Abstract LB560: Engineering genetically-encoded synthetic biomarkers for breath-based cancer detection
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».