An Investigation into the Feasibility of Detecting Microscopic Disease Using Machine Learning

The prognosis for many cancers could be improved dramatically if they could be detected while still at the microscopic disease stage. We are investigating the possibility of detecting microscopic disease using machine learning approaches based on features derived from gene expression levels and metabolic profiles. We use immunochemistry and QRT-PCR to measure the gene expression profiles from a number of antigens such as cyclin E, P27KIP1, FHIT, Ki-67, PCNA, Bax, Bcl-2, P53, Fas, FasL and hTERT in several particular types of neuroendocrine tumors such as pheochromocytomas, paragangliomas; and the adrenocortical carcinomas (ACC), adenomas (ACA), and hyperplasia (ACH) in Cushing's syndrome. We provide statistical evidence that, higher expression levels of hTERT, PCNA and Ki-67 etc. are associated with a higher risk that the tumors are malignant or borderline, as opposed to benign. We also investigated whether higher expression levels of the P27KIP and FHIT etc. are associated with a decreased risk of adrenomedullary tumors. While no significant difference was found between cell-arrest antigens such as P27KIP1 for malignant, borderline, and benign tumors, there was a significant difference between expression levels of such antigens in normal adrenal medulla samples and in adrenomedullary tumors.