Evidence map›Paper›PMID 28936799›Full record

ArticleAnnals of surgical oncology2017

Proteomic Features of Colorectal Cancer Identify Tumor Subtypes Independent of Oncogenic Mutations and Independently Predict Relapse-Free Survival.

Callisia N Clarke, Michael S Lee, Wei Wei, Ganiraju Manyam, Zhi-Qin Jiang, Yiling Lu, Jeffrey Morris, Bradley Broom, David Menter, Eduardo Vilar-Sanchez and 9 more

Open access · greenAbstract read
In one paragraph

Article in Annals of surgical oncology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
1.8field-weighted citation impact, top 17% of its field
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 36 citations in OpenAlex.

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  5. Annals of surgical treatment and research · 2024
    Article
  6. Biomarker Identification through Proteomics in Colorectal Cancer.International journal of molecular sciences · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors at 5 institutions in 2 countries.

Callisia N ClarkeDivision of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, USA. cnclarke@mcw.edu.
Michael S LeeDivision of Hematology and Oncology, University of North Carolina, Chapel Hill, NC, USA.
Wei WeiDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Ganiraju ManyamDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Zhi-Qin JiangDepartment of GI Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Yiling LuDepartment of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Jeffrey MorrisDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Bradley BroomDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
David MenterDepartment of GI Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Eduardo Vilar-SanchezDepartment of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Kanwal RaghavDepartment of GI Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Cathy EngDepartment of GI Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
George J ChangDepartment of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Iris SimonAgendia BV, Amsterdam, The Netherlands.
Rene BernardsDivision of Molecular Carcinogenesis, Cancer Systems Biology Centre and Cancer Genomics Centre, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
Michael OvermanDepartment of GI Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Gordon B MillsDepartment of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Dipen MaruDepartment of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Scott KopetzDepartment of GI Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
The University of Texas MD Anderson Cancer Center · USAgendia (Netherlands) · NLCancer Genomics Centre · NLMedical College of Wisconsin · USUniversity of North Carolina at Chapel Hill · US

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
TRAINING OF ACADEMIC SURGICAL ONCOLOGISTST32CA009599 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Ashley M Holder, Jennifer A. Wargo · 1988 to 2026
$13.2M
UNC Oncology K12K12CA120780 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI WILLIAM Y. KIM · 2007 to 2026
$9.6M
NCI NIH HHS K12 CA120780NCI NIH HHS P30 CA016672NCI NIH HHS T32 CA009599
6 · The paper itself

Abstract

backgroundThe directed study of the functional proteome in colorectal cancer (CRC) has identified critical protein markers and signaling pathways; however, the prognostic relevance of many of these proteins remains unclear.

methodsWe determined the prognostic implications of the functional proteome in 263 CRC tumor samples from patients treated at MD Anderson Cancer Center (MDACC) and 462 patients from The Cancer Genome Atlas (TCGA) to identify patterns of protein expression that drive tumorigenesis. A total of 163 validated proteins were analyzed by reverse phase protein array (RPPA). Unsupervised hierarchical clustering of the tumor proteins from the MDACC cohort was performed, and clustering was validated using RPPA data from TCGA CRC. Cox regression was used to identify predictors of tumor recurrence.

resultsClustering revealed dichotomization, with subtype A notable for a high epithelial-mesenchymal transition (EMT) protein signature, while subtype B was notable for high Akt/TSC/mTOR pathway components. Survival data were only available for the MDACC cohort and were used to evaluate prognostic relevance of these protein signatures. Group B demonstrated worse relapse-free survival (hazard ratio 2.11, 95% confidence interval 1.04-4.27, p = 0.039), although there was no difference in known genomic drivers between the two proteomic groups. Proteomic grouping and stage were significant predictors of recurrence on multivariate analysis. Eight proteins were found to be significant predictors of tumor recurrence on multivariate analysis: Collagen VI, FOXO3a, INPP4B, LcK, phospho-PEA15, phospho-PRAS40, Rad51, phospho-S6.

conclusionCRC can be classified into distinct subtypes by proteomic features independent of common oncogenic driver mutations. Proteomic analysis has identified key biomarkers with prognostic importance, however these findings require further validation in an independent cohort.

Indexed as

MutationBiomarkers, TumorCohort StudiesColorectal NeoplasmsFemaleFollow-Up StudiesGene Expression Regulation, NeoplasticHumansMaleMiddle AgedPrognosisProteomicsSurvival RateBiomarkers, Tumor

Identifiers

PMID28936799
PMCPMC6063735
OpenAlexW2760698245

What OpenQuestion holds

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Registered trials

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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.