BACKGROUND: Cell proliferation is a major determinant of the biologic behavior of breast carcinoma. MIB-1 monoclonal antibody is a promising tool for determining cell proliferation on routine histologic material. The objectives of this study were to compare MIB-1 evaluation to other methods of measuring cell proliferation, with a view to refining the cutoff used to classify tumors with low and high proliferation rates in therapeutic trials. METHODS: One hundred eighty-five invasive breast carcinomas were evaluated for cell proliferation by determining monoclonal antibody MIB-1 staining, histologic parameters (Scarff-Bloom-Richardson grade and mitotic index) on paraffin sections, S-phase fraction (SPF) by flow cytometry, and thymidine-kinase (TK) content of frozen samples. RESULTS: There was a high correlation (P = 0.0001) between the percentage of MIB-1 positive tumor cells and SPF, TK, histologic grade, and the mitotic index. Multivariate analyses including MIB-1 at 5 different cutoffs (10%, 15%, 17% [median], 20%, 25%) and the other proliferative markers showed that the optimal MIB-1 cutoff was 25% and that the mitotic index was the proliferative variable that best discriminated between low and high MIB-1 samples. A MIB-1 cutoff of 25% adequately identified highly proliferative tumors. Conversely, with a MIB-1 cutoff of 10%, few tumors with low proliferation were misclassified. CONCLUSIONS: The choice of MIB-1 cutoff depends on the following clinical objective: if MIB-1 is used to exclude patients with slowly proliferating tumors from chemotherapeutic protocols, a cutoff of 10% will help to avoid overtreatment. In contrast, if MIB-1 is used to identify patients sensitive to chemotherapy protocols, it is preferable to set the cutoff at 25%. The MIB-1 index should be combined with some other routinely used proliferative markers, such as the mitotic index. Copyright 2002 American Cancer Society.
BACKGROUND: Cell proliferation is a major determinant of the biologic behavior of breast carcinoma. MIB-1 monoclonal antibody is a promising tool for determining cell proliferation on routine histologic material. The objectives of this study were to compare MIB-1 evaluation to other methods of measuring cell proliferation, with a view to refining the cutoff used to classify tumors with low and high proliferation rates in therapeutic trials. METHODS: One hundred eighty-five invasive breast carcinomas were evaluated for cell proliferation by determining monoclonal antibody MIB-1 staining, histologic parameters (Scarff-Bloom-Richardson grade and mitotic index) on paraffin sections, S-phase fraction (SPF) by flow cytometry, and thymidine-kinase (TK) content of frozen samples. RESULTS: There was a high correlation (P = 0.0001) between the percentage of MIB-1 positive tumor cells and SPF, TK, histologic grade, and the mitotic index. Multivariate analyses including MIB-1 at 5 different cutoffs (10%, 15%, 17% [median], 20%, 25%) and the other proliferative markers showed that the optimal MIB-1 cutoff was 25% and that the mitotic index was the proliferative variable that best discriminated between low and high MIB-1 samples. A MIB-1 cutoff of 25% adequately identified highly proliferative tumors. Conversely, with a MIB-1 cutoff of 10%, few tumors with low proliferation were misclassified. CONCLUSIONS: The choice of MIB-1 cutoff depends on the following clinical objective: if MIB-1 is used to exclude patients with slowly proliferating tumors from chemotherapeutic protocols, a cutoff of 10% will help to avoid overtreatment. In contrast, if MIB-1 is used to identify patients sensitive to chemotherapy protocols, it is preferable to set the cutoff at 25%. The MIB-1 index should be combined with some other routinely used proliferative markers, such as the mitotic index. Copyright 2002 American Cancer Society.
Authors: Laura J Esserman; Donald A Berry; Angela DeMichele; Lisa Carey; Sarah E Davis; Meredith Buxton; Cliff Hudis; Joe W Gray; Charles Perou; Christina Yau; Chad Livasy; Helen Krontiras; Leslie Montgomery; Debasish Tripathy; Constance Lehman; Minetta C Liu; Olufunmilayo I Olopade; Hope S Rugo; John T Carpenter; Lynn Dressler; David Chhieng; Baljit Singh; Carolyn Mies; Joseph Rabban; Yunn-Yi Chen; Dilip Giri; Laura van 't Veer; Nola Hylton Journal: J Clin Oncol Date: 2012-05-29 Impact factor: 44.544
Authors: Annette Lischka; Natalie Doberstein; Sandra Freitag-Wolf; Ayla Koçak; Timo Gemoll; Kerstin Heselmeyer-Haddad; Thomas Ried; Gert Auer; Jens K Habermann Journal: Clin Cancer Res Date: 2020-06-10 Impact factor: 12.531
Authors: Lukas B Been; Albert J H Suurmeijer; David C P Cobben; Pieter L Jager; Harald J Hoekstra; Philip H Elsinga Journal: Eur J Nucl Med Mol Imaging Date: 2004-12 Impact factor: 9.236
Authors: Rohit Bhargava; Joan Striebel; Sushil Beriwal; John C Flickinger; Agnieszka Onisko; Gretchen Ahrendt; David J Dabbs Journal: Int J Clin Exp Pathol Date: 2009-02-09