Role of perioperative immunotherapy in localized renal cell carcinoma

Treatment Landscape of Renal Cell Carcinoma

OPINION STATEMENT

The treatment landscape of renal cell carcinoma (RCC) has evolved significantly over the past three decades. Active surveillance and tumor ablation are alternatives to extirpative therapy in appropriately selected patients. Stereotactic body radiation therapy (SBRT) is an emerging noninvasive alternative to treat primary RCC tumors. The advent of immune checkpoint inhibitors (ICIs) has greatly improved the overall survival of advanced RCC, and now the ICI-based doublet (dual ICI-ICI doublet; or ICI in combination with a vascular endothelial growth factor tyrosine kinase inhibitor, ICI-TKI doublet) has become the standard frontline therapy. Based on unprecedented outcomes in the metastatic with ICIs, they are also being explored in the neoadjuvant and adjuvant setting for patients with high-risk disease. Adjuvant pembrolizumab has proven efficacy to reduce the risk of RCC recurrence after nephrectomy. Historically considered a radioresistant tumor, SBRT occupies an expanding role to treat RCC with oligometastasis or oligoprogression in combination with systemic therapy. Furthermore, SBRT is being investigated in combination with ICI-doublet in the advanced disease setting. Lastly, given the treatment paradigm is shifting to adopt ICIs at earlier disease course, the prospective studies guiding treatment sequencing in the post-ICI setting is maturing. The effort is ongoing in search of predictive biomarkers to guide optimal treatment option in RCC.

Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review

This review explores the multifaceted landscape of renal cell carcinoma (RCC) by delving into both mechanistic and machine learning models. While machine learning models leverage patients' gene expression and clinical data through a variety of techniques to predict patients' outcomes, mechanistic models focus on investigating cells' and molecules' interactions within RCC tumors. These interactions are notably centered around immune cells, cytokines, tumor cells, and the development of lung metastases. The insights gained from both machine learning and mechanistic models encompass critical aspects such as signature gene identification, sensitive interactions in the tumors' microenvironments, metastasis development in other organs, and the assessment of survival probabilities. By reviewing the models of RCC, this study aims to shed light on opportunities for the integration of machine learning and mechanistic modeling approaches for treatment optimization and the identification of specific targets, all of which are essential for enhancing patient outcomes.