PRECOG update: an augmented resource of clinical outcome associations with gene expression for adult, pediatric, and immunotherapy cohorts.
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Gene expression can be used to define prognostic and predictive biomarkers across cancers and treatment modalities. PRECOG (https://precog.stanford.edu) is a compendium of datasets with gene expression and clinical outcomes that facilitates visualization of associations between genomic profiles and patient survival. Here, we augment the existing PRECOG with over 10 000 new patients in previously poorly represented adult cancer types, as well as adding annotated pediatric and immunotherapy treated cohorts. Pediatric PRECOG comprises over 3000 patients across 12 cancers; while the checkpoint inhibitor (ICI) PRECOG contains over 4000 patients across 20 cancer subtypes from 80 distinct datasets across 51 studies. Together this represents a doubling in the size of the PRECOG database. We compute and visualize associations of gene expression with survival outcomes using Cox regression for time-to-event, or logistic regression for responder versus nonresponder, across all datasets. We also estimate cell type fractions in samples via computational deconvolution using CIBERSORTx, to identify survival associations at the level of cell types. All expression data, clinical annotations, and gene and cell type survival z-scores and meta-z scores for adult, pediatric, and ICI PRECOG, are available for interactive analysis and download, along with Kaplan-Meier and boxplot visualizations. This updated resource will provide new insights into biomarkers for specific therapies, populations, and cancer types.