CAREER: Design and validation of a novel part-brain-part-engineered gas sensor for noninvasive detection of lung cancer

Michigan State University, East Lansing MI

Investigators

Abstract

It is well known that the presence of cancer changes the volatile chemical composition of human exhaled breath (i.e., changes its “smell”), and this can be used to detect cancer noninvasively. This project aims to develop a lung cancer detection device based on a biological chemical sensory array and biological neural circuitries from insects. Neuronal voltage responses evoked by the smell of lung cancer will be used to classify lung cancer vs. noncancer and to differentiate between different types of lung cancers. This study will be performed using lung cancer cell cultures over multiple days. When completed, this novel gas sensor for noninvasive detection of lung cancer will add a powerful new dimension to biosensing. This project will advance the science of complex ‘smell’ processing in the brain and has potential to improve human health by advancing the development of biosensors for diverse medical applications. This project will also address the critical need to provide early and authentic research exposure to historically underrepresented students by providing research exposure to high school students and personalized research experiences to freshman undergraduates. The investigator's long-term career goal is to employ insect brain-based sensors in clinical settings for early detection of different cancers from exhaled breath samples. Towards this goal, the objective of this CAREER project is to develop an olfactory neuronal response-based gas sensor for sensitive, robust, and real-time detection of lung cancer. An antennae-attached ex vivo insect (locust) brain will constitute the central gas sensing device, which will be coupled with a miniaturized and implantable electrode array, a multi-channel amplifier, and biological neural computation schemes for developing the part-brain, part-engineered gas sensor. This gas sensor will utilize cancer volatiles-evoked neuronal spiking (voltage) responses for classifying human lung cancer from noncancer. The sensor’s ability to distinguish between human small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) cell lines will be systematically tested using volatile organic compound (VOC) signatures of individual cell cultures. The sensor’s performance will be compared with the detection performance of gas chromatography mass spectrometry (GC-MS) technology. Finally, this sensor will be optimized for one-shot and real-time detection of human lung cancer by increasing the recording electrode numbers and by implementing a recurrent neural network (RNN) model for data analysis. When completed, this novel technology will incorporate fully functional biological chemosensory arrays, neuronal signal transduction, and neural circuit computations all together in one single VOC sensing device for sensitive and real-time detection of lung cancer. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.