Pretreatment gene expression profiles can be used to predict response to neoadjuvant chemoradiotherapy in esophageal cancer
| Autor: | Adam Kowalczyk, Wayne A. Phillips, Cuong Duong, William K. Murray, Danielle M. Greenawalt, Robert J. Thomas, Garvesh Raskutti, Marianne L. Ciavarella |
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| Rok vydání: | 2007 |
| Předmět: |
Oncology
Male medicine.medical_specialty Esophageal Neoplasms medicine.medical_treatment Adenocarcinoma Immunoenzyme Techniques Internal medicine Antineoplastic Combined Chemotherapy Protocols medicine Carcinoma Biomarkers Tumor Humans Survival rate Neoadjuvant therapy Aged Neoplasm Staging Oligonucleotide Array Sequence Analysis business.industry Gene Expression Profiling Cancer Esophageal cancer Middle Aged medicine.disease Prognosis Combined Modality Therapy Neoadjuvant Therapy Gene expression profiling Esophagectomy Survival Rate Treatment Outcome Lymphatic Metastasis Carcinoma Squamous Cell Surgery Female business Chemoradiotherapy |
| Zdroj: | Annals of surgical oncology. 14(12) |
| ISSN: | 1534-4681 |
| Popis: | 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. 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. 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. 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. |
| Databáze: | OpenAIRE |
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