Augmented Mycobiome-Based Cancer Detection by an Interpretable Large Model

The microbiome has emerged as a promising predictor of human cancers. Fungi are important components of the human microbiome and are closely associated with cancer. However, our understanding of the function and efficacy of fungal cells in tumors remains limited, making it challenging to accurately detect cancer tissues using limited tumor mycobiome data. Transfer learning has recently revolutionized the bioinformatics field by leveraging deep learning models pre-trained on large-scale general datasets, effectively addressing the predicted issue in tasks with limited data. Here, we propose an interpretable large model, MCaPred, that encodes different tumor tissue sites with microbiome features. The model was pre-trained on large-scale microbiome data from different tumor tissue sites to predict cancer in limited mycobiome data samples, thereby exploring the association between fungi and cancer-type specificity. Studies have shown that MCaPred outperforms recent deep learning models in cancer screening based on tumor mycobiome data and that mycobiome-based cancer detection is augmented by pretrained models. More importantly, MCaPred can capture key factors from mycobiome data related to specific cancer occurrences because of its high interpretability. The proposed model, analysis code, and supplementary materials used in this study are available at GitHub (https://github.com/cskyan/MCaPred.git). Contact: [email protected] or [email protected]