Distinct structural and numerical chromosome abnormalities determine the MYC status in diffuse large B-Cell lymphoma and help differentiate from Burkitt lymphoma: a cytogenetic data analysis using unsupervised and AI-driven prediction models.

The aim of this study was to identify recurrent chromosome abnormalities (RCAs) to distinguish these entities and to test their specificities in a set of predictor models. The study analyzed publicly available cytogenetic data to construct models to predict DLBCL and BL. The Fisher Exact test (2-tail) was used to assess the significance of differences in the number of aberrations between groups, as well as to determine correlations between RCAs and the two entities. A p-value less than 0.05 was considered significant. Discrimination analysis was determined by the receiver operating curve (ROC). All analyses were performed using the R package. The SAS software package was used to develop a logistic regression model. Two subsequent supervised models were constructed using a larger dataset (n = 515) to confirm initial findings. A p-value < 0.05 was considered significant. Several RCAs were associated with DLBCL, including 1p-, 1q-, -2, + 3, -4, + 5, 6p gain, 6q-, + 7, -8, 9q-, -10/-15, -10/-14, + 11, +12, 14q-, 15q-, + 16, 16q-,17p-, + 18, 19p-, and 22q-. Of these, + 7, 15q-, + 16 and + 18 were more prevalent in MYC + DLBCL vs. BL, whereas 1q gain and 13q- were consistent with BL. The specificity of supervised models ranged from 90 to 100%, whereas the accuracy of the unsupervised logistic regression model was 85%. Our findings revealed unique RCAs that may be used in combination with model classifiers to augment diagnostic accuracy and help clinicians better manage these patients.