oncode Institute, Amsterdam, The Netherlands
| | - Jacco van Rheenen
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Oncode Institute, Amsterdam, The Netherlands
| | - Jelle Wesseling
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Pathology, Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
| | - Colinda Lgj Scheele
- Department of Oncology, VIB Center for Cancer Biology, KU Leuven, Leuven, Belgium
| |
Collapse
|
7
|
Treekitkarnmongkol W, Shah V, Kai K, Katayama H, Wong J, Ladha FA, Nguyen T, Menegaz B, Lu W, Yang F, Mino B, Tang X, Gagea M, Batra H, Raso MG, Wistuba II, Krishnamurthy S, Pinder SE, Sawyer EJ, Thompson AM, Sen S. Epigenetic activation of SOX11 is associated with recurrence and progression of ductal carcinoma in situ to invasive breast cancer. Br J Cancer 2024; 131:171-183. [PMID: 38760444 PMCID: PMC11231151 DOI: 10.1038/s41416-024-02697-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 03/26/2024] [Accepted: 04/15/2024] [Indexed: 05/19/2024] Open
Abstract
BACKGROUND Risk of recurrence and progression of ductal carcinoma in situ (DCIS) to invasive cancer remains uncertain, emphasizing the need for developing predictive biomarkers of aggressive DCIS. METHODS Human cell lines and mouse models of disease progression were analyzed for candidate risk predictive biomarkers identified and validated in two independent DCIS cohorts. RESULTS RNA profiling of normal mammary and DCIS tissues (n = 48) revealed that elevated SOX11 expression correlates with MKI67, EZH2, and DCIS recurrence score. The 21T human cell line model of DCIS progression to invasive cancer and two mouse models developing mammary intraepithelial neoplasia confirmed the findings. AKT activation correlated with chromatin accessibility and EZH2 enrichment upregulating SOX11 expression. AKT and HER2 inhibitors decreased SOX11 expression along with diminished mammosphere formation. SOX11 was upregulated in HER2+ and basal-like subtypes (P < 0.001). Longitudinal DCIS cohort (n = 194) revealed shorter recurrence-free survival in SOX11+ than SOX11- patients (P = 0.0056 in all DCIS; P < 0.0001 in HER2+ subtype) associated with increased risk of ipsilateral breast event/IBE (HR = 1.9, 95%CI = 1.2-2.9; P = 0.003). DISCUSSION Epigenetic activation of SOX11 drives recurrence of DCIS and progression to invasive cancer, suggesting SOX11 as a predictive biomarker of IBE.
Collapse
MESH Headings
- Humans
- Female
- Breast Neoplasms/genetics
- Breast Neoplasms/pathology
- Breast Neoplasms/metabolism
- Animals
- Carcinoma, Intraductal, Noninfiltrating/genetics
- Carcinoma, Intraductal, Noninfiltrating/pathology
- Carcinoma, Intraductal, Noninfiltrating/metabolism
- SOXC Transcription Factors/genetics
- SOXC Transcription Factors/metabolism
- Mice
- Disease Progression
- Neoplasm Recurrence, Local/genetics
- Neoplasm Recurrence, Local/pathology
- Epigenesis, Genetic
- Cell Line, Tumor
- Neoplasm Invasiveness
- Biomarkers, Tumor/genetics
- Biomarkers, Tumor/metabolism
- Gene Expression Regulation, Neoplastic
- Receptor, ErbB-2/genetics
- Receptor, ErbB-2/metabolism
- Enhancer of Zeste Homolog 2 Protein/genetics
- Enhancer of Zeste Homolog 2 Protein/metabolism
Collapse
Affiliation(s)
- Warapen Treekitkarnmongkol
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Vandna Shah
- School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, Guy's Cancer Centre, King's College London, London, UK
| | - Kazuharu Kai
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Hiroshi Katayama
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Justin Wong
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Farah A Ladha
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tristian Nguyen
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Brian Menegaz
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Wei Lu
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Fei Yang
- Department of Anatomic Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Barbara Mino
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ximing Tang
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mihai Gagea
- Department of Veterinary Medicine & Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Harsh Batra
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Maria Gabriela Raso
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ignacio I Wistuba
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Savitri Krishnamurthy
- Department of Anatomic Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sarah E Pinder
- School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, Guy's Cancer Centre, King's College London, London, UK
| | - Elinor J Sawyer
- School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, Guy's Cancer Centre, King's College London, London, UK
| | - Alastair M Thompson
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA.
| | - Subrata Sen
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| |
Collapse
|
8
|
Delaloge S, Khan SA, Wesseling J, Whelan T. Ductal carcinoma in situ of the breast: finding the balance between overtreatment and undertreatment. Lancet 2024; 403:2734-2746. [PMID: 38735296 DOI: 10.1016/s0140-6736(24)00425-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 01/10/2024] [Accepted: 02/29/2024] [Indexed: 05/14/2024]
Abstract
Ductal carcinoma in situ (DCIS) accounts for 15-25% of all breast cancer diagnoses. Its prognosis is excellent overall, the main risk being the occurrence of local breast events, as most cases of DCIS do not progress to invasive cancer. Systematic screening has greatly increased the incidence of this non-obligate precursor of invasion, lending urgency to the need to identify DCIS that is prone to invasive progression and distinguish it from non-invasion-prone DCIS, as the latter can be overdiagnosed and therefore overtreated. Treatment strategies, including surgery, radiotherapy, and optional endocrine therapy, decrease the risk of local events, but have no effect on survival outcomes. Active surveillance is being evaluated as a possible new option for low-risk DCIS. Considerable efforts to decipher the biology of DCIS have led to a better understanding of the factors that determine its variable natural history. Given this variability, shared decision making regarding optimal, personalised treatment strategies is the most appropriate course of action. Well designed, risk-based de-escalation studies remain a major need in this field.
Collapse
Affiliation(s)
- Suzette Delaloge
- Department of Cancer Medicine, Interception Programme, Gustave Roussy, Villejuif, France.
| | - Seema Ahsan Khan
- Department of Surgery, Northwestern University, Chicago, IL, USA
| | - Jelle Wesseling
- Divisions of Molecular Pathology & Department of Pathology, Netherlands Cancer Institute, Amsterdam, Netherlands; Department of Pathology, Leiden University Medical Center, Leiden, Netherlands
| | - Timothy Whelan
- Department of Oncology, McMaster University, Hamilton, ON, Canada
| |
Collapse
|
9
|
Jin Y, Zuo Y, Li G, Liu W, Pan Y, Fan T, Fu X, Yao X, Peng Y. Advances in spatial transcriptomics and its applications in cancer research. Mol Cancer 2024; 23:129. [PMID: 38902727 PMCID: PMC11188176 DOI: 10.1186/s12943-024-02040-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Accepted: 06/10/2024] [Indexed: 06/22/2024] Open
Abstract
Malignant tumors have increasing morbidity and high mortality, and their occurrence and development is a complicate process. The development of sequencing technologies enabled us to gain a better understanding of the underlying genetic and molecular mechanisms in tumors. In recent years, the spatial transcriptomics sequencing technologies have been developed rapidly and allow the quantification and illustration of gene expression in the spatial context of tissues. Compared with the traditional transcriptomics technologies, spatial transcriptomics technologies not only detect gene expression levels in cells, but also inform the spatial location of genes within tissues, cell composition of biological tissues, and interaction between cells. Here we summarize the development of spatial transcriptomics technologies, spatial transcriptomics tools and its application in cancer research. We also discuss the limitations and challenges of current spatial transcriptomics approaches, as well as future development and prospects.
Collapse
Affiliation(s)
- Yang Jin
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yuanli Zuo
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Gang Li
- Department of Thoracic Surgery, The Public Health Clinical Center of Chengdu, Chengdu, 610061, China
| | - Wenrong Liu
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yitong Pan
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Ting Fan
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xin Fu
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiaojun Yao
- Department of Thoracic Surgery, The Public Health Clinical Center of Chengdu, Chengdu, 610061, China.
| | - Yong Peng
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Frontier Medical Center, Tianfu Jincheng Laboratory, Chengdu, 610212, China.
| |
Collapse
|
10
|
Hulahan TS, Spruill L, Wallace EN, Park Y, West RB, Marks JR, Hwang ES, Drake RR, Angel PM. Extracellular Microenvironment Alterations in Ductal Carcinoma In Situ and Invasive Breast Cancer Pathologies by Multiplexed Spatial Proteomics. Int J Mol Sci 2024; 25:6748. [PMID: 38928454 PMCID: PMC11203487 DOI: 10.3390/ijms25126748] [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: 05/07/2024] [Revised: 06/10/2024] [Accepted: 06/16/2024] [Indexed: 06/28/2024] Open
Abstract
Ductal carcinoma in situ (DCIS) is a heterogeneous breast disease that remains challenging to treat due to its unpredictable progression to invasive breast cancer (IBC). Contemporary literature has become increasingly focused on extracellular matrix (ECM) alterations with breast cancer progression. However, the spatial regulation of the ECM proteome in DCIS has yet to be investigated in relation to IBC. We hypothesized that DCIS and IBC present distinct ECM proteomes that could discriminate between these pathologies. Tissue sections of pure DCIS, mixed DCIS-IBC, or pure IBC (n = 22) with detailed pathological annotations were investigated by multiplexed spatial proteomics. Across tissues, 1,005 ECM peptides were detected in pathologically annotated regions and their surrounding extracellular microenvironments. A comparison of DCIS to IBC pathologies demonstrated 43 significantly altered ECM peptides. Notably, eight fibrillar collagen peptides could distinguish with high specificity and sensitivity between DCIS and IBC. Lesion-targeted proteomic imaging revealed heterogeneity of the ECM proteome surrounding individual DCIS lesions. Multiplexed spatial proteomics reported an invasive cancer field effect, in which DCIS lesions in closer proximity to IBC shared a more similar ECM profile to IBC than distal counterparts. Defining the ECM proteomic microenvironment provides novel molecular insights relating to DCIS and IBC.
Collapse
Affiliation(s)
- Taylor S. Hulahan
- Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, SC 29425, USA; (T.S.H.); (E.N.W.); (R.R.D.)
| | - Laura Spruill
- Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, SC 29425, USA;
| | - Elizabeth N. Wallace
- Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, SC 29425, USA; (T.S.H.); (E.N.W.); (R.R.D.)
| | - Yeonhee Park
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53726, USA;
| | - Robert B. West
- Department of Pathology Clinical, Stanford University, Stanford, CA 94305, USA;
| | - Jeffrey R. Marks
- Department of Surgery, Duke University, Durham, NC 27710, USA; (J.R.M.); (E.S.H.)
| | - E. Shelley Hwang
- Department of Surgery, Duke University, Durham, NC 27710, USA; (J.R.M.); (E.S.H.)
| | - Richard R. Drake
- Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, SC 29425, USA; (T.S.H.); (E.N.W.); (R.R.D.)
| | - Peggi M. Angel
- Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, SC 29425, USA; (T.S.H.); (E.N.W.); (R.R.D.)
| |
Collapse
|
11
|
Thakur C, Qiu Y, Pawar A, Chen F. Epigenetic regulation of breast cancer metastasis. Cancer Metastasis Rev 2024; 43:597-619. [PMID: 37857941 DOI: 10.1007/s10555-023-10146-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 10/02/2023] [Indexed: 10/21/2023]
Abstract
Breast cancer is the most frequently diagnosed malignancy and the second leading cause of cancer-related mortality among women worldwide. Recurrent metastasis is associated with poor patient outcomes and poses a significant challenge in breast cancer therapies. Cancer cells adapting to a new tissue microenvironment is the key event in distant metastasis development, where the disseminating tumor cells are likely to acquire genetic and epigenetic alterations during the process of metastatic colonization. Despite several decades of research in this field, the exact mechanisms governing metastasis are not fully understood. However, emerging body of evidence indicates that in addition to genetic changes, epigenetic reprogramming of cancer cells and the metastatic niche are paramount toward successful metastasis. Here, we review and discuss the latest knowledge about the salient attributes of metastasis and epigenetic regulation in breast cancer and crucial research domains that need further investigation.
Collapse
Affiliation(s)
- Chitra Thakur
- Stony Brook Cancer Center, Renaissance School of Medicine, Stony Brook University, Lauterbur Drive, Stony Brook, NY, 11794, USA.
| | - Yiran Qiu
- Stony Brook Cancer Center, Renaissance School of Medicine, Stony Brook University, Lauterbur Drive, Stony Brook, NY, 11794, USA
| | - Aashna Pawar
- Stony Brook Cancer Center, Renaissance School of Medicine, Stony Brook University, Lauterbur Drive, Stony Brook, NY, 11794, USA
| | - Fei Chen
- Stony Brook Cancer Center, Renaissance School of Medicine, Stony Brook University, Lauterbur Drive, Stony Brook, NY, 11794, USA.
| |
Collapse
|
12
|
Kaplan HG, Dowdell AK, Berry AB, Shimol RB, Robinson FL, Carney CA, Piening BD. Multi-omic profiling of simultaneous ductal carcinoma in situ and invasive breast cancer. Breast Cancer Res Treat 2024; 205:451-464. [PMID: 38523186 PMCID: PMC11101558 DOI: 10.1007/s10549-024-07270-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 01/24/2024] [Indexed: 03/26/2024]
Abstract
PURPOSE The progression of ductal carcinoma in situ (DCIS) to invasive breast carcinoma (IBC) in humans is highly variable. To better understand the relationship between them, we performed a multi-omic characterization of co-occurring DCIS and IBC lesions in a cohort of individuals. METHODS Formalin-fixed paraffin-embedded tissue samples from 50 patients with co-occurring DCIS and IBC lesions were subjected to DNA-seq and whole transcriptome RNA-seq. Paired DCIS and IBC multi-omics profiles were then interrogated for DNA mutations, gene expression profiles and pathway analysis. RESULTS Most small variants and copy number variations were shared between co-occurring DCIS and IBC lesions, with IBC exhibiting on average a higher degree of additional mutations. However, 36% of co-occurring lesions shared no common mutations and 49% shared no common copy number variations. The most frequent genomic variants in both DCIS and IBC were PIK3CA, TP53, KMT2C, MAP3K1, GATA3 and SF3B1, with KMT2C being more frequent in DCIS and TP53 and MAP3K1 more frequent in IBC, though the numbers are too small for definitive conclusions. The most frequent copy number variations were seen in MCL1, CKSB1 and ERBB2. ERBB2 changes were not seen in IBC unless present in the corresponding DCIS. Transcriptional profiles were highly distinct between DCIS and IBC, with DCIS exhibiting upregulation of immune-related signatures, while IBC showed significant overexpression in genes and pathways associated with cell division and proliferation. Interestingly, DCIS and IBC exhibited significant differential expression of different components of extracellular matrix (ECM) formation and regulation, with DCIS showing overexpression of ECM-membrane interaction components while IBC showed upregulation of genes associated with fibronectin and invadopodia. CONCLUSION While most co-occurring DCIS and IBC were mutationally similar and suggestive of a common clonal progenitor, transcriptionally the lesions are highly distinct, with IBC expressing key pathways that facilitate invasion and proliferation. These results are suggestive of additional levels of regulation, epigenetic or other, that facilitate the acquisition of invasive properties during tumor evolution.
Collapse
MESH Headings
- Humans
- Female
- Carcinoma, Intraductal, Noninfiltrating/genetics
- Carcinoma, Intraductal, Noninfiltrating/pathology
- Breast Neoplasms/genetics
- Breast Neoplasms/pathology
- Mutation
- DNA Copy Number Variations
- Gene Expression Profiling/methods
- Carcinoma, Ductal, Breast/genetics
- Carcinoma, Ductal, Breast/pathology
- Carcinoma, Ductal, Breast/metabolism
- Biomarkers, Tumor/genetics
- Middle Aged
- Neoplasm Invasiveness
- Gene Expression Regulation, Neoplastic
- Transcriptome
- Aged
- Adult
- Genomics/methods
- Multiomics
Collapse
Affiliation(s)
- Henry G Kaplan
- Swedish Cancer Institute, 1221 Madison St., Suite 920, Seattle, WA, 98104, USA.
| | - Alexa K Dowdell
- Earle A. Chiles Research Institute, Providence Health, Portland, OR, 97213, USA
| | - Anna B Berry
- Swedish Cancer Institute, 1221 Madison St., Suite 920, Seattle, WA, 98104, USA
| | - Racheli Ben Shimol
- Earle A. Chiles Research Institute, Providence Health, Portland, OR, 97213, USA
| | - Fred L Robinson
- Earle A. Chiles Research Institute, Providence Health, Portland, OR, 97213, USA
| | | | - Brian D Piening
- Earle A. Chiles Research Institute, Providence Health, Portland, OR, 97213, USA
| |
Collapse
|
13
|
Del Pino Herrera A, Ferrall-Fairbanks MC. A war on many fronts: cross disciplinary approaches for novel cancer treatment strategies. Front Genet 2024; 15:1383676. [PMID: 38873108 PMCID: PMC11169904 DOI: 10.3389/fgene.2024.1383676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 04/26/2024] [Indexed: 06/15/2024] Open
Abstract
Cancer is a disease characterized by uncontrolled cellular growth where cancer cells take advantage of surrounding cellular populations to obtain resources and promote invasion. Carcinomas are the most common type of cancer accounting for almost 90% of cancer cases. One of the major subtypes of carcinomas are adenocarcinomas, which originate from glandular cells that line certain internal organs. Cancers such as breast, prostate, lung, pancreas, colon, esophageal, kidney are often adenocarcinomas. Current treatment strategies include surgery, chemotherapy, radiation, targeted therapy, and more recently immunotherapy. However, patients with adenocarcinomas often develop resistance or recur after the first line of treatment. Understanding how networks of tumor cells interact with each other and the tumor microenvironment is crucial to avoid recurrence, resistance, and high-dose therapy toxicities. In this review, we explore how mathematical modeling tools from different disciplines can aid in the development of effective and personalized cancer treatment strategies. Here, we describe how concepts from the disciplines of ecology and evolution, economics, and control engineering have been applied to mathematically model cancer dynamics and enhance treatment strategies.
Collapse
Affiliation(s)
- Adriana Del Pino Herrera
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Meghan C. Ferrall-Fairbanks
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
- University of Florida Health Cancer Center, University of Florida, Gainesville, FL, United States
| |
Collapse
|
14
|
Myers MA, Arnold BJ, Bansal V, Balaban M, Mullen KM, Zaccaria S, Raphael BJ. HATCHet2: clone- and haplotype-specific copy number inference from bulk tumor sequencing data. Genome Biol 2024; 25:130. [PMID: 38773520 PMCID: PMC11110434 DOI: 10.1186/s13059-024-03267-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 05/03/2024] [Indexed: 05/24/2024] Open
Abstract
Bulk DNA sequencing of multiple samples from the same tumor is becoming common, yet most methods to infer copy-number aberrations (CNAs) from this data analyze individual samples independently. We introduce HATCHet2, an algorithm to identify haplotype- and clone-specific CNAs simultaneously from multiple bulk samples. HATCHet2 extends the earlier HATCHet method by improving identification of focal CNAs and introducing a novel statistic, the minor haplotype B-allele frequency (mhBAF), that enables identification of mirrored-subclonal CNAs. We demonstrate HATCHet2's improved accuracy using simulations and a single-cell sequencing dataset. HATCHet2 analysis of 10 prostate cancer patients reveals previously unreported mirrored-subclonal CNAs affecting cancer genes.
Collapse
Affiliation(s)
- Matthew A Myers
- Department of Computer Science, Princeton University, Princeton, USA
| | - Brian J Arnold
- Center for Statistics and Machine Learning, Princeton University, Princeton, USA
| | - Vineet Bansal
- Princeton Research Computing, Princeton University, Princeton, NJ, USA
| | - Metin Balaban
- Department of Computer Science, Princeton University, Princeton, USA
| | - Katelyn M Mullen
- Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Simone Zaccaria
- Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK.
| | | |
Collapse
|
15
|
Cao J, Li C, Cui Z, Deng S, Lei T, Liu W, Yang H, Chen P. Spatial Transcriptomics: A Powerful Tool in Disease Understanding and Drug Discovery. Theranostics 2024; 14:2946-2968. [PMID: 38773973 PMCID: PMC11103497 DOI: 10.7150/thno.95908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 04/25/2024] [Indexed: 05/24/2024] Open
Abstract
Recent advancements in modern science have provided robust tools for drug discovery. The rapid development of transcriptome sequencing technologies has given rise to single-cell transcriptomics and single-nucleus transcriptomics, increasing the accuracy of sequencing and accelerating the drug discovery process. With the evolution of single-cell transcriptomics, spatial transcriptomics (ST) technology has emerged as a derivative approach. Spatial transcriptomics has emerged as a hot topic in the field of omics research in recent years; it not only provides information on gene expression levels but also offers spatial information on gene expression. This technology has shown tremendous potential in research on disease understanding and drug discovery. In this article, we introduce the analytical strategies of spatial transcriptomics and review its applications in novel target discovery and drug mechanism unravelling. Moreover, we discuss the current challenges and issues in this research field that need to be addressed. In conclusion, spatial transcriptomics offers a new perspective for drug discovery.
Collapse
Affiliation(s)
- Junxian Cao
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
| | - Caifeng Li
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Zhao Cui
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Shiwen Deng
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
| | - Tong Lei
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Wei Liu
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Hongjun Yang
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
| | - Peng Chen
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
| |
Collapse
|
16
|
Borowsky A, Glencer A, Ramalingam K, Schindler N, Mori H, Ghule P, Lee K, Nachmanson D, Officer A, Harismendy O, Stein J, Stein G, Evans M, Weaver D, Yau C, Hirst G, Campbell M, Esserman L. Tumor microenvironmental determinants of high-risk DCIS progression. RESEARCH SQUARE 2024:rs.3.rs-4126092. [PMID: 38766192 PMCID: PMC11100907 DOI: 10.21203/rs.3.rs-4126092/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Ductal carcinoma in situ (DCIS) constitutes an array of morphologically recognized intraductal neoplasms in the mammary ductal tree defined by an increased risk for subsequent invasive carcinomas at or near the site of biopsy detection. However, only 15-45% of untreated DCIS cases progress to invasive cancer, so understanding mechanisms that prevent progression is key to avoid overtreatment and provides a basis for alternative therapies and prevention. This study was designed to characterize the tumor microenvironment and molecular profile of high-risk DCIS that grew to a large size but remained as DCIS. All patients had DCIS lesions >5cm in size with at least one additional high-risk feature: young age (<45 years), high nuclear grade, hormone receptor negativity, HER2 positivity, the presence of comedonecrosis, or a palpable mass. The tumor immune microenvironment was characterized using multiplex immunofluorescence to identify immune cells and their spatial relationships within the ducts and stroma. Gene copy number analysis and whole exome DNA sequencing identified the mutational burden and driver mutations, and quantitative whole-transcriptome/gene expression analyses were performed. There was no association between the percent of the DCIS genome characterized by copy number variants (CNAs) and recurrence events (DCIS or invasive). Mutations, especially missense mutations, in the breast cancer driver genes PIK3CA and TP53 were common in this high-risk DCIS cohort (47% of evaluated lesions). Tumor infiltrating lymphocyte (TIL) density was higher in DCIS lesions with TP53 mutations (p=0.0079) compared to wildtype lesions, but not in lesions with PIK3CA mutations (p=0.44). Immune infiltrates were negatively associated with hormone receptor status and positively associated with HER2 expression. High levels of CD3+CD8- T cells were associated with good outcomes with respect to any subsequent recurrence (DCIS or invasive cancer), whereas high levels of CD3+Foxp3+ Treg cells were associated with poor outcomes. Spatial proximity analyses of immune cells and tumor cells demonstrated that close proximity of T cells with tumor cells was associated with good outcomes with respect to any recurrence as well as invasive recurrences. Interestingly, we found that myoepithelial continuity (distance between myoepithelial cells surrounding the involved ducts) was significantly lower in DCIS lesions compared to normal tissue (p=0.0002) or to atypical ductal hyperplasia (p=0.011). Gene set enrichment analysis identified several immune pathways associated with low myoepithelial continuity and a low myoepithelial continuity score was associated with better outcomes, suggesting that gaps in the myoepithelial layer may allow access/interactions between immune infiltrates and tumor cells. Our study demonstrates the immune microenvironment of DCIS, in particular the spatial proximity of tumor cells and T cells, and myoepithelial continuity are important determinants for progression of disease.
Collapse
|
17
|
Ali HR, West RB. Spatial Biology of Breast Cancer. Cold Spring Harb Perspect Med 2024; 14:a041335. [PMID: 38110242 PMCID: PMC11065165 DOI: 10.1101/cshperspect.a041335] [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] [Indexed: 12/20/2023]
Abstract
Spatial findings have shaped on our understanding of breast cancer. In this review, we discuss how spatial methods, including spatial transcriptomics and proteomics and the resultant understanding of spatial relationships, have contributed to concepts regarding cancer progression and treatment. In addition to discussing traditional approaches, we examine how emerging multiplex imaging technologies have contributed to the field and how they might influence future research.
Collapse
Affiliation(s)
- H Raza Ali
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge CB2 0RE, United Kingdom
| | - Robert B West
- Department of Pathology, Stanford University Medical Center, Stanford, California 94305, USA
| |
Collapse
|
18
|
Ginzel JD, Chapman H, Sills JE, Allen EJ, Barak LS, Cardiff RD, Borowsky AD, Lyerly HK, Rogers BW, Snyder JC. Nonlinear progression across the occult transition establishes cancer lethality. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.23.590826. [PMID: 38712192 PMCID: PMC11071403 DOI: 10.1101/2024.04.23.590826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Cancer screening is based upon a linear model of growth and invasion. Yet, early dissemination during the lengthy pre-diagnostic phase suggests that nonlinearity in growth can also occur. Therefore, we quantitatively traced the invisible and visible phases of tumorigenesis in the mammary gland for more than two-thousand tumors. Dynamic mathematical models of the invisible phase revealed an occult checkpoint resulting in nonlinear progression of transformed field cells. We found that expansile fields have increased dwell time at the occult checkpoint resulting in a large reservoir of image detectable precursors prior to invasion. In contrast, slowly proliferating lesions disseminate early and then transition rapidly through an occult checkpoint in a process we term nascent lethality. Our data illustrate how nonlinear growth across an occult checkpoint can account for a paradoxical increase in early-stage cancer detection without a dramatic reduction in metastatic burden. Highlights Growth during the invisible phase of tumorigenesis is a nonlinear processField size and field growth rate are uncoupled from metastatic potentialOccult transition rates vary by genotypeNascent lethal lesions are currently undetectable.
Collapse
|
19
|
Manso BA, Rodriguez y Baena A, Forsberg EC. From Hematopoietic Stem Cells to Platelets: Unifying Differentiation Pathways Identified by Lineage Tracing Mouse Models. Cells 2024; 13:704. [PMID: 38667319 PMCID: PMC11048769 DOI: 10.3390/cells13080704] [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: 03/29/2024] [Revised: 04/17/2024] [Accepted: 04/17/2024] [Indexed: 04/28/2024] Open
Abstract
Platelets are the terminal progeny of megakaryocytes, primarily produced in the bone marrow, and play critical roles in blood homeostasis, clotting, and wound healing. Traditionally, megakaryocytes and platelets are thought to arise from multipotent hematopoietic stem cells (HSCs) via multiple discrete progenitor populations with successive, lineage-restricting differentiation steps. However, this view has recently been challenged by studies suggesting that (1) some HSC clones are biased and/or restricted to the platelet lineage, (2) not all platelet generation follows the "canonical" megakaryocytic differentiation path of hematopoiesis, and (3) platelet output is the default program of steady-state hematopoiesis. Here, we specifically investigate the evidence that in vivo lineage tracing studies provide for the route(s) of platelet generation and investigate the involvement of various intermediate progenitor cell populations. We further identify the challenges that need to be overcome that are required to determine the presence, role, and kinetics of these possible alternate pathways.
Collapse
Affiliation(s)
- Bryce A. Manso
- Institute for the Biology of Stem Cells, University of California-Santa Cruz, Santa Cruz, CA 95064, USA
- Department of Biomolecular Engineering, University of California-Santa Cruz, Santa Cruz, CA 95064, USA
| | - Alessandra Rodriguez y Baena
- Institute for the Biology of Stem Cells, University of California-Santa Cruz, Santa Cruz, CA 95064, USA
- Program in Biomedical Sciences and Engineering, Department of Molecular, Cell, and Developmental Biology, University of California-Santa Cruz, Santa Cruz, CA 95064, USA
| | - E. Camilla Forsberg
- Institute for the Biology of Stem Cells, University of California-Santa Cruz, Santa Cruz, CA 95064, USA
- Department of Biomolecular Engineering, University of California-Santa Cruz, Santa Cruz, CA 95064, USA
| |
Collapse
|
20
|
Wu J, Xu QQ, Jiang YR, Chen JB, Ying WX, Fan QX, Wang HF, Wang Y, Shi SW, Pan JZ, Fang Q. One-Shot Single-Cell Proteome and Metabolome Analysis Strategy for the Same Single Cell. Anal Chem 2024; 96:5499-5508. [PMID: 38547315 DOI: 10.1021/acs.analchem.3c05659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
Characterizing the profiles of proteome and metabolome at the single-cell level is of great significance in single-cell multiomic studies. Herein, we proposed a novel strategy called one-shot single-cell proteome and metabolome analysis (scPMA) to acquire the proteome and metabolome information in a single-cell individual in one injection of LC-MS/MS analysis. Based on the scPMA strategy, a total workflow was developed to achieve the single-cell capture, nanoliter-scale sample pretreatment, one-shot LC injection and separation of the enzyme-digested peptides and metabolites, and dual-zone MS/MS detection for proteome and metabolome profiling. Benefiting from the scPMA strategy, we realized dual-omic analysis of single tumor cells, including A549, HeLa, and HepG2 cells with 816, 578, and 293 protein groups and 72, 91, and 148 metabolites quantified on average. A single-cell perspective experiment for investigating the doxorubicin-induced antitumor effects in both the proteome and metabolome aspects was also performed.
Collapse
Affiliation(s)
- Jie Wu
- Institute of Microanalytical Systems, Department of Chemistry, Zhejiang University, Hangzhou 310058, China
| | - Qin-Qin Xu
- Institute of Microanalytical Systems, Department of Chemistry, Zhejiang University, Hangzhou 310058, China
| | - Yi-Rong Jiang
- Institute of Microanalytical Systems, Department of Chemistry, Zhejiang University, Hangzhou 310058, China
| | - Jian-Bo Chen
- Institute of Microanalytical Systems, Department of Chemistry, Zhejiang University, Hangzhou 310058, China
| | - Wei-Xin Ying
- Institute of Microanalytical Systems, Department of Chemistry, Zhejiang University, Hangzhou 310058, China
| | - Qian-Xi Fan
- Institute of Microanalytical Systems, Department of Chemistry, Zhejiang University, Hangzhou 310058, China
| | - Hui-Feng Wang
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 311200, China
| | - Yu Wang
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 311200, China
| | - Shao-Wen Shi
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 311200, China
| | - Jian-Zhang Pan
- Institute of Microanalytical Systems, Department of Chemistry, Zhejiang University, Hangzhou 310058, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 311200, China
| | - Qun Fang
- Institute of Microanalytical Systems, Department of Chemistry, Zhejiang University, Hangzhou 310058, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 311200, China
- Key Laboratory for Biomedical Engineering of Ministry of Education, Cancer Center, Zhejiang University, Hangzhou 310007, China
- Key Laboratory of Excited-State Materials of Zhejiang Province, Zhejiang University, Hangzhou 310007, China
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, China
| |
Collapse
|
21
|
Wang J, Li B, Luo M, Huang J, Zhang K, Zheng S, Zhang S, Zhou J. Progression from ductal carcinoma in situ to invasive breast cancer: molecular features and clinical significance. Signal Transduct Target Ther 2024; 9:83. [PMID: 38570490 PMCID: PMC10991592 DOI: 10.1038/s41392-024-01779-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 02/14/2024] [Accepted: 02/26/2024] [Indexed: 04/05/2024] Open
Abstract
Ductal carcinoma in situ (DCIS) represents pre-invasive breast carcinoma. In untreated cases, 25-60% DCIS progress to invasive ductal carcinoma (IDC). The challenge lies in distinguishing between non-progressive and progressive DCIS, often resulting in over- or under-treatment in many cases. With increasing screen-detected DCIS in these years, the nature of DCIS has aroused worldwide attention. A deeper understanding of the biological nature of DCIS and the molecular journey of the DCIS-IDC transition is crucial for more effective clinical management. Here, we reviewed the key signaling pathways in breast cancer that may contribute to DCIS initiation and progression. We also explored the molecular features of DCIS and IDC, shedding light on the progression of DCIS through both inherent changes within tumor cells and alterations in the tumor microenvironment. In addition, valuable research tools utilized in studying DCIS including preclinical models and newer advanced technologies such as single-cell sequencing, spatial transcriptomics and artificial intelligence, have been systematically summarized. Further, we thoroughly discussed the clinical advancements in DCIS and IDC, including prognostic biomarkers and clinical managements, with the aim of facilitating more personalized treatment strategies in the future. Research on DCIS has already yielded significant insights into breast carcinogenesis and will continue to pave the way for practical clinical applications.
Collapse
Affiliation(s)
- Jing Wang
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Breast Surgery and Oncology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
| | - Baizhou Li
- Department of Pathology, the Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Meng Luo
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
- Department of Plastic Surgery, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jia Huang
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
| | - Kun Zhang
- Department of Breast Surgery and Oncology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shu Zheng
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
| | - Suzhan Zhang
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China.
| | - Jiaojiao Zhou
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Department of Breast Surgery and Oncology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China.
- Cancer Center, Zhejiang University, Hangzhou, China.
| |
Collapse
|
22
|
Yin W, Wan Y, Zhou Y. SpatialcoGCN: deconvolution and spatial information-aware simulation of spatial transcriptomics data via deep graph co-embedding. Brief Bioinform 2024; 25:bbae130. [PMID: 38557675 PMCID: PMC10982953 DOI: 10.1093/bib/bbae130] [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: 05/18/2023] [Revised: 02/22/2024] [Accepted: 03/08/2024] [Indexed: 04/04/2024] Open
Abstract
Spatial transcriptomics (ST) data have emerged as a pivotal approach to comprehending the function and interplay of cells within intricate tissues. Nonetheless, analyses of ST data are restricted by the low spatial resolution and limited number of ribonucleic acid transcripts that can be detected with several popular ST techniques. In this study, we propose that both of the above issues can be significantly improved by introducing a deep graph co-embedding framework. First, we establish a self-supervised, co-graph convolution network-based deep learning model termed SpatialcoGCN, which leverages single-cell data to deconvolve the cell mixtures in spatial data. Evaluations of SpatialcoGCN on a series of simulated ST data and real ST datasets from human ductal carcinoma in situ, developing human heart and mouse brain suggest that SpatialcoGCN could outperform other state-of-the-art cell type deconvolution methods in estimating per-spot cell composition. Moreover, with competitive accuracy, SpatialcoGCN could also recover the spatial distribution of transcripts that are not detected by raw ST data. With a similar co-embedding framework, we further established a spatial information-aware ST data simulation method, SpatialcoGCN-Sim. SpatialcoGCN-Sim could generate simulated ST data with high similarity to real datasets. Together, our approaches provide efficient tools for studying the spatial organization of heterogeneous cells within complex tissues.
Collapse
Affiliation(s)
- Wang Yin
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, 38 Xueyuan Road, Beijing 100191, China
- Department of Neurobiology, School of Basic Medical Sciences, Neuroscience Research Institute, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - You Wan
- Department of Neurobiology, School of Basic Medical Sciences, Neuroscience Research Institute, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - Yuan Zhou
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, 38 Xueyuan Road, Beijing 100191, China
| |
Collapse
|
23
|
Duan B, Chen S, Cheng X, Liu Q. Multi-slice spatial transcriptome domain analysis with SpaDo. Genome Biol 2024; 25:73. [PMID: 38504325 PMCID: PMC10949687 DOI: 10.1186/s13059-024-03213-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 03/08/2024] [Indexed: 03/21/2024] Open
Abstract
With the rapid advancements in spatial transcriptome sequencing, multiple tissue slices are now available, enabling the integration and interpretation of spatial cellular landscapes. Herein, we introduce SpaDo, a tool for multi-slice spatial domain analysis, including modules for multi-slice spatial domain detection, reference-based annotation, and multiple slice clustering at both single-cell and spot resolutions. We demonstrate SpaDo's effectiveness with over 40 multi-slice spatial transcriptome datasets from 7 sequencing platforms. Our findings highlight SpaDo's potential to reveal novel biological insights in multi-slice spatial transcriptomes.
Collapse
Affiliation(s)
- Bin Duan
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201804, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
| | - Shaoqi Chen
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201804, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
| | - Xiaojie Cheng
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201804, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
| | - Qi Liu
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201804, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
| |
Collapse
|
24
|
Celià-Terrassa T, Kang Y. How important is EMT for cancer metastasis? PLoS Biol 2024; 22:e3002487. [PMID: 38324529 PMCID: PMC10849258 DOI: 10.1371/journal.pbio.3002487] [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] [Indexed: 02/09/2024] Open
Abstract
Epithelial-to-mesenchymal transition (EMT), a biological phenomenon of cellular plasticity initially reported in embryonic development, has been increasingly recognized for its importance in cancer progression and metastasis. Despite tremendous progress being made in the past 2 decades in our understanding of the molecular mechanism and functional importance of EMT in cancer, there are several mysteries around EMT that remain unresolved. In this Unsolved Mystery, we focus on the variety of EMT types in metastasis, cooperative and collective EMT behaviors, spatiotemporal characterization of EMT, and strategies of therapeutically targeting EMT. We also highlight new technical advances that will facilitate the efforts to elucidate the unsolved mysteries of EMT in metastasis.
Collapse
Affiliation(s)
- Toni Celià-Terrassa
- Cancer Research Program, Hospital del Mar Research Institute, Barcelona, Spain
| | - Yibin Kang
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, United States of America
- Ludwig Institute for Cancer Research Princeton Branch, Princeton, New Jersey, United States of America
| |
Collapse
|
25
|
Xue Y, Su Z, Lin X, Ho MK, Yu KHO. Single-cell lineage tracing with endogenous markers. Biophys Rev 2024; 16:125-139. [PMID: 38495438 PMCID: PMC10937880 DOI: 10.1007/s12551-024-01179-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 01/18/2024] [Indexed: 03/19/2024] Open
Abstract
Resolving lineage relationships between cells in an organism provides key insights into the fate of individual cells and drives a fundamental understanding of the process of development and disease. A recent rapid increase in experimental and computational advances for detecting naturally occurring somatic nuclear and mitochondrial mutation at single-cell resolution has expanded lineage tracing from model organisms to humans. This review discusses the advantages and challenges of experimental and computational techniques for cell lineage tracing using somatic mutation as endogenous DNA barcodes to decipher the relationships between cells during development and tumour evolution. We outlook the advantages of spatial clonal evolution analysis and single-cell lineage tracing using endogenous genetic markers.
Collapse
Affiliation(s)
- Yan Xue
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Units 1201-1206, 1223 & 1225, 12/F, Building 19W, 19 Science Park West Avenue, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Zezhuo Su
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Units 1201-1206, 1223 & 1225, 12/F, Building 19W, 19 Science Park West Avenue, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
- Department of Orthopaedics and Traumatology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Xinyi Lin
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Mun Kay Ho
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Ken H. O. Yu
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Units 1201-1206, 1223 & 1225, 12/F, Building 19W, 19 Science Park West Avenue, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
| |
Collapse
|
26
|
Li Z, Yang W, Wu P, Shan Y, Zhang X, Chen F, Yang J, Yang JR. Reconstructing cell lineage trees with genomic barcoding: approaches and applications. J Genet Genomics 2024; 51:35-47. [PMID: 37269980 DOI: 10.1016/j.jgg.2023.05.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 05/19/2023] [Accepted: 05/20/2023] [Indexed: 06/05/2023]
Abstract
In multicellular organisms, developmental history of cell divisions and functional annotation of terminal cells can be organized into a cell lineage tree (CLT). The reconstruction of the CLT has long been a major goal in developmental biology and other related fields. Recent technological advancements, especially those in editable genomic barcodes and single-cell high-throughput sequencing, have sparked a new wave of experimental methods for reconstructing CLTs. Here we review the existing experimental approaches to the reconstruction of CLT, which are broadly categorized as either image-based or DNA barcode-based methods. In addition, we present a summary of the related literature based on the biological insight provided by the obtained CLTs. Moreover, we discuss the challenges that will arise as more and better CLT data become available in the near future. Genomic barcoding-based CLT reconstructions and analyses, due to their wide applicability and high scalability, offer the potential for novel biological discoveries, especially those related to general and systemic properties of the developmental process.
Collapse
Affiliation(s)
- Zizhang Li
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Wenjing Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Peng Wu
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Yuyan Shan
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Xiaoyu Zhang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Feng Chen
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Junnan Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Jian-Rong Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou, Guangdong 510080, China.
| |
Collapse
|
27
|
Yadav S, Zhou S, He B, Du Y, Garmire LX. Deep learning and transfer learning identify breast cancer survival subtypes from single-cell imaging data. COMMUNICATIONS MEDICINE 2023; 3:187. [PMID: 38114659 PMCID: PMC10730890 DOI: 10.1038/s43856-023-00414-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 11/23/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Single-cell multiplex imaging data have provided new insights into disease subtypes and prognoses recently. However, quantitative models that explicitly capture single-cell resolution cell-cell interaction features to predict patient survival at a population scale are currently missing. METHODS We quantified hundreds of single-cell resolution cell-cell interaction features through neighborhood calculation, in addition to cellular phenotypes. We applied these features to a neural-network-based Cox-nnet survival model to identify survival-associated features. We used non-negative matrix factorization (NMF) to identify patient survival subtypes. We identified atypical subpopulations of triple-negative breast cancer (TNBC) patients with moderate prognosis and Luminal A patients with poor prognosis and validated these subpopulations by label transferring using the UNION-COM method. RESULTS The neural-network-based Cox-nnet survival model using all cellular phenotype and cell-cell interaction features is highly predictive of patient survival in the test data (Concordance Index > 0.8). We identify seven survival subtypes using the top survival features, presenting distinct profiles of epithelial, immune, and fibroblast cells and their interactions. We reveal atypical subpopulations of TNBC patients with moderate prognosis (marked by GATA3 over-expression) and Luminal A patients with poor prognosis (marked by KRT6 and ACTA2 over-expression and CDH1 under-expression). These atypical subpopulations are validated in TCGA-BRCA and METABRIC datasets. CONCLUSIONS This work provides an approach to bridge single-cell level information toward population-level survival prediction.
Collapse
Affiliation(s)
- Shashank Yadav
- Department of Computational Medicine and Bioinformatics, University of Michigan, Michigan, MI, 48105, USA
| | - Shu Zhou
- Department of Computational Medicine and Bioinformatics, University of Michigan, Michigan, MI, 48105, USA
| | - Bing He
- Department of Computational Medicine and Bioinformatics, University of Michigan, Michigan, MI, 48105, USA
| | - Yuheng Du
- Department of Computational Medicine and Bioinformatics, University of Michigan, Michigan, MI, 48105, USA
| | - Lana X Garmire
- Department of Computational Medicine and Bioinformatics, University of Michigan, Michigan, MI, 48105, USA.
| |
Collapse
|
28
|
Beumers L, Vlachavas EI, Borgoni S, Schwarzmüller L, Penso-Dolfin L, Michels BE, Sofyali E, Burmester S, Heiss D, Wilhelm H, Yarden Y, Helm D, Will R, Goncalves A, Wiemann S. Clonal heterogeneity in ER+ breast cancer reveals the proteasome and PKC as potential therapeutic targets. NPJ Breast Cancer 2023; 9:97. [PMID: 38042915 PMCID: PMC10693625 DOI: 10.1038/s41523-023-00604-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 11/17/2023] [Indexed: 12/04/2023] Open
Abstract
Intratumoral heterogeneity impacts the success or failure of anti-cancer therapies. Here, we investigated the evolution and mechanistic heterogeneity in clonal populations of cell models for estrogen receptor positive breast cancer. To this end, we established barcoded models of luminal breast cancer and rendered them resistant to commonly applied first line endocrine therapies. By isolating single clones from the resistant cell pools and characterizing replicates of individual clones we observed inter- (between cell lines) and intra-tumor (between different clones from the same cell line) heterogeneity. Molecular characterization at RNA and phospho-proteomic levels revealed private clonal activation of the unfolded protein response and respective sensitivity to inhibition of the proteasome, and potentially shared sensitivities for repression of protein kinase C. Our in vitro findings are consistent with tumor-heterogeneity that is observed in breast cancer patients thus highlighting the need to uncover heterogeneity at an individual patient level and to adjust therapies accordingly.
Collapse
Affiliation(s)
- Lukas Beumers
- Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120, Heidelberg, Germany.
- Faculty of Biosciences, University of Heidelberg, Im Neuenheimer Feld 234, 69120, Heidelberg, Germany.
| | - Efstathios-Iason Vlachavas
- Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120, Heidelberg, Germany
| | - Simone Borgoni
- Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120, Heidelberg, Germany
| | - Luisa Schwarzmüller
- Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120, Heidelberg, Germany
- Faculty of Biosciences, University of Heidelberg, Im Neuenheimer Feld 234, 69120, Heidelberg, Germany
| | - Luca Penso-Dolfin
- Division of Somatic Evolution and Early Detection, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120, Heidelberg, Germany
| | - Birgitta E Michels
- Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120, Heidelberg, Germany
| | - Emre Sofyali
- Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120, Heidelberg, Germany
| | - Sara Burmester
- Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120, Heidelberg, Germany
| | - Daniela Heiss
- Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120, Heidelberg, Germany
| | - Heike Wilhelm
- Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120, Heidelberg, Germany
| | - Yosef Yarden
- Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Dominic Helm
- Proteomics Core Facility, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120, Heidelberg, Germany
| | - Rainer Will
- Cellular Tools Core Facility, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120, Heidelberg, Germany
| | - Angela Goncalves
- Division of Somatic Evolution and Early Detection, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120, Heidelberg, Germany
| | - Stefan Wiemann
- Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120, Heidelberg, Germany.
- Faculty of Biosciences, University of Heidelberg, Im Neuenheimer Feld 234, 69120, Heidelberg, Germany.
| |
Collapse
|
29
|
Makhmut A, Qin D, Fritzsche S, Nimo J, König J, Coscia F. A framework for ultra-low-input spatial tissue proteomics. Cell Syst 2023; 14:1002-1014.e5. [PMID: 37909047 DOI: 10.1016/j.cels.2023.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 08/03/2023] [Accepted: 10/06/2023] [Indexed: 11/02/2023]
Abstract
Spatial proteomics combining microscopy-based cell phenotyping with ultrasensitive mass-spectrometry-based proteomics is an emerging and powerful concept to study cell function and heterogeneity in (patho)physiology. However, optimized workflows that preserve morphological information for phenotype discovery and maximize proteome coverage of few or even single cells from laser microdissected tissue are currently lacking. Here, we report a robust and scalable workflow for the proteomic analysis of ultra-low-input archival material. Benchmarking in murine liver resulted in up to 2,000 quantified proteins from single hepatocyte contours and nearly 5,000 proteins from 50-cell regions. Applied to human tonsil, we profiled 146 microregions including T and B lymphocyte niches and quantified cell-type-specific markers, cytokines, and transcription factors. These data also highlighted proteome dynamics within activated germinal centers, illuminating sites undergoing B cell proliferation and somatic hypermutation. This approach has broad implications in biomedicine, including early disease profiling and drug target and biomarker discovery. A record of this paper's transparent peer review process is included in the supplemental information.
Collapse
Affiliation(s)
- Anuar Makhmut
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Spatial Proteomics Group, Berlin, Germany
| | - Di Qin
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Spatial Proteomics Group, Berlin, Germany
| | - Sonja Fritzsche
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Spatial Proteomics Group, Berlin, Germany
| | - Jose Nimo
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Spatial Proteomics Group, Berlin, Germany
| | - Janett König
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Spatial Proteomics Group, Berlin, Germany
| | - Fabian Coscia
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Spatial Proteomics Group, Berlin, Germany.
| |
Collapse
|
30
|
Chai R, Zhao Y, Su Z, Liang W. Integrative analysis reveals a four-gene signature for predicting survival and immunotherapy response in colon cancer patients using bulk and single-cell RNA-seq data. Front Oncol 2023; 13:1277084. [PMID: 38023180 PMCID: PMC10644708 DOI: 10.3389/fonc.2023.1277084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 09/25/2023] [Indexed: 12/01/2023] Open
Abstract
Background Colon cancer (CC) ranks as one of the leading causes of cancer-related mortality globally. Single-cell transcriptome sequencing (scRNA-seq) offers precise gene expression data for distinct cell types. This study aimed to utilize scRNA-seq and bulk transcriptome sequencing (bulk RNA-seq) data from CC samples to develop a novel prognostic model. Methods scRNA-seq data was downloaded from the GSE161277 database. R packages including "Seurat", "Harmony", and "singleR" were employed to categorize eight major cell types within normal and tumor tissues. By comparing tumor and normal samples, differentially expressed genes (DEGs) across these major cell types were identified. Gene Ontology (GO) enrichment analyses of DEGs for each cell type were conducted using "Metascape". DEGs-based signature construction involved Cox regression and least absolute shrinkage operator (LASSO) analyses, performed on The Cancer Genome Atlas (TCGA) training cohort. Validation occurred in the GSE39582 and GSE33382 datasets. The expression pattern of prognostic genes was verified using spatial transcriptome sequencing (ST-seq) data. Ultimately, an established prognostic nomogram based on the gene signature and age was established and calibrated. Sensitivity to chemotherapeutic drugs was predicted with the "oncoPredict" R package. Results Using scRNA-Seq data, we examined 33,213 cells, categorizing them into eight cell types within normal and tumor samples. GO enrichment analysis revealed various cancer-related pathways across DEGs in these cell types. Among the 55 DEGs identified via univariate Cox regression, four independent prognostic genes emerged: PTPN6, CXCL13, SPINK4, and NPDC1. Expression validation through ST-seq confirmed PTPN6 and CXCL13 predominance in immune cells, while SPINK4 and NPDC1 were relatively epithelial cell-specific. Creating a four-gene prognostic signature, Kaplan-Meier survival analyses emphasized higher risk scores correlating with unfavorable prognoses, confirmed across training and validation cohorts. The risk score emerged as an independent prognostic factor, supported by a reliable nomogram. Intriguingly, drug sensitivity analysis unveiled contrasting anti-cancer drug responses in the two risk groups, suggesting significant clinical implications. Conclusion We developed a novel prognostic four-gene risk model, and these genes may act as potential therapeutic targets for CC.
Collapse
Affiliation(s)
- Ruoyang Chai
- Department of General Practice, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yajie Zhao
- Department of Geriatrics, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Zhengjia Su
- Department of Geriatrics, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Wei Liang
- Department of Geriatrics, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| |
Collapse
|
31
|
Behbod F, Chen JH, Thompson A. Human Ductal Carcinoma In Situ: Advances and Future Perspectives. Cold Spring Harb Perspect Med 2023; 13:a041319. [PMID: 36781223 PMCID: PMC10547390 DOI: 10.1101/cshperspect.a041319] [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] [Indexed: 02/15/2023]
Abstract
Due to widespread adoption of screening mammography, there has been a significant increase in new diagnoses of ductal carcinoma in situ (DCIS). However, DCIS outcomes remain unclear. A large fraction of human DCIS (>50%) may not need the multimodality treatment options currently offered to all DCIS patients. More importantly, while we may be overtreating many, we cannot identify those most at risk of invasion or metastasis following a DCIS diagnosis. This review summarizes the studies that have furthered our understanding of DCIS pathology and mechanisms of invasive progression by using advanced technologies including spatial genomics, transcriptomics, and multiplex proteomics. This review also highlights a need for rethinking DCIS with a more focused view on epithelial states and programs and their cross talk with the microenvironment.
Collapse
Affiliation(s)
- Fariba Behbod
- Department of Pathology and Laboratory Medicine, MS 3045, The University of Kansas Medical Center, Kansas City, Kansas 66160, USA
| | - Jennifer H Chen
- Michael E. Debakey Department of Surgery, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Alastair Thompson
- Section of Breast Surgery, Baylor College of Medicine, Co-Director, Lester and Sue Smith Breast Center, Dan L Duncan Comprehensive Cancer Center, Houston, Texas 77030, USA
| |
Collapse
|
32
|
Ryser MD, Greenwald MA, Sorribes IC, King LM, Hall A, Geradts J, Weaver DL, Mallo D, Holloway S, Monyak D, Gumbert G, Vaez-Ghaemi S, Wu E, Murgas K, Grimm LJ, Maley CC, Marks JR, Shibata D, Hwang ES. Growth Dynamics of Ductal Carcinoma in Situ Recapitulate Normal Breast Development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.01.560370. [PMID: 37873488 PMCID: PMC10592867 DOI: 10.1101/2023.10.01.560370] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Ductal carcinoma in situ (DCIS) and invasive breast cancer share many morphologic, proteomic, and genomic alterations. Yet in contrast to invasive cancer, many DCIS tumors do not progress and may remain indolent over decades. To better understand the heterogenous nature of this disease, we reconstructed the growth dynamics of 18 DCIS tumors based on the geo-spatial distribution of their somatic mutations. The somatic mutation topographies revealed that DCIS is multiclonal and consists of spatially discontinuous subclonal lesions. Here we show that this pattern of spread is consistent with a new 'Comet' model of DCIS tumorigenesis, whereby multiple subclones arise early and nucleate the buds of the growing tumor. The discontinuous, multiclonal growth of the Comet model is analogous to the branching morphogenesis of normal breast development that governs the rapid expansion of the mammary epithelium during puberty. The branching morphogenesis-like dynamics of the proposed Comet model diverges from the canonical model of clonal evolution, and better explains observed genomic spatial data. Importantly, the Comet model allows for the clinically relevant scenario of extensive DCIS spread, without being subjected to the selective pressures of subclone competition that promote the emergence of increasingly invasive phenotypes. As such, the normal cell movement inferred during DCIS growth provides a new explanation for the limited risk of progression in DCIS and adds biologic rationale for ongoing clinical efforts to reduce DCIS overtreatment.
Collapse
Affiliation(s)
- Marc D. Ryser
- Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA
- Department of Mathematics, Duke University, Durham, NC, USA
| | | | | | - Lorraine M. King
- Department of Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Allison Hall
- Department of Pathology, Duke University School of Medicine, Durham, NC, USA
| | - Joseph Geradts
- Department of Pathology, East Carolina University School of Medicine, Greenville, NC, USA
| | - Donald L. Weaver
- Larner College of Medicine, University of Vermont and UVM Cancer Center, Burlington, VT, USA
| | - Diego Mallo
- Arizona Cancer Evolution Center, Arizona State University, Tempe, AZ, USA
- Biodesign Center for Biocomputing, Security and Society, Arizona State University, Tempe, AZ, USA
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Shannon Holloway
- Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Daniel Monyak
- Trinity College of Arts and Sciences, Duke University, Durham, NC
| | - Graham Gumbert
- Trinity College of Arts and Sciences, Duke University, Durham, NC
| | | | - Ethan Wu
- Trinity College of Arts and Sciences, Duke University, Durham, NC
| | - Kevin Murgas
- Department of Biomedical Informatics, Stony Brook University School of Medicine, Stony Brook, NY, USA
| | - Lars J. Grimm
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Carlo C. Maley
- Arizona Cancer Evolution Center, Arizona State University, Tempe, AZ, USA
- Biodesign Center for Biocomputing, Security and Society, Arizona State University, Tempe, AZ, USA
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Jeffrey R. Marks
- Department of Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Darryl Shibata
- Department of Pathology, University of Southern California Keck School of Medicine, Los Angeles, CA, USA
| | - E. Shelley Hwang
- Department of Surgery, Duke University School of Medicine, Durham, NC, USA
| |
Collapse
|
33
|
Liu Y, Yu T. Clinicopathological characteristics and prognosis of triple-negative breast cancer invasive ductal carcinoma with ductal carcinoma in situ. J Cancer Res Clin Oncol 2023; 149:11181-11191. [PMID: 37354223 PMCID: PMC10465373 DOI: 10.1007/s00432-023-04895-9] [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: 04/19/2023] [Accepted: 05/20/2023] [Indexed: 06/26/2023]
Abstract
PURPOSE The purpose of this study is to compare and analyze the clinicopathological characteristics and prognosis of patients with invasive ductal carcinoma coexisting with ductal carcinoma in situ (IDC-DCIS) and invasive ductal carcinoma (IDC) in triple-negative breast cancer (TNBC), and to explore the factors affecting the prognosis, so as to provide new ideas for clinical diagnosis and treatment of these patients. METHODS The patients with TNBC underwent surgery in the Department of Breast Surgery of Harbin Medical University Cancer Hospital from October 2012 to December 2018 were retrospectively analyzed and divided into IDC-DCIS group and IDC group. The clinicopathological characteristics and prognosis of the two groups were compared. P < 0.05 was considered statistically significant. RESULTS A total of 358 patients were enrolled. There were significant differences in age (P = 0.002), family history (P = 0.016), menopausal status (P = 0.003), KI-67% (P < 0.001), lymphovascular invasion (P = 0.010), histologic grade of IDC (P < 0.001) and multifocal (P < 0.001) between the two groups. The disease-free survival (DFS) of the IDC-DCIS group was better than that of the IDC group (the 5-year DFS was 87.9% vs. 82.6%, P = 0.045), but the overall survival (OS) of the two groups was not statistically significant (the 5-year OS was 96.2% vs. 96.0%, P = 0.573). In addition, the coexistence of DCIS (P = 0.030), lymph node pathologic stage (P = 0.001), tumor location (P = 0.011), and adjuvant chemotherapy (P < 0.001) were independent prognostic factors for DFS. CONCLUSION In TNBC, the IDC-DCIS group had less invasive biological characteristics. The DFS of the IDC-DCIS group was better than that of the IDC group, but there was no statistical difference in OS between the two groups. In addition, the coexistence of DCIS, lymph node stage, tumor location and adjuvant chemotherapy may be independent prognostic factors for DFS.
Collapse
Affiliation(s)
- Yang Liu
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, China.
| | - Tong Yu
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, China
| |
Collapse
|
34
|
Chen H, Li X, Li F, Li Y, Chen F, Zhang L, Ye F, Gong M, Bu H. Prediction of coexisting invasive carcinoma on ductal carcinoma in situ (DCIS) lesions by mass spectrometry imaging. J Pathol 2023; 261:125-138. [PMID: 37555360 DOI: 10.1002/path.6154] [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: 10/15/2022] [Revised: 05/16/2023] [Accepted: 06/07/2023] [Indexed: 08/10/2023]
Abstract
Due to limited biopsy samples, ~20% of DCIS lesions confirmed by biopsy are upgraded to invasive ductal carcinoma (IDC) upon surgical resection. Avoiding underestimation of IDC when diagnosing DCIS has become an urgent challenge in an era discouraging overtreatment of DCIS. In this study, the metabolic profiles of 284 fresh frozen breast samples, including tumor tissues and adjacent benign tissues (ABTs) and distant surrounding tissues (DSTs), were analyzed using desorption electrospray ionization-mass spectrometry (DESI-MS) imaging. Metabolomics analysis using DESI-MS data revealed significant differences in metabolite levels, including small-molecule antioxidants, long-chain polyunsaturated fatty acids (PUFAs) and phospholipids between pure DCIS and IDC. However, the metabolic profile in DCIS with invasive carcinoma components clearly shifts to be closer to adjacent IDC components. For instance, DCIS with invasive carcinoma components showed lower levels of antioxidants and higher levels of free fatty acids compared to pure DCIS. Furthermore, the accumulation of long-chain PUFAs and the phosphatidylinositols (PIs) containing PUFA residues may also be associated with the progression of DCIS. These distinctive metabolic characteristics may offer valuable indications for investigating the malignant potential of DCIS. By combining DESI-MS data with machine learning (ML) methods, various breast lesions were discriminated. Importantly, the pure DCIS components were successfully distinguished from the DCIS components in samples with invasion in postoperative specimens by a Lasso prediction model, achieving an AUC value of 0.851. In addition, pixel-level prediction based on DESI-MS data enabled automatic visualization of tissue properties across whole tissue sections. Summarily, DESI-MS imaging on histopathological sections can provide abundant metabolic information about breast lesions. By analyzing the spatial metabolic characteristics in tissue sections, this technology has the potential to facilitate accurate diagnosis and individualized treatment of DCIS by inferring the presence of IDC components surrounding DCIS lesions. © 2023 The Pathological Society of Great Britain and Ireland.
Collapse
Affiliation(s)
- Hong Chen
- Department of Pathology and Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, PR China
- Key Laboratory of Transplant Engineering and Immunology of the National Health Commission, West China Hospital, Sichuan University, Chengdu, PR China
| | - Xin Li
- Laboratory of Clinical Proteomics and Metabolomics, Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, PR China
| | - Fengling Li
- Department of Pathology and Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, PR China
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, PR China
| | - Yijie Li
- Department of Pathology and Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, PR China
- Key Laboratory of Transplant Engineering and Immunology of the National Health Commission, West China Hospital, Sichuan University, Chengdu, PR China
| | - Fei Chen
- Department of Pathology and Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, PR China
| | - Lu Zhang
- Image Processing and Parallel Computing Laboratory, School of Computer Science, Southwest Petroleum University, Chengdu, PR China
| | - Feng Ye
- Department of Pathology and Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, PR China
- Key Laboratory of Transplant Engineering and Immunology of the National Health Commission, West China Hospital, Sichuan University, Chengdu, PR China
| | - Meng Gong
- Laboratory of Clinical Proteomics and Metabolomics, Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, PR China
| | - Hong Bu
- Department of Pathology and Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, PR China
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, PR China
| |
Collapse
|
35
|
Aktar S, Islam F, Cheng T, Gamage SMK, Choudhury IN, Islam MS, Lu CT, Hamid FB, Ishida H, Abe I, Xie N, Gopalan V, Lam AK. Correlation between KRAS Mutation and CTLA-4 mRNA Expression in Circulating Tumour Cells: Clinical Implications in Colorectal Cancer. Genes (Basel) 2023; 14:1808. [PMID: 37761948 PMCID: PMC10530465 DOI: 10.3390/genes14091808] [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: 08/10/2023] [Revised: 09/07/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
Combination strategies of KRAS inhibition with immunotherapy in treating advanced or recurrent colorectal carcinoma (CRC) may need to be assessed in circulating tumour cells (CTCs) to achieve better clinical outcomes. This study aimed to investigate the genomic variations of KRAS in CTCs and matched CRC tissues and compared mRNA expression of KRAS and CTLA-4 between wild-type and KRAS-mutated CTCs and CRC tissues. Clinicopathological correlations were also compared. Six known mutations of KRAS were identified at both codon 12 and codon 13 (c.35G>T/G12V, c.35G>A7/G12D, c.35G>C/G12A, c.34G>A/G12S, c.38G>C/G13A, and c.38G>A/G13D). Three CTC samples harboured the identified mutations (16.7%; 3/18), while fifteen matched primary tumour tissues (65.2%, 15/23) showed the mutations. CTCs harbouring the KRAS variant were different from matched CRC tissue. All the mutations were heterozygous. Though insignificant, CTLA-4 mRNA expression was higher in patients carrying KRAS mutations. Patients harbouring KRAS mutations in CTCs were more likely to have poorly differentiated tumours (p = 0.039) and with lymph node metastasis (p = 0.027) and perineural invasion (p = 0.014). KRAS mutations in CTCs were also significantly correlated with overall pathological stages (p = 0.027). These findings imply the genetic basis of KRAS with immunotherapeutic target molecules based on a real-time platform. This study also suggests the highly heterogeneous nature of cancer cells, which may facilitate the assessment of clonal dynamics across a single patient's disease.
Collapse
Affiliation(s)
- Sharmin Aktar
- Cancer Molecular Pathology, School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia; (S.A.); (T.C.); (S.M.K.G.); (M.S.I.); (F.B.H.); (H.I.); (I.A.); (N.X.)
- Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD 4222, Australia;
- Department of Biochemistry and Molecular Biology, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh
| | - Farhadul Islam
- Department of Biochemistry and Molecular Biology, University of Rajshahi, Rajshahi 6205, Bangladesh;
| | - Tracie Cheng
- Cancer Molecular Pathology, School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia; (S.A.); (T.C.); (S.M.K.G.); (M.S.I.); (F.B.H.); (H.I.); (I.A.); (N.X.)
- Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD 4222, Australia;
| | - Sujani Madhurika Kodagoda Gamage
- Cancer Molecular Pathology, School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia; (S.A.); (T.C.); (S.M.K.G.); (M.S.I.); (F.B.H.); (H.I.); (I.A.); (N.X.)
- Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD 4222, Australia;
- Faculty of Health Sciences & Medicine, Bond University, Gold Coast, QLD 4229, Australia
| | - Indra Neil Choudhury
- Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD 4222, Australia;
| | - Md Sajedul Islam
- Cancer Molecular Pathology, School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia; (S.A.); (T.C.); (S.M.K.G.); (M.S.I.); (F.B.H.); (H.I.); (I.A.); (N.X.)
- Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD 4222, Australia;
- Department of Biochemistry & Biotechnology, University of Barishal, Barishal 8254, Bangladesh
| | - Cu Tai Lu
- Department of Surgery, Gold Coast University Hospital, Gold Coast, QLD 4215, Australia;
| | - Faysal Bin Hamid
- Cancer Molecular Pathology, School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia; (S.A.); (T.C.); (S.M.K.G.); (M.S.I.); (F.B.H.); (H.I.); (I.A.); (N.X.)
- Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD 4222, Australia;
| | - Hirotaka Ishida
- Cancer Molecular Pathology, School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia; (S.A.); (T.C.); (S.M.K.G.); (M.S.I.); (F.B.H.); (H.I.); (I.A.); (N.X.)
| | - Ichiro Abe
- Cancer Molecular Pathology, School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia; (S.A.); (T.C.); (S.M.K.G.); (M.S.I.); (F.B.H.); (H.I.); (I.A.); (N.X.)
| | - Nan Xie
- Cancer Molecular Pathology, School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia; (S.A.); (T.C.); (S.M.K.G.); (M.S.I.); (F.B.H.); (H.I.); (I.A.); (N.X.)
| | - Vinod Gopalan
- Cancer Molecular Pathology, School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia; (S.A.); (T.C.); (S.M.K.G.); (M.S.I.); (F.B.H.); (H.I.); (I.A.); (N.X.)
- Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD 4222, Australia;
| | - Alfred K. Lam
- Cancer Molecular Pathology, School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia; (S.A.); (T.C.); (S.M.K.G.); (M.S.I.); (F.B.H.); (H.I.); (I.A.); (N.X.)
- Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD 4222, Australia;
- Pathology Queensland, Gold Coast University Hospital, Southport, QLD 4215, Australia
| |
Collapse
|
36
|
Yadav S, Zhou S, He B, Du Y, Garmire LX. Deep-learning and transfer learning identify new breast cancer survival subtypes from single-cell imaging data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.14.23295578. [PMID: 37745392 PMCID: PMC10516066 DOI: 10.1101/2023.09.14.23295578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Quantitative models that explicitly capture single-cell resolution cell-cell interaction features to predict patient survival at population scale are currently missing. Here, we computationally extracted hundreds of features describing single-cell based cell-cell interactions and cellular phenotypes from a large, published cohort of cyto-images of breast cancer patients. We applied these features to a neural-network based Cox-nnet survival model and obtained high accuracy in predicting patient survival in test data (Concordance Index > 0.8). We identified seven survival subtypes using the top survival features, which present distinct profiles of epithelial, immune, fibroblast cells, and their interactions. We identified atypical subpopulations of TNBC patients with moderate prognosis (marked by GATA3 over-expression) and Luminal A patients with poor prognosis (marked by KRT6 and ACTA2 over-expression and CDH1 under-expression). These atypical subpopulations are validated in TCGA-BRCA and METABRIC datasets. This work provides important guidelines on bridging single-cell level information towards population-level survival prediction. STATEMENT OF TRANSLATIONAL RELEVANCE Our findings from a breast cancer population cohort demonstrate the clinical utility of using the single-cell level imaging mass cytometry (IMC) data as a new type of patient prognosis prediction marker. Not only did the prognosis prediction achieve high accuracy with a Concordance index score greater than 0.8, it also enabled the discovery of seven survival subtypes that are more distinguishable than the molecular subtypes. These new subtypes present distinct profiles of epithelial, immune, fibroblast cells, and their interactions. Most importantly, this study identified and validated atypical subpopulations of TNBC patients with moderate prognosis (GATA3 over-expression) and Luminal A patients with poor prognosis (KRT6 and ACTA2 over-expression and CDH1 under-expression), using multiple large breast cancer cohorts.
Collapse
|
37
|
Hershey BJ, Barozzi S, Orsenigo F, Pompei S, Iannelli F, Kamrad S, Matafora V, Pisati F, Calabrese L, Fragale G, Salvadori G, Martini E, Totaro MG, Magni S, Guan R, Parazzoli D, Maiuri P, Bachi A, Patil KR, Cosentino Lagomarsino M, Havas KM. Clonal cooperation through soluble metabolite exchange facilitates metastatic outgrowth by modulating Allee effect. SCIENCE ADVANCES 2023; 9:eadh4184. [PMID: 37713487 PMCID: PMC10881076 DOI: 10.1126/sciadv.adh4184] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 08/14/2023] [Indexed: 09/17/2023]
Abstract
Cancers feature substantial intratumoral heterogeneity of genetic and phenotypically distinct lineages. Although interactions between coexisting lineages are emerging as a potential contributor to tumor evolution, the extent and nature of these interactions remain largely unknown. We postulated that tumors develop ecological interactions that sustain diversity and facilitate metastasis. Using a combination of fluorescent barcoding, mathematical modeling, metabolic analysis, and in vivo models, we show that the Allee effect, i.e., growth dependency on population size, is a feature of tumor lineages and that cooperative ecological interactions between lineages alleviate the Allee barriers to growth in a model of triple-negative breast cancer. Soluble metabolite exchange formed the basis for these cooperative interactions and catalyzed the establishment of a polyclonal community that displayed enhanced metastatic dissemination and outgrowth in xenograft models. Our results highlight interclonal metabolite exchange as a key modulator of tumor ecology and a contributing factor to overcoming Allee effect-associated growth barriers to metastasis.
Collapse
Affiliation(s)
| | - Sara Barozzi
- IFOM ETS The AIRC Institute of Molecular Oncology, Milan, Italy
| | | | - Simone Pompei
- IFOM ETS The AIRC Institute of Molecular Oncology, Milan, Italy
| | - Fabio Iannelli
- IFOM ETS The AIRC Institute of Molecular Oncology, Milan, Italy
| | | | | | | | | | | | | | | | | | - Serena Magni
- IFOM ETS The AIRC Institute of Molecular Oncology, Milan, Italy
| | - Rui Guan
- Medical Research Council Toxicology Unit, Cambridge, UK
| | - Dario Parazzoli
- IFOM ETS The AIRC Institute of Molecular Oncology, Milan, Italy
| | | | - Angela Bachi
- IFOM ETS The AIRC Institute of Molecular Oncology, Milan, Italy
| | | | | | | |
Collapse
|
38
|
Yaghoubi Naei V, Bordhan P, Mirakhorli F, Khorrami M, Shrestha J, Nazari H, Kulasinghe A, Ebrahimi Warkiani M. Advances in novel strategies for isolation, characterization, and analysis of CTCs and ctDNA. Ther Adv Med Oncol 2023; 15:17588359231192401. [PMID: 37692363 PMCID: PMC10486235 DOI: 10.1177/17588359231192401] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 07/19/2023] [Indexed: 09/12/2023] Open
Abstract
Over the past decade, the detection and analysis of liquid biopsy biomarkers such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) have advanced significantly. They have received recognition for their clinical usefulness in detecting cancer at an early stage, monitoring disease, and evaluating treatment response. The emergence of liquid biopsy has been a helpful development, as it offers a minimally invasive, rapid, real-time monitoring, and possible alternative to traditional tissue biopsies. In resource-limited settings, the ideal platform for liquid biopsy should not only extract more CTCs or ctDNA from a minimal sample volume but also accurately represent the molecular heterogeneity of the patient's disease. This review covers novel strategies and advancements in CTC and ctDNA-based liquid biopsy platforms, including microfluidic applications and comprehensive analysis of molecular complexity. We discuss these systems' operational principles and performance efficiencies, as well as future opportunities and challenges for their implementation in clinical settings. In addition, we emphasize the importance of integrated platforms that incorporate machine learning and artificial intelligence in accurate liquid biopsy detection systems, which can greatly improve cancer management and enable precision diagnostics.
Collapse
Affiliation(s)
- Vahid Yaghoubi Naei
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
- Faculty of Medicine, Frazer Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Pritam Bordhan
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
- Faculty of Science, Institute for Biomedical Materials & Devices, University of Technology Sydney, Australia
| | - Fatemeh Mirakhorli
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
| | - Motahare Khorrami
- Immunology Research Center, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Jesus Shrestha
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
| | - Hojjatollah Nazari
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
| | - Arutha Kulasinghe
- Faculty of Medicine, Frazer Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Majid Ebrahimi Warkiani
- School of Biomedical Engineering, University of Technology Sydney, 1, Broadway, Ultimo New South Wales 2007, Australia
| |
Collapse
|
39
|
Guo W, Hu Y, Qian J, Zhu L, Cheng J, Liao J, Fan X. Laser capture microdissection for biomedical research: towards high-throughput, multi-omics, and single-cell resolution. J Genet Genomics 2023; 50:641-651. [PMID: 37544594 DOI: 10.1016/j.jgg.2023.07.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 07/28/2023] [Accepted: 07/28/2023] [Indexed: 08/08/2023]
Abstract
Spatial omics technologies have become powerful methods to provide valuable insights into cells and tissues within a complex context, significantly enhancing our understanding of the intricate and multifaceted biological system. With an increasing focus on spatial heterogeneity, there is a growing need for unbiased, spatially resolved omics technologies. Laser capture microdissection (LCM) is a cutting-edge method for acquiring spatial information that can quickly collect regions of interest (ROIs) from heterogeneous tissues, with resolutions ranging from single cells to cell populations. Thus, LCM has been widely used for studying the cellular and molecular mechanisms of diseases. This review focuses on the differences among four types of commonly used LCM technologies and their applications in omics and disease research. Key attributes of application cases are also highlighted, such as throughput and spatial resolution. In addition, we comprehensively discuss the existing challenges and the great potential of LCM in biomedical research, disease diagnosis, and targeted therapy from the perspective of high-throughput, multi-omics, and single-cell resolution.
Collapse
Affiliation(s)
- Wenbo Guo
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, Zhejiang 314100, China
| | - Yining Hu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, Zhejiang 314100, China
| | - Jingyang Qian
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, Zhejiang 314100, China
| | - Lidan Zhu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, Zhejiang 314100, China
| | - Junyun Cheng
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, Zhejiang 314100, China
| | - Jie Liao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, Zhejiang 314100, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, Zhejiang 314100, China.
| |
Collapse
|
40
|
Wang Y, Ali MA, Vallon-Christersson J, Humphreys K, Hartman J, Rantalainen M. Transcriptional intra-tumour heterogeneity predicted by deep learning in routine breast histopathology slides provides independent prognostic information. Eur J Cancer 2023; 191:112953. [PMID: 37494846 DOI: 10.1016/j.ejca.2023.112953] [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: 01/11/2023] [Revised: 06/05/2023] [Accepted: 06/17/2023] [Indexed: 07/28/2023]
Abstract
BACKGROUND Intra-tumour heterogeneity (ITH) causes diagnostic challenges and increases the risk for disease recurrence. Quantification of ITH is challenging and has not been demonstrated in large studies. It has previously been shown that deep learning can enable spatially resolved prediction of molecular phenotypes from digital histopathology whole slide images (WSIs). Here we propose a novel method (Deep-ITH) to predict and measure ITH, and we evaluate its prognostic performance in breast cancer. METHODS Deep convolutional neural networks were used to spatially predict gene-expression (PAM50 set) from WSIs. For each predicted transcript, 12 measures of heterogeneity were extracted in the training data set (N = 931). A prognostic score to dichotomise patients into Deep-ITH low- and high-risk groups was established using an elastic-net regularised Cox proportional hazards model (recurrence-free survival). Prognostic performance was evaluated in two independent data sets: SöS-BC-1 (N = 1358) and SCAN-B-Lund (N = 1262). RESULTS We observed an increase in risk of recurrence in the high-risk group with hazard ratio (HR) 2.11 (95%CI:1.22-3.60; p = 0.007) using nested cross-validation. Subgroup analyses confirmed the prognostic performance in oestrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, grade 3, and large tumour subgroups. The prognostic value was confirmed in the independent SöS-BC-1 cohort (HR=1.84; 95%CI:1.03-3.3; p = 3.99 ×10-2). In the other external cohort, significant HR was observed in the subgroup of histological grade 2 patients, as well as in the subgroup of patients with small tumours (<20 mm). CONCLUSION We developed a novel method for an automated, scalable, and cost-efficient measure of ITH from WSIs that provides independent prognostic value for breast cancer. SIGNIFICANCE Transcriptional ITH predicted by deep learning models enables prediction of patient survival from routine histopathology WSIs in breast cancer.
Collapse
Affiliation(s)
- Yinxi Wang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Maya Alsheh Ali
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | | | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Johan Hartman
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden; MedTechLabs, BioClinicum, Karolinska University Hospital, Solna, Sweden
| | - Mattias Rantalainen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; MedTechLabs, BioClinicum, Karolinska University Hospital, Solna, Sweden.
| |
Collapse
|
41
|
Cheng C, Nguyen TT, Tang M, Wang X, Jiang C, Liu Y, Gorlov I, Gorlova O, Iafrate J, Lanuti M, Christiani DC, Amos CI. Immune Infiltration in Tumor and Adjacent Non-Neoplastic Regions Codetermines Patient Clinical Outcomes in Early-Stage Lung Cancer. J Thorac Oncol 2023; 18:1184-1198. [PMID: 37146750 PMCID: PMC10528252 DOI: 10.1016/j.jtho.2023.04.022] [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: 08/16/2022] [Revised: 04/24/2023] [Accepted: 04/26/2023] [Indexed: 05/07/2023]
Abstract
INTRODUCTION In recent years, the proportion of patients with NSCLC diagnosed at an early stage has increased continuously. METHODS In this study, we analyzed samples and data collected from 119 samples from 67 early stage patients with NSCLC, including 52 pairs of tumor and adjacent non-neoplastic samples, and performed RNA-sequencing analysis with high sequencing depth. RESULTS We found that immune-related genes were highly enriched among the differentially expressed genes and observed significantly higher inferred immune infiltration levels in adjacent non-neoplastic samples than in tumor samples. In survival analysis, the infiltration of certain immune cell types in tumor, but not adjacent non-neoplastic, samples were associated with overall patient survival, and excitingly, the differential infiltration between paired samples (tumor minus non-neoplastic) was more prognostic than expression in either non-neoplastic or tumor tissues. We also performed B cell receptor (BCR) and T cell receptor (TCR) repertoire analysis and observed more BCR/TCR clonotypes and increased BCR clonality in tumor than in non-neoplastic samples. Finally, we carefully quantified the fraction of the five histologic subtypes in our adenocarcinoma samples and found that higher histologic pattern complexity was associated with higher immune infiltration and low TCR clonality in the tumor-proximal regions. CONCLUSIONS Our results indicated significantly differential immune characteristics between tumor and adjacent non-neoplastic samples and suggested that the two regions provided complementary prognostic values in early-stage NSCLCs.
Collapse
Affiliation(s)
- Chao Cheng
- Department of Medicine, Baylor College of Medicine, Houston, Texas; Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas; The Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Thinh T Nguyen
- Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Mabel Tang
- Department of Biosciences, Rice University, Houston, Texas
| | - Xinan Wang
- Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts
| | - Chongming Jiang
- Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Yanhong Liu
- Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Ivan Gorlov
- Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Olga Gorlova
- Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - John Iafrate
- Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts
| | - Michael Lanuti
- Department of Surgery, Thoracic Surgery Division, Massachusetts General Hospital, Boston, Massachusetts
| | - David C Christiani
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts; Pulmonary and Critical Care Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Christopher I Amos
- Department of Medicine, Baylor College of Medicine, Houston, Texas; Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas; The Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas.
| |
Collapse
|
42
|
Wang K, Kumar T, Wang J, Minussi DC, Sei E, Li J, Tran TM, Thennavan A, Hu M, Casasent AK, Xiao Z, Bai S, Yang L, King LM, Shah V, Kristel P, van der Borden CL, Marks JR, Zhao Y, Zurita AJ, Aparicio A, Chapin B, Ye J, Zhang J, Gibbons DL, Sawyer E, Thompson AM, Futreal A, Hwang ES, Wesseling J, Lips EH, Navin NE. Archival single-cell genomics reveals persistent subclones during DCIS progression. Cell 2023; 186:3968-3982.e15. [PMID: 37586362 DOI: 10.1016/j.cell.2023.07.024] [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: 01/08/2023] [Revised: 05/09/2023] [Accepted: 07/17/2023] [Indexed: 08/18/2023]
Abstract
Ductal carcinoma in situ (DCIS) is a common precursor of invasive breast cancer. Our understanding of its genomic progression to recurrent disease remains poor, partly due to challenges associated with the genomic profiling of formalin-fixed paraffin-embedded (FFPE) materials. Here, we developed Arc-well, a high-throughput single-cell DNA-sequencing method that is compatible with FFPE materials. We validated our method by profiling 40,330 single cells from cell lines, a frozen tissue, and 27 FFPE samples from breast, lung, and prostate tumors stored for 3-31 years. Analysis of 10 patients with matched DCIS and cancers that recurred 2-16 years later show that many primary DCIS had already undergone whole-genome doubling and clonal diversification and that they shared genomic lineages with persistent subclones in the recurrences. Evolutionary analysis suggests that most DCIS cases in our cohort underwent an evolutionary bottleneck, and further identified chromosome aberrations in the persistent subclones that were associated with recurrence.
Collapse
Affiliation(s)
- Kaile Wang
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Tapsi Kumar
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA; MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA; Department of Genomic Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Junke Wang
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA; MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Darlan Conterno Minussi
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA; MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Emi Sei
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jianzhuo Li
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Tuan M Tran
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Aatish Thennavan
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Min Hu
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Anna K Casasent
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Zhenna Xiao
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Shanshan Bai
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Lei Yang
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA; MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Lorraine M King
- Department of Surgery, Duke University School of Medicine, Durham, NC 27707, USA
| | - Vandna Shah
- School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, Guy's Cancer Centre, King's College London, London WC2R 2LS, UK
| | - Petra Kristel
- Division of Molecular Pathology, the Netherlands Cancer Institute, Amsterdam 1066 CX, the Netherlands
| | - Carolien L van der Borden
- Division of Molecular Pathology, the Netherlands Cancer Institute, Amsterdam 1066 CX, the Netherlands
| | - Jeffrey R Marks
- Department of Surgery, Duke University School of Medicine, Durham, NC 27707, USA
| | - Yuehui Zhao
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Amado J Zurita
- Department of Genitourinary Medical Oncology, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Ana Aparicio
- Department of Genitourinary Medical Oncology, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Brian Chapin
- Department of Urology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jie Ye
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA; MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA; Department of Thoracic/Head and Neck Medical Oncology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jianjun Zhang
- Department of Genomic Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Thoracic/Head and Neck Medical Oncology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Don L Gibbons
- Department of Thoracic/Head and Neck Medical Oncology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ellinor Sawyer
- School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, Guy's Cancer Centre, King's College London, London WC2R 2LS, UK
| | - Alastair M Thompson
- Department of Surgery, Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Andrew Futreal
- Department of Genomic Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - E Shelley Hwang
- Department of Surgery, Duke University School of Medicine, Durham, NC 27707, USA
| | - Jelle Wesseling
- Department of Pathology, the Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam 1066 CX, the Netherlands; Department of Pathology, Leiden University Medical Center, Leiden 2333 ZC, the Netherlands
| | - Esther H Lips
- Department of Pathology, the Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam 1066 CX, the Netherlands; Department of Pathology, Leiden University Medical Center, Leiden 2333 ZC, the Netherlands
| | - Nicholas E Navin
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA; MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA; Department of Bioinformatics, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
| |
Collapse
|
43
|
Martino F, Lupi M, Giraudo E, Lanzetti L. Breast cancers as ecosystems: a metabolic perspective. Cell Mol Life Sci 2023; 80:244. [PMID: 37561190 PMCID: PMC10415483 DOI: 10.1007/s00018-023-04902-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/18/2023] [Accepted: 07/28/2023] [Indexed: 08/11/2023]
Abstract
Breast cancer (BC) is the most frequently diagnosed cancer and one of the major causes of cancer death. Despite enormous progress in its management, both from the therapeutic and early diagnosis viewpoints, still around 700,000 patients succumb to the disease each year, worldwide. Late recurrency is the major problem in BC, with many patients developing distant metastases several years after the successful eradication of the primary tumor. This is linked to the phenomenon of metastatic dormancy, a still mysterious trait of the natural history of BC, and of several other types of cancer, by which metastatic cells remain dormant for long periods of time before becoming reactivated to initiate the clinical metastatic disease. In recent years, it has become clear that cancers are best understood if studied as ecosystems in which the impact of non-cancer-cell-autonomous events-dependent on complex interaction between the cancer and its environment, both local and systemic-plays a paramount role, probably as significant as the cell-autonomous alterations occurring in the cancer cell. In adopting this perspective, a metabolic vision of the cancer ecosystem is bound to improve our understanding of the natural history of cancer, across space and time. In BC, many metabolic pathways are coopted into the cancer ecosystem, to serve the anabolic and energy demands of the cancer. Their study is shedding new light on the most critical aspect of BC management, of metastatic dissemination, and that of the related phenomenon of dormancy and fostering the application of the knowledge to the development of metabolic therapies.
Collapse
Affiliation(s)
- Flavia Martino
- Department of Oncology, University of Torino Medical School, Turin, Italy
- Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy
| | - Mariadomenica Lupi
- Department of Oncology, University of Torino Medical School, Turin, Italy
- Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy
| | - Enrico Giraudo
- Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy
- Department of Science and Drug Technology, University of Torino, Turin, Italy
| | - Letizia Lanzetti
- Department of Oncology, University of Torino Medical School, Turin, Italy.
- Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy.
| |
Collapse
|
44
|
Thakur S, Haider S, Natrajan R. Implications of tumour heterogeneity on cancer evolution and therapy resistance: lessons from breast cancer. J Pathol 2023; 260:621-636. [PMID: 37587096 DOI: 10.1002/path.6158] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/11/2023] [Accepted: 06/14/2023] [Indexed: 08/18/2023]
Abstract
Tumour heterogeneity is pervasive amongst many cancers and leads to disease progression, and therapy resistance. In this review, using breast cancer as an exemplar, we focus on the recent advances in understanding the interplay between tumour cells and their microenvironment using single cell sequencing and digital spatial profiling technologies. Further, we discuss the utility of lineage tracing methodologies in pre-clinical models of breast cancer, and how these are being used to unravel new therapeutic vulnerabilities and reveal biomarkers of breast cancer progression. © 2023 The Pathological Society of Great Britain and Ireland.
Collapse
Affiliation(s)
- Shefali Thakur
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Syed Haider
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Rachael Natrajan
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| |
Collapse
|
45
|
Myers MA, Arnold BJ, Bansal V, Mullen KM, Zaccaria S, Raphael BJ. HATCHet2: clone- and haplotype-specific copy number inference from bulk tumor sequencing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.13.548855. [PMID: 37502835 PMCID: PMC10370020 DOI: 10.1101/2023.07.13.548855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Multi-region DNA sequencing of primary tumors and metastases from individual patients helps identify somatic aberrations driving cancer development. However, most methods to infer copy-number aberrations (CNAs) analyze individual samples. We introduce HATCHet2 to identify haplotype- and clone-specific CNAs simultaneously from multiple bulk samples. HATCHet2 introduces a novel statistic, the mirrored haplotype B-allele frequency (mhBAF), to identify mirrored-subclonal CNAs having different numbers of copies of parental haplotypes in different tumor clones. HATCHet2 also has high accuracy in identifying focal CNAs and extends the earlier HATCHet method in several directions. We demonstrate HATCHet2's improved accuracy using simulations and a single-cell sequencing dataset. HATCHet2 analysis of 50 prostate cancer samples from 10 patients reveals previously-unreported mirrored-subclonal CNAs affecting cancer genes.
Collapse
Affiliation(s)
- Matthew A. Myers
- Department of Computer Science, Princeton University, Princeton, USA
| | - Brian J. Arnold
- Center for Statistics and Machine Learning, Princeton University, Princeton, USA
| | - Vineet Bansal
- Princeton Research Computing, Princeton University, Princeton, NJ, USA
| | - Katelyn M. Mullen
- Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Simone Zaccaria
- Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | | |
Collapse
|
46
|
van Amerongen R, Bentires-Alj M, van Boxtel AL, Clarke RB, Fre S, Suarez EG, Iggo R, Jechlinger M, Jonkers J, Mikkola ML, Koledova ZS, Sørlie T, Vivanco MDM. Imagine beyond: recent breakthroughs and next challenges in mammary gland biology and breast cancer research. J Mammary Gland Biol Neoplasia 2023; 28:17. [PMID: 37450065 PMCID: PMC10349020 DOI: 10.1007/s10911-023-09544-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 06/25/2023] [Indexed: 07/18/2023] Open
Abstract
On 8 December 2022 the organizing committee of the European Network for Breast Development and Cancer labs (ENBDC) held its fifth annual Think Tank meeting in Amsterdam, the Netherlands. Here, we embraced the opportunity to look back to identify the most prominent breakthroughs of the past ten years and to reflect on the main challenges that lie ahead for our field in the years to come. The outcomes of these discussions are presented in this position paper, in the hope that it will serve as a summary of the current state of affairs in mammary gland biology and breast cancer research for early career researchers and other newcomers in the field, and as inspiration for scientists and clinicians to move the field forward.
Collapse
Affiliation(s)
- Renée van Amerongen
- Developmental, Stem Cell and Cancer Biology, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands.
| | - Mohamed Bentires-Alj
- Laboratory of Tumor Heterogeneity, Metastasis and Resistance, Department of Biomedicine, University of Basel and University Hospital of Basel, Basel, Switzerland
| | - Antonius L van Boxtel
- Developmental, Stem Cell and Cancer Biology, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands
| | - Robert B Clarke
- Manchester Breast Centre, Division of Cancer Sciences, School of Medical Sciences, University of Manchester, Manchester, UK
| | - Silvia Fre
- Institut Curie, Genetics and Developmental Biology Department, PSL Research University, CNRS UMR3215, U93475248, InsermParis, France
| | - Eva Gonzalez Suarez
- Transformation and Metastasis Laboratory, Molecular Oncology, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
- Oncobell, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
| | - Richard Iggo
- INSERM U1312, University of Bordeaux, 33076, Bordeaux, France
| | - Martin Jechlinger
- Cell Biology and Biophysics Department, EMBL, Heidelberg, Germany
- Molit Institute of Personalized Medicine, Heilbronn, Germany
| | - Jos Jonkers
- Division of Molecular Pathology, Oncode Institute, Netherlands Cancer Institute, Plesmanlaan 121, 1066CX, Amsterdam, The Netherlands
| | - Marja L Mikkola
- Institute of Biotechnology, HiLIFE Helsinki Institute of Life Science, University of Helsinki, P.O.B. 56, 00014, Helsinki, Finland
| | - Zuzana Sumbalova Koledova
- Department of Histology and Embryology, Faculty of Medicine, Masaryk University, Kamenice 5, 625 00, Brno, Czech Republic
| | - Therese Sørlie
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Maria dM Vivanco
- Cancer Heterogeneity Lab, CIC bioGUNE, Basque Research and Technology Alliance, BRTA, Technological Park Bizkaia, 48160, Derio, Spain
| |
Collapse
|
47
|
Houser AE, Kazmi A, Nair AK, Ji AL. The Use of Single-Cell RNA-Sequencing and Spatial Transcriptomics in Understanding the Pathogenesis and Treatment of Skin Diseases. JID INNOVATIONS 2023; 3:100198. [PMID: 37205302 PMCID: PMC10186616 DOI: 10.1016/j.xjidi.2023.100198] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 02/15/2023] [Accepted: 02/27/2023] [Indexed: 05/21/2023] Open
Abstract
The development of multiomic profiling tools has rapidly expanded in recent years, along with their use in profiling skin tissues in various contexts, including dermatologic diseases. Among these tools, single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST) have emerged as widely adopted and powerful assays for elucidating key cellular components and their spatial arrangement within skin disease. In this paper, we review the recent biological insights gained from the use of scRNA-seq and ST and the advantages of combining both for profiling skin diseases, including aberrant wound healing, inflammatory skin diseases, and cancer. We discuss the role of scRNA-seq and ST in improving skin disease treatments and moving toward the goal of achieving precision medicine in dermatology, whereby patients can be optimally matched to treatments that maximize therapeutic response.
Collapse
Affiliation(s)
- Aubrey E. Houser
- Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Abiha Kazmi
- Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Arjun K. Nair
- Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Andrew L. Ji
- Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| |
Collapse
|
48
|
Massimino M, Martorana F, Stella S, Vitale SR, Tomarchio C, Manzella L, Vigneri P. Single-Cell Analysis in the Omics Era: Technologies and Applications in Cancer. Genes (Basel) 2023; 14:1330. [PMID: 37510235 PMCID: PMC10380065 DOI: 10.3390/genes14071330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 07/30/2023] Open
Abstract
Cancer molecular profiling obtained with conventional bulk sequencing describes average alterations obtained from the entire cellular population analyzed. In the era of precision medicine, this approach is unable to track tumor heterogeneity and cannot be exploited to unravel the biological processes behind clonal evolution. In the last few years, functional single-cell omics has improved our understanding of cancer heterogeneity. This approach requires isolation and identification of single cells starting from an entire population. A cell suspension obtained by tumor tissue dissociation or hematological material can be manipulated using different techniques to separate individual cells, employed for single-cell downstream analysis. Single-cell data can then be used to analyze cell-cell diversity, thus mapping evolving cancer biological processes. Despite its unquestionable advantages, single-cell analysis produces massive amounts of data with several potential biases, stemming from cell manipulation and pre-amplification steps. To overcome these limitations, several bioinformatic approaches have been developed and explored. In this work, we provide an overview of this entire process while discussing the most recent advances in the field of functional omics at single-cell resolution.
Collapse
Affiliation(s)
- Michele Massimino
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Federica Martorana
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Stefania Stella
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Silvia Rita Vitale
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Cristina Tomarchio
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Livia Manzella
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Paolo Vigneri
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
- Humanitas Istituto Clinico Catanese, University Oncology Department, 95045 Catania, Italy
| |
Collapse
|
49
|
Zhu X, Zhao W, Zhou Z, Gu X. Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools. J Mol Evol 2023:10.1007/s00239-023-10117-0. [PMID: 37246992 DOI: 10.1007/s00239-023-10117-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 05/09/2023] [Indexed: 05/30/2023]
Abstract
Cancer originates from somatic cells that have accumulated mutations. These mutations alter the phenotype of the cells, allowing them to escape homeostatic regulation that maintains normal cell numbers. The emergence of malignancies is an evolutionary process in which the random accumulation of somatic mutations and sequential selection of dominant clones cause cancer cells to proliferate. The development of technologies such as high-throughput sequencing has provided a powerful means to measure subclonal evolutionary dynamics across space and time. Here, we review the patterns that may be observed in cancer evolution and the methods available for quantifying the evolutionary dynamics of cancer. An improved understanding of the evolutionary trajectories of cancer will enable us to explore the molecular mechanism of tumorigenesis and to design tailored treatment strategies.
Collapse
Affiliation(s)
- Xunuo Zhu
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Wenyi Zhao
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Zhan Zhou
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, 322000, China.
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 310058, China.
| | - Xun Gu
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, 50011, USA.
| |
Collapse
|
50
|
Yu X, Liu R, Gao W, Wang X, Zhang Y. Single-cell omics traces the heterogeneity of prostate cancer cells and the tumor microenvironment. Cell Mol Biol Lett 2023; 28:38. [PMID: 37161356 PMCID: PMC10170780 DOI: 10.1186/s11658-023-00450-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 04/17/2023] [Indexed: 05/11/2023] Open
Abstract
Prostate cancer is one of the more heterogeneous tumour types. In recent years, with the rapid development of single-cell sequencing and spatial transcriptome technologies, researchers have gained a more intuitive and comprehensive understanding of the heterogeneity of prostate cancer. Tumour-associated epithelial cells; cancer-associated fibroblasts; the complexity of the immune microenvironment, and the heterogeneity of the spatial distribution of tumour cells and other cancer-promoting molecules play a crucial role in the growth, invasion, and metastasis of prostate cancer. Single-cell multi-omics biotechnology, especially single-cell transcriptome sequencing, reveals the expression level of single cells with higher resolution and finely dissects the molecular characteristics of different tumour cells. We reviewed the recent literature on prostate cancer cells, focusing on single-cell RNA sequencing. And we analysed the heterogeneity and spatial distribution differences of different tumour cell types. We discussed the impact of novel single-cell omics technologies, such as rich omics exploration strategies, multi-omics joint analysis modes, and deep learning models, on future prostate cancer research. In this review, we have constructed a comprehensive catalogue of single-cell omics studies in prostate cancer. This article aimed to provide a more thorough understanding of the diagnosis and treatment of prostate cancer. We summarised and proposed several key issues and directions on applying single-cell multi-omics and spatial transcriptomics to understand the heterogeneity of prostate cancer. Finally, we discussed single-cell omics trends and future directions in prostate cancer.
Collapse
Affiliation(s)
- Xudong Yu
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China
- Beijing Tumour Minimally Invasive Medical Center of Integrated Traditional Chinese and Western Medicine, Beijing, 101121, China
| | - Ruijia Liu
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China
| | - Wenfeng Gao
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China
| | - Xuyun Wang
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, 100010, China.
| | - Yaosheng Zhang
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China.
- Beijing Tumour Minimally Invasive Medical Center of Integrated Traditional Chinese and Western Medicine, Beijing, 101121, China.
| |
Collapse
|