DNA and RNA Alterations Associated with Colorectal Peritoneal Metastases: A Systematic Review

Background: As colorectal cancer (CRC) patients with peritoneal metastases (PM) have a poor prognosis, new treatment options are currently being investigated for CRC patients. Specific biomarkers in the primary tumor could serve as a prediction tool to estimate the risk of distant metastatic spread. This would help identify patients eligible for early treatment. Aim: To give an overview of previously studied DNA and RNA alterations in the primary tumor correlated to colorectal PM and investigate which gene mutations should be further studied. Methods: A systematic review of all published studies reporting genomic analyses on the primary tissue of CRC tumors in relation to PM was undertaken according to PRISMA guidelines. Results: Overall, 32 studies with 18,906 patients were included. BRAF mutations were analyzed in 17 articles, of which 10 found a significant association with PM. For all other reported genes, no association with PM was found. Two analyses with broader cancer panels did not reveal any new biomarkers. Conclusion: An association of specific biomarkers in the primary tumors of CRC patients with metastatic spread into peritoneum could not be proven. The role of BRAF mutations should be further investigated. In addition, studies searching for potential novel biomarkers are still required

Applying the electronic nose for pre-operative SARS-CoV-2 screening

Background: Infection with SARS-CoV-2 causes corona virus disease (COVID-19). The most standard diagnostic method is reverse transcription-polymerase chain reaction (RT-PCR) on a nasopharyngeal and/or an oropharyngeal swab. The high occurrence of false-negative results due to the non-presence of SARS-CoV-2 in the oropharyngeal environment renders this sampling method not ideal. Therefore, a new sampling device is desirable. This proof-of-principle study investigated the possibility to train machine-learning classifiers with an electronic nose (Aeonose) to differentiate between COVID-19-positive and negative persons based on volatile organic compounds (VOCs) analysis. Methods: Between April and June 2020, participants were invited for breath analysis when a swab for RT-PCR was collected. If the RT-PCR resulted negative, the presence of SARS-CoV-2-specific antibodies was checked to confirm the negative result. All participants breathed through the Aeonose for five minutes. This device contains metal-oxide sensors that change in conductivity upon reaction with VOCs in exhaled breath. These conductivity changes are input data for machine learning and used for pattern recognition. The result is a value between − 1 and + 1, indicating the infection probability. Results: 219 participants were included, 57 of which COVID-19 positive. A sensitivity of 0.86 and a negative predictive value (NPV) of 0.92 were found. Adding clinical variables to machine-learning classifier via multivariate logistic regression analysis, the NPV improved to 0.96. Conclusions: The Aeonose can distinguish COVID-19 positive from negative participants based on VOC patterns in exhaled breath with a high NPV. The Aeonose might be a promising, non-invasive, and low-cost triage tool for excluding SARS-CoV-2 infection in patients elected for surgery

Applying the electronic nose for pre-operative SARS-CoV-2 screening

Background Infection with SARS-CoV-2 causes corona virus disease (COVID-19). The most standard diagnostic method is reverse transcription-polymerase chain reaction (RT-PCR) on a nasopharyngeal and/or an oropharyngeal swab. The high occurrence of false-negative results due to the non-presence of SARS-CoV-2 in the oropharyngeal environment renders this sampling method not ideal. Therefore, a new sampling device is desirable. This proof-of-principle study investigated the possibility to train machine-learning classifiers with an electronic nose (Aeonose) to differentiate between COVID-19-positive and negative persons based on volatile organic compounds (VOCs) analysis. Methods Between April and June 2020, participants were invited for breath analysis when a swab for RT-PCR was collected. If the RT-PCR resulted negative, the presence of SARS-CoV-2-specific antibodies was checked to confirm the negative result. All participants breathed through the Aeonose for five minutes. This device contains metal-oxide sensors that change in conductivity upon reaction with VOCs in exhaled breath. These conductivity changes are input data for machine learning and used for pattern recognition. The result is a value between - 1 and + 1, indicating the infection probability. Results 219 participants were included, 57 of which COVID-19 positive. A sensitivity of 0.86 and a negative predictive value (NPV) of 0.92 were found. Adding clinical variables to machine-learning classifier via multivariate logistic regression analysis, the NPV improved to 0.96. Conclusions The Aeonose can distinguish COVID-19 positive from negative participants based on VOC patterns in exhaled breath with a high NPV. The Aeonose might be a promising, non-invasive, and low-cost triage tool for excluding SARS-CoV-2 infection in patients elected for surgery

Predictive Genetic Biomarkers for the Development of Peritoneal Metastases in Colorectal Cancer

Metastatic colorectal cancer (CRC) is a common cause of cancer-related mortality, of which peritoneal metastases (PMs) have the worse outcome. Metastasis-specific markers may help predict the spread of tumor cells and select patients for preventive strategies. This exploratory pilot study aimed to gain more insight into genetic alterations in primary CRC tumors, which might be a predictive factor for the development of PM. Forty patients with T3 stage CRC were retrospectively divided in three groups: without metachronous metastases during 5-year follow-up (M0, n = 20), with metachronous liver metastases (LM, n = 10) and with metachronous PM (PM, n = 10). Patients with synchronous metastases were excluded. Primary formalin-fixed paraffin-embedded tumor samples were analyzed via comprehensive genome sequencing (TSO500 analysis) to identify DNA alterations and RNA fusion transcripts in 523 genes and 55 genes, respectively. Thirty-eight samples were included for final analysis. Four M0 tumors and one PM tumor were microsatellite instable. BRAF mutations were uniquely identified in three microsatellite-stable (MSS) PM tumors (37.5%, p = 0.010). RNA analysis showed an additional FAM198A-RAF1 fusion in one PM sample. BRAF p.V600E mutations were only present in PM patients with MSS tumors. Greater attention should be paid to BRAF-mutated tumors in relation to the development of metachronous PM