Protein Profiling Identify Distinct Tumor-Tissue Protein Expression Pattern Specific for Relapsing Diffuse Large B-Cell Lymphoma
Bibliographic record
Abstract
Abstract Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease. Several genetic drivers have been suggested based on gene-expression and RNA sequencing. However, clinical trials based on these molecular subclassifications have failed to improve treatment results and standard of care in DLBCL is still R-CHOP (rituximab, cyclophosphamide, doxorubicin, prednisone) based therapy, which results in approximately an 70% 5-year overall survival rate. Nordic Lymphoma Group (NLG) has conducted two large phase II trials (NLG-LBC-04; NCT 01502982 and NLG-LBC-05; NCT 01325194) during the last decade with the aim of improving the treatment outcome for young (<65 years) high-risk patients with primary DLBCL. Both protocols were based on biweekly administered R-CHOP-14 with etoposide (R-CHOEP) and systemic central nervous system (CNS) prophylaxis. Various factors acting at different biological levels may influence pathology of the disease, and discrepancies between gene expression and the final combination of the functional proteins may be present due to, e.g., translational inhibition/activation and posttranslational modifications. Mass spectrometry-based protein profiling is a high throughput methodology that enables identification of the proteins in a given cell or tissue and the global protein expression can be compared between patients. The aim of our study was to search for prognostic markers and possible therapeutic targets at the protein level in a uniformly treated patient cohort from the two Nordic trials (n=64) with available tumor-tissue. Thus, in this study, we investigated the protein expression pattern in the pre-therapeutic formalin-fixed paraffin embedded tumor samples by nano liquid-chromatography coupled to a mass spectrometer (Orbitrap Fusion) through an EASY-Spray nano-electrospray ion source (nLC-MS/MS). We identified 4,622 proteins with at least two unique peptides across all samples. In the combined cohort, we were able to, based on differential protein expression of 190 proteins between patients (p<0.05) with treatment sensitive (n=53) and relapsing (n=11) lymphoma, identify three clusters with one of the clusters containing only patients with experienced relapsing disease. Interestingly, among other identified differentially expressed proteins, we emphasize the identification of voltage dependent anion channel 3 (VDAC3) and the GTPase RAC1. This pattern of protein expression suggests disturbance in reactive oxidative species (ROS) signaling and regulation of tumor cell proliferation between relapsing and therapy-responding patients. Assessment of these specific protein expression patterns in the three clusters may provide a useful parameter for prediction of relapsing disease in DLBCL patients but still awaits further validation in a separate patient cohort. Disclosures Holte: Gilead: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees; Nordic: Membership on an entity's Board of Directors or advisory committees; Nanovector: Membership on an entity's Board of Directors or advisory committees, Other: lectures honorarias; Novartis: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Jørgensen: Gilead/Kite: Consultancy; Novartis: Consultancy; Celgene/BMS: Consultancy; Roche: Consultancy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".