Machine learning models of human tissue microbiomes for tissue-of-origin prediction

Abstract

There is increasing interest in using microbial data diagnostically for tissue health monitoring such as in early cancer detection. To build such models, we need to understand whether normal tissue microbiomes can also be predictive of tissue of origin, and importantly ask how contaminants may contribute to model performance. In this study, using the Tabula Sapiens Microbiome dataset, we built machine learning models of human tissue microbiomes that can predict tissue of origin. This may in part explain how tumor types can be predicted based on the tumor microbiomes. We also demonstrate that machine learning models built using contaminants alone, though not as powerful as those built on true signal, can still predict tissue of origin. Reassuringly, the addition of contaminants to true signal does not increase the performance over models built on true signal. Overall, our findings raise the burden of proof for predictive models of the human tissue and tumor microbiomes. Without addressing the magnitude of contribution from contaminants to model performance, a models reproducibility and its clinical value becomes questionable. We also discuss the optimal microbial taxonomic resolution for building these models.