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- 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.
- THOR is a targetable epigenetic biomarker with clinical implications in breast cancerPublication . Apolónio, Joana; Dias, João S.; Fernandes, Mónica T.; Komosa, Martin; Lipman, Tatiana; Zhang, Cindy H.; Leão, Ricardo; Lee, Donghyun; Nunes, Nuno M.; Maia, Ana-Teresa; Morera, José L.; Vicioso, Luis; Tabori, Uri; Castelo-Branco, PedroBreast cancer (BC) is the most frequently diagnosed cancer and a leading cause of death among women worldwide. Early BC is potentially curable, but the mortality rates still observed among BC patients demon‑ strate the urgent need of novel and more efective diagnostic and therapeutic options. Limitless self-renewal is a hallmark of cancer, governed by telomere maintenance. In around 95% of BC cases, this process is achieved by telom‑ erase reactivation through upregulation of the human telomerase reverse transcriptase (hTERT). The hypermethylation of a specifc region within the hTERT promoter, termed TERT hypermethylated oncological region (THOR) has been associated with increased hTERT expression in cancer. However, its biological role and clinical potential in BC have never been studied to the best of our knowledge. Therefore, we aimed to investigate the role of THOR as a biomarker and explore the functional impact of THOR methylation status in hTERT upregulation in BC.
- 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.
- Allele-specific miRNA-binding analysis identifies candidate target genes for breast cancer riskPublication . Jacinta-Fernandes, Ana; Xavier, Joana M.; Magno, Ramiro; Lage, Joel; Maia, Ana-TeresaMost breast cancer (BC) risk-associated single-nucleotide polymorphisms (raSNPs) identified in genome-wide association studies (GWAS) are believed to cis-regulate the expression of genes. We hypothesise that cis-regulatory variants contributing to disease risk may be affecting microRNA (miRNA) genes and/or miRNA binding. To test this, we adapted two miRNA-binding prediction algorithms-TargetScan and miRanda-to perform allele-specific queries, and integrated differential allelic expression (DAE) and expression quantitative trait loci (eQTL) data, to query 150 genome-wide significant ( P≤5×10-8 ) raSNPs, plus proxies. We found that no raSNP mapped to a miRNA gene, suggesting that altered miRNA targeting is an unlikely mechanism involved in BC risk. Also, 11.5% (6 out of 52) raSNPs located in 3'-untranslated regions of putative miRNA target genes were predicted to alter miRNA::mRNA (messenger RNA) pair binding stability in five candidate target genes. Of these, we propose RNF115, at locus 1q21.1, as a strong novel target gene associated with BC risk, and reinforce the role of miRNA-mediated cis-regulation at locus 19p13.11. We believe that integrating allele-specific querying in miRNA-binding prediction, and data supporting cis-regulation of expression, improves the identification of candidate target genes in BC risk, as well as in other common cancers and complex diseases.
- Colocalised genetic associations reveal alternative splicing variants as candidate causal links for breast cancer risk in 10 LociPublication . Duarte, André; Carrasqueiro, Beatriz; Vieira de Sousa, Cármen Sofia; Gonçalves de Gouveia Maia Xavier, Joana; Maia, Ana-TeresaSimple Summary Hundreds of common genetic variants have been linked to breast cancer, but their exact mechanisms of action remain unclear. Understanding these mechanisms could lead to better prevention strategies and improved survival rates. Our study focused on how these variants influence splicing-a process by which a gene's coding elements are rearranged to produce different proteins. By analysing data from healthy breast tissue, we identified 43 variants within twelve genes associated with both alternative splicing and breast cancer risk. We then used advanced computational tools and existing experimental data to explore the biological significance of these findings.Abstract Genome-wide association studies (GWASs) have revealed numerous loci associated with breast cancer risk, yet the precise causal variants, their impact on molecular mechanisms, and the affected genes often remain elusive. We hypothesised that specific variants exert their influence by affecting cis-regulatory alternative splice elements. An analysis of splicing quantitative trait loci (sQTL) in healthy breast tissue from female individuals identified multiple variants linked to alterations in splicing ratios. Through colocalisation analysis, we pinpointed 43 variants within twelve genes that serve as candidate causal links between sQTL and GWAS findings. In silico splice analysis highlighted a potential mechanism for three genes-FDPS, SGCE, and MRPL11-where variants in proximity to or on the splice site modulate usage, resulting in alternative splice transcripts. Further in vitro/vivo studies are imperative to fully understand how these identified changes contribute to breast oncogenesis. Moreover, investigating their potential as biomarkers for breast cancer risk could enhance screening strategies and early detection methods for breast cancer.
- Identification of candidate causal variants and target genes at 41 breast cancer risk loci through differential allelic expression analysisPublication . Gonçalves de Gouveia Maia Xavier, Joana; Magno, Ramiro; Russell, Roslin; Almeida, Bernardo P. de; Jacinta-Fernandes, Ana; Duarte, André; Besouro-Duarte, André; Dunning, Mark; Samarajiwa, Shamith; O’Reilly, Martin; MARQUES MAIA DE ALMEIDA, JOSE ANTONIO; Rocha, Cátia L.; Rosli, Nordiana; Ponder, Bruce A. J.; Maia, Ana-TeresaUnderstanding breast cancer genetic risk relies on identifying causal variants and candidate target genes in risk loci identified by genome-wide association studies (GWAS), which remains challenging. Since most loci fall in active gene regulatory regions, we developed a novel approach facilitated by pinpointing the variants with greater regulatory potential in the disease’s tissue of origin. Through genome-wide differential allelic expression (DAE) analysis, using microarray data from 64 normal breast tissue samples, we mapped the variants associated with DAE (daeQTLs). Then, we intersected these with GWAS data to reveal candidate risk regulatory variants and analysed their cis-acting regulatory potential. Finally, we validated our approach by extensive functional analysis of the 5q14.1 breast cancer risk locus. We observed widespread gene expression regulation by cis-acting variants in breast tissue, with 65% of coding and noncoding expressed genes displaying DAE (daeGenes). We identified over 54 K daeQTLs for 6761 (26%) daeGenes, including 385 daeGenes harbouring variants previously associated with BC risk. We found 1431 daeQTLs mapped to 93 different loci in strong linkage disequilibrium with risk-associated variants (risk-daeQTLs), suggesting a link between risk-causing variants and cis-regulation. There were 122 risk-daeQTL with stronger cis-acting potential in active regulatory regions with protein binding evidence. These variants mapped to 41 risk loci, of which 29 had no previous report of target genes and were candidates for regulating the expression levels of 65 genes. As validation, we identified and functionally characterised five candidate causal variants at the 5q14.1 risk locus targeting the ATG10 and ATP6AP1L genes, likely acting via modulation of alternative transcription and transcription factor binding. Our study demonstrates the power of DAE analysis and daeQTL mapping to identify causal regulatory variants and target genes at breast cancer risk loci, including those with complex regulatory landscapes. It additionally provides a genome-wide resource of variants associated with DAE for future functional studies.