Evidence map›Paper›PMID 42755149›Full record

ArticleHGG advances2026

Massively parallel functional profiling prioritizes breast cancer risk variants and implicates CCDC88C in ER-positive breast cancer.

Kate Mackie, Harriet Kemp, Andrea Gunnell, James B Studd, Molly Went, Philip Law, Katarzyna Tomczyk, Rafaela Ferreira, Ernesto Lopez, Selin Sevgi and 7 more

Abstract read
In one paragraph

Article in HGG advances, 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
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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

17 authors.

Kate MackieThe Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW3 6JB, UK.
Harriet KempThe Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW3 6JB, UK.
Andrea GunnellDivision of Genetics and Epidemiology, The Institute of Cancer Research, London SW7 3RP, UK.
James B StuddDivision of Genetics and Epidemiology, The Institute of Cancer Research, London SW7 3RP, UK.
Molly WentDivision of Genetics and Epidemiology, The Institute of Cancer Research, London SW7 3RP, UK.
Philip LawDivision of Genetics and Epidemiology, The Institute of Cancer Research, London SW7 3RP, UK.
Katarzyna TomczykThe Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW3 6JB, UK.
Rafaela FerreiraThe Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW3 6JB, UK.
Ernesto LopezThe Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW3 6JB, UK.
Selin SevgiThe Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW3 6JB, UK.
Yunjiao LuThe Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW3 6JB, UK.
Alisa ZverevaThe Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW3 6JB, UK.
Nick OrrThe Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast BT9 7AE, UK.
Richard S HoulstonDivision of Genetics and Epidemiology, The Institute of Cancer Research, London SW7 3RP, UK.
Nichola JohnsonThe Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW3 6JB, UK.
Olivia FletcherThe Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW3 6JB, UK.
Syed HaiderThe Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW3 6JB, UK. Electronic address: Syed.Haider@icr.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genome-wide association studies (GWASs), combined with fine-mapping, have identified 196 independent signals associated with breast cancer risk. Deciphering the functional basis of these associations can inform our understanding of the biology and etiology of breast cancer. Decoding GWAS risk associations is challenging due to linkage disequilibrium between variants and because most variants map to non-coding regions, influencing breast cancer risk via cis-regulatory mechanisms that modulate the expression of target genes. To prioritize variants with allele-specific regulatory activity at breast cancer risk loci, we carried out a lentivirus-based massively parallel reporter assay (lentiMPRA) to screen 5,116 credible causal variants (CCVs) across these signals. We identified 709 variants mapping to 140 risk regions that are associated with significant variation between reference (REF) and alternative (ALT) alleles. A follow-up investigation at 14q32.11 revealed that rs7153397 may influence breast cancer risk through expression of CCDC88C, which in turn could impact prognosis. These findings provide a prioritized set of functional variants for downstream analyses, advancing our understanding of breast cancer risk mechanisms.

Indexed as

Breast NeoplasmsGenetic Predisposition to DiseaseReceptors, EstrogenAllelesFemaleGenome-Wide Association StudyHumansPolymorphism, Single NucleotideReceptors, Estrogenbreast cancerMPRA profilingrisk targets

Identifiers

PMID42755149
PMCPMC13657374

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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.