Multicancer screening test based on the detection of circulating non haematological proliferating atypical cells

• Published in: Molecular cancer Vol. 23; no. 1; p. 32
• Main Authors: Malara, Natalia, Coluccio, Maria Laura, Grillo, Fabiana, Ferrazzo, Teresa, Garo, Nastassia C, Donato, Giuseppe, Lavecchia, Annamaria, Fulciniti, Franco, Sapino, Anna, Cascardi, Eliano, Pellegrini, Antonella, Foxi, Prassede, Furlanello, Cesare, Negri, Giovanni, Fadda, Guido, Capitanio, Arrigo, Pullano, Salvatore, Garo, Virginia M, Ferrazzo, Francesca, Lowe, Alarice, Torsello, Angela, Candeloro, Patrizio, Gentile, Francesco
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 13.02.2024; BioMed Central; BMC
• Subjects: Analysis; Antigens; Artificial intelligence; Biopsy; Blood; Cancer; Cancer prevention; Cancer screening; Cell proliferation; Cells; Correspondence; Diagnosis; Disease prevention; Genotype & phenotype; Hematology; Hyperplasia; Invasiveness; Latency; Liquid biopsy; Machine learning; Medical screening; Medical tests; Metastasis; Multicancer diagnosis; Neural networks; Non heamatological proliferating cells; Patients; Prediction models; Predictive model; Prevention; Preventive medicine; Supervised machine learning; Tumor cells; Tumors; Variables; Supervised machine learning; Multicancer diagnosis; Predictive model; Cancer prevention; Liquid biopsy; Neural network algorithm; Non heamatological proliferating cells
• ISSN: 1476-4598; 1476-4598
• DOI: 10.1186/s12943-024-01951-x

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. 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. the developed test combining circulating non-haematological cell proliferation data and artificial intelligence shows 98.8% of accuracy, 100% sensitivity, and 95% specificity. 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.