OGG1 activation improves T cell resilience to oxidative stress after allo-SCT and T cell engager exposure
Bibliographic record
Abstract
Relapse remains the major cause of treatment failure after allogeneic stem cell transplantation (allo-SCT), often related to impaired immune surveillance by donor-derived T cells. Oxidative stress is a hallmark of allo-SCT leading to accumulation of oxidized DNA lesions, most notably 8-hydroxy-2’-deoxyguanosine (8-ohdg) in reconstituting T cells [ 1 ]. High 8-ohdg level correlated with impaired functionality and increased relapse risk [ 1 ]. The clinical success of histamine dihydrochloride, an antioxidant agent, in combination with interleukin-2 for relapse prevention in AML highlights the therapeutic relevance of redox modulation to restore T and NK cell functionality [ 2 ]. Sustained stimulation of the T cell receptor (TCR) in alloreactive T cells results in mitochondrial hyperreactivity and abundant superoxide generation [ 3 ]. Oxidized bases lead to replication stress and double-strand breaks, which not only impair gene expression but also induce premature senescence, a state that limits efficacy of T cell-based therapies. T cell fitness, which encompasses metabolic resilience and adaptability, has emerged as a key determinant of successful immune reconstitution and therapeutic efficacy [ 4 ]. In fact, DNA integrity, particularly resistance to oxidative DNA damage, is an underappreciated facet of T cell fitness, as DNA damage accelerates T cell aging [ 5 ]. In this context, “DNA fitness” emerges as a potential prerequisite for sustained T cell function. The DNA-glycosylase OGG1 is the key enzyme initiating base excision repair of 8-ohdg, thereby maintaining genomic stability [ 6 ]. Recent work has identified TH10785, a small-molecule OGG1 activator, which enhances its repair activity [ 7 ]. Therapeutic benefit of OGG1 activation has already been demonstrated in preclinical models, where it improved tissue function [ 8 ]. Here, we explore the potential of OGG1 activation as a strategy to restore and preserve T cell function under oxidative stress. By improving DNA repair capacity, we aim to improve T cell-based therapies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".