Pancreatic ductal adenocarcinoma (PDAC) continues to be the third leading cause of cancer-related deaths in the United States with a five-year relative survival rate below ten percent. Detection and classification tools are urgently needed to improve PDAC patients’ survival. Current methods for PDAC categorization rely on traditional mutational status of genes involved in DNA repair, homologous recombination, or other pathways. PDACs with these mutations generally respond better to different therapies. Nevertheless, traditional mutational analysis may not capture all perturbations to these pathways, especially where a large number of genes are involved. It has been demonstrated that mutations due to structural DNA aberrations are a better measure of DNA damage. Hence, we aim to use this measure to better classify PDACs to predict therapeutic response. Our goal is to advance PDAC classification by developing computational tools that uses structural (conformational) descriptors of the human genome.
Figure. DNA sequence-structure model for mutational predictions in PDAC.
(A) Mutational profiles within PDAC preliminary samples: MSI (signature SBS6, SBS stands for a single base substitution) and APOBEC (SBS2/13). (B) The APOBEC SBS2/13 mutational signature from preliminary data. (C) Mapping of the structural DNA parameters on DNA sequence, where N can be any of the four DNA bases. Structural features: minor groove width (mgw), base-pair parameters (bp, here propeller), base-pair step parameters (bp step, here roll), and their loci within the 5nt-long DNA motif. (D) Mutational signature SBS 2/13 from DNA structure. (E) Contribution of each signature to a given cancer type (2 signatures shown). (F) Contribution of detected signature to tumor sample (PDAC samples, high heterogeneity).