Research Papers:

Exploring the synergistic effects of cabozantinib and a programmed cell death protein 1 inhibitor in metastatic renal cell carcinoma with machine learning

Ignacio Durán _, Daniel Castellano, Javier Puente, Lidia Martín-Couce, Esther Bello, Urbano Anido, José Manuel Mas and Luis Costa

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Oncotarget. 2022; 13:237-256. https://doi.org/10.18632/oncotarget.28183

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Ignacio Durán1, Daniel Castellano2, Javier Puente3, Lidia Martín-Couce4, Esther Bello4, Urbano Anido5, José Manuel Mas6 and Luis Costa7,8

1 Medical Oncology Department, University Hospital Marqués de Valdecilla, IDIVAL, Santander, Spain

2 Medical Oncology Department, University Hospital 12 de Octubre, Madrid, Spain

3 Medical Oncology Department, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), CIBERONC, Madrid, Spain

4 IPSEN, Planta 7, Torre Realia, L’hospitalet de Llobregat, Barcelona, Spain

5 Department of Medical Oncology, University Clinic Hospital of Santiago, Health Research Institute (IDIS), ONCOMET, Santiago de Compostela, Spain

6 Anaxomics Biotech, Barcelona, Spain

7 Oncology Department, Hospital de Santa Maria, Centro Hospitalar Universitário Lisboa Norte, Lisbon, Portugal

8 Instituto de Medicina Molecular-João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal

Correspondence to:

Ignacio Durán, email: [email protected]

Keywords: machine learning; cabozantinib; renal cell carcinoma; tumour microenvironment; systems biology

Received: October 28, 2021     Accepted: December 10, 2021     Published: January 27, 2022

Copyright: © 2022 Durán et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Clinical evidence supports the combination of cabozantinib with an immune checkpoint inhibitor for the treatment of metastatic clear cell renal cell carcinoma (mccRCC) and suggests a synergistic antitumour activity of this combination. Nevertheless, the biological basis of this synergy is not fully characterized. We studied the mechanisms underpinning the potential synergism of cabozantinib combined with a PD1 inhibitor in mccRCC and delved into cabozantinib monotherapy properties supporting its role to partner these combinations. To model physiological drug action, we used a machine learning-based technology known as Therapeutic Performance Mapping Systems, applying two approaches: Artificial Neural Networks and Sampling Methods. We found that the combined therapy was predicted to exert a wide therapeutic action in the tumour and the microenvironment. Cabozantinib may enhance the effects of PD1 inhibitors on immunosurveillance by modulating the innate and adaptive immune system, through the inhibition of VEGF-VEGFR and Gas6-AXL/TYRO3/MER (TAM) axes, while the PD1 inhibitors may boost the antiangiogenic and pro–apoptotic effects of cabozantinib by modulating angiogenesis and T-cell cytotoxicity. Cabozantinib alone was predicted to restore cellular adhesion and hamper tumour proliferation and invasion. These data provide a biological rationale and further support for cabozantinib plus PD1 inhibitor combination and may guide future nonclinical and clinical research.

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