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Hori, S. S.

Publications and source records attributed to Hori, S. S..

2 recordsLinked to original sources

Engineering genetically-encoded synthetic biomarkers for breath-based cancer detection

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 StatementWe 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.

bioengineering↗

A mathematical model of ctDNA shedding predicts tumor detection size

Early cancer detection aims to find tumors before they progress to an incurable stage. We developed a stochastic mathematical model of tumor evolution and circulating tumor DNA (ctDNA) shedding to determine the potential and the limitations of cancer early detection tests. We inferred normalized ctDNA shedding rates from 176 early stage lung cancer subjects and calculated that a 15 mL blood sample contains on average 1.7 genome equivalents of ctDNA for lung tumors with a volume of 1 cm3. For annual screening, the model predicts median detection sizes between 3.8 and 6.6 cm3 corresponding to lead times between 310 and 450 days compared to current lung tumor sizes at diagnosis. For monthly cancer relapse testing based on 20 a priori known mutations, the model predicts a median detection size of 0.26 cm3 corresponding to a lead time of 150 days. This mechanistic framework can help to optimize early cancer detection approaches.

cancer biology↗