title: In silico modeling predicts drug sensitivity of patient-derived cancer cells.
abstract: BACKGROUND
  Glioblastoma (GBM) is an aggressive disease associated with poor survival. It is essential to account for the complexity of GBM biology to improve diagnostic and therapeutic strategies. This complexity is best represented by the increasing amounts of profiling ("omics") data available due to advances in biotechnology. The challenge of integrating these vast genomic and proteomic data can be addressed by a comprehensive systems modeling approach.
  
  
  METHODS
  Here, we present an in silico model, where we simulate GBM tumor cells using genomic profiling data. We use this in silico tumor model to predict responses of cancer cells to targeted drugs. Initially, we probed the results from a recent hypothesis-independent, empirical study by Garnett and co-workers that analyzed the sensitivity of hundreds of profiled cancer cell lines to 130 different anticancer agents. We then used the tumor model to predict sensitivity of patient-derived GBM cell lines to different targeted therapeutic agents.
  
  
  RESULTS
  Among the drug-mutation associations reported in the Garnett study, our in silico model accurately predicted ~85% of the associations. While testing the model in a prospective manner using simulations of patient-derived GBM cell lines, we compared our simulation predictions with experimental data using the same cells in vitro. This analysis yielded a ~75% agreement of in silico drug sensitivity with in vitro experimental findings.
  
  
  CONCLUSIONS
  These results demonstrate a strong predictability of our simulation approach using the in silico tumor model presented here. Our ultimate goal is to use this model to stratify patients for clinical trials. By accurately predicting responses of cancer cells to targeted agents a priori, this in silico tumor model provides an innovative approach to personalizing therapy and promises to improve clinical management of cancer.
authors.forename: Sandeep C
authors.initials: SC
authors.lastname: Pingle
authors.forename: Zeba
authors.initials: Z
authors.lastname: Sultana
authors.forename: Sandra
authors.initials: S
authors.lastname: Pastorino
authors.forename: Pengfei
authors.initials: P
authors.lastname: Jiang
authors.forename: Rajesh
authors.initials: R
authors.lastname: Mukthavaram
authors.forename: Ying
authors.initials: Y
authors.lastname: Chao
authors.forename: Ila Sri
authors.initials: IS
authors.lastname: Bharati
authors.forename: Natsuko
authors.initials: N
authors.lastname: Nomura
authors.forename: Milan
authors.initials: M
authors.lastname: Makale
authors.forename: Taher
authors.initials: T
authors.lastname: Abbasi
authors.forename: Shweta
authors.initials: S
authors.lastname: Kapoor
authors.forename: Ansu
authors.initials: A
authors.lastname: Kumar
authors.forename: Shahabuddin
authors.initials: S
authors.lastname: Usmani
authors.forename: Ashish
authors.initials: A
authors.lastname: Agrawal
authors.forename: Shireen
authors.initials: S
authors.lastname: Vali
authors.affiliation: Translational Neuro-Oncology Laboratories, Moores Cancer Center, UC San Diego, La Jolla, CA 92093, USA. skesari@ucsd.edu.
authors.forename: Santosh
authors.initials: S
authors.lastname: Kesari
mesh_terms: D000970:Antineoplastic Agents / Q000627:therapeutic use; D045744:Cell Line, Tumor; D003198:Computer Simulation; D004354:Drug Screening Assays, Antitumor; D006801:Humans; D012189:Retrospective Studies
chemical_list: D000970:Antineoplastic Agents