PARP1 as a biomarker for early detection and intraoperative tumor delineation in epithelial cancers – first-in-human results
Bibliographic record
Abstract
Abstract Major determining factors for survival of patients with oral, oropharyngeal, and esophageal cancer are early detection, the quality of surgical margins, and the contemporaneous detection of residual tumor. Intuitively, the exposed location at the epithelial surface qualifies these tumor types for utilization of visual aids to assist in discriminating tumor from healthy surrounding tissue. Here, we explored the DNA repair enzyme PARP1 as imaging biomarker and conducted optical imaging in animal models, human tissues and as part of a first-in-human clinical trial. Our data suggests that PARP1 is a quantitative biomarker for oral, oropharyngeal, and esophageal cancer and can be visualized with PARPi-FL, a fluorescently labeled small molecule contrast agent for topical or intravenous delivery. We show feasibility of PARPi-FL-assisted tumor detection in esophageal cancer, oropharyngeal and oral cancer. We developed a contemporaneous PARPi-FL topical staining protocol for human biospecimens. Using fresh oral cancer tissues within 25 min of biopsy, tumor and margin samples were correctly identified with >95% sensitivity and specificity without terminal processing. PARPi-FL imaging can be integrated into clinical workflows, potentially providing instantaneous assessment of the presence or absence of microscopic disease at the surgical margin. Additionally, we showed first-in-human PARPi-FL imaging in oral cancer. In aggregate, our preclinical and clinical studies have the unifying goal of verifying the clinical value of PARPi-FL-based optical imaging for early detection and intraoperative margin assignment.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".