Targeting TCTP sensitizes tumor to T cell-mediated therapy by reversing immune-refractory phenotypes
Bibliographic record
Abstract
Immunotherapy has emerged as a powerful approach to cancer treatment. However, immunotherapeutic resistance limits its clinical application. Therefore, identifying immune-resistant factors, which can be targeted by clinically available drugs and it also can be a companion diagnostic marker, is needed to develop combination strategies. Here, using the transcriptome data of patients, and immune-refractory tumor models, we identify TCTP as an immune-resistance factor that correlates with clinical outcome of anti-PD-L1 therapy and confers immune-refractory phenotypes, decreased T cell trafficking to the tumor and resistance to cytotoxic T lymphocyte-mediated tumor cell killing. Mechanistically, TCTP activates the EGFR-AKT-MCL-1/CXCL10 pathway by phosphorylation-dependent interaction with Na, K ATPase. Furthermore, treatment with dihydroartenimsinin, the most effective agent impending the TCTP-mediated-refractoriness, synergizes with T cell-mediated therapy to control immune-refractory tumors. Thus, our findings suggest a role of TCTP in promoting immune-refractoriness, thereby encouraging a rationale for combination therapies to enhance the efficacy of T cell-mediated therapy.
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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".