A roadmap towards personalized immunology

Abstract

Big data generation and computational processing will enable medicine to evolve from a "one-size-fits-all" approach to precise patient stratification and treatment. Significant achievements using "Omics" data have been made especially in personalized oncology. However, immune cells relative to tumor cells show a much higher degree of complexity in heterogeneity, dynamics, memory-capability, plasticity and "social" interactions. There is still a long way ahead on translating our capability to identify potentially targetable personalized biomarkers into effective personalized therapy in immune-centralized diseases. Here, we discuss the recent advances and successful applications in "Omics" data utilization and network analysis on patients' samples of clinical trials and studies, as well as the major challenges and strategies towards personalized stratification and treatment for infectious or non-communicable inflammatory diseases such as autoimmune diseases or allergies. We provide a roadmap and highlight experimental, clinical, computational analysis, data management, ethical and regulatory issues to accelerate the implementation of personalized immunology.

Fig. 1

A roadmap proposed towards personalized immunology. There exist both horizontal and vertical roadmaps towards personalized immunology. Vertically, to translate sample stratification to clinical therapies, we need to utilize the state-of-the-art “Omics” analysis and network integration approaches to stratify patients into subgroups and then implement personalized therapeutic approaches to treat individual patients, which needs to overcome various types of barriers at different steps. Horizontally, we might need to go through at least 7 steps to enable personalized immunotherapies, 1) classic symptom-based approach, 2) deep phenotyping approach, 3) multi-layer “Omics”-based profiling, 4) cell type-specific “Omics”, 5) state-specific “Omics”, 6) single-cell (sc) “Omics” and dynamic response analysis of immune cells, 7) integrated network analysis. FACS, fluorescence activated cell sorting; TCR/BCR, T cell receptor/B-cell receptor; DEG, differential expression gene; PEEP, personalized expression perturbation profile; SSN, sample-specific network; SVM, support vector machine; KNN, K-nearest neighbors; under the first layer (the so-called stratification layer), different colors of patients indicate individual patients with different cellular and/or molecular profiles while brackets represent patient subgroups; under the second layer (the so-called technique layers), different small circles with distinct colors indicate different immune cells while big circles represent patient (sub)groups; under the technique layers, the snapshot of microarray representing either microarray-based or RNA-seq-based transcriptome analysis; under the third layer (the so-called therapeutic layer), the syringes with different colors or tonalities indicate different therapeutic approaches; P1,..., Pn at step 7 designate different patients; G1, G2, G3, G4 represent different genes, the arrows between them representing regulatory relationships. Three images in the second layer of step 1 are used with permissions from Fotolia.com

Fig. 2

Longitudinal studies and dynamic measurement are critical for discovering various types of biomarkers. a Longitudinal follow-up of individual patients with multilayer “Omics” analysis is essential for identifying different types of biomarkers. The check marker at the given time point indicates the necessary “Omics” measurement and clinical assessment for revealing the given type of biomarker while the cross symbol indicates an unnecessary involvement at the given time point for the given type of biomarker. b Time-series “Omics” analysis of the cultured isolated immune cells from the first visit (T₀ at panel a) following certain stimulation or stresses will also be able to help extract various types of biomarkers. c Various types of dynamic patterns of different pathways or modules or subnetworks of the given relevant type of immune cells isolated from PBMC or other tissues of individual patients might be valuable for patient subgroup stratification. Subnetwork activities at the given time can be defined either by the expression levels of the co-expressed genes, or by the expression levels of the effector genes (such as cytokines) or any other readouts which could define the activities or outputs of the given pathway or subnetwork or module