Evidence map›Paper›PMID 26930401›Full record

ArticlePloS one2016

Stratification of Prognosis of Triple-Negative Breast Cancer Patients Using Combinatorial Biomarkers.

Yong Yue, Kristine Astvatsaturyan, Xiaojiang Cui, Xiao Zhang, Benedick Fraass, Shikha Bose

Abstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing 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.

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

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

  1. Pooled it
  2. Trial
  3. Cancers · 2024
    Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. Review
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

6 authors.

Yong YueDepartment of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, California, United States of America.
Kristine AstvatsaturyanDepartment of Pathology, Cedars-Sinai Medical Center, Los Angeles, California, United States of America.
Xiaojiang CuiDepartments of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America.
Xiao ZhangBiostatistics and Bioinformatics Core, Cedars-Sinai Medical Center, Los Angeles, California, United States of America.
Benedick FraassDepartment of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, California, United States of America.
Shikha BoseDepartment of Pathology, Cedars-Sinai Medical Center, Los Angeles, California, United States of America.

Funding

The Role of FOXCI in Basal-like Breast CancerR01CA151610 · NCI · SAINT JOHN'S CANCER INSTITUTE · PI CUI, XIAOJIANG · 2011 to 2022
$3.8M
NCI NIH HHS CA151610NCI NIH HHS R01 CA151610
6 · The paper itself

Abstract

backgroundTriple-negative breast cancer (TNBC) is highly diverse group of cancers, and generally considered an aggressive disease associated with poor survival. Stratification of TNBC is highly desired for both prognosis and treatment decisions to identify patients who may benefit from less aggressive therapy.

methodsThis study retrieved 192 consecutive non-metastasis TNBC patients who had undergone a resection of a primary tumor from 2008 to 2012. All samples were negative for ER, PR, and HER2/neu. Disease-free-survival (DFS) and overall-survival (OS) were evaluated for expression of immunohistochemical biomarkers (P53, Ki-67, CK5/6 and EGFR), as well as clinicopathological variables including age, tumor size, grade, lymph node status, pathologic tumor and nodal stages. The cutoff values of the basal biomarkers, EGFR and CK5/6, were estimated by time-dependent ROC curves. The prognostic values of combinatorial variables were identified by univariate and multivariate Cox analysis. Patients were stratified into different risk groups based on expression status of identified prognostic variables.

resultsMedian age was 57 years (range, 28-92 years). Patients' tumor stage and nodal stage were significantly associated with OS and DFS. EGFR and CK5/6 were significant prognostic variables at cutoff points of 15% (p = 0.001, AUC = 0.723), and 50% (p = 0.006, AUC = 0.675), respectively. Multivariate Cox analysis identified five significant variables: EGFR (p = 0.016), CK5/6 (p = 0.018), Ki-67 (p = 0.048), tumor stage (p = 0.010), and nodal stage (p = 0.003). Patients were stratified into low basal (EGFR≤15% and CK5/6≤50%) and high basal (EGFR>15% and/or CK5/6>50%) expression groups. In the low basal expression group, patients with low expressions of Ki-67, low tumor and nodal stage had significantly better survival than those with high expressions/stages of three variables, log-rank p = 0.015 (100% vs 68% at 50 months). In the high basal expression group, patient with high basal expression of both biomarkers (EGFR >15% and CK5/6 >50%) had worse survival (mean DFS = 25 months, 41.7% event rate) than those patient with high expression of either one marker (mean DFS = 34 months, 25.5% event rate).

conclusionsImmunoexpression of basal biomarkers, EGFR and CK5/6, is useful in predicting survival of TNBC patients. Integrated with Ki-67, tumor and nodal stages, combinatorial biomarker analysis provides a feasible clinical solution to stratify patient risks and help clinical decision-making with respect to selecting the appropriate therapies for individual patients.

Indexed as

AdultAgedAged, 80 and overBiomarkers, TumorBreastDisease-Free SurvivalErb-b2 Receptor Tyrosine KinasesErbB ReceptorsFemaleHumansImmunohistochemistryKaplan-Meier EstimateMiddle AgedPrognosisReceptors, EstrogenReceptors, ProgesteroneBiomarkers, TumorERBB2 protein, humanErb-b2 Receptor Tyrosine KinasesErbB ReceptorsReceptors, EstrogenReceptors, Progesterone

Identifiers

PMID26930401
PMCPMC4773063

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.