TY - JOUR
T1 - Best practices for spatial profiling for breast cancer research with the GeoMx® digital spatial profiler
AU - GeoMx Breast Cancer Consortium
AU - Bergholtz, Helga
AU - Carter, Jodi M.
AU - Cesano, Alessandra
AU - Cheang, Maggie Chon U.
AU - Church, Sarah E.
AU - Divakar, Prajan
AU - Fuhrman, Christopher A.
AU - Goel, Shom
AU - Gong, Jingjing
AU - Guerriero, Jennifer L.
AU - Hoang, Margaret L.
AU - Hwang, E. Shelley
AU - Kuasne, Hellen
AU - Lee, Jinho
AU - Liang, Yan
AU - Mittendorf, Elizabeth A.
AU - Perez, Jessica
AU - Prat, Aleix
AU - Pusztai, Lajos
AU - Reeves, Jason W.
AU - Riazalhosseini, Yasser
AU - Richer, Jennifer K.
AU - Sahin, Özgür
AU - Sato, Hiromi
AU - Schlam, Ilana
AU - Sørlie, Therese
AU - Stover, Daniel G.
AU - Swain, Sandra M.
AU - Swarbrick, Alexander
AU - Thompson, E. Aubrey
AU - Tolaney, Sara M.
AU - Warren, Sarah E.
N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/9
Y1 - 2021/9
N2 - 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.
AB - 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.
KW - Biomarker discovery
KW - Breast cancer
KW - Cancer transcriptome atlas
KW - Digital spatial profiler
KW - GeoMx
KW - RNA and protein profiling
KW - Spatial biology
KW - Tumor heterogeneity
KW - Tumor microenvironment
KW - Whole transcriptome atlas
UR - http://www.scopus.com/inward/record.url?scp=85114259085&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85114259085&partnerID=8YFLogxK
U2 - 10.3390/cancers13174456
DO - 10.3390/cancers13174456
M3 - Article
AN - SCOPUS:85114259085
SN - 2072-6694
VL - 13
JO - Cancers
JF - Cancers
IS - 17
M1 - 4456
ER -