bioRxivCancer Biology
Germline APOBEC3B deletion in Asian women increases somatic hypermutation in breast cancer that is associated with Her2 subtype, PIK3CA mutations, immune activation, and increased survival
J.-W. Pan, M. M. A. Zabidi, B.-K. Chong, M.-Y. Meng, P.-S. Ng, S. N. Hasan, B. Sandey, S. Bahnu, P. Rajadurai, C.-H. Yip, O. M. Rueda, C. Caldas, S.-F. Chin, S.-H. Teo
Abstract
A 30-kb deletion that eliminates the coding region of APOBEC3B (A3B) is >5 times more common in women of Asian compared to European descent. This polymorphism creates a chimera with the APOBEC3A (A3A) coding region and A3B 3UTR, and is associated with an increased risk for breast cancer in Asian women. Here, we explored the relationship between the A3B deletion polymorphism with tumour characteristics in Asian women. Using whole exome and whole transcriptome sequencing data of 527 breast tumours, we report that germline A3B deletion polymorphism leads to expression of the A3A-B hybrid isoform and increased APOBEC-associated somatic hypermutation. Hypermutated tumours, regardless of A3B germline status, were associated with the Her2 molecular subtype and PIK3CA mutations. Compared to non-hypermutated tumours, hypermutated tumours also had higher neoantigen burden, tumour heterogeneity and immune activation. Taken together, our results suggest that the germline A3B deletion polymorphism, via the A3A-B hybrid isoform, contributes to APOBEC-mutagenesis in a significant proportion of Asian breast cancers. In addition, APOBEC somatic hypermutation, regardless of A3B background, may be an important clinical biomarker for Asian breast cancers. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/135251v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@45b329org.highwire.dtl.DTLVardef@1f5770corg.highwire.dtl.DTLVardef@5ac017org.highwire.dtl.DTLVardef@1f939ad_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: human
- assay: whole-exome sequencing
- assay: whole-genome sequencing
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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