Biology subjects

Fonseca, M. A. S.

Publications and source records attributed to Fonseca, M. A. S..

4 recordsLinked to original sources

bioRxiv2021

A cellular and molecular portrait of endometriosis subtypes

Endometriosis is a common, benign condition characterized by extensive heterogeneity in lesion appearance and patient symptoms. We profiled transcriptomes of 207,949 individual cells from endometriomata (n=7), extra-ovarian endometriosis (n=19), eutopic endometrium (n=4), unaffected ovary (n=1) and endometriosis-free peritoneum (n=4) to create a cellular atlas of endometrial-type epithelial cells, endometrial-type stromal cells and microenvironmental cell populations across tissue sites. Signatures of endometrial-type epithelium and stroma differed markedly across eutopic endometrium, endometrioma, superficial extra-ovarian disease and deep infiltrating endometriosis, suggesting that extensive transcriptional reprogramming is a core component of the disease process. Endometriomas were notable for the dysregulation of pro-inflammatory pathways and upregulation of complement proteins C3 and C7. Somatic ARID1A mutation in epithelial cells was associated with upregulation of pro-angiogenic factor SOX17 and remodeling of the endothelial cell compartment. Finally, signatures of endometriosis-associated endometrial-type epithelial clusters were enriched in ovarian cancers, reinforcing the epidemiologic associations between these two diseases.

genomics↗

bioRxiv2020

Reprogramming of the FOXA1 cistrome in treatment-emergent neuroendocrine prostate cancer

Lineage plasticity, the ability of a cell to alter its identity, is an increasingly common mechanism of adaptive resistance to targeted therapy in cancer1,2. An archetypal example is the development of neuroendocrine prostate cancer (NEPC) after treatment of prostate adenocarcinoma (PRAD) with inhibitors of androgen signaling. NEPC is an aggressive variant of prostate cancer that aberrantly expresses genes characteristic of neuroendocrine (NE) tissues and no longer depends on androgens. To investigate the epigenomic basis of this resistance mechanism, we profiled histone modifications in NEPC and PRAD patient-derived xenografts (PDXs) using chromatin immunoprecipitation and sequencing (ChIP-seq). We identified a vast network of cis-regulatory elements (N~15,000) that are recurrently activated in NEPC. The FOXA1 transcription factor (TF), which pioneers androgen receptor (AR) chromatin binding in the prostate epithelium3,4, is reprogrammed to NE-specific regulatory elements in NEPC. Despite loss of dependence upon AR, NEPC maintains FOXA1 expression and requires FOXA1 for proliferation and expression of NE lineage-defining genes. Ectopic expression of the NE lineage TFs ASCL1 and NKX2-1 in PRAD cells reprograms FOXA1 to bind to NE regulatory elements and induces enhancer activity as evidenced by histone modifications at these sites. Our data establish the importance of FOXA1 in NEPC and provide a principled approach to identifying novel cancer dependencies through epigenomic profiling.

cancer biology↗

bioRxiv2019

Predicting master transcription factors from pan-cancer expression data

The function of critical developmental regulators can be subverted by cancer cells to control expression of oncogenic transcriptional programs. These "master transcription factors" (MTFs) are often essential for cancer cell survival and represent vulnerabilities that can be exploited therapeutically. The current approaches to identify candidate MTFs examine super-enhancer associated transcription factor-encoding genes with high connectivity in network models. This relies on chromatin immunoprecipitation-sequencing (ChIP-seq) data, which is technically challenging to obtain from primary tumors, and is currently unavailable for many cancer types and clinically relevant subtypes. In contrast, gene expression data are more widely available, especially for rare tumors and subtypes where MTFs have yet to be discovered. We have developed a predictive algorithm called CaCTS (Cancer Core Transcription factor Specificity) to identify candidate MTFs using pan-cancer RNA-sequencing data from The Cancer Genome Atlas. The algorithm identified 273 candidate MTFs across 34 tumor types and recovered known tumor MTFs. We also made novel predictions, including for cancer types and subtypes for which MTFs have not yet been characterized. Clustering based on MTF predictions reproduced anatomic groupings of tumors that share 1-2 lineage-specific candidates, but also dictated functional groupings, such as a squamous group that comprised five tumor subtypes sharing 3 common MTFs. PAX8, SOX17, and MECOM were candidate factors in high-grade serous ovarian cancer (HGSOC), an aggressive tumor type where the core regulatory circuit is currently uncharacterized. PAX8, SOX17, and MECOM are required for cell viability and lie proximal to super-enhancers in HGSOC cells. ChIP-seq revealed that these factors co-occupy HGSOC regulatory elements globally and co-bind at critical gene loci including MUC16 (CA-125). Addiction to these factors was confirmed in studies using THZ1 to inhibit transcription in HGSOC cells, suggesting early down-regulation of these genes may be responsible for cytotoxic effects of THZ1 on HGSOC models. Identification of MTFs across 34 tumor types and 140 subtypes, especially for those with limited understanding of transcriptional drivers paves the way to therapeutic targeting of MTFs in a broad spectrum of cancers.

cancer biology↗

bioRxiv2017

In silico identification of non-coding RNAs in Halobacterium salinarum NRC-1 model archeon organism

BackgroundIn addition to the regulatory elements already known, for instance, transcription factors or post-translation modifications, there is growing interest in the regulatory role played by non-coding RNA molecules (ncRNA), whose functions are performed at a different level of biological information processing. Model organisms provide a convenient way of working in the laboratory, and different research groups use these models to conduct studies on the cellular mechanisms present in these organisms. Although some ncRNAs elements have been found in the Halobacterium salinarum model organism, we believe that not enough is known about these genomic regions.\n\nMethodsTherefore, an in silico analysis for ncRNA identification was conducted on H. salinarum NRC-1. Considering a data integration perspective and some available methodologies, several machine learning models were built and used to designate candidate ncRNAs genome regions.\n\nResultsA total of 42 new ncRNAs were identified. Combing analysis with other available tools, it had been observed that some suggested candidates also was found with different methodologies and thus, it highlights the proposed results.

bioinformatics↗