Characterisation of GATA3 expression in invasive breast cancer: differences in histological subtypes and immunohistochemically defined molecular subtypes

Tang, Shao-hui; Yu, Bo; Xu, Xiaohua; Cheng, Yu‐Fan; Tu, Xiaoyu; Lu, Haizhou; Bi, Rui; Sun, Xin; Shui, Ruo Hong; Yang, Wentao

doi:10.1136/jclinpath-2016-204137

Cited by 57 publications

(43 citation statements)

References 43 publications

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“…Luminal A-specific candidates were not dependencies in basal-type cell lines, and vice versa. We detected some known subtypespecific factors including GATA3 in the luminal A subtype (Ciriello et al, 2015;Shaoxian et al, 2017), and FOXC1 and SOX9 in triple negative breast cancer (which is enriched in the basal subtype) (Wang et al, 2015).…”

Section: Subtype-specific Mtf Predictions Reveal Subtype-specific Regmentioning

confidence: 95%

Predicting master transcription factors from pan-cancer expression data

Reddy

Fonseca

Fuente

et al. 2019

Preprint

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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 pancancer 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. ChIPseq 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.

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Section: Subtype-specific Mtf Predictions Reveal Subtype-specific Regmentioning

confidence: 95%

Predicting master transcription factors from pan-cancer expression data

Reddy

Fonseca

Fuente

et al. 2019

Preprint

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“…BRCA had 323 marker genes (gene-effect score > 0.5). Well-known specific markers of BRCA, such as GATA3 [26] and ESR1 [27] were ranked at the 13 th and 98 th among all genes. Their classifying capability was predominately in BRCA (gene-effect scores, 0.89 and 0.67; Figure 4F).…”

Section: Interpretation Of the 1d-cnn Model To Investigate Cancer Marmentioning

confidence: 99%

Convolutional neural network models for cancer type prediction based on gene expression

Chiu²,

et al. 2020

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BackgroundPrecise prediction of cancer types is vital for cancer diagnosis and therapy. Important cancer marker genes can be inferred through predictive model. Several studies have attempted to build machine learning models for this task however none has taken into consideration the effects of tissue of origin that can potentially bias the identification of cancer markers. ResultsIn this paper, we introduced several Convolutional Neural Network (CNN) models that take unstructured gene expression inputs to classify tumor and non-tumor samples into their designated cancer types or as normal. Based on different designs of gene embeddings and convolution schemes, we implemented three CNN models: 1D-CNN, 2D-Vanilla-CNN, and 2D-Hybrid-CNN. The models were trained and tested on combined 10,340 samples of 33 cancer types and 731 matched normal tissues of The Cancer Genome Atlas (TCGA). Our models achieved excellent prediction accuracies (93.9-95.0%) among 34 classes (33 cancers and normal). Furthermore, we interpreted one of the models, known as 1D-CNN model, with a guided saliency technique and identified a total of 2,090 cancer markers (108 per class). The concordance of differential expression of these markers between the cancer type they represent and others is confirmed. In breast cancer, for instance, our model identified well-known markers, such as GATA3 and ESR1. Finally, we extended the 1D-CNN model for prediction of breast cancer subtypes and achieved an average accuracy of 88.42% among 5 subtypes. The codes can be found at -3 -https://github.com/chenlabgccri/CancerTypePrediction. ConclusionsHere we present novel CNN designs for accurate and simultaneous cancer/normal and cancer types prediction based on gene expression profiles, and unique model interpretation scheme to elucidate biologically relevance of cancer marker genes after eliminating the effects of tissue-of-origin. The proposed model had light hyperparameters to be trained and thus can be easily adapt to facilitate cancer diagnosis in the future. BackgroundCancer is the second leading cause of death worldwide, an average of one in six deaths is due to cancer [1]. Considerable research efforts have been devoted to cancer diagnosis and treatment techniques to lessen its impact on human health. Cancer prediction's major focus is on cancer susceptibility, recurrence, and prognosis, while the aim of cancer detection is the classification of tumor types and identification of markers for each cancer such that we can build a learning machine to identify specific metastatic tumor type or detect cancer at their earlier stage. With the increased awareness of precision medicine and early detection techniques matured over years of technology development [2][3][4], including particularly many detection screens achieving a sensitivity around 70-80% [5], the demand for applying novel machine learning methods to discover new biomarkers has become one of the key driving factors in many clinical and translational applications.Deep learning (DL), a branch of Artificial ...

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“…The GATA family of transcription factors (TFs) contain one or two zinc-finger domains that bind to the consensus DNA sequence A/T GATA A/G and is known to play crucial roles in the development and physiology of vertebrates, invertebrates, plants and fungi (Lentjes et al, 2016;Merika and Orkin, 1993). Aside from their developmental roles, vertebrate GATA factors are associated to diverse types of cancers, including intestinal tumors, leukemia, and breast cancer (Zheng and Blobel, 2010;Pihlajoki et al, 2016;Hellebrekers et al, 2009;Shaoxian et al, 2017). In humans, at least two GATA factors are expressed in the kidney (GATA3 and GATA5).…”

Section: Introductionmentioning

confidence: 99%

Novel roles for GATAe in growth, maintenance and proliferation of cell populations in the Drosophila renal tubule

et al. 2019

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The GATA family of transcription factors is implicated in numerous developmental and physiological processes in metazoans. In Drosophila melanogaster, five different GATA factor genes (pannier, serpent, grain, GATAd and GATAe) have been reported as essential in the development and identity of multiple tissues, including the midgut, heart and brain. Here, we present a novel role for GATAe in the function and homeostasis of the Drosophila renal (Malpighian) tubule. We demonstrate that reduced levels of GATAe gene expression in tubule principal cells induce uncontrolled cell proliferation, resulting in tumorous growth with associated altered expression of apoptotic and carcinogenic key genes. Furthermore, we uncover the involvement of GATAe in the maintenance of stellate cells and migration of renal and nephritic stem cells into the tubule. Our findings of GATAe as a potential master regulator in the events of growth control and cell survival required for the maintenance of the Drosophila renal tubule could provide new insights into the molecular pathways involved in the formation and maintenance of a functional tissue and kidney disease.

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Characterisation of GATA3 expression in invasive breast cancer: differences in histological subtypes and immunohistochemically defined molecular subtypes

Abstract: GATA3 exhibits a relatively high sensitivity for breast carcinomas. It is more sensitive than GCDFP15 and mammaglobin in luminal-like and HER2 overexpression subtypes. GATA3 expression is associated with breast carcinomas of luminal subtype and low histological grade.

Cited by 57 publications

References 43 publications

Predicting master transcription factors from pan-cancer expression data

Predicting master transcription factors from pan-cancer expression data

Convolutional neural network models for cancer type prediction based on gene expression

Novel roles for GATAe in growth, maintenance and proliferation of cell populations in the Drosophila renal tubule

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