M&M: an RNA-seq based pan-cancer classifier for paediatric tumours

Abstract

Background: With many rare tumour types, acquiring the correct diagnosis is a challenging but crucial process in paediatric oncology. Historically, this is done based on histology and morphology of the disease. However, advances in genome wide profiling techniques such as RNA sequencing now allow the development of molecular classification tools. Methods: Here, we present M&M, a pan-paediatric cancer ensemble-based machine learning algorithm tailored towards inclusion of rare tumour types. Findings: The RNA-seq based algorithm can classify 52 different tumour types (precision ∼99%, recall ∼80%), plus the underlying 96 tumour subtypes (precision ∼96%, recall ∼70%). For low-confidence classifications, a comparable precision is achieved when including the three highest-scoring labels. We then validated M&M on an internal dataset (precision 99%, recall 76%) and an external dataset from the KidsFirst initiative (precision 98%, recall 77%). Finally, we show that M&M has similar performance as existing disease or domain specific classification algorithms based on RNA sequencing or methylation data. Interpretation: M&M's pan-cancer setup allows for easy clinical implementation, requiring only one classifier for all incoming diagnostic samples, including samples from different tumour stages and treatment statuses. Simultaneously, its performance is comparable to existing tumour- and tissue-specific classifiers. The introduction of an extensive pan-cancer classifier in diagnostics has the potential to increase diagnostic accuracy for many paediatric cancer cases, thereby contributing towards optimal patient survival and quality of life. Funding: Financial support was provided by theFoundation Children Cancer Free (KiKa core funding) andAdessium Foundation.