Integrative Analysis of Inflammation, Immune Profiles, and Genetic Factors Reveals Novel Prognostic Subgroups in Myelodysplastic Syndromes

핵심 개념

Integrative analysis of multi-omics data, including clinical, genetic, and transcriptomic profiles, identifies inflammation, immune signatures, and retrotransposon expression as key factors impacting prognosis in myelodysplastic syndromes.

초록

The study employed a Multi-Omics Factor Analysis (MOFA) approach to integrate three data modalities (clinical, genotypic, and transcriptomic) and seven different "views" derived from these modalities to identify factors that may impact prognosis in myelodysplastic syndromes (MDS).

Key highlights:

• MOFA identified ten latent factors, with Factor 1 being the most dominant, linking immune profile, cell-type composition, and inflammation/aging profile in both the bone marrow mononuclear cell (BMMNC) and CD34+ cell cohorts.
• High expression of retrotransposable elements (RTEs) was identified as a risk factor, while inflammation was found to be a protective factor for MDS prognosis.
• Factor 4 was associated with a phenotype characterized by depletion of healthy hematopoietic stem cells, increased malignant cells, and decreased leukocytes, leading to a higher risk of progression to acute myeloid leukemia.
• SF3B1 mutant cases showed high inflammation, potentially conferring a better prognosis, while SRSF2 mutant cases exhibited high granulocyte-monocyte progenitor content and increased senescence/immunosenescence, leading to a poorer prognosis.

The study demonstrates the power of integrative multi-omics analysis to uncover novel prognostic factors in MDS beyond the currently established genetic and clinical markers.

통계

Patients with high levels of inflammatory cytokines and chemokines had superior overall survival compared to those with low levels in the BMMNC cohort.
Patients with high levels of the interferon-I (IFN-I) signature had poorer overall survival compared to those with low levels in the CD34+ cohorts.

인용구

"Inflammation can either suppress or promote cancer development."
"Inflammaging is the process by which an age-related increase in chronic inflammation occurs, but the extent to which this process and other age-related events can impact the overall prognosis in MDS is yet to be uncovered."
"Recent work has explored the relationship between transcriptional signatures and critical signalling pathways to determine survival prognosis and diagnostic efficacy in MDS patient cohorts."

핵심 통찰 요약

Identification of novel myelodysplastic syndromes prognostic subgroups by integration of inflammation, cell-type composition, and immune signatures in the bone marrow

by Gerlevik,S.,... 게시일 www.biorxiv.org 03-13-2024

https://www.biorxiv.org/content/10.1101/2024.03.11.584361v1

더 깊은 질문

How do the identified prognostic factors, such as inflammation and RTE expression, interact with the known genetic drivers of MDS, and can this information be leveraged to develop more personalized treatment strategies?

The identified prognostic factors, such as inflammation and RTE expression, play crucial roles in the pathogenesis and progression of Myelodysplastic Syndromes (MDS). Inflammation can have a dual role in cancer development, either promoting or suppressing tumor growth. In the context of MDS, low-level chronic inflammation can create an immunosuppressive environment, impacting the innate and adaptive immune responses. On the other hand, inflammation can enhance antitumor immunity by activating dendritic cells and promoting effector T cell responses. The study found that high inflammation levels were associated with a good prognosis for MDS patients, indicating a potential protective role of inflammation in certain cases.
RTEs, which are genomic remnants of ancient DNA sequences, are usually silenced in somatic cells through DNA methylation and histone modifications. However, mutations in genes involved in these processes, such as DNMT3A and TET2, can lead to the reactivation of RTEs. The study identified RTEs expression as a risk factor for MDS, suggesting that global hypomethylation and RTE reactivation may contribute to disease progression.
When these prognostic factors interact with known genetic drivers of MDS, such as SF3B1 and SRSF2 mutations, a complex interplay emerges. For example, SF3B1 mutant cases were associated with high inflammation levels, potentially due to macrophage activation, leading to a better prognosis. On the other hand, SRSF2 mutant cases showed increased senescence and immunosenescence, contributing to a poor prognosis. Understanding these interactions can help in developing more personalized treatment strategies for MDS patients. By considering the specific genetic mutations and their interactions with inflammatory and RTE expression profiles, clinicians can tailor treatment approaches to target the underlying biology of each patient's disease, potentially leading to more effective outcomes.

How do the identified prognostic factors, such as inflammation and RTE expression, interact with the known genetic drivers of MDS, and can this information be leveraged to develop more personalized treatment strategies?

The mechanisms by which SRSF2 mutations lead to increased senescence and immunosenescence in Myelodysplastic Syndromes (MDS) are multifaceted and interconnected. SRSF2 mutations are known to be associated with poor prognosis in MDS, and the study identified a correlation between SRSF2 mutations and high levels of granulocyte-monocyte progenitor (GMP) cells, as well as increased senescence and immunosenescence markers.
One potential mechanism by which SRSF2 mutations contribute to increased senescence and immunosenescence is through the dysregulation of splicing processes. SRSF2 is a splicing factor that plays a critical role in mRNA splicing, and mutations in SRSF2 can lead to aberrant splicing patterns, affecting the expression of genes involved in senescence and immune responses. This dysregulation can result in the accumulation of senescent cells and impaired immune function, contributing to disease progression and poor outcomes in MDS patients with SRSF2 mutations.
Understanding the specific pathways and molecular mechanisms underlying the effects of SRSF2 mutations on senescence and immunosenescence can provide insights for the development of targeted therapies. By targeting the dysregulated splicing events and downstream pathways associated with SRSF2 mutations, it may be possible to mitigate the effects of senescence and enhance immune responses in MDS patients. Additionally, therapies aimed at modulating senescence or rejuvenating immune function could be explored as potential treatment strategies for patients with SRSF2 mutant MDS.

Could the integration of additional data modalities, such as epigenomic profiles or single-cell analyses, further refine the prognostic subgroups identified in this study and provide deeper insights into the underlying biology of MDS?

The integration of additional data modalities, such as epigenomic profiles and single-cell analyses, has the potential to significantly enhance the refinement of prognostic subgroups identified in the study and provide deeper insights into the underlying biology of Myelodysplastic Syndromes (MDS).
Epigenomic profiles, including DNA methylation patterns and histone modifications, can offer valuable information about the regulatory mechanisms that govern gene expression in MDS. By integrating epigenomic data with the existing multi-omics analysis, researchers can uncover epigenetic alterations associated with specific prognostic factors and genetic drivers of MDS. This comprehensive approach can help identify epigenetic signatures that influence disease progression and treatment response, leading to more precise prognostic stratification and personalized therapeutic interventions.
Single-cell analyses, such as single-cell RNA sequencing, can provide insights into the heterogeneity of cell populations within the bone marrow microenvironment of MDS patients. By characterizing individual cells at a single-cell resolution, researchers can identify rare cell populations, transitional states, and cellular interactions that may not be captured in bulk analyses. This detailed understanding of cellular composition and dynamics can refine the identification of prognostic subgroups, uncover novel disease mechanisms, and identify potential therapeutic targets for MDS.
Overall, the integration of additional data modalities, such as epigenomic profiles and single-cell analyses, holds great promise for advancing our understanding of MDS biology, refining prognostic stratification, and guiding the development of more effective and personalized treatment strategies for MDS patients.