PURPOSE: The prognosis of hepatocellular carcinoma (HCC) is very poor, particularly in patients with tumors that have invaded the major branches of the portal vein. Combination chemotherapy with intra-arterial 5-fluorouracil and subcutaneous interferon-alpha has shown promising results for such advanced HCC, but it is important to develop the ability to accurately predict chemotherapeutic responses. EXPERIMENTAL DESIGN: We analyzed the expression of 3,080 genes using a polymerase chain reaction-based array in 20 HCC patients who were treated with combination chemotherapy after reduction surgery. After unsupervised analyses, a supervised classification method for predicting chemotherapeutic responses was constructed. To minimize the number of predictive genes, we used a random permutation test to select only significant (P < 0.01) genes. A leave-one-out cross-validation confirmed the gene selection. We also prepared an additional 11 cases for validation of predictive performance. RESULTS: Hierarchical clustering analysis and principal component analysis with all 3,080 genes revealed distinct gene expression patterns in responders (those with complete response or partial response) and nonresponders (those with stable disease or progressive disease) to the combination chemotherapy. Using a weighted-voting classification method with either all genes or only significant genes as assessed by permutation testing, the objective responses to treatment were correctly predicted in 17 of 20 cases (accuracy, 85%; positive predictive value, 100%; negative predictive value, 80%). Moreover, patients in the validation dataset could be classified into two distinct prognostic groups using 63 predictive genes. CONCLUSIONS: Molecular analysis of 63 genes can predict the response of patients with advanced HCC and major portal vein tumor thrombi to combination chemotherapy with 5-fluorouracil and interferon-alpha.
PURPOSE: The prognosis of hepatocellular carcinoma (HCC) is very poor, particularly in patients with tumors that have invaded the major branches of the portal vein. Combination chemotherapy with intra-arterial 5-fluorouracil and subcutaneous interferon-alpha has shown promising results for such advanced HCC, but it is important to develop the ability to accurately predict chemotherapeutic responses. EXPERIMENTAL DESIGN: We analyzed the expression of 3,080 genes using a polymerase chain reaction-based array in 20 HCC patients who were treated with combination chemotherapy after reduction surgery. After unsupervised analyses, a supervised classification method for predicting chemotherapeutic responses was constructed. To minimize the number of predictive genes, we used a random permutation test to select only significant (P < 0.01) genes. A leave-one-out cross-validation confirmed the gene selection. We also prepared an additional 11 cases for validation of predictive performance. RESULTS: Hierarchical clustering analysis and principal component analysis with all 3,080 genes revealed distinct gene expression patterns in responders (those with complete response or partial response) and nonresponders (those with stable disease or progressive disease) to the combination chemotherapy. Using a weighted-voting classification method with either all genes or only significant genes as assessed by permutation testing, the objective responses to treatment were correctly predicted in 17 of 20 cases (accuracy, 85%; positive predictive value, 100%; negative predictive value, 80%). Moreover, patients in the validation dataset could be classified into two distinct prognostic groups using 63 predictive genes. CONCLUSIONS: Molecular analysis of 63 genes can predict the response of patients with advanced HCC and major portal vein tumor thrombi to combination chemotherapy with 5-fluorouracil and interferon-alpha.
Authors: Thorsten Maass; Ioannis Sfakianakis; Frank Staib; Markus Krupp; Peter R Galle; Andreas Teufel Journal: Curr Genomics Date: 2010-06 Impact factor: 2.236
Authors: T Noda; H Nagano; I Takemasa; S Yoshioka; M Murakami; H Wada; S Kobayashi; S Marubashi; Y Takeda; K Dono; K Umeshita; N Matsuura; K Matsubara; Y Doki; M Mori; M Monden Journal: Br J Cancer Date: 2009-04-28 Impact factor: 7.640
Authors: H Ota; H Nagano; M Sakon; H Eguchi; M Kondo; T Yamamoto; M Nakamura; B Damdinsuren; H Wada; S Marubashi; A Miyamoto; K Dono; K Umeshita; S Nakamori; K Wakasa; M Monden Journal: Br J Cancer Date: 2005-09-05 Impact factor: 7.640