Treatment and Outcome of Metastatic Renal Cell Carcinoma With Sarcomatoid Differentiation: A Single-Center, Real-World Analysis of Retrospective Data

Background: Sarcomatoid differentiation/histology of renal cell carcinoma (sRCC) in patients with metastatic renal cell carcinoma (mRCC) is still underresearched in current therapy regimes. We aimed to evaluate the impact of sRCC on outcomes in patients with mRCC treated with tyrosine kinase inhibitors (TKIs).Methods: We collected complete data of 262 consecutive mRCC patients from our institutional database for this retrospective study. All patients were treated with TKIs within a single or multimodal treatment approach. All analyses were adjusted for the presence of sRCC. Descriptive statistics as well as uni- and multivariable outcome metrics, including progression-free (PFS) and overall survival (OS) as endpoints were performed.Results: Overall, 18 patients had sRCC (6.9%). Patients with sRCC had more often clear-cell histology (p = 0.047), a higher T-stage (p = 0.048), and underwent cytoreductive nephrectomy more frequently (p < 0.001). The most common first-line TKIs were Sunitinib (65.6%), Sorafenib (19.5%), and Pazopanib (10.3%), respectively. At a median follow-up of 32 months, patients with sRCC had significantly reduced PFS (p = 0.02) and OS (p = 0.01) compared to patients without sRCC. In multivariable analyses that adjusted for the effects of standard mRCC predictors, the sarcomatoid feature retained its independent association with inferior PFS (HR: 2.39; p = 0.007) and OS (HR: 2.37; p = 0.001). This association remained statistically significant in subgroup analyses of patients with Sunitinib as first-line therapy (PFS p < 0.001; OS: p < 0.001).Conclusion: Despite its rare occurrence, our findings confirm sRCC as a powerful predictor for inferior outcomes in mRCC treated with targeted therapies. This suggests a need for more tailored treatment strategies in patients harboring mRCC with sarcomatoid histology to improve oncological outcomes.

Durable Remission with Immunotherapy in a Patient with Sarcomatoid Renal Cell Carcinoma

Sarcomatoid differentiation is a rare and aggressive histologic subtype with poor prognosis, seen in several malignancies. In sarcomatoid renal cell carcinoma (RCC), the degree of sarcomatoid differentiation and the stage at presentation determines the prognosis. Despite resection, chemotherapy and targeted therapy response is modest, with relapse usually occurring within a few months. We present a case of a gentleman with sarcomatoid RCC managed with pembrolizumab, who has had no evidence of recurrence for over 4 years since the last dose of immunotherapy. RCCs with sarcomatoid differentiation have a high presence of programmed cell death protein 1 and programmed cell death ligand 1 in T cells and tumor cells, respectively, making immunotherapy an attractive option in this setting. Clinical trials are ongoing to further define the benefit of immunotherapy in sarcomatoid RCC.

Outcomes of renal cell carcinoma with associated venous tumor thrombus: experience from a large cohort and short time span in a single center

Background The surgical management and outcomes of renal cell carcinoma (RCC) with venous tumor thrombus (VTT) have been reported in limited sample size, and there remain discrepancies over the factors that influence oncologic outcomes after radical nephrectomy with thrombectomy (RNTE). The aim of the study was to analyze the outcomes of the patients with RCC with VTT in our institution and identify the independent prognostic factors. Methods Patients with RCC with VTT were enrolled for the study from February 2015 to December 2018. All patients underwent RNTE. Clinical data were compared using Mann-Whitney U test and the chi-square test for continuous and categorical variables respectively. Survival analysis was estimated using the Kaplan-Meier method. Univariable and multivariable survival analyses were performed using Cox regression model. Results 121 patients (91 men & 30 women) were identified with a median age of 60 years. VTT level was 0 in 25 patients, I in 20, II in 50, III in 12 and IV in 14. The median follow-up time was 24 months. During the follow-up period, 51 (42%) patients died and 69 (57%) patients experienced recurrence or metastasis. The 3-year and 5-year over-all survival (OS) were 58 and 39%. Among the several factors examined, positive lymph node (P = 0.016), metastasis at surgery (P = 0.034), tumor necrosis (P = 0.023) and sarcomatoid differentiation (P < 0.001) were demonstrated as independent significant risk factors on multivariable analysis. Conclusion The OS was poor for patients with RCC with VTT. Rather than VTT level, positive lymph node, metastasis at surgery, tumor necrosis and sarcomatoid differentiation were independent prognostic predictors.

Prognostic Value of Positive Lymph Nodes in Patients with Renal Cell Carcinoma and Tumor Thrombus Undergoing Nephrectomy and Thrombectomy

<b><i>Introductions:</i></b> The objective of this study was to determine the prognostic value of positive lymph nodes (LNs) in patients with renal cell carcinoma (RCC) and tumor thrombus (TT) and to explore risk factors predicting LNs metastasis. <b><i>Methods:</i></b> We retrospectively analyzed 216 patients with RCC and TT treated at a single institution from January 2015 to December 2019. Overall survival (OS) and progression-free survival (PFS) was estimated using the Kaplan-Meier curves divided by pathological LN status. Associations between clinicopathological features and survival outcomes were evaluated using Cox regression models. Logistic regression model was performed to determine risk factors associated with LN metastasis. <b><i>Results:</i></b> We identified 216 patients with RCC and TT including 85 (39.4%) who did and 131 (60.6%) who did not undergo lymph node dissection. Pathologically positive LNs were found in 18 (8.3%) cases. pN1 had significant worse OS (median: 21 vs. 41 and 56 months, <i>p</i> &#x3c; 0.001) and PFS (median:14 vs. 29 and 33 months, <i>p</i> &#x3c; 0.001) than pN0 and pNx respectively. However, survival outcomes of OS and PFS were similar between pNx-0/M1 and pN1/M0 group and between 1- and ≥2-node-positive group. Non-CCRCC (<i>p</i> = 0.001), sarcomatoid differentiation (<i>p</i> &#x3c; 0.001), and pathologically positive LNs (<i>p</i> = 0.025) were independent prognostic predictors predicting worse OS while distance metastasis (<i>p</i> = 0.009), non-CCRCC (<i>p</i> = 0.023), necrosis (<i>p</i> = 0.014), sarcomatoid differentiation (<i>p</i> = 0.003), and pathologically positive LNs (<i>p</i> = 0.007) were independent prognostic indicators predicting worse PFS. Clinically positive LNs (<i>p</i> = 0.014) and sarcomatoid differentiation (<i>p</i> = 0.009) were predictors of positive LNs. <b><i>Conclusions:</i></b> LNs metastasis independently associated with worse survival outcomes in RCC and TT populations, with similar survival outcomes compared to distance metastasis. Therefore, more accurate risk stratification is warranted for guiding postoperative surveillance and adjuvant therapy.

Identification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric MRI

Sarcomatoid differentiation in RCC (sRCC) is associated with a poor prognosis, necessitating more aggressive management than RCC without sarcomatoid components (nsRCC). Since suspected renal cell carcinoma (RCC) tumors are not routinely biopsied for histologic evaluation, there is a clinical need for a non-invasive method to detect sarcomatoid differentiation pre-operatively. We utilized unsupervised self-organizing map (SOM) and supervised Learning Vector Quantizer (LVQ) machine learning to classify RCC tumors on T2-weighted, non-contrast T1-weighted fat-saturated, contrast-enhanced arterial-phase T1-weighted fat-saturated, and contrast-enhanced venous-phase T1-weighted fat-saturated MRI images. The SOM was trained on 8 nsRCC and 8 sRCC tumors, and used to compute Activation Maps for each training, validation (3 nsRCC and 3 sRCC), and test (5 nsRCC and 5 sRCC) tumor. The LVQ classifier was trained and optimized on Activation Maps from the 22 training and validation cohort tumors, and tested on Activation Maps of the 10 unseen test tumors. In this preliminary study, the SOM-LVQ model achieved a hold-out testing accuracy of 70% in the task of identifying sarcomatoid differentiation in RCC on standard multiparameter MRI (mpMRI) images. We have demonstrated a combined SOM-LVQ machine learning approach that is suitable for analysis of limited mpMRI datasets for the task of differential diagnosis.