For instance, expanding imaging genomics into the analysis

For instance, expanding imaging genomics into the analysis 5-FU cost of gliomas could focus on the intra-tumoral heterogeneity in high- and low-grade lesions. Correlation of quantitative imaging parameters with locus-specific gene expression will help identify not just a genomic basis for specific

imaging phenotypes, but pave the way to monitor any phenotypic changes occurring during the treatment/observation phase with serial imaging, using imaging as surrogate markers, as surveillance tools. Tumor heterogeneity is multidimensional. For example, within a tumor, there can be genetic and epigenetic heterogeneity; differences in microenvironments; phenotype differences; heterogeneity arising over time; and heterogeneity between primary tumor and metastases. Imaging phenotype can be characterized by one or more spatially registered imaging modalities (e.g., CT, PET, molecular imaging, MR, and ultrasound). Imaging is the only technique that can characterize the whole tumor as well as any pertinent

surrounding tissues; it is non-invasive and can be repeated over time (assuming issues of radiation dose, where applicable, are addressed). Specific attention should be paid to “serial imaging,” to Galunisertib mw understand molecular mechanisms behind treatment success/failure and changes in spatial/temporal/habitats that accompany treatment, and to observe tumor evolution over time (e.g., resistance development). Image analysis methods to predict and detect the emergence of resistance, correlate with genomic heterogeneity, and

identify homogeneous subtypes within a heterogeneous tumor would be invaluable. Within the context of tumor heterogeneity, microscopic images represent an extremely valuable resource of disease phenotype data. Visual analysis of microscopic images is considered the gold standard diagnostic modality for virtually all cancer types [47] and [48]. Importantly, a large amount SPTBN5 of cell type-specific and tissue region-specific biomedical knowledge encoded in morphological data is not directly recoverable from -omics data, which requires destroying tissue structure prior to extraction of molecular analytes and molecular profiling. This suggests that there may be value in integrating molecular and morphological phenotype data to take advantage of the unique strengths of each data type (depicted in Figure 10). Similarly, within the context of tumor heterogeneity, image-guided (IG) semi-automated needle core biopsy methods will prove to be very important. These IG methods, capable of extracting 30 + mg tumor tissue samples suitable for micro-fluidic -omic analysis, are now available, but have not yet been widely deployed. Such targeted tumor sampling, coupled with increased fresh frozen biospecimens pioneered by TCGA, could extend the reliability of -omic sampling and analysis procedures. Many individual comprehensive cancer centers are currently engaged in this type of biospecimen harvesting but further standardization is required.

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