Proteomic Analysis Adds Predictive Precision in MCL

A new proteomic analysis of mantle cell lymphoma (MCL)—believed to be the first of its kind—is offering new insights into the disease’s pathogenesis and may offer a more precise understanding of patient prognoses. The report was published in the journal Blood Advances.¹

The molecular underpinnings of MCL have been well-explored using whole-exome sequencing (WES) and transcriptome analysis, the authors noted. Those scientific undertakings have helped investigators better understand the genetic alterations that contribute to the cancer’s heterogeneity, they added.

“However, genomic and transcriptomic data alone do not fully capture the complex proteomic landscape that ultimately determines cellular behavior,” they noted.

Recent advancements in mass spectrometry-based proteomics have made it possible to more deeply analyze the post-transcriptional landscapes of tumors, the authors explained. Still, they said most of the recent studies involving protein-level analysis of MCL have been targeted in nature. “A comprehensive and unbiased survey of proteomics in MCL is thus urgently needed,” the authors wrote.

The authors had previously analyzed the genomic landscapes of MCL in 134 patients.² That study helped the investigators identify 4 genetic subtypes of the disease. In the new report, the authors conducted further analysis on peripheral blood samples from a subset of 27 of those patients.¹ The patients were chosen because their biospecimens were sufficient to perform deep proteomic profiling. In addition, the authors used samples from 4 healthy donors to act as a control.

The 27 patients had a range of clinical characteristics, but they all were male and all had bone marrow involvement at diagnosis. The cohort’s median age was 57 years, and the 3-year overall survival (OS) in the group was 37.5% (95% CI, 22.5%-62.5%), due largely to the inclusion of several high-risk cases.

The investigators conducted protein measurements using data-independent acquisition mass spectrometry and then added those data to WES and RNA-sequencing data from matched samples. They then integrated genomic and transcriptomic data from their previous study, they said.

The study found that 5702 proteins were consistently detected in the samples. When the authors compared MCL cells to normal B cells, they found 468 proteins were significantly upregulated in MCL and 1,296 proteins were significantly downregulated in MCL. CCND1, a key diagnostic marker of MCL, was among the most upregulated proteins, the authors said.

The analysis further showed that splicing pathways were significantly upregulated at the mRNA and protein levels, “suggesting a critical role for aberrant RNA splicing in MCL pathogenesis,” they added.

The new findings also had prognostic value. By combining proteomic data and genetic alterations, the investigators found distinct transcriptomic and proteomic profiles that were associated with immunoglobulin heavy chain variable (IGHV) mutational status and CCND1 mutation. These profiles, in turn, were associated with clear differences in clinical outcomes. When the investigators compared a multi-omics molecular stratification to a single-omics stratification model, they found the former had a significantly improved predictive power when it came to patient survival (concordance index, 0.83 vs 0.74).

The authors acknowledged that their study had a small sample size and the cohort was made up entirely of male patients with peripheral blood involvement.

“While this design allowed for a focused analysis of tumor cell-intrinsic proteomic differences, it implies that caution should be exercised when extrapolating our findings to pure nodal disease or female patients,” they said.

Even with those limitations, the investigators concluded the findings show the value of proteomic analysis when it comes to risk stratification. “This work establishes the unique biological and clinical value of protein abundance data in MCL,” they wrote.

References

1. Yan Y, Chen W, Ge X, et al. Proteogenomic features define subtypes of mantle cell lymphoma. Blood Adv. Published online March 5, 2026. doi:10.1182/bloodadvances.2025018701

2. Yi S, Yan Y, Jin M, et al. Genomic and transcriptomic profiling reveals distinct molecular subsets associated with outcomes in mantle cell lymphoma. J Clin Invest. 2022;132(3):e153283. doi:10.1172/JCI153283