1. The association between inflammatory markers and renal cell carcinoma prognosis
Inflammation has a pivotal role at each step of cancer biology: The cancer-related inflammatory microenvironment triggers cell-malignant conversion, local invasion, and distant metastasis . The connection between the magnitude of inflammatory response and renal cell carcinoma (RCC) aggressiveness has been extensively investigated , with the main goal of better prognosticating patient outcome using inflammatory markers (IMs). Serum C-reactive protein , serum albumin , platelet count , erythrocyte sedimentation rate , and fluctuations in the relative proportion of the white blood cell subpopulation  (eg, lymphocyte:monocyte ratio) have been demonstrated to be associated with poor oncologic outcomes.
Nonetheless, no international guidelines recommend IM evaluation at any step of the clinical decision-making strategy  and . Such a cautious attitude is indeed justified by several pitfalls in the available evidence with regard to statistical methodology, study design, and clinical applicability.
2. Limitations related to statistical methodology and study design
Regardless of the individual biomarker of interest, the statistics that are usually used consist of multivariable Cox regression analyses . Although this approach is sufficient to test for an association between risk factors and outcome, it represents only the first step in the process of novel biomarker evaluation and clinical implementation: The vast majority of the available reports on IMs and RCC prognosis do not provide risk estimates to externally apply the study findings. Moreover, assessment of model discrimination is often omitted; if assessed, it is striking that the inclusion of IM results in a marginal increase of predictive accuracy or does not increase predictive accuracy at all . In this light, it is important to consider that no study relied on decision curve analyses to evaluate the net benefit associated with the inclusion of IMs within a base predictive model, which represents another relevant method of measuring the magnitude and the clinical impact of a novel biomarker. As a direct consequence of the above-mentioned observations, no IM-based predictive model has been deemed worthy of formal external validation.
Study designs also limit the applicability of the findings to daily practice. Indeed, all of the available studies testing the correlation between IMs and RCC prognosis share a similar design: The IM of interest was measured in a cohort of RCC patients already selected for medical or surgical treatment. Consequently, the findings cannot be used to predict RCC prognosis if alternative options are taken into consideration (eg, active surveillance or minimally invasive treatments). Moreover, IMs measure a systemic host-to-cancer reaction, and it is arguable that the presence of systemic symptoms due to RCC (eg, paraneoplastic syndromes) might share the same pathogenesis of IM elevation . The validity of regression model–based analyses is invariably dependent by the comprehensiveness of potential confounders included in each specific model. Accordingly, lack of adjustment for presence of symptoms or concomitant comorbidities is another essential limitation of most of the available literature supporting IM. Finally, similar considerations are applicable to any other cause of inflammatory momentum that might operate as a potential bias, eventually masking the cancer-related inflammatory response, such as obesity , diabetes and cardiovascular disease , smoking , or even environmental pollution .
3. The missing link between evidence of association and clinical applicability
In conclusion, the relationship between inflammation and RCC has been investigated for more than a decade; however, this research line has failed to yield concrete clinical consequences. Although an association between IMs and RCC prognosis has been proved, how to translate such information into treatment strategies remains to be determined. The strongest argument against the use of IMs in the management of patients diagnosed with RCC is the missing link between the evidence of association and the clinical applicability of IMs. Moreover, with the rapid advent of translational research, it is difficult to imagine that an unspecific phenotypic characteristic, such as IM profile, will provide more informative data relative to individual genotypic predictors.
Conflicts of interest
The authors have nothing to disclose.
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a Unit of Urology, University Vita-Salute, San Raffaele Scientific Institute, Milan, Italy
b Division of Experimental Oncology, URI, Urological Research Institute, IRCCS San Raffaele Scientific Institute, Milan, Italy
Corresponding author. Department of Urology, Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Via Olgettina 60, 20132 Milan, Italy. Tel. +39 02 26437286.
© 2016 European Association of Urology, Published by Elsevier B.V.