Evidence map›Paper›PMID 30691046›Full record

ArticleInternational journal of molecular sciences2019

Molecular Insights into the Classification of Luminal Breast Cancers: The Genomic Heterogeneity of Progesterone-Negative Tumors.

Gianluca Lopez, Jole Costanza, Matteo Colleoni, Laura Fontana, Stefano Ferrero, Monica Miozzo, Nicola Fusco

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2019. 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 16% 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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  19. Prognostic Value of theCancer management and research · 2021
    Article
  20. Frontiers in oncology · 2021
    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

7 authors at 2 institutions in 1 country.

Gianluca LopezDivision of Pathology, Fondazione IRCCS Ca' Granda-Ospedale Maggiore Policlinico, 20122 Milan, Italy. gianluca.lopez@unimi.it.
Jole CostanzaResearch Laboratory Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, 20122, Milan, Italy. jole.costanza@policlinico.mi.it.
Matteo ColleoniDivision of Pathology, Fondazione IRCCS Ca' Granda-Ospedale Maggiore Policlinico, 20122 Milan, Italy. matteo.colleoni1@studenti.unimi.it.
Laura FontanaMedical Genetics, Department of Pathophysiology and Transplantation, University of Milan, 20122, Milan, Italy. laura.fontana@unimi.it.
Stefano FerreroDivision of Pathology, Fondazione IRCCS Ca' Granda-Ospedale Maggiore Policlinico, 20122 Milan, Italy. stefano.ferrero@unimi.it.
Monica MiozzoResearch Laboratory Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, 20122, Milan, Italy. monica.miozzo@unimi.it.
Nicola FuscoDivision of Pathology, Fondazione IRCCS Ca' Granda-Ospedale Maggiore Policlinico, 20122 Milan, Italy. nicola.fusco@unimi.it.
Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico · ITUniversity of Milan · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Estrogen receptor (ER)-positive progesterone receptor (PR)-negative breast cancers are infrequent but clinically challenging. Despite the volume of genomic data available on these tumors, their biology remains poorly understood. Here, we aimed to identify clinically relevant subclasses of ER+/PR- breast cancers based on their mutational landscape. The Cancer Genomics Data Server was interrogated for mutational and clinical data of all ER+ breast cancers with information on PR status from The Cancer Genome Atlas (TCGA), Memorial Sloan Kettering (MSK), and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) projects. Clustering analysis was performed using gplots, ggplot2, and ComplexHeatmap packages. Comparisons between groups were performed using the Student's t-test and the test of Equal or Given Proportions. Survival curves were built according to the Kaplan⁻Meier method; differences in survival were assessed with the log-rank test. A total of 3570 ER+ breast cancers (PR- n = 959, 27%; PR+ n = 2611, 73%) were analyzed. Mutations in well-known cancer genes such as TP53, GATA3, CDH1, HER2, CDH1, and BRAF were private to or enriched for in PR- tumors. Mutual exclusivity analysis revealed the presence of four molecular clusters with significantly different prognosis on the basis of PIK3CA and TP53 status. ER+/PR- breast cancers are genetically heterogeneous and encompass a variety of distinct entities in terms of prognostic and predictive information.

Indexed as

Genetic HeterogeneityAdultAgedAged, 80 and overAntigens, CDBreast NeoplasmsCadherinsClass I Phosphatidylinositol 3-KinasesCluster AnalysisDNA Mutational AnalysisErb-b2 Receptor Tyrosine KinasesFemaleGATA3 Transcription FactorHumansMiddle AgedProto-Oncogene Proteins B-rafAntigens, CDBRAF protein, humanCadherinsCDH1 protein, humanClass I Phosphatidylinositol 3-KinasesERBB2 protein, humanErb-b2 Receptor Tyrosine KinasesGATA3 protein, humanGATA3 Transcription FactorPIK3CA protein, humanProto-Oncogene Proteins B-rafReceptors, ProgesteroneTP53 protein, humanTumor Suppressor Protein p53breast cancermutational profilingPI3K pathwayprogesterone receptor negativeTP53

Identifiers

PMID30691046
PMCPMC6386970
OpenAlexW2911710003

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.