Advances in machine learning for tumour classification in cancer of unknown primary: A mini-review

• Published in: Cancer letters 2025-02, Vol.611, p.217348, Article 217348
• Main Authors: Oróstica, Karen, Mardones, Felipe, Bernal, Yanara A., Molina, Samuel, Orchard, Marcos, Verdugo, Ricardo A., Carvajal-Hausdorf, Daniel, Marcelain, Katherine, Contreras, Seba, Armisen, Ricardo
• Format: Article
• Language: English
• Subjects: Cancers of unknown primary (CUP); Diagnostic methods; Machine learning (ML); Mutational signatures; Somatic mutations; Tumour classification
• ISSN: 0304-3835; 1872-7980; 1872-7980
• DOI: 10.1016/j.canlet.2024.217348

Cancers of unknown primary (CUP) are a heterogeneous group of aggressive metastatic cancers where standardised diagnostic techniques fail to identify the organ where it originated, resulting in a poor prognosis and resistance to treatment. Recent advances in large-scale sequencing techniques have enabled the identification of mutational signatures specific to particular tumour subtypes, even from liquid biopsy samples such as blood. This breakthrough paves the way for the development of new cost-effective diagnostic strategies. This mini-review explores recent advancements in Machine Learning (ML) and its application to tumour classification methods for CUP patients, identifying its weaknesses and strengths when classifying the tumour type. In the era of multi-omics, integrating several sources of information (e.g., imaging, molecular biomarkers, and family history) requires important theoretical advancements: increasing the dimensionality of the problem can result in lowering the predictive accuracy and robustness when data is scarce. Here, we review and discuss different architectures and strategies for incorporating cutting-edge machine learning into CUP diagnosis, aiming to bridge the gap between theory and clinical practice. •Cancers of Unknown Primary (CUP) are a diagnostic and clinical challenge in oncology.•Mutational signatures can differentiate tumour tissue and subtypes even in CUP.•Machine Learning leverages hidden and complex patterns in CUP mutational data.•We review recent advancements in ML applied to CUP diagnosis and classification.