PURPOSE: Classification based on a combination of molecular and pathologic predictors had never been done using hierarchical cluster analysis. For this purpose, we identified prognostic classification based on molecular predictors, pathologic and molecular predictors, and compared their respective prognostic efficacy together with that of tumor-node-metastasis (TNM) stage. Moreover, we investigated the prognostic significance of molecular classification in different TNM stage. EXPERIMENTAL DESIGN: Six pathologic predictors (p) and 13 immunohistochemical predictors (m) were investigated in 221 colorectal carcinomas. Unsupervised hierarchical clustering analysis was done to group the data. Survival analysis was done by Kaplan-Meier method and log-rank test, and by multivariate COX proportional hazard model. RESULTS: Six pathologic predictors and four molecular predictors were of significant prognostic value (P <or= 0.05). One molecular predictor showed a trend toward significance (P = 0.085). Hierarchical clustering analysis was done based on different combinations (5p, 13m, 5m, 5p13m, and 5p5m), and distinct groups were produced except 5p (the TNM stage was excluded). Groups identified by 5m (P = 0.053) and 5p5m (P = 0.000) showed significant differences in prognosis. Groups identified by 5p5m and TNM stage were confirmed as the independent prognostic factors in a multivariate COX proportional hazard model. Moreover, groups identified by 5m could predict different prognoses in patients with stage II disease. CONCLUSIONS: Classification based on pathologic and immunohistochemical predictors is superior to that based only on molecular predictors on prognosis. Classification based on 5m could identify additional different prognoses in patients with stage II disease.
PURPOSE: Classification based on a combination of molecular and pathologic predictors had never been done using hierarchical cluster analysis. For this purpose, we identified prognostic classification based on molecular predictors, pathologic and molecular predictors, and compared their respective prognostic efficacy together with that of tumor-node-metastasis (TNM) stage. Moreover, we investigated the prognostic significance of molecular classification in different TNM stage. EXPERIMENTAL DESIGN: Six pathologic predictors (p) and 13 immunohistochemical predictors (m) were investigated in 221 colorectal carcinomas. Unsupervised hierarchical clustering analysis was done to group the data. Survival analysis was done by Kaplan-Meier method and log-rank test, and by multivariate COX proportional hazard model. RESULTS: Six pathologic predictors and four molecular predictors were of significant prognostic value (P <or= 0.05). One molecular predictor showed a trend toward significance (P = 0.085). Hierarchical clustering analysis was done based on different combinations (5p, 13m, 5m, 5p13m, and 5p5m), and distinct groups were produced except 5p (the TNM stage was excluded). Groups identified by 5m (P = 0.053) and 5p5m (P = 0.000) showed significant differences in prognosis. Groups identified by 5p5m and TNM stage were confirmed as the independent prognostic factors in a multivariate COX proportional hazard model. Moreover, groups identified by 5m could predict different prognoses in patients with stage II disease. CONCLUSIONS: Classification based on pathologic and immunohistochemical predictors is superior to that based only on molecular predictors on prognosis. Classification based on 5m could identify additional different prognoses in patients with stage II disease.
Authors: B Weixler; F Renetseder; I Facile; N Tosti; E Cremonesi; A Tampakis; T Delko; S Eppenberger-Castori; A Tzankov; G Iezzi; C Kettelhack; S D Soysal; U von Holzen; G C Spagnoli; L Terracciano; L Tornillo; Raoul A Droeser; S Däster Journal: Cell Oncol (Dordr) Date: 2017-09-21 Impact factor: 6.730
Authors: Daniel Ayude; Francisco Javier Rodríguez-Berrocal; José Ayude; Sonia Blanco-Prieto; Lorena Vázquez-Iglesias; Marta Vázquez-Cedeira; María Páez de la Cadena Journal: BMC Cancer Date: 2013-11-12 Impact factor: 4.430