Literature DB >> 16319691

A novel logistic model based on clinicopathological features predicts microsatellite instability in colorectal carcinomas.

Anna Colomer1, Nadina Erill, August Vidal, Miquel Calvo, Ruth Roman, Montse Verdú, Carlos Cordon-Cardo, Xavier Puig.   

Abstract

High-frequency microsatellite instability has been reported to be associated with good prognosis in colorectal adenocarcinoma. However, methods to assess microsatellite instability (MIN) are based on genetic assays and are not ideally suited to most histopathology laboratories. The aim of the present study was to develop a model for prediction of MIN status in colorectal cancer based on phenotypic characteristics. Clinicopathological features of a cohort of 204 patients with primary colon cancer were retrospectively reviewed following predetermined criteria. Genetic assessment of MIN status was performed on DNA extracted from sections of formalin-fixed, paraffin-embedded specimens by testing a panel of 11 microsatellite markers. Logistic regression analysis generated a mathematical tool capable of identifying colorectal tumors displaying MIN status with a sensitivity of 77.8% and a specificity of 96.8%. Features associated with instability included the proximal location of the lesions, occurrence of solid and/or mucinous differentiation, absence of cribriform structures, presence of peritumoral Crohn-like reaction, expansive growth pattern, high Ki67 proliferative index, and p53-negative phenotype. This approach predicts microsatellite instability in colorectal carcinoma with an overall assigned accuracy of 95.1% and a negative predictive value of 97.8%. Implementation of this tool to routine histopathological studies could improve the management of patients with colorectal cancer, especially those presenting with stage II and III of the disease. It will also assist in identifying a subset of patients likely to benefit from adjuvant chemotherapy.

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Year:  2005        PMID: 16319691     DOI: 10.1097/01.pas.0000177800.65959.48

Source DB:  PubMed          Journal:  Diagn Mol Pathol        ISSN: 1052-9551


  6 in total

1.  Prognostic significance and molecular associations of tumor growth pattern in colorectal cancer.

Authors:  Teppei Morikawa; Aya Kuchiba; Zhi Rong Qian; Mari Mino-Kenudson; Jason L Hornick; Mai Yamauchi; Yu Imamura; Xiaoyun Liao; Reiko Nishihara; Jeffrey A Meyerhardt; Charles S Fuchs; Shuji Ogino
Journal:  Ann Surg Oncol       Date:  2011-12-22       Impact factor: 5.344

2.  Microsatellite instability of the colorectal carcinoma can be predicted in the conventional pathologic examination. A prospective multicentric study and the statistical analysis of 615 cases consolidate our previously proposed logistic regression model.

Authors:  Ruth Román; Montse Verdú; Miquel Calvo; August Vidal; Xavier Sanjuan; Mireya Jimeno; Antonio Salas; Josefina Autonell; Isabel Trias; Marta González; Beatriz García; Natalia Rodón; Xavier Puig
Journal:  Virchows Arch       Date:  2010-05       Impact factor: 4.064

3.  KRAS gene mutations are more common in colorectal villous adenomas and in situ carcinomas than in carcinomas.

Authors:  Peter Zauber; Stephen Marotta; Marlene Sabbath-Solitare
Journal:  Int J Mol Epidemiol Genet       Date:  2013-03-18

4.  Predictive model for high-frequency microsatellite instability in colorectal cancer patients over 50 years of age.

Authors:  Kenji Fujiyoshi; Tatsuro Yamaguchi; Miho Kakuta; Akemi Takahashi; Yoshiko Arai; Mina Yamada; Gou Yamamoto; Sachiko Ohde; Misato Takao; Shin-Ichiro Horiguchi; Soichiro Natsume; Shinsuke Kazama; Yusuke Nishizawa; Yoji Nishimura; Yoshito Akagi; Hirohiko Sakamoto; Kiwamu Akagi
Journal:  Cancer Med       Date:  2017-05-23       Impact factor: 4.452

5.  Efficient and reproducible identification of mismatch repair deficient colon cancer: validation of the MMR index and comparison with other predictive models.

Authors:  Patrick Joost; Pär-Ola Bendahl; Britta Halvarsson; Eva Rambech; Mef Nilbert
Journal:  BMC Clin Pathol       Date:  2013-12-17

6.  Development and validation of MMR prediction model based on simplified clinicopathological features and serum tumour markers.

Authors:  Yinghao Cao; Tao Peng; Han Li; Ming Yang; Liang Wu; Zili Zhou; Xudan Zhang; Shengbo Han; Haijun Bao; Kailin Cai; Ning Zhao
Journal:  EBioMedicine       Date:  2020-10-20       Impact factor: 8.143

  6 in total

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