Evidence map›Paper›PMID 30594216›Full record

ArticleJournal of experimental & clinical cancer research : CR2018

Classification of triple-negative breast cancers based on Immunogenomic profiling.

Yin He, Zehang Jiang, Cai Chen, Xiaosheng Wang

Open access · goldAbstract read
In one paragraph

Article in Journal of experimental & clinical cancer research : CR, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 412 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
412citing papers in PubMed, 2 pooled it
19.4field-weighted citation impact, top 1% 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

412 citing papers in PubMed, 2 syntheses or guidelines pooled it, 686 citations in OpenAlex.

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352 more citing papers are in PubMed but not listed here.

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

4 authors at 2 institutions in 2 countries.

Yin HeBiomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, Nanjing, 211198, China.
Zehang JiangBiomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, Nanjing, 211198, China.
Cai ChenDepartment of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA.
Xiaosheng WangBiomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, Nanjing, 211198, China. xiaosheng.wang@cpu.edu.cn.ORCID http://orcid.org/0000-0002-7199-7093
China Pharmaceutical University · CNUniversity of California San Diego · US

Funding

China Pharmaceutical University 2632018YX01China Pharmaceutical University 3150120001
6 · The paper itself

Abstract

backgroundAbundant evidence shows that triple-negative breast cancer (TNBC) is heterogeneous, and many efforts have been devoted to identifying TNBC subtypes on the basis of genomic profiling. However, few studies have explored the classification of TNBC specifically based on immune signatures that may facilitate the optimal stratification of TNBC patients responsive to immunotherapy.

methodsUsing four publicly available TNBC genomics datasets, we classified TNBC on the basis of the immunogenomic profiling of 29 immune signatures. Unsupervised and supervised machine learning methods were used to perform the classification.

resultsWe identified three TNBC subtypes that we named Immunity High (Immunity_H), Immunity Medium (Immunity_M), and Immunity Low (Immunity_L) and demonstrated that this classification was reliable and predictable by analyzing multiple different datasets. Immunity_H was characterized by greater immune cell infiltration and anti-tumor immune activities, as well as better survival prognosis compared to the other subtypes. Besides the immune signatures, some cancer-associated pathways were hyperactivated in Immunity_H, including apoptosis, calcium signaling, MAPK signaling, PI3K-Akt signaling, and RAS signaling. In contrast, Immunity_L presented depressed immune signatures and increased activation of cell cycle, Hippo signaling, DNA replication, mismatch repair, cell adhesion molecule binding, spliceosome, adherens junction function, pyrimidine metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and RNA polymerase pathways. Furthermore, we identified a gene co-expression subnetwork centered around five transcription factor (TF) genes (CORO1A, STAT4, BCL11B, ZNF831, and EOMES) specifically significant in the Immunity_H subtype and a subnetwork centered around two TF genes (IRF8 and SPI1) characteristic of the Immunity_L subtype.

conclusionsThe identification of TNBC subtypes based on immune signatures has potential clinical implications for TNBC treatment.

Indexed as

Cell Line, TumorFemaleHumansImmunogeneticsMachine LearningSignal TransductionSurvival AnalysisTriple Negative Breast NeoplasmsClassificationImmunogenomic profilingMachine learningTriple-negative breast cancerTumor immunity

Identifiers

PMID30594216
PMCPMC6310928
OpenAlexW2907190362

What OpenQuestion holds

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LicenceCC BY
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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.