NSCLC presents metabolic heterogeneity, and there is still some leeway for EGF stimuli in EGFR-mutated NSCLC

Precision detection of select human lung cancer biomarkers and cell lines using honeybee olfactory neural circuitry as a novel gas sensor

Human breath contains biomarkers (odorants) that can be targeted for early disease detection. It is well known that honeybees have a keen sense of smell and can detect a wide variety of odors at low concentrations. Here, we employ honeybee olfactory neuronal circuitry to classify human lung cancer volatile biomarkers at different concentrations and their mixtures at concentration ranges relevant to biomarkers in human breath from parts-per-billion to parts-per-trillion. We also validated this brain-based sensing technology by detecting human non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) cell lines using the 'smell' of the cell cultures. Different lung cancer biomarkers evoked distinct spiking response dynamics in the honeybee antennal lobe neurons indicating that those neurons encoded biomarker-specific information. By investigating lung cancer biomarker-evoked population neuronal responses from the honeybee antennal lobe, we classified individual human lung cancer biomarkers successfully (88% success rate). When we mixed six lung cancer biomarkers at different concentrations to create 'synthetic lung cancer' vs. 'synthetic healthy' human breath, honeybee population neuronal responses were able to classify those complex breath mixtures reliably with exceedingly high accuracy (93-100% success rate with a leave-one-trial-out classification method). Finally, we employed this sensor to detect human NSCLC and SCLC cell lines and we demonstrated that honeybee brain olfactory neurons could distinguish between lung cancer vs. healthy cell lines and could differentiate between different NSCLC and SCLC cell lines successfully (82% classification success rate). These results indicate that the honeybee olfactory system can be used as a sensitive biological gas sensor to detect human lung cancer.

H2S-Synthesizing Enzymes Are Putative Determinants in Lung Cancer Management toward Personalized Medicine

Lung cancer is a lethal disease with no truly efficient therapeutic management despite the progresses, and metabolic profiling can be a way of stratifying patients who may benefit from new therapies. The present study is dedicated to profiling cysteine metabolic pathways in NSCLC cell lines and tumor samples. This was carried out by analyzing hydrogen sulfide (H2S) and ATP levels, examining mRNA and protein expression patterns of cysteine catabolic enzymes and transporters, and conducting metabolomics analysis using nuclear magnetic resonance (NMR) spectroscopy. Selenium-chrysin (SeChry) was tested as a therapeutic alternative with the aim of having an effect on cysteine catabolism and showed promising results. NSCLC cell lines presented different cysteine metabolic patterns, with A549 and H292 presenting a higher reliance on cystathionine β-synthase (CBS) and cystathionine γ-lyase (CSE) to maintain H2S levels, while the PC-9 cell line presented an adaptive behavior based on the use of mercaptopyruvate sulfurtransferase (MST) and cysteine dioxygenase (CDO1), both contributing to the role of cysteine as a pyruvate source. The analyses of human lung tumor samples corroborated this variability in profiles, meaning that the expression of certain genes may be informative in defining prognosis and new targets. Heterogeneity points out individual profiles, and the identification of new targets among metabolic players is a step forward in cancer management toward personalized medicine.