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BACKGROUND: the problem in early diagnosis of sporadic cancer is understanding the individual's risk to develop disease. In response to this need, global scientific research is focusing on developing predictive models based on non-invasive screening tests. A tentative solution to the problem may be a cancer screening blood-based test able to discover those cell requirements triggering subclinical and clinical onset latency, at the stage when the cell disorder, i.e. atypical epithelial hyperplasia, is still in a subclinical stage of proliferative dysregulation. METHODS: a well-established procedure to identify proliferating circulating tumor cells was deployed to measure the cell proliferation of circulating non-haematological cells which may suggest tumor pathology. Moreover, the data collected were processed by a supervised machine learning model to make the prediction. RESULTS: the developed test combining circulating non-haematological cell proliferation data and artificial intelligence shows 98.8% of accuracy, 100% sensitivity, and 95% specificity. CONCLUSION: this proof of concept study demonstrates that integration of innovative non invasive methods and predictive-models can be decisive in assessing the health status of an individual, and achieve cutting-edge results in cancer prevention and management.
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Inteligencia Artificial , Neoplasias , HumanosRESUMEN
Cardiac myxoma (CM) is a potentially life-threatening disease because frequently asymptomatic or debuts with aspecific manifestations. Definitive diagnosis is established by histopathological assessment including tumor and endothelial cell markers. To derive a specific panel of circulating cells antigenically detectable, pre-surgery peripheral blood samples of CM patients were analyzed. Pre-surgery peripheral blood samples from patients with CM were simultaneously analyzed for Circulating tumor cells (CTCs) and circulating endothelial cells (CECs) that were matched with tumor tissue profiles and with patient-derived xenografts (PDXs) distinguishing tumor regions. Moreover, CECs values in CM patients were further matched with CEC's levels in cardiovascular disease and control subjects. The blood-derived cytological specimens detected at least 1-3 CTCs/ml in 10 tested CM samples (p = 0.0001) showing specific CM features preserved in the central zones of the tumor. The central zone of the primary tumor, supported by a vessel density rate (55 ± 7%), with a proliferative profile of 32 ± 3% and a percentage of Calretininpos cells (p = 0.03), is the principal site of CTCs (r = 00) dissemination. The subsets of endothelial cells recognized in the blood were indifferent to their topological distribution within the tumor and corresponding PDXs. With further refinement and validation in large cohorts, multiparametric liquid biopsies can optimally integrate clinically informative datasets and maximize their utility in pre-surgery evaluation of CM patients. Blood-derived culture's protocol provides a versatile method capable of viable analysis of CTCs of non-hematological rare tumors which conventional antibody-mediated analytical platform is unable to perform. Distinctive blood- based cell phenotype contributes to differentiate CM from other differentials assuring its prompt surgical resection by combining blood-based cell biomarkers integrated with clinically informative datasets.
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Enfermedades Cardiovasculares , Neoplasias Pulmonares , Células Neoplásicas Circulantes , Humanos , Células Endoteliales/patología , Biomarcadores de Tumor , Células Neoplásicas Circulantes/patología , Neoplasias Pulmonares/patologíaRESUMEN
Although the role of liquid biopsy (LB) to measure minimal residual disease (MRD) in the treatment of epithelial cancer is well known, the biology of the change in the availability of circulating biomarkers arising throughout treatments such as radiotherapy and interventional radio-oncology is less explained. Deep knowledge of how therapeutic effects can influence the biology of the release mechanism at the base of the biomarkers available in the bloodstream is needed for selecting the appropriate treatment-induced tumor circulating biomarker. Combining existing progress in the LB and interventional oncology (IO) fields, a proof of concept is provided, discussing the advantages of the traditional risk assessment of relapsing lesions, limitations, and the timing of detection of the circulating biomarker. The current review aims to help both interventional radiologists and interventional radiation oncologists evaluate the possibility of drawing a tailor-made board of blood-based surveillance markers to reveal subclinical diseases and avoid overtreatment.
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Protein A has long been used in different research fields due to its ability to specifically recognize immunoglobulins (Ig). The protein derived from Staphylococcus aureus binds Ig through the Fc region of the antibody, showing its strongest binding in immunoglobulin G (IgG), making it the most used protein in its purification and detection. The research presented here integrates, for the first time, protein A to a silicon surface patterned with gold nanoparticles for the oriented binding of IgG. The signal detection is conveyed through a metal enhanced fluorescence (MEF) system. Orienting immunoglobulins allows the exposition of the fragment antigen-binding (Fab) region for the binding to its antigen, substantially increasing the binding capacity per antibody immobilized. Antibodies orientation is of crucial importance in many diagnostics devices, particularly when either component is in limited quantities.
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The altered glucose metabolism characterising cancer cells determines an increased amount of methylglyoxal in their secretome. Previous studies have demonstrated that the methylglyoxal, in turn, modifies the protonation state (PS) of soluble proteins contained in the secretomes of cultivated circulating tumour cells (CTCs). In this study, we describe a method to assess the content of methylglyoxal adducts (MAs) in the secretome by near-infrared (NIR) portable handheld spectroscopy and the extreme learning machine (ELM) algorithm. By measuring the vibration absorption functional groups containing hydrogen, such as C-H, O-H and N-H, NIR generates specific spectra. These spectra reflect alterations of the energy frequency of a sample bringing information about its MAs concentration levels. The algorithm deciphers the information encoded in the spectra and yields a quantitative estimate of the concentration of MAs in the sample. This procedure was used for the comparative analysis of different biological fluids extracted from patients suspected of having cancer (secretome, plasma, serum, interstitial fluid and whole blood) measured directly on the solute left on a surface upon a sample-drop cast and evaporation, without any sample pretreatment. Qualitative and quantitative regression models were built and tested to characterise the different levels of MAs by ELM. The final model we selected was able to automatically segregate tumour from non-tumour patients. The method is simple, rapid and repeatable; moreover, it can be integrated in portable electronic devices for point-of-care and remote testing of patients.
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The molecular protonation profiles obtained by means of an organic electrochemical transistor, which is used for analysis of molecular products released by blood-derived cultures, contain a large amount of information The transistor is based on the conductive polymer PEDOT:PSS comprising super hydrophobic SU8 pillars positioned on the substrate to form a non-periodic square lattice to measure the state of protonation on secretomes derived from liquid biopsies. In the extracellular space of cultured cells, the number of glycation products increase, driven both by a glycolysis metabolism and by a compromised function of the glutathione redox system. Glycation products are a consequence of the interaction of the reactive aldehydes and side glycolytic products with other molecules. As a result, the amount of the glycation products reflects the anti-oxidative cellular reserves, counteracting the reactive aldehyde production of which both the secretome protonation profile and cancer risk are related. The protonation profiles can be profitably exploited through the use of mathematical techniques and multivariate statistics. This study provides a novel chemometric approach for molecular analysis of protonation and discusses the possibility of constructing a predictive cancer risk model based on the exploration of data collected by conventional analysis techniques and novel nanotechnological devices.