دورية أكاديمية
Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx(R) Digital Spatial Profiler
| العنوان: | Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx(R) Digital Spatial Profiler |
|---|---|
| المؤلفون: | 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, O, Sato, H, Schlam, I, Sorlie, T, Stover, DG, Swain, SM, Swarbrick, A, Thompson, EA, Tolaney, SM, Warren, SE |
| بيانات النشر: | MDPI |
| سنة النشر: | 2021 |
| المجموعة: | The University of Melbourne: Digital Repository |
| الوصف: | 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. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 2072-6694 |
| العلاقة: | pii: cancers13174456; 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. ,. Warren, S. E. (2021). Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx(R) Digital Spatial Profiler. CANCERS, 13 (17), https://doi.org/10.3390/cancers13174456Test.; http://hdl.handle.net/11343/296926Test |
| الإتاحة: | https://doi.org/10.3390/cancers13174456Test http://hdl.handle.net/11343/296926Test |
| حقوق: | CC BY ; https://creativecommons.org/licenses/by/4.0Test |
| رقم الانضمام: | edsbas.929FC6DC |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 20726694 |
|---|