title: Collaborative network of predictive markers complicates formation of prognostic groups in patients with advanced lung cancer.
abstract: BACKGROUND
  Evaluation of cancer therapies is mainly based on prolonging remission status and effect of survival. Various serological, clinical or histological markers are used to estimate the patient's prognosis, and to tailor specific therapies for patients with poor prognosis. However, it is still a challenge to combine all this information into a comprehensive risk prediction.
  
  
  METHODS
  In 58 patients with advanced non small cell lung cancer we recorded 38 parameters (15 from clinic, 10 from histology, 13 from serology) to analyze their impact on survival. We both used univariate as well as multivariate approaches and decision tree analysis.
  
  
  RESULTS
  Univariate analysis showed that ECOG status, stage, and the presence of cerebral or bone metastasis had a significant impact on survival, as well as the serum markers CA15-3, TPA, Cyfra. In a multivariate approach only ECOG and stage had a significant impact on survival. Considering correlation coefficients of >0.3 as an indicator of a functional relationship, we found several relations among the clinical (9), histological (8) or the serological parameters (13). Survival was related to 9 parameters by significant direct and cross-relation coefficients. The use of already few variables with its different possible options led to many different patterns in the cohort, almost all being specific for individual patients, and thereby underlining their heterogeneity. Decision tree analysis revealed that by including either stage and kind of therapy or stage and expression of YB-1 allows to identify sub-groups with distinct prognosis.
  
  
  CONCLUSIONS
  Clinical, serological and histological markers, all provide prognostic information. Because they are all linked in a collaborative network, the formation of homogenous prognostic groups by use of single markers is limited. Alternative statistical approaches with focus on decision trees may allow use of various information to assess individual patients into distinct risk groups.
authors.affiliation: Department of General, Visceral and Tranplant surgery, University Hospital of the RWTH Aachen, Aachen, Germany.
authors.forename: Bernice
authors.initials: B
authors.lastname: Schwab
authors.affiliation: Universität der Bundeswehr München, Institut für Mathematik und Informatik FB BW Werner-Heisenberg-Weg, Neubiberg, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Rudolph
authors.affiliation: Department for Oncology, Hematology and Stem Cell Transplantation, Medical Faculty, RWTH Aachen University, Aachen, Germany.
authors.forename: Edgar
authors.initials: E
authors.lastname: Jost
authors.affiliation: Department of General, Visceral and Tranplant surgery, University Hospital of the RWTH Aachen, Aachen, Germany uklinge@ukaachen.de.
authors.forename: Uwe
authors.initials: U
authors.lastname: Klinge
mesh_terms: D014408:Biomarkers, Tumor / Q000378:metabolism*; D001859:Bone Neoplasms / Q000378:metabolism* / Q000401:mortality / Q000556:secondary; D001932:Brain Neoplasms / Q000378:metabolism* / Q000401:mortality / Q000556:secondary; D002289:Carcinoma, Non-Small-Cell Lung / Q000378:metabolism* / Q000401:mortality / Q000556:secondary; D002294:Carcinoma, Squamous Cell / Q000378:metabolism* / Q000401:mortality / Q000556:secondary; D015331:Cohort Studies; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008175:Lung Neoplasms / Q000378:metabolism* / Q000401:mortality / Q000473:pathology; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009364:Neoplasm Recurrence, Local / Q000378:metabolism / Q000401:mortality / Q000473:pathology; D009367:Neoplasm Staging; D011237:Predictive Value of Tests; D015996:Survival Rate
chemical_list: D014408:Biomarkers, Tumor
keywords: Non-small cell lung cancer; collaborative network; predictive tumor markers