title: Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma.
abstract: OBJECTIVE
  To determine whether conventional contrast-enhanced computed tomography (CT) could be used to identify imaging phenotypes associated with a doxorubicin drug response gene expression program in hepatocellular carcinoma (HCC) by using an integrated imaging-genomic approach.
  
  
  METHODS
  Thirty HCCs were analyzed and scored individually across six predefined imaging phenotypes. Unsupervised and supervised bioinformatics analyses were performed to correlate the imaging scores with the corresponding tumor microarray data (each microarray contained gene expression measurements across approximately 18,000 genes) to identify relationships between the imaging traits and underlying tumor gene expression. Enrichment for a predefined doxorubicin-response gene expression program was then performed against the imaging phenotype-associated genes and enrichment determined.
  
  
  RESULTS
  An imaging phenotype related to tumor margins on arterial phase images demonstrated significant correlation with the doxorubicin-response transcriptional program (P < .05, q < 0.1). It was also significantly associated with HCC venous invasion and tumor stage (P < .05, q < 0.1). Tumors with higher tumor margin scores were more strongly associated with the doxorubicin resistance transcriptional program and had a greater prevalence of venous invasion and worse stage. Tumors with lower tumor margin scores, however, demonstrated a converse relationship.
  
  
  CONCLUSIONS
  It is possible to identify HCC imaging phenotypes at CT that correlate with a doxorubicin drug response gene expression program. Given the role of doxorubicin in regional therapies for HCC management, it is possible that such an approach could be used to guide HCC therapy on a tumor-by-tumor basis on the basis of underlying tumor gene expression patterns.
authors.affiliation: Department of Radiology, University of California, San Diego Medical Center, San Diego, CA 92103, USA. mkuo@ucsd.edu
authors.forename: Michael D
authors.initials: MD
authors.lastname: Kuo
authors.forename: Jeremy
authors.initials: J
authors.lastname: Gollub
authors.forename: Claude B
authors.initials: CB
authors.lastname: Sirlin
authors.forename: Clara
authors.initials: C
authors.lastname: Ooi
authors.forename: Xin
authors.initials: X
authors.lastname: Chen
mesh_terms: D000903:Antibiotics, Antineoplastic / Q000494:pharmacology*; D006528:Carcinoma, Hepatocellular / Q000000981:diagnostic imaging* / Q000188:drug therapy / Q000235:genetics* / Q000473:pathology; D003287:Contrast Media; D004317:Doxorubicin / Q000494:pharmacology*; D019008:Drug Resistance, Neoplasm / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D007479:Iopamidol; D016018:Least-Squares Analysis; D008113:Liver Neoplasms / Q000000981:diagnostic imaging* / Q000188:drug therapy / Q000235:genetics* / Q000473:pathology; D046228:Microarray Analysis; D010641:Phenotype; D014057:Tomography, X-Ray Computed
chemical_list: D000903:Antibiotics, Antineoplastic; D003287:Contrast Media; D004317:Doxorubicin; D007479:Iopamidol