Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules.

We report on an artificially intelligent nanoarray based on molecularly modified gold nanoparticles and a random network of single-walled carbon nanotubes for noninvasive diagnosis and classification of a number of diseases from exhaled breath. The performance of this artificially intelligent nanoarray was clinically assessed on breath samples collected from 1404 subjects having one of 17 different disease conditions included in the study or having no evidence of any disease (healthy controls). Blind experiments showed that 86% accuracy could be achieved with the artificially intelligent nanoarray, allowing both detection and discrimination between the different disease conditions examined. Analysis of the artificially intelligent nanoarray also showed that each disease has its own unique breathprint, and that the presence of one disease would not screen out others. Cluster analysis showed a reasonable classification power of diseases from the same categories. The effect of confounding clinical and environmental factors on the performance of the nanoarray did not significantly alter the obtained results. The diagnosis and classification power of the nanoarray was also validated by an independent analytical technique, i.e., gas chromatography linked with mass spectrometry. This analysis found that 13 exhaled chemical species, called volatile organic compounds, are associated with certain diseases, and the composition of this assembly of volatile organic compounds differs from one disease to another. Overall, these findings could contribute to one of the most important criteria for successful health intervention in the modern era, viz. easy-to-use, inexpensive (affordable), and miniaturized tools that could also be used for personalized screening, diagnosis, and follow-up of a number of diseases, which can clearly be extended by further development.

Differentiation between genetic mutations of breast cancer by breath volatolomics.

Mapping molecular sub-types in breast cancer (BC) tumours is a rapidly evolving area due to growing interest in, for example, targeted therapy and screening high-risk populations for early diagnosis. We report a new concept for profiling BC molecular sub-types based on volatile organic compounds (VOCs). For this purpose, breath samples were collected from 276 female volunteers, including healthy, benign conditions, ductal carcinoma in situ (DCIS) and malignant lesions. Breath samples were analysed by gas chromatography mass spectrometry (GC-MS) and artificially intelligent nanoarray technology. Applying the non-parametric Wilcoxon/Kruskal-Wallis test, GC-MS analysis found 23 compounds that were significantly different (p < 0.05) in breath samples of BC patients with different molecular sub-types. Discriminant function analysis (DFA) of the nanoarray identified unique volatolomic signatures between cancer and non-cancer cases (83% accuracy in blind testing), and for the different molecular sub-types with accuracies ranging from 82 to 87%, sensitivities of 81 to 88% and specificities of 76 to 96% in leave-one-out cross-validation. These results demonstrate the presence of detectable breath VOC patterns for accurately profiling molecular sub-types in BC, either through specific compound identification by GC-MS or by volatolomic signatures obtained through statistical analysis of the artificially intelligent nanoarray responses.

Assessment of ovarian cancer conditions from exhaled breath.

We present a pilot study that aims to examine the possibility to easily and noninvasively detect and discriminate females with ovarian cancer (OC) from females that have no tumor(s) and from females that have benign genital tract neoplasia, using exhaled breath samples. The study is based on clinical samples and data from 182 females, as follows: 48 females with OC, 48 tumor-free controls and 86 females with benign gynecological neoplasia. Analysis of the breath samples with gas chromatography linked with mass spectrometry shows that decanal, nonanal, styrene, 2-butanone and hexadecane could serve as potential volatile markers for OC. Analysis of the same samples with tailor-made nanoarrays shows good discrimination between females with OC and females that have either no tumor or benign genital tract neoplasia (71% for accuracy, sensitivity and specificity). Conversely, the nanoarray output shows excellent discrimination between the OC patients and the tumor-free controls (79% sensitivity, 100% specificity and 89% accuracy). These results suggest that the nanoarray approach might be useful to avoid unnecessary complicated or expensive tests for tumor-free females in case of a negative result. In the case of positive result, the test will indicate with high probability the presence of OC.