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Predicting Genomic Alterations of Phosphatidylinositol-3 Kinase Signaling in Hepatocellular Carcinoma: A Radiogenomics Study Based on Next-Generation Sequencing and Contrast-Enhanced CT

  • Translational Research
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Abstract

Background

Exploring the genomic landscape of hepatocellular carcinoma (HCC) provides clues for therapeutic decision-making. Phosphatidylinositol-3 kinase (PI3K) signaling is one of the key pathways regulating HCC aggressiveness, and its genomic alterations have been correlated with sorafenib response. In this study, we aimed to predict somatic mutations of the PI3K signaling pathway in HCC samples through machine-learning-based radiomic analysis.

Methods

HCC patients who underwent next-generation sequencing and preoperative contrast-enhanced CT were recruited from West China Hospital and The Cancer Genome Atlas for model training and validation, respectively. Radiomic features were extracted from volumes of interest (VOIs) covering the tumor (VOItumor) and peritumoral areas (5 mm [VOI5mm], 10 mm [VOI10mm], and 20 mm [VOI20mm] from tumor margin). Factor analysis, logistic regression analysis, least absolute shrinkage and selection operator, and random forest analysis were applied for feature selection and model construction. Model performance was characterized based on the area under the receiver operating characteristic curve (AUC).

Results

A total of 132 HCC patients (mean age: 61.1 ± 14.7 years; 108 men) were enrolled. In the training set, the AUCs of radiomic signatures based on single CT phases were moderate (AUC 0.694–0.771). In the external validation set, the radiomic signature based on VOI10mm in arterial phase demonstrated the highest AUC (0.733) among all models. No improvement in model performance was achieved after adding the tumor radiomic features or manually assessed qualitative features.

Conclusions

Machine-learning-based radiomic analysis had potential for characterizing alterations of PI3K signaling in HCC and could help identify potential candidates for sorafenib treatment.

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Data Availability

All datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgement

We are most grateful to West China Biobanks, Department of Clinical Research Management, West China Hospital, Sichuan University for their support regarding human tissue samples. We thank the TCGA working group for generating publicly available data.

Funding

This work was supported by grants from the National Key Technologies R&D Program (2018YFC1106800), the Natural Science Foundation of China (82173124, 82173248, 82103533, 82002572, 82002967, 81972747, 81872004, and 8210071122), the Fellowship of China National Postdoctoral Program for Innovative Talents (BX20200225 and BX20200227), the China Postdoctoral Science Foundation (2021M692278 and 2020M673231), the Science and Technology Support Program of Sichuan Province (2021YJ0436 and 2021YFS0141), the Postdoctoral Science Foundation of Sichuan University (2021SCU12007), the 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYJC18008), and the Postdoctoral Science Foundation of West China Hospital (2020HXBH075 and 2020HXBH007).

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  1. Haotian Liao and Hanyu Jiang authors contributed equally to this work

Authors and Affiliations

  1. Department of Liver Surgery & Liver Transplantation, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China

    Haotian Liao MD, Kefei Yuan PhD & Yong Zeng PhD

  2. Department of Radiology, West China Hospital, Sichuan University, Chengdu, China

    Hanyu Jiang MD, Yuntian Chen MD, Ting Duan MD, Ting Yang MD & Bin Song PhD

  3. Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China

    Miaofei Han PhD, Zhong Xue PhD, Feng Shi PhD & Dinggang Shen PhD

  4. Department of Radiology and Center for Advanced Magnetic Resonance Development, Duke University Medical Center, Durham, NC, USA

    Mustafa R Bashir PhD

  5. School of Biomedical Engineering, ShanghaiTech University, Shanghai, China

    Dinggang Shen PhD

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  1. Haotian Liao MD
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Contributions

Conceptualization: HL, HJ, DS, BS and YZ; Bioinformatics analyses: HL, HJ and YC; VOI segmentation: HJ and TD; Methodology: HL, HJ, TY, MH, ZX, FS; Data analysis: HL, HJ, YC and MH; Writing and revision: HL, HJ, KY and MB; Reviewing: all authors. The author(s) read and approved the final manuscript.

Corresponding authors

Correspondence to Dinggang Shen PhD, Bin Song PhD or Yong Zeng PhD.

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Conflict of interest

Mustafa R. Bashir: Research support to institution (federal): 5U01-DK061713-19, 1R01CA249765-01. Research support to institution (industry): Carmot Therapeutics, Corcept Therapeutics, CymaBay Therapeutics, Diabetes & Endocrinology Consultants, Madrigal Pharmaceuticals, Metacrine, NGM Biopharmaceuticals, Pinnacle Clinical Research, Polarean Imaging, ProSciento Inc, Siemens Healthineers, Consulting: MedPace, ICON, Corcept Therapeutics.

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Liao, H., Jiang, H., Chen, Y. et al. Predicting Genomic Alterations of Phosphatidylinositol-3 Kinase Signaling in Hepatocellular Carcinoma: A Radiogenomics Study Based on Next-Generation Sequencing and Contrast-Enhanced CT. Ann Surg Oncol 29, 4552–4564 (2022). https://doi.org/10.1245/s10434-022-11505-4

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