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Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx® Digital Spatial Profiler

1
Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, 0450 Oslo, Norway
2
Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
3
ESSA Pharma Inc., South San Francisco, CA 94080, USA
4
ICR Clinical Trials and Statistics Unit, Division of Clinical Studies, The Institute of Cancer Research, London SM2 5NG, UK
5
NanoString® Technologies Inc., Seattle, WA 98109, USA
6
Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia
7
Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC 3010, Australia
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Division of Breast Surgery, Department of Surgery, Brigham and Women’s Hospital, Boston, MA 02115, USA
9
Duke Cancer Institute, Duke University, Durham, NC 27710, USA
10
Rosalind and Morris Goodman Cancer Centre, McGill University, Montreal, QC H3A 0G4, Canada
11
Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA
12
Breast Oncology Program, Dana-Farber Cancer Institute, Boston, MA 02215, USA
13
Harvard Medical School, Boston, MA 02115, USA
14
Translational Genomics and Targeted Therapies in Solid Tumors, August Pi i Sunyer Biomedical Research Institute, 08036 Barcelona, Spain
15
Yale Cancer Center, Yale School of Medicine, New Haven, CT 06510, USA
16
Department of Human Genetics, McGill University, Montreal, QC H3A 0G4, Canada
17
McGill University Genome Centre, McGill University, Montreal, QC H3A 0G4, Canada
18
Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
19
Department of Drug Discovery and Biomedical Sciences, University of South Carolina, Columbia, SC 29208, USA
20
MedStar Washington Hospital Center, Washington, DC 20010, USA
21
Tufts Medical Center, Boston, MA 02111, USA
22
Institute of Clinical Medicine, University of Oslo, 0315 Oslo, Norway
23
Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA
24
Georgetown Lombardi Comprehensive Cancer Center, Washington, DC 20057, USA
25
Georgetown University Medical Center, Washington, DC 20057, USA
26
MedStar Health, Washington, DC 20057, USA
27
Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia
28
St Vincent’s Clinical School, Faculty of Medicine, UNSW Sydney, Sydney NSW 2052, Australia
29
Department of Cancer Biology, Mayo Clinic Florida, Jacksonville, FL 32224, USA
30
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
*
Author to whom correspondence should be addressed.
Representative: Sarah E. Warren.
Academic Editors: Karel Pacak and Mineko Terao
Cancers 2021, 13(17), 4456; https://doi.org/10.3390/cancers13174456
Received: 30 June 2021 / Revised: 31 August 2021 / Accepted: 1 September 2021 / Published: 4 September 2021
In breast cancer, there is a high degree of variability in tumors and the surrounding tissue called the tumor microenvironment (TME). To better understand tumor biology and metastasis, as well as to predict response to cancer treatments or the course of the disease, it is important to characterize molecular diversity in the breast TME. The GeoMx Digital Spatial Profiler (DSP) enables researchers to spatially analyze proteins and RNA transcripts in tumors and surrounding tissues from patients or preclinical models. Using the GeoMx DSP, protein expression and RNA transcripts in the distinct regions of a tumor can be quantified up to and including the whole transcriptome level. Herein, the GeoMx Breast Cancer Consortium presents best practices for GeoMx spatial profiling of tumors to promote the collection of high-quality data, optimization of data analysis and integration of datasets to accelerate biomarker discovery. These best practices can also be applied to any tumor type to provide information about the tumor and the TME.
Breast cancer is a heterogenous disease with variability in tumor cells and in the surrounding tumor microenvironment (TME). Understanding the molecular diversity in breast cancer is critical for improving prediction of therapeutic response and prognostication. High-plex spatial profiling of tumors enables characterization of heterogeneity in the breast TME, which can holistically illuminate the biology of tumor growth, dissemination and, ultimately, response to therapy. The GeoMx Digital Spatial Profiler (DSP) enables researchers to spatially resolve and quantify proteins and RNA transcripts from tissue sections. The platform is compatible with both formalin-fixed paraffin-embedded and frozen tissues. RNA profiling was developed at the whole transcriptome level for human and mouse samples and protein profiling of 100-plex for human samples. Tissue can be optically segmented for analysis of regions of interest or cell populations to study biology-directed tissue characterization. The GeoMx Breast Cancer Consortium (GBCC) is composed of breast cancer researchers who are developing innovative approaches for spatial profiling to accelerate biomarker discovery. Here, the GBCC presents best practices for GeoMx profiling to promote the collection of high-quality data, optimization of data analysis and integration of datasets to advance collaboration and meta-analyses. Although the capabilities of the platform are presented in the context of breast cancer research, they can be generalized to a variety of other tumor types that are characterized by high heterogeneity. View Full-Text
Keywords: breast cancer; spatial biology; RNA and protein profiling; GeoMx; digital spatial profiler; tumor microenvironment; biomarker discovery; whole transcriptome atlas; cancer transcriptome atlas; tumor heterogeneity breast cancer; spatial biology; RNA and protein profiling; GeoMx; digital spatial profiler; tumor microenvironment; biomarker discovery; whole transcriptome atlas; cancer transcriptome atlas; tumor heterogeneity
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Figure 1

MDPI and ACS Style

Bergholtz, H.; Carter, J.M.; Cesano, A.; Cheang, M.C.U.; Church, S.E.; Divakar, P.; Fuhrman, C.A.; Goel, S.; Gong, J.; Guerriero, J.L.; Hoang, M.L.; Hwang, E.S.; Kuasne, H.; Lee, J.; Liang, Y.; Mittendorf, E.A.; Perez, J.; Prat, A.; Pusztai, L.; Reeves, J.W.; Riazalhosseini, Y.; Richer, J.K.; Sahin, Ö.; Sato, H.; Schlam, I.; Sørlie, T.; Stover, D.G.; Swain, S.M.; Swarbrick, A.; Thompson, E.A.; Tolaney, S.M.; Warren, S.E.; on behalf of the GeoMx Breast Cancer Consortium. Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx® Digital Spatial Profiler. Cancers 2021, 13, 4456. https://doi.org/10.3390/cancers13174456

AMA Style

Bergholtz H, Carter JM, Cesano A, Cheang MCU, Church SE, Divakar P, Fuhrman CA, Goel S, Gong J, Guerriero JL, Hoang ML, Hwang ES, Kuasne H, Lee J, Liang Y, Mittendorf EA, Perez J, Prat A, Pusztai L, Reeves JW, Riazalhosseini Y, Richer JK, Sahin Ö, Sato H, Schlam I, Sørlie T, Stover DG, Swain SM, Swarbrick A, Thompson EA, Tolaney SM, Warren SE, on behalf of the GeoMx Breast Cancer Consortium. Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx® Digital Spatial Profiler. Cancers. 2021; 13(17):4456. https://doi.org/10.3390/cancers13174456

Chicago/Turabian Style

Bergholtz, Helga, Jodi M. Carter, Alessandra Cesano, Maggie C.U. Cheang, Sarah E. Church, Prajan Divakar, Christopher A. Fuhrman, Shom Goel, Jingjing Gong, Jennifer L. Guerriero, Margaret L. Hoang, E. S. Hwang, Hellen Kuasne, Jinho Lee, Yan Liang, Elizabeth A. Mittendorf, Jessica Perez, Aleix Prat, Lajos Pusztai, Jason W. Reeves, Yasser Riazalhosseini, Jennifer K. Richer, Özgür Sahin, Hiromi Sato, Ilana Schlam, Therese Sørlie, Daniel G. Stover, Sandra M. Swain, Alexander Swarbrick, E. A. Thompson, Sara M. Tolaney, Sarah E. Warren, and on behalf of the GeoMx Breast Cancer Consortium. 2021. "Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx® Digital Spatial Profiler" Cancers 13, no. 17: 4456. https://doi.org/10.3390/cancers13174456

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