An artificial intelligence system applied to recurrent cytogenetic aberrations and genetic progression scores predicts MYC rearrangements in large B‐cell lymphoma

Abstract

Abstract Diffuse large B‐cell lymphoma (DLBCL), the most common type of non‐Hodgkin lymphoma, is characterized by MYC rearrangements (MYC R) in up to 15% of cases, and these have unfavorable prognosis. Due to cryptic rearrangements and variations in MYC breakpoints, MYC R may be undetectable by conventional methods in up to 10%–15% of cases. In this study, a retrospective proof of concept study, we sought to identify recurrent cytogenetic aberrations (RCAs), generate genetic progression scores (GP) from RCAs and apply these to an artificial intelligence (AI) algorithm to predict MYC status in the karyotypes of published cases. The developed AI algorithm is validated for its performance on our institutional cases. In addition, cytogenetic evolution pattern and clinical impact of RCAs was performed. Chromosome losses were associated with MYC‐, while partial gain of chromosome 1 was significant in MYC R tumors. MYC R was the sole driver alteration in MYC‐rearranged tumors, and evolution patterns revealed RCAs associated with gene expression signatures. A higher GPS value was associated with MYC R tumors. A subsequent AI algorithm (composed of RCAs + GPS) obtained a sensitivity of 91.4 and specificity of 93.8 at predicting MYC R. Analysis of an additional 59 institutional cases with the AI algorithm showed a sensitivity and specificity of 100% and 87% each with positive predictive value of 92%, and a negative predictive value of 100%. Cases with a MYC R showed a shorter survival.

Keywords

• AI
• chromosome aberrations
• DLBCL
• genetic progression scores
• MYC rearrangement