bioRxivCancer Biology
Allelic expression imbalance of PIK3CA mutations is frequent in breast cancer and prognostically significant
L. W. Correia, J. M. Xavier, R. Magno, B. P. de Almeida, F. Esteves, I. Duarte, M. Eldridge, C. Sun, A. Bosma, L. Mittempergher, A. Marreiros, R. Bernards, C. Caldas, S.-F. Chin, A.-T. Maia
Abstract
PIK3CA mutations are the most common in breast cancer, particularly in the estrogen receptor positive cohort, but the benefit of PI3K inhibitors has had limited success compared with approaches targeting other less common mutations. We found allelic imbalances in the expression of PIK3CA in normal breast tissue and mapped a germline candidate regulatory variant. An imbalance was also frequently observed in the expression of the missense mutant and wild-type PIK3CA alleles in breast tumors from METABRIC and TCGA projects. Moreover, although 60% of tumors preferentially expressed the mutant allele, 10% did preferentially express the wild-type allele. Mechanistically, we show that these imbalances are more frequently due to regulatory variants in cis than altered copy-number and predict that somatic variants have a more significant role than germline ones. We further found that imbalanced allelic expression between mutant and wild-type alleles due to cis-regulatory variants associated with poor prognosis (p=0.0081). Interestingly, ER+, PR+, and Her2- tumors expressing very low levels of the mutant allele had the poorest prognosis (DSS <7.5yrs for ER+ and PR+ tumors and <5yrs for Her2- tumors). Hence, our work provides compelling evidence to support the clinical utility of PIK3CA allelic expression in breast cancer in identifying this cohort of low mutant allele expressing patients of poorer prognosis, who will unlikely benefit from PI3K inhibitors. Furthermore, our work establishes a new model of differential regulation of critical cancer-promoting genes.
- organism: human
- assay: DNA methylation
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: protein structure prediction
- research focus: protein design
- research focus: drug response / drug discovery
- approach: statistical model
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