Online-only supplementary tables: Table S1. Clinicopathological characteristics of patients per cohort. Table S2. ST gene panel. Table S3. The list of features used in the neural network based classification. Table S4. Results of pre-study hyperparameter tunning on 5 genes. Table S5. Prediction performance for the 1011 genes on validation and test sets. Table S6. Prediction performance for transcripts from established biomarker panels. Table S7. Correlation of gene expression with ER status (internal test set). Table S8. Correlation of gene expression with ER status (external test set). Table S9. Enriched pathways for genes cannot be predicted (Reactome gene sets). Table S10. Enriched pathways for genes cannot be predicted (Hallmark gene sets). Table S11. List of proliferation genes.
Funding
Cancer Foundation Finland
CSC - IT Center for Science
Tampere University graduate school
Vetenskapsrådet (VR)
Cancerfonden (Swedish Cancer Society)
Swedish e-science research centre (SeRC)
MedTechLabs
ERA PerMed
Karolinska Institutet (Cancer Research KI; StratCan)
Stockholm Region
Stockholm Cancer Society
Swedish Breast Cancer Association
Academy of Finland (Suomen Akatemia)
Academy of Finland Center of Excellence programme
ARTICLE ABSTRACT
Molecular profiling is central in cancer precision medicine but remains costly and is based on tumor average profiles. Morphologic patterns observable in histopathology sections from tumors are determined by the underlying molecular phenotype and therefore have the potential to be exploited for prediction of molecular phenotypes. We report here the first transcriptome-wide expression–morphology (EMO) analysis in breast cancer, where individual deep convolutional neural networks were optimized and validated for prediction of mRNA expression in 17,695 genes from hematoxylin and eosin–stained whole slide images. Predicted expressions in 9,334 (52.75%) genes were significantly associated with RNA sequencing estimates. We also demonstrated successful prediction of an mRNA-based proliferation score with established clinical value. The results were validated in independent internal and external test datasets. Predicted spatial intratumor variabilities in expression were validated through spatial transcriptomics profiling. These results suggest that EMO provides a cost-efficient and scalable approach to predict both tumor average and intratumor spatial expression from histopathology images.
Transcriptome-wide expression morphology deep learning analysis enables prediction of mRNA expression and proliferation markers from routine histopathology whole slide images in breast cancer.
