D/2 Predictors of Favorable Outcome in Cancer

  • Zoltán Pós
  • Jérôme Galon


Prediction of disease outcome in cancer is usually achieved by histological evaluation of tissue samples obtained during surgical extirpation of the primary tumor, mostly focusing on histological characteristics of cancer cells in the tumor mass, such as the extent of atypical cell morphology, of tissue integrity, aberrant expression of protein markers or malignant transformation, senescence and proliferation, various characteristics of the invasive margin and surrounding tumor stroma, depth of invasion, and the extent of vascularization. In addition, histological or radiological analysis of both tumor draining- and distant lymph nodes and remote organs can be carried out looking for evidence of metastases. Based on these data, evaluation of cancer progression can be performed and serve as an estimate of patient prognosis. This is done on the basis of statistical data available of patients exhibiting similar progression characteristics and their actual outcome parameters, such as average disease-free (DFS), disease-specific (DSS) and overall survival (OS). To this end, several dozens of tumor-type specific staging and grading systems have been developed such as Clark’s and Breslow’s indexes for melanoma, Gleason’s score for prostate cancer, Duke’s for colorectal cancer, Boden-Gibb’s staging for testicular cancer, the Evans staging system for neuroblastoma, etc., and also more universal ones such as the TNM system, which summarizes data on tumor burden (T), presence of cancer cells in draining and distant lymph nodes (N) and evidence for metastases (M). With the large body of statistical data available on cancer patients’ survival with a given progression stage, such approaches have been shown to be valuable and in many cases of acceptable accuracy in estimating disease outcome in cancer.


Vascular Endothelial Growth Factor Overall Survival Renal Cell Carcinoma Renal Cell Cancer Immune Marker 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Infectious Disease and Immunogenetics Section, Department of Transfusion Medicine, Clinical Center, and Center for Human ImmunologyNational Institutes of HealthBethesdaUSA

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