Evaluation of immune dysregulation and its clinical implications in Myelodysplastic Syndromes

BACKGROUND. Myelodysplastic syndromes (MDS) are a heterogeneous group of myeloid neoplasms with variable clinical outcomes and an increased risk of progression to Acute Myeloid Leukemia (AML). Given their heterogeneity, the use of classifications and scoring systems is of fundamental importance to identify the disease subtype and to evaluate patient’s prognosis but fails in predicting who will respond to hypomethylating agents (HMAs), which represent the first line treatment for high-risk patients. Biological heterogeneity of MDS is partly driven by tumor-associated genomic lesions, but increasing evidence suggests that immune tumor microenvironment plays a significant role. However, dissecting these cell states and understanding their clinical relevance on a large scale remains challenging because there is still no standard method for evaluating patient’s immune status .

 

AIMS. In this study, we characterized the immune ecosystems in a large cohort of MDS and AML postMDS patients. Specific objectives were: 1) to analyze the contribution of immune ecosystems in refining patients' prognosis; 2) to define specific immune profiles to predict probability of response to HMAs; and 3) to develop a panel for immune monitoring in clinical practice.

 

METHODS. We prospectively studied 190 MDS patients at Humanitas Cancer Center Milan, Italy (evaluated at diagnosis and at multiple time points throughout the natural history of the disease). T lymphocytes, Natural Killer (NK) and myeloid cells were investigated in bone marrow (BM) by extensive multi-color flow cytometry. Phenograph was used to analyze immune cell subset distribution and phenotype, while HDBSCAN identified clusters of patients with homogeneous immune features (defined as ecosystems). Each ecosystem was further characterized by integrating RNA-seq data from CD34+ progenitors. DURAClone dry pre-formulated antibody panels (Beckman Coulter) were designed for the implementation of an immune monitoring approach in the clinical work-up by using freshly collected peripheral blood (PB).

 

RESULTS. We identified five immune ecosystems in the BM, each characterized by varying functionality and maturation stages of immune cells. Two clusters exhibited features of a healthy-like immune system: one with an expanded pool of immature and plastic Naïve T cells and CD56bright NK cells (referred to as "Naïve" ecosystem); and another enriched with memory T cells and functionally activated T and NK cells, termed "Memory, activated". The other three clusters showed progressive levels of immune dysfunction: one was characterized by overall immune inactivity, labeled as “Not activated" ecosystem; another one displayed a skewing of immune cell maturation towards advanced stages accompanied by immune suppression, and was named as "Terminally differentiated, immunosuppressed", while the final group was marked by T and NK cell exhaustion and suppression, identified as "Exhausted, immunosuppressed". Transcriptome analysis of CD34+ MDS progenitors revealed distinct inflammatory signatures and immunosuppressive pathways associated with immune dysfunction.

Depending on the grade of immune dysfunction, the immune ecosystems exhibited distinct probabilities of survival and risk of leukemic transformation (P<0.05). Moreover, they were able to further refine the prognosis of patients stratified according to IPSS-M risk group. In patients treated with HMAs, baseline immune ecosystems were associated with different probabilities of achieving a complete response, ranging from >75% to <10% (P<0.01). At relapse, all patients exhibited severe immune dysregulation.

In the end, we observed that BM immune ecosystems can be easily detected by the analysis of PB cells, providing a proof of concept for a non-invasive immune monitoring approach. We therefore assessedthe reliability of the designed dry pre-formulated antibody panel for clinical work-up in 100 MDS prospectively evaluated.

 

CONCLUSION. Immune ecosystems capture the clinical heterogeneity of MDS within existing subtypes and extend beyond genotypic classifications. These findings provide a system-level resolution of the MDS microenvironment and identify opportunities for patient immune monitoring in clinical practice, thereby improving the clinical decision-making process.

 

 

Learning Objectives