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Massively parallel functional profiling identifies CCDC88C as a risk gene for ER-positive breast cancer

Mackie, K.; Kemp, H.; Gunnell, A.; Studd, J. B.; Went, M.; Law, P.; Tomczyk, K.; Sevgi, S.; Lu, Y.; Orr, N.; Houlston, R. S.; Johnson, N.; Fletcher, O.; Haider, S.

2026-03-03 genetic and genomic medicine
10.64898/2026.03.02.26347419 medRxiv
Show abstract

Genome wide association studies (GWAS), 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 aetiology 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 identify the functional variants driving breast cancer risk associations, we carried out a lentivirus-based massively parallel reporter assay (lentiMPRA) to screen 5,116 credible causal variants across these signals. We identified 709 variants mapping to 140 risk regions, that are associated with significant variation between REF and ALT alleles. A follow-up investigation at 14q32.11 revealed rs7153397 may impact expression of CCDC88C to influence both breast cancer risk and prognosis. These findings provide a prioritised set of functional variants for downstream analyses, advancing our understanding of breast cancer risk mechanisms.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.