Chromatin accessibility landscape and active transcription factors in primary human invasive lobular and ductal breast carcinomas

Lee, Sanghoon; Castel, Pau

doi:10.1186/s13058-022-01550-y

Cited by 4 publications

(5 citation statements)

References 98 publications

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“…9a–c ). A previous study in which TCGA–BRCA ATAC-seq data were reanalysed revealed that the FOXA1 motif was more enriched in ER + /HER2 − invasive ductal carcinoma (IDC) tumours than in ER + /HER2 − invasive lobular carcinoma (ILC) tumours [ 26 ]. The JFCR–BRCA cohort included two ILC tumours, which were all assigned to CA-B; therefore, we conducted differential analysis between CA-A and CA-B but only for IDC tumours.…”

Section: Resultsmentioning

confidence: 99%

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Chromatin profile-based identification of a novel ER-positive breast cancer subgroup with reduced ER-responsive element accessibility

et al. 2023

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Background Oestrogen receptor (ER) signalling-dependent cancer cell growth is one of the major features of ER-positive breast cancer (BC). Inhibition of ER function is a standard and effective treatment for ER-positive tumours; however, ~20% of patients with ER-positive BC experience early or late recurrence. In this study, we examined intertumour heterogeneity from an epigenetic perspective based on the hypothesis that the intrinsic difference in epigenetic states around ER signalling pathway underlies endocrine therapy resistance. Methods We performed transposase-accessible chromatin sequencing (ATAC-seq) analysis of 42 BC samples, including 35 ER-positive(+) human epidermal growth factor receptor 2 (HER2)-negative(−) and 7 triple-negative tumours. We also reanalysed ATAC-seq data of 45 ER + /HER2 − tumours in the Cancer Genome Atlas (TCGA) BC cohort to validate our observations. Results We conducted a comprehensive analysis of cis-regulatory elements (CREs) using ATAC-seq, identifying three subgroups based on chromatin accessibility profiles. We identified a subgroup of ER-positive BCs with a distinctive chromatin accessibility pattern including reduced accessibility to ER-responsive elements (EREs). The same subgroup was also observed in TCGA BC cohort. Despite the reduced accessibility to EREs, the expression of ER and potential ER target genes were not decreased in these tumours. Conclusion Our findings highlight the existence of a subset of ER-positive BCs with unchanged ER expression but reduced EREs accessibility that cannot be distinguished by conventional immunostaining for ER. Future studies should determine whether these tumours are associated with resistance to endocrine therapy.

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Section: Resultsmentioning

confidence: 99%

“…In one study, ATAC-seq was used to profile 410 tumour samples from 23 cancer types at The Cancer Genome Atlas (TCGA), including 75 BC samples [ 25 ]. Although these data have already been utilised in another study [ 26 ], patient stratification of BC via ATAC-seq has not been reported.…”

Section: Introductionmentioning

confidence: 99%

Chromatin profile-based identification of a novel ER-positive breast cancer subgroup with reduced ER-responsive element accessibility

et al. 2023

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“…In the MLP model, critical genes such as TNNT1 [37, 38] and AQP7 [39–41] were also identified as potential therapeutic targets. Five genes ( TACC3 [42–45], CDC20 [46–50], FOXI1 [51–53], KIF11 [54–57], and MKI67 [17, 19–21]) were identified as critical in both the LOG and MLP models and have been proposed to be therapeutic targets, with MKI67 being particularly noteworthy because it encodes the Ki-67 protein. Additionally, our analysis employing the CNN model identified several key genes as potential therapeutic targets for breast cancer.…”

Section: Resultsmentioning

confidence: 99%

“…Five genes (TACC3 [42][43][44][45], CDC20 [46][47][48][49][50], FOXI1 [51][52][53], KIF11 [54][55][56][57], and MKI67 [17,[19][20][21])…”

Section: Biological Relevancementioning

confidence: 99%

A Model-agnostic Computational Method for Discovering Gene–Phenotype Relationships and Inferring Gene Networks viain silicoGene Perturbation

Stojšin,

Chen,

Zhao

2024

Preprint

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BackgroundDeep learning architectures have advanced genotype‒phenotype mappings with precision but often obscure the roles of specific genes and their interactions. Our research introduces a model-agnostic computational methodology, capitalizing on the analytical strengths of deep learning models to serve as biological proxies, enabling interpretation of key gene interactions and their impact on phenotypic outcomes. The objective of this research is to refine the understanding of genetic networks in complex traits by leveraging the nuanced decision-making of advanced models.ResultsTesting was conducted across several computational models representing varying levels of complexity trained on gene expression datasets for the prediction of the Ki-67 biomarker, which is known for its prognostic value in breast cancer. The methodology is capable of using models as proxies to identify biologically significant genes and to infer relevant gene networks from an entirely data-driven analysis. Notably, the model-derived biomarkers (p-values of 0.013 and 0.003) outperformed the conventional Ki-67 biomarker (0.021) in terms of prognostic efficacy. Moreover, our analysis revealed high congruence between model precision and the biological relevance of the genes and gene relationships identified. Furthermore, we demonstrated that the complexity of the identified gene relationships was consistent with the decision-making intricacy of the model, with complex models capturing greater proportions of complex gene–gene interactions (61.2% and 31.1%) than simpler models (4.6%), reinforcing that the approach effectively captures biologically relevant in-model decision-making processes.ConclusionsThis methodology offers researchers a powerful tool to examine the decision-making processes within their genotype–phenotype mapping models. It accurately identifies critical genes and their interactions, revealing the biological rationale behind model decisions. It also enables comparisons of decision-making between different models. Furthermore, by discovering in-model critical gene networks, our approach helps bridge the gap between research and clinical applications. It facilitates the translation of complex, model-driven genetic discoveries into actionable clinical insights. This capability is pivotal for advancing personalized medicine, as it leverages the precision of deep learning models to uncover biologically relevant genes and gene networks and opens pathways for discovering new gene biomarker combinations and previously unknown gene interactions.

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“…The hazard agents that must be highlighting it are epidemiological agents "such as age, obesity, alcohol use, and exposure to estrogen in lifetime", but history of family for breast carcinogenesis have more strongest one. Nearly "20%" for carcinomas "family origin", as well as following the presence of "specific predisposing gene" for disease occurring (19,20,21).…”

Section: Family History and Genetic Factors In Breast Cancermentioning

confidence: 99%

Review on Breast Cancer in Iraq Women

Jassim¹,

Hamad²,

Hussein³

2022

jsci

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breast cancer" which is common type of carcinogenesis in females, surpassing even bronchogenic cancer "accounting for approximately one-third of the registered female cancers according to the latest Iraqi Cancer Registry". According to "World Health Organization", that discovery as well as examination early, particularly in combination together with sufficient therapy, present the appropriate method which decrease in the mortality rate for "breast cancer". Rate of "breast cancer" rise in Iraq, a source of a significant health problem. Labors are necessary on the nationalist scale and establishing comprehensive breast cancer control programs in Iraq for better estimate of the problem

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Chromatin accessibility landscape and active transcription factors in primary human invasive lobular and ductal breast carcinomas

Cited by 4 publications

References 98 publications

Chromatin profile-based identification of a novel ER-positive breast cancer subgroup with reduced ER-responsive element accessibility

Chromatin profile-based identification of a novel ER-positive breast cancer subgroup with reduced ER-responsive element accessibility

A Model-agnostic Computational Method for Discovering Gene–Phenotype Relationships and Inferring Gene Networks viain silicoGene Perturbation

Review on Breast Cancer in Iraq Women

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