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. Results from this study could pave the way for in vivo gene delivery and tumor-specific expression of exogenous volatile cancer reporters. Breath-based cancer detection using synthetic reporters has broad applicability to the early diagnosis of a wide variety of cancers.
Whole-body magnetic resonance imaging of Li-Fraumeni syndrome patients: observations from a two rounds screening of Brazilian patients