title: Mining heterogeneous causal effects for personalized cancer treatment.
abstract: BACKGROUND
  Cancer is not a single disease and involves different subtypes characterized by different sets of molecules. Patients with different subtypes of cancer often react heterogeneously towards the same treatment. Currently, clinical diagnoses rather than molecular profiles are used to determine the most suitable treatment. A molecular level approach will allow a more precise and informed way for making treatment decisions, leading to a better survival chance and less suffering of patients. Although many computational methods have been proposed to identify cancer subtypes at molecular level, to the best of our knowledge none of them are designed to discover subtypes with heterogeneous treatment responses.
  
  
  RESULTS
  In this article we propose the Survival Causal Tree (SCT) method. SCT is designed to discover patient subgroups with heterogeneous treatment effects from censored observational data. Results on TCGA breast invasive carcinoma and glioma datasets have shown that for each subtype identified by SCT, the patients treated with radiotherapy exhibit significantly different relapse free survival pattern when compared to patients without the treatment. With the capability to identify cancer subtypes with heterogeneous treatment responses, SCT is useful in helping to choose the most suitable treatment for individual patients.
  
  
  METHODS
  Data and code are available at https://github.com/WeijiaZhang24/SurvivalCausalTree .
  
  
  BACKGROUND
  weijia.zhang@mymail.uinsa.edu.au.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: School of Information Technology and Mathematical Sciences.
authors.forename: Weijia
authors.initials: W
authors.lastname: Zhang
authors.affiliation: School of Information Technology and Mathematical Sciences.|Centre for Cancer Biology, University of South Australia, Adelaide 5000, Australia.
authors.forename: Thuc Duy
authors.initials: TD
authors.lastname: Le
authors.affiliation: School of Information Technology and Mathematical Sciences.
authors.forename: Lin
authors.initials: L
authors.lastname: Liu
authors.affiliation: National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China.
authors.forename: Zhi-Hua
authors.initials: ZH
authors.lastname: Zhou
authors.affiliation: School of Information Technology and Mathematical Sciences.
authors.forename: Jiuyong
authors.initials: J
authors.lastname: Li
mesh_terms: D000328:Adult; D000970:Antineoplastic Agents / Q000627:therapeutic use*; D001943:Breast Neoplasms / Q000188:drug therapy* / Q000532:radiotherapy; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D018572:Disease-Free Survival; D005260:Female; D005910:Glioma / Q000188:drug therapy* / Q000532:radiotherapy; D006801:Humans; D057285:Precision Medicine / Q000379:methods*; D016896:Treatment Outcome
chemical_list: D000970:Antineoplastic Agents