M&M: An RNA-seq based Pan-Cancer Classifier for Pediatric Malignancies
Wallis, F. S. A.; Baker-Hernandez, J. L.; van Tuil, M.; van Hamersveld, C. A.; Koudijs, M. J.; Verwiel, E. T. P.; Janse, A.; Hiemcke-Jiwa, L. S.; de Krijger, R. R.; Kranendonk, M. E. G.; Vermeulen, M. A.; Wesseling, P.; Flucke, U. E.; de Haas, V.; Luesink, M.; Hoving, E. W.; Vormoor, H. J.; van Noesel, M. M.; Hehir-Kwa, J. Y.; Tops, B. B. J.; Kemmeren, P.; Kester, L. A.
Show abstract
With many rare tumor types, acquiring the correct diagnosis is a challenging but crucial process in pediatric oncology. Here, we present M&M, a pan-cancer ensemble-based machine learning algorithm tailored towards inclusion of rare tumor types. The RNA-seq based algorithm can classify 52 different tumor types (precision[~] 99%, recall[~] 80%), plus the underlying 96 tumor subtypes (precision[~] 96%, recall[~] 70%). For low-confidence classifications, a comparable precision is achieved when including the three highest-scoring labels. M&Ms pan-cancer setup allows for easy clinical implementation, requiring only one classifier for all incoming diagnostic samples, including samples from different tumor stages and treatment statuses. Simultaneously, its performance is comparable to existing tumor- and tissue-specific classifiers. The introduction of an extensive pan-cancer classifier in diagnostics has the potential to increase diagnostic accuracy for many pediatric cancer cases, thereby contributing towards optimal patient survival and quality of life.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Predicting the Tumor Microenvironment Composition and Immunotherapy Response in Non-Small Cell Lung Cancer from Digital Histopathology Images 95%
- Generalizing AI-driven Assessment of Immunohistochemistry across Immunostains and Cancer Types: A Universal Immunohistochemistry Analyzer 95%
- Deep learning inference of cell type-specific gene expression from breast tumor histopathology 94%
Similar papers in this journal
- Comprehensive analysis of mutational signatures in pediatric cancers 95%
- Clinical interpretation of integrative molecular profiles to guide precision cancer medicine 95%
- Cancer-associated fibroblast compositions change with breast cancer progression linking S100A4 and PDPN ratios with clinical outcome 94%
Similar papers in this journal
- Non-invasive multi-cancer detection using DNA hypomethylation of LINE-1 retrotransposons 95%
- Tamoxifen Response at Single Cell Resolution in Estrogen Receptor-Positive Primary Human Breast Tumors 93%
- cfTrack : Exome-wide mutation analysis of cell-free DNA to simultaneously monitor the full spectrum of cancer treatment outcomes: MRD, recurrence, and evolution 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.