Abstract
Simple SummaryIn 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.
Highlights
GeoMx spatial profiling opens a new window into tumor biology and accelerates our understanding of the collaboration and competition between different cells in the breast tumor microenvironment (TME) across location and time
A.C.: (1) Participated in the GeoMx Breast Cancer Consortium (GBCC) user group meetings, where the concept, content and organization of the manuscript was discussed in detail, contributed to the design of the work in the manuscript development meetings and contributed to the analysis/interpretation of data and workflow for best practices
S.E.C.: (1) Organized and participated in the GBCC user group meetings, where the concept, content and organization of the manuscript was discussed in detail, and contributed to the design of the work in the manuscript development meetings
Summary
Improvements in diagnostic tools and treatments have led to better outcomes for patients with breast cancer; there are still subgroups of patients with aggressive disease where effective therapies remain elusive [1,2]. Recent studies have shown that tumor-infiltrating lymphocytes (TILs) in the primary tumor play an important role in therapy response and prognosis in breast cancer [4,16,17,18,19,20,21,22,23,24,25,26]. An opportunity exists to improve available tools for characterizing and quantifying the molecular heterogeneity of breast tumors (as well as other highly histologically and clinically heterogeneous tumors) and associating those signatures with clinical outcomes to improve the performance of prognostic and predictive biomarkers [35,36]
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