Evidence map›Paper›PMID 35842534›Full record

ArticleAnnals of surgical oncology2022

Machine Learning-Based Epigenetic Classifiers for Axillary Staging of Patients with ER-Positive Early-Stage Breast Cancer.

Javier I J Orozco, Julie Le, Miquel Ensenyat-Mendez, Jennifer L Baker, Joanne Weidhaas, Alexandra Klomhaus, Diego M Marzese, Maggie L DiNome

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of surgical oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 30% 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 2 countries.

Javier I J Orozco *Saint John's Cancer Institute, Providence Saint John's Health Center, Santa Monica, CA, USA.
Julie LeDepartment of Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
Miquel Ensenyat-MendezCancer Epigenetics Laboratory at the Cancer Cell Biology Group, Institut d'Investigació Sanitària Illes Balears (IdISBa), Carretera de Valldemosa 79, -1F, Palma, Spain.
Jennifer L BakerDepartment of Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
Joanne WeidhaasDepartment of Radiation Oncology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
Alexandra KlomhausDepartment of Medicine Statistics Core, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Diego M MarzeseCancer Epigenetics Laboratory at the Cancer Cell Biology Group, Institut d'Investigació Sanitària Illes Balears (IdISBa), Carretera de Valldemosa 79, -1F, Palma, Spain. diego.marzese@ssib.es.
Maggie L DiNomeDepartment of Surgery, Duke University School of Medicine, Durham, NC, USA. Maggie.dinome@duke.edu.ORCID http://orcid.org/0000-0002-1926-292X
University of California, Los Angeles · USHealth Research Institute of the Balearic Islands · ESDuke University · USSaint John's Health Center · US

Funding

UCLA Clinical Translational Science InstituteUL1TR001881 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ARLEEN F. BROWN, ARASH NAEIM · 2016 to 2026
$118.1M
MicroRNAs to Understand Cause and Outcome in Breast CancerR01CA157749 · NCI · YALE UNIVERSITY · PI SLACK, FRANK J., WEIDHAAS, JOANNE B · 2012 to 2016
$1.7M
NCATS NIH HHS UL1 TR001881NCATS NIH HHS UL1TR001881NCI NIH HHS R01 CA157749
6 · The paper itself

Abstract

backgroundIn the era of molecular stratification and effective multimodality therapies, surgical staging of the axilla is becoming less relevant for patients with estrogen receptor (ER)-positive early-stage breast cancer (EBC). Therefore, a nonsurgical method for accurately predicting lymph node disease is the next step in the de-escalation of axillary surgery. This study sought to identify epigenetic signatures in the primary tumor that accurately predict lymph node status. PATIENTS AND

methodsWe selected a cohort of patients in The Cancer Genome Atlas (TCGA) with ER-positive, HER2-negative invasive ductal carcinomas, and clinically-negative axillae (n = 127). Clinicopathological nomograms from the Memorial Sloan Kettering Cancer Center (MSKCC) and the MD Anderson Cancer Center (MDACC) were calculated. DNA methylation (DNAm) patterns from primary tumor specimens were compared between patients with pN0 and those with > pN0. The cohort was divided into training (n = 85) and validation (n = 42) sets. Random forest was employed to obtain the combinations of DNAm features with the highest accuracy for stratifying patients with > pN0. The most efficient combinations were selected according to the area under the curve (AUC).

resultsClinicopathological models displayed a modest predictive potential for identifying > pN0 disease (MSKCC AUC 0.76, MDACC AUC 0.69, p = 0.15). Differentially methylated sites (DMS) between patients with pN0 and those with > pN0 were identified (n = 1656). DMS showed a similar performance to the MSKCC model (AUC = 0.76, p = 0.83). Machine learning approaches generated five epigenetic classifiers, which showed higher discriminative potential than the clinicopathological variables tested (AUC > 0.88, p < 0.05).

conclusionsEpigenetic classifiers based on primary tumor characteristics can efficiently stratify patients with no lymph node involvement from those with axillary lymph node disease, thereby providing an accurate method of staging the axilla.

Indexed as

Breast NeoplasmsAxillaEpigenesis, GeneticFemaleHumansLymphatic MetastasisLymph Node ExcisionLymph NodesMachine LearningNeoplasm StagingNomogramsROC CurveSentinel Lymph Node Biopsy

Identifiers

PMID35842534
PMCPMC10413094
OpenAlexW4285587111

What OpenQuestion holds

Textmetadata
LicenceTDM
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.