title: Personalized identification of altered pathways in cancer using accumulated normal tissue data.
abstract: BACKGROUND
  Identifying altered pathways in an individual is important for understanding disease mechanisms and for the future application of custom therapeutic decisions. Existing pathway analysis techniques are mainly focused on discovering altered pathways between normal and cancer groups and are not suitable for identifying the pathway aberrance that may occur in an individual sample. A simple way to identify individual's pathway aberrance is to compare normal and tumor data from the same individual. However, the matched normal data from the same individual are often unavailable in clinical situation. Therefore, we suggest a new approach for the personalized identification of altered pathways, making special use of accumulated normal data in cases when a patient's matched normal data are unavailable. The philosophy behind our method is to quantify the aberrance of an individual sample's pathway by comparing it with accumulated normal samples. We propose and examine personalized extensions of pathway statistics, overrepresentation analysis and functional class scoring, to generate individualized pathway aberrance score.
  
  
  RESULTS
  Collected microarray data of normal tissue of lung and colon mucosa are served as reference to investigate a number of cancer individuals of lung adenocarcinoma (LUAD) and colon cancer, respectively. Our method concurrently captures known facts of cancer survival pathways and identifies the pathway aberrances that represent cancer differentiation status and survival. It also provides more improved validation rate of survival-related pathways than when a single cancer sample is interpreted in the context of cancer-only cohort. In addition, our method is useful in classifying unknown samples into cancer or normal groups. Particularly, we identified 'amino acid synthesis and interconversion' pathway is a good indicator of LUAD (Area Under the Curve (AUC) 0.982 at independent validation). Clinical importance of the method is providing pathway interpretation of single cancer, even though its matched normal data are unavailable.
  
  
  METHODS
  The method was implemented using the R software, available at our Web site: http://bibs.snu.ac.kr/ipas.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Samsung Advanced Institute of Technology, 130, Suwon-si, Gyeonggi-do, 443-803, Korea, Samsung Genome Institute, Seoul, 135-710, Korea, Interdisciplinary Program in Bioinformatics and Department of Statistics, Seoul National University, Seoul, South Korea Samsung Advanced Institute of Technology, 130, Suwon-si, Gyeonggi-do, 443-803, Korea, Samsung Genome Institute, Seoul, 135-710, Korea, Interdisciplinary Program in Bioinformatics and Department of Statistics, Seoul National University, Seoul, South Korea Samsung Advanced Institute of Technology, 130, Suwon-si, Gyeonggi-do, 443-803, Korea, Samsung Genome Institute, Seoul, 135-710, Korea, Interdisciplinary Program in Bioinformatics and Department of Statistics, Seoul National University, Seoul, South Korea.
authors.forename: TaeJin
authors.initials: T
authors.lastname: Ahn
authors.affiliation: Samsung Advanced Institute of Technology, 130, Suwon-si, Gyeonggi-do, 443-803, Korea, Samsung Genome Institute, Seoul, 135-710, Korea, Interdisciplinary Program in Bioinformatics and Department of Statistics, Seoul National University, Seoul, South Korea Samsung Advanced Institute of Technology, 130, Suwon-si, Gyeonggi-do, 443-803, Korea, Samsung Genome Institute, Seoul, 135-710, Korea, Interdisciplinary Program in Bioinformatics and Department of Statistics, Seoul National University, Seoul, South Korea.
authors.forename: Eunjin
authors.initials: E
authors.lastname: Lee
authors.affiliation: Samsung Advanced Institute of Technology, 130, Suwon-si, Gyeonggi-do, 443-803, Korea, Samsung Genome Institute, Seoul, 135-710, Korea, Interdisciplinary Program in Bioinformatics and Department of Statistics, Seoul National University, Seoul, South Korea.
authors.forename: Nam
authors.initials: N
authors.lastname: Huh
authors.affiliation: Samsung Advanced Institute of Technology, 130, Suwon-si, Gyeonggi-do, 443-803, Korea, Samsung Genome Institute, Seoul, 135-710, Korea, Interdisciplinary Program in Bioinformatics and Department of Statistics, Seoul National University, Seoul, South Korea Samsung Advanced Institute of Technology, 130, Suwon-si, Gyeonggi-do, 443-803, Korea, Samsung Genome Institute, Seoul, 135-710, Korea, Interdisciplinary Program in Bioinformatics and Department of Statistics, Seoul National University, Seoul, South Korea.
authors.forename: Taesung
authors.initials: T
authors.lastname: Park
mesh_terms: D000230:Adenocarcinoma / Q000235:genetics / Q000378:metabolism / Q000401:mortality; D000077192:Adenocarcinoma of Lung; D003106:Colon / Q000378:metabolism; D003110:Colonic Neoplasms / Q000235:genetics / Q000378:metabolism / Q000401:mortality; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008168:Lung / Q000378:metabolism; D008175:Lung Neoplasms / Q000235:genetics / Q000378:metabolism / Q000401:mortality; D009369:Neoplasms / Q000235:genetics*; D016019:Survival Analysis