Abstract LB560: Engineering genetically-encoded synthetic biomarkers for breath-based cancer detection
Bibliographic record
Abstract
Abstract Background: Breath analysis holds great promise for rapid and noninvasive early cancer detection. However, clinical implementation of endogenous volatile organic compound (VOC) signatures in breath is limited by low signal from nascent tumors and high background expression by nonmalignant tissues. By engineering tumors to express synthetic reporters that are not naturally produced in the human body, background signal from healthy tissues can be minimized, thereby maximizing sensitivity and specificity for tumor detection. Humans and plants share a common cholesterol biosynthesis (mevalonate) pathway, but in plants this pathway also generates volatile secondary metabolites (e.g. that attract pollinators). We therefore hypothesized that cancer cells could be coaxed to produce plant VOCs by genetically introducing the appropriate plant enzymes, and that these VOCs would be detectable in the breath as unique biomarkers of cancer. Aims: 1) To express the citrus VOC, limonene, in a cultured human cancer cell line; 2) To determine the smallest tumor size at which exhaled limonene can be detected in mice implanted with limonene-expressing tumor cells. Methods: HeLa cervical cancer cells were stably transfected with DNA vectors encoding limonene synthase (LS) alone or in combination with a truncated version of HMG-CoA reductase (HMGR), a key regulatory enzyme of the mevalonate pathway. Truncation of HMGR (tHMGR) by deletion of its regulatory domain renders it insensitive to feedback inhibition, augmenting flux through the mevalonate pathway and increasing limonene precursors. Cell culture headspace was analyzed using solid phase microextraction (SPME) and gas chromatography-mass spectrometry (GC-MS), confirming the presence of limonene. A xenograft murine tumor model was created by subcutaneously implanting HeLa-LS, HeLa-LS-tHMGR, or untransfected control HeLa cells in both flanks of 10-week-old athymic nude mice. For weekly VOC measurements, mice (n = 12) were placed in 1-liter chambers with continuous flow of highly pure air, and VOCs were collected using Tenax sorbent tubes, which were subsequently analyzed by GC-MS. Results: Limonene production in HeLa-LS-tHMGR cells was double that of HeLa-LS cells (11.0 vs. 5.6 fg/cell/day) with LODs of 107,000 and 360,000 cells, respectively, and was undetectable in untransfected HeLa cells. In xenograft mice, tumor detection improved proportionally with breath sampling time: a 10-fold increase in sampling duration resulted in 9.4-fold greater limonene production (94 ng vs. 10 ng), and dynamic headspace sampling was ~100-fold more sensitive than static sampling. Importantly, limonene was a sensitive volatile reporter, permitting detection of tumors as small as 5 mm, and increased linearly with tumor size (R2 = 0.97), demonstrating strong utility for monitoring tumor progression. Pharmacokinetic modeling of tumor-derived limonene predicts detection of tumors as small as 7 mm in humans, equivalent to the detection limit of PET imaging, yet far more economical. In future work, this strategy will be incorporated into an inhalable nonviral vector formulation with a tumor-activatable promoter (e.g. survivin) for safe, non-invasive in vivo gene delivery and tumor-specific expression of limonene for breath-based early detection of non-small cell lung cancer. Citation Format: Ophir Vermesh, Aloma D'Souza, Israt Alam, Mirwais Wardak, Theresa McLaughlin, Fadi El Rami, Ataya Sathirachinda, John Bell, Sharon Pitteri, Michelle James, Sharon Hori, Eric Gross, Sanjiv Gambhir. Engineering genetically-encoded synthetic biomarkers for breath-based cancer detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB560.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".