The promise of proteomic analysis
Powerful -omics tools are unlocking new insights in systemic mastocytosis (SM). This summary highlights the latest proteomics data from Iribarren et al., which aim to identify biomarkers that differentiate SM subtypes with varying prognoses. By comparing SM with another myeloproliferative disorder, the study also identifies SM-specific biomarkers and delves into their cellular origins.1
Recent history of SM proteomics analysis
Recent advances in proteomics analysis have sparked interest in biomarkers for SM. Several groups have explored their potential, linking biomarkers to disease subtypes or severity,2–5 identifying novel candidates,6 and establishing cut-off values for prognostication (previously summarised, click here to view).7
Here, Iribarren et al. investigated SM biomarkers through a large, targeted profile of 275 proteins from 92 patients, using proximity extension assay techniques from a single drop of plasma each. The data underwent various statistical analyses with the aim of improving SM subtyping and understanding of disease mechanisms, including principal components analysis (PCA). The study also incorporated single-cell RNA sequencing (scRNA-seq) to validate and explore the cellular origins of relevant biomarkers.1
Results
Differences between mastocytosis subtypes: PCA analysis showed clear separation between cutaneous mastocytosis (CM)/indolent systemic mastocytosis (ISM) and advanced SM (AdvSM) clusters of proteins (P < 0.001; Table 1A). Further analysis based on the Boruta algorithm confirmed 29 biomarkers distinguishing ISM from AdvSM (Table 1B), with the top five being IL-1RT1, LAG3, TNFSF13B, EGLN1, and IL-18BP.1
Potential predictors of SM subtypes: Our previous publication summary discussed traditional biomarkers that can differentiate ISM and AdvSM, with data on optimal cut-off values for differentiation and prognostication (click here).7 A computer modelling approach in the Iribarren proteomics study showed that IL-1RT1 and LAG3 contributed the most to predicting ISM diagnosis versus AdvSM (P < 0.01).1
Comparison with polycythemia vera: The study compared SM protein profiles with those of polycythemia vera (PCV), another myeloproliferative disorder, and showed significant differences in 186 proteins altogether (P < 0.001; Table 1A); TPSAB1 (tryptase) was the most important (aligned with previous reporting; click here to view). For AdvSM versus PCV, 25 biomarkers were confirmed via the Boruta algorithm (Table 1B), with MILR1 identified as the leading distinguishing protein (P < 0.0001). These findings highlight the distinct molecular signatures of SM within the myeloproliferative disease space. 1
Table 1. Overall proteomics analysis results for SM
Cellular origin of biomarkers: Analysis of scRNA-seq data provided insights into the cellular origins of some identified biomarkers. This information could help in understanding the complex cellular interactions involved in SM pathogenesis, such as the effects of KIT and stem cell factor (SCF) ligand signalling, which play a key role in mast cell function. Iribarren et al. suggest that the low levels of SCF (referred to as ‘KITLG’ in the publication) observed in AdvSM compared to ISM may indicate over-expression of mutated KIT protein in AdvSM patients, potentially leading to a greater imbalance in the KIT signalling cascade.1 How KIT-activating mutations affect cell signalling is not yet fully understood, but possible prognostic associations of KIT D816V mutational status have been discussed in a previous publication summary (click here).8 The physiologic role of other imbalanced proteins found in SM are listed in Table 2, although their diagnostic implication remains to be determined.
Table 2. Cellular origins of key protein biomarkers identified
Conclusions and future research
The rarity and heterogeneity of SM make diagnosis and subtype classification difficult, necessitating sensitive and specific testing. As reported in previous publication summaries (click here or here to view summaries), receiver operating characteristic (ROC) analyses identified optimal cut-off values for differentiation between ISM and AdvSM for serum tryptase at 125 μg/L.7,9 However, this test still lacks the ability to differentiate from other myeloproliferative disorders, as demonstrated by Iribarren et al., and many AdvSM patients still have tryptase levels < 125 μg/L.7 Studies such as Iribarren et al. and Lübke et al. could lead to testing of panels of markers such as [TNFSF13B + IL-18BP + IL2-RA] versus MILR1 ratio,1,7 enabling more efficient diagnosis and subtyping using biomarkers specific to SM.
Iribarren et al. suggest future research could focus on: investigating the functional roles of biomarkers in pathogenesis; developing further diagnostic biomarker assays; and conducting longitudinal studies. Such studies could provide valuable insights into disease symptoms, severity, or progression, as carried out by Navarro-Navarro et al. on KIT D816V mutational status.1,8 They showed that 49% of patients who had unstable KIT mutation status, regardless of the subtype or specific diagnosis, had lower 5-year progression-free survival rates (click here).8 Taken together, biomarker assessments represent a promising step toward improving care for patients living with SM.1
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