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Decoding global gene expression programs in liver cancer by noninvasive imaging


Paralleling the diversity of genetic and protein activities, pathologic human tissues also exhibit diverse radiographic features. Here we show that dynamic imaging traits in non-invasive computed tomography (CT) systematically correlate with the global gene expression programs of primary human liver cancer. Combinations of twenty-eight imaging traits can reconstruct 78% of the global gene expression profiles, revealing cell proliferation, liver synthetic function, and patient prognosis. Thus, genomic activity of human liver cancers can be decoded by noninvasive imaging, thereby enabling noninvasive, serial and frequent molecular profiling for personalized medicine.

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Figure 1: Linking imaging traits and global gene expression.
Figure 2: An association map of imaging traits and global gene expression.
Figure 3: Molecular portraits of HCC from imaging traits.
Figure 4: Imaging traits predict venous invasion and survival.


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Supported by grants from UC San Diego (M.D.K.), the Israel Science Foundation (E.S.), and the National Institutes of Health (X.C., H.Y.C.). E.S. is the incumbent of the Soretta and Henry Shapiro career development chair. H.Y.C. is the Kenneth G. and Elaine A. Langone Scholar of the Damon Runyon Cancer Research Foundation. M.D.K. is the Bracco Diagnostics Research Scholar of the Radiological Society of North America.

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E.S., H.Y.C. and M.D.K. conceived of the project, analyzed the data and wrote the paper. All other authors provided data or analysis tools.

Corresponding authors

Correspondence to Howard Y Chang or Michael D Kuo.

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Competing interests

E.S., H.Y.C. and M.D.K. have filed a provisional patent [AU: Correct?] application for this work.

Supplementary information

Supplementary Table 1

Imaging trait name and definition. (XLS 3678 kb)

Supplementary Table 2

Modules and imaging trait splits. (PDF 21 kb)

Supplementary Table 3

Venous invasion module analysis. (XLS 16 kb)

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Segal, E., Sirlin, C., Ooi, C. et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol 25, 675–680 (2007).

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