Lecture dedicated to radiogenomics of renal cell carcinoma by Ph.d., MD, Mytsyk Yulian, Department of Urology/Radiology, Lviv National Medical University, presented at IV Ukrainian-Polish Conference on Radiology 2018, Rzeszów

1. RADIOGENOMICS OF RENAL CELL
CARCINOMA
DANYLO HALYTSKY LVIV NATIONAL MEDICAL UNIVERSITY
DEPARTMRNT OF RADIOLOGY/UROLOGY
MEDICAL CENTER “EUROCLINIC”
LVIV, UKRAINE
Doc. Mycyk Julian

2. “Intratumor heterogeneity can lead to underestimation of the tumor genomics
landscape portrayed from single tumor-biopsy samples and may present major
challenges to personalized medicine and biomarker development.”
New England Journal of Medicine 366;10 nejm.org march 8, 2012
2

3. Recession Approaching
More precise approach to treatment
3

4. Imaging data
4

5. 5

6. Since the turn of the twentieth century, radiological images have been used
to diagnose disease on a large scale, because tissue imaging correlates with
tissue pathology.
RAD-PATH 1.0 PARADIGM
Kuo, Michael D., and Shota Yamamoto. “Next Generation Radiologic-Pathologic Correlation in Oncology: Rad-Path 2.0.” AJR. American Journal of
Roentgenology 197, no. 4 (October 2011): 990–97. https://doi.org/10.2214/AJR.11.7163.
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7. The addition of genomic data in the last twenty years, including
• DNA microarrays
• miRNA
• RNA-Seq
allows new correlations to be made between cellular genomics and tissue-
scale imaging.
RAD-PATH 2.0 PARADIGM
Kuo, Michael D., and Shota Yamamoto. “Next Generation Radiologic-Pathologic Correlation in Oncology: Rad-Path 2.0.” AJR. American Journal of
Roentgenology 197, no. 4 (October 2011): 990–97. https://doi.org/10.2214/AJR.11.7163.
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8. Еvolving to a Rad-Path 2.0 paradigm would entail realigning current radiologic-
histopathologic correlation basis to
- radiologic-molecular or
- radiologic-genomic correlation.
Rad-Path 2.0 will be about finding
- ways to extend correlation of the information already obtained from existing
clinical imaging modalities, such as
- CT
- MRI
- PET
beyond conventional
histopathology to also include
large-scale molecular or genomic
information.
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9. Different radiology-pathology (Rad-Path) paradigms
Kuo, Michael D., and Shota Yamamoto. “Next Generation Radiologic-Pathologic Correlation in Oncology: Rad-Path 2.0.” AJR. American Journal of
Roentgenology 197, no. 4 (October 2011): 990–97. https://doi.org/10.2214/AJR.11.7163.
9

10. RADIOGENOMICS
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11. 11
Its correlation between cancer
imaging features and gene
expression1
What is
Radiogenomics?
Applications
1. Barnett GC, Elliott RM, Alsner J, Andreassen CN, Abdelhay O, Burnet NG, Chang-Claude J, Coles CE, Gutiérrez-Enríquez S, Fuentes-Raspall MJ, Alonso-Muñoz MC,
Kerns S, Raabe A, Symonds RP, Seibold P, Talbot CJ, Wenz F, Wilkinson J, Yarnold J, Dunning AM, Rosenstein BS, West CM, Bentzen SM (2012). "Individual patient
data meta-analysis shows no association between the SNP rs1800469 in TGFB and late radiotherapy toxicity.". Radioth Oncol.
2. Rutman, Aaron M.; Kuo, Michael D. (2009). "Radiogenomics: Creating a link between molecular diagnostics and diagnostic imaging". European Journal of
Radiology.
• Radiogenomics can be used to
create imaging biomarkers that can
identify the genomics of a disease.
• Especially in cancer without the
use of a biopsy, prognosis,
prediction of the response to the
treatment of the disease2

12. Radiogenomics
CT
MRI
USG
Genetics (gene
expression)
Unique radiogenomic profile
12

13. IMAGING FEATURES NEEDED TO CREATE
RADIOGENOMIC MAPS
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14. 14

15. APPLICATIONS OF
RADIOGENOMICS
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16. 16

17. RADIOGENOMICS IN CANCER
Radiogenomics in prostate cancer
Evolving research field to
establish a bridge between
diagnostic imaging and the
underlying gene expression
patterns
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18. 18

19. RADIOGENOMICS
IN RCC
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20. RENAL CELL CARCINOMA
Renal cell carcinoma (RCC) represents about 3% of total oncologic pathology1
1. Jemal A, Siegel R, Ward E, et al. Cancer statistics, 2009. CA: A Cancer Journal for Clinicians 2009;59(4):225–49.
2. Howlader N, Noone AM, Krapcho M, Garshell J, Miller D, Altekruse SF, Kosary CL, Yu M, Ruhl J, Tatalovich Z,Mariotto A, Lewis DR, Chen HS, Feuer EJ, Cronin KA (eds).
SEER Cancer Statistics Review, 1975-2011, National Cancer Institute.
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21. Radiology. 2011 Dec;261(3):854-62. doi: 10.1148/radiol.11101508. Epub 2011 Oct 24.
• On unenhanced scans, 7% of ccRCCs with
the loss of chromosome 3p were calcified,
• whereas 37% of lesions without this anomaly were
сalcified (odds ratio, 0.13)
• During the corticomedullary phase, ccRCCs with the loss
of chromosome Y enhanced more than those without this
anomaly (130.0 vs 102.5 HU, P = .04)
• ccRCCs with trisomy 7 enhanced less than
those without this anomaly (105.8 vs 139.3 HU, P = .04).
• During the excretory phase, ccRCCs with trisomy 5
enhanced more than those without this anomaly
(115.5 vs 83.4 HU, P = .03).
Conclusion: The genetic makeup of
ccRCCs affects their imaging
features at multidetector CT
examinations. Multidetector CT
imaging characteristics may help
suggest differences at the
cytogenetic level among ccRCCs.
n=58
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22. Radiology. 2014 Feb;270(2):464-71. doi: 10.1148/radiol.13130663. Epub 2013 Oct 28.
n=233
CT-features:
A - Necrosis.
B - Renal vein invasion.
C - Ill-defined margin.
D - Nodular enhancement,
E - Calcifications.
F - Multicystic architecture.
G - Collecting system invasion.
H - Gross appearance of intratumoral
vasculature.
Mutations in
genes:
VHL
PBRM1
SETD2
BAP1
KDM5C
• Mutations of VHL were significantly associated with well-
defined tumor margins (P = .013), nodular tumor enhancement
(P = .021), and gross appearance of intratumoral vascularity (P
=.018).
• Mutations of KDM5C and BAP1 were significantly associated
with evidence of renal vein invasion (P = .022 and .046,
respectively).
• Mutations of SETD2, KDM5C, and BAP1 were absent in
multicystic clear cell RCC.
• Mutations of VHL (P = .016) and PBRM1 (P = .017) were
significantly more common among solid clear cell RCC.22

23. Subject-based color map illustrates mutations and CT features
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24. n=72
Use of volumetric contrast-enhance CT to correlate
primary RCC vascularity with IMP3 expression in the
tumor
• IMP3 expression positively correlated with
CT vascular enhancement (p<0.01).
• IMP3 expression by IHC was strongly positive
in all RCC, but weak in PC bone metastases.
• Real time RT-PCR demonstrated a significant
4-fold increase in IMP-3 expression in RCC vs.
PC cells in vitro (p<0.001).
Conclusion: Quantitation of pre-operative CT
is a feasible method to phenotype primary
RCC vascularity, which correlates with IMP-3
expression.
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25. Radiology. 2015 Oct;277(1):114-23. doi: 10.1148/radiol.2015150800. Epub 2015 Aug 19.
n=70
Diagram shows the
three-step process
for constructing an
Radiogenomic Risk
Score Predictor
• The RRS scaled with the RCC gene
signature classification accuracy was
70.1% and predicted disease-specific
survival (log rank P < .001).
• Independent validation confirmed the
relationship between the RRS and
the RCC gene signature
• RRS was independent of stage,
grade, and performance status
(multivariate Cox model P < .05, log-
rank P < .001).
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26. Definition of the Radiogenomic Risk Score phenotype.
CT images show the four imaging traits that constitute the RRS, with examples of
high (red) and low (green) scores for each imaging feature, and the composite
high- (yellow) and low- (blue) risk score phenotype.
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27. OUR DATA
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• Melanoma
• Glioma
• Prostate cancer
• Breast cancer
• CLL
• Nasopharyngeal carcinoma
MicroRNA-15a

29. MicroRNA-15a
P <0.001
92,31%
54,8%
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30. Investigation was based on retrospective study that enrolled 64
patients with clear cell RCC (ccRCC) after radical nephrectomy
Six imaging parameters of CT or MRI were analyzed
(size, necrosis, exophytic growth >50%, nodular
enhancement, cystic component, macroscopic fat)
qPCR of miRna-15a expression in paraffin-embedded
tumor samples was executed (TaqMan MicroRNA
Assays (Applied Biosystems, USA).
Analysis of associations between miRna-15a expression,
RCC’s imaging qualitative features was accomplished
(www.r-project.org).
30

31. 29,69
70,31
23,44
76,56
32,81
67,19
20,31
79,69
37,5
62,5
0,35
2,01
0,34
1,88
4,01
0,82
0,29
1,83
2,91
0,68
-1
0
1
2
3
4
5
6
0
10
20
30
40
50
60
70
80
90
miRNA-15аexpression
%incidenceoffeature
Imaging feature of the TCC 31

32. PREDICTION OF MIR-15A EXPRESSION
• Among all imaging features, RCCs size had strongest relation with
miR-15a expression
• Pearson correlation coefficient = 0.724 (р<0.001)
• Using only size of the tumor for miR-15a expression prediction:
• R² = 0.8281 (р<0.001)
• 82.81% of miR-15a expression can be explained with RCC size
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33. RCC imaging feature Coefficient Std. error t Pr(>|t|)
Constant 1.5798 1.9440 0.813 0.419852
Log(tumor size), DF 1 -8.4927 2.1705 -3.913 0.000249
Log(tumor size), DF 2 3.1016 0.5923 5.236 2.55e-06
Absence of cystic component 0.3172 0.2688 1.180 0.243005
Presence of exophytic growth
≥50% outside kidney margin
-0.3382 0.2849 -1.187 0.240242
Presence of tumor necrosis 0.4085 0.3242 1.260 0.212925
Presence of macroscopic fat
within tumor
-0.2048 0.3150 -0.650 0.498233
Presence of nodular contrast
encancement
0.3326 0.2854 1.165 0.248904
POLINOMIAL REGRESSION MODEL
• Using model for miR-15a expression prediction:
• R² = 0.8336 (р<0.001)
• 83.36% of miR-15a expression can be explained with model 33

34. log(miR-15a) = 1,5798 – 8,4927 × log(tumor size) + 8,4927 × log(tumor size)2 + 0,3172 × cystic component (no=1,
yes=0) – 0,3382 × exophytic growth (yes=1, no=0) + 0,4085 × necrosis (yes=1, no=0) – 0,2048 × macroscopic fat
(yes=1, no=0) + 0,3326 × nodular enhancement (yes=1, no=0)
RCC imaging feature Coefficient
Constant 1.5798
Log(tumor size), DF 1 -8.4927
Log(tumor size), DF 2 3.1016
Absence of cystic component 0.3172
Presence of exophytic growth
≥50% outside kidney margin
-0.3382
Presence of tumor necrosis 0.4085
Presence of macroscopic fat within
tumor
-0.2048
Presence of nodular contrast
encancement
0.3326
POLINOMIAL REGRESSION MODEL FORMULA:
Visual scale of the model
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35. CONCLUSIONS
• The genetic makeup of ccRCCs affects their imaging features on CT and
MRI examinations.
• Combination of RCC imaging features can predict miR-15a expression
level and thus can be used as surrogate prognostic biomarker of cancer-
specific survival.
• The use of non-invasive imaging as a surrogate for gene expression
profiling of solid tumors provides a fast and repeatable method that is
non-invasive and provides a potential replacement for high-risk invasive
biopsy procedures and subsequent histologic examination.
• In this fashion, radiogenomics could allow for more accurate diagnosis
and prognostication, in addition to shaping the clinical decisions in
regards to the form and extent of treatment.
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