A bioinformatics approach for precision medicine off-label drug drug selection among triple negative breast cancer patients.
Cancer has been extensively characterized on the basis of genomics. The integration of genetic information about cancers with data on how the cancers respond to target based therapy to help to optimum cancer treatment.



The increasing usage of sequencing technology in cancer research and clinical practice has enormously advanced our understanding of cancer mechanisms. The cancer precision medicine is becoming a reality. Although off-label drug usage is a common practice in treating cancer, it suffers from the lack of knowledge base for proper cancer drug selections. This eminent need has become even more apparent considering the upcoming genomics data.



In this paper, a personalized medicine knowledge base is constructed by integrating various cancer drugs, drug-target database, and knowledge sources for the proper cancer drugs and their target selections. Based on the knowledge base, a bioinformatics approach for cancer drugs selection in precision medicine is developed. It integrates personal molecular profile data, including copy number variation, mutation, and gene expression.



By analyzing the 85 triple negative breast cancer (TNBC) patient data in the Cancer Genome Altar, we have shown that 71.7% of the TNBC patients have FDA approved drug targets, and 51.7% of the patients have more than one drug target. Sixty-five drug targets are identified as TNBC treatment targets and 85 candidate drugs are recommended. Many existing TNBC candidate targets, such as Poly (ADP-Ribose) Polymerase 1 (PARP1), Cell division protein kinase 6 (CDK6), epidermal growth factor receptor, etc., were identified. On the other hand, we found some additional targets that are not yet fully investigated in the TNBC, such as Gamma-Glutamyl Hydrolase (GGH), Thymidylate Synthetase (TYMS), Protein Tyrosine Kinase 6 (PTK6), Topoisomerase (DNA) I, Mitochondrial (TOP1MT), Smoothened, Frizzled Class Receptor (SMO), etc. Our additional analysis of target and drug selection strategy is also fully supported by the drug screening data on TNBC cell lines in the Cancer Cell Line Encyclopedia.



The proposed bioinformatics approach lays a foundation for cancer precision medicine. It supplies much needed knowledge base for the off-label cancer drug usage in clinics.
Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN 46202, USA Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, IN 46202, USA.
Lijun
L
Cheng
Division of Hematology and Oncology, Department of Medicine, School of Medicine, Indiana University, Indianapolis, IN 46202, USA Indiana Institute of Personalized Medicine, School of Medicine, Indiana University, Indianapolis, IN 46202, USA.
Bryan P
BP
Schneider
Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN 46202, USA Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, IN 46202, USA Indiana Institute of Personalized Medicine, School of Medicine, Indiana University, Indianapolis, IN 46202, USA lali@iu.edu.
Lang
L
Li
D000465:Algorithms; D000970:Antineoplastic Agents / Q000627:therapeutic use*; D045744:Cell Line, Tumor; D019295:Computational Biology; D004273:DNA, Neoplasm / Q000032:analysis; D030541:Databases, Genetic; D005260:Female; D006801:Humans; D051188:Knowledge Bases; D056687:Off-Label Use; D057285:Precision Medicine; D017422:Sequence Analysis, DNA; D064726:Triple Negative Breast Neoplasms / Q000188:drug therapy* / Q000235:genetics*
D000970:Antineoplastic Agents; D004273:DNA, Neoplasm
bioinformatics; drug selection; precision medicine