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Updated: Jun 29, 2025
From a 2DE-Gel Spot to Protein Function: Lesson Learned From HS1 in Chronic Lymphocytic Leukemia
Published on: October 19, 2014
Inflammatory and subtype-dependent serum protein signatures predict survival beyond the ctDNA in aggressive B cell
Maare Arffman1, Leo Meriranta1, Matias Autio1
1Research Programs Unit, Applied Tumor Genomics, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Oncology, Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland; iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland.
Background:
Biological heterogeneity of large B cell lymphomas (LBCLs) is poorly captured by current prognostic tools, hampering optimal treatment decisions.
Methods:
We dissected the levels of 1,463 serum proteins in a uniformly treated trial cohort of 109 patients with high-risk primary LBCL (ClinicalTrials.gov: NCT01325194) and correlated the profiles with molecular data from tumor tissue and circulating tumor DNA (ctDNA) together with clinical data.
Findings:
We discovered clinically and biologically relevant associations beyond established clinical estimates and ctDNA. We identified an inflamed serum protein profile, which reflected host response to lymphoma, associated with inflamed and exhausted tumor microenvironment features and high ctDNA burden, and translated to poor outcome. We composed an inflammation score based on the identified inflammatory proteins and used the score to predict survival in an independent LBCL trial cohort (ClinicalTrials.gov: NCT03293173). Furthermore, joint analyses with ctDNA uncovered multiple serum proteins that correlate with tumor burden. We found that SERPINA9, TACI, and TARC complement minimally invasive subtype profiling and that TACI and TARC can be used to evaluate treatment response in a subtype-dependent manner in the liquid biopsy.
Conclusions:
Altogether, we discovered distinct serum protein landscapes that dissect the heterogeneity of LBCLs and provide agile, minimally invasive tools for precision oncology.
Funding:
This research was funded by grants from the Research Council of Finland, Finnish Cancer Organizations, Sigrid Juselius Foundation, University of Helsinki, iCAN Digital Precision Cancer Medicine Flagship, Orion Research Foundation sr, and Helsinki University Hospital.
