Introduction This is an investigation of the capability of FAIMS (Field Asymmetric Ion Mobility Spectrometry) technology as a tool for non-invasive detection of Colorectal Cancer (CRC) through urinary volatile organic compound analysis. It expands on "Detection of Colorectal Cancer (CRC) by Urinary Volatile Organic Compound Analysis" (Ramesh P. Arasaradnam et al, 2014) with an increased sample population and local to Japan, also comparing in-house data of CA19-9 and carcinoembryonic antigen (CEA) markers. Experiment Conducted at Nippon Medical School Chiba Hokusoh Hospital, urine samples were collected and frozen at -80°C from 139 patients at various stages of CRC and 78 healthy control samples. The samples were thawed in batches and placed in ice hours prior to testing. 2 ml of each urine sample was aliquoted into 10 ml vials for processing with the commercial FAIMS device (Lonestar, Owlstone, UK). Each vial was heated in the device to 40°C to create headspace with sufficient VOCs then a carrier gas (clean dry air) delivered the headspace (0.5 L/min) diluted with a make-up flow (2 L/min). The FAIMS device was set to scan at 0 to 100% electric dispersion field in 51 steps and compensation voltage between -6 V and +6 V in 512 steps, producing data matrices for each sample's analysis. Principal Component Analysis (PCA) followed by Partial Least Squares Discriminant Analysis (PLS-DA) of each sample was conducted using SIMCA 13 (Umetrics, Sweden). See Table Conclusions FAIMS technology achieved a high rate of separation between the CRC and healthy control urine samples with 64.7% sensitivity and 82.1% specificity overall. As the CRC stage advances the sensitivity increased from 27.3% to 100%. Results show excellent potential to use FAIMS technology as an early screening tool for CRC, particularly impressive compared to in-house sensitivity data of CA19-9 and CEA markers. Further research into FAIMS screening of other cancer types through VOC biomarker analysis of urine, breath, and feces is recommended. >Detection ResultsCRC STAGEFAIMSOTHER MARKERSCA19-9CEATrue PositiveFalse NegativeSensitivityFalse PositiveTrue NegativeSpecificityNo. of SamplesSensitivitySensitivityI123227.3%146482.1%434.7%18.6%II181260.0%146482.1%3116.1%32.3%III45590.0%146482.1%5219.2%36.5%IV150100%146482.1%1735.3%64.7%ALL904964.7%146482.1%14316.1%33.6% Citation Format: Christopher Psutka, Marina Yamada, Akihisa Matsuda, Kazuya Yamahatsu, Satoshi Matsumoto, Toshihiko Kitayama, Nobuo Nakano, Jyunichi Koyano, Tohru Mikoshiba, Masao Miyashita. FAIMS technology in urinary volatile organic compound analysis to detect colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5303. doi:10.1158/1538-7445.AM2017-5303
Safe and noninvasive methods for breast cancer screening with improved accuracy are urgently needed. Volatile organic compounds (VOCs) in biological samples such as breath and blood have been investigated as noninvasive novel markers of cancer. We investigated volatile organic compounds in urine to assess their potential for the detection of breast cancer. One hundred and ten women with biopsy-proven breast cancer and 177 healthy volunteers were enrolled. The subjects were divided into two groups: a training set and an external validation set. Urine samples were collected and analyzed by gas chromatography and mass spectrometry. A predictive model was constructed by multivariate analysis, and the sensitivity and specificity of the model were confirmed using both a training set and an external set with reproducibility tests. The training set included 60 breast cancer patients (age 34–88 years, mean 60.3) and 60 healthy controls (age 34–81 years, mean 58.7). The external validation set included 50 breast cancer patients (age 35–85 years, mean 58.8) and 117 healthy controls (age 18–84 years, mean 51.2). One hundred and ninety-one compounds detected in at least 80% of the samples from the training set were used for further analysis. The predictive model that best-detected breast cancer at various clinical stages was constructed using a combination of two of the compounds, 2-propanol and 2-butanone. The sensitivity and specificity in the training set were 93.3% and 83.3%, respectively. Triplicated reproducibility tests were performed by randomly choosing ten samples from each group, and the results showed a matching rate of 100% for the breast cancer patient group and 90% for the healthy control group. Our prediction model using two VOCs is a useful complement to the current diagnostic tools. Further studies inclusive of benign tumors and non-breast malignancies are warranted.
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