Engineering genetically-encoded synthetic biomarkers for breath-based cancer detection
Bibliographic record
Abstract
Abstract Breath analysis holds great promise for rapid, noninvasive early cancer detection; however, clinical implementation is impeded by limited signal from nascent tumors and high background expression by non-malignant tissues. To address this issue, we developed a novel breath-based reporter system for early cancer detection using D-limonene, a volatile organic compound (VOC) from citrus fruit that is not produced in humans, in order to minimize background signal and maximize sensitivity and specificity for cancer detection. We metabolically engineered HeLa human cervical cancer cells to express limonene at levels detectable by mass spectrometry by introducing a single plant gene encoding limonene synthase. To improve limonene production and detection sensitivity twofold, we genetically co-expressed a modified form of a key enzyme in the cholesterol biosynthesis pathway. In a HeLa xenograft tumor mouse model, limonene is a sensitive and specific volatile reporter of tumor presence and growth, permitting detection of tumors as small as 5 mm. Moreover, tumor detection in mice improves proportionally with breath sampling time. By continuously collecting VOCs for 10 hours, we improve sensitivity for cancer detection 100-fold over static headspace sampling methods. Whole-body physiologically-based pharmacokinetic (PBPK) modeling and simulation of tumor-derived limonene predicts detection of tumors as small as 7 mm in humans, equivalent to the detection limit of clinical imaging modalities, such as PET, yet far more economical. Significance Statement We developed a breath-based reporter system using the plant terpene, D-limonene – a volatile secondary metabolite that gives citrus fruit its characteristic scent but is not produced in human tissues – as a biomarker for early cancer detection. Results from this study could pave the way for in vivo gene delivery and tumor-specific expression of exogenous volatile cancer reporters with broad applicability to the early diagnosis of a wide variety of cancers.
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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 | 0.001 | 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".