Evidence map›Paper›PMID 24613583›Full record

ArticleEuropean urology2014

ClearCode34: A prognostic risk predictor for localized clear cell renal cell carcinoma.

Samira A Brooks, A Rose Brannon, Joel S Parker, Jennifer C Fisher, Oishee Sen, Michael W Kattan, A Ari Hakimi, James J Hsieh, Toni K Choueiri, Pheroze Tamboli and 5 more

2 registry-linked trialsAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European urology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 166 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
166citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT04891055 unknown statusnot on this mapstarted 2018, after this paper: background citation

Prospective Translational Study Investigating Predictors of Outcome in Metastatic Renal Cell Carcinoma Patients Treated With Nivolumab (I-Rene Trial)

TypeobservationalSponsorFondazione IRCCS Istituto Nazionale dei Tumori, MilanoRan2018 to 2021Enrolled90ConditionsMetastatic Renal Cell Carcinoma
NCT05782400 active not recruitingnot on this mapstarted 2023, after this paper: background citation

Multiomics Approach for Patients Stratification and Novel Target Identification in Metastatic Clear Renal Cell Carcnoma

TypeobservationalSponsorFondazione IRCCS Istituto Nazionale dei Tumori, MilanoRan2023 to 2027Enrolled100ConditionsMetastatic Clear Cell Renal CarcinomaArmsCT scan, Plasma collection
3 · Its place in the literature

Who cites it

166 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  4. Axitinib versus placebo as an adjuvant treatment of renal cell carcinoma: results from the phase III, randomized ATLAS trial.Annals of oncology : official journal of the European Society for Medical Oncology · 2018
    Trial
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106 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Samira A BrooksUNC Lineberger Cancer Center, Chapel Hill, NC, USA.
A Rose BrannonUNC Lineberger Cancer Center, Chapel Hill, NC, USA; Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Joel S ParkerUNC Lineberger Cancer Center, Chapel Hill, NC, USA.
Jennifer C FisherUNC Lineberger Cancer Center, Chapel Hill, NC, USA.
Oishee SenUNC Lineberger Cancer Center, Chapel Hill, NC, USA.
Michael W KattanDepartment of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA.
A Ari HakimiDepartment of Surgery, Urology Service, Memorial Sloan Kettering Cancer Center, New York, USA.
James J HsiehDepartment of Surgery, Urology Service, Memorial Sloan Kettering Cancer Center, New York, USA.
Toni K ChoueiriDepartment of Medical Oncology and Kidney Cancer Center, Dana Farber Cancer Institute, Boston, MA, USA.
Pheroze TamboliDepartment of Pathology, MD Anderson Cancer Center, Houston, TX, USA.
Jodi K MaranchieDepartment of Urologic Oncology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
Peter HindsDepartment of Urologic Oncology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
C Ryan MillerUNC Lineberger Cancer Center, Chapel Hill, NC, USA; Department of Pathology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Matthew E NielsenUNC Lineberger Cancer Center, Chapel Hill, NC, USA; Department of Urology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
W Kimryn RathmellUNC Lineberger Cancer Center, Chapel Hill, NC, USA; Department of Medicine, Division of Hematology and Oncology, and Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: Rathmell@med.unc.edu.

Funding

TOXICOLOGYT32ES007126 · NIEHS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ILONA JASPERS, Bernard E. Weissman · 1985 to 2026
$13.0M
Translational Research in Oncology Training ProgramT32CA160001 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI SAWYERS, CHARLES L. · 2011 to 2020
$2.0M
Enhancing Translational Science via Prospective InvestigationK24CA172355 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI RATHMELL, WENDY KIMRYN · 2013 to 2017
$870k
Howard Hughes Medical InstituteNCI NIH HHS 5T32CA160001NCI NIH HHS K24 CA172355NCI NIH HHS T32 CA160001NIEHS NIH HHS T32 ES007126
6 · The paper itself

Abstract

backgroundGene expression signatures have proven to be useful tools in many cancers to identify distinct subtypes of disease based on molecular features that drive pathogenesis, and to aid in predicting clinical outcomes. However, there are no current signatures for kidney cancer that are applicable in a clinical setting.

objectiveTo generate a signature biomarker for the clear cell renal cell carcinoma (ccRCC) good risk (ccA) and poor risk (ccB) subtype classification that could be readily applied to clinical samples to develop an integrated model for biologically defined risk stratification. DESIGN, SETTING, AND

participantsA set of 72 ccRCC sample standards was used to develop a 34-gene classifier (ClearCode34) for assigning ccRCC tumors to subtypes. The classifier was applied to RNA-sequencing data from 380 nonmetastatic ccRCC samples from the Cancer Genome Atlas (TCGA), and to 157 formalin-fixed clinical samples collected at the University of North Carolina. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Kaplan-Meier analyses were performed on the individual cohorts to calculate recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS). Training and test sets were randomly selected from the combined cohorts to assemble a risk prediction model for disease recurrence. RESULTS AND LIMITATIONS: The subtypes were significantly associated with RFS (p<0.01), CSS (p<0.01), and OS (p<0.01). Hazard ratios for subtype classification were similar to those of stage and grade in association with recurrence risk, and remained significant in multivariate analyses. An integrated molecular/clinical model for RFS to assign patients to risk groups was able to accurately predict CSS above established, clinical risk-prediction algorithms.

conclusionsThe ClearCode34-based model provides prognostic stratification that improves upon established algorithms to assess risk for recurrence and death for nonmetastatic ccRCC patients. PATIENT SUMMARY: We developed a 34-gene subtype predictor to classify clear cell renal cell carcinoma tumors according to ccA or ccB subtypes and built a subtype-inclusive model to analyze patient survival outcomes.

Indexed as

Gene ExpressionAdultAgedAged, 80 and overAnimalsBiomarkers, TumorCarcinoma, Renal CellDisease-Free SurvivalFemaleGene Expression ProfilingHumansKidney NeoplasmsMaleMiddle AgedNeoplasm Recurrence, LocalRisk AssessmentBiomarkers, TumorRNA, NeoplasmBiomarkerccRCCKidney cancerPrognosisRenal cell carcinomaTCGA

Identifiers

PMID24613583
PMCPMC4058355

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.