Adjuvant Systemic Therapies for Patients with Renal Cell Carcinoma: Choosing Treatment Based on Patient-level Characteristics

Obesity and renal cell carcinoma: Biological mechanisms and perspectives

Obesity, defined by body mass index (BMI), is an established risk factor for specific renal cell carcinoma (RCC) subtypes such as clear cell RCC, the most common RCC histology. Many studies have identified an association between obesity and improved survival after diagnosis of RCC, a potential "obesity paradox." Clinically, there is uncertainty whether improved outcomes observed after diagnosis are driven by stage, type of treatment received, or artifacts of longitudinal changes in weight and body composition. The biological mechanisms underlying obesity's influence on RCC are not fully established, but multiomic and mechanistic studies suggest an impact on tumor metabolism, particularly fatty acid metabolism, angiogenesis, and peritumoral inflammation, which are known to be key biological hallmarks of clear cell RCC. Conversely, high-intensity exercise associated with increased muscle mass may be a risk factor for renal medullary carcinoma, a rare RCC subtype that predominantly occurs in individuals with sickle hemoglobinopathies. Herein, we highlight methodologic challenges associated with studying the influence of obesity on RCC and review the clinical evidence and potential underlying mechanisms associating RCC with BMI and body composition.

Adjuvant Therapy for Renal Cell Carcinoma: End Points, Outcomes, and Risk Assessments

PURPOSE

Targeted tyrosine kinase inhibitors (TKIs) and immune-checkpoint inhibitors (ICIs) revolutionized the treatment of metastatic renal cell carcinoma (RCC). Efforts to translate these therapies into the adjuvant setting for local and locoregional RCC have been pursued over the past decade. We sought to provide an updated review of the literature regarding adjuvant therapy in RCC, as well as an analysis of patient characteristics that may portend the most favorable responses.

MATERIALS AND METHODS

Using PubMed, Google Scholar, and Wiley Online Library, we reviewed articles between 2000 and 2022. Search terms included "tyrosine kinase inhibitors," "adjuvant," "immunotherapy," and "renal cell carcinoma." The articles included were original and published in English. Information on clinical trials was collected from ClinicalTrials.gov, accessed in June 2022.

RESULTS

Landmark trials investigating adjuvant vascular endothelial growth factor (VEGF) inhibitors produced conflicting results, with only a single trial of sunitinib (S-TRAC) resulting in US Food and Drug Administration-approval on the basis of a slightly prolonged progression-free survival (PFS). Subsequent meta-analyses failed to show a benefit for adjuvant VEGF inhibitors. Several trials evaluating ICIs are currently ongoing, with pembrolizumab (KEYNOTE-564) earning US Food and Drug Administration-approval for a prolonged PFS, although overall survival data are not yet mature. Preliminary results from other adjuvant ICI trials have been conflicting.

CONCLUSION

There remains a lack of clear benefit for the use of adjuvant VEGF inhibitors in local and locoregional RCC. Adjuvant ICI investigations are ongoing, with promising results from KEYNOTE-564. It remains to be seen if PFS is an adequate surrogate end point for overall survival. Selection of patients at greatest risk for recurrence, and identification of those at greatest risk of rare but serious adverse events, may improve outcomes.

Less is More? First Impressions From COSMIC-313

The COSMIC-313 phase 3 randomized controlled trial tested the triplet combination of cabozantinib with nivolumab and ipilimumab in comparison with nivolumab plus ipilimumab control as fist-line systemic therapy in metastatic clear cell renal cell carcinoma. The first results presented at the 2022 European Society of Medical Oncology Congress are a milestone for the renal cell carcinoma field because they signal the advent of triplet combinations as potential treatment options for our patients. The present commentary highlights some considerations and potential next steps based on these first impressions.

A Causal Framework for Making Individualized Treatment Decisions in Oncology

Simple Summary

Physicians routinely make individualized treatment decisions by accounting for the joint effects of patient prognostic covariates and treatments on clinical outcomes. Ideally, this is performed using historical randomized clinical trial (RCT) data. Randomization ensures that unbiased estimates of causal treatment effect parameters can be obtained from the historical RCT data and used to predict each new patient’s outcome based on the joint effect of their baseline covariates and each treatment being considered. However, this process becomes problematic if a patient seen in the clinic is very different from the patients who were enrolled in the RCT. That is, if a new patient does not satisfy the entry criteria of the RCT, then the patient does not belong to the population represented by the patients who were studied in the RCT. In such settings, it still may be possible to utilize the RCT data to help choose a new patient’s treatment. This may be achieved by combining the RCT data with data from other clinical trials, or possibly preclinical experiments, and using the combined dataset to predict the patient’s expected outcome for each treatment being considered. In such settings, combining data from multiple sources in a way that is statistically reliable is not entirely straightforward, and correctly identifying and estimating the effects of treatments and patient covariates on clinical outcomes can be complex. Causal diagrams provide a rational basis to guide this process. The first step is to construct a causal diagram that reflects the plausible relationships between treatment variables, patient covariates, and clinical outcomes. If the diagram is correct, it can be used to determine what additional data may be needed, how to combine data from multiple sources, how to formulate a statistical model for clinical outcomes as a function of treatment and covariates, and how to compute an unbiased treatment effect estimate for each new patient. We use adjuvant therapy of renal cell carcinoma to illustrate how causal diagrams may be used to guide these steps.

Abstract

We discuss how causal diagrams can be used by clinicians to make better individualized treatment decisions. Causal diagrams can distinguish between settings where clinical decisions can rely on a conventional additive regression model fit to data from a historical randomized clinical trial (RCT) to estimate treatment effects and settings where a different approach is needed. This may be because a new patient does not meet the RCT’s entry criteria, or a treatment’s effect is modified by biomarkers or other variables that act as mediators between treatment and outcome. In some settings, the problem can be addressed simply by including treatment–covariate interaction terms in the statistical regression model used to analyze the RCT dataset. However, if the RCT entry criteria exclude a new patient seen in the clinic, it may be necessary to combine the RCT data with external data from other RCTs, single-arm trials, or preclinical experiments evaluating biological treatment effects. For example, external data may show that treatment effects differ between histological subgroups not recorded in an RCT. A causal diagram may be used to decide whether external observational or experimental data should be obtained and combined with RCT data to compute statistical estimates for making individualized treatment decisions. We use adjuvant treatment of renal cell carcinoma as our motivating example to illustrate how to construct causal diagrams and apply them to guide clinical decisions.

The Big Data Paradox in Clinical Practice

The big data paradox is a real-world phenomenon whereby as the number of patients enrolled in a study increases, the probability that the confidence intervals from that study will include the truth decreases. This occurs in both observational and experimental studies, including randomized clinical trials, and should always be considered when clinicians are interpreting research data. Furthermore, as data quantity continues to increase in today's era of big data, the paradox is becoming more pernicious. Herein, I consider three mechanisms that underlie this paradox, as well as three potential strategies to mitigate it: (1) improving data quality; (2) anticipating and modeling patient heterogeneity; (3) including the systematic error, not just the variance, in the estimation of error intervals.