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Paraneoplastic granulomatous disease occurs in approximately 7.3% of patients with non-Hodgkin lymphoma, most commonly among patients with chronic lymphocytic leukemia (CLL). These lesions are often reported to appear similar to sarcoidosis in clinical presentation and under light microscopy. However, comprehensive descriptions of the cytomorphologic characteristics of these paraneoplastic granulomas are lacking, and the mechanisms involved in their formation remain ill-defined. Noninfectious dermal granulomatous reactions have also been reported in many primary immunodeficiencies, including common variable immune deficiency and ataxia-telangiectasia. We present a case of noninfectious CD8+predominant granulomatous dermatitis with ocular involvement occurring in the setting of CLL and marked hypogammaglobulinemia. Based on the analysis of shared factors in patients with primary immunodeficiencies and CLL, we conclude that the presence of pan-humoral immunodeficiency could itself be a risk factor for developing a CD8+lymphogranulomatous reaction. This report and associated discussion evince that CD8+predominant granulomatous reactions, distinct from sarcoidosis, may represent a previously unappreciated segment of the paraneoplastic granulomas observed in hematologic malignancies.

Introduction:Cutaneous T-cell lymphomas (CTCL) are heterogeneous lymphoproliferative disorders on a spectrum of disease presentation and severity. Around two-thirds of cutaneous T-cell lymphomas can be classified as mycosis fungoides (MF) or Sézary syndrome (SS). While advanced stages of MF and SS are associated with decreased survival and worse outcomes, even early-stage patients can possess a variable course. Numerous deep sequencing studies have fallen short in identifying genetic abnormalities that drive disease pathogenesis and predict prognosis. Large-cell transformation and elevated lactate dehydrogenase levels are associated with worse prognosis in SS; however, such features cannot accurately prognosticate patient survival. There is a need for investigation that may assist in the prognostication of survival in patients with CTCL. Machine learning methods may help to elucidate correlations between clinical and genetic factors to predict disease progression and outcomes.