title: Personalization of cancer treatment using predictive simulation.
abstract: BACKGROUND
  The personalization of cancer treatments implies the reconsideration of a one-size-fits-all paradigm. This move has spawned increased use of next generation sequencing to understand mutations and copy number aberrations in cancer cells. Initial personalization successes have been primarily driven by drugs targeting one patient-specific oncogene (e.g., Gleevec, Xalkori, Herceptin). Unfortunately, most cancers include a multitude of aberrations, and the overall impact on cancer signaling and metabolic networks cannot be easily nullified by a single drug.
  
  
  METHODS
  We used a novel predictive simulation approach to create an avatar of patient cancer cells using point mutations and copy number aberration data. Simulation avatars of myeloma patients were functionally screened using various molecularly targeted drugs both individually and in combination to identify drugs that are efficacious and synergistic. Repurposing of drugs that are FDA-approved or under clinical study with validated clinical safety and pharmacokinetic data can provide a rapid translational path to the clinic. High-risk multiple myeloma patients were modeled, and the simulation predictions were assessed ex vivo using patient cells.
  
  
  RESULTS
  Here, we present an approach to address the key challenge of interpreting patient profiling genomic signatures into actionable clinical insights to make the personalization of cancer therapy a practical reality. Through the rational design of personalized treatments, our approach also targets multiple patient-relevant pathways to address the emergence of single therapy resistance. Our predictive platform identified drug regimens for four high-risk multiple myeloma patients. The predicted regimes were found to be effective in ex vivo analyses using patient cells.
  
  
  CONCLUSIONS
  These multiple validations confirm this approach and methodology for the use of big data to create personalized therapeutics using predictive simulation approaches.
authors: New York University School of Medicine, New York, NY, USA. nicole.doudican@gmail.com.
authors: Nicole A
authors: NA
authors: Doudican
authors: Cellworks Research India Pvt. Ltd, Bangalore, India. ansu@cellworksgroup.com.
authors: Ansu
authors: A
authors: Kumar
authors: Cellworks Research India Pvt. Ltd, Bangalore, India. neeraj@cellworksgroup.com.
authors: Neeraj Kumar
authors: NK
authors: Singh
authors: Cellworks Research India Pvt. Ltd, Bangalore, India. prashant@cellworksgroup.com.
authors: Prashant R
authors: PR
authors: Nair
authors: Cellworks Research India Pvt. Ltd, Bangalore, India. deepak@cellworksgroup.com.
authors: Deepak A
authors: DA
authors: Lala
authors: Cellworks Research India Pvt. Ltd, Bangalore, India. Kabya@cellworksgroup.com.
authors: Kabya
authors: K
authors: Basu
authors: Cellworks Research India Pvt. Ltd, Bangalore, India. anay@cellworksgroup.com.
authors: Anay A
authors: AA
authors: Talawdekar
authors: Cellworks Research India Pvt. Ltd, Bangalore, India. zeba@cellworksgroup.com.
authors: Zeba
authors: Z
authors: Sultana
authors: Cellworks Research India Pvt. Ltd, Bangalore, India. krishna@cellworksgroup.com.
authors: Krishna Kumar
authors: KK
authors: Tiwari
authors: Cellworks Research India Pvt. Ltd, Bangalore, India. Anuj@cellworksgroup.com.
authors: Anuj
authors: A
authors: Tyagi
authors: Cellworks Group, Inc, San Jose, CA, USA. taher@cellworksgroup.com.
authors: Taher
authors: T
authors: Abbasi
authors: Cellworks Group, Inc, San Jose, CA, USA. shireen@cellworksgroup.com.
authors: Shireen
authors: S
authors: Vali
authors: Washington University School of Medicine, St. Louis, MO, USA. rvij@DOM.wustl.edu.
authors: Ravi
authors: R
authors: Vij
authors: Washington University School of Medicine, St. Louis, MO, USA. MFIALA@DOM.wustl.edu.
authors: Mark
authors: M
authors: Fiala
authors: Washington University School of Medicine, St. Louis, MO, USA. jking@DOM.wustl.edu.
authors: Justin
authors: J
authors: King
authors: New York University School of Medicine, New York, NY, USA. maryann.perle@nyumc.org.
authors: MaryAnn
authors: M
authors: Perle
authors: New York University Cancer Center, New York, NY, USA. amitabha.mazumder@nyumc.org.
authors: Amitabha
authors: A
authors: Mazumder
mesh_terms: D045744:Cell Line, Tumor; D003198:Computer Simulation; D023281:Genomics; D006801:Humans; D009101:Multiple Myeloma / Q000473:pathology / Q000628:therapy*; D057285:Precision Medicine
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