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

• Published in: Annals of oncology Vol. 21; no. 7; pp. 1472 - 1481
• Main Authors: Vermaat, J.S., van der Tweel, I., Mehra, N., Sleijfer, S., Haanen, J.B., Roodhart, J.M., Engwegen, J.Y., Korse, C.M., Langenberg, M.H., Kruit, W., Groenewegen, G., Giles, R.H., Schellens, J.H., Beijnen, J.H., Voest, E.E.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.07.2010; Oxford University Press
• Subjects: Aged; Antineoplastic agents; Apolipoprotein A-II - blood; apolipoprotein A2 (ApoA2); Biological and medical sciences; Biomarkers - blood; Carcinoma, Renal Cell - blood; Carcinoma, Renal Cell - pathology; Cohort Studies; Enzyme-Linked Immunosorbent Assay; Female; Humans; Kidney Neoplasms - blood; Kidney Neoplasms - pathology; Kidneys; Male; Medical sciences; metastatic renal cell cancer patients; Middle Aged; Neoplasm Metastasis; Nephrology. Urinary tract diseases; overall survival; Pharmacology. Drug treatments; prognosis; Proteomics; Retrospective Studies; risk model; Serum Amyloid A Protein - metabolism; serum amyloid alpha (SAA); Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Survival Rate; Tumors of the urinary system; apolipoprotein A2 (ApoA2); overall survival; serum amyloid alpha (SAA); prognosis; metastatic renal cell cancer patients; risk model; Biological fluid; Carcinoma; Prognosis; Biological marker; Serology; Metastasis; Grawitz tumor; Advanced stage; Serum; Amyloid; Kidney disease; Human; Urinary system disease; Patient; Malignant tumor; Survival; Protein; Kidney cancer; Risk factor; Models; Predictive factor; Cancer
• ISSN: 0923-7534; 1569-8041
• DOI: 10.1093/annonc/mdp559

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). 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. 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 × 10-9, P = 1.1 × 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 × 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 × 10-11) more robustly than the MSKCC model (AIC = 729, P = 1.3 × 10-7). Applying this protein-based model instead of the MSKCC model would have changed the risk group in 38% of the patients. 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.