Abdominal Imaging

, Volume 40, Issue 6, pp 1684–1692 | Cite as

Radiogenomics of clear cell renal cell carcinoma: preliminary findings of The Cancer Genome Atlas–Renal Cell Carcinoma (TCGA–RCC) Imaging Research Group

  • Atul B. ShinagareEmail author
  • Raghu Vikram
  • Carl Jaffe
  • Oguz Akin
  • Justin Kirby
  • Erich Huang
  • John Freymann
  • Nisha I. Sainani
  • Cheryl A. Sadow
  • Tharakeswara K. Bathala
  • Daniel L. Rubin
  • Aytekin Oto
  • Matthew T. Heller
  • Venkateswar R. Surabhi
  • Venkat Katabathina
  • Stuart G. Silverman



To investigate associations between imaging features and mutational status of clear cell renal cell carcinoma (ccRCC).

Materials and methods

This multi-institutional, multi-reader study included 103 patients (77 men; median age 59 years, range 34–79) with ccRCC examined with CT in 81 patients, MRI in 19, and both CT and MRI in three; images were downloaded from The Cancer Imaging Archive, an NCI-funded project for genome-mapping and analyses. Imaging features [size (mm), margin (well-defined or ill-defined), composition (solid or cystic), necrosis (for solid tumors: 0%, 1%–33%, 34%–66% or >66%), growth pattern (endophytic, <50% exophytic, or ≥50% exophytic), and calcification (present, absent, or indeterminate)] were reviewed independently by three readers blinded to mutational data. The association of imaging features with mutational status (VHL, BAP1, PBRM1, SETD2, KDM5C, and MUC4) was assessed.


Median tumor size was 49 mm (range 14–162 mm), 73 (71%) tumors had well-defined margins, 98 (95%) tumors were solid, 95 (92%) showed presence of necrosis, 46 (45%) had ≥50% exophytic component, and 18 (19.8%) had calcification. VHL (n = 52) and PBRM1 (n = 24) were the most common mutations. BAP1 mutation was associated with ill-defined margin and presence of calcification (p = 0.02 and 0.002, respectively, Pearson’s χ 2 test); MUC4 mutation was associated with an exophytic growth pattern (p = 0.002, Mann–Whitney U test).


BAP1 mutation was associated with ill-defined tumor margins and presence of calcification; MUC4 mutation was associated with exophytic growth. Given the known prognostic implications of BAP1 and MUC4 mutations, these results support using radiogenomics to aid in prognostication and management.


Clear cell renal cell carcinoma CT MRI Mutational status Radiogenomics 



Image data used in this research were obtained from The Cancer Imaging Archive ( sponsored by the Cancer Imaging Program, Division of Cancer Treatment and Diagnosis (DCTD)/National Cancer Institute (NCI)/National Institutes of Health (NIH). This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. We thank Alessandro Furlan, MD for participating as a study reader. We thank Ms. Brenda Fevrier-Sullivan from NCI for administrative support.


  1. 1.
    Siegel RL, Miller KD, Jemal A (2015) Cancer statistics, 2015. CA Cancer J Clin 65(1):5–29PubMedCrossRefGoogle Scholar
  2. 2.
    Lam JS, Shvarts O, Leppert JT, Figlin RA, Belldegrun AS (2005) Renal cell carcinoma 2005: new frontiers in staging, prognostication and targeted molecular therapy. J Urol 173(6):1853–1862PubMedCrossRefGoogle Scholar
  3. 3.
    Gerlinger M, Rowan AJ, Horswell S, et al. (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366(10):883–892PubMedCrossRefGoogle Scholar
  4. 4.
    Cancer Genome Atlas Research Network (2013) Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499(7456):43–49CrossRefGoogle Scholar
  5. 5.
    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):751–759PubMedCentralPubMedCrossRefGoogle Scholar
  6. 6.
    Dalgliesh GL, Furge K, Greenman C, et al. (2010) Systematic sequencing of renal carcinoma reveals inactivation of histone modifying genes. Nature 463(7279):360–363PubMedCentralPubMedCrossRefGoogle Scholar
  7. 7.
    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):539–542PubMedCentralPubMedCrossRefGoogle Scholar
  8. 8.
    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):17–19CrossRefGoogle Scholar
  9. 9.
    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):4287–4291PubMedCrossRefGoogle Scholar
  10. 10.
    Shinagare AB, Giardino AA, Jagannathan JP, Van den Abbeele AD, Ramaiya NH (2011) Hereditary cancer syndromes: a radiologist’s perspective. Am J Roentgenol 197(6):W1001–W1007CrossRefGoogle Scholar
  11. 11.
    Kim WY, Kaelin WG (2004) Role of VHL gene mutation in human cancer. J Clin Oncol 22(24):4991–5004PubMedCrossRefGoogle Scholar
  12. 12.
    Shuin T, Kondo K, Torigoe S, et al. (1994) Frequent somatic mutations and loss of heterozygosity of the von Hippel-Lindau tumor suppressor gene in primary human renal cell carcinomas. Cancer Res 54(11):2852–2855PubMedGoogle Scholar
  13. 13.
    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):159–167PubMedCrossRefGoogle Scholar
  14. 14.
    Hakimi AA, Chen Y-B, Wren J, et al. (2013) Clinical and pathologic impact of select chromatin-modulating tumor suppressors in clear cell renal cell carcinoma. Eur Urol 63(5):848–854PubMedCentralPubMedCrossRefGoogle Scholar
  15. 15.
  16. 16.
    Brugarolas J (2014) Molecular genetics of clear-cell renal cell carcinoma. J Clin Oncol 32(18):1968–1976PubMedCentralPubMedCrossRefGoogle Scholar
  17. 17.
    Kuo MD, Jamshidi N (2014) Behind the numbers: decoding molecular phenotypes with radiogenomics—guiding principles and technical considerations. Radiology 270(2):320–325PubMedCrossRefGoogle Scholar
  18. 18.
    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):464–471PubMedCentralPubMedCrossRefGoogle Scholar
  19. 19.
    CIP TCGA Radiology Initiative—The Cancer Imaging Archive (TCIA) Public Access—Cancer Imaging Archive Wiki [Internet].;jsessionid=92ACC1CC632A2219F9A6F965E2325B68. Accessed 23 Sep 2014
  20. 20.
    Software Tools: Department of Radiology: Feinberg School of Medicine: Northwestern University [Internet]. Accessed 18 Sep 2014
  21. 21.
    Mongkolwat P, Kleper V, Talbot S, Rubin D (2014) The National Cancer Informatics Program (NCIP) Annotation and Image Markup (AIM) Foundation Model. J Digit Imaging. 27:692–701PubMedCrossRefGoogle Scholar
  22. 22.
    Mongkolwat P, Channin DS, Kleper V, Rubin DL (2012) Informatics in radiology: an open-source and open-access cancer biomedical informatics grid annotation and image markup template builder. Radiographics 32(4):1223–1232PubMedCentralPubMedCrossRefGoogle Scholar
  23. 23.
    McGraw KO, Wong SP (1996) Forming inferences about some intraclass correlation coefficients. Psychol Methods 1(1):30–46CrossRefGoogle Scholar
  24. 24.
    Swiger LA, Harvey WR, Everson DO, Gregory KE (1964) The variance of intraclass correlation involving groups with one observation. Biometrics 20:818–826CrossRefGoogle Scholar
  25. 25.
    Krippendorff K (1970) Estimating the reliability, systematic error, and random error of interval data. Educ Psychol Meas 30:61–70CrossRefGoogle Scholar
  26. 26.
    Krippendorff K (2004) Content analysis: an introduction to its methodology, 2nd edn. Sage: Thousand OaksGoogle Scholar
  27. 27.
    Efron B, Tibshirani RJ (1998) An Introduction to the Bootstrap. Boca Raton: Chapman and Hall/CRCGoogle Scholar
  28. 28.
    Efron B, Tibshirani RJ (1993) An Introduction to the Bootstrap. London: Chapman and HallCrossRefGoogle Scholar
  29. 29.
    Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29–36PubMedCrossRefGoogle Scholar
  30. 30.
    Bickel PJ, Freedman DA (1984) Asymptotic normality and the Bootstrap in stratified sampling. Ann Statist 12(2):470–482CrossRefGoogle Scholar
  31. 31.
    Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57(1):289–300Google Scholar
  32. 32.
    R Project for Statistical Computing. R (Version 3.1.1, 2014). Accessed 23 Sep 2014
  33. 33.
    Fukatsu A, Tsuzuki T, Sassa N, et al. (2013) Growth pattern, an important pathologic prognostic parameter for clear cell renal cell carcinoma. Am J Clin Pathol 140(4):500–505PubMedCrossRefGoogle Scholar
  34. 34.
    Ro JY, Ayala AG, Sella A, Samuels ML, Swanson DA (1987) Sarcomatoid renal cell carcinoma: clinicopathologic. A study of 42 cases. Cancer 59(3):516–526PubMedCrossRefGoogle Scholar
  35. 35.
    Venkatesh R, Weld K, Ames CD, et al. (2006) Laparoscopic partial nephrectomy for renal masses: effect of tumor location. Urology 67(6):1169–1174 (discussion 1174)PubMedCrossRefGoogle Scholar
  36. 36.
    Kutikov A, Uzzo RG (2009) The R.E.N.A.L. nephrometry score: a comprehensive standardized system for quantitating renal tumor size, location and depth. J Urol 182(3):844–853PubMedCrossRefGoogle Scholar
  37. 37.
    Sankin A, Hakimi AA, Mikkilineni N, et al. (2014) The impact of genetic heterogeneity on biomarker development in kidney cancer assessed by multiregional sampling. Cancer Med 3:1485–1492PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Atul B. Shinagare
    • 1
    Email author
  • Raghu Vikram
    • 2
  • Carl Jaffe
    • 3
  • Oguz Akin
    • 4
  • Justin Kirby
    • 5
  • Erich Huang
    • 5
  • John Freymann
    • 5
  • Nisha I. Sainani
    • 1
  • Cheryl A. Sadow
    • 1
  • Tharakeswara K. Bathala
    • 2
  • Daniel L. Rubin
    • 6
  • Aytekin Oto
    • 7
  • Matthew T. Heller
    • 8
  • Venkateswar R. Surabhi
    • 9
  • Venkat Katabathina
    • 10
  • Stuart G. Silverman
    • 1
  1. 1.Brigham and Women’s HospitalBostonUSA
  2. 2.University of Texas MD Anderson Cancer CenterHoustonUSA
  3. 3.Boston Medical CenterBostonUSA
  4. 4.Memorial Sloan Kettering Cancer CenterNew YorkUSA
  5. 5.National Cancer InstituteBethesdaUSA
  6. 6.Stanford University School of MedicineStanfordUSA
  7. 7.University of ChicagoChicagoUSA
  8. 8.University of Pittsburgh Medical CenterPittsburghUSA
  9. 9.University of Texas Health Science CenterHoustonUSA
  10. 10.University of Texas Health Science CenterSan AntonioUSA

Personalised recommendations