Loading...
2 results
Search Results
Now showing 1 - 2 of 2
- Allelic expression imbalance of PIK3CA mutations is frequent in breast cancer and prognostically significantPublication . Correia, Lizelle; Magno, Ramiro; Xavier, JM; Almeida, Bernardo; Duarte, Isabel; Esteves, Filipa; Ghezzo, Marinella; Eldridge, Matthew; Sun, Chong; Bosma, Astrid; Mittempergher, Lorenza; Marreiros, Ana; Bernards, Rene; Caldas, Carlos; Chin, Suet-Feung; Maia, Ana-TeresaPIK3CA 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 a frequent allelic expression imbalance between the missense mutant and wild-type PIK3CA alleles in breast tumors from the METABRIC (70.2%) and the TCGA (60.1%) projects. When considering the mechanisms controlling allelic expression, 27.7% and 11.8% of tumors showed imbalance due to regulatory variants in cis, in the two studies respectively. Furthermore, preferential expression of the mutant allele due to cis-regulatory variation is associated with poor prognosis in the METABRIC tumors (P = 0.031). Interestingly, ER-, PR-, and HER2+ tumors showed significant preferential expression of the mutated allele in both datasets. Our work provides compelling evidence to support the clinical utility of PIK3CA allelic expression in breast cancer in identifying patients of poorer prognosis, and those with low expression of the mutated allele, who will unlikely benefit from PI3K inhibitors. Furthermore, our work proposes a model of differential regulation of a critical cancer-promoting gene in breast cancer.
- Quincunx: an R package to query, download and wrangle PGS catalog dataPublication . Magno, Ramiro; Duarte, Isabel; Maia, Ana-TeresaFor two decades, GWAS identified individual variants associated with risk for complex diseases. These associations can be combined into polygenic scores (PGS) aiming at quantifying an individual’s risk to disease, inform on prognosis and even treatment response (Lambert et al., 2019). Broadly, PGS use summary statistics produced by GWAS to calculate a weighted sum of trait-associated alleles carried by each individual, in which the weights correspond to the per-allele size effects. Initially used to validate associations with disease and uncover interactions between variants, PGS have been more challenging to implement in the clinic. In 2020, over 1400 publications on PGS appeared in PubMed, raising the need for a standardized distribution of studies’ key data, assuring their wide evaluation and accurate use. The Polygenic Score (PGS) Catalog, created in 2019, is a publicly available, manually curated database of PGS and relevant metadata, that responds to this need (Lambert et al., 2020). Its current release [date 2021-02-03] includes data from 133 publications and 721 PGS associated with 194 traits. Currently, data is accessed via three ways: (i) the web graphical user interface (GUI); (ii) by downloading database dumps; and (iii) the recent PGS Catalog representational state transfer (REST) application programming interface (API), the preferred method for batch analyses.