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Translational Therapeutics

Radiogenomics for predicting p53 status, PD-L1 expression, and prognosis with machine learning in pancreatic cancer

Abstract

Background

Radiogenomics is an emerging field that integrates “Radiomics” and “Genomics”. In the current study, we aimed to predict the genetic information of pancreatic tumours in a simple, inexpensive, and non-invasive manner, using cancer imaging analysis and radiogenomics. We focused on p53 mutations, which are highly implicated in pancreatic ductal adenocarcinoma (PDAC), and PD-L1, a biomarker for immune checkpoint inhibitor-based therapies.

Methods

Overall, 107 patients diagnosed with PDAC were retrospectively examined. The relationship between p53 mutations as well as PD-L1 abnormal expression and clinicopathological factors was investigated using immunohistochemistry. Imaging features (IFs) were extracted from CT scans and were used to create prediction models of p53 and PD-L1 status.

Results

We found that p53 and PD-L1 are significant independent prognostic factors (P = 0.008, 0.013, respectively). The area under the curve for p53 and PD-L1 predictive models was 0.795 and 0.683, respectively. Radiogenomics-predicted p53 mutations were significantly associated with poor prognosis (P = 0.015), whereas the predicted abnormal expression of PD-L1 was not significant (P = 0.096).

Conclusions

Radiogenomics could predict p53 mutations and in turn the prognosis of PDAC patients. Hence, prediction of genetic information using radiogenomic analysis may aid in the development of precision medicine.

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Fig. 1: Immunohistochemistry of p53 and PD-L1 in PDAC.
Fig. 2: Machine learning processing was summarised.
Fig. 3: Kaplan-Meier plots of p53 and PD-L1 status by IHC compared with status by machine-learning, and ROC curve.

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Author information

Affiliations

Authors

Contributions

Y.I. and I.H. analysed and interpreted patient data. Y.I. was a major contributor to the writing of the paper. F.I., S.C., H.A., H.Yanagibashi., H.N., and W.T. performed the CT scans in PDAC patients and registered the images. H.Yokota and Y.M. extracted the imaging features from CT scans and analysed the relationship between imaging features and clinicopathological features. M.I. performed the pathological examination of pancreatic cancer samples and interpreted IHC results. All authors read and approved the final paper.

Corresponding author

Correspondence to Isamu Hoshino.

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Ethics approval and consent to participate

This study was approved by the Chiba Cancer Center Review Board (H29-006). All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation and with the Helsinki Declaration of 1964 and its later amendments. Informed consent was obtained from all patients in this study.

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Written informed consent was obtained from the patients for publication of this study and accompanying clinicopathological data.

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The data that support the findings of this study are available from the corresponding author upon reasonable request.

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The authors declare no competing interests.

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Iwatate, Y., Hoshino, I., Yokota, H. et al. Radiogenomics for predicting p53 status, PD-L1 expression, and prognosis with machine learning in pancreatic cancer. Br J Cancer 123, 1253–1261 (2020). https://doi.org/10.1038/s41416-020-0997-1

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