Periodic Reporting for period 2 - COLOVOC (Reliable and specific urinary biomarkers for colorectal cancer)
Reporting period: 2021-07-01 to 2022-06-30
When you pee, you can easily know when you eat something specific, like asparagus. It was also by smelling urine that antic physicians could determine diabetes disease. We know urine can contain compounds specifics of food and disease. But it is complicated to determine which ones are specific to a particular disease. Therefore, we use advanced analytical techniques to analyze the urine and determine which compounds, the metabolites, are biomarkers of a disease. In the COLOVOC project, we analyze both the smell (volatiles) and the ‘flavor’ (liquid) of urine with mass spectrometry techniques. We have determined which metabolites are specific of colorectal cancer (CRC), the second most frequent neoplasia, after breast cancer in women and lung cancer in men. It is the most malignant digestive neoplasia in the western world, with a higher incidence than all other tumours combined. It is estimated that in a standard population, compliance with CRC screening is only ~30-50%.
The research performed within the COLOVOC project has shown that 2 urine biomarkers can predict the recurrence and metastasis of CRC with an accuracy of 81% (82% sensitivity, 80% specificity), and it outperforms the current blood carcinoembryonic antigen (CEA) test (47% sensitivity, 80% specificity). Our next steps are the validation of those two biomarkers and the development of a portable device for the clinics. Our long-term aim is to improve the detection of CRC and to reduce the burden on the health systems widely affected by the pandemic.
The research performed within the COLOVOC project has shown that 2 urine biomarkers can predict the recurrence and metastasis of CRC with an accuracy of 81% (82% sensitivity, 80% specificity), and it outperforms the current blood carcinoembryonic antigen (CEA) test (47% sensitivity, 80% specificity). Our next steps are the validation of those two biomarkers and the development of a portable device for the clinics. Our long-term aim is to improve the detection of CRC and to reduce the burden on the health systems widely affected by the pandemic.
A comprehensive metabolomics urine study has been done for the first time with both volatile and liquid fractions of urine for CRC. We have analyzed by mass spectrometry (MNS) either with gas chromatography (GC-MS, GCxGC-MS), and liquid chromatography (LC-MS) over 200 urine samples from a CRC screening program. Big part of the work has been the learning of new analytical techniques by the researcher fellow at UC Davis, in US. Even though the project has been affected by the pandemic, we have finalized the samples analysis during the outgoing phase. During the lock-down periods in which we could not do laboratory work, we performed a systematic review and meta-analysis of CRC markers in urine reported so far for volatiles (volatilomics) and metabolites (metabolomics).
Comprehensive gas chromatography is very well suited for the measurement of complex matrices, such those found in metabolomics. However, the high dimensionality of the raw data obtained makes the analysis and processing difficult to use in an automated way. A new algorithm for GCxGC has been developed, in an open-source code.
The repeatability and reproducibility of the GC-MS method was assessed, and it was found that samples kept at 4ºC before analysis exhibited an increase in the number of linear and reproducible peaks compared to room temperature (RT). Furthermore, repeatability and reproducibility of the method also improved when samples were stored at 4ºC. This issue was only observed in automatized sampling, meaning the samples remained longer hours sitting at RT. To overcome this issue, a Peltier cooled drawer was acquired for the samples while waiting for their processing (stirring, heating, solid phase microextraction (SPME) exposure and finally, chromatographic analysis).
From the univariate and multivariate statistical analysis, 10 metabolites were found relevant, however they were reduced to 2 with a LASSO selection. Noteworthy, one of the biomarkers is specific of CRC staging. The area under the curve (AUC) of the CRC progression model is 0.890 meaning we have a very good model to explain the differences between early and metastatic CRC groups. The model ROC curve was created with a random forest algorithm. The prediction test was further evaluated with a validation set of metastatic cancer patients, obtaining a significant model with a similar AUC of 0.869.
Comprehensive gas chromatography is very well suited for the measurement of complex matrices, such those found in metabolomics. However, the high dimensionality of the raw data obtained makes the analysis and processing difficult to use in an automated way. A new algorithm for GCxGC has been developed, in an open-source code.
The repeatability and reproducibility of the GC-MS method was assessed, and it was found that samples kept at 4ºC before analysis exhibited an increase in the number of linear and reproducible peaks compared to room temperature (RT). Furthermore, repeatability and reproducibility of the method also improved when samples were stored at 4ºC. This issue was only observed in automatized sampling, meaning the samples remained longer hours sitting at RT. To overcome this issue, a Peltier cooled drawer was acquired for the samples while waiting for their processing (stirring, heating, solid phase microextraction (SPME) exposure and finally, chromatographic analysis).
From the univariate and multivariate statistical analysis, 10 metabolites were found relevant, however they were reduced to 2 with a LASSO selection. Noteworthy, one of the biomarkers is specific of CRC staging. The area under the curve (AUC) of the CRC progression model is 0.890 meaning we have a very good model to explain the differences between early and metastatic CRC groups. The model ROC curve was created with a random forest algorithm. The prediction test was further evaluated with a validation set of metastatic cancer patients, obtaining a significant model with a similar AUC of 0.869.
Found biomarkers will be classified in 3 categories: 1) diagnostic biomarkers of CRC and/or CRC stage-specific biomarkers; 2) biomarkers of adenomatous polyps detection; and 3) healthy human biomarkers. Robust biomarkers would be obtained as they would be validated at least twice, by two independent cohorts, and in 2-independent laboratory facilities. Expected results would be to find a new and robust panel of compound to improve the CRC detection by screening programs, and reduce the burden of the health systems widely affected by the pandemic. We also have the expected output of the evaluation of the creation of a CRC test (at-home kit) for the general public.