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Integration of histopathological images and multi-dimensional omics analyses predicts molecular features and prognosis in high-grade serous ovarian cancer

  • Author Footnotes
    1 Hao Zeng and Linyan Chen are co-first authors of the article.
    Hao Zeng
    Footnotes
    1 Hao Zeng and Linyan Chen are co-first authors of the article.
    Affiliations
    Department of Biotherapy, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, and Collaborative Innovation Center, Chengdu, China
    Search for articles by this author
  • Author Footnotes
    1 Hao Zeng and Linyan Chen are co-first authors of the article.
    Linyan Chen
    Footnotes
    1 Hao Zeng and Linyan Chen are co-first authors of the article.
    Affiliations
    Department of Biotherapy, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, and Collaborative Innovation Center, Chengdu, China
    Search for articles by this author
  • Mingxuan Zhang
    Affiliations
    Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
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  • Yuling Luo
    Affiliations
    Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
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  • Xuelei Ma
    Correspondence
    Corresponding author at: Department of Biotherapy, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, No. 37, Guoxue Alley, Chengdu 610041, China.
    Affiliations
    Department of Biotherapy, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, and Collaborative Innovation Center, Chengdu, China
    Search for articles by this author
  • Author Footnotes
    1 Hao Zeng and Linyan Chen are co-first authors of the article.

      Highlights

      • Computer-aided image analysis system can provide quantitative assessment of histopathological images.
      • Histopathological image features help to predict mutations, microsatellite instability, molecular subtypes and prognosis.
      • Integrative model of histopathological images and omics data may improve prognosis prediction and risk stratification.

      Abstract

      Objective

      This study used histopathological image features to predict molecular features, and combined with multi-dimensional omics data to predict overall survival (OS) in high-grade serous ovarian cancer (HGSOC).

      Methods

      Patients from The Cancer Genome Atlas (TCGA) were distributed into training set (n = 115) and test set (n = 114). In addition, we collected tissue microarrays of 92 patients as an external validation set. Quantitative features were extracted from histopathological images using CellProfiler, and utilized to establish prediction models by machine learning methods in training set. The prediction performance was assessed in test set and validation set.

      Results

      The prediction models were able to identify BRCA1 mutation (AUC = 0.952), BRCA2 mutation (AUC = 0.912), microsatellite instability-high (AUC = 0.919), microsatellite stable (AUC = 0.924), and molecular subtypes: proliferative (AUC = 0.961), differentiated (AUC = 0.952), immunoreactive (AUC = 0.941), mesenchymal (AUC = 0.918) in test set. The prognostic model based on histopathological image features could predict OS in test set (5-year AUC = 0.825) and validation set (5-year AUC = 0.703). We next explored the integrative prognostic models of image features, genomics, transcriptomics and proteomics. In test set, the models combining two omics had higher prediction accuracy, such as image features and genomics (5-year AUC = 0.834). The multi-omics model including all features showed the best prediction performance (5-year AUC = 0.911). According to risk score of multi-omics model, the high-risk and low-risk groups had significant survival differences (HR = 18.23, p < 0.001).

      Conclusions

      These results indicated the potential ability of histopathological image features to predict above molecular features and survival risk of HGSOC patients. The integration of image features and multi-omics data may improve prognosis prediction in HGSOC patients.

      Keywords

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