On-treatment biomarkers in metastatic renal cell carcinoma: towards individualization of prognosis?

Logical Imputation to Optimize Prognostic Risk Classification in Metastatic Renal Cell Cancer

BACKGROUND: Application of the MSKCC and IMDC models is recommended for prognostication in metastatic renal cell cancer (mRCC). Patient classification in MSKCC and IMDC risk groups in real-world observational studies is often hampered by missing data on required pre-treatment characteristics. OBJECTIVE: To evaluate the effect of application of easy-to-use logical, or deductive, imputation on MSKCC and IMDC risk classification in an observational study setting. PATIENTS AND METHODS: We used data on 713 mRCC patients with first-line sunitinib treatment from our observational European multi-centre study EuroTARGET. Pre-treatment characteristics and follow-up were derived from medical files. Hospital-specific cut-off values for laboratory measurements were requested. The effect of logical imputation of missing data and consensus versus hospital-specific cut-off values on patient classification and the subsequent models’ predictive performance for progression-free and overall survival (OS) was evaluated. RESULTS: 45% of the patients had missing data for≥1 pre-treatment characteristic for either model. Still, 72% of all patients could be unambiguously classified using logical imputation. Use of consensus instead of hospital-specific cut-offs led to a shift in risk group for 12% and 7% of patients for the MSKCC and IMDC model, respectively. Using logical imputation or other cut-offs did not influence the models’ predictive performance. These were in line with previous reports (c-statistic ∼0.64 for OS) CONCLUSIONS: Logical imputation leads to a substantial increase in the proportion of patients that can be correctly classified into poor and intermediate MSKCC and IMDC risk groups in observational studies and its use in the field should be advocated.

Tyrosine Kinase Inhibitor-Induced Hypertension: Role of Hypertension as a Biomarker in Cancer Treatment

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Cancer treatment is an area of continuous improvement. Therapy is becoming more targeted and the use of anti-angiogenic agents in multiple cancers, specifically tyrosine kinase inhibitors (TKIs), has demonstrated prolonged survival outcomes compared with previous drugs. Therefore, they have become a well-established part of the treatment.

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Despite good results, there is a broad range of moderate to severe adverse effects associated with treatment. Hypertension (HTN) is one of the most frequent adverse effects and has been associated with favourable outcomes (in terms of cancer treatment) of TKI treatment.

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High blood pressure is considered a class effect of TKI treatment, although the mechanisms have not been fully described. Three current hypotheses of TKI-associated HTN are highlighted in this narrative review. These include nitric oxide decrease, a change in endothelin-1 levels and capillary rarefaction.

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Several studies have investigated HTN as a potential biomarker of TKI efficacy. HTN is easy to measure and adding this factor to prognostic models has been shown to improve specificity. HTN may become a potential biomarker in clinical practice involving treating advanced cancers. However, data are currently limited by the number of studies and knowledge of the mechanism of action.