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Updated: Jun 23, 2026

A Syngeneic Mouse Model of Metastatic Renal Cell Carcinoma for Quantitative and Longitudinal Assessment of Preclinical Therapies
Published on: April 12, 2017
Treatment selection for patients with metastatic renal cell carcinoma
Michael B Atkins1, Toni K Choueiri, Daniel Cho
1Division of Hematology/Oncology, Beth Israel Deaconess Medical Center, MASCO Bldg., Room 412, 375 Longwood Avenue, Boston, MA 02115, USA. Matkins@bidmc.harvard.edu
Abstract:
The availability of approved agents with distinct mechanisms of action has encouraged investigations to identify optimal treatment strategies for specific patients and specific tumor features. Study of tumors from patients treated with interleukin-2 (IL-2) has suggested that response was unlikely in patients with tumors with papillary features or low carbonic anhydrase IX (CAIX) expression. A model combining histologic features and CAIX expression separated patients into 2 groups of roughly equal size, with 96% of the responding patients being in the favorable prognostic group. Additional studies have begun to identify molecular features that might predict response to IL-2 therapy. In contrast, clinical trial data suggest that temsirolimus was relatively more active than interferon in patients with tumors containing non-clear cell features. Furthermore, pathologic examination showed no correlation of response with CAIX expression, but an apparent association with high expression of either phospho-AKT or phospho-S6, proteins either upstream or downstream of mammalian target of rapamycin. Preliminary investigations of tumor specimens from patients receiving vascular endothelial growth factor-targeted therapy suggested that high hypoxia-inducible factor expression might predict response. In addition, response appeared more likely in tumors with mutated or methylated VHL genes; however, substantial antitumor activity was still observed in patients with VHL wild-type tumors, particularly in patients treated with either sunitinib or axitinib, rather than bevacizumab or sorafenib. Although these data provide some guidance in treatment selection, considerably more research is needed to identify and validate selection models for particular treatment approaches, and to enable rational and optimal utilization of the available treatment options.
Insights
Identifying optimal cancer treatments involves analyzing tumor features. Interleukin-2 (IL-2) therapy response is linked to specific tumor characteristics, while other targeted therapies associate with different molecular markers, guiding personalized treatment strategies.
Area of Science:
- Oncology
- Molecular Biology
- Pathology
Background:
- Distinct therapeutic agents necessitate personalized treatment strategies based on patient and tumor specifics.
- Interleukin-2 (IL-2) therapy response is influenced by tumor histology and carbonic anhydrase IX (CAIX) expression.
- Other targeted therapies show varying efficacy based on tumor molecular profiles.
Purpose of the Study:
- To investigate predictive biomarkers for interleukin-2 (IL-2) and other targeted cancer therapies.
- To correlate specific tumor features and molecular markers with treatment response.
- To guide the development of optimal treatment selection models.
Main Methods:
- Analysis of tumor specimens from patients treated with IL-2, temsirolimus, interferon, and vascular endothelial growth factor (VEGF)-targeted agents.
- Histopathological examination including assessment of CAIX, phospho-AKT, and phospho-S6 expression.
- Genetic analysis of VHL gene mutations and methylation status.
Main Results:
- IL-2 response was associated with favorable histology and high CAIX expression, with a predictive model identifying 96% of responders.
- Temsirolimus showed greater activity in non-clear cell tumors, correlating with high phospho-AKT or phospho-S6 expression, independent of CAIX.
- VEGF-targeted therapy response was linked to high hypoxia-inducible factor and VHL gene alterations, though activity was observed in VHL wild-type tumors.
Conclusions:
- Tumor features and molecular markers can predict response to specific cancer therapies like IL-2 and VEGF-targeted agents.
- Further research is required to validate predictive models for optimal treatment selection and utilization.
- Personalized medicine approaches are crucial for maximizing therapeutic outcomes in oncology.
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