Evidence map›Paper›PMID 33226082›Full record

ArticleBioscience reports2020

A 23-gene prognostic classifier for prediction of recurrence and survival for Asian breast cancer patients.

Ting-Hao Chen, Jian-Ying Chiu, Kuan-Hui Shih

Abstract read
In one paragraph

Article in Bioscience reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Prognostic Comparison between Oncotype DXDiagnostics (Basel, Switzerland) · 2022
    Article
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

3 authors.

Ting-Hao ChenDepartment of Medical Operation, Amwise Diagnostics Pte. Ltd., Singapore.
Jian-Ying ChiuDepartment of Medical Operation, Amwise Diagnostics Pte. Ltd., Singapore.
Kuan-Hui ShihDepartment of Medical Operation, Amwise Diagnostics Pte. Ltd., Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We report a 23- gene-classifier profiled from Asian women, with the primary purpose of assessing its clinical utility towards improved risk stratification for relapse for breast cancer patients from Asian cohorts within 10 years' following mastectomy. Four hundred and twenty-two breast cancer patients underwent mastectomy and were used to train the classifier on a logistic regression model. A subset of 197 patients were chosen to be entered into the follow-up studies post mastectomy who were examined to determine the patterns of recurrence and survival analysis based on gene expression of the gene classifier, age at diagnosis, tumor stage and lymph node status, over a 5 and 10 years follow-up period. Metastasis to lymph node (N2-N3) with N0 as the reference (N2 vs. N0 hazard ratio: 2.02 (1.05-8.70), N3 vs. N0 hazard ratio: 4.32 (1.41-13.22) for 5 years) and gene expression of the 23-gene panel (P=0.06, 5 years and 0.02, 10 years, log-rank test) were found to have significant discriminatory effects on the risk of relapse (HR (95%CI):2.50 (0.95-6.50)). Furthermore, survival curves for subgroup analysis with N0-N1 and T1-T2 predicted patients with higher risk scores. The study provides robust evidence of the effectiveness of the 23-gene-classifier and could be used to determine the risk of relapse event (locoregional and distant recurrence) in Asian patients, leading to a meaningful reduction in chemotherapy recommendations.

Indexed as

Gene Expression ProfilingTranscriptomeAdultAsian PeopleBiomarkers, TumorBreast NeoplasmsClinical Decision-MakingDatabases, GeneticDisease ProgressionFemaleHumansLymphatic MetastasisMiddle AgedNeoplasm Recurrence, LocalPredictive Value of TestsRisk AssessmentBiomarkers, TumorAsianclassifier modelgene expression profilerecurrence

Identifiers

PMID33226082
PMCPMC7711061

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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.