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&M′s 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.

The authors have declared no competing interest.

We gratefully acknowledge that financial support was provided by the Foundation Children Cancer Free (KiKa core funding) and Adessium Foundation.

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The Biobank and Data Access Committee (BDAC) of the Princess Máxima Center for Pediatric Oncology gave ethical approval for this work.

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