Abstract IA24: Computational dissection of phenotypic and functional heterogeneity in cancer

Cells within a single tumor are known to display extensive phenotypic and functional heterogeneity. Many life-threatening features of cancer, including drug resistance, metastasis and relapse, are facets of intratumor heterogeneity. With emerging single-cell measurement technologies, the field is poised to make important strides in understanding and controlling this heterogeneity. However, these technologies require advances in analytical methods to interpret the complex data they produce. Using mass cytometry, which measures single cells in ~31 simultaneous proteomic features, we developed novel methods for analyzing phenotypic heterogeneity in cancer. We use AML as an example to demonstrate the power of our approach. The heart of our approach is Phenograph, a graph-based representation of single-cells which represents the phenotypic structure of the sample and can be partitioned into subsets of densely interconnected nodes, called communities. Using Phenograph, we deconstructed several AML samples into discrete phenotypes. Analyzing the resulting subpopulations provided insights into functional heterogeneity of AML. Phenograph can be applied to characterize heterogeneity and primitive subpopulations in additional cancers. Citation Format: Dana Pe'er. Computational dissection of phenotypic and functional heterogeneity in cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr IA24.