As tumors grow and evolve, tumor, stromal, and immune cells change, displaying new and variable phenotypes as a result of both genetic and non-genetic influences. This intratumoral phenotypic heterogeneity is an important consideration for cancer therapy, as some cells may be high-priority targets (i.e. cancer stem cells), and others may be innately resistant to therapy (i.e. hypoxic cells). Intratumoral phenotypic heterogeneity can only be studied using single-cell approaches such as microscopy or flow cytometry, but these are limited by the amount of information that can be assessed per cell using optical detectors. A recently introduced non-optical cytometry technology, termed mass cytometry, enables the measurement of up to 45 antibody-based parameters per cell. Compared even to advanced 15-parameter flow cytometry, mass cytometry can potentially differentiate 109-fold more cellular phenotypes. In our proof-of-principle experiments, we have begun using this technology to define the cellular differentiation and intracellular signaling of cancer systems. In this application, we detail a robust pipeline for the systematic investigation o a large number of solid tumors by mass cytometry, including optimized sample preparation and advanced data analysis techniques. We will perform these studies on a cohort of human glioblastoma tumors with well-characterized gene expression profiles. Gene expression data represents the average genetic program of many cells in the tumor, and we predict that the rich layer of single-cell data obtained by mass cytometry will provide complementary information. We will pursue the following Specific Aims: (1) to develop optimized tissue preparation methods and mass cytometry reagents for characterizing the intratumoral phenotypic heterogeneity of glioblastoma samples; (2) to identify single-cell signatures that predict major tumor subsets, using peer-reviewed gene expression profiles as a gold standard; (3) to leverage the complementary nature of single-cell mass cytometry data and gene expression data to yield an integrated systems-level signature that predicts the patient's response to therapy. Successful completion of these aims will immediately impact our understanding of glioblastoma by revealing tumor cell subpopulations that contribute to the molecular and clinical differences between patients. Once mass cytometry is validated in a cancer research setting, the proposed analysis workflow can be used for studying fundamental tumor biology, discovering novel drug targets, and prospectively stratifying patients for precision medicine.
Mass cytometry, an innovative technology similar to flow cytometry, can quantify 4.3 billion possible cell phenotypes (compared with 256 for flow), providing a detailed snapshot of molecular diversity in complex tumors. Focusing on glioblastoma, we propose an analysis pipeline to identify single-cell signatures correlated with overall gene expression patterns and response to therapy. This advanced analysis methodology will enable new research into tumor biology, mechanisms of therapeutic resistance, and biomarkers that can inform personalized treatment plans.
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