title: Comparing Patient-Derived Xenograft and Computational Response Prediction for Targeted Therapy in Patients of Early-Stage Large Cell Lung Cancer.
abstract: Targeted therapy (TT) provides highly effective cancer treatment for appropriately selected individuals. A major challenge of TT is to select patients who would benefit most.
  
  
  
  The study uses cancer material from 25 patients primarily diagnosed with non-small cell lung cancer (NSCLC). Patient-derived xenografts (PDXs) are treated with cetuximab and erlotinib. Treatment response is measured by tumor shrinkage comparing tumor volume at day 25 (V25) with tumor volume at baseline (V0). Shrinkage below 40% is considered as treatment response: V25/V0 < 0.4 (<40%). Furthermore, RNA-seq data from each tumor sample are used to predict tumor response to either treatment using an in silico molecular signaling map (MSM) approach.
  
  
  
  PDX response was 40% (10/25; 95% CI [21.13%, 61.34%]) under cetuximab and 20% (5/25; 95% CI [6.83%, 40.70%]) under erlotinib. MSM predicted response was 48% (12/25; 95% CI [27.8%, 68.7%]) under cetuximab and 40% (10/25; 95% CI [21.13%, 61.34%]) under erlotinib. Agreement between PDX and MSM response prediction is substantial under cetuximab and erlotinib: 84% (21/25, P = 0.001) and 80% (20/25, P = 0.003). A total of 5 from the 25 patients have been treated with cetuximab showing a clinical response identical to both predictions.
  
  
  
  For NSCLC patients, this proof-of-concept study shows a considerable agreement in response prediction from MSM and PDX approaches, but MSM saves time and laboratory resources. Our result indicates the potential of MSM-based approach for clinical decision making when selecting cancer TTs. Clin Cancer Res; 22(9); 2167-76. ©2015 AACR.
authors.affiliation: Institute for Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany. German Cancer Consortium (DKTK), Heidelberg, Germany. German Cancer Research Center (DKFZ), Heidelberg, Germany. lijian@ibe.med.uni-muenchen.de.
authors.forename: Jian
authors.initials: J
authors.lastname: Li
authors.affiliation: Medical Research Center of Yu Huang Hospital, Yu Huang, Zhejiang, PR China.
authors.forename: Changkun
authors.initials: C
authors.lastname: Ye
authors.affiliation: Institute for Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany. German Cancer Consortium (DKTK), Heidelberg, Germany.
authors.forename: Ulrich R
authors.initials: UR
authors.lastname: Mansmann
mesh_terms: D000328:Adult; D000368:Aged; D000818:Animals; D000970:Antineoplastic Agents / Q000627:therapeutic use; D000074322:Antineoplastic Agents, Immunological / Q000627:therapeutic use; D002289:Carcinoma, Non-Small-Cell Lung / Q000188:drug therapy / Q000473:pathology*; D000068818:Cetuximab / Q000627:therapeutic use; D000069347:Erlotinib Hydrochloride / Q000627:therapeutic use; D005260:Female; D064593:Heterografts / Q000473:pathology*; D006801:Humans; D008175:Lung Neoplasms / Q000188:drug therapy / Q000473:pathology*; D008297:Male; D051379:Mice; D008819:Mice, Nude; D016513:Mice, SCID; D008875:Middle Aged; D023041:Xenograft Model Antitumor Assays / Q000379:methods
chemical_list: D000970:Antineoplastic Agents; D000074322:Antineoplastic Agents, Immunological; D000069347:Erlotinib Hydrochloride; D000068818:Cetuximab