Biological markers – or biomarkers – are an area of extreme interest in medicine, as they can be used to diagnose illness, predict the likely course of a disease, or to predict patients’ response to a particular intervention. Diagnostic, prognostic, and predictive biomarkers can be any biological material, such as DNA, RNA, proteins or metabolites.
In order to be useful, biomarkers need to be easily quantifiable, and their presence or absence needs to be highly associated with a biological state or outcome. For example, a commonly used diagnostic biomarker is prostate specific antigen (PSA), which allows prostate health to be inferred from a simple blood test.
There are currently eight FDA-approved systemic treatments available to treat metastatic renal cancers, and more are being developed. However, patients do not respond equally well to the different treatments. Thus finding biomarkers to accurately predict which patients will benefit from which treatment will improve the prognosis for patients with advanced kidney cancer.
A recent study by Motzer et al. (2014) investigated a number of potential biomarkers to predict the efficiacy of the tyrosine kinase inhibitor Sunitinib, a commonly used first line therapy for metastatic renal cancer. This study was performed as part of the Renal EFFECT Trial testing the efficacy of Sunitinib administered on 4/2 schedule of 50 mg/day for 4 weeks, followed by a two week rest period, versus a continuous dosage schedule of 37.5 mg/day. 292 patients were enrolled in this study and continued treatment for up to two years.
Motzer et al. investigated several previously reported biomarkers linked to tyrosine kinase inhibitor efficacy – single nucleotide polymorphisms (SNPs) in VEGF-A and VEGFR3 (Garcia-Donas et al., 2011, Schneider et al., 2008), HIF1α and CA-IX expression in tumours (Dornbusch et al., 2013, Muriel Lopez et al., 2012), and VHL gene inactivation by mutation, gene deletion or promoter methylation (Moore et al., 2011) – and analysed the levels of serum soluble proteins in patients’ blood before and after treatment.
No statistically significant link was observed between the tumour response to treatment and the VEGF-A or VEGFR3 SNPs analysed, or CA-IX tumour expression. Lower HIF1α tumour expression and VHL inactivation was associated with increased time to tumour progression and progression free survival was observed in the patients receiving Sunitinib on the 4/2 schedule, but not the continuous dosing schedule. Finally, low ANG2 and high MMP2 levels in blood serum before treatment were both associated with increased tumour response to Sunitinib treatment.
Defining a set of biomarkers for all systemic kidney cancer therapies will allow clinicians to choose the best drug for each patient. Furthermore, the fact that HIF1α expression and VHL gene inactivation only correlated with outcomes for patients on the 4/2 dosage schedule suggests that biomarkers may also indicate the optimal dosage schedule, thus allowing a personalised medicine approach for patients with advanced kidney cancer. However, this study did not replicate earlier findings linking CA-IX tumour expression, VEGF-A SNPs and VEGFR3 SNPs to treatment response, suggesting that finding reliable biomarkers will be a difficult undertaking and that multiple biomarkers will be required in order to accurately predict treatment efficacy.
- Dornbusch J, Zacharis A, Meinhardt M, Erdmann K, Wolff I, Froehner M, Wirth MP, Zastrow S, & Fuessel S (2013). Analyses of potential predictive markers and survival data for a response to sunitinib in patients with metastatic renal cell carcinoma. PloS one, 8 (9) PMID: 24086736
- Garcia-Donas J, Esteban E, Leandro-García LJ, Castellano DE, del Alba AG, Climent MA, Arranz JA, Gallardo E, Puente J, Bellmunt J, Mellado B, Martínez E, Moreno F, Font A, Robledo M, & Rodríguez-Antona C (2011). Single nucleotide polymorphism associations with response and toxic effects in patients with advanced renal-cell carcinoma treated with first-line sunitinib: a multicentre, observational, prospective study. The Lancet. Oncology, 12 (12), 1143-50 PMID: 22015057
- Moore LE, Nickerson ML, Brennan P, Toro JR, Jaeger E, Rinsky J, Han SS, Zaridze D, Matveev V, Janout V, Kollarova H, Bencko V, Navratilova M, Szeszenia-Dabrowska N, Mates D, Schmidt LS, Lenz P, Karami S, Linehan WM, Merino M, Chanock S, Boffetta P, Chow WH, Waldman FM, & Rothman N (2011). Von Hippel-Lindau (VHL) inactivation in sporadic clear cell renal cancer: associations with germline VHL polymorphisms and etiologic risk factors. PLoS genetics, 7 (10) PMID: 22022277
- Motzer et al., PMID: 25100134
- Muriel López C, Esteban E, Astudillo A, Pardo P, Berros JP, Izquierdo M, Crespo G, Fonseca PJ, Sanmamed M, & Martínez-Camblor P (2012). Predictive factors for response to treatment in patients with advanced renal cell carcinoma. Investigational new drugs, 30 (6), 2443-9 PMID: 22644070
- Schneider BP, Wang M, Radovich M, Sledge GW, Badve S, Thor A, Flockhart DA, Hancock B, Davidson N, Gralow J, Dickler M, Perez EA, Cobleigh M, Shenkier T, Edgerton S, Miller KD, & ECOG 2100 (2008). Association of vascular endothelial growth factor and vascular endothelial growth factor receptor-2 genetic polymorphisms with outcome in a trial of paclitaxel compared with paclitaxel plus bevacizumab in advanced breast cancer: ECOG 2100. Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 26 (28), 4672-8 PMID: 18824714