2021
Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx® Digital Spatial Profiler
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, Consortium O. Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx® Digital Spatial Profiler. Cancers 2021, 13: 4456. PMID: 34503266, PMCID: PMC8431590, DOI: 10.3390/cancers13174456.Peer-Reviewed Original ResearchWhole transcriptome levelDigital Spatial ProfilerSpatial profilingTranscriptome levelRNA profilingRNA transcriptsTumor microenvironmentMolecular diversityProtein profilingCancer researchIntegration of datasetsGeoMx Digital Spatial ProfilerProfilingCell populationsMouse samplesBiomarker discoveryBreast tumor microenvironmentBreast Cancer ConsortiumCancer researchers
2013
Cancer heterogeneity: implications for targeted therapeutics
Fisher R, Pusztai L, Swanton C. Cancer heterogeneity: implications for targeted therapeutics. British Journal Of Cancer 2013, 108: 479-485. PMID: 23299535, PMCID: PMC3593543, DOI: 10.1038/bjc.2012.581.Peer-Reviewed Original ResearchConceptsIntra-tumoural heterogeneityIntra-tumor heterogeneityClinical trial designCancer therapeuticsDistinct genomic alterationsClinical outcomesMalignant tumorsCurrent evidenceTrial designSolid tumorsSubpopulation of cellsSame tumorTumorsTissue collectionGenomic alterationsTherapeuticsBiomarker discoveryWidespread implementationEvidence