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Background Metastatic renal cell carcinoma (RCC) can present as synchronous metastatic disease, where primary metastases are detected at the time of diagnosis, or metachronous disease, where metastases appear during follow-up after initial RCC diagnosis. The effect of time to metastasis on patient outcomes is not well characterized in the era of immune checkpoint inhibitor (ICI)-based combinations. Herein, we assess the differences in clinical outcomes between patients with synchronous and metachronous mRCC. Methods Data for mRCC patients treated with first line ICI-based combination therapies between 2014 and 2023 were retrospectively collected from two NCI-designated comprehensive cancer centers. Patients were categorized as having synchronous or metachronous disease based on the time of presentation of metastases. Synchronous disease was defined as the presence of metastases at the time of RCC diagnosis or within 3 months thereafter. The study endpoints were time to treatment failure (TTF), overall survival (OS), and disease control rate (DCR). TTF and OS were estimated by Kaplan-Meier method and compared based on the time of presentation of metastases using log-rank tests. Univariable and multivariable Cox proportional hazard models and logistic regression models were used to examine the impact of sex, IMDC risk score, histology, and age at treatment start on TTF, OS and DCR. Results A total of 223 patients (126 synchronous and 97 metachronous) diagnosed with mRCC were included in the analysis. No significant difference was seen in gender distribution or age at first line treatment between these two groups. Although not statistically significant, the synchronous group had a higher proportion of patients with non-clear cell histology compared to those with metachronous disease (21% vs. 11%, P = 0.057). The median TTF was shorter in patients with synchronous disease but did not reach statistical significance (9 vs. 19.8 months for synchronous and metachronous, respectively, HR 1.37, 95% CI [0.98, 1.92], P = 0.064). Median OS was significantly shorter in patients with synchronous disease (28.0 vs. 50.9 months, adjusted HR 2.23, 95% CI [1.36, 3.65], P = 0.001). Similarly, patients with synchronous mRCC had a lower DCR than patients with metachronous (58.7% vs 78.4%, adjusted odds ratio (OR) 0.29, 95% CI [0.13, 0.64], P = 0.002). In the multivariable analysis, synchronous disease remained an independent factor associated with worse OS and DCR. Conclusions Patients with metastatic disease at the time of RCC diagnosis who were treated with first line ICI-based combinations have a poorer OS, and worse DCR than those who develop metastatic disease during follow-up. Although further validation is needed, these findings could be valuable in designing clinical trials and selecting patients for treatment escalation and closer clinical follow-up. CDMRP DOD Funding: no [ABSTRACT FROM AUTHOR]

Background In addition to the previously studied socioeconomic factors associated with health disparities, insurance status can serve as a prognostic factor associated with overall survival in RCC patients (Zhang et al., Future Oncol 2019). Moreover, within this population, having access to health insurance has been reported to result in earlier detection of disease (Javier-DesLoges et al., JAMA Netw Open 2021). In this study, we explored the impact of primary and secondary insurance status on PFS and OS in patients receiving first line systemic therapy for mRCC. Methods Patients with mRCC from two NCI-designated comprehensive cancer centers diagnosed between 1990 and 2022 with available insurance information were retrospectively identified using institutional databases. Primary insurance information was categorized into three groups—Medicare, private insurance, and Medicaid/no insurance—while secondary insurance was defined by the presence or absence of secondary coverage. PFS and OS were estimated by Kaplan-Meier method and compared based on insurance status using log-rank tests. Univariate and multivariate Cox proportional hazard regression models were used to examine the impact of insurance status on PFS and OS. Results In total, 645 patients with mRCC had accessible information and were included in our analysis. Of these, 344 (53.3%), 250 (38.8%), and 51 (7.9%) had primary Medicare, private insurance, and Medicaid/no insurance, respectively. Overall, most patients were male (73.0%), with a median age of 60.0 (22.0-94.0) at time of diagnosis. The most commonly rendered first-line treatments were monotherapy with targeted agents (66.4%), dual immunotherapy (13.6%), and targeted/immunotherapy combinations (10.2%). Median PFS for the entire cohort was 6.6 months (95% CI, 5.9-7.7). Median PFS for patients with primary Medicare was 7.7 months (95%, CI 7.0-9.0), 5.5 months (95% CI, 4.0-7.0) for patients with private insurance, and 4.9 months (95% CI, 3.8-8.1) for Medicaid/uninsured patients. Using an overall log-rank test, a significant difference in PFS among the three groups with different primary insurance was observed (p<0.0001). The median PFS for patients with secondary insurance was 8.1 months (95% CI, 6.6-11.3) compared to 6.1 months (95% CI, 5.5-7.4) for patients without secondary insurance. A multivariate Cox model with adjustment for other factors (such as age, gender, and ethnicity) revealed a statistically significant difference in PFS between patients with and without secondary insurance (p=0.0281). Median OS for the entire cohort was 36.8 months (95% CI, 32.4-44.3). Median OS for patients with primary Medicare, private insurance, and Medicaid/no insurance was 49.0 months (95%, CI 41.8-55.3), 28.5 months (95% CI, 24.1-35.7), and 21.6 months (95% CI, 17.5-42.3), respectively. By an overall log-rank test, a significant difference in OS across the three groups of primary insurance was observed (p=0.0003). No statistically significant differences in OS were observed between patients with and without secondary insurance. Overall, patients with primary Medicare had superior median PFS (p=0.0327) and OS (p=0.0004) compared to those with Medicaid/no insurance; although patients with private insurance had a lower risk of progression and death compared to those with Medicaid/no insurance, the result was not statistically significant. Conclusions In this real-world study, we investigated the impact of insurance status on clinical outcomes in patients with mRCC receiving systemic therapy. mRCC patients with primary Medicaid/no insurance or private insurance had inferior median PFS and OS compared to those with primary Medicare. Moreover, patients with secondary insurance had superior PFS over those with primary insurance alone. Overall, our findings suggest that insurance status may serve as a determinant of clinical outcomes. These hypothesis-generating data warrant external validation in prospective studies. [ABSTRACT FROM AUTHOR]

Background Radiogenomics is an emerging tool with applications in screening for molecular biomarkers in diagnostic and prognostic assessment through the extraction of quantitative data from medical images (Shui et al., Front Oncol 2021). In this study, we aim to develop machine learning (ML) models to predict the most common genomic alterations present in our subset of renal cell carcinoma patients (pts). Methods Retrospectively, pts with tissue genomic testing from CT-guided biopsy samples were identified. Genomic testing was done via GEM ExTra assay, a CAP-accredited, CLIA-certified test encompassing tumor whole exome sequencing and whole transcriptome sequencing (TGen; Phoenix, AZ). Biopsy sample collection sites were identified from pre-biopsy contrast CT images, and the lesions were segmented with ITK-SNAP software, from which 510 radiomic features were extracted with Pyradiomics. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select the most relevant features. Logistic regression (LR) and support vector machine (SVM) classifiers were built for the prediction of PBRM1, VHL, and SETD2 gene alterations. Multiple metrics were used to evaluate the predictive performance via leave-one-out cross-validation, including the area under the receiver operating characteristic (AUROC) and the area under the precision-recall curve (AUPRC). Feature importance was evaluated with Shapley additive explanations (SHAP) method. Results A total of 14 RCC pts (10:4 M:F) with genomic testing from CT-guided biopsies were identified. The majority of pts were White (85.7%) and had clear cell histology (71.4%). The most common locations for the CT-guided biopsy were lung (18.2%), soft tissue (13.6%), kidney (9.1%), and bone (9.1%). The most common alterations were seen in PBRM1 (50%), VHL (43.9%), and SETD2 (35.7%) genes. The PBRM1 gene was predicted with the highest AUROC (0.84) and AUPRC (0.88) with the SVM classifier, followed by the SETD2 gene (AUROC=0.78 and AUPRC=0.66) with LR classifier and VHL gene (AUROC=0.56 and AUPRC=0.65) with SVM classifier. Notably, all three models showed good sensitivity in classifying gene mutation status (PBRM1: 0.86; VHL: 0.88; SETD2: 0.89). Among all radiomic features, first-order features, Gray Level Size Zone Matrix features, and Gray Level Dependence Matrix features were found to be the most important features for predictions. Conclusions Using a CT-based radiomics analysis of the biopsy area, we showed that SVM and LR prediction models could predict PBRM1, VHL, and SETD2 mutations with high accuracy. These models may assist in identifying potentially actionable alterations and yield ease in treatment selection for RCC. Further extensive studies are warranted to validate our findings and improve our model. CDMRP DOD Funding: no [ABSTRACT FROM AUTHOR]