Predicting Tissue of Origin from Bulk Tumor Gene Expression using a Pre-trained Transformer Model

Mellors, T.; Spitmann, M.

doi:10.1101/2024.12.01.626105

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bioRxiv · 10.1101/2024.12.01.626105

Predicting Tissue of Origin from Bulk Tumor Gene Expression using a Pre-trained Transformer Model

Mellors, T.·Spitmann, M.

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Abstract

Identifying the tissue of origin for cancers is essential for enhancing diagnostic precision, selecting effective treatments, and guiding clinical decision-making. In this study, we developed a predictive model to classify the tissue of origin across various cancer types. Using a dataset with 10,300 samples from 32 unique tissue types, the model achieved an overall accuracy of 88% in distinguishing among all 32 classes, with an average accuracy of 99.2% within each class. When tested on metastatic skin tumors, it reached an accuracy of 87%, underscoring its potential in addressing challenging metastatic cases. These results demonstrate the models reliability in oncology applications, offering a promising tool for improving diagnostic accuracy and supporting personalized cancer treatment strategies.

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Mellors, T., Spitmann, M.. 2024-12-05. Predicting Tissue of Origin from Bulk Tumor Gene Expression using a Pre-trained Transformer Model. https://doi.org/10.1101/2024.12.01.626105

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