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Published in:

01-06-2019

Radiogenomics in renal cell carcinoma

Authors: Francesco Alessandrino, Atul B. Shinagare, Dominick Bossé, Toni K. Choueiri, Katherine M. Krajewski

Published in: Abdominal Radiology | Issue 6/2019

Abstract

Radiogenomics, a field of radiology investigating the association between the imaging features of a disease and its gene expression pattern, has expanded considerably in the last few years. Recent advances in whole-genome sequencing of clear cell renal cell carcinoma (ccRCC) and the identification of mutations with prognostic significance have led to increased interest in the relationship between imaging and genomic data. ccRCC is particularly suitable for radiogenomic analysis as the relative paucity of mutated genes allows for more straightforward genomic-imaging associations. The ultimate aim of radiogenomics of ccRCC is to retrieve additional data for accurate diagnosis, prognostic stratification, and optimization of therapy. In this review article, we will present the state-of-the-art of radiogenomics of ccRCC, and after briefly reviewing updates in genomics, we will discuss imaging-genomic associations for diagnosis and staging, prognosis, and for assessment of optimal therapy in ccRCC.

Kuo MD, Jamshidi N (2014) Behind the numbers: decoding molecular phenotypes with radiogenomics-guiding principles and technical considerations. Radiology 270(2):320–325CrossRefPubMed

Lander ES, Linton LM, Birren B, et al. (2001) Initial sequencing and analysis of the human genome. Nature 409(6822):860–921CrossRefPubMed

Alessandrino F, Krajewski KM, Shinagare AB (2016) Update on radiogenomics of clear cell renal cell carcinoma. Eur Urol Focus 2(6):572–573CrossRefPubMed

Kuo MD, Gollub J, Sirlin CB, Ooi C, Chen X (2007) Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma. J Vasc Interv Radiol 18(7):821–831CrossRefPubMed

Diehn M, Nardini C, Wang DS, et al. (2008) Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci USA 105(13):5213–5218CrossRefPubMedPubMedCentral

Rutman AM, Kuo MD (2009) Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging. Eur J Radiol 70(2):232–241CrossRefPubMed

The Cancer Genome Atlas Research Network (2013) Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499(7456):43–49CrossRefPubMedCentral

Karlo CA, Di Paolo PL, Chaim J, et al. (2014) Radiogenomics of clear-cell renal cell carcinoma: associations between CT imaging features and mutations. Radiology 270(2):464–471CrossRefPubMed

Shinagare AB, Vikram R, Jaffe C, et al. (2015) Radiogenomics of clear cell renal cell carcinoma: preliminary findings of The Cancer Genome Atlas-Renal Cell Carcinoma (TCGA–RCC) Imaging Research Group. Abdom Imaging 40(6):1684–1692CrossRefPubMedPubMedCentral

10.

Seizinger BR, Rouleau GA, Ozelius LJ, et al. (1988) Von Hippel–Lindau disease maps to the region of chromosome 3 associated with renal cell carcinoma. Nature 332(6161):268–269CrossRefPubMed

11.

Dalgliesh GL, Furge K, Greenman C, et al. (2010) Systematic sequencing of renal carcinoma reveals inactivation of histone modifying genes. Nature 463(7279):360–363CrossRefPubMedPubMedCentral

12.

Duns G, van den Berg E, van Duivenbode I, et al. (2010) Histone methyltransferase gene SETD2 is a novel tumor suppressor gene in clear cell renal cell carcinoma. Cancer Res 70(11):4287–4291CrossRefPubMed

13.

Guo G, Gui Y, Gao S, et al. (2012) Frequent mutations of genes encoding ubiquitin-mediated proteolysis pathway components in clear cell renal cell carcinoma. Nat Genet 44(1):17–19CrossRef

14.

Peña-Llopis S, Vega-Rubín-de-Celis S, Liao A, et al. (2012) BAP1 loss defines a new class of renal cell carcinoma. Nat Genet 44(7):751–759CrossRefPubMedPubMedCentral

15.

Varela I, Tarpey P, Raine K, et al. (2011) Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma. Nature 469(7331):539–542CrossRefPubMedPubMedCentral

16.

Gerlinger M, Rowan AJ, Horswell S, et al. (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366(10):883–892CrossRefPubMedPubMedCentral

17.

Kreso A, O’Brien CA, van Galen P, et al. (2013) Variable clonal repopulation dynamics influence chemotherapy response in colorectal cancer. Science 339:543–548CrossRefPubMed

18.

Beroukhim R, Mermel CH, Porter D, et al. (2010) The landscape of somatic copy-number alteration across human cancers. Nature 463:899–905CrossRefPubMedPubMedCentral

19.

Sato Y, Yoshizato T, Shiraishi Y, et al. (2013) Integrated molecular analysis of clear-cell renal cell carcinoma. Nat Genet 45(8):860–867CrossRefPubMed

20.

Forbes SA, Beare D, Boutselakis H, et al. (2017) COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res 45(D1):D777–D783. http://cancer.sanger.ac.uk. Accessed 31 Jan 2018

21.

Brugarolas J (2014) Molecular genetics of clear-cell renal cell carcinoma. J Clin Oncol 32(18):1968–1976CrossRefPubMedPubMedCentral

22.

Stebbins CE, Kaelin WG Jr, Pavletich NP (1999) Structure of the VHL-ElonginC–ElonginB complex: implications for VHL tumor suppressor function. Science 284:455–461CrossRefPubMed

23.

Maxwell PH, Wiesener MS, Chang GW, et al. (1999) The tumor suppressor protein VHL targets hypoxia-inducible factors for oxygen-dependent proteolysis. Nature 399(6733):271–275CrossRefPubMed

24.

Kim BJ, Kim JH, Kim HS, Zang DY (2017) Prognostic and predictive value of VHL gene alteration in renal cell carcinoma: a meta-analysis and review. Oncotarget 8(8):13979–13985PubMedPubMedCentral

25.

Nargund AM, Osmanbeyoglu HU, Cheng EH, et al. (2017) SWI/SNF tumor suppressor gene PBRM1/BAF180 in human clear cell kidney cancer. Mol Cell Oncol 4(4):e1342747CrossRefPubMedPubMedCentral

26.

Joseph RW, Kapur P, Serie DJ, et al. (2016) Clear cell renal cell carcinoma subtypes identified by BAP1 and PBRM1 expression. J Urol 195(1):180–187CrossRefPubMed

27.

Kim SH, Park WS, Park EY, et al. (2017) The prognostic value of BAP1, PBRM1, pS6, PTEN, TGase2, PD-L1, CA9, PSMA, and Ki-67 tissue markers in localized renal cell carcinoma: a retrospective study of tissue microarrays using immunohistochemistry. PLoS ONE 12(6):e0179610CrossRefPubMedPubMedCentral

28.

Fay AP, de Velasco G, Ho TH, et al. (2016) Whole-exome sequencing in two extreme phenotypes of response to VEGF-targeted therapies in patients with metastatic clear cell renal cell carcinoma. J Natl Compr Canc Netw 14(7):820–824CrossRefPubMedPubMedCentral

29.

Hsieh JJ, Chen D, Wang PI, et al. (2017) Genomic biomarkers of a randomized trial comparing first-line everolimus and sunitinib in patients with metastatic renal cell carcinoma. Eur Urol 71(3):405–414CrossRefPubMed

30.

Miao D, Margolis CA, Gao W, et al. (2018) Genomic correlates of response to immune checkpoint therapies in clear cell renal cell carcinoma. Science . https://doi.org/10.1126/science.aan5951 CrossRefPubMedPubMedCentral

31.

Liao L, Testa JR, Yang H (2015) The roles of chromatin-remodelers and epigenetic modifiers in kidney cancer. Cancer Genet 208(5):206–214CrossRefPubMedPubMedCentral

32.

Bielecka ZF, Czarnecka AM, Szczylik C (2014) Genomic analysis as the first step toward personalized treatment in renal cell carcinoma. Front Oncol 4:194CrossRefPubMedPubMedCentral

33.

Kapur P, Peña-Llopis S, Christie A, et al. (2013) Effects on survival of BAP1 and PBRM1 mutations in sporadic clear-cell renal cell carcinoma: a retrospective analysis with independent validation. Lancet Oncol 14(2):159–167CrossRefPubMedPubMedCentral

34.

Hakimi AA, Ostrovnaya I, Reva B, et al. (2013) Adverse outcomes in clear cell renal cell carcinoma with mutations of 3p21 epigenetic regulators BAP1 and SETD2: a report by MSKCC and the KIRC TCGA research network. Clin Cancer Res 19(12):3259–3267CrossRefPubMedPubMedCentral

35.

Ge Y-Z, Xu L-W, Zhou C-C, et al. (2017) A BAP1 mutation-specific MicroRNA signature predicts clinical outcomes in clear cell renal cell carcinoma patients with wild-type BAP1. J Cancer 8(13):2643–2652CrossRefPubMedPubMedCentral

36.

Manley BJ, Zabor EC, Casuscelli J, et al. (2016) Integration of recurrent somatic mutations with clinical outcomes: a pooled analysis of 1049 patients with clear cell renal cell carcinoma. Eur Urol Focus . https://doi.org/10.1016/j.euf.2016.06.015 CrossRefPubMedPubMedCentral

37.

Tennenbaum DM, Manley BJ, Zabor E, et al. (2017) Genomic alterations as predictors of survival among patients within a combined cohort with clear cell renal cell carcinoma undergoing cytoreductive nephrectomy. Urol Oncol 35(8):532.e7–537.e13CrossRef

38.

Liu W, Fu Q, An H, et al. (2015) Decreased expression of SETD2 predicts unfavorable prognosis in patients with nonmetastatic clear-cell renal cell carcinoma. Medicine 94(45):e2004CrossRefPubMedPubMedCentral

39.

Hoffmann I, Roatsch M, Schmitt ML, et al. (2012) The role of histone demethylases in cancer therapy. Mol Oncol 6(6):683–703CrossRefPubMedPubMedCentral

40.

Manley BJ, Reznik E, Ghanaat M, et al. (2017) Characterizing recurrent and lethal small renal masses in clear cell renal cell carcinoma using recurrent somatic mutations. Urol Oncol . https://doi.org/10.1016/j.urolonc.2017.10.012 CrossRefPubMedPubMedCentral

41.

Ho TH, Choueiri TK, Wang K, et al. (2016) Correlation between molecular subclassifications of clear cell renal cell carcinoma and targeted therapy response. Eur Urol Focus 2(2):204–209CrossRefPubMed

42.

Chaturvedi P, Singh AP, Chakraborty S, et al. (2008) MUC4 mucin interacts with and stabilizes the HER2 oncoprotein in human pancreatic cancer cells. Cancer Res 68(7):2065–2070CrossRefPubMedPubMedCentral

43.

Ponnusamy MP, Singh AP, Jain M, et al. (2008) MUC4 activates HER2 signalling and enhances the motility of human ovarian cancer cells. Br J Cancer 99(3):520–526CrossRefPubMedPubMedCentral

44.

Fu H, Liu Y, Xu L, et al. (2016) Low expression of mucin-4 predicts poor prognosis in patients with clear-cell renal cell carcinoma. Medicine 95(17):e3225CrossRefPubMedPubMedCentral

45.

Finlay CA, Hinds PW, Levine AJ (1989) The p53 proto-oncogene can act as a suppressor of transformation. Cell 57(7):1083–1093CrossRefPubMed

46.

Baker SJ, Fearon ER, Nigro JM, et al. (1989) Chromosome 17 deletions and p53 gene mutations in colorectal carcinomas. Science 244(4901):217–221CrossRefPubMed

47.

Santibáñez-Koref MF, Birch JM, Hartley AL, et al. (1991) p53 germline mutations in Li–Fraumeni syndrome. Lancet 338(8781):1490–1491CrossRefPubMed

48.

Jamshidi N, Jonasch E, Zapala M, et al. (2015) The radiogenomic risk score: construction of a prognostic quantitative, noninvasive image-based molecular assay for renal cell carcinoma. Radiology 277(1):114–123CrossRefPubMed

49.

Jamshidi N, Jonasch E, Zapala M, et al. (2016) The radiogenomic risk score stratifies outcomes in a renal cell cancer phase 2 clinical trial. Eur Radiol 26(8):2798–2807CrossRefPubMed

50.

Mazurowski MA (2015) Radiogenomics: what it is and why it is important. J Am Coll Radiol 12(8):862–866CrossRefPubMed

51.

Bai HX, Lee AM, Yang L, et al. (2016) Imaging genomics in cancer research: limitations and promises. Br J Radiol 89(1061):20151030CrossRefPubMedPubMedCentral

52.

Lawrence MS, Stojanov P, Polak P, et al. (2013) Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499:214–218CrossRefPubMedPubMedCentral

53.

O’Connor JPB, Rose CJ, Waterton JC, et al. (2014) Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clin Cancer Res 21:249–257CrossRefPubMedPubMedCentral

54.

Kwiatkowski DJ, Choueiri TK, Fay AP, et al. (2016) Mutations in TSC1, TSC2, and MTOR are associated with response to rapalogs in patients with metastatic renal cell carcinoma. Clin Cancer Res 22(10):2445–2452CrossRefPubMedPubMedCentral

55.

Gevaert O, Mitchell LA, Achrol AS, et al. (2014) Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. Radiology 273(1):168–174CrossRefPubMed

56.

Esquela-Kerscher A, Slack FJ (2006) Oncomirs—microRNAs with a role in cancer. Nat Rev Cancer 6(4):259–269CrossRefPubMed

57.

Gámez-Pozo A, Antón-Aparicio LM, Bayona C, et al. (2012) MicroRNA expression profiling of peripheral blood samples predicts resistance to first-line sunitinib in advanced renal cell carcinoma patients. Neoplasia 14:1144–1152CrossRefPubMedPubMedCentral

58.

Khella HWZ, Daniel N, Youssef L, et al. (2017) miR-10b is a prognostic marker in clear cell renal cell carcinoma. J Clin Pathol 70(10):854–859CrossRefPubMed

59.

Weiss GJ, Ganeshan B, Miles KA, et al. (2014) Noninvasive image texture analysis differentiates K-ras mutation from pan-wildtype NSCLC and is prognostic. PLoS ONE 9(7):e100244CrossRefPubMedPubMedCentral

60.

Miles KA, Ganeshan B, Hayball MP (2013) CT texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging 13(3):400–406CrossRefPubMedPubMedCentral

61.

Chee CG, Kim YH, Lee KH, et al. (2017) CT texture analysis in patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy: a potential imaging biomarker for treatment response and prognosis. PLoS ONE 12(8):e0182883CrossRefPubMedPubMedCentral

62.

Goh V, Ganeshan B, Nathan P, et al. (2011) Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker. Radiology 261(1):165–171CrossRefPubMed

63.

Lubner MG, Stabo N, Abel EJ, Del Rio AM, Pickhardt PJ (2016) CT textural analysis of large primary renal cell carcinomas: pretreatment tumor heterogeneity correlates with histologic findings and clinical outcomes. AJR 207(1):96–105CrossRefPubMed

64.

Haider MA, Vosough A, Khalvati F, et al. (2017) CT texture analysis: a potential tool for prediction of survival in patients with metastatic clear cell carcinoma treated with sunitinib. Cancer Imaging 17:4CrossRefPubMedPubMedCentral

65.

Rios Velazquez E, Parmar C, Liu Y, et al. (2017) Somatic mutations drive distinct imaging phenotypes in lung cancer. Cancer Res 77(14):3922–3930CrossRefPubMed

66.

Yin Q, Hung S-C, Wang L, et al. (2017) Associations between tumor vascularity, vascular endothelial growth factor expression and PET/MRI radiomic signatures in primary clear-cell–renal-cell-carcinoma: proof-of-concept study. Sci Rep 7:43356CrossRefPubMedPubMedCentral

67.

Aerts HJWL (2017) Data science in radiology: a path forward. Clin Cancer Res . https://doi.org/10.1158/1078-0432.CCR-17-2804 CrossRefPubMedPubMedCentral

68.

Aerts HJWL, Grossmann P, Tan Y, et al. (2016) Defining a radiomic response phenotype: a pilot study using targeted therapy in NSCLC. Sci Rep 6:33860CrossRefPubMedPubMedCentral

69.

Li Z, Wang Y, Yu J, Guo Y, Cao W (2017) Deep learning based radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma. Sci Rep 7:5467CrossRefPubMedPubMedCentral

70.

Aerts HJ (2016) The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol 2(12):1636–1642CrossRefPubMed

71.

Aerts HJ, Velazquez ER, Leijenaar R, et al. (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 3(5):4006CrossRef

72.

Weinstein JN, Collisson EA, Mills GB, et al. (2013) The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45(10):1113–1120CrossRefPubMedPubMedCentral

73.

Clark K, Vendt B, Smith K, et al. (2013) The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26(6):1045–1057CrossRefPubMedPubMedCentral

Title: Radiogenomics in renal cell carcinoma
Authors: Francesco Alessandrino
Atul B. Shinagare
Dominick Bossé
Toni K. Choueiri
Katherine M. Krajewski
Publication date: 01-06-2019
Publisher: Springer US
Published in: Abdominal Radiology / Issue 6/2019
Print ISSN: 2366-004X
Electronic ISSN: 2366-0058
DOI: https://doi.org/10.1007/s00261-018-1624-y

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