High-Dimensional Phenotyping Identifies Age-Emergent Cells in Human Mammary Epithelia. Pelissier Vatter Fanny A,Schapiro Denis,Chang Hang,Borowsky Alexander D,Lee Jonathan K,Parvin Bahram,Stampfer Martha R,LaBarge Mark A,Bodenmiller Bernd,Lorens James B Cell reports Aging is associated with tissue-level changes in cellular composition that are correlated with increased susceptibility to disease. Aging human mammary tissue shows skewed progenitor cell potency, resulting in diminished tumor-suppressive cell types and the accumulation of defective epithelial progenitors. Quantitative characterization of these age-emergent human cell subpopulations is lacking, impeding our understanding of the relationship between age and cancer susceptibility. We conducted single-cell resolution proteomic phenotyping of healthy breast epithelia from 57 women, aged 16-91 years, using mass cytometry. Remarkable heterogeneity was quantified within the two mammary epithelial lineages. Population partitioning identified a subset of aberrant basal-like luminal cells that accumulate with age and originate from age-altered progenitors. Quantification of age-emergent phenotypes enabled robust classification of breast tissues by age in healthy women. This high-resolution mapping highlighted specific epithelial subpopulations that change with age in a manner consistent with increased susceptibility to breast cancer. 10.1016/j.celrep.2018.03.114

Determinants of response and resistance to CD19 chimeric antigen receptor (CAR) T cell therapy of chronic lymphocytic leukemia. Fraietta Joseph A,Lacey Simon F,Orlando Elena J,Pruteanu-Malinici Iulian,Gohil Mercy,Lundh Stefan,Boesteanu Alina C,Wang Yan,O'Connor Roddy S,Hwang Wei-Ting,Pequignot Edward,Ambrose David E,Zhang Changfeng,Wilcox Nicholas,Bedoya Felipe,Dorfmeier Corin,Chen Fang,Tian Lifeng,Parakandi Harit,Gupta Minnal,Young Regina M,Johnson F Brad,Kulikovskaya Irina,Liu Li,Xu Jun,Kassim Sadik H,Davis Megan M,Levine Bruce L,Frey Noelle V,Siegel Donald L,Huang Alexander C,Wherry E John,Bitter Hans,Brogdon Jennifer L,Porter David L,June Carl H,Melenhorst J Joseph Nature medicine Tolerance to self-antigens prevents the elimination of cancer by the immune system. We used synthetic chimeric antigen receptors (CARs) to overcome immunological tolerance and mediate tumor rejection in patients with chronic lymphocytic leukemia (CLL). Remission was induced in a subset of subjects, but most did not respond. Comprehensive assessment of patient-derived CAR T cells to identify mechanisms of therapeutic success and failure has not been explored. We performed genomic, phenotypic and functional evaluations to identify determinants of response. Transcriptomic profiling revealed that CAR T cells from complete-responding patients with CLL were enriched in memory-related genes, including IL-6/STAT3 signatures, whereas T cells from nonresponders upregulated programs involved in effector differentiation, glycolysis, exhaustion and apoptosis. Sustained remission was associated with an elevated frequency of CD27CD45ROCD8 T cells before CAR T cell generation, and these lymphocytes possessed memory-like characteristics. Highly functional CAR T cells from patients produced STAT3-related cytokines, and serum IL-6 correlated with CAR T cell expansion. IL-6/STAT3 blockade diminished CAR T cell proliferation. Furthermore, a mechanistically relevant population of CD27PD-1CD8 CAR T cells expressing high levels of the IL-6 receptor predicts therapeutic response and is responsible for tumor control. These findings uncover new features of CAR T cell biology and underscore the potential of using pretreatment biomarkers of response to advance immunotherapies. 10.1038/s41591-018-0010-1

Multivariate Computational Analysis of Gamma Delta T Cell Inhibitory Receptor Signatures Reveals the Divergence of Healthy and ART-Suppressed HIV+ Aging. Frontiers in immunology Even with effective viral control, HIV-infected individuals are at a higher risk for morbidities associated with older age than the general population, and these serious non-AIDS events (SNAEs) track with plasma inflammatory and coagulation markers. The cell subsets driving inflammation in aviremic HIV infection are not yet elucidated. Also, whether ART-suppressed HIV infection causes premature induction of the inflammatory events found in uninfected elderly or if a novel inflammatory network ensues when HIV and older age co-exist is unclear. In this study we measured combinational expression of five inhibitory receptors (IRs) on seven immune cell subsets and 16 plasma markers from peripheral blood mononuclear cells (PBMC) and plasma samples, respectively, from a HIV and Aging cohort comprised of ART-suppressed HIV-infected and uninfected controls stratified by age (≤35 or ≥50 years old). For data analysis, multiple multivariate computational algorithms [cluster identification, characterization, and regression (CITRUS), partial least squares regression (PLSR), and partial least squares-discriminant analysis (PLS-DA)] were used to determine if immune parameter disparities can distinguish the subject groups and to investigate if there is a cross-impact of aviremic HIV and age on immune signatures. IR expression on gamma delta (γδ) T cells exclusively separated HIV+ subjects from controls in CITRUS analyses and secretion of inflammatory cytokines and cytotoxic mediators from γδ T cells tracked with TIGIT expression among HIV+ subjects. Also, plasma markers predicted the percentages of TIGIT+ γδ T cells in subjects with and without HIV in PSLR models, and a PLS-DA model of γδ T cell IR signatures and plasma markers significantly stratified all four of the subject groups (uninfected younger, uninfected older, HIV+ younger, and HIV+ older). These data implicate γδ T cells as an inflammatory driver in ART-suppressed HIV infection and provide evidence of distinct "inflamm-aging" processes with and without ART-suppressed HIV infection. 10.3389/fimmu.2018.02783

Supervised Machine Learning with CITRUS for Single Cell Biomarker Discovery. Polikowsky Hannah G,Drake Katherine A Methods in molecular biology (Clifton, N.J.) CITRUS is a supervised machine learning algorithm designed to analyze single cell data, identify cell populations, and identify changes in the frequencies or functional marker expression patterns of those populations that are significantly associated with an outcome. The algorithm is a black box that includes steps to cluster cell populations, characterize these populations, and identify the significant characteristics. This chapter describes how to optimize the use of CITRUS by combining it with upstream and downstream data analysis and visualization tools. 10.1007/978-1-4939-9454-0_20