Evidence map›Paper›PMID 29788166›Full record

ArticleAnnals of oncology : official journal of the European Society for Medical Oncology2018

Combined pathologic-genomic algorithm for early-stage breast cancer improves cost-effective use of the 21-gene recurrence score assay.

M M Gage, W C Mylander, M Rosman, T Fujii, F Le Du, A Raghavendra, A K Sinha, J R Espinosa Fernandez, A James, N T Ueno and 2 more

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Article in Annals of oncology : official journal of the European Society for Medical Oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

M M GageDepartment of Surgery, Johns Hopkins Hospital, Baltimore.
W C MylanderThe Rebecca Fortney Breast Center, Anne Arundel Medical Center, Annapolis.
M RosmanThe Rebecca Fortney Breast Center, Anne Arundel Medical Center, Annapolis.
T FujiiDepartment of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA.
F Le DuDepartment of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA.
A RaghavendraDepartment of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA.
A K SinhaDepartment of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA.
J R Espinosa FernandezDepartment of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA.
A JamesDepartment of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA.
N T UenoDepartment of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA. Electronic address: rjackson1@aahs.org.
L TafraThe Rebecca Fortney Breast Center, Anne Arundel Medical Center, Annapolis.
R S JacksonThe Rebecca Fortney Breast Center, Anne Arundel Medical Center, Annapolis. Electronic address: rjackson1@aahs.org.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Fabien Delerue · 1985 to 2026
$290.8M
National Cancer Institute P30 CA016672
6 · The paper itself

Abstract

Background: The 21-gene recurrence score (RS) (Oncotype DX®; Genomic Health, Redwood City, CA) partitions hormone receptor positive, node negative breast cancers into three risk groups for recurrence. The Anne Arundel Medical Center (AAMC) model has previously been shown to accurately predict RS risk categories using standard pathology data. A pathologic-genomic (P-G) algorithm then is presented using the AAMC model and reserving the RS assay only for AAMC intermediate-risk patients. Patients and methods: A survival analysis was done using a prospectively collected institutional database of newly diagnosed invasive breast cancers that underwent RS assay testing from February 2005 to May 2015. Patients were assigned to risk categories based on the AAMC model. Using Kaplan-Meier methods, 5-year distant recurrence rates (DRR) were evaluated within each risk group and compared between AAMC and RS-defined risk groups. Five-year DRR were calculated for the P-G algorithm and compared with DRR for RS risk groups and the AAMC model's risk groups. Results: A total of 1268 cases were included. Five-year DRR were similar between the AAMC low-risk group (2.7%, n = 322) and the RS < 18 low-risk group (3.4%, n = 703), as well as between the AAMC high-risk group (22.8%, n = 230) and the RS > 30 high-risk group (23.0%, n = 141). Using the P-G algorithm, more patients were categorized as either low or high risk and the distant metastasis rate was 3.3% for the low-risk group (n = 739) and 24.2% for the high-risk group (n = 272). Using the P-G algorithm, 44% (552/1268) of patients would have avoided RS testing. Conclusions: AAMC model is capable of predicting 5-year recurrences in high- and low-risk groups similar to RS. Further, using the P-G algorithm, reserving RS for AAMC intermediate cases, results in larger low- and high-risk groups with similar prognostic accuracy. Thus, the P-G algorithm reliably identifies a significant portion of patients unlikely to benefit from RS assay and with improved ability to categorize risk.

Indexed as

Models, GeneticAlgorithmsBiomarkers, TumorBreastBreast NeoplasmsChemotherapy, AdjuvantCost-Benefit AnalysisFemaleFollow-Up StudiesGenetic TestingHumansIncidenceMastectomyMiddle AgedNeoplasm GradingNeoplasm Recurrence, LocalBiomarkers, Tumor

Identifiers

PMID29788166
PMCPMC5961181

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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.