PURPOSE: FOLFOX (a combination of leucovorin, fluorouracil and oxaliplatin) has achieved substantial success in the treatment of colorectal cancer (CRC) patients. However, about half of all patients show resistance to this regimen and some develop adverse symptoms such as neurotoxicity. In order to select patients who would benefit most from this therapy, we aimed to build a predictor for the response to FOLFOX using microarray gene expression profiles of primary CRC samples. PATIENTS AND METHODS: Forty patients who underwent surgery for primary lesions were examined. All patients had metastatic or recurrent CRC and received modified FOLFOX6. Responders and nonresponders were determined according to the best observed response at the end of the first-line treatment. Gene-expression profiles of primary CRC were determined using Human Genome GeneChip arrays U133. We identified discriminating genes whose expression differed significantly between responders and nonresponders and then carried out supervised class prediction using the k-nearest-neighbour method. RESULTS: We identified 27 probes that were differentially expressed between responders and nonresponders at significant levels. Based on the expression of these genes, we constructed a FOLFOX response predictor with an overall accuracy of 92.5%. The sensitivity, specificity, positive and negative predictive values were 78.6%, 100%, 100% and 89.7%, respectively. CONCLUSION: The present model suggests the possibility of selecting patients who would benefit from FOLFOX therapy both in the metastatic and the adjuvant setting. To our knowledge, this is the first study to establish a prediction model for the response to FOLFOX chemotherapy based on gene expression by microarray analysis.
PURPOSE:FOLFOX (a combination of leucovorin, fluorouracil and oxaliplatin) has achieved substantial success in the treatment of colorectal cancer (CRC) patients. However, about half of all patients show resistance to this regimen and some develop adverse symptoms such as neurotoxicity. In order to select patients who would benefit most from this therapy, we aimed to build a predictor for the response to FOLFOX using microarray gene expression profiles of primary CRC samples. PATIENTS AND METHODS: Forty patients who underwent surgery for primary lesions were examined. All patients had metastatic or recurrent CRC and received modified FOLFOX6. Responders and nonresponders were determined according to the best observed response at the end of the first-line treatment. Gene-expression profiles of primary CRC were determined using Human Genome GeneChip arrays U133. We identified discriminating genes whose expression differed significantly between responders and nonresponders and then carried out supervised class prediction using the k-nearest-neighbour method. RESULTS: We identified 27 probes that were differentially expressed between responders and nonresponders at significant levels. Based on the expression of these genes, we constructed a FOLFOX response predictor with an overall accuracy of 92.5%. The sensitivity, specificity, positive and negative predictive values were 78.6%, 100%, 100% and 89.7%, respectively. CONCLUSION: The present model suggests the possibility of selecting patients who would benefit from FOLFOX therapy both in the metastatic and the adjuvant setting. To our knowledge, this is the first study to establish a prediction model for the response to FOLFOX chemotherapy based on gene expression by microarray analysis.
Authors: P Therasse; S G Arbuck; E A Eisenhauer; J Wanders; R S Kaplan; L Rubinstein; J Verweij; M Van Glabbeke; A T van Oosterom; M C Christian; S G Gwyther Journal: J Natl Cancer Inst Date: 2000-02-02 Impact factor: 13.506
Authors: Y Shirota; J Stoehlmacher; J Brabender; Y P Xiong; H Uetake; K D Danenberg; S Groshen; D D Tsao-Wei; P V Danenberg; H J Lenz Journal: J Clin Oncol Date: 2001-12-01 Impact factor: 44.544
Authors: Howard S Hochster; Lowell L Hart; Ramesh K Ramanathan; Barrett H Childs; John D Hainsworth; Allen L Cohn; Lucas Wong; Louis Fehrenbacher; Yousif Abubakr; M Wasif Saif; Lee Schwartzberg; Eric Hedrick Journal: J Clin Oncol Date: 2008-07-20 Impact factor: 44.544
Authors: S L Cheeseman; S P Joel; J D Chester; G Wilson; J T Dent; F J Richards; M T Seymour Journal: Br J Cancer Date: 2002-08-12 Impact factor: 7.640
Authors: Alexander Nguyen; Jia Min Loo; Rohit Mital; Ethan M Weinberg; Fung Ying Man; Zhaoshi Zeng; Philip B Paty; Leonard Saltz; Yelena Y Janjigian; Elisa de Stanchina; Sohail F Tavazoie Journal: J Clin Invest Date: 2016-01-19 Impact factor: 14.808
Authors: Purificacion Estevez-Garcia; Fernando Rivera; Sonia Molina-Pinelo; Marta Benavent; Javier Gómez; Maria Luisa Limón; Maria Dolores Pastor; Julia Martinez-Perez; Luis Paz-Ares; Amancio Carnero; Rocio Garcia-Carbonero Journal: Oncotarget Date: 2015-03-20