Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2022 Dec 15:47:12-19.
doi: 10.1016/j.euros.2022.11.006. eCollection 2023 Jan.

Can Molecular Classifications Help Tailor First-line Treatment of Metastatic Renal Cell Carcinoma? A Systematic Review of Available Models

Affiliations
Review

Can Molecular Classifications Help Tailor First-line Treatment of Metastatic Renal Cell Carcinoma? A Systematic Review of Available Models

Idir Ouzaid et al. Eur Urol Open Sci. .

Abstract

Context: The advent of immune check inhibitors (ICIs) has tremendously changed the prognosis of metastatic renal cell carcinoma (mRCC), adding an unseen substantial overall survival benefit. These agents could be administered alone or in combination with anti-vascular endothelial growth factor (anti-VEGF) therapies. So far, treatment allocation is based only on clinical stratification risk models.

Objective: Herein, we aimed to report the different molecular classifications reported in the first-line treatment of mRCC and discuss the awaited clinical implications in terms of treatment selection.

Evidence acquisition: Medline database as well as European Society for Medical Oncology (ESMO)/American Society of Clinical Oncology (ASCO) conference proceedings were searched to identify biomarker studies. Inclusion criteria comprised randomized and nonrandomized clinical trials that included patients treated in the first line of mRCC setting, patients treated with anti-VEGF therapies or ICIs, biological modeling, and available survival outcomes.

Evidence synthesis: Four classification models were identified with subsequent clinical implications: Beuselinck model (34 gene signatures), IMmotion150, Hakimi, and JAVELIN 101 model. Tumor profiling shows distinct outcomes when treated with one or other combination. Patients are clustered into two gene signatures: angiogenic and proinflammatory (as per JAVELIN). The first is more likely to respond to therapy that includes anti-VEGF agents, while the best outcomes are obtained with an ICI combination with the second.

Conclusions: The findings presented here were mostly derived from ancillary registered studies of new drugs in the setting of mRCC. Further validation is needed, which sets new paradigms for investigation in clinical research based on tumor biology for treatment allocation and not only on clinical stratification tools.

Patient summary: First-line treatment of metastatic kidney includes immunotherapy alone or in combination with antiangiogenic therapy. However, clinical practice demonstrated that the "one treatment fits all" strategy might not be the best approach. In fact, recent studies showed that the addition of immunotherapy agents will not benefit all patients equally, and some still respond either equally to or better than anti-vascular endothelial growth factor alone. This review revealed biomarker modeling that impacts treatment selection. Recent tumor profiling into "angiogenic signature" more sensitive to angiogenic agents versus "immune signature" more likely to achieve the best response with immunotherapy should be validated. Tumor biology features might be more powerful than clinical classification for a tailored treatment approach.

Keywords: Angiogenesis; Biomarkers; Immune check inhibitors; Immunotherapy; Metastases; PD-L1; Renal cell carcinoma; Tyrosine kinase inhibitor.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1
Flowchart of evidence acquisition and search strategy. RCC = renal cell carcinoma.
Fig. 2
Fig. 2
Hypothetic representation of the response to tyrosine kinase inhibitors and immune check inhibitors according to molecular classification in every model.

Similar articles

References

    1. Leibovich B.C., Lohse C.M., Crispen P.L., et al. Histological subtype is an independent predictor of outcome for patients with renal cell carcinoma. J Urol. 2010;183:1309–1315. - PubMed
    1. Lipworth L., Morgans A.K., Edwards T.L., et al. Renal cell cancer histological subtype distribution differs by race and sex. BJU Int. 2016;117:260–265. - PubMed
    1. SEER. Cancer of the kidney and renal pelvis—Cancer Stat Facts. https://seer.cancer.gov/statfacts/html/kidrp.html.
    1. Motzer R.J., Tannir N.M., McDermott D.F., et al. Nivolumab plus ipilimumab versus sunitinib in advanced renal-cell carcinoma. N Engl J Med. 2018;378:1277–1290. - PMC - PubMed
    1. Rini B.I., Plimack E.R., Stus V., et al. Pembrolizumab plus axitinib versus sunitinib for advanced renal-cell carcinoma. N Engl J Med. 2019;380:1116–1127. - PubMed