Bioinformatory‐assisted analysis of next‐generation sequencing data for precision medicine in pancreatic cancer

• Published in: Molecular oncology 2017-10, Vol.11 (10), p.1413-1429
• Main Authors: Malgerud, Linnéa, Lindberg, Johan, Wirta, Valtteri, Gustafsson‐Liljefors, Maria, Karimi, Masoud, Moro, Carlos Fernández, Stecker, Katrin, Picker, Alexander, Huelsewig, Carolin, Stein, Martin, Bohnert, Regina, Del Chiaro, Marco, Haas, Stephan L., Heuchel, Rainer L., Permert, Johan, Maeurer, Markus J., Brock, Stephan, Verbeke, Caroline S., Engstrand, Lars, Jackson, David B., Grönberg, Henrik, Löhr, Johannes‐Matthias
• Format: Article
• Language: English
• Subjects: Adenocarcinoma; Analysis; bioinformatics; Biomarkers; Breast cancer; Cancer; Cancer therapies; Chemoresistance; Chemotherapy; Computer programs; Data analysis; drug–drug interactions; Ethylenediaminetetraacetic acid; evidence‐based; FDA approval; Feasibility studies; Gene mutations; Genomes; Health aspects; Kinases; Medical research; Medicin och hälsovetenskap; Mutation; NGS; p53 Protein; Pancreatic cancer; Patients; Pharmacogenetics; Pharmacogenomics; Precision medicine; Proteins; Side effects; Software; Toxicity; Tumor proteins
• ISSN: 1574-7891; 1878-0261; 1878-0261
• DOI: 10.1002/1878-0261.12108

Pancreatic ductal adenocarcinoma (PDAC) is a tumor with an extremely poor prognosis, predominantly as a result of chemotherapy resistance and numerous somatic mutations. Consequently, PDAC is a prime candidate for the use of sequencing to identify causative mutations, facilitating subsequent administration of targeted therapy. In a feasibility study, we retrospectively assessed the therapeutic recommendations of a novel, evidence‐based software that analyzes next‐generation sequencing (NGS) data using a large panel of pharmacogenomic biomarkers for efficacy and toxicity. Tissue from 14 patients with PDAC was sequenced using NGS with a 620 gene panel. FASTQ files were fed into treatmentmap. The results were compared with chemotherapy in the patients, including all side effects. No changes in therapy were made. Known driver mutations for PDAC were confirmed (e.g. KRAS, TP53). Software analysis revealed positive biomarkers for predicted effective and ineffective treatments in all patients. At least one biomarker associated with increased toxicity could be detected in all patients. Patients had been receiving one of the currently approved chemotherapy agents. In two patients, toxicity could have been correctly predicted by the software analysis. The results suggest that NGS, in combination with an evidence‐based software, could be conducted within a 2‐week period, thus being feasible for clinical routine. Therapy recommendations were principally off‐label use. Based on the predominant KRAS mutations, other drugs were predicted to be ineffective. The pharmacogenomic biomarkers indicative of increased toxicity could be retrospectively linked to reported negative side effects in the respective patients. Finally, the occurrence of somatic and germline mutations in cancer syndrome‐associated genes is noteworthy, despite a high frequency of these particular variants in the background population. These results suggest software‐analysis of NGS data provides evidence‐based information on effective, ineffective and toxic drugs, potentially forming the basis for precision cancer medicine in PDAC. Examining precision medicine using bioinformatics assisted analysis of next generation sequencing (NGS) data in patients with (pancreatic) cancer. Tumor tissue and blood samples were subjected to DNA extraction and NGS. Sequencing data were run against several data bases containing biomedical knowledge (‘dataome’). Based on the entirety of patients biomarkers, a report was gener