The The Prognosis and Immune Microenvironment Related Factor Interleukin-23 in Clear Cell Renal Cell Carcinoma: A Radiological Investigation
Purpose: To explore the ccRCC clinical and immune characteristics correlated with IL-23 expression level and build pre-operative prediction models based on contrast CT scans.
Materials and Methods: The study included the cancer genome atlas kidney renal clear cell carcinoma cases to build a bioinformatics cohort. The cases with qualified contrast CT images were selected as radiographic and radiomics cohort. The IL-23 expression level groups were defined by median-based thresholding. The clinical characteristics were compared between groups. The impacts of IL-23 on immune microenvironment composition were measured via the CIBERSORT. Two radiologists evaluated the pre-operative contrast CT images. The radiomics features were automatically extracted. IL-23 group-specific radiographic and radiomics features were collected and used for prediction model establishment via Orange Data Mining Toolbox. P < 0.05 was set as statistically significant.
Results: For total, 530 ccRCC cases were included. The IL-23 group was significantly associated with survival, histologic grade, AJCC tumor stage, AJCC cancer stage, and plasma calcium level. Except for Treg and other T cells, IL-23 showed correlation with NK cell, mast cell, monocyte infiltration. Axial length was the only significant radiographic measurement between IL-23 groups. The radiomics features established an IL-23 group prediction model with the highest 10-fold cross-verification AUC of 0.842.
Conclusion: The clear cell renal cell carcinoma IL-23 expression level had prognosis and immune microenvironment correlation and could be predicted by pre-operative radiomics features.
- Clear cell renal cell carcinoma
- Tumor microenvironment
- Computed Tomography
How to Cite
2. Motzer RJ, Hutson TE, Tomczak P, et al. Overall survival and updated results for sunitinib compared with interferon alfa in patients with metastatic renal cell carcinoma. Journal of clinical oncology. 2009;27:3584.
3. Motzer RJ, Escudier B, McDermott DF, et al. Nivolumab versus everolimus in advanced renal-cell carcinoma. New England Journal of Medicine. 2015;373:1803-13.
4. Rini BI, Plimack ER, Stus V, et al. Pembrolizumab plus axitinib versus sunitinib for advanced renal-cell carcinoma. New England Journal of Medicine. 2019;380:1116-27.
5. Motzer RJ, Rini BI, McDermott DF, et al. Nivolumab for metastatic renal cell carcinoma: results of a randomized phase II trial. Journal of clinical oncology. 2015;33:1430.
6. McDermott DF, Huseni MA, Atkins MB, et al. Clinical activity and molecular correlates of response to atezolizumab alone or in combination with bevacizumab versus sunitinib in renal cell carcinoma. Nature medicine. 2018;24:749-57.
7. Gaffen SL, Jain R, Garg AV, Cua DJ. The IL-23–IL-17 immune axis: from mechanisms to therapeutic testing. Nature reviews immunology. 2014;14:585-600.
8. Fu Q, Xu L, Wang Y, et al. Tumor-associated macrophage-derived interleukin-23 interlinks kidney cancer glutamine addiction with immune evasion. European urology. 2019;75:752-63.
9. Nie P, Yang G, Wang Z, et al. A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma. European radiology. 2020;30:1274-84.
10. Jamshidi N, Jonasch E, Zapala M, et al. The radiogenomic risk score: construction of a prognostic quantitative, noninvasive image-based molecular assay for renal cell carcinoma. Radiology. 2015;277:114-23.
11. Karlo CA, Di Paolo PL, Chaim J, et al. Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations. Radiology. 2014;270:464-71.
12. Stanzione A, Ricciardi C, Cuocolo R, et al. MRI radiomics for the prediction of Fuhrman grade in clear cell renal cell carcinoma: A machine learning exploratory study. Journal of digital imaging. 2020;33:879-87.
13. Clark K, Vendt B, Smith K, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. Journal of digital imaging. 2013;26:1045-57.
14. Cohan RH, Sherman LS, Korobkin M, Bass JC, Francis IR. Renal masses: assessment of corticomedullary-phase and nephrographic-phase CT scans. Radiology. 1995;196:445-51.
15. Ihaka R, Gentleman R. R: a language for data analysis and graphics. Journal of computational and graphical statistics. 1996;5:299-314.
16. Jonasch E, Walker CL, Rathmell WK. Clear cell renal cell carcinoma ontogeny and mechanisms of lethality. Nature Reviews Nephrology. 20201-17.
17. McDermott D, Ghebremichael M, Signoretti S, et al. The high-dose aldesleukin (HD IL-2)“SELECT” trial in patients with metastatic renal cell carcinoma (mRCC). Journal of Clinical Oncology. 2010;28:4514-.
18. Ljungberg B, Bensalah K, Canfield S, et al. EAU guidelines on renal cell carcinoma: 2014 update. European urology. 2015;67:913-24.
19. Mazza C, Escudier B, Albiges L. Nivolumab in renal cell carcinoma: latest evidence and clinical potential. Therapeutic advances in medical oncology. 2017;9:171-81.
20. González-Navajas JM, Fan DD, Yang S, et al. The impact of Tregs on the anticancer immunity and the efficacy of immune checkpoint inhibitor therapies. Frontiers in Immunology. 2021;12:416.
21. Blom JH, van Poppel H, Maréchal JM, et al. Radical nephrectomy with and without lymph-node dissection: final results of European Organization for Research and Treatment of Cancer (EORTC) randomized phase 3 trial 30881. European urology. 2009;55:28-34.
22. Zhi Y, Li X, Qi F, Hu X, Xu W. Association of Tumor Size with Risk of Lymph Node Metastasis in Clear Cell Renal Cell Carcinoma: A Population-Based Study. Journal of oncology. 2020;2020.
23. Heng DY, Xie W, Regan MM, et al. External validation and comparison with other models of the International Metastatic Renal-Cell Carcinoma Database Consortium prognostic model: a population-based study. The lancet oncology. 2013;14:141-8.
24. Gravallese EM, Schett G. Effects of the IL-23–IL-17 pathway on bone in spondyloarthritis. Nature Reviews Rheumatology. 2018;14:631-40.
25. Dimitrov V, Bouttier M, Boukhaled G, et al. Hormonal vitamin D up-regulates tissue-specific PD-L1 and PD-L2 surface glycoprotein expression in humans but not mice. Journal of Biological Chemistry. 2017;292:20657-68.
26. Pincikova T, Paquin‐Proulx D, Sandberg J, Flodström‐Tullberg M, Hjelte L. Vitamin D treatment modulates immune activation in cystic fibrosis. Clinical & Experimental Immunology. 2017;189:359-71.
27. Borcherding N, Vishwakarma A, Voigt AP, et al. Mapping the immune environment in clear cell renal carcinoma by single-cell genomics. Communications biology. 2021;4:1-11.
28. Nakano N, Nishiyama C, Kanada S, et al. Involvement of mast cells in IL-12/23 p40 production is essential for survival from polymicrobial infections. Blood, The Journal of the American Society of Hematology. 2007;109:4846-55.
29. Shinagare AB, Vikram R, Jaffe C, et al. Radiogenomics of clear cell renal cell carcinoma: Preliminary findings of the cancer genome atlas–renal cell carcinoma (TCGA–RCC) imaging research group. Abdominal imaging. 2015;40:1684-92.
30. Kocak B, Durmaz ES, Ates E, Ulusan MB. Radiogenomics in clear cell renal cell carcinoma: machine learning–based high-dimensional quantitative CT texture analysis in predicting PBRM1 mutation status. American Journal of Roentgenology. 2019;212:W55-W63.
- Abstract Viewed: 0 times
- Just Accepted/6825 Downloaded: 0 times