Acyl-coenzyme a binding protein (ACBP) - a risk factor for cancer diagnosis and an inhibitor of immunosurveillance
Bibliographic record
Abstract
BACKGROUND: The plasma concentrations of acyl coenzyme A binding protein (ACBP, also known as diazepam-binding inhibitor, DBI, or 'endozepine') increase with age and obesity, two parameters that are also amongst the most important risk factors for cancer. METHODS: We measured ACBP/DBI in the plasma from cancer-free individuals, high-risk patients like the carriers of TP53 or BRCA1/2 mutations, and non-syndromic healthy subjects who later developed cancer. In mice, the neutralization of ACBP/DBI was used in models of non-small cell lung cancer (NSCLC) and breast cancer development and as a combination treatment with chemoimmunotherapy (chemotherapy + PD-1 blockade) in the context of NSCLC and sarcomas. The anticancer T cell response upon ACBP/DBI neutralization was characterized by flow cytometry and single-cell RNA sequencing. RESULTS: Circulating levels of ACBP/DBI were higher in patients with genetic cancer predisposition (BRCA1/2 or TP53 germline mutations) than in matched controls. In non-syndromic cases, high ACBP/DBI levels were predictive of future cancer development, and especially elevated in patients who later developed lung cancer. In preclinical models, ACBP/DBI neutralization slowed down breast cancer and NSCLC development and enhanced the efficacy of chemoimmunotherapy in NSCLC and sarcoma models. When combined with chemoimmunotherapy, the neutralizing monoclonal antibody against ACBP/DBI reduced the frequency of regulatory T cells in the tumor bed, modulated the immune checkpoint profile, and increased activation markers. CONCLUSION: These findings suggest that ACBP/DBI acts as an endogenous immune suppressor. We conclude that elevation of ACBP/DBI constitutes a risk factor for the development of cancer and that ACBP/DBI is an actionable target for improving cancer immunosurveillance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".