Evidence map›Paper›PMID 32880016›Full record

ArticleBreast cancer research and treatment2020

Development and validation of prognostic gene signature for basal-like breast cancer and high-grade serous ovarian cancer.

Yi Zhang, Jianfang Liu, Praveen-Kumar Raj-Kumar, Lori A Sturtz, Anupama Praveen-Kumar, Howard H Yang, Maxwell P Lee, J Leigh Fantacone-Campbell, Jeffrey A Hooke, Albert J Kovatich and 2 more

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In one paragraph

Article in Breast cancer research and treatment, 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
0.3field-weighted citation impact, top 39% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

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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

12 authors at 3 institutions in 1 country.

Yi ZhangChan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA.
Jianfang LiuChan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA.
Praveen-Kumar Raj-KumarChan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA.ORCID http://orcid.org/0000-0001-6596-9283
Lori A SturtzChan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA.
Anupama Praveen-KumarChan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA.
Howard H YangCenter for Cancer Research, National Cancer Institute, Rockville, MD, USA.
Maxwell P LeeCenter for Cancer Research, National Cancer Institute, Rockville, MD, USA.
J Leigh Fantacone-CampbellMurtha Cancer Center Research Program, Bethesda, MD, USA.
Jeffrey A HookeMurtha Cancer Center Research Program, Bethesda, MD, USA.
Albert J KovatichMurtha Cancer Center Research Program, Bethesda, MD, USA.
Craig D ShriverMurtha Cancer Center Research Program, Bethesda, MD, USA.
Hai HuChan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA. h.hu@wriwindber.org.ORCID http://orcid.org/0000-0001-5345-8371
Windber Research Institute · USHenry M. Jackson Foundation · USNational Cancer Institute · US

Funding

Integrated analyses of genetics, epigenetics, and gene expressionZIHBC011703 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI LEE, MAXWELL · 2016 to 2025
$10.9M
Intramural NIH HHS ZIH BC011703Uniformed Services University of the Health Sciences HU0001-16-2-0004;Subawards 3406 and 3425
6 · The paper itself

Abstract

purposeMolecular similarities have been reported between basal-like breast cancer (BLBC) and high-grade serous ovarian cancer (HGSOC). To date, there have been no prognostic biomarkers that can provide risk stratification and inform treatment decisions for both BLBC and HGSOC. In this study, we developed a molecular signature for risk stratification in BLBC and further validated this signature in HGSOC.

methodsRNA-seq data was downloaded from The Cancer Genome Atlas (TCGA) project for 190 BLBC and 314 HGSOC patients. Analyses of differentially expressed genes between recurrent vs. non-recurrent cases were performed using different bioinformatics methods. Gene Signature was established using weighted linear combination of gene expression levels. Their prognostic performance was evaluated using survival analysis based on progression-free interval (PFI) and disease-free interval (DFI).

results63 genes were differentially expressed between 18 recurrent and 40 non-recurrent BLBC patients by two different methods. The recurrence index (RI) calculated from this 63-gene signature significantly stratified BLBC patients into two risk groups with 38 and 152 patients in the low-risk (RI-Low) and high-risk (RI-High) groups, respectively (p = 0.0004 and 0.0023 for PFI and DFI, respectively). Similar performance was obtained in the HGSOC cohort (p = 0.0131 and 0.004 for PFI and DFI, respectively). Multivariate Cox regression adjusting for age, grade, and stage showed that the 63-gene signature remained statistically significant in stratifying HGSOC patients (p = 0.0005).

conclusionA gene signature was identified to predict recurrence in BLBC and HGSOC patients. With further validation, this signature may provide an additional prognostic tool for clinicians to better manage BLBC, many of which are triple-negative and HGSOC patients who are currently difficult to treat.

Indexed as

Breast NeoplasmsCystadenocarcinoma, SerousOvarian NeoplasmsBiomarkers, TumorFemaleHumansNeoplasm Recurrence, LocalPrognosisBiomarkers, TumorBasal-like breast cancerGene signatureHigh-grade serous ovarian cancerPrognosisRecurrence

Identifiers

PMID32880016
PMCPMC8916168
OpenAlexW3082585563

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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.