title: Big data and computational biology strategy for personalized prognosis.
abstract: The era of big data and precision medicine has led to accumulation of massive datasets of gene expression data and clinical information of patients. For a new patient, we propose that identification of a highly similar reference patient from an existing patient database via similarity matching of both clinical and expression data could be useful for predicting the prognostic risk or therapeutic efficacy.Here, we propose a novel methodology to predict disease/treatment outcome via analysis of the similarity between any pair of patients who are each characterized by a certain set of pre-defined biological variables (biomarkers or clinical features) represented initially as a prognostic binary variable vector (PBVV) and subsequently transformed to a prognostic signature vector (PSV). Our analyses revealed that Euclidean distance rather correlation distance measure was effective in defining an unbiased similarity measure calculated between two PSVs.We implemented our methods to high-grade serous ovarian cancer (HGSC) based on a 36-mRNA predictor that was previously shown to stratify patients into 3 distinct prognostic subgroups. We studied and revealed that patient's age, when converted into binary variable, was positively correlated with the overall risk of succumbing to the disease. When applied to an independent testing dataset, the inclusion of age into the molecular predictor provided more robust personalized prognosis of overall survival correlated with the therapeutic response of HGSC and provided benefit for treatment targeting of the tumors in HGSC patients.Finally, our method can be generalized and implemented in many other diseases to accurately predict personalized patients' outcomes.
authors.affiliation: Bioinformatics Institute, Singapore 138671.
authors.forename: Ghim Siong
authors.initials: GS
authors.lastname: Ow
authors.affiliation: Bioinformatics Institute, Singapore 138671.
authors.forename: Zhiqun
authors.initials: Z
authors.lastname: Tang
authors.affiliation: Bioinformatics Institute, Singapore 138671.|School of Computer Engineering, Nanyang Technological University, Singapore 639798.
authors.forename: Vladimir A
authors.initials: VA
authors.lastname: Kuznetsov
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008875:Middle Aged; D010051:Ovarian Neoplasms / Q000235:genetics / Q000378:metabolism; D057285:Precision Medicine / Q000379:methods*; D011379:Prognosis; D012333:RNA, Messenger / Q000378:metabolism; D012306:Risk
chemical_list: D012333:RNA, Messenger
keywords: aging; big data; ovarian cancer; personalized prognosis; risk stratification