Interpreting Sequence Variation in PDAC-Predisposing Genes Using a Multi-Tier Annotation Approach Performed at the...

We investigated germline variation in pancreatic ductal adenocarcinoma (PDAC) predisposition genes in 535 patients, using a custom-built panel and a new complementary bioinformatic approach. Our panel assessed genes belonging to DNA repair, cell cycle checkpoints, migration, and preneoplastic pancreatic conditions. Our bioinformatics approach integrated annotations of variants by using data derived from both germline and somatic references. This integrated approach with expanded evidence enabled us to consider patterns even among private mutations, supporting a functional role for certain alleles, which we believe enhances individualized medicine beyond classic gene-centric approaches. Concurrent evaluation of three levels of evidence, at the gene, sample, and cohort level, has not been previously done. Overall, we identified in PDAC patient germline samples, 12% with mutations previously observed in pancreatic cancers, 23% with mutations previously discovered by sequencing other human tumors, and 46% with mutations with germline associations to cancer. Non-polymorphic protein-coding pathogenic variants were found in 18.4% of patient samples. Moreover, among patients with metastatic PDAC, 16% carried at least one pathogenic variant, and this subgroup was found to have an improved overall survival (22.0 months versus 9.8; p=0.008) despite a higher pre-treatment CA19-9 level (p=0.02). Genetic alterations in DNA damage repair genes were associated with longer overall survival among patients who underwent resection surgery (92 months vs. 46; p=0.06). ATM alterations were associated with more frequent metastatic stage (p = 0.04) while patients with BRCA1 or BRCA2 alterations had improved overall survival (79 months vs. 39; p=0.05). We found that mutations in genes associated with chronic pancreatitis were more common in non-white patients (p<0.001) and associated with longer overall survival (52 months vs. 26; p=0.004), indicating the need for greater study of the relationship among these factors. More than 90% of patients were found to have variants of uncertain significance, which is higher than previously reported. Furthermore, we generated 3D models for selected mutant proteins, which suggested distinct mechanisms underlying their dysfunction, likely caused by genetic alterations. Notably, this type of information is not predictable from sequence alone, underscoring the value of structural bioinformatics to improve genomic interpretation. In conclusion, the variation in PDAC predisposition genes appears to be more extensive than anticipated. This information adds to the growing body of literature on the genomic landscape of PDAC and brings us closer to a more widespread use of precision medicine for this challenging disease.