title: Pretreatment gene expression profiles can be used to predict response to neoadjuvant chemoradiotherapy in esophageal cancer.
abstract: BACKGROUND
  The use of neoadjuvant therapy, in particular chemoradiotherapy (CRT), in the treatment of esophageal cancer (EC) remains controversial. The ability to predict treatment response in an individual EC patient would greatly aid therapeutic planning. Gene expression profiles of EC were measured and relationship to therapeutic response assessed.
  
  
  METHODS
  Tumor biopsy samples taken from 46 EC patients before neoadjuvant CRT were analyzed on 10.5K cDNA microarrays. Response to treatment was assessed and correlated to gene expression patterns by using a support vector machine learning algorithm.
  
  
  RESULTS
  Complete clinical response at conclusion of CRT was achieved in 6 of 21 squamous cell carcinoma (SCC) and 11 of 25 adenocarcinoma (AC) patients. CRT response was an independent prognostic factor for survival (P < .001). A range of support vector machine models incorporating 10 to 1000 genes produced a predictive performance of tumor response to CRT peaking at 87% in SCC, but a distinct positive prediction profile was unobtainable for AC. A 32-gene classifier was produced, and by means of this classifier, 10 of 21 SCC patients could be accurately identified as having disease with an incomplete response to therapy, and thus unlikely to benefit from neoadjuvant CRT.
  
  
  CONCLUSIONS
  Our study identifies a 32-gene classifier that can be used to predict response to neoadjuvant CRT in SCC. However, because of the molecular diversity between the two histological subtypes of EC, when considering the AC and SCC samples as a single cohort, a predictive profile could not be resolved, and a negative predictive profile was observed for AC.
authors: Division of Surgical Oncology, Peter MacCallum Cancer Centre, St. Andrew's Place, East Melbourne, Victoria, 3002, Australia.
authors: Cuong
authors: C
authors: Duong
authors: Danielle M
authors: DM
authors: Greenawalt
authors: Adam
authors: A
authors: Kowalczyk
authors: Marianne L
authors: ML
authors: Ciavarella
authors: Garvesh
authors: G
authors: Raskutti
authors: William K
authors: WK
authors: Murray
authors: Wayne A
authors: WA
authors: Phillips
authors: Robert J S
authors: RJ
authors: Thomas
mesh_terms: D000230:Adenocarcinoma / Q000378:metabolism / Q000556:secondary / Q000628:therapy; D000368:Aged; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use*; D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism*; D002294:Carcinoma, Squamous Cell / Q000378:metabolism / Q000556:secondary / Q000628:therapy; D003131:Combined Modality Therapy; D004938:Esophageal Neoplasms / Q000188:drug therapy / Q000378:metabolism* / Q000532:radiotherapy / Q000628:therapy*; D016629:Esophagectomy; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D007124:Immunoenzyme Techniques; D008207:Lymphatic Metastasis / Q000473:pathology; D008297:Male; D008875:Middle Aged; D020360:Neoadjuvant Therapy; D009367:Neoplasm Staging; D020411:Oligonucleotide Array Sequence Analysis; D011379:Prognosis; D015996:Survival Rate; D016896:Treatment Outcome
chemical_list: D014408:Biomarkers, Tumor