Radiogenomics Analysis Linking Multiparametric MRI and Transcriptomics in Prostate Cancer

Catarina Dinis Fernandes, Annekoos Schaap, Joan Kant, Petra van Houdt, Hessel Wijkstra, Elise Bekers, Simon Linder, Andries M. Bergman, Uulke van der Heide, Massimo Mischi, Wilbert Zwart (Corresponding author), Federica Eduati (Corresponding author), Simona Turco (Corresponding author)

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Abstract

Prostate cancer (PCa) is a highly prevalent cancer type with a heterogeneous prognosis. An accurate assessment of tumor aggressiveness can pave the way for tailored treatment strategies, potentially leading to better outcomes. While tumor aggressiveness is typically assessed based on invasive methods (e.g., biopsy), radiogenomics, combining diagnostic imaging with genomic information can help uncover aggressive (imaging) phenotypes, which in turn can provide non-invasive advice on individualized treatment regimens. In this study, we carried out a parallel analysis on both imaging and transcriptomics data in order to identify features associated with clinically significant PCa (defined as an ISUP grade ≥ 3), subsequently evaluating the correlation between them. Textural imaging features were extracted from multi-parametric MRI sequences (T2W, DWI, and DCE) and combined with DCE-derived parametric pharmacokinetic maps obtained using magnetic resonance dispersion imaging (MRDI). A transcriptomic analysis was performed to derive functional features on transcription factors (TFs), and pathway activity from RNA sequencing data, here referred to as transcriptomic features. For both the imaging and transcriptomic features, different machine learning models were separately trained and optimized to classify tumors in either clinically insignificant or significant PCa. These models were validated in an independent cohort and model performance was used to isolate a subset of relevant imaging and transcriptomic features to be further investigated. A final set of 31 imaging features was correlated to 33 transcriptomic features obtained on the same tumors. Five significant correlations (p < 0.05) were found, of which, three had moderate strength (|r| ≥ 0.5). The strongest significant correlations were seen between a perfusion-based imaging feature—MRDI A median—and the activities of the TFs STAT6 (−0.64) and TFAP2A (−0.50). A higher-order T2W textural feature was also significantly correlated to the activity of the TF STAT6 (−0.58). STAT6 plays an important role in controlling cell proliferation and migration. Loss of the AP2alpha protein expression, quantified by TFAP2A, has been strongly associated with aggressiveness and progression in PCa. According to our findings, a combination of texture features extracted from T2W and DCE, as well as perfusion-based pharmacokinetic features, can be considered for the prediction of clinically significant PCa, with the pharmacokinetic MRDI A feature being the most correlated with the underlying transcriptomic information. These results highlight a link between quantitative imaging features and the underlying transcriptomic landscape of prostate tumors.

Original languageEnglish
Article number3074
Number of pages21
JournalCancers
Volume15
Issue number12
DOIs
Publication statusPublished - 2 Jun 2023

Bibliographical note

Funding Information:
This research was funded by the Hanarth Fonds fellowship for AI in Oncology, granted to S.T. in 2019.

Publisher Copyright:
© 2023 by the authors.

Funding

This research was funded by the Hanarth Fonds fellowship for AI in Oncology, granted to S.T. in 2019.

FundersFunder number
Hanarth Fonds fellowship

    Keywords

    • machine learning
    • magnetic resonance imaging
    • prostate cancer
    • radiogenomics

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