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Mulholland EJ, Leedham SJ. Redefining clinical practice through spatial profiling: a revolution in tissue analysis. Ann R Coll Surg Engl 2024; 106:305-312. [PMID: 38555868 PMCID: PMC10981989 DOI: 10.1308/rcsann.2023.0091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/25/2023] [Indexed: 04/02/2024] Open
Abstract
Spatial biology, which combines molecular biology and advanced imaging, enhances our understanding of tissue cellular organisation. Despite its potential, spatial omics encounters challenges related to data complexity, computational requirements and standardisation of analysis. In clinical applications, spatial omics has the potential to revolutionise biomarker discovery, disease stratification and personalised treatments. It can identify disease-specific cell patterns, and could help risk stratify patients for clinical trials and disease-appropriate therapies. Although there are challenges in adopting it in clinical practice, spatial omics has the potential to significantly enhance patient outcomes. In this paper, we discuss the recent evolution of spatial biology, and its potential for improving our tissue level understanding and treatment of disease, to help advance precision and effectiveness in healthcare interventions.
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Yan W, Ren Z, Chen X, Zhang R, Lv J, Verma V, Wu M, Chen D, Yu J. Potential Role of Lymphocyte CD44 in Determining Treatment Selection Between Stereotactic Body Radiation Therapy and Surgery for Early-Stage Non-Small Cell Lung Cancer. Int J Radiat Oncol Biol Phys 2024:S0360-3016(24)00356-0. [PMID: 38447611 DOI: 10.1016/j.ijrobp.2024.02.048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 02/03/2024] [Accepted: 02/12/2024] [Indexed: 03/08/2024]
Abstract
PURPOSE Stereotactic body radiation therapy (SBRT) versus surgery for operable early-stage non-small cell lung cancer (ES-NSCLC) remains highly debated. Herein, we used spatial proteomics to identify whether any molecular biomarker(s) associate with the efficacy of either modality, in efforts to optimize treatment selection between surgery and SBRT for this population. METHODS AND MATERIALS We evaluated biopsy tissue samples from 44 patients with ES-NSCLC treated with first-line SBRT (cohort 1) by GeoMx Digital Spatial Profiling (DSP) with a panel of 70 proteins in 5 spatial molecular compartments: tumor (panCK+), leukocyte (CD45+), lymphocyte (CD3+), macrophage (CD68+), and stroma (α-SMA+). To validate the findings in cohort 1, biopsy samples from 52 patients with ES-NSCLC who received SBRT (cohort 2) and 62 patients with ES-NSCLC who underwent surgery (cohort 3) were collected and analyzed by multiplex immunofluorescence (mIF). RESULTS In cohort 1, higher CD44 expression in the lymphocyte compartment was associated with poorer recurrence-free survival (RFS) (DSP: P < .001; mIF: P < .001) and higher recurrence rate (DSP: P = .001; mIF: P = .004). mIF data from cohort 2 validated these findings (P < .05 for all). From cohort 3, higher lymphocyte CD44 predicted higher RFS after surgery (P = .003). Intermodality comparisons demonstrated that SBRT was associated with significantly higher RFS over surgery in CD44-low patients (P < .001), but surgery was superior to SBRT in CD44-high cases (P = .016). CONCLUSIONS Lymphocyte CD44 may not only be a predictor of SBRT efficacy in this population but also an important biomarker (pending validation by large prospective data) that could better sharpen selection for SBRT versus surgery in ES-NSCLC.
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Affiliation(s)
- Weiwei Yan
- Cheeloo College of Medicine, Shandong University Cancer Center, Jinan, Shandong, China; Department of Radiation Oncology and Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Ziyuan Ren
- Cheeloo College of Medicine, Shandong University Cancer Center, Jinan, Shandong, China; Department of Radiation Oncology and Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Xi Chen
- Cheeloo College of Medicine, Shandong University Cancer Center, Jinan, Shandong, China; Department of Radiation Oncology and Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Ran Zhang
- Department of Radiation Oncology and Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Juncai Lv
- Department of Radiation Oncology and Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Vivek Verma
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Meng Wu
- Department of Radiation Oncology and Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Dawei Chen
- Cheeloo College of Medicine, Shandong University Cancer Center, Jinan, Shandong, China; Department of Radiation Oncology and Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China.
| | - Jinming Yu
- Cheeloo College of Medicine, Shandong University Cancer Center, Jinan, Shandong, China; Department of Radiation Oncology and Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China.
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Masotti M, Osher N, Eliason J, Rao A, Baladandayuthapani V. DIMPLE: An R package to quantify, visualize, and model spatial cellular interactions from multiplex imaging with distance matrices. Patterns (N Y) 2023; 4:100879. [PMID: 38106614 PMCID: PMC10724356 DOI: 10.1016/j.patter.2023.100879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 09/11/2023] [Accepted: 10/24/2023] [Indexed: 12/19/2023]
Abstract
A major challenge in the spatial analysis of multiplex imaging (MI) data is choosing how to measure cellular spatial interactions and how to relate them to patient outcomes. Existing methods to quantify cell-cell interactions do not scale to the rapidly evolving technical landscape, where both the number of unique cell types and the number of images in a dataset may be large. We propose a scalable analytical framework and accompanying R package, DIMPLE, to quantify, visualize, and model cell-cell interactions in the TME. By applying DIMPLE to publicly available MI data, we uncover statistically significant associations between image-level measures of cell-cell interactions and patient-level covariates.
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Affiliation(s)
- Maria Masotti
- University of Michigan, Department of Biostatistics, Ann Arbor, MI 48109, USA
| | - Nathaniel Osher
- University of Michigan, Department of Biostatistics, Ann Arbor, MI 48109, USA
| | - Joel Eliason
- University of Michigan, Department of Computational Medicine and Bioinformatics, Ann Arbor, MI 48109, USA
| | - Arvind Rao
- University of Michigan, Department of Biostatistics, Ann Arbor, MI 48109, USA
- University of Michigan, Department of Computational Medicine and Bioinformatics, Ann Arbor, MI 48109, USA
| | - Veerabhadran Baladandayuthapani
- University of Michigan, Department of Biostatistics, Ann Arbor, MI 48109, USA
- University of Michigan, Department of Computational Medicine and Bioinformatics, Ann Arbor, MI 48109, USA
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Lim Y, Choi S, Oh HJ, Kim C, Song S, Kim S, Song H, Park S, Kim JW, Kim JW, Kim JH, Kang M, Kang SB, Kim DW, Oh HK, Lee HS, Lee KW. Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes for prediction of prognosis in resected colon cancer. NPJ Precis Oncol 2023; 7:124. [PMID: 37985785 PMCID: PMC10662481 DOI: 10.1038/s41698-023-00470-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 10/24/2023] [Indexed: 11/22/2023] Open
Abstract
Tumor-infiltrating lymphocytes (TIL) have been suggested as an important prognostic marker in colorectal cancer, but assessment usually requires additional tissue processing and interpretational efforts. The aim of this study is to assess the clinical significance of artificial intelligence (AI)-powered spatial TIL analysis using only a hematoxylin and eosin (H&E)-stained whole-slide image (WSI) for the prediction of prognosis in stage II-III colon cancer treated with surgery and adjuvant therapy. In this retrospective study, we used Lunit SCOPE IO, an AI-powered H&E WSI analyzer, to assess intratumoral TIL (iTIL) and tumor-related stromal TIL (sTIL) densities from WSIs of 289 patients. The patients with confirmed recurrences had significantly lower sTIL densities (mean sTIL density 630.2/mm2 in cases with confirmed recurrence vs. 1021.3/mm2 in no recurrence, p < 0.001). Additionally, significantly higher recurrence rates were observed in patients having sTIL or iTIL in the lower quartile groups. Risk groups defined as high-risk (both iTIL and sTIL in the lowest quartile groups), low-risk (sTIL higher than the median), or intermediate-risk (not high- or low-risk) were predictive of recurrence and were independently associated with clinical outcomes after adjusting for other clinical factors. AI-powered TIL analysis can provide prognostic information in stage II/III colon cancer in a practical manner.
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Affiliation(s)
| | - Songji Choi
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Hyeon Jeong Oh
- Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
| | - Chanyoung Kim
- Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | | | | | | | | | - Ji-Won Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Jin Won Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Jee Hyun Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Minsu Kang
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Sung-Bum Kang
- Department of Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Duck-Woo Kim
- Department of Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Heung-Kwon Oh
- Department of Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Hye Seung Lee
- Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Keun-Wook Lee
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea.
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Hathaway CA, Townsend MK, Conejo-Garcia JR, Fridley BL, Moran Segura C, Nguyen JV, Armaiz-Pena GN, Sasamoto N, Saeed-Vafa D, Terry KL, Kubzansky LD, Tworoger SS. The relationship of lifetime history of depression on the ovarian tumor immune microenvironment. Brain Behav Immun 2023; 114:52-60. [PMID: 37557966 PMCID: PMC10592154 DOI: 10.1016/j.bbi.2023.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 08/04/2023] [Accepted: 08/06/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND Depression is associated with a higher ovarian cancer risk. Prior work suggests that depression can lead to systemic immune suppression, which could potentially alter the anti-tumor immune response. METHODS We evaluated the association of pre-diagnosis depression with features of the anti-tumor immune response, including T and B cells and immunoglobulins, among women with ovarian tumor tissue collected in three studies, the Nurses' Health Study (NHS; n = 237), NHSII (n = 137) and New England Case-Control Study (NECC; n = 215). Women reporting depressive symptoms above a clinically relevant cut-point, antidepressant use, or physician diagnosis of depression at any time prior to diagnosis of ovarian cancer were considered to have pre-diagnosis depression. Multiplex immunofluorescence was performed on tumor tissue microarrays to measure immune cell infiltration. In pooled analyses, we estimated odds ratios (OR) and 95% confidence intervals (CI) for the positivity of tumor immune cells using a beta-binomial model comparing those with and without depression. We used Bonferroni corrections to adjust for multiple comparisons. RESULTS We observed no statistically significant association between depression status and any immune markers at the Bonferroni corrected p-value of 0.0045; however, several immune markers were significant at a nominal p-value of 0.05. Specifically, there were increased odds of having recently activated cytotoxic (CD3+CD8+CD69+) and exhausted-like T cells (CD3+Lag3+) in tumors of women with vs. without depression (OR = 1.36, 95 %CI = 1.09-1.69 and OR = 1.24, 95 %CI = 1.01-1.53, respectively). Associations were comparable when considering high grade serous tumors only (comparable ORs = 1.33, 95 %CI = 1.05-1.69 and OR = 1.25, 95 %CI = 0.99-1.58, respectively). There were decreased odds of having tumor infiltrating plasma cells (CD138+) in women with vs. without depression (OR = 0.54, 95 %CI = 0.33-0.90), which was similar among high grade serous carcinomas, although not statistically significant. Depression was also related to decreased odds of having naïve and memory B cells (CD20+: OR = 0.54, 95 %CI = 0.30-0.98) and increased odds of IgG (OR = 1.22, 95 %CI = 0.97-1.53) in high grade serous carcinomas. CONCLUSION Our results provide suggestive evidence that depression may influence ovarian cancer outcomes through changes in the tumor immune microenvironment, including increasing T cell activation and exhaustion and reducing antibody-producing B cells. Further studies with clinical measures of depression and larger samples are needed to confirm these results.
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Affiliation(s)
| | - Mary K Townsend
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | | | - Brooke L Fridley
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Carlos Moran Segura
- Advanced Analytical and Digital Laboratory, Moffitt Cancer Center, Tampa, FL, USA
| | - Jonathan V Nguyen
- Advanced Analytical and Digital Laboratory, Moffitt Cancer Center, Tampa, FL, USA
| | - Guillermo N Armaiz-Pena
- Department of Basic Sciences, Division of Pharmacology, School of Medicine, Ponce Health Sciences University, Ponce, PR, USA
| | - Naoko Sasamoto
- Department of Obstetrics, Gynecology, and Reproductive Biology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Daryoush Saeed-Vafa
- Advanced Analytical and Digital Laboratory, Moffitt Cancer Center, Tampa, FL, USA; Department of Anatomic Pathology, Moffitt Cancer Center, Tampa, FL, USA
| | - Kathryn L Terry
- Department of Obstetrics, Gynecology, and Reproductive Biology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Laura D Kubzansky
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Shelley S Tworoger
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA.
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Gray S, Ottensmeier CH. Advancing Understanding of Non-Small Cell Lung Cancer with Multiplexed Antibody-Based Spatial Imaging Technologies. Cancers (Basel) 2023; 15:4797. [PMID: 37835491 PMCID: PMC10571797 DOI: 10.3390/cancers15194797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/22/2023] [Accepted: 09/27/2023] [Indexed: 10/15/2023] Open
Abstract
Non-small cell lung cancer (NSCLC) remains a cause of significant morbidity and mortality, despite significant advances made in its treatment using immune checkpoint inhibitors (ICIs) over the last decade; while a minority experience prolonged responses with ICIs, benefit is limited for most patients. The development of multiplexed antibody-based (MAB) spatial tissue imaging technologies has revolutionised analysis of the tumour microenvironment (TME), enabling identification of a wide range of cell types and subtypes, and analysis of the spatial relationships and interactions between them. Such study has the potential to translate into a greater understanding of treatment susceptibility and resistance, factors influencing prognosis and recurrence risk, and identification of novel therapeutic approaches and rational treatment combinations to improve patient outcomes in the clinic. Herein we review studies that have leveraged MAB technologies to deliver novel insights into the TME of NSCLC.
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Affiliation(s)
- Simon Gray
- Department of Molecular and Clinical Cancer Medicine, Faculty of Health and Life Sciences, University of Liverpool, Ashton St., Liverpool L69 3GB, UK
- Department of Medical Oncology, The Clatterbridge Cancer Centre NHS Foundation Trust, Pembroke Pl., Liverpool L7 8YA, UK
| | - Christian H. Ottensmeier
- Department of Molecular and Clinical Cancer Medicine, Faculty of Health and Life Sciences, University of Liverpool, Ashton St., Liverpool L69 3GB, UK
- Department of Medical Oncology, The Clatterbridge Cancer Centre NHS Foundation Trust, Pembroke Pl., Liverpool L7 8YA, UK
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Yi M, Zhan T, Peck AR, Hooke JA, Kovatich AJ, Shriver CD, Hu H, Sun Y, Rui H, Chervoneva I. Quantile Index Biomarkers Based on Single-Cell Expression Data. J Transl Med 2023; 103:100158. [PMID: 37088463 PMCID: PMC10524910 DOI: 10.1016/j.labinv.2023.100158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 04/06/2023] [Accepted: 04/15/2023] [Indexed: 04/25/2023] Open
Abstract
Current histocytometry methods enable single-cell quantification of biomolecules in tumor tissue sections by multiple detection technologies, including multiplex fluorescence-based immunohistochemistry or in situ hybridization. Quantitative pathology platforms can provide distributions of cellular signal intensity (CSI) levels of biomolecules across the entire cell populations of interest within the sampled tumor tissue. However, the heterogeneity of CSI levels is usually ignored, and the simple mean signal intensity value is considered a cancer biomarker. Here we consider the entire distribution of CSI expression levels of a given biomolecule in the cancer cell population as a predictor of clinical outcome. The proposed quantile index (QI) biomarker is defined as the weighted average of CSI distribution quantiles in individual tumors. The weight for each quantile is determined by fitting a functional regression model for a clinical outcome. That is, the weights are optimized so that the resulting QI has the highest power to predict a relevant clinical outcome. The proposed QI biomarkers were derived for proteins expressed in cancer cells of malignant breast tumors and demonstrated improved prognostic value compared with the standard mean signal intensity predictors. The R package Qindex implementing QI biomarkers has been developed. The proposed approach is not limited to immunohistochemistry data and can be based on any cell-level expressions of proteins or nucleic acids.
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Affiliation(s)
- Misung Yi
- Division of Biostatistics, Department of Pharmacology and Experimental Therapeutics, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania.
| | - Tingting Zhan
- Division of Biostatistics, Department of Pharmacology and Experimental Therapeutics, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Amy R Peck
- Department of Pathology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Jeffrey A Hooke
- John P. Murtha Cancer Center, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, Maryland
| | - Albert J Kovatich
- John P. Murtha Cancer Center, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, Maryland
| | - Craig D Shriver
- John P. Murtha Cancer Center, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, Maryland
| | - Hai Hu
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, Pennsylvania
| | - Yunguang Sun
- Department of Pathology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Hallgeir Rui
- Department of Pathology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Inna Chervoneva
- Division of Biostatistics, Department of Pharmacology and Experimental Therapeutics, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania.
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MASOTTI MARIA, OSHER NATHANIEL, ELIASON JOEL, RAO ARVIND, BALADANDAYUTHAPANI VEERABHADRAN. DIMPLE: AN R PACKAGE TO QUANTIFY, VISUALIZE, AND MODEL SPATIAL CELLULAR INTERACTIONS FROM MULTIPLEX IMAGING WITH DISTANCE MATRICES. bioRxiv 2023:2023.07.20.548170. [PMID: 37503048 PMCID: PMC10370183 DOI: 10.1101/2023.07.20.548170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
The tumor microenvironment (TME) is a complex ecosystem containing tumor cells, other surrounding cells, blood vessels, and extracellular matrix. Recent advances in multiplexed imaging technologies allow researchers to map several cellular markers in the TME at the single cell level while preserving their spatial locations. Evidence is mounting that cellular interactions in the TME can promote or inhibit tumor development and contribute to drug resistance. Current statistical approaches to quantify cell-cell interactions do not readily scale to the outputs of new imaging technologies which can distinguish many unique cell phenotypes in one image. We propose a scalable analytical framework and accompanying R package, DIMPLE, to quantify, visualize, and model cell-cell interactions in the TME. In application of DIMPLE to publicly available MI data, we uncover statistically significant associations between image-level measures of cell-cell interactions and patient-level covariates.
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Affiliation(s)
| | | | - JOEL ELIASON
- University of Michigan Department of Computation Medicine and Bioinformatics
| | - ARVIND RAO
- University of Michigan Department Biostatistics
- University of Michigan Department of Computation Medicine and Bioinformatics
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9
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Hathaway CA, Conejo-Garcia JR, Fridley BL, Rosner B, Saeed-Vafa D, Segura CM, Nguyen JV, Hecht JL, Sasamoto N, Terry KL, Tworoger SS, Townsend MK. Measurement of Ovarian Tumor Immune Profiles by Multiplex Immunohistochemistry: Implications for Epidemiologic Studies. Cancer Epidemiol Biomarkers Prev 2023; 32:848-853. [PMID: 36940177 PMCID: PMC10239319 DOI: 10.1158/1055-9965.epi-22-1285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 02/22/2023] [Accepted: 03/16/2023] [Indexed: 03/21/2023] Open
Abstract
BACKGROUND Despite the immunogenic nature of many ovarian tumors, treatment with immune checkpoint therapies has not led to substantial improvements in ovarian cancer survival. To advance population-level research on the ovarian tumor immune microenvironment, it is critical to understand methodologic issues related to measurement of immune cells on tissue microarrays (TMA) using multiplex immunofluorescence (mIF) assays. METHODS In two prospective cohorts, we collected formalin-fixed, paraffin-embedded ovarian tumors from 486 cases and created seven TMAs. We measured T cells, including several sub-populations, and immune checkpoint markers on the TMAs using two mIF panels. We used Spearman correlations, Fisher exact tests, and multivariable-adjusted beta-binomial models to evaluate factors related to immune cell measurements in TMA tumor cores. RESULTS Between-core correlations of intratumoral immune markers ranged from 0.52 to 0.72, with more common markers (e.g., CD3+, CD3+CD8+) having higher correlations. Correlations of immune cell markers between the whole core, tumor area, and stromal area were high (range 0.69-0.97). In multivariable-adjusted models, odds of T-cell positivity were lower in clear cell and mucinous versus type II tumors (ORs, 0.13-0.48) and, for several sub-populations, were lower in older tissue (sample age > 30 versus ≤ 10 years; OR, 0.11-0.32). CONCLUSIONS Overall, high correlations between cores for immune markers measured via mIF support the use of TMAs in studying ovarian tumor immune infiltration, although very old samples may have reduced antigenicity. IMPACT Future epidemiologic studies should evaluate differences in the tumor immune response by histotype and identify modifiable factors that may alter the tumor immune microenvironment.
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Affiliation(s)
| | | | - Brooke L. Fridley
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, Florida, USA
| | - Bernard Rosner
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Daryoush Saeed-Vafa
- Department of Anatomic Pathology, Moffitt Cancer Center, Tampa, Florida, USA
- Advanced Analytical and Digital Laboratory, Moffitt Cancer Center, Tampa, Florida, USA
| | - Carlos Moran Segura
- Advanced Analytical and Digital Laboratory, Moffitt Cancer Center, Tampa, Florida, USA
| | - Jonathan V. Nguyen
- Advanced Analytical and Digital Laboratory, Moffitt Cancer Center, Tampa, Florida, USA
| | - Jonathan L. Hecht
- Department of Pathology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Naoko Sasamoto
- Department of Obstetrics, Gynecology, and Reproductive Biology, Brigham and Women's Hospital and Harvard Medical School; Boston, Massachusetts, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Kathryn L. Terry
- Department of Obstetrics, Gynecology, and Reproductive Biology, Brigham and Women's Hospital and Harvard Medical School; Boston, Massachusetts, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Shelley S. Tworoger
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida, USA
| | - Mary K. Townsend
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida, USA
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10
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Bull JA, Byrne HM. Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions. PLoS Comput Biol 2023; 19:e1010994. [PMID: 36972297 PMCID: PMC10079237 DOI: 10.1371/journal.pcbi.1010994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 04/06/2023] [Accepted: 03/04/2023] [Indexed: 03/29/2023] Open
Abstract
We introduce a new spatial statistic, the weighted pair correlation function (wPCF). The wPCF extends the existing pair correlation function (PCF) and cross-PCF to describe spatial relationships between points marked with combinations of discrete and continuous labels. We validate its use through application to a new agent-based model (ABM) which simulates interactions between macrophages and tumour cells. These interactions are influenced by the spatial positions of the cells and by macrophage phenotype, a continuous variable that ranges from anti-tumour to pro-tumour. By varying model parameters that regulate macrophage phenotype, we show that the ABM exhibits behaviours which resemble the 'three Es of cancer immunoediting': Equilibrium, Escape, and Elimination. We use the wPCF to analyse synthetic images generated by the ABM. We show that the wPCF generates a 'human readable' statistical summary of where macrophages with different phenotypes are located relative to both blood vessels and tumour cells. We also define a distinct 'PCF signature' that characterises each of the three Es of immunoediting, by combining wPCF measurements with the cross-PCF describing interactions between vessels and tumour cells. By applying dimension reduction techniques to this signature, we identify its key features and train a support vector machine classifier to distinguish between simulation outputs based on their PCF signature. This proof-of-concept study shows how multiple spatial statistics can be combined to analyse the complex spatial features that the ABM generates, and to partition them into interpretable groups. The intricate spatial features produced by the ABM are similar to those generated by state-of-the-art multiplex imaging techniques which distinguish the spatial distribution and intensity of multiple biomarkers in biological tissue regions. Applying methods such as the wPCF to multiplex imaging data would exploit the continuous variation in biomarker intensities and generate more detailed characterisation of the spatial and phenotypic heterogeneity in tissue samples.
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Affiliation(s)
- Joshua A. Bull
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Helen M. Byrne
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
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11
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Hathaway CA, Wang T, Townsend MK, Vinci C, Jake-Schoffman DE, Saeed-Vafa D, Segura CM, Nguyen JV, Conejo-Garcia JR, Fridley BL, Tworoger SS. Lifetime Exposure to Cigarette Smoke and Risk of Ovarian Cancer by T-cell Tumor Immune Infiltration. Cancer Epidemiol Biomarkers Prev 2023; 32:66-73. [PMID: 36318652 PMCID: PMC9839509 DOI: 10.1158/1055-9965.epi-22-0877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/21/2022] [Accepted: 10/27/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Exposure to cigarette smoke, particularly in early life, is modestly associated with ovarian cancer risk and may impact systemic immunity and the tumor immune response. However, no studies have evaluated whether cigarette smoke exposure impacts the ovarian tumor immune microenvironment. METHODS Participants in the Nurses' Health Study (NHS) and NHSII reported on early life exposure to cigarette smoke and personal smoking history on questionnaires (n = 165,760). Multiplex immunofluorescence assays were used to measure markers of T cells and immune checkpoints in tumor tissue from 385 incident ovarian cancer cases. We used Cox proportional hazards models to evaluate HRs and 95% confidence intervals (CI) for developing ovarian tumors with a low (<median) or high (≥median) immune cell percentage by cigarette exposure categories. RESULTS Women exposed versus not to cigarette smoke early in life had a higher risk of developing ovarian cancer with low levels of T cells overall (CD3+: HR: 1.54, 95% CI: 1.08-2.20) and recently activated cytotoxic T cells (CD3+CD8+CD69+: HR: 1.45, 95% CI: 1.05-2.00). These findings were not statistically significant at the Bonferroni-corrected P value of 0.0083. Adult smoking was not significantly associated with tumor immune markers after Bonferroni correction. CONCLUSIONS These results suggest early life cigarette smoke exposure may modestly increase risk of developing ovarian tumors with low abundance of total T cells and recently activated cytotoxic T cells. IMPACT Future research should focus on understanding the impact of exposures throughout the life course on the ovarian tumor immune microenvironment.
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Affiliation(s)
| | - Tianyi Wang
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida, USA
| | - Mary K. Townsend
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida, USA
| | - Christine Vinci
- Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, Florida, USA
| | | | - Daryoush Saeed-Vafa
- Department of Anatomic Pathology, Moffitt Cancer Center, Tampa, Florida, USA.,Advanced Analytical and Digital Laboratory, Moffitt Cancer Center, Tampa, Florida, USA
| | - Carlos Moran Segura
- Advanced Analytical and Digital Laboratory, Moffitt Cancer Center, Tampa, Florida, USA
| | - Jonathan V. Nguyen
- Advanced Analytical and Digital Laboratory, Moffitt Cancer Center, Tampa, Florida, USA
| | | | - Brooke L. Fridley
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, Florida, USA
| | - Shelley S. Tworoger
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida, USA
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12
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Wisniewski L, Braak S, Klamer Z, Gao C, Shi C, Allen P, Haab BB. Heterogeneity of Glycan Biomarker Clusters as an Indicator of Recurrence in Pancreatic Cancer. bioRxiv 2023:2023.01.05.522607. [PMID: 36711795 PMCID: PMC9881915 DOI: 10.1101/2023.01.05.522607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Outcomes following tumor resection vary dramatically among patients with pancreatic cancer. A challenge in defining predictive biomarkers is to discern within the complex tumor tissue the specific subpopulations and relationships that drive recurrence. Multiplexed immunofluorescence is valuable for such studies when supplied with markers of relevant subpopulations and analysis methods to sort out the intra-tumor relationships that are informative of tumor behavior. We hypothesized that the glycan biomarkers CA19-9 and STRA, which detect separate subpopulations of cancer cells, define intra-tumoral features associated with recurrence. We probed this question using automated signal thresholding and spatial cluster analysis applied to the immunofluorescence images of the STRA and CA19-9 glycan biomarkers in whole-block tumor sections. The tumors (N = 22) displayed extreme diversity between them in the amounts of the glycans and in the levels of spatial clustering, but neither the amounts nor the clusters of the individual and combined glycans associated with recurrence. The combined glycans, however, marked divergent types of spatial clusters, alternatively only STRA, only CA19-9, or both. The co-occurrence of more than one cluster type within a tumor associated significantly with disease recurrence, in contrast to the independent occurrence of each type of cluster. In addition, intra-tumoral regions with heterogeneity in biomarker clusters spatially aligned with pathology-confirmed cancer cells, whereas regions with homogeneous biomarker clusters aligned with various non-cancer cells. Thus, the STRA and CA19-9 glycans are markers of distinct and co-occurring subpopulations of cancer cells that in combination are associated with recurrence. Furthermore, automated signal thresholding and spatial clustering provides a tool for quantifying intra-tumoral subpopulations that are informative of outcome.
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13
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Wisniewski L, Braak S, Klamer Z, Gao C, Shi C, Allen P, Haab BB. Heterogeneity of glycan biomarker clusters as an indicator of recurrence in pancreatic cancer. Front Oncol 2023; 13:1135405. [PMID: 37124496 PMCID: PMC10130372 DOI: 10.3389/fonc.2023.1135405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 03/17/2023] [Indexed: 05/02/2023] Open
Abstract
Introduction Outcomes following tumor resection vary dramatically among patients with pancreatic ductal adenocarcinoma (PDAC). A challenge in defining predictive biomarkers is to discern within the complex tumor tissue the specific subpopulations and relationships that drive recurrence. Multiplexed immunofluorescence is valuable for such studies when supplied with markers of relevant subpopulations and analysis methods to sort out the intra-tumor relationships that are informative of tumor behavior. We hypothesized that the glycan biomarkers CA19-9 and STRA, which detect separate subpopulations of cancer cells, define intra-tumoral features associated with recurrence. Methods We probed this question using automated signal thresholding and spatial cluster analysis applied to the immunofluorescence images of the STRA and CA19-9 glycan biomarkers in whole-block sections of PDAC tumors collected from curative resections. Results The tumors (N = 22) displayed extreme diversity between them in the amounts of the glycans and in the levels of spatial clustering, but neither the amounts nor the clusters of the individual and combined glycans associated with recurrence. The combined glycans, however, marked divergent types of spatial clusters, alternatively only STRA, only CA19-9, or both. The co-occurrence of more than one cluster type within a tumor associated significantly with disease recurrence, in contrast to the independent occurrence of each type of cluster. In addition, intra-tumoral regions with heterogeneity in biomarker clusters spatially aligned with pathology-confirmed cancer cells, whereas regions with homogeneous biomarker clusters aligned with various non-cancer cells. Conclusion Thus, the STRA and CA19-9 glycans are markers of distinct and co-occurring subpopulations of cancer cells that in combination are associated with recurrence. Furthermore, automated signal thresholding and spatial clustering provides a tool for quantifying intra-tumoral subpopulations that are informative of outcome.
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Affiliation(s)
- Luke Wisniewski
- Department of Cell Biology, Van Andel Institute, Grand Rapids, MI, United States
| | - Samuel Braak
- Department of Cell Biology, Van Andel Institute, Grand Rapids, MI, United States
| | - Zachary Klamer
- Department of Cell Biology, Van Andel Institute, Grand Rapids, MI, United States
| | - ChongFeng Gao
- Department of Cell Biology, Van Andel Institute, Grand Rapids, MI, United States
| | - Chanjuan Shi
- Department of Pathology, Duke University School of Medicine, Durham, NC, United States
| | - Peter Allen
- Department of Surgery, Duke University School of Medicine, Durham, NC, United States
| | - Brian B. Haab
- Department of Cell Biology, Van Andel Institute, Grand Rapids, MI, United States
- *Correspondence: Brian B. Haab,
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14
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Peng H, Wu X, Liu S, He M, Xie C, Zhong R, Liu J, Tang C, Li C, Xiong S, Zheng H, He J, Lu X, Liang W. Multiplex immunofluorescence and single-cell transcriptomic profiling reveal the spatial cell interaction networks in the non-small cell lung cancer microenvironment. Clin Transl Med 2023; 13:e1155. [PMID: 36588094 PMCID: PMC9806015 DOI: 10.1002/ctm2.1155] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 12/06/2022] [Accepted: 12/12/2022] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Conventional immunohistochemistry technologies were limited by the inability to simultaneously detect multiple markers and the lack of identifying spatial relationships among cells, hindering understanding of the biological processes in cancer immunology. METHODS Tissue slices of primary tumours from 553 IA∼IIIB non-small cell lung cancer (NSCLC) cases were stained by multiplex immunofluorescence (mIF) assay for 10 markers, including CD4, CD38, CD20, FOXP3, CD66b, CD8, CD68, PD-L1, CD133 and CD163, evaluating the amounts of 26 phenotypes of cells in tumour nest and tumour stroma. StarDist depth learning model was utilised to determine the spatial location of cells based on mIF graphs. Single-cell RNA sequencing (scRNA-seq) on four primary NSCLC cases was conducted to investigate the putative cell interaction networks. RESULTS Spatial proximity among CD20+ B cells, CD4+ T cells and CD38+ T cells (r2 = 0.41) was observed, whereas the distribution of regulatory T cells was associated with decreased infiltration levels of CD20+ B cells and CD38+ T cells (r2 = -0.45). Univariate Cox analyses identified closer proximity between CD8+ T cells predicted longer disease-free survival (DFS). In contrast, closer proximity between CD133+ cancer stem cells (CSCs), longer distances between CD4+ T cells and CD20+ B cells, CD4+ T cells and neutrophils, and CD20+ B cells and neutrophils were correlated with dismal DFS. Data from scRNA-seq further showed that spatially adjacent N1-like neutrophils could boost the proliferation and activation of T and B lymphocytes, whereas spatially neighbouring M2-like macrophages showed negative effects. An immune-related risk score (IRRS) system aggregating robust quantitative and spatial prognosticators showed that high-IRRS patients had significantly worse DFS than low-IRRS ones (HR 2.72, 95% CI 1.87-3.94, p < .001). CONCLUSIONS We developed a framework to analyse the cell interaction networks in tumour microenvironment, revealing the spatial architecture and intricate interplays between immune and tumour cells.
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Affiliation(s)
- Haoxin Peng
- Department of Thoracic Oncology and SurgeryChina State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Diseasethe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhouChina
- Department of Clinical MedicineNanshan SchoolGuangzhou Medical UniversityGuangzhouChina
| | - Xiangrong Wu
- Department of Thoracic Oncology and SurgeryChina State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Diseasethe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhouChina
- Department of Clinical MedicineNanshan SchoolGuangzhou Medical UniversityGuangzhouChina
| | - Shaopeng Liu
- Department of Computer ScienceGuangdong Polytechnic Normal UniversityGuangzhouChina
- Department of Artificial Intelligence ResearchPazhou LabGuangzhouChina
| | - Miao He
- Department of Thoracic Oncology and SurgeryChina State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Diseasethe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhouChina
| | - Chao Xie
- Department of Computer ScienceGuangdong Polytechnic Normal UniversityGuangzhouChina
| | - Ran Zhong
- Department of Thoracic Oncology and SurgeryChina State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Diseasethe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhouChina
| | - Jun Liu
- Department of Thoracic Oncology and SurgeryChina State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Diseasethe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhouChina
| | - Chenshuo Tang
- Department of Computer ScienceGuangdong Polytechnic Normal UniversityGuangzhouChina
| | - Caichen Li
- Department of Thoracic Oncology and SurgeryChina State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Diseasethe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhouChina
| | - Shan Xiong
- Department of Thoracic Oncology and SurgeryChina State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Diseasethe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhouChina
| | - Hongbo Zheng
- Medical DepartmentGenecast Biotechnology Co., LtdBeijingChina
| | - Jianxing He
- Department of Thoracic Oncology and SurgeryChina State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Diseasethe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhouChina
| | - Xu Lu
- Department of Computer ScienceGuangdong Polytechnic Normal UniversityGuangzhouChina
- Department of Artificial Intelligence ResearchPazhou LabGuangzhouChina
| | - Wenhua Liang
- Department of Thoracic Oncology and SurgeryChina State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Diseasethe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhouChina
- Department of Medical OncologyThe First People's Hospital of ZhaoqingZhaoqingChina
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15
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Wrobel J, Harris C, Vandekar S. Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data. Methods Mol Biol 2023; 2629:141-168. [PMID: 36929077 DOI: 10.1007/978-1-0716-2986-4_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Advances in multiplexed single-cell immunofluorescence (mIF) and multiplex immunohistochemistry (mIHC) imaging technologies have enabled the analysis of cell-to-cell spatial relationships that promise to revolutionize our understanding of tissue-based diseases and autoimmune disorders. Multiplex images are collected as multichannel TIFF files; then denoised, segmented to identify cells and nuclei, normalized across slides with protein markers to correct for batch effects, and phenotyped; and then tissue composition and spatial context at the cellular level are analyzed. This chapter discusses methods and software infrastructure for image processing and statistical analysis of mIF/mIHC data.
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Affiliation(s)
- Julia Wrobel
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
| | - Coleman Harris
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Simon Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
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16
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Wang LC, Wang YL, He B, Zheng YJ, Yu HC, Liu ZY, Fan RR, Zan X, Liang RC, Wu ZP, Tang X, Wang GQ, Xu JG, Zhou LX. Expression and clinical significance of VISTA, B7-H3, and PD-L1 in glioma. Clin Immunol 2022; 245:109178. [DOI: 10.1016/j.clim.2022.109178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/15/2022] [Accepted: 11/01/2022] [Indexed: 11/09/2022]
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17
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Creed JH, Wilson CM, Soupir AC, Colin-Leitzinger CM, Kimmel GJ, Ospina OE, Chakiryan NH, Markowitz J, Peres LC, Coghill A, Fridley BL. spatialTIME and iTIME: R package and Shiny application for visualization and analysis of immunofluorescence data. Bioinformatics 2021; 37:4584-4586. [PMID: 34734969 PMCID: PMC8652029 DOI: 10.1093/bioinformatics/btab757] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 09/10/2021] [Accepted: 10/29/2021] [Indexed: 01/19/2023] Open
Abstract
Summary Multiplex immunofluorescence (mIF) staining combined with quantitative digital image analysis is a novel and increasingly used technique that allows for the characterization of the tumor immune microenvironment (TIME). Generally, mIF data is used to examine the abundance of immune cells in the TIME; however, this does not capture spatial patterns of immune cells throughout the TIME, a metric increasingly recognized as important for prognosis. To address this gap, we developed an R package spatialTIME that enables spatial analysis of mIF data, as well as the iTIME web application that provides a robust but simplified user interface for describing both abundance and spatial architecture of the TIME. The spatialTIME package calculates univariate and bivariate spatial statistics (e.g. Ripley’s K, Besag’s L, Macron’s M and G or nearest neighbor distance) and creates publication quality plots for spatial organization of the cells in each tissue sample. The iTIME web application allows users to statistically compare the abundance measures with patient clinical features along with visualization of the TIME for one tissue sample at a time. Availability and implementation spatialTIME is implemented in R and can be downloaded from GitHub (https://github.com/FridleyLab/spatialTIME) or CRAN. An extensive vignette for using spatialTIME can also be found at https://cran.r-project.org/web/packages/spatialTIME/index.html. iTIME is implemented within a R Shiny application and can be accessed online (http://itime.moffitt.org/), with code available on GitHub (https://github.com/FridleyLab/iTIME). Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jordan H Creed
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Christopher M Wilson
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Alex C Soupir
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA.,Department of Tumor Biology, Moffitt Cancer Center, Tampa, FL, USA
| | | | - Gregory J Kimmel
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Oscar E Ospina
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | | | - Joseph Markowitz
- Department of Cutaneous Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Lauren C Peres
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Anna Coghill
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Brooke L Fridley
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
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18
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Wang J, Browne L, Slapetova I, Shang F, Lee K, Lynch J, Beretov J, Whan R, Graham PH, Millar EKA. Multiplexed immunofluorescence identifies high stromal CD68 +PD-L1 + macrophages as a predictor of improved survival in triple negative breast cancer. Sci Rep 2021; 11:21608. [PMID: 34732817 PMCID: PMC8566595 DOI: 10.1038/s41598-021-01116-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 10/15/2021] [Indexed: 12/14/2022] Open
Abstract
Triple negative breast cancer (TNBC) comprises 10-15% of all breast cancers and has a poor prognosis with a high risk of recurrence within 5 years. PD-L1 is an important biomarker for patient selection for immunotherapy but its cellular expression and co-localization within the tumour immune microenvironment and associated prognostic value is not well defined. We aimed to characterise the phenotypes of immune cells expressing PD-L1 and determine their association with overall survival (OS) and breast cancer-specific survival (BCSS). Using tissue microarrays from a retrospective cohort of TNBC patients from St George Hospital, Sydney (n = 244), multiplexed immunofluorescence (mIF) was used to assess staining for CD3, CD8, CD20, CD68, PD-1, PD-L1, FOXP3 and pan-cytokeratin on the Vectra Polaris™ platform and analysed using QuPath. Cox multivariate analyses showed high CD68+PD-L1+ stromal cell counts were associated with improved prognosis for OS (HR 0.56, 95% CI 0.33-0.95, p = 0.030) and BCSS (HR 0.47, 95% CI 0.25-0.88, p = 0.018) in the whole cohort and in patients receiving chemotherapy, improving incrementally upon the predictive value of PD-L1+ alone for BCSS. These data suggest that CD68+PD-L1+ status can provide clinically useful prognostic information to identify sub-groups of patients with good or poor prognosis and guide treatment decisions in TNBC.
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Affiliation(s)
- James Wang
- St George and Sutherland Clinical School, University of New South Wales Sydney, Kensington, Australia
| | - Lois Browne
- Cancer Care Centre, St George Hospital, Kogarah, Australia
| | - Iveta Slapetova
- Biomedical Imaging Facility, Mark Wainwright Analytical Centre, University of New South Wales Sydney, Kensington, Australia
| | - Fei Shang
- Biomedical Imaging Facility, Mark Wainwright Analytical Centre, University of New South Wales Sydney, Kensington, Australia
| | - Kirsty Lee
- Department of Clinical Oncology, Prince of Wales Hospital, Chinese University of Hong Kong, Shatin, Hong Kong
| | - Jodi Lynch
- St George and Sutherland Clinical School, University of New South Wales Sydney, Kensington, Australia
- Cancer Care Centre, St George Hospital, Kogarah, Australia
| | - Julia Beretov
- St George and Sutherland Clinical School, University of New South Wales Sydney, Kensington, Australia
- Cancer Care Centre, St George Hospital, Kogarah, Australia
- Department of Anatomical Pathology, New South Wales Health Pathology, St George Hospital, Kogarah, Australia
| | - Renee Whan
- Biomedical Imaging Facility, Mark Wainwright Analytical Centre, University of New South Wales Sydney, Kensington, Australia
| | - Peter H Graham
- St George and Sutherland Clinical School, University of New South Wales Sydney, Kensington, Australia
- Cancer Care Centre, St George Hospital, Kogarah, Australia
| | - Ewan K A Millar
- St George and Sutherland Clinical School, University of New South Wales Sydney, Kensington, Australia.
- Department of Anatomical Pathology, New South Wales Health Pathology, St George Hospital, Kogarah, Australia.
- Faculty of Medicine and Health Sciences, Western Sydney University, Campbelltown, Australia.
- University of Technology, Sydney, Australia.
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19
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Wharton KA, Wood D, Manesse M, Maclean KH, Leiss F, Zuraw A. Tissue Multiplex Analyte Detection in Anatomic Pathology - Pathways to Clinical Implementation. Front Mol Biosci 2021; 8:672531. [PMID: 34386519 PMCID: PMC8353449 DOI: 10.3389/fmolb.2021.672531] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 07/14/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Multiplex tissue analysis has revolutionized our understanding of the tumor microenvironment (TME) with implications for biomarker development and diagnostic testing. Multiplex labeling is used for specific clinical situations, but there remain barriers to expanded use in anatomic pathology practice. Methods: We review immunohistochemistry (IHC) and related assays used to localize molecules in tissues, with reference to United States regulatory and practice landscapes. We review multiplex methods and strategies used in clinical diagnosis and in research, particularly in immuno-oncology. Within the framework of assay design and testing phases, we examine the suitability of multiplex immunofluorescence (mIF) for clinical diagnostic workflows, considering its advantages and challenges to implementation. Results: Multiplex labeling is poised to radically transform pathologic diagnosis because it can answer questions about tissue-level biology and single-cell phenotypes that cannot be addressed with traditional IHC biomarker panels. Widespread implementation will require improved detection chemistry, illustrated by InSituPlex technology (Ultivue, Inc., Cambridge, MA) that allows coregistration of hematoxylin and eosin (H&E) and mIF images, greater standardization and interoperability of workflow and data pipelines to facilitate consistent interpretation by pathologists, and integration of multichannel images into digital pathology whole slide imaging (WSI) systems, including interpretation aided by artificial intelligence (AI). Adoption will also be facilitated by evidence that justifies incorporation into clinical practice, an ability to navigate regulatory pathways, and adequate health care budgets and reimbursement. We expand the brightfield WSI system “pixel pathway” concept to multiplex workflows, suggesting that adoption might be accelerated by data standardization centered on cell phenotypes defined by coexpression of multiple molecules. Conclusion: Multiplex labeling has the potential to complement next generation sequencing in cancer diagnosis by allowing pathologists to visualize and understand every cell in a tissue biopsy slide. Until mIF reagents, digital pathology systems including fluorescence scanners, and data pipelines are standardized, we propose that diagnostic labs will play a crucial role in driving adoption of multiplex tissue diagnostics by using retrospective data from tissue collections as a foundation for laboratory-developed test (LDT) implementation and use in prospective trials as companion diagnostics (CDx).
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