Evidence map›Paper›PMID 42657161›Full record

ReviewFrontiers in oncology2026

Oral selective estrogen receptor degraders and biomarker-driven therapy in ER-positive breast cancer: mechanisms, clinical evidence, and future directions.

Anton Yu Alasheev, Raneem Hamama, Elena V Petersen, Philipp Y Maximov

Abstract readReview
In one paragraph

Review in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Anton Yu AlasheevInstitute of Future Biophysics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia.
Raneem HamamaInstitute of Future Biophysics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia.
Elena V PetersenInstitute of Future Biophysics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia.
Philipp Y MaximovInstitute of Future Biophysics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer (BC) remains one of the most prevalent malignancies worldwide, with approximately 70% of cases being estrogen receptor-positive (ER+). Endocrine therapy targeting the estrogen receptor has revolutionized BC treatment, evolving from surgical oophorectomy to selective estrogen receptor modulators (SERMs) and aromatase inhibitors (AIs). Selective estrogen receptor degraders (SERDs) represent the latest advancement in hormonal therapy, offering complete receptor blockade and degradation. This review comprehensively examines the mechanisms of SERD action, their role in overcoming resistance to conventional endocrine therapy, and clinical applications of approved agents including fulvestrant, elacestrant, imlunestrant, and vepdegestrant, the first FDA-approved PROTAC estrogen receptor degrader. We discuss emerging oral SERDs such as giredestrant and camizestrant, novel PROTAC-based approaches, and combination strategies with CDK4/6 inhibitors, PI3K inhibitors, and other targeted agents. Special attention is given to ESR1 mutations as biomarkers for patient selection and therapy optimization. The review highlights key clinical trials including EMERALD, EMBER-3, SERENA-6, and lidERA that have shaped current treatment guidelines and points toward future directions in SERD development.

Indexed as

biomarker-driven therapybreast cancerctDNAelacestrantendocrine therapyESR1 mutationsestrogen receptorfulvestrant

Identifiers

PMID42657161
PMCPMC13508036

What OpenQuestion holds

Textmetadata
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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.