Successful identification of predictive profiles for infection utilising systems‐level immune analysis: a pilot study in patients with relapsed and refractory multiple myeloma

Abstract

Objectives. Patients with multiple myeloma (MM) are at increased risk for infection. Clinical assessment of infection risk is increasingly challenging in the era of immune-based therapy. A pilot systems-level immune analysis study to identify predictive markers for infection was conducted. Methods. Patients with relapsed and/or refractory MM (RRMM) who participated in a treatment trial of lenalidomide and dexamethasone were evaluated. Data on patient demographics, disease and episodes of infection were extracted from clinical records. Peripheral blood mononuclear cells (PBMCs) collected at defined intervals were analysed, with or without mitogen re-stimulation, using RNA sequencing and mass cytometry (CyTOF). CyTOF-derived cell subsets and RNAseq gene expression profiles were compared between patients that did and did not develop infection to identify immune signatures that predict infection over a 3-month period. Results. Twenty-three patients participated in the original treatment trial, and we were able to access samples from 17 RRMM patients for further evaluation in our study. Nearly half the patients developed an infection (8/17) within 3 months of sample collection. Infections were mostly clinically diagnosed (62.5%), and the majority involved the respiratory tract (87.5%). We did not detect phenotypic or numerical differences in immune cell populations between patients that did and did not develop infections. Transcriptional profiling of stimulated PBMCs revealed distinct Th2 immune pathway signatures in patients that developed infection. Conclusion. Immune cell counts were not useful predictors of infection risk. Functional assessment of