Exploiting the Epigenome to Control Cancer-Promoting Gene-Expression Programs.
Ontology highlight
ABSTRACT:
SUBMITTER: Brien GL
PROVIDER: S-EPMC4889129 | biostudies-literature | 2016 Apr
REPOSITORIES: biostudies-literature
Publications
Exploiting the Epigenome to Control Cancer-Promoting Gene-Expression Programs.
Brien Gerard L GL Valerio Daria G DG Armstrong Scott A SA
Cancer cell 20160401 4
The epigenome is a key determinant of transcriptional output. Perturbations within the epigenome are thought to be a key feature of many, perhaps all cancers, and it is now clear that epigenetic changes are instrumental in cancer development. The inherent reversibility of these changes makes them attractive targets for therapeutic manipulation, and a number of small molecules targeting chromatin-based mechanisms are currently in clinical trials. In this perspective we discuss how understanding t ...[more]
Similar Datasets
Project description:Chronic obstructive pulmonary disease (COPD) is an independent risk factor for lung cancer, suggesting that COPD stroma favors cancer initiation. Therefore, we used proteomics and polysome-profiling to identify gene expression programs that distinguish stroma of patients harboring lung cancer from those that do not, with varied COPD severities. This profiling unveiled distinct COPD-dependent cancer-associated gene expression programs predominantly manifesting as alterations in mRNA translation. Mechanistically, such programs are downstream of the mammalian target of rapamycin pathway in mild COPD and pathological extracellular matrix in more severe COPD; and both programs parallel activation of distinct pro-cancer fibroblast-derived secretomes. Therefore, depending upon COPD severity, the lung stroma can exist in two states favoring cancer initiation, which likely result in distinct disease entities.
2021-11-03 | PXD007743 | Pride
Project description:Epigenetic alterations play a central role in cancer development and progression. The acetylation of histone 3 at lysine 27 (H3K27ac) specifically marks active genes. While chromatin immunoprecipitation (ChIP) followed by next-generation sequencing (ChIP-seq) analyses are commonly performed in cell lines, only limited data are available from primary tumors. We therefore examined whether cancer-specific alterations in H3K27ac occupancy can be identified in primary rectal cancer. Tissue samples from primary rectal cancer and matched mucosa were obtained. ChIP-seq for H3K27ac was performed and differentially occupied regions were identified. The expression of selected genes displaying differential occupancy between tumor and mucosa were examined in gene expression data from an independent patient cohort. Differential expression of four proteins was further examined by immunohistochemistry. ChIP-seq for H3K27ac in primary rectal cancer and matched mucosa was successfully performed and revealed differential binding on 44 regions. This led to the identification of genes with increased H3K27ac, i.e., RIPK2, FOXQ1, KRT23, and EPHX4, which were also highly upregulated in primary rectal cancer in an independent dataset. The increased expression of these four proteins was confirmed by immunohistochemistry. This study demonstrates the feasibility of ChIP-seq-based epigenome mapping of primary rectal cancer and confirms the value of H3K27ac occupancy to predict gene expression differences.
| S-EPMC6721540 | biostudies-literature
Project description:Precision medicine offers remarkable potential for the treatment of cancer, but is largely focused on tumors that harbor actionable mutations. Gene expression signatures can expand the scope of precision medicine by predicting response to traditional (cytotoxic) chemotherapy agents without relying on changes in mutational status. We present a new signature extraction method, inspired by the principle of convergent phenotypes, which states that tumors with disparate genetic backgrounds may evolve similar phenotypes independently. This evolutionary-informed method can be utilized to produce consensus signatures predictive of response to over 200 chemotherapeutic drugs found in the Genomics of Drug Sensitivity in Cancer (GDSC) Database. Here, we demonstrate its use by extracting the Cisplatin Response Signature (CisSig). We show that this signature can predict cisplatin response within carcinoma-based cell lines from the GDSC database, and expression of the signatures aligns with clinical trends seen in independent datasets of tumor samples from The Cancer Genome Atlas (TCGA) and Total Cancer Care (TCC) database. Finally, we demonstrate preliminary validation of CisSig for use in muscle-invasive bladder cancer, predicting overall survival in a small cohort of patients who undergo cisplatin-containing chemotherapy. This methodology can be used to produce robust signatures that, with further clinical validation, may be used for the prediction of traditional chemotherapeutic response, dramatically increasing the reach of personalized medicine in cancer.
| S-EPMC10115855 | biostudies-literature
Project description:LIN28 is an evolutionarily conserved RNA-binding protein with critical functions in developmental timing and cancer. However, the molecular mechanisms underlying LIN28's oncogenic properties are yet to be described. RNA-protein immunoprecipitation coupled with genome-wide sequencing (RIP-Seq) analysis revealed significant LIN28 binding within 843 mRNAs in breast cancer cells. Many of the LIN28-bound mRNAs are implicated in the regulation of RNA and cell metabolism. We identify heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1), a protein with multiple roles in mRNA metabolism, as a LIN28-interacting partner. Subsequently, we used a custom computational method to identify differentially spliced gene isoforms in LIN28 and hnRNP A1 small interfering RNA (siRNA)-treated cells. The results reveal that these proteins regulate alternative splicing and steady-state mRNA expression of genes implicated in aspects of breast cancer biology. Notably, cells lacking LIN28 undergo significant isoform switching of the ENAH gene, resulting in a decrease in the expression of the ENAH exon 11a isoform. The expression of ENAH isoform 11a has been shown to be elevated in breast cancers that express HER2. Intriguingly, analysis of publicly available array data from the Cancer Genome Atlas (TCGA) reveals that LIN28 expression in the HER2 subtype is significantly different from that in other breast cancer subtypes. Collectively, our data suggest that LIN28 may regulate splicing and gene expression programs that drive breast cancer subtype phenotypes.
| S-EPMC4539381 | biostudies-literature
Project description:Rationale: Chronic obstructive pulmonary disease is an independent risk factor for lung cancer, but the underlying molecular mechanisms are unknown. We hypothesized that lung stromal cells activate pathological gene expression programs that support oncogenesis.Objectives: To identify molecular mechanisms operating in the lung stroma that support the development of lung cancer.Methods: The study included subjects with and without lung cancer across a spectrum of lung-function values. We conducted a multiomics analysis of nonmalignant lung tissue to quantify the transcriptome, translatome, and proteome.Measurements and Main Results: Cancer-associated gene expression changes predominantly manifested as alterations in the efficiency of mRNA translation modulating protein levels in the absence of corresponding changes in mRNA levels. The molecular mechanisms that drove these cancer-associated translation programs differed based on lung function. In subjects with normal to mildly impaired lung function, the mammalian target of rapamycin (mTOR) pathway served as an upstream driver, whereas in subjects with severe airflow obstruction, pathways downstream of pathological extracellular matrix emerged. Consistent with a role during cancer initiation, both the mTOR and extracellular matrix gene expression programs paralleled the activation of previously identified procancer secretomes. Furthermore, an in situ examination of lung tissue showed that stromal fibroblasts expressed cancer-associated proteins from two procancer secretomes: one that included IL-6 (in cases of mild or no airflow obstruction), and one that included BMP1 (in cases of severe airflow obstruction).Conclusions: Two distinct stromal gene expression programs that promote cancer initiation are activated in patients with lung cancer depending on lung function. Our work has implications both for screening strategies and for personalized approaches to cancer treatment.
| S-EPMC6680296 | biostudies-literature
Project description:Noncoding transcription is a defining feature of active enhancers, linking transcription factor (TF) binding to the molecular mechanisms controlling gene expression. To determine the relationship between enhancer activity and biological outcomes in breast cancers, we profiled the transcriptomes (using GRO-seq and RNA-seq) and epigenomes (using ChIP-seq) of 11 different human breast cancer cell lines representing five major molecular subtypes of breast cancer, as well as two immortalized ("normal") human breast cell lines. In addition, we developed a robust and unbiased computational pipeline that simultaneously identifies putative subtype-specific enhancers and their cognate TFs by integrating the magnitude of enhancer transcription, TF mRNA expression levels, TF motif P-values, and enrichment of H3K4me1 and H3K27ac. When applied across the 13 different cell lines noted above, the Total Functional Score of Enhancer Elements (TFSEE) identified key breast cancer subtype-specific TFs that act at transcribed enhancers to dictate gene expression patterns determining growth outcomes, including Forkhead TFs, FOSL1, and PLAG1. FOSL1, a Fos family TF, (1) is highly enriched at the enhancers of triple negative breast cancer (TNBC) cells, (2) acts as a key regulator of the proliferation and viability of TNBC cells, but not Luminal A cells, and (3) is associated with a poor prognosis in TNBC breast cancer patients. Taken together, our results validate our enhancer identification pipeline and reveal that enhancers transcribed in breast cancer cells direct critical gene regulatory networks that promote pathogenesis.
| S-EPMC5793780 | biostudies-literature
Project description:Breast cancer biology varies markedly among patients. Basal-like breast cancer is one of the most challenging subtypes to treat because it lacks effective therapeutic targets. Despite numerous studies on potential targetable molecules in this subtype, few targets have shown promise. However, the present study revealed that FOXD1, a transcription factor that functions in both normal development and malignancy, is associated with poor prognosis in basal-like breast cancer. We analyzed publicly available RNA sequencing data and conducted FOXD1-knockdown experiments, finding that FOXD1 maintains gene expression programs that contribute to tumor progression. We first conducted survival analysis of patients grouped via a Gaussian mixture model based on gene expression in basal-like tumors, finding that FOXD1 is a prognostic factor specific to this subtype. Then, our RNA sequencing and chromatin immunoprecipitation sequencing experiments using the basal-like breast cancer cell lines BT549 and Hs578T with FOXD1 knockdown revealed that FOXD1 regulates enhancer-gene programs related to tumor progression. These findings suggest that FOXD1 plays an important role in basal-like breast cancer progression and may represent a promising therapeutic target.
| S-EPMC10206236 | biostudies-literature
Project description:Background: A national priority in the United States is to promote patient engagement in cancer genomics research, especially among diverse and understudied populations. Several cancer genomics research programs have emerged to accomplish this priority, yet questions remain about the meaning and methods of patient engagement. This study explored how cancer genomics research programs define engagement and what strategies they use to engage patients across stages in the conduct of research. Methods: An environmental scan was conducted of cancer genomics research programs focused on patient engagement. Research programs were identified and characterized using materials identified from publicly available sources (e.g., websites), a targeted literature review, and interviews with key informants. Descriptive information about the programs and their definitions of engagement, were synthesized using thematic analysis. The engagement strategies were synthesized and mapped to different stages in the conduct of research, including recruitment, consent, data collection, sharing results, and retention. Results: Ten research programs were identified, examples of which include the Cancer Moonshot Biobank, the MyPART Network, NCI-CONNECT, and the Participant Engagement and Cancer Genome Sequencing (PE-CGS) Network. All programs aimed to include understudied or underrepresented populations. Based on publicly available information, four programs explicitly defined engagement. These definitions similarly characterized engagement as being interpersonal, reciprocal, and continuous. Five general strategies of engagement were identified across the programs: 1) digital (such as websites) and 2) non-digital communications (such as radio broadcasts, or printed brochures); 3) partnering with community organizations; 4) providing incentives; and 5) affiliating with non-academic medical centers. Digital communications were the only strategy used across all stages of the conduct of research. Programs tailored these strategies to their study goals, including overcoming barriers to research participation among diverse populations. Conclusion: Programs studying cancer genomics are deeply committed to increasing research participation among diverse populations through patient engagement. Yet, the field needs to reach a consensus on the meaning of patient engagement, develop a taxonomy of patient engagement measures in cancer genomics research, and identify optimal strategies to engage patients in cancer genomics. Addressing these needs could enable patient engagement to fulfill its potential and accelerate the pace of cancer genomic discoveries.
| S-EPMC9889863 | biostudies-literature
Project description:Background: Selected gene mutations are routinely used to guide the selection of cancer drugs for a given patient tumour. Large pharmacogenomic data sets, such as those by Genomics of Drug Sensitivity in Cancer (GDSC) consortium, were introduced to discover more of these single-gene markers of drug sensitivity. Very recently, machine learning regression has been used to investigate how well cancer cell line sensitivity to drugs is predicted depending on the type of molecular profile. The latter has revealed that gene expression data is the most predictive profile in the pan-cancer setting. However, no study to date has exploited GDSC data to systematically compare the performance of machine learning models based on multi-gene expression data against that of widely-used single-gene markers based on genomics data. Methods: Here we present this systematic comparison using Random Forest (RF) classifiers exploiting the expression levels of 13,321 genes and an average of 501 tested cell lines per drug. To account for time-dependent batch effects in IC 50 measurements, we employ independent test sets generated with more recent GDSC data than that used to train the predictors and show that this is a more realistic validation than standard k-fold cross-validation. Results and Discussion: Across 127 GDSC drugs, our results show that the single-gene markers unveiled by the MANOVA analysis tend to achieve higher precision than these RF-based multi-gene models, at the cost of generally having a poor recall (i.e. correctly detecting only a small part of the cell lines sensitive to the drug). Regarding overall classification performance, about two thirds of the drugs are better predicted by the multi-gene RF classifiers. Among the drugs with the most predictive of these models, we found pyrimethamine, sunitinib and 17-AAG. Conclusions: Thanks to this unbiased validation, we now know that this type of models can predict in vitro tumour response to some of these drugs. These models can thus be further investigated on in vivo tumour models. R code to facilitate the construction of alternative machine learning models and their validation in the presented benchmark is available at http://ballester.marseille.inserm.fr/gdsc.transcriptomicDatav2.tar.gz.
| S-EPMC5310525 | biostudies-literature
Project description:This article reviews neural interface technology and its relationship with neuroplasticity. Two types of neural interface technology are reviewed, highlighting specific technologies that the authors directly work with: (1) neural interface technology for neural recording, such as the micro-ECoG BCI system for hand prosthesis control, and the comprehensive rehabilitation paradigm combining MEG-BCI, action observation, and motor imagery training; (2) neural interface technology for functional neural stimulation, such as somatosensory neural stimulation for restoring somatosensation, and non-invasive cortical stimulation using rTMS and tDCS for modulating cortical excitability and stroke rehabilitation. The close interaction between neural interface devices and neuroplasticity leads to increased efficacy of neural interface devices and improved functional recovery of the nervous system. This symbiotic relationship between neural interface technology and the nervous system is expected to maximize functional gain for individuals with various sensory, motor, and cognitive impairments, eventually leading to better quality of life.
| S-EPMC2788507 | biostudies-other