title: Two-protein signature of novel serological markers apolipoprotein-A2 and serum amyloid alpha predicts prognosis in patients with metastatic renal cell cancer and improves the currently used prognostic survival models.
abstract: BACKGROUND
  In metastatic renal cell cancer (mRCC), the Memorial Sloan-Kettering Cancer Center (MSKCC) risk model is widely used for clinical trial design and patient management. To improve prognostication, we applied proteomics to identify novel serological proteins associated with overall survival (OS).
  
  
  METHODS
  Sera from 114 mRCC patients were screened by surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF MS). Identified proteins were related to OS. Three proteins were subsequently validated with enzyme-linked immunosorbent assays and immunoturbidimetry. Prognostic models were statistically bootstrapped to correct for overestimation.
  
  
  RESULTS
  SELDI-TOF MS detected 10 proteins associated with OS. Of these, apolipoprotein A2 (ApoA2), serum amyloid alpha (SAA) and transthyretin were validated for their association with OS (P = 5.5 x 10(-9), P = 1.1 x 10(-7) and P = 0.0004, respectively). Combining ApoA2 and SAA yielded a prognostic two-protein signature [Akaike's Information Criteria (AIC) = 732, P = 5.2 x 10(-7)]. Including previously identified prognostic factors, multivariable Cox regression analysis revealed ApoA2, SAA, lactate dehydrogenase, performance status and number of metastasis sites as independent factors for survival. Using these five factors, categorization of patients into three risk groups generated a novel protein-based model predicting patient prognosis (AIC = 713, P = 4.3 x 10(-11)) more robustly than the MSKCC model (AIC = 729, P = 1.3 x 10(-7)). Applying this protein-based model instead of the MSKCC model would have changed the risk group in 38% of the patients.
  
  
  CONCLUSIONS
  Proteomics and subsequent validation yielded two novel prognostic markers and survival models which improved prediction of OS in mRCC patients over commonly used risk models. Implementation of these models has the potential to improve current risk stratification, although prospective validation will still be necessary.
authors.affiliation: Department of Medical Oncology.
authors.forename: J S
authors.initials: JS
authors.lastname: Vermaat
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht.
authors.forename: I
authors.initials: I
authors.lastname: van der Tweel
authors.affiliation: Department of Medical Oncology.
authors.forename: N
authors.initials: N
authors.lastname: Mehra
authors.affiliation: Department of Medical Oncology, Erasmus Medical Center, Rotterdam.
authors.forename: S
authors.initials: S
authors.lastname: Sleijfer
authors.affiliation: Department of Medical Oncology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital.
authors.forename: J B
authors.initials: JB
authors.lastname: Haanen
authors.affiliation: Department of Medical Oncology.
authors.forename: J M
authors.initials: JM
authors.lastname: Roodhart
authors.affiliation: Department of Pharmacy and Pharmacology, The Netherlands Cancer Institute-Slotervaart Hospital.
authors.forename: J Y
authors.initials: JY
authors.lastname: Engwegen
authors.affiliation: Department of Clinical Chemistry, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam.
authors.forename: C M
authors.initials: CM
authors.lastname: Korse
authors.affiliation: Department of Medical Oncology.
authors.forename: M H
authors.initials: MH
authors.lastname: Langenberg
authors.affiliation: Department of Medical Oncology, Erasmus Medical Center, Rotterdam.
authors.forename: W
authors.initials: W
authors.lastname: Kruit
authors.affiliation: Department of Medical Oncology.
authors.forename: G
authors.initials: G
authors.lastname: Groenewegen
authors.affiliation: Department of Medical Oncology.
authors.forename: R H
authors.initials: RH
authors.lastname: Giles
authors.affiliation: Department of Medical Oncology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital; Department of Biomedical Analysis, Faculty of Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands.
authors.forename: J H
authors.initials: JH
authors.lastname: Schellens
authors.affiliation: Department of Pharmacy and Pharmacology, The Netherlands Cancer Institute-Slotervaart Hospital; Department of Biomedical Analysis, Faculty of Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands.
authors.forename: J H
authors.initials: JH
authors.lastname: Beijnen
authors.affiliation: Department of Medical Oncology. Electronic address: e.e.voest@umcutrecht.nl.
authors.forename: E E
authors.initials: EE
authors.lastname: Voest
mesh_terms: D000368:Aged; D016633:Apolipoprotein A-II / Q000097:blood*; D015415:Biomarkers / Q000097:blood*; D002292:Carcinoma, Renal Cell / Q000097:blood* / Q000473:pathology; D015331:Cohort Studies; D004797:Enzyme-Linked Immunosorbent Assay; D005260:Female; D006801:Humans; D007680:Kidney Neoplasms / Q000097:blood* / Q000473:pathology; D008297:Male; D008875:Middle Aged; D009362:Neoplasm Metastasis; D040901:Proteomics; D012189:Retrospective Studies; D000685:Serum Amyloid A Protein / Q000378:metabolism*; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; D015996:Survival Rate
chemical_list: D016633:Apolipoprotein A-II; D015415:Biomarkers; C521404:SAA1 protein, human; D000685:Serum Amyloid A Protein