Eric Jonasch

Metastatic clear cell renal cell carcinoma (ccRCC) exhibits heterogeneous clinical outcomes, and limited biomarkers are available to predict response to targeted therapies. This study evaluated cell compartment-specific transcriptional biomarkers predicting clinical response to the tyrosine kinase inhibitor (TKI) sunitinib. In this phase 2 clinical trial (NCT00715442), 46 patients with metastatic ccRCC received sunitinib pre- and post-cytoreductive nephrectomy, with a primary endpoint of time-to-progression (TTP) and secondary endpoints of overall survival (OS) and sunitinib toxicity. To investigate spatial dynamics of therapeutic response to sunitinib, we applied whole-transcriptome digital spatial profiling (DSP) to tumor tissue from responders and non-responders. For 46 trial patients, median TTP was 8.2 months (95% CI: 6.1-19.3) and OS 36.3 months (95% CI: 19.7-70.5). Responders displayed a tumor microenvironment enriched in pro-inflammatory/immunostimulatory programs, while non-responders displayed enrichment of immunosuppressive programs with elevated PD-L1 expression in myeloid cells and exhausted CD8 + T-cell signatures. Sunitinib resistance mechanisms involving VEGF-dependent and independent angiogenic pathways were identified, revealing potential therapeutic targets. DSP-based machine learning models showed strong prognostic and predictive performance in sunitinib-treated patients in an external JAVELIN Renal 101 cohort, but attenuated performance in the avelumab plus axitinib cohort, indicating treatment-context specificity. Our findings highlight immune regulation as a key determinant of sunitinib response in metastatic ccRCC. Spatial profiling revealed potentially targetable mechanisms of TKI resistance involving SPP1-associated myeloid programs, CD8 + T-cell exhaustion, and angiogenic signaling, highlighting interactions between angiogenesis, lipid metabolism, and tumor immunity as determinants of therapeutic outcome.

Background: Brain metastases (BM) from renal cell carcinoma (RCC) significantly affect survival, and disease-specific prognostic tools for patients undergoing stereotactic radiosurgery (SRS) remain limited. We aimed to identify prognostic factors and develop an interpretable machine learning (ML) model for individualized risk stratification following SRS. Methods: We retrospectively analyzed 169 patients with RCC BM treated with SRS between November 2015 and May 2021 in this retrospective cohort study. Univariate Cox regression were performed. ML analyses used a comprehensive survival modeling framework integrating multiple algorithms and ensemble strategies, with final model selection based on nested cross-validation (CV) performance and interpretation using Shapley Additive Explanations (SHAP). Performance was evaluated using the concordance index (C-index) and time-dependent area under the receiver operating characteristic curve (AUC), Brier score, and calibration metrics; a web-based calculator was developed. Results: Median overall survival (OS) was 1.73 years (95% confidence interval [CI], 1.26-2.60). In the test cohort, the selected model elastic net-regularized Cox regression (α = 0.2) achieved a C-index of 0.68 with 95% CI, 0.60-0.76 and a 1-year AUC of 0.74 with 95% CI, 0.61-0.86. Using the training cohort median risk score, patients were stratified into low- and high-risk groups with significantly different OS in both cohorts. SHAP analysis identified pre-intervention Karnofsky Performance Status, BM locations, age at intervention, pre-intervention tumor volume, and number of BM as influential predictors. Conclusions: We developed an interpretable ML survival model that enables individualized OS prediction and ­clinically meaningful risk stratification after SRS while reflecting established prognostic factors.

In the management of advanced clear cell renal cell carcinoma (ccRCC), antiangiogenic tyrosine kinase inhibitors (TKIs) provide limited long-term durability, even when combined with immune checkpoint blockade (ICB), and the underlying mechanisms remain poorly understood. In this study, we applied 10x Genomics Xenium profiling using a 5,000-gene panel to characterize the tumor ecosystem in TKI-treated vs. untreated ccRCC to identify the escape mechanisms and propose strategies to overcome them. Sunitinib treatment triggered a profound collapse of the vascular and perivascular niche, but induced a more inflammatory yet immunosuppressive tumor microenvironment. Sunitinib did not significantly influence the overall abundance of tumor-associated macrophages (TAMs). However, the spatial organization and intercellular interactions of TAMs were markedly altered, shifting from sequestration alongside endothelial cells to increased communication with T cells. We further identified two TAM subsets—interferon-primed TAMs (IFN-TAMs) and proliferating TAMs (Prolif-TAMs)—that were enriched in sunitinib-treated tumors and associated with shorter progression-free survival. IFN-TAMs displayed strong IFNg signaling activity, elevated PD-L1/PD-L2 expression, and engagement with multiple cell types, including PD-1⁺ T cell subsets. In contrast, Prolif-TAMs expressed distinct tumor-promoting molecules, including CSF1R, MSR1, VSIG4, and TREM2 , and predominantly interacted with tumor cells rather than immune cells. Notably, IFN-TAMs and Prolif-TAMs exhibited high expression of the A2A adenosine receptor ( A2AR ), while T-cell subsets expressed ENTPD1 , enabling adenosine production and A2AR-mediated suppression. In an animal model, PD-1 blockade and pharmacologic inhibition of A2AR signaling potentiated the therapeutic efficacy of axitinib. Collectively, these findings demonstrate that sunitinib promotes an immunosuppressive tumor microenvironment that contributes to therapeutic resistance through macrophage reprogramming and spatial redistribution. Targeting these macrophage subsets may represent a promising strategy to improve the efficacy of TKI-based therapies.

Targeting DNA repair defects has shown therapeutic benefits in solid tumors with genetic mutations that disrupt DNA damage repair (DDR) pathways. Clear cell renal cell carcinoma (ccRCC) demonstrates an intermediate level of genomic instability, while it rarely carries mutations in these genes. Instead, it is characterized by the loss of chromosome 3p, the von Hippel-Lindau (VHL) tumor suppressor gene inactivation, and secondary mutations in Polybromo-1 (PBRM1), SET domain-containing 2 (SETD2), and BRCA-associated protein 1 (BAP1). Here, we summarize and discuss how these essential mutations impair the DDR, activate the cytosolic DNA sensing pathway, alter the tumor immune microenvironment, and offer promising therapeutic targets.

Background: Patients with von Hippel-Lindau-associated central nervous system hemangioblastoma (VHL-CNS-Hb) have a large economic burden, but there is limited real-world evidence describing their clinical burden in the United States (US). This study compared treatment patterns and monitoring procedures between patients with VHL-CNS-Hb and matched controls in the US. Methods: Patients with VHL-CNS-Hb and matched controls were identified from Optum’s de-identified Clinformatics ^® Data Mart Database. The index date was the date of first CNS-Hb diagnosis (VHL-CNS-Hb cohort) or the date of a random medical claim within the patient’s claims (control cohort). Treatment patterns were assessed in the VHL-CNS-Hb cohort using unadjusted incidence rates. Pain management drug use, disease monitoring procedures, and visits to medical specialists were compared between cohorts using unadjusted and adjusted generalized linear models. Clinical trial number: not applicable. Results: The most common treatment observed in the VHL-CNS-Hb cohort ( N = 220) was targeted therapy for renal cell carcinoma (2.34 treatment events per 10 person-years). Pain management drug use, monitoring, and specialist visits peaked during the first/second year post-index. Compared to controls ( N = 1100), VHL-CNS-Hb patients had higher rates of pain management drug use (adjusted incidence rate ratio [IRR]: 1.96), monitoring procedures (adjusted IRR: 3.93), and specialist visits (adjusted IRRs: 2.49–25.44; all P ≤ 0.001). Conclusions: Patients with VHL-CNS-Hb had a substantial, long-term clinical burden that included treatment for multiple VHL manifestations—reflecting the multi-organ nature of the disease—frequent pain management drug use, and high levels of healthcare use for disease monitoring and specialist visits.

Background: Immune checkpoint inhibitor therapy (ICI) with nivolumab+ipilimumab is a first-line (1L) standard for metastatic clear cell renal cell carcinoma (ccRCC), yet outcomes remain heterogeneous. Increasing evidence suggests that host factors influence the tumor microenvironment and response to ICI. Although higher body mass index (BMI) has been associated with improved outcomes in several malignancies, BMI is an imprecise surrogate for underlying adipose and muscle compartments. We evaluated the association between body composition and outcomes with 1L nivolumab+ipilimumab in metastatic ccRCC. Methods: We retrospectively analyzed patients with mccRCC treated with 1L nivolumab+ipilimumab at MD Anderson Cancer Center between June 2015 and February 2024. Body composition was measured using an artificial intelligence segmentation tool at the L3 vertebra from pretreatment CT scans obtained within 45 days prior to starting therapy. Endpoints included real-world progression-free survival (PFS) and overall survival (OS). Multivariable Cox regression models, guided by directed acyclic graphs, evaluated associations between continuous body composition measures and outcomes, incorporating non-linear cubic splines. An exploratory analysis used single-cell RNA sequencing from 12 treatment-naïve patients with metastatic ccRCC, stratified by median Skeletal Muscle Mass Index (SMMi) and Subcutaneous Adipose Tissue Index (SATi) values. Results: Among 309 patients (80.3% male, median age 61.9 years; 61.8% intermediate-risk and 28.5% poor-risk), increasing SMMi and SATi were independently associated with shorter PFS (HR 1.50, 95% CI 1.05 to 2.15 per 3-unit increase; and HR 1.39, 95% CI 1.01 to 1.90 per 10-unit increase). The associations of BMI, visceral adiposity, and skeletal muscle density with PFS were inconclusive. OS associations for all body-composition measures were likewise indeterminate. In the single-cell cohort, low SMMi was associated with numerically higher T-cell fractions (p=0.064), fewer myeloid cell proportions (p=0.10), and higher IDO1 expression. Conclusions: Greater subcutaneous adiposity and skeletal muscle mass were associated with shorter PFS among patients with metastatic ccRCC treated with 1L nivolumab+ipilimumab. These findings support the concept that host body composition influences the heterogeneous clinical benefit observed with ICI in metastatic ccRCC.

Background: Brain metastases (BM) from renal cell carcinoma (RCC) are associated with poor prognosis and limited survival. Prognostic tools specific to patients with RCC undergoing surgical resection of BM are lacking, and current models do not incorporate advanced machine learning (ML) approaches. This study aimed to develop and validate an ML-based model to predict overall survival (OS) after BM resection in RCC. Methods: We retrospectively analyzed 253 patients with histologically confirmed RCC and radiographically or pathologically confirmed BM who underwent neurosurgical resection at a tertiary referral center (1993-2021). Clinical and radiologic features were used to train and internally validate multiple ML models for OS prediction. Model performance was assessed using the concordance index (C-index) and time-dependent Area Under the Curve (AUC) at 1, 2, and 5 years. Feature importance and interpretability were evaluated using SHapley Additive exPlanations (SHAP). Results: The XGBoostCox + plsRcox model outperformed other algorithms, achieving a test C-Index of 0.59. AUCs at 1, 2, and 5 years were 0.61, 0.64, and 0.69 in the test cohort. SHAP analysis revealed extracranial disease status, number of BM, preoperative symptoms, and age at surgical resection as the most influential predictors. Kaplan-Meier analysis using an optimal cutoff based on the training cohort demonstrated significant survival differences between high- and low-risk groups in the test cohort (HR: 2.06 [1.26-3.35], P = .004). Conclusions and Relevance: An explainable XGBoostCox + plsRcox model accurately predicts OS after BM resection in RCC and enables personalized risk assessment via an online calculator (https://hasanovlab-rcc-bm-resect.share.connect.posit.cloud/).

The therapeutic landscape for renal cell carcinoma (RCC) and urinary tract cancer (UTC) has transformed dramatically, creating complexity in treatment selection and sequencing. The 2025 Advanced Urologic Cancer Consensus Conference was convened to establish evidence‐based expert consensus recommendations for optimal management. A multidisciplinary panel of 51 experts participated in a modified Delphi process addressing questions developed through iterative consensus‐building covering RCC and UTC management. Voting occurred before and after the conference, and analyses focused on postmeeting responses. Consensus was defined as ≥75% agreement, with strong consensus as >90%. Strong consensus was found on the use of adjuvant pembrolizumab for higher risk RCC (pathologic T2 [pT2], grade 4; pT3–pT4, any grade; pTXN1; or fully resected metastatic disease) and on neoadjuvant therapy before cystectomy for localized UTC. There was strong consensus on the use of enfortumab vedotin plus pembrolizumab as frontline therapy for metastatic UTC and the use of platinum‐based chemotherapy postprogression in biomarker‐negative UTC. For RCC, there was consensus on the role of single‐agent vascular endothelial growth factor receptor–tyrosine kinase inhibitor therapy after progression on frontline immune checkpoint inhibitor/vascular endothelial growth factor receptor–tyrosine kinase inhibitor therapy or dual immune checkpoint inhibitor therapy. However, there was a lack of consensus on other critical areas in the management of RCC and UTC. The 2025 Advanced Urologic Cancer Consensus Conference provides evidence‐informed guidance for complex clinical scenarios while identifying critical research priorities. The group recognizes that the lack of consensus across multiple areas highlights the need for improved patient selection and prospective studies enabling optimal combination and sequencing approaches. This iterative annual process will address evolving treatment paradigms to optimize outcomes.