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

PRECOG update: An augmented resource of clinical outcome associations with gene expression for pediatric and immunotherapy cohorts | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results PRECOG update: An augmented resource of clinical outcome associations with gene expression for pediatric and immunotherapy cohorts Brooks A. Benard , Chinmay K. Lalgudi , Ilayda Ilerten , Ruohan Wang , View ORCID Profile Andrew J. Gentles doi: https://doi.org/10.1101/2025.08.22.671849 Brooks A. Benard 1 Department of Pathology, Stanford University , Stanford, CA, 94035, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chinmay K. Lalgudi 2 Department of Biochemistry, Stanford University , Stanford, CA, 94035, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ilayda Ilerten 1 Department of Pathology, Stanford University , Stanford, CA, 94035, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ruohan Wang 1 Department of Pathology, Stanford University , Stanford, CA, 94035, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andrew J. Gentles 1 Department of Pathology, Stanford University , Stanford, CA, 94035, USA 3 Department of Medicine, Stanford University , Stanford, CA, 94035, USA 4 Department of Biomedical Data Science, Stanford University , Stanford, CA, 94035, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andrew J. Gentles For correspondence: andrewg{at}stanford.edu Abstract Full Text Info/History Metrics Data/Code Preview PDF ABSTRACT 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 new datasets in previously poorly represented adult cancer types, as well as adding annotated pediatric and immunotherapy treated cohorts. Pediatric PRECOG comprises ∼4,000 patients across 12 cancers; while the immunotherapy cohort (ICI PRECOG) contains ∼4,500 patients across 20 cancer subtypes from 80 distinct datasets across 52 studies. We compute and visualize associations of gene expression with survival outcomes using Cox regression for time-to-event, or logistic regression for responder vs non-responder, 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. Download figure Open in new tab 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 Genomics) 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 TCGA (The Cancer Genome Atlas). The PRECOG resource provides freely downloadable data and annotations, along with precomputed associations between gene expression and outcome, and the ability to visualize these associations through Kaplan-Meier plots. Although the influence of the immune system on patient responses and survival across cancers and treatment modalities is now well recognized, at the time this was one of the first high-level views of just how pervasive this influence is. In the decade since these resources have been publicly available, immune checkpoint inhibitor (ICI) therapy has revolutionized the treatment of many cancers including, remarkably, in the context of advanced metastatic disease 3 . However, robust prognostic biomarkers that identify which patients will effectively respond to ICI therapies remain elusive for many cancers. For example, tumor mutational burden and parameters such as number of PD1 positive cells are often used, but have limited predictive accuracy 4 , 5 . Several resources have consolidated gene expression and clinical outcomes for ICI-treated cohorts into databases for data download, exploratory analysis, and hypothesis generation 6 – 13 . These resources represent useful platforms from which to glean insights into broad and specific predictors of ICI response. However, due to differences across them in areas such as study inclusion criteria (e.g. open source vs. controlled data access), statistical approaches to identify biomarkers of response (e.g. differential expression vs regression), and level of subgrouping across clinical annotations (e.g. considering non small cell lung cancer as a single entity versus separate analysis of adenocarcinoma and squamous cell carcinoma), no single resource is necessarily the most comprehensive or useful compared to the others. Here we extend PRECOG to the immunotherapy era by performing a comprehensive literature review for studies reporting ICI-treated cancers with transcriptomic profiles and clinical outcomes publicly available for analysis. This search identified numerous studies meeting the previously described criteria. We focused on publicly available data, rather than ones which required data access requests that preclude dissemination - which represents a barrier to open science and reproducibility. Expression associations with ICI response for these reclusive studies have been recently described 11 , 14 . We also integrate pediatric cancers into PRECOG, facilitating identification of expression biomarkers, and comparison with adult malignancies ( Fig 1 ). Download figure Open in new tab Figure 1. PRECOG update: Integration of existing and novel resources to the site. The PRECOG website previously contained only adult datasets of survival and expression from the original PRECOG publication and TCGA. Our augmentation of the PRECOG site now includes expression and survival associations for ∼4,000 pediatric samples across 12 cancers, in addition to ∼4,500 ICI-treated adult samples across 20 cancers. Precomputed gene expression and cell type abundance correlations with outcomes for individual studies, as well as meta-z scores across dataset, allow comparison between cancers, adult and pediatric malignancies, and treatment modalities. MATERIAL AND METHODS Dataset curation PRECOG Update At the time of publication, PRECOG 1 included only one dataset for pancreatic cancer (PDAC: GSE215011 15 ; n = 102) compared with at least two for most other cancers. To improve the signal-to-noise and robustness of associations in PDAC, we added six new PDAC studies with gene expression and clinical outcomes publicly available through GEO: GSE224564 16 (n = 175), GSE79668 17 (n = 51), GSE183795 18 (n = 134), GSE205154 19 (n = 218), GSE62452 20 (n = 65), and GSE71729 21 (n = 125). In combination, these increase the number of PDAC samples from 102 to 870. Gene-level z-scores were generated for each study as previously described 1 and new PDAC meta-z scores were calculated for all genes across the seven datasets (metaZfunction in package WGCNA v1.73). Curation and analysis of Pediatric PRECOG has been previously described 2 . We also updated annotations for head and neck cancer. Previously, these had not been separated into distinct disease subtypes in PRECOG. We now more rigorously separated entities such as oral squamous carcinoma and hypopharyngeal carcinoma, as described previously 22 . All clinical metadata related to survival were updated including: demographic information (age, sex etc); clinicopathological variables such as tumor subsite, HPV status; and data pertaining to HNC-related risk habits including smoking and alcohol consumption status and intensity measures. Immune Checkpoint Inhibitor (ICI) PRECOG Studies are included if patients were (1) treated with an immune checkpoint inhibitor, (2) profiled by bulk gene expression, (3) had clinical outcome data such as time-to-event or clinical response available, and (4) all data were publicly accessible. Studies were included regardless of the number of genes measured (i.e. targeted panels vs unbiased assays such as RNA-seq). If studies contained clinically distinct subtypes/histologies (e.g. adenocarcinoma vs squamous in NSCLC), treatment arms, timepoints, sample site (e.g. primary vs metastatic) etc., the study was subset into distinct datasets which were analyzed separately. For studies with time-to-event outcomes data (e.g. overall or progression-free survival, OS/PFS), we excluded datasets with less than ten patients per dataset. Response Evaluation Criteria in Solid Tumors (RECIST) categories were collapsed to either Responder (complete response CR, partial response PR) or Non-Responder (stable disease SD, progressive disease PD). For studies with RECIST or responder status available, we excluded datasets with less than five patients per response group. Clinical metadata Clinical annotations for all samples were either retrieved from the Gene Expression Omnibus (GEO; package GEOquery v2.70.0), from the study supplementary material, or from publication GitHub repositories 23 – 26 . All studies were manually cleaned to maintain a consistent nomenclature and labeling across studies as follows: For all studies, we ensure each sample is annotated with respect to primary vs metastatic status, treatment exposure, ICI target, sample timepoint, and cancer type/subtype. In cases of ambiguous, inconsistent, or incomplete sample labeling as provided by the authors, annotations were assigned manually by either extracting the relevant metadata from the manuscript or by recapitulating analyses to assign labels. Expression pre-processing and Z-score calculation Pre-processing of expression matrices For each study, reported gene IDs were updated to their currently approved HGNC HUGO IDs (package biomaRt v2.58.2). Any duplicate gene IDs were combined by taking their average expression level. Next, we (1) ensure the expression matrix is in log2 space, (2) remove all genes with no variance (SD < 0.00001), (3) remove genes/samples with ≥80% missing values, (4) quantile normalize, (5) standardize (scale each gene to have mean zero and unit variance across samples), and (6) impute missing values (package impute v1.76.0). Further details and motivation are provided in the original PRECOG study. Calculating z-scores for gene expression association with outcomes For studies with sufficient time-to-event outcomes data (n ≥ 10 PFS/OS), univariate Cox proportional hazards regression was used to calculate gene-level z-scores for expression associations with outcomes (package Survival v3.5-8). For studies with responder/non-responder status (n ≥ 5 per group), we used univariate logistic regression to calculate gene-level z-scores for expression associations with responses (package Stats v4.3.1). Positive z-scores indicate that increased gene expression is associated with worse outcomes (i.e. shorter survival time or time to progression, or non-response to therapy). Z-scores were calculated for all outcomes labels when available; for datasets with more than one outcome measure (i.e. OS and PFS, OS and Response), we report all dataset z-scores for the different outcomes measures, prioritizing z-scores for analysis in the following order: OS>PFS>Response. Comparison of Cox proportional hazards z-scores with logistic regression z-scores PRECOG and Pediatric PRECOG z-scores were all generated from PFS or OS data using Cox proportional hazards regression. ICI PRECOG contains studies with a mixture of OS/PFS, RECIST, and/or responder/non-responder clinical annotations. In order to justify combining z-scores calculated from Cox proportional hazards regression and those calculated using logistic regression, we compared z-scores (for both gene and cell type proportion) from both methods in studies with both time-to-event and responder/non-responder status (n = 25 datasets). Cell type abundances and associations with outcome through CIBERSORTx deconvolution Estimating cell type proportions and z-scores CIBERSORTx 27 was run on all datasets using default parameters, with the LM22 signature matrix which can enumerate the proportions of 22 different immune cell subtypes in a bulk expression profile. For each dataset with sufficient time-to-event or responder/non-responder status, univariate Cox or logistic regression (respectively) were used to calculate z-scores for each CIBERSORTx cell type fraction and outcomes, analogously to the analysis performed at the level of individual genes. Positive z-scores indicate that increased cell type proportion is associated with worse outcomes/response. RESULTS Updated PDAC meta z-scores in PRECOG We added six additional treatment naive primary PDAC datasets (n = 768, Fig 2A ) to the single (n=102) study in the original PRECOG. This increased the gene-level meta z-score range from −5.1–4.6 to −8.5–9.3. Gene z-scores between the new and old PDAC meta-z were correlated across 20,610 genes (R = 0.51, p<2.2e-16; Fig 2B ). Hallmark genesets 28 that were statistically associated with outcome (i.e. enriched for individual genes associated with outcome) in the original PRECOG PDAC data all maintained the directionality of association but with increased significance in the updated PRECOG ( Fig 2C ). Importantly, 26 hallmark sets (out of 50 total tested) that previously did not reach statistical significance were significant (adjusted p<0.05) in the updated PDAC compendium. These included “IFNa Response” and “TNFA Signaling via NFKB” as being associated with worse outcomes in PDAC ( Fig 2C ), consistent with known biology of PDAC disease 29 – 31 . There were no examples where genesets lost significance through augmentation of PDAC studies. This illustrates that the updated PRECOG provides an improved resource for researchers to identify clinically-relevant biomarkers and investigate biological processes underlying outcomes. Download figure Open in new tab Figure 2. Impact of augmenting adult PRECOG, and comparison to pediatric PRECOG. A) Additional PDAC datasets increase coverage from 102 to 870 patients. B) Old vs updated gene-wise meta-Z scores for PDAC are correlated and show improved significance for many genes which achieve higher absolute Z. C) Geneset-level analysis of old and augmented PDAC data boosts significance of PDAC-relevant pathways and processes associated with survival. D) Summary of number of pediatric cancer studies and cohort sizes. Cancer abbreviations: acute lymphatic leukemia (ALL), atypical teratoid rhabdoid tumor (ATRT), Ewing’s sarcoma family of tumors (ESFT), germ cell tumors (GCT), high grade gliomas (HGG), medulloblastomas (MB), neuroblastoma (NB), osteosarcoma (OSTEO), central nervous system primitive neuroectodermal tumors (CNS-PNETs), rhabdomyosarcoma (RMS), synovial sarcomas (SS) and Wilms tumors (WILMS). E) Comparison between gene-level z-score associations between adult and pediatric cancers shows significant correlation (R=0.51, p<2.2×10-16). F) Hallmarks gene set associations with outcome are highly correlated between adult and pediatric cancers with the exception of protein secretion and adult tissue related development (androgen and estrogen signaling). G) Summary of immune cell type fractions across pediatric cancer types. H) Average cell type fractions across adult cancers and across pediatric cancer are well correlated except M2 macrophages and CD8 T cells (higher in pediatric) and plasma cells (higher in adult). I) Associations between cell type fractions and outcome are discordant between adult and pediatric cancer except neutrophils which are generally unfavorable in both. Pediatric PRECOG Pediatric PRECOG overview Pediatric cancers remain understudied 32 . We previously generated a comprehensive resource of gene expression, estimated cell type proportions, and survival associations across 32 studies of 4,068 patients for 12 pediatric cancers (Pediatric PRECOG; Fig 2D ) 2 . However, Pediatric PRECOG was not previously available on https://precog.stanford.edu . To make this resource usable to the broader community, we added all datasets and annotations, study/gene-level survival z-scores, CIBERSORT cell type proportions, and cell type proportion z-scores 2 to the site. Comparison of pediatric and adult gene-level meta-z scores showed significant correlation (R = 0.51, p<2.2e-16; Fig 2E ). Interestingly, GSEA enrichment scores for Hallmark pathways between adult and pediatric meta-z scores were highly correlated (R = 0.9, p<2.2e-16; Fig 2F ), with the exception of “Estrogen/Androgen Response” and “Protein Secretion”. These make sense as they are fundamental differences in biology and development between pediatric and adult cancers, with estrogen and androgen receptor signaling playing key roles driving cancer-relevant processes such as proliferation in adult tissues. We estimated the proportions of 22 leukocyte cell types 33 across Pediatric PRECOG datasets as previously described 2 ( Fig 1G ). Comparing adult and pediatric cancer, the average proportions of most cell types were highly correlated (R = 0.78, p = 2.3×10-5; Fig 2H ). However, the association of cell type proportion with outcomes was uncorrelated, with neutrophils being the only cell type with a consistent, unfavorable, association with outcome between adult and pediatric cancers ( Fig 2I ). The neutrophil signal potentially clarifies their increasingly controversial impact of inflammation in cancer 34 , 35 . This provides one example highlighting the utility of Pediatric PRECOG and provides a rich resource for uncovering developmentally conserved and distinct biomarkers for further research. ICI PRECOG ICI PRECOG cohort description Our literature review for immunotherapy studies with publicly-available gene expression and clinical outcomes data for treatment naive patients identified 4,045 patients across 51 studies and 20 cancers, of which 851 samples were lymph node or distant metastases ( Fig 3A-C ) 36 – 87 . We subset “studies” into “datasets” if there were at least 10 samples with the same cancer subtype (e.g. adenocarcinoma vs squamous), ICI target, primary/metastatic annotation, and treatment time point (e.g. naive vs post therapy). This defined 69 datasets for which we calculated outcome-associated gene z-scores (49 primary naive, 20 metastatic naive). To define prognostic associations for distinct clinical categories, we collapsed ICI datasets based on shared cancer type, ICI target, primary/metastatic status, and treatment exposure time point (e.g. pre/post therapy) and calculated meta-z scores for each category ( Fig 3D ). For datasets for which both OS/PFS and binary response were available, we compared gene-level z-scores and observed that they were well correlated ( Fig 3 . D; R=0.73, p=0.0004). Download figure Open in new tab Figure 3. Development and high level analysis of ICI PRECOG. A) Summary of the number of patients per cancer type, together with specific ICI treatment. B) Distribution of ICI treatments across ICI PRECOG datasets. C) ICI PRECOG distribution of primary versus metastatic samples. D) Top-level heatmap of ICI PRECOG gene expression associations with outcome. Red indicates that a gene (row) is associated with worse survival (Cox regression) or binary lack-of-response (logistic regression) in a specific dataset (column). Cancer types correspond to TCGA designations. E) Comparison between gene-level and cell-type-level z-scores for datasets where both time-to-event and binary response are available. F) Gene-level z-scores are modestly correlated between Adult PRECOG (non-ICI therapy) and ICI PRECOG (R=0.3, p<2,2×10-16), with most agreement occurring in favorable genes (bottom left quadrant) which primarily reflect immune activity. G) Concordance between the association of specific immune cell types with outcome in Adult PRECOG vs ICI PRECOG shows significant correlation except for mast cells and M1 macrophages. ICI PRECOG compared to PRECOG When gene expression levels were associated with ICI response, many known “T cell exhaustion” genes such as TIGIT and LAG3 emerged as favorable prognostic markers; while the single most unfavorable marker for ICI response was DOK4 which encodes for an adapter protein that has recently been implicated in negative regulation of T-cell activation 88 . Gene-level meta-z scores showed modest correlation between ICI PRECOG and PRECOG (R = 0.3, p < 2.2e-19; Fig 1F ). Immune related genes were often favorably prognostic in both ICI and non-ICI contexts; whereas genes associated with poor survival differed between them. Hence, ICI PRECOG retrieves both familiar and also less-known markers of immunotherapy response, including ones that are shared or distinct from those that are prognostic for older therapies. This illustrates the potential for discovery of new markers and mechanisms underlying ICI response. We also estimated proportions of 22 different immune cell types for each sample in ICI PRECOG and computed z-scores for their association with ICI response or survival. These were broadly correlated with similar measures in the original PRECOG ( Fig 3G ), with neutrophils being unfavorable in both, and CD8 T cells and γδT cells favorable in both. However, there were strong differences for some cell types, such as M1 macrophages and mast cells, whose presence was unfavorable in the context of ICI therapy, but otherwise favorable. DISCUSSION Here, we integrated and augmented our PRECOG database of adult 1 and pediatric cancers 2 ; and added a large curated, processed, and pre-analyzed compendium of immunotherapy response datasets of over 4,000 patients. All data and annotations are freely available for download from the PRECOG website at https://precog.stanford.edu . This resource also provides capability for basic analyses such as comparing gene z-score association with outcomes between cancer types and individual datasets. The updated PRECOG represents a significant centralization of gene expression and cell type abundance associations with outcomes across pediatric and adult cancers with immunotherapy treatment. While there are similar resources for ICI treated cohorts 6 – 13 , PRECOG is the most comprehensive in terms of treatment types, data availability, consistent and curated sample annotations, and the ability to integrate with non-ICI cohorts, as well as comparing and contrasting to pediatric cancer. The vignettes we present here illustrate the potential for discovery of new biomarkers related to ICI response. However, we also anticipate that more in-depth analysis, paired with comparison to older therapies, will be informative about mechanisms of response and resistance development. DATA AVAILABILITY Only publicly available datasets were used in this study. All clinical and expression data used, as well as dataset level CIBERSORT(x) cell-type fractions and gene/cell-type z-scores generated in this study can be downloaded at https://precog.stanford.edu . AUTHOR CONTRIBUTIONS Brooks Benard: Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Software, Supervision, Visualization, Writing—original draft. Chinmay Lalgudi: Resource, Software, Visualization, Writing—review & editing. Ilayda Ilerten: Formal analysis, Visualization. Ruohan Wang: Formal analysis. Andrew Gentles: Conceptualization, Formal analysis, Funding acquisition, Project administration, Methodology, Supervision, Writing—review & editing. FUNDING This work was supported by the Advanced Research Projects Agency for Health ADAPT program (ARPA-H 140D042590010 to A.J.G.); and the National Institutes of Health (R01CA276828 to A.J.G.). CONFLICT OF INTEREST The authors declare no conflict of interest. ACKNOWLEDGEMENTS We would like to thank all the authors of the included studies for making their data publicly available and useful to the scientific community. Funder Information Declared National Institutes of Health , R01CA276828 Advanced Research Projects Agency - Health Footnotes ↵ † Joint Authors https://precog.stanford.edu REFERENCES (1). ↵ Gentles , A. J. ; Newman , A. M. ; Liu , C. L. ; Bratman , S. V. ; Feng , W. ; Kim , D. ; Nair , V. S. ; Xu , Y. ; Khuong , A. ; Hoang , C. D. ; Diehn , M. ; West , R. B. ; Plevritis , S. K. ; Alizadeh , A. A . The Prognostic Landscape of Genes and Infiltrating Immune Cells across Human Cancers . Nat. Med . 2015 , 21 ( 8 ), 938 – 945 . doi: 10.1038/nm.3909 . OpenUrl CrossRef PubMed (2). ↵ Stahl , D. ; Knoll , R. ; Gentles , A. J. ; Vokuhl , C. ; Buness , A. ; Gütgemann , I. Prognostic Gene Expression, Stemness and Immune Microenvironment in Pediatric Tumors . 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