Discovery of pharmaceutically-targetable pathways and prediction of survivorship for pneumonia and sepsis patients from the view point of ensemble gene noise

Tristan de Jong; Victor Guryev; Yury M. Moshkin

Finally, we explored the possibility to use ensemble gene noise in the prediction of clinical outcomes. Previously some promising biomarkers and gene expression endotypes associated with septic shock and mortality have been identified based on DGE analysis [8, 9] . However, as already mentioned, ensemble gene noise looks at gene expression from a different, yet complementary, angle, thus enabling the identification of novel pathways and biomarkers for sepsis and other diseases. To that, models predicting pathology based on ensemble gene noise could potentially be more robust, as inter-individual variability for ensemble gene noise is lower than that for log gene expression ( Figure S3 ). Furthermore, Gradient boosted regression tree models trained on CAP and sepsis patients to predict their mortality had a good accuracy on validation cohort ( Figure 3 , Table 2 ). These outperformed predictions based on the Mars1 gene expression endotype, which was shown to associate with a poor prognosis [8] , both on the discovery and validation cohorts ( Figure 3C ). Interestingly, some ensemble gene noise features selected statistically for the models predicting mortality in both CAP/sepsis-, CAPand sepsis-patients couldimmediately be related to the host's response to infection. For example, increases in ensemble gene noise in legionellosis, epithelial cell signalling in . CC-BY-NC 4.0 International license author/funder. It is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.10.035717 doi: bioRxiv preprint