Abstract 2180: Investigating lung adenocarcinoma tumor heterogeneity with single-cell mass cytometry

• Published in: Cancer research (Chicago, Ill.) Ill.), 2018-07, Vol.78 (13_Supplement), p.2180-2180
• Main Authors: Senosain-Ortega, Maria-Fernanda, Zou, Yong, Doxie, Deon B., Roe, Caroline E., Lehman, Jonathan M., Irish, Jonathan M., Massion, Pierre P.
• Format: Article
• Language: English
• ISSN: 0008-5472; 1538-7445
• DOI: 10.1158/1538-7445.AM2018-2180

The lack of accuracy in predicting behavior of early detected lung adenocarcinoma (ADC) presents a major challenge to patients and their providers. Indolent tumors may be overdiagnosed and overtreated. While imaging tools may contribute to better prediction of tumor behavior, unveiling tumor heterogeneity of ADCs at the single-cell level will elucidate mechanisms of tumor progression. Mass cytometry allows us to profile tumor heterogeneity and identify cell populations driven by the activation of specific signaling pathways. We report the development of an antibody panel for mass cytometry and preliminary experiments in cancer cell lines and in ADC. ADCs were collected at the time of surgery, dissociated into suspension and cryopreserved within 24 hours. A mass cytometry antibody panel was developed, including commercially available and in-house metal conjugated antibodies. Biaxial gating or unsupervised analysis approaches SPADE and viSNE were used to compare major populations of cells. Our comprehensive antibody panel includes markers for cellular lineage (14) (immune, epithelial, mesenchymal, fibroblasts), cancer cells (3), signaling pathways (15) and quality control markers (2). The in-house conjugated antibodies (9/34) were titrated in concentrations going from 0 to 1 ug/mL using cell lines with known expression of the marker of interest. A panel of previously validated antibodies was used to gate cell populations. Signal medians of positive and negative populations and standard deviations of negative population were used to determine the stain index and the signal/noise ratio. Based on titration curves, optimal concentrations of antibody were selected for use. In the tumor studied, four cell populations were identified: endothelial cells, fibroblasts, epithelial tumor cells and leukocytes. Basal kinase activity was detected in cancer cells and infiltrating leukocytes, both exhibiting p-STAT5 activation, and cancer cells showing high p-AKT activation. Using our Marker Enrichment Modeling algorithm, we identified 3 differentially enriched subpopulations of tumor cells, as well as different leukocyte populations. These preliminary data proved mass cytometry and the panel developed are suitable tools to characterize tumor heterogeneity in lung ADC. The work in progress studying indolent and aggressive ADCs promises to identify detailed phenotypes and respective signaling pathway activation that may ultimately allow us to better predict tumor behavior and