PhenoGraph and viSNE facilitate the identification of abnormal T‐cell populations in routine clinical flow cytometric data

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Background Flow cytometric identification of neoplastic T‐cell populations is complicated by the wide range of phenotypic abnormalities in T‐cell neoplasia, and the diverse repertoire of reactive T‐cell phenotypes. We evaluated whether a recently described clustering algorithm, PhenoGraph, and dimensionality‐reduction algorithm, viSNE, might facilitate the identification of abnormal T‐cell populations in routine clinical flow cytometric data. Methods We applied PhenoGraph and viSNE to peripheral blood mononuclear cells labeled with a single 8‐color T/NK‐cell antibody combination. Individual peripheral blood samples containing either a T‐cell neoplasm or reactive lymphocytosis were analyzed together with a cohort of 10 normal samples, which established the location and identity of normal mononuclear‐cell subsets in viSNE displays. Results PhenoGraph‐derived subpopulations from the normal samples formed regions of phenotypic similarity in the viSNE display describing normal mononuclear‐cell subsets, which correlated with those obtained by manual gating (r2 = 0.99, P < 0.0001). In 24 of 24 cases of T‐cell neoplasia with an aberrant phenotype, compared with 4 of 17 cases of reactive lymphocytosis (P = 1.4 × 10−7, Fisher Exact test), PhenoGraph‐derived subpopulations originating exclusively from the abnormal sample formed one or more distinct phenotypic regions in the viSNE display, which represented the neoplastic T cells, and reactive T‐cell subpopulations not present in the normal cohort, respectively. The numbers of neoplastic T cells identified using PhenoGraph/viSNE correlated with those obtained by manual gating (r2 = 0.99; P < 0.0001). Conclusions PhenoGraph and viSNE may facilitate the identification of abnormal T‐cell populations in routine clinical flow cytometric data.