1. Mining heterogeneous causal effects for personalized cancer treatment
- Author
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Thuc Duy Le, Lin Liu, Weijia Zhang, Zhi-Hua Zhou, Jiuyong Li, Zhang, Weijia, Le, Thuc Duy, Liu, Lin, Zhou, Zhi-Hua, and Li, Jiuyong
- Subjects
Adult ,0301 basic medicine ,Statistics and Probability ,Oncology ,medicine.medical_specialty ,breast-cancer ,propensity score ,gene-expression ,microarray data ,class discovery ,survival ,subtypes ,models ,regression ,algorithm ,medicine.medical_treatment ,MEDLINE ,Antineoplastic Agents ,Breast Neoplasms ,Disease ,Biochemistry ,Disease-Free Survival ,03 medical and health sciences ,Internal medicine ,Data Mining ,Humans ,Medicine ,Precision Medicine ,Medical diagnosis ,Molecular Biology ,business.industry ,Causal effect ,Computational Biology ,Cancer ,Glioma ,medicine.disease ,Precision medicine ,Computer Science Applications ,Radiation therapy ,Computational Mathematics ,Treatment Outcome ,030104 developmental biology ,Computational Theory and Mathematics ,Female ,Observational study ,business - Abstract
Motivation 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. Availability and implementation Data and code are available at https://github.com/WeijiaZhang24/SurvivalCausalTree . Contact weijia.zhang@mymail.uinsa.edu.au. Supplementary information Supplementary data are available at Bioinformatics online.
- Published
- 2017