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The Radiogenomic Risk Score: Construction of a Prognostic Quantitative, Noninvasive Image-based Molecular Assay for
Neema Jamshidi1, Eric Jonasch1, Matthew Zapala1
1From the Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Box 951721, CHS 17-135, 10833 LeConte Ave, Los Angeles, CA 90095-1721 (N.J., M.Z., S.B., M.D.K.); Department of Genitourinary Medical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, Tex (E.J.); Department of Radiology, Hospital of Veterans Affairs, University of California-San Diego, San Diego, Calif (M.Z., L.A.); Scottsdale Medical Imaging, Scottsdale, Ariz (R.K.); Department of Urology, Stanford University School of Medicine, Stanford, Calif (H.Z., J.D.B.); Department of Surgical and Perioperative Sciences, Urology and Andrology, Umea Hospital, Umea, Sweden (R.T.S., B.L.); and Department of Statistics, Stanford University, Stanford, Calif (R.J.T.).
Purpose:
To evaluate the feasibility of constructing radiogenomic-based surrogates of molecular assays (SOMAs) in patients with clear-cell renal cell carcinoma (CCRCC) by using data extracted from a single computed tomographic (CT) image.
Materials And Methods:
In this institutional review board approved study, gene expression profile data and contrast material-enhanced CT images from 70 patients with CCRCC in a training set were independently assessed by two radiologists for a set of predefined imaging features. A SOMA for a previously validated CCRCC-specific supervised principal component (SPC) risk score prognostic gene signature was constructed and termed the radiogenomic risk score (RRS). It uses the microarray data and a 28-trait image array to evaluate each CT image with multiple regression of gene expression analysis. The predictive power of the RRS SOMA was then prospectively validated in an independent dataset to confirm its relationship to the SPC gene signature (n = 70) and determination of patient outcome (n = 77). Data were analyzed by using multivariate linear regression-based methods and Cox regression modeling, and significance was assessed with receiver operator characteristic curves and Kaplan-Meier survival analysis.
Results:
Our SOMA faithfully represents the tissue-based molecular assay it models. The RRS scaled with the SPC gene signature (R = 0.57, P < .001, classification accuracy 70.1%, P < .001) and predicted disease-specific survival (log rank P < .001). Independent validation confirmed the relationship between the RRS and the SPC gene signature (R = 0.45, P < .001, classification accuracy 68.6%, P < .001) and disease-specific survival (log-rank P < .001) and that it was independent of stage, grade, and performance status (multivariate Cox model P < .05, log-rank P < .001).
Conclusion:
A SOMA for the CCRCC-specific SPC prognostic gene signature that is predictive of disease-specific survival and independent of stage was constructed and validated, confirming that SOMA construction is feasible.
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