Mutated RAS-associating proteins and ERK activation in relapse/refractory diffuse large B cell lymphoma
Bibliographic record
Abstract
Diffuse large B cell lymphoma (DLBCL) is successfully treated with combination immuno-chemotherapy, but relapse with resistant disease occurs in ~ 40% of patients. However, little is known regarding relapsed/refractory DLBCL (rrDLBCL) genetics and alternative therapies. Based on findings from other tumors, we hypothesized that RAS-MEK-ERK signaling would be upregulated in resistant tumors, potentially correlating with mutations in RAS, RAF, or associated proteins. We analyzed mutations and phospho-ERK levels in tumor samples from rrDLBCL patients. Unlike other tumor types, rrDLBCL is not mutated in any Ras or Raf family members, despite having increased expression of p-ERK. In paired biopsies comparing diagnostic and relapsed specimens, 33% of tumors gained p-ERK expression, suggesting a role in promoting survival. We did find mutations in several Ras-associating proteins, including GEFs, GAPs, and downstream effectors that could account for increased ERK activation. We further investigated mutations in one such protein, RASGRP4. In silico modeling indicated an increased interaction between H-Ras and mutant RASGRP4. In cell lines, mutant RASGRP4 increased basal p-ERK expression and lead to a growth advantage in colony forming assays when challenged with doxorubicin. Relapsed/refractory DLBCL is often associated with increased survival signals downstream of ERK, potentially corresponding with mutations in protein controlling RAS/MEK/ERK signaling.
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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 | 0.000 | 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".