Virchows Archiv

, Volume 456, Issue 5, pp 533–541 | Cite as

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

  • Ruth RománEmail author
  • Montse Verdú
  • Miquel Calvo
  • August Vidal
  • Xavier Sanjuan
  • Mireya Jimeno
  • Antonio Salas
  • Josefina Autonell
  • Isabel Trias
  • Marta González
  • Beatriz García
  • Natalia Rodón
  • Xavier Puig
Original Article


High microsatellite instability (MSI-H) allows the identification of a subset of colorectal carcinomas associated with good prognosis and a higher incidence of Lynch syndrome. The aim of this work was to assess the interobserver variability and optimize our MSI-H prediction model previously published based on phenotypic features. The validation series collected from five different hospitals included 265 primary colorectal carcinomas from the same number of patients. The eight clinicopathological parameters that integrate our original model were evaluated in the corresponding centers. Homogeneity assessment revealed significant differences between hospitals in the estimation of the growth pattern, presence of Crohn-like reaction, percentage of cribriform structures, and Ki-67 positivity. Despite this observation, our model was globally able to predict MSI-H with a negative predictive value of 97.0%. The optimization studies were carried out with 615 cases and resulted in a new prediction model RERtest8, which includes the presence of tumor infiltrating lymphocytes at the expense of the percentage of cribriform structures. This refined model achieves a negative predictive value of 97.9% that is maintained even when the immunohistochemical parameters are left out, RERtest6. The high negative predictive value achieved by our models allows the reduction of the cases to be tested for MSI to less than 10%. Furthermore, the easy evaluation of the parameters included in the model renders it a useful tool for the routine practice and can reinforce other published models and the current clinical protocols to detect the subset of colorectal cancer patients bearing hereditary nonpolyposis colorectal cancers risk and/or MSI-H phenotype.


Microsatellite instability Prediction model Colorectal cancer Pathological parameters Hereditary nonpolyposis colorectal cancer 



The authors thank Eva Torija from BIOPAT for her secretarial assistance in data collection.

Conflict of interest statement

We declare that we have no conflict of interest.


  1. 1.
    Ogino S, Goel A (2008) Molecular classification and correlates in colorectal cancer. J Mol Diagnostics 10:13–27, s.lCrossRefGoogle Scholar
  2. 2.
    Gryfe RH, Kim H, Hsieh ET (2000) Tumor microsatellite instability and clinical outcome in young patients with colorectal cancer. N Engl J Med 342:69–77, s.lCrossRefPubMedGoogle Scholar
  3. 3.
    Fearon ER, Volgestein B (1990) A generic model for colorectal tumorigenesis. Cell 61:759–767CrossRefPubMedGoogle Scholar
  4. 4.
    Thibodeau SN, Bren G, Schaid D (1993) Microsatellite instability in cancer of proximal colon. Science 260:816–819, s.lCrossRefPubMedGoogle Scholar
  5. 5.
    Jass JR, Do KA, Simms LA (1998) Morphology of sporadic colorectal cancer with DNA replication errors. Gut 42:673–679, s.lPubMedCrossRefGoogle Scholar
  6. 6.
    Fujiwara T, Stolker JM, Watanabe T et al (1998) Accumulated clonal genetic alterations in familial and sporadic colorectal carcinomas with widespread instability in microsatellite sequences. Am J Pathol 153:1063–1078, s.lPubMedGoogle Scholar
  7. 7.
    Herman JG, Umar A, Polyak K et al (1998) Incidence and functional consequences of hMLH1 promoter hypermethylation in colorectal carcinoma. Proc Natl Acad Sci 95:6870–6875, s.lCrossRefPubMedGoogle Scholar
  8. 8.
    Arnold CN, Goel A, Boland CR (2003) Role of hMLH1 promoter hypermethylation in drug resistance to 5-fluorouracil in colorectal cancer cell lines. Int J Cancer 106:66–73, s.lCrossRefPubMedGoogle Scholar
  9. 9.
    Ribic CM, Sargent DJ, Moore MJ et al (2003) Tumor microsatellite-instability status as a predictor of benefit from fluorouracil-based adjuvant chemotherapy for colon cancer. N Engl J Med 349:247–257CrossRefPubMedGoogle Scholar
  10. 10.
    Jass JR (2004) HNPCC and sporadic MSI-H colorectal cancer: a review of the morphological similarities and differences. Familial Cancer 3:93–100, s.lCrossRefPubMedGoogle Scholar
  11. 11.
    Kakar S, Aksoy S, Burgart LJ, Smyrk TC (2004) Mucinous carcinoma of the colon: correlation of loss of mismatch repair enzymes with clinicopathologic features and survival. Mod Pathol 17:696–700CrossRefPubMedGoogle Scholar
  12. 12.
    Ogino S, Brahmandam M, Cantor M et al (2006) Distinct molecular features of colorectal carcinoma with signet ring cell component and colorectal carcinoma with mucinous component. Mod Pathol 19:59–68, s.lCrossRefPubMedGoogle Scholar
  13. 13.
    Jenkins MA, Hayashi S, O'Shea et al (2007) Pathology features in Bethesda guidelines predict colorectal cancer microsatellite instability: a population-based study. Gastroenterology 133:48–56CrossRefPubMedGoogle Scholar
  14. 14.
    Greenson JK, Huang S, Herron C et al (2009) Pathologic predictors of mirosatellite instability in colorectal cancer. Am J Surg Pathol 33:126–133CrossRefPubMedGoogle Scholar
  15. 15.
    Colomer A, Erill N, Vidal A et al (2005) A novel logistic model based on clinicopathological features predicts microsatellite instability in colorectal carcinomas. Diagn Mol Pathol 14:213–223CrossRefPubMedGoogle Scholar
  16. 16.
    Michael-Robinson JM, Biemer-Huttmann A, Purdie DM et al (2001) Tumour infiltrating lymphocytes and apoptosis are independent features in colorectal cancer stratified according to microsatellite instability status. Gut 48:360–366CrossRefPubMedGoogle Scholar
  17. 17.
    Hamilton SR, Aaltonen LA (2000) Pathology and genetics of tumours of the digestive system. eds World Health Organization Classification of Tumors, IARC Press, Lyon, pp 103–143Google Scholar
  18. 18.
    Boland CR, Thibodeau SN, Hamilton SR et al (1998) A National Cancer Institute Workshop on microsatellite instability for cancer detection and familial predisposition: development of international criteria for the determination of microsatellite instability in colorectal cancer. Cancer Res 58:5248–5257PubMedGoogle Scholar
  19. 19.
    Jen J, Kim H, Piantadosi S et al (1994) Allelic loss of chromosome 18q and prognosis in colorectal cancer. N Engl J Med 331:213–221CrossRefPubMedGoogle Scholar
  20. 20.
    Jones MH, Nakamura Y (1992) Detection of loss of heterozygosity at the human TP53 locus using a dinucleotide repeat polymorphism. Genes Chromosom Cancer 5:89–90CrossRefPubMedGoogle Scholar
  21. 21.
    Friedman J, Hastie T, Tibshirani R (2008) Glmnet: Lasso and elastic-net regularized generalized linear models:∼hastie/Papers/glmnet.pdfGoogle Scholar
  22. 22.
    Friedman J, Hastie T, Höfling H, Tibshirani R (2007) Pathwise coordinate optimization. Ann Appl Stat 1(2):302–332CrossRefGoogle Scholar
  23. 23.
    R Development Core Team. R (2008) A language and environment for statistical computing. s.l.: R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL
  24. 24.
    Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Ruth Román
    • 1
    Email author
  • Montse Verdú
    • 1
    • 3
  • Miquel Calvo
    • 4
  • August Vidal
    • 3
    • 5
  • Xavier Sanjuan
    • 5
  • Mireya Jimeno
    • 6
  • Antonio Salas
    • 7
  • Josefina Autonell
    • 8
  • Isabel Trias
    • 9
  • Marta González
    • 1
  • Beatriz García
    • 1
  • Natalia Rodón
    • 1
  • Xavier Puig
    • 1
    • 2
    • 3
  1. 1.BIOPAT. Biopatologia Molecular, SLGrup AssistènciaBarcelonaSpain
  2. 2.Hospital de Barcelona-SCIASGrup AssistènciaBarcelonaSpain
  3. 3.Histopat LaboratorisBarcelonaSpain
  4. 4.Statistics Department, Universitat de BarcelonaBarcelonaSpain
  5. 5.Department of PathologyHospital Universitari de BellvitgeBarcelonaSpain
  6. 6.Department of PathologyHospital del MarBarcelonaSpain
  7. 7.Department of PathologyHospital Mútua de TerrassaTerrassaSpain
  8. 8.Department of PathologyHospital General de VicVicSpain
  9. 9.Department of PathologyHospital PlatóBarcelonaSpain

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