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

PurposeTo investigate associations between imaging features and mutational status of clear cell renal cell carcinoma (ccRCC).Materials and methodsThis 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.ResultsMedian 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 χ2 test); MUC4 mutation was associated with an exophytic growth pattern (p = 0.002, Mann–Whitney U test).ConclusionsBAP1 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.

[1]  L. A. Swiger,et al.  THE VARIANCE OF INTRACLASS CORRELATION INVOLVING GROUPS WITH ONE OBSERVATION , 1964 .

[2]  Klaus Krippendorff,et al.  Estimating the Reliability, Systematic Error and Random Error of Interval Data , 1970 .

[3]  Klaus Krippendorff,et al.  Content Analysis: An Introduction to Its Methodology , 1980 .

[4]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[5]  D. Freedman,et al.  Asymptotic Normality and the Bootstrap in Stratified Sampling , 1984 .

[6]  J. Ro,et al.  Sarcomatoid renal cell carcinoma: Clinicopathologic. A study of 42 cases , 1987, Cancer.

[7]  N. Dubrawsky Cancer statistics , 1989, CA: a cancer journal for clinicians.

[8]  Y Kubota,et al.  Frequent somatic mutations and loss of heterozygosity of the von Hippel-Lindau tumor suppressor gene in primary human renal cell carcinomas. , 1994, Cancer research.

[9]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[10]  K. McGraw,et al.  Forming inferences about some intraclass correlation coefficients. , 1996 .

[11]  S. Kunte,et al.  Statistical computing , 1999 .

[12]  W. Kaelin,et al.  Role of VHL gene mutation in human cancer. , 2004, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[13]  J. Lam,et al.  Renal cell carcinoma 2005: new frontiers in staging, prognostication and targeted molecular therapy. , 2005, The Journal of urology.

[14]  Ralph V Clayman,et al.  Laparoscopic partial nephrectomy for renal masses: effect of tumor location. , 2006, Urology.

[15]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[16]  C. Pipper,et al.  [''R"--project for statistical computing]. , 2008, Ugeskrift for laeger.

[17]  Robert G Uzzo,et al.  The R.E.N.A.L. nephrometry score: a comprehensive standardized system for quantitating renal tumor size, location and depth. , 2009, The Journal of urology.

[18]  Gurpreet W. Tang,et al.  Systematic sequencing of renal carcinoma reveals inactivation of histone modifying genes , 2009, Nature.

[19]  Paul Russo,et al.  Commentary on The R.E.N.A.L. nephrometry score: A comprehensive standardized system for quantitating renal tumor size, location and depth , 2010 .

[20]  Gerben Duns,et al.  Histone methyltransferase gene SETD2 is a novel tumor suppressor gene in clear cell renal cell carcinoma. , 2010, Cancer research.

[21]  P. A. Futreal,et al.  Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma , 2010, Nature.

[22]  A. D. Van den Abbeele,et al.  Hereditary cancer syndromes: a radiologist's perspective. , 2011, AJR. American journal of roentgenology.

[23]  Pattanasak Mongkolwat,et al.  Informatics in radiology: An open-source and open-access cancer biomedical informatics grid annotation and image markup template builder. , 2012, Radiographics : a review publication of the Radiological Society of North America, Inc.

[24]  Huanming Yang,et al.  Frequent mutations of genes encoding ubiquitin-mediated proteolysis pathway components in clear cell renal cell carcinoma , 2011, Nature Genetics.

[25]  P. A. Futreal,et al.  Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. , 2012, The New England journal of medicine.

[26]  N. Grishin,et al.  BAP1 loss defines a new class of renal cell carcinoma , 2012, Nature Genetics.

[27]  Steven J. M. Jones,et al.  Comprehensive molecular characterization of clear cell renal cell carcinoma , 2013, Nature.

[28]  P. Kapur,et al.  Effects on survival of BAP1 and PBRM1 mutations in sporadic clear-cell renal-cell carcinoma: a retrospective analysis with independent validation. , 2013, The Lancet. Oncology.

[29]  T. Tsuzuki,et al.  Growth pattern, an important pathologic prognostic parameter for clear cell renal cell carcinoma. , 2013, American journal of clinical pathology.

[30]  Han Liu,et al.  Clinical and pathologic impact of select chromatin-modulating tumor suppressors in clear cell renal cell carcinoma. , 2013, European urology.

[31]  The Cancer Genome Atlas Research Network COMPREHENSIVE MOLECULAR CHARACTERIZATION OF CLEAR CELL RENAL CELL CARCINOMA , 2013, Nature.

[32]  M. Kuo,et al.  Behind the numbers: Decoding molecular phenotypes with radiogenomics--guiding principles and technical considerations. , 2014, Radiology.

[33]  H. Hricak,et al.  Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations. , 2013, Radiology.

[34]  J. Brugarolas Molecular genetics of clear-cell renal cell carcinoma. , 2014, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[35]  Daniel L. Rubin,et al.  The National Cancer Informatics Program (NCIP) Annotation and Image Markup (AIM) Foundation Model , 2014, Journal of Digital Imaging.

[36]  R. Motzer,et al.  The impact of genetic heterogeneity on biomarker development in kidney cancer assessed by multiregional sampling , 2014, Cancer medicine.

[37]  A. Jemal,et al.  Cancer statistics, 2015 , 2015, CA: a cancer journal for clinicians.