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Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx(®) Digital Spatial Profiler
SIMPLE SUMMARY: 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 t...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8431590/ https://www.ncbi.nlm.nih.gov/pubmed/34503266 http://dx.doi.org/10.3390/cancers13174456 |
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author | Bergholtz, Helga Carter, Jodi M. Cesano, Alessandra Cheang, Maggie Chon U Church, Sarah E. Divakar, Prajan Fuhrman, Christopher A. Goel, Shom Gong, Jingjing Guerriero, Jennifer L. Hoang, Margaret L. Hwang, E. Shelley Kuasne, Hellen Lee, Jinho Liang, Yan Mittendorf, Elizabeth A. Perez, Jessica Prat, Aleix Pusztai, Lajos Reeves, Jason W. Riazalhosseini, Yasser Richer, Jennifer K. Sahin, Özgür Sato, Hiromi Schlam, Ilana Sørlie, Therese Stover, Daniel G. Swain, Sandra M. Swarbrick, Alexander Thompson, E. Aubrey Tolaney, Sara M. Warren, Sarah E. |
author_facet | Bergholtz, Helga Carter, Jodi M. Cesano, Alessandra Cheang, Maggie Chon U Church, Sarah E. Divakar, Prajan Fuhrman, Christopher A. Goel, Shom Gong, Jingjing Guerriero, Jennifer L. Hoang, Margaret L. Hwang, E. Shelley Kuasne, Hellen Lee, Jinho Liang, Yan Mittendorf, Elizabeth A. Perez, Jessica Prat, Aleix Pusztai, Lajos Reeves, Jason W. Riazalhosseini, Yasser Richer, Jennifer K. Sahin, Özgür Sato, Hiromi Schlam, Ilana Sørlie, Therese Stover, Daniel G. Swain, Sandra M. Swarbrick, Alexander Thompson, E. Aubrey Tolaney, Sara M. Warren, Sarah E. |
author_sort | Bergholtz, Helga |
collection | PubMed |
description | SIMPLE SUMMARY: 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. ABSTRACT: 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. |
format | Online Article Text |
id | pubmed-8431590 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84315902021-09-11 Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx(®) Digital Spatial Profiler Bergholtz, Helga Carter, Jodi M. Cesano, Alessandra Cheang, Maggie Chon U Church, Sarah E. Divakar, Prajan Fuhrman, Christopher A. Goel, Shom Gong, Jingjing Guerriero, Jennifer L. Hoang, Margaret L. Hwang, E. Shelley Kuasne, Hellen Lee, Jinho Liang, Yan Mittendorf, Elizabeth A. Perez, Jessica Prat, Aleix Pusztai, Lajos Reeves, Jason W. Riazalhosseini, Yasser Richer, Jennifer K. Sahin, Özgür Sato, Hiromi Schlam, Ilana Sørlie, Therese Stover, Daniel G. Swain, Sandra M. Swarbrick, Alexander Thompson, E. Aubrey Tolaney, Sara M. Warren, Sarah E. Cancers (Basel) Guidelines SIMPLE SUMMARY: 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. ABSTRACT: 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. MDPI 2021-09-04 /pmc/articles/PMC8431590/ /pubmed/34503266 http://dx.doi.org/10.3390/cancers13174456 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Guidelines Bergholtz, Helga Carter, Jodi M. Cesano, Alessandra Cheang, Maggie Chon U Church, Sarah E. Divakar, Prajan Fuhrman, Christopher A. Goel, Shom Gong, Jingjing Guerriero, Jennifer L. Hoang, Margaret L. Hwang, E. Shelley Kuasne, Hellen Lee, Jinho Liang, Yan Mittendorf, Elizabeth A. Perez, Jessica Prat, Aleix Pusztai, Lajos Reeves, Jason W. Riazalhosseini, Yasser Richer, Jennifer K. Sahin, Özgür Sato, Hiromi Schlam, Ilana Sørlie, Therese Stover, Daniel G. Swain, Sandra M. Swarbrick, Alexander Thompson, E. Aubrey Tolaney, Sara M. Warren, Sarah E. Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx(®) Digital Spatial Profiler |
title | Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx(®) Digital Spatial Profiler |
title_full | Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx(®) Digital Spatial Profiler |
title_fullStr | Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx(®) Digital Spatial Profiler |
title_full_unstemmed | Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx(®) Digital Spatial Profiler |
title_short | Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx(®) Digital Spatial Profiler |
title_sort | best practices for spatial profiling for breast cancer research with the geomx(®) digital spatial profiler |
topic | Guidelines |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8431590/ https://www.ncbi.nlm.nih.gov/pubmed/34503266 http://dx.doi.org/10.3390/cancers13174456 |
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