AIMS: Receiver operating characteristic (ROC) curve analysis is a well-established method to study the accuracies of biological markers. It may, however, be suboptimal for analysing outcomes over time, such as prognosis. Here, the clinical value of time-dependent ROC curve analysis for improving the identification of high-risk patients with colon cancers and diffuse large B-cell lymphomas (DLBCL) is explored. METHODS: Using tissue microarrays, immunohistochemistry was performed on two matched sets (N = 469, each) of colon cancers (p53, CD8(+) tumour infiltrating lymphocytes (TILs), mammalian sterile-like 20 kinase 1 (MST1), mucin 2 (MUC2) and urokinase plasminogen activator receptor (uPAR)) and on 208 DLBCL (Bcl2, Bcl6, CD10, FOXP1 and Ki67). The area-under-the-curve (AUC)-over-time plots, cut-off scores for tumour marker positivity and Kaplan-Meier survival curves were analysed. RESULTS: With the exception of uPAR, all markers were most accurate within the first 18 months following diagnosis. Expression of p53 (AUC = 0.75), uPAR (AUC = 0.64), Bcl2 (AUC = 0.58) and FOXp1 (AUC = 0.68) was linked to more aggressive tumours, while TILs (AUC = 0.38), MST1 (AUC = 0.39), MUC2 (AUC = 0.38), Bcl6 (AUC = 0.4), CD10 (AUC = 0.49) and Ki67 (AUC = 0.41) were predictive of improved survival. Cut-off scores for markers at their peak accuracies as well as survival time differences were reproducible between colon cancer groups. Only FOXp1 at its optimal cut-off of 60% had significant effects on survival in DLBCL (p = 0.019). CONCLUSIONS: Time-dependent ROC curve analysis is a novel tool for identifying potential immunohistochemical prognostic markers across varying follow-up times. Use of this tool could facilitate the identification of high-risk patients not only with colon cancer and DLBCL but with a range of other tumour types.
AIMS: Receiver operating characteristic (ROC) curve analysis is a well-established method to study the accuracies of biological markers. It may, however, be suboptimal for analysing outcomes over time, such as prognosis. Here, the clinical value of time-dependent ROC curve analysis for improving the identification of high-risk patients with colon cancers and diffuse large B-cell lymphomas (DLBCL) is explored. METHODS: Using tissue microarrays, immunohistochemistry was performed on two matched sets (N = 469, each) of colon cancers (p53, CD8(+) tumour infiltrating lymphocytes (TILs), mammalian sterile-like 20 kinase 1 (MST1), mucin 2 (MUC2) and urokinase plasminogen activator receptor (uPAR)) and on 208 DLBCL (Bcl2, Bcl6, CD10, FOXP1 and Ki67). The area-under-the-curve (AUC)-over-time plots, cut-off scores for tumour marker positivity and Kaplan-Meier survival curves were analysed. RESULTS: With the exception of uPAR, all markers were most accurate within the first 18 months following diagnosis. Expression of p53 (AUC = 0.75), uPAR (AUC = 0.64), Bcl2 (AUC = 0.58) and FOXp1 (AUC = 0.68) was linked to more aggressive tumours, while TILs (AUC = 0.38), MST1 (AUC = 0.39), MUC2 (AUC = 0.38), Bcl6 (AUC = 0.4), CD10 (AUC = 0.49) and Ki67 (AUC = 0.41) were predictive of improved survival. Cut-off scores for markers at their peak accuracies as well as survival time differences were reproducible between colon cancer groups. Only FOXp1 at its optimal cut-off of 60% had significant effects on survival in DLBCL (p = 0.019). CONCLUSIONS: Time-dependent ROC curve analysis is a novel tool for identifying potential immunohistochemical prognostic markers across varying follow-up times. Use of this tool could facilitate the identification of high-risk patients not only with colon cancer and DLBCL but with a range of other tumour types.
Authors: Yong Yue; Arsen Osipov; Benedick Fraass; Howard Sandler; Xiao Zhang; Nicholas Nissen; Andrew Hendifar; Richard Tuli Journal: J Gastrointest Oncol Date: 2017-02
Authors: M H Kang; S-I Go; H-N Song; A Lee; S-H Kim; J-H Kang; B-K Jeong; K M Kang; H Ling; G-W Lee Journal: Br J Cancer Date: 2014-06-12 Impact factor: 7.640