PRECOG update: an augmented resource of clinical outcome associations with gene expression for adult, pediatric, and immunotherapy cohorts

Sep 22, 2026

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Gene expression enables prognostic and predictive biomarkers across cancer types and treatment modalities. PRECOG (PREdiction of Clinical Outcomes from Ge-nomics) is a compendium of datasets linking gene expression with clinical outcomes to visualize associations with patient survival. This update adds 10,000+ patients to previously underrepresented adult cancers and incorporates annotated pediatric and immunotherapy-treated cohorts, doubling the database size. Cox and logistic regression quantify gene-expression–survival links, while CIBERSORTx deconvolution estimates cell-type fractions to assess cell-level survival associations. Interactive analysis and downloads include adult, pediatric, and ICI gene and cell-type survival z-scores plus Kaplan–Meier and boxplot visualizations.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Abstract

Graphical abstract

Introduction

PRECOG background and prior work

Need for robust ICI biomarkers

Approach to extend PRECOG to immunotherapy era

Nucleic Acids Research, 2026, 54, D1579–D1589 [https://doi.org/10.1093/nar/gkaf1215](https://doi.org/10.1093/nar/gkaf1215) Advance access publication date: 17 November 2025 Database issue PRECOG update: an augmented resource of clinical outcome associations with gene expression for adult, pediatric, and immunotherapy cohorts Brooks A Benard 1 ,†, Chinmay K Lalgudi2 ,†, Ilayda Ilerten 1 , Ruo Han Wang1 , Andrew J Gentles 1,3,4 ,* 1 Department of Pathology, Stanford University, Stanford, CA 94035, United States 2 Department of Biochemistry, Stanford University, Stanford CA 94035, United States 3 Department of Medicine, Stanford University, Stanford, CA 94035, United States 4 Department of Biomedical Data Science, Stanford University, Stanford, CA 94035, United States ∗ To whom correspondence should be addressed. Email: [andrewg@stanford.edu](andrewg@stanford.edu)[ ](andrewg@stanford.edu)†The first two authors should be regarded as Joint First Authors. Abstract Gene expression can be used to define prognostic and predictive biomarkers across cancers and treatment modalities. PRECOG ([https://prec](https://prec)[og.stanford.edu](og.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 associationsat 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. Graphical abstract Introduction We previously described the prognostic landscape of genes and infiltrating immune cell types across adult and pediatric cancers and developed an associated freely available resource called PRECOG (PREdiction of Clinical Outcomes from Ge- nomics) [1, 2]. PRECOG curated 166 gene expression datasets comprising ∼18 000 tumor samples for which patient outcomes such as overall survival are known. It also includes ∼11 000 samples from The Cancer Genome Atlas (TCGA) . The PRECOG resource provides freely downloadable data Received: August 15, 2025. Revised: October 16, 2025. Accepted: October 16, 2025 © The Author(s) 2025. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ([https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/)), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [reprints@oup.com](reprints@oup.com) for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact [journals.permissions@oup.com](journals.permissions@oup.com). D1580 Benard et al. and annotations, alon

FAQs

What does PRECOG provide for researchers?

PRECOG provides a compendium of gene-expression datasets linked to clinical outcomes, enabling visualization of associations with patient survival and related metrics.

What new cohorts were added in the PRECOG update?

The update adds 10,000+ new patients to better cover adult cancer types, and includes annotated pediatric cohorts plus immunotherapy (ICI) treated cohorts.

How are associations between gene expression, cell types, and survival computed?

Associations are computed using Cox regression for time-to-event outcomes and logistic regression for responder vs nonresponder. Cell-type fractions are estimated via computational deconvolution using CIBERSORTx, supporting cell-level survival association analyses.

Sep 22, 2026

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