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A Syngeneic Mouse Model of Metastatic Renal Cell Carcinoma for Quantitative and Longitudinal Assessment of Preclinical Therapies
Published on: April 12, 2017
Clinical outcome predictors for metastatic renal cell carcinoma: a retrospective multicenter real-life case series
Mimma Rizzo1, Gaetano Pezzicoli2, Valentina Tibollo3
1Medical Oncology Unit, Azienda Ospedaliera Universitaria Consorziale, Policlinico di Bari, Bari, Italy. rizzo.mimma@gmail.com.
Abstract:
Over the last decades, the therapeutic armamentarium of metastatic renal cell carcinoma (mRCC) has been revolutionized by the advent of tyrosin-kinase inhibitors (TKI), immune-checkpoint inhibitors (ICI), and immune-combinations. RCC is heterogeneous, and even the most used validated prognostic systems, fail to describe its evolution in real-life scenarios. Our aim is to identify potential easily-accessible clinical factors and design a disease course prediction system. Medical records of 453 patients with mRCC receiving sequential systemic therapy in two high-volume oncological centres were reviewed. The Kaplan-Meier method and Cox proportional hazard model were used to estimate and compare survival between groups. As first-line treatment 366 patients received TKI monotherapy and 64 patients received ICI, alone or in combination. The mean number of therapy lines was 2.5. A high Systemic Inflammation Index, a BMI under 25 Kg/m2, the presence of bone metastases before systemic therapy start, age over 65 years at the first diagnosis, non-clear-cell histology and sarcomatoid component were correlated with a worse OS. No significant OS difference was observed between patients receiving combination therapies and those receiving exclusively monotherapies in the treatment sequence. Our relapse prediction system based on pathological stage and histological grade was effective in predicting the time between nephrectomy and systemic treatment. Our multicentric retrospective analysis reveals additional potential prognostic factors for mRCC, not included in current validated prognostic systems, suggests a model for disease course prediction and describes the outcomes of the most common therapeutic strategies currently available.
Insights
New factors like inflammation and BMI impact metastatic renal cell carcinoma (mRCC) patient outcomes. A predictive system using these factors and pathology can forecast disease course and relapse timing.
Area of Science:
- Oncology
- Medical Informatics
Background:
- Metastatic renal cell carcinoma (mRCC) treatment has advanced with tyrosin-kinase inhibitors (TKI) and immune-checkpoint inhibitors (ICI).
- Current prognostic systems for mRCC lack precision in real-world scenarios due to tumor heterogeneity.
Purpose of the Study:
- To identify accessible clinical factors for predicting mRCC disease course.
- To develop a novel disease course prediction system for mRCC.
Main Methods:
- Retrospective analysis of 453 mRCC patients from two centers.
- Kaplan-Meier and Cox proportional hazard models used for survival analysis.
- Evaluation of a relapse prediction system based on pathological stage and histological grade.
Main Results:
- High Systemic Inflammation Index, low BMI (<25 Kg/m2), bone metastases, age >65, non-clear-cell histology, and sarcomatoid component correlated with worse overall survival (OS).
- No significant OS difference found between combination therapies and monotherapies in sequential treatment.
- Relapse prediction system effectively predicted time from nephrectomy to systemic treatment.
Conclusions:
- Identified novel prognostic factors for mRCC beyond current systems.
- Proposed a new model for mRCC disease course prediction.
- Characterized outcomes of common mRCC therapeutic strategies.
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