Differentiation between genetic mutations of breast cancer by breath volatolomics

Orna Barash, Wei Zhang, Jeffrey M Halpern, Qing-Ling Hua, Yue-Yin Pan, Haneen Kayal, Kayan Khoury, Hu Liu, Michael P A Davies, Hossam Haick, Orna Barash, Wei Zhang, Jeffrey M Halpern, Qing-Ling Hua, Yue-Yin Pan, Haneen Kayal, Kayan Khoury, Hu Liu, Michael P A Davies, Hossam Haick

Abstract

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.

Keywords: breast cancer; molecular; sensor; spectrometry; volatolomic.

Conflict of interest statement

CONFLICTS OF INTERESTS

No conflict of interest to declare related to the study.

Figures

Figure 1. Schematic figure describing the collection…
Figure 1. Schematic figure describing the collection and analysis procedures of the exhaled breath using two approaches
Following lung-wash the patient inhale into a collection bag (A), which is then being collected and concentrated on Tenax® TA sorption tubes (B). The sorbent tube is then exposed both to GC-MS for specific compound identification (C) and to artificially intelligence nanoarray for volatolomic signature of breast cancer genetic mutations (D).
Figure 2. Study design
Figure 2. Study design
Figure 3. Representative DFA plots of CV…
Figure 3. Representative DFA plots of CV values obtained from the response of the sensor array to breath VOCs from different sub-groups (Group B)
The boxes represent 95% CI of CV values; error bars represent the standard deviation. Central dotted line represents Youden's cut-point. Each graph refers to different comparison. Values are given in Table 3 and Table S3 of SI. Comparisons are shown for breast cancer with healthy and benign (A) benign (B) and DCIS (C) cases [DCIS have been treated as blind samples in A and B]. Comparisons are also shown for each molecular sub-group of breast cancer with all other types (D–G) distinction between luminal and non-luminal cancers (H) and between different luminal types (I) distinction of HER2 status within luminal (J) and non-luminal (K) breast cancers.

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