Everolimus For Second-line Treatment Research Articles

Risk Prediction Using Bayesian Networks: An Immunotherapy Case Study in Patients With Metastatic Renal Cell Carcinoma

PURPOSETo address the need for more accurate risk stratification models for cancer immuno-oncology, this study aimed to develop a machine-learned Bayesian network model (BNM) for predicting outcomes in patients with metastatic renal cell carcinoma (mRCC) being treated with immunotherapy.METHODSPatient-level data from the randomized, phase III CheckMate 025 clinical trial comparing nivolumab with everolimus for second-line treatment in patients with mRCC were used to develop the BNM. Outcomes of interest were overall survival (OS), all-cause adverse events, and treatment-related adverse events (TRAE) over 36 months after treatment initiation. External validation of the model's predictions for OS was conducted using data from select centers from the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC).RESULTSAreas under the receiver operating characteristic curve (AUCs) for BNM-based classification of OS using baseline data were 0.74, 0.71, and 0.68 over months 12, 24, and 36, respectively. AUC for OS at 12 months increased to 0.86 when treatment response and progression status in year 1 were included as predictors; progression and response at 12 months were highly prognostic of all outcomes over the 36-month period. AUCs for adverse events and treatment-related adverse events were approximately 0.6 at 12 months but increased to approximately 0.7 by 36 months. Sensitivity analysis comparing the BNM with machine learning classifiers showed comparable performance. Test AUC on IMDC data for 12-month OS was 0.71 despite several variable imbalances. Notably, the BNM outperformed the IMDC risk score alone.CONCLUSIONThe validated BNM performed well at prediction using baseline data, particularly with the inclusion of response and progression at 12 months. Additionally, the results suggest that 12 months of follow-up data alone may be sufficient to inform long-term survival projections in patients with mRCC.

Renal cell carcinoma: Drug therapy and prognostic models

Renal cell carcinoma (RCC) represents the sixth-leading cancer-specific cause of death worldwide. This is mainly caused by metastatic or locally advanced RCC. Approximately 25-30% of patients present with metastasis during the initial diagnosis. Furthermore, 20-30% of patients develop metastatic disease following initial curative surgery. Metastatic RCC is characterized by a poor prognosis with a median overall survival of less than 2 years. Today, targeted therapies such as VEGF receptor inhibitors and antagonists as well as mTOR inhibitors represent the standard of care in metastatic RCC. Conventional chemotherapies or cytokine-based medications have been abandoned due to inferior clinical efficacy compared with targeted therapies. In Germany, sunitinib, pazopanib, temsirolimus, and bevacizumab have been approved for first-line treatment and sorafenib, axitinib, and everolimus for second-line treatment. Prognostic models, assessing individual risk profiles, have been developed in the last 15 years, which are crucial for the design of trials, patient counseling, and initiation of goal-directed therapies.

Systematic review and meta-analysis of target terapies for the treatment of metastatic renal cancer

At present there are several drugs for the treatment of advanced renal cell carcinoma (ARCC). The main objective of this work was to perform a systematic review (SR) and meta-analysis (MA) of clinical randomized studies that compared target cell therapies (TCT). SR identified clinical randomized trials that compared TCT versus interferon-alpha in the treatment of patients with ARCC. In order to analyze efficiency, it was evaluated free-survival progression (FSP), total survival (TS) and response rate (RR). In relation to first line treatment, seven studies of TCT were identified using sunitinib, sorafenib, bevacizumab and temsirolimus; and two studies with sorafenib and everolimus for second line treatment. Relative risk (RRi) of MA for FSP of first line therapies was: 0.83, CI = 0.78-0.87, I2 = 94% and p < 0.00001. Best results of RR of specific FSP among studies were: 0.38, sunitinib, CI = 0.25-0.58, bevacizumab, 0.62, CI = 0.47-0.83; and temsirolimus, 0.78, CI = 0.70-0.87. MA didn't show any benefit regarding TS of first line treatment of all analyzed drugs. As for RR significant results were: sunitinib, 3.83 CI = 2.86-5.12; bevacizumab, 2.52 CI = 1.78-3.57 and bevacizumab, 1.97 CI = 1.43-2.71. For first line treatment, sunitinib was the most effective TCT in relation to FPS; there was no alteration of TS and RR was small but significant for sunitinib and bevacizumab. Available studies could not conclude any results for second line treatments.

Survival following initiation of everolimus for second-line treatment of metastatic renal cell carcinoma: Prognostic factors in clinical practice and comparison to clinical trials.

e15500 Background: Current overall survival (OS) for metastatic renal cell carcinoma (mRCC) patients in clinical practice may differ from clinical trials. We aimed to identify and validate prognostic factors for OS in practice-based patients receiving 2nd-line everolimus (EVE), and to compare their OS vs. the RECORD-1 trial of EVE for mRCC. Methods: Two separate samples of oncologists/hematologists reviewed charts for patients initiating 2nd-line EVE between 2009 and 2011 following a 1st-line tyrosine kinase inhibitor (TKI). OS was defined as time from EVE initiation to death (censored at last follow-up). In the 1st sample of charts (the study sample), prognostic factors were identified via multivariable Cox proportional hazards models with stepwise selection. Prognostic factors considered included age, duration of mRCC and 1st-line treatment, metastatic sites, diabetes, histological subtype, ECOG and KPS score, and progression during 1st-line treatment. Model performance was assessed in the 2nd sample (the validation sample). Kaplan-Meier (KM) estimates for OS were compared between chart data and RECORD-1. Results: The study and validation samples included 220 and 97 patients, respectively. Significant prognostic factors were clear cell histology (hazard ratio (HR) = 2.9), KPS score &lt;80% (HR = 2.9), duration of mRCC &lt;1 year (HR = 2.7), progression on 1st-line TKI (HR = 2.2), and liver metastasis (HR = 1.9) (all P &lt;.05). In the validation sample, KM estimates for 1-year OS were 90% for patients with 0-2 risk factors, 62% for patients with 3 risk factors, and 20% for patients with 4-5 risk factors (log-rank P &lt;.001). OS estimates were consistent between both chart samples with 1-year OS probabilities of 67% and 68% and median OS of 19 and 23 months. In RECORD-1 1-year OS was 60% and median OS was 14.8 months (Motzer et al., 2010, Cancer). Conclusions: Prognostic factors for OS following 2nd-line EVE for mRCC in clinical practice were consistent with those previously identified in trial data. However, OS with 2nd-line EVE in clinical practice was longer than observed in trial data, and was not associated with type of 1st-line TKI.

PCN101 Cost Effectiveness of Everolimus for Second Line Treatment of Metastatic Renal Cell Cancer in Serbia

PCN101 Cost Effectiveness of Everolimus for Second Line Treatment of Metastatic Renal Cell Cancer in Serbia