Gene Expression-Based Signature Can Predict Sorafenib Response in Kidney Cancer

Alexander Gudkov; Valery Shirokorad; Kirill Kashintsev; Dmitriy Sokov; Daniil Nikitin; Andrey Anisenko; Nicolas Borisov; Marina Sekacheva; Nurshat Gaifullin; Andrew Garazha; Maria Suntsova; Elena Koroleva; Anton Buzdin; Maksim Sorokin

doi:10.3389/fmolb.2022.753318

Gene Expression-Based Signature Can Predict Sorafenib Response in Kidney Cancer

Front Mol Biosci. 2022 Mar 14:9:753318. doi: 10.3389/fmolb.2022.753318. eCollection 2022.

Authors

Alexander Gudkov¹, Valery Shirokorad², Kirill Kashintsev², Dmitriy Sokov³, Daniil Nikitin⁴, Andrey Anisenko⁴, Nicolas Borisov⁵, Marina Sekacheva⁶, Nurshat Gaifullin⁷, Andrew Garazha⁸, Maria Suntsova⁶, Elena Koroleva⁵, Anton Buzdin^{5

8

6

9}, Maksim Sorokin^{1

5

8

10}

Affiliations

¹ I. M. Sechenov First Moscow State Medical University, Moscow, Russia.
² Moscow City Oncological Hospital №. 62, Moscow, Russia.
³ Moscow City Clinical Oncological Dispensary №. 1, Moscow, Russia.
⁴ Oncobox Ltd., Moscow, Russia.
⁵ Moscow Institute of Physics and Technology, Moscow, Russia.
⁶ World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia.
⁷ Department of Pathology, Faculty of Medicine, Lomonosov Moscow State University, Moscow, Russia.
⁸ OmicsWay Corp, Walnut, CA, United States.
⁹ Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.
¹⁰ European Organization for Research and Treatment of Cancer (EORTC), Biostatistics and Bioinformatics Subgroup, Brussels, Belgium.

Abstract

Sorafenib is a tyrosine kinase inhibitory drug with multiple molecular specificities that is approved for clinical use in second-line treatments of metastatic and advanced renal cell carcinomas (RCCs). However, only 10-40% of RCC patients respond on sorafenib-containing therapies, and personalization of its prescription may help in finding an adequate balance of clinical efficiency, cost-effectiveness, and side effects. We investigated whether expression levels of known molecular targets of sorafenib in RCC can serve as prognostic biomarker of treatment response. We used Illumina microarrays to profile RNA expression in pre-treatment formalin-fixed paraffin-embedded (FFPE) samples of 22 metastatic or advanced RCC cases with known responses on next-line sorafenib monotherapy. Among them, nine patients showed partial response (PR), three patients-stable disease (SD), and 10 patients-progressive disease (PD) according to Response Evaluation Criteria In Solid Tumors (RECIST) criteria. We then classified PR + SD patients as "responders" and PD patients as "poor responders". We found that gene signature including eight sorafenib target genes was congruent with the drug response characteristics and enabled high-quality separation of the responders and poor responders [area under a receiver operating characteristic curve (AUC) 0.89]. We validated these findings on another set of 13 experimental annotated FFPE RCC samples (for 2 PR, 1 SD, and 10 PD patients) that were profiled by RNA sequencing and observed AUC 0.97 for 8-gene signature as the response classifier. We further validated these results in a series of qRT-PCR experiments on the third experimental set of 12 annotated RCC biosamples (for 4 PR, 3 SD, and 5 PD patients), where 8-gene signature showed AUC 0.83.

Keywords: RNA sequencing; gene signature; kidney cancer; mRNA expression; microarray profiling; renal cell carcinoma; sorafenib response; tyrosine kinase inhibitor.