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Self-supervised learning for predicting transcriptomic groups on whole slides images in intrahepatic cholangiocarcinoma

ABSTRACT: Backgrounds & Aims: Transcriptomic classification of intrahepatic cholangiocarcinoma (iCCA) has been recently improved from two classes to five classes, associated with pathological features, targetable genetic alterations and survival. Despite its prognostic and therapeutic value, the classification is not routinely performed due to technical limits such as insufficient material or the cost of molecular analyses. Our aim was to predict iCCA transcriptomic classes on whole-slide digital histological images (WSI) using a self-supervised learning (SSL) model. Methods: Gene expression was investigated using RNA sequencing to class samples in the five transcriptomic classes. We then used a SSL method called GigaSSL. We first trained our model on a discovery set of 769 slides (biopsy and surgical samples, 246 patients) in a 5-fold cross-validation scheme. We then validated the model in a TCGA (n= 29) and a French external validation set (n=32). Results: Our model showed good performance for predicting each transcriptomic class in the discovery set (Area under the curve, AUC: 0.55-0.81), particularly for the Hepatic stem-like class (most frequent group, 37% of cases), with AUC 0.81. The model generalised well for the prediction of four on five of the transcriptomic classes into the two validation sets with AUCs ranging from 0.76 to 0.80 in the TCGA set and from 0.62 to 0.92 in the French external set. Conclusion: We have developed and validated a SSL based model able to predict iCCA transcriptomic classes on routine histological slides (biopsy and surgical samples), which could have an impact on the management of patients.

ORGANISM(S): Homo sapiens

PROVIDER: GSE244807 | GEO | 2023/11/01

REPOSITORIES: GEO

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Project description:Background We have recently constructed a DNA methylation classifier that can discriminate between pancreatic ductal adenocarcinoma (PAAD) liver metastasis and intrahepatic cholangiocarcinoma (iCCA) with high accuracy (PAAD-iCCA-Classifier). PAAD is one of the leading causes of cancer of unknown primary and diagnosis is based on exclusion of other malignancies. Therefore, our focus was to investigate whether the PAAD-iCCA-Classifier can be used to diagnose PAAD metastases from other sites. Methods For this scope, the anomaly detection filter of the initial classifier was expanded by 8 additional mimicker carcinomas, amounting to a total of 10 carcinomas in the negative class. We validated the updated version of the classifier on a validation set, which consisted of a biological cohort (n = 3579) and a technical one (n = 15). We then assessed the performance of the classifier on a test set, which included a positive control cohort of 16 PAAD metastases from various sites and a cohort of 124 negative control samples consisting of 96 breast cancer metastases from 18 anatomical sites and 28 carcinoma metastases to the brain. Results The updated PAAD-iCCA-Classifier achieved 98.21% accuracy on the biological validation samples, and on the technical validation ones it reached 100%. The classifier also correctly identified 15/16 (93.75%) metastases of the positive control as PAAD, and on the negative control, it correctly classified 122/124 samples (98.39%) for a 97.85% overall accuracy on the test set. We used this DNA methylation dataset to explore the organotropism of PAAD metastases and observed that PAAD liver metastases are distinct from PAAD peritoneal carcinomatosis and primary PAAD, and are characterized by specific copy number alterations and hypomethylation of enhancers involved in epithelial-mesenchymal-transition. Conclusions The updated PAAD-iCCA-Classifier (available at https://classifier.tgc-research.de/) can accurately classify PAAD samples from various metastatic sites and it can serve as a diagnostic aid.

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Self-supervised learning for predicting transcriptomic groups on whole slides images in intrahepatic cholangiocarcinoma

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