Browse
Submit Data
Databases
API
Help

Dataset Information

20 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Functional genomics for breast cancer drug target discovery.

ABSTRACT: Breast cancer is a heterogeneous disease that develops through a multistep process via the accumulation of genetic/epigenetic alterations in various cancer-related genes. Current treatment options for breast cancer patients include surgery, radiotherapy, and chemotherapy including conventional cytotoxic and molecular-targeted anticancer drugs for each intrinsic subtype, such as endocrine therapy and antihuman epidermal growth factor receptor 2 (HER2) therapy. However, these therapies often fail to prevent recurrence and metastasis due to resistance. Overall, understanding the molecular mechanisms of breast carcinogenesis and progression will help to establish therapeutic modalities to improve treatment. The recent development of comprehensive omics technologies has led to the discovery of driver genes, including oncogenes and tumor-suppressor genes, contributing to the development of molecular-targeted anticancer drugs. Here, we review the development of anticancer drugs targeting cancer-specific functional therapeutic targets, namely, MELK (maternal embryonic leucine zipper kinase), TOPK (T-lymphokine-activated killer cell-originated protein kinase), and BIG3 (brefeldin A-inhibited guanine nucleotide-exchange protein 3), as identified through comprehensive breast cancer transcriptomics.

SUBMITTER: Yoshimaru T

PROVIDER: S-EPMC8384626 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

The Discovery of New Drug-Target Interactions for Breast Cancer Treatment.

Project description:Drug-target interaction (DTIs) prediction plays a vital role in probing new targets for breast cancer research. Considering the multifaceted challenges associated with experimental methods identifying DTIs, the in silico prediction of such interactions merits exploration. In this study, we develop a feature-based method to infer unknown DTIs, called PsePDC-DTIs, which fuses information regarding protein sequences extracted by pseudo-position specific scoring matrix (PsePSSM), detrended cross-correlation analysis coefficient (DCCA coefficient), and an FP2 format molecular fingerprint descriptor of drug compounds. In addition, the synthetic minority oversampling technique (SMOTE) is employed for dealing with the imbalanced data after Lasso dimensionality reduction. Then, the processed feature vectors are put into a random forest classifier to perform DTIs predictions on four gold standard datasets, including nuclear receptors (NR), G-protein-coupled receptors (GPCR), ion channels (IC), and enzymes (E). Furthermore, we explore new targets for breast cancer treatment using its risk genes identified from large-scale genome-wide genetic studies using PsePDC-DTIs. Through five-fold cross-validation, the average values of accuracy in NR, GPCR, IC, and E datasets are 95.28%, 96.19%, 96.74%, and 98.22%, respectively. The PsePDC-DTIs model provides us with 10 potential DTIs for breast cancer treatment, among which erlotinib (DB00530) and FGFR2 (hsa2263), caffeine (DB00201) and KCNN4 (hsa3783), as well as afatinib (DB08916) and FGFR2 (hsa2263) are found with direct or inferred evidence. The PsePDC-DTIs model has achieved good prediction results, establishing the validity and superiority of the proposed method.

| S-EPMC8704452 | biostudies-literature

A functional genomics process for systematic dissection and mutation-specific target discovery in breast cancer PIK3CA hotspot mutations

Project description:This SuperSeries is composed of the SubSeries listed below.

2023-12-01 | GSE247822 | GEO

Cancer proliferation gene discovery through functional genomics.

Project description:Retroviral short hairpin RNA (shRNA)-mediated genetic screens in mammalian cells are powerful tools for discovering loss-of-function phenotypes. We describe a highly parallel multiplex methodology for screening large pools of shRNAs using half-hairpin barcodes for microarray deconvolution. We carried out dropout screens for shRNAs that affect cell proliferation and viability in cancer cells and normal cells. We identified many shRNAs to be antiproliferative that target core cellular processes, such as the cell cycle and protein translation, in all cells examined. Moreover, we identified genes that are selectively required for proliferation and survival in different cell lines. Our platform enables rapid and cost-effective genome-wide screens to identify cancer proliferation and survival genes for target discovery. Such efforts are complementary to the Cancer Genome Atlas and provide an alternative functional view of cancer cells.

| S-EPMC2981870 | biostudies-literature

Breast cancer PDxO cultures for drug discovery and functional precision oncology.

Project description:Patient-derived xenografts (PDXs) have clinical value but are time-, cost-, and labor-intensive and thus ill-suited for large-scale experiments. Here, we present a protocol to convert PDX tumors into PDxOs for long-term cultures amenable to moderate-throughput drug screens, including in-depth PDxO validation. We describe steps for PDxO preparation and mouse cell removal. We then detail PDxO validation and characterization and drug response assay. Our PDxO drug screening platform can predict therapy response in vivo and inform functional precision oncology for patients. For complete details on the use and execution of this protocol, please refer to Guillen et al.1.

| S-EPMC10339058 | biostudies-literature

Functional genomics reveals an off-target dependency of drug synergy in gastric cancer therapy.

Project description:The rational combination of anticancer agents is critical to improving patient outcomes in cancer. Nonetheless, most combination regimens in the clinic result from empirical methodologies disregarding insight into the mechanism of action and missing the opportunity to improve therapy outcomes incrementally. Deciphering the genetic dependencies and vulnerabilities responsible for synergistic interactions is crucial for rationally developing effective anticancer drug combinations. Hence, we screened pairwise pharmacological interactions between molecular-targeted agents and conventional chemotherapeutics and examined the genome-scale genetic dependencies in gastric adenocarcinoma cell models. Since this type of cancer is mainly chemoresistant and incurable, clinical situations demand effective combination strategies. Our pairwise combination screen revealed SN38/erlotinib as the drug pair with the most robust synergism. Genome-wide CRISPR screening and a shRNA-based signature assay indicated that the genetic dependency/vulnerability signature of SN38/erlotinib is the same as SN38 alone. Additional investigation revealed that the enhanced cell death with improved death kinetics caused by the SN38/erlotinib combination is surprisingly due to erlotinib's off-target effect that inhibits ABCG2 but not its on-target effect on EGFR. Our results confirm that a genetic dependency signature different from the single-drug application may not be necessary for the synergistic interaction of molecular-targeted agents with conventional chemotherapeutics in gastric adenocarcinoma. The findings also demonstrated the efficacy of functional genomics approaches in unveiling biologically validated mechanisms of pharmacological interactions.

| S-EPMC10592690 | biostudies-literature

A functional genomics process for systematic dissection and mutation-specific target discovery in breast cancer PIK3CA hotspot mutations [RNA-seq]

Project description:Despite the general understanding that different mutations impart different phenotypic changes within a cell, the majority of targeted therapies in cancer treatment are used to treat all mutations in the same gene regardless of location or phenotypic effects. There is a significant unmet need to distinguish distinct changes in cellular signaling which may provide opportunities for mutation-specific treatment with reduced toxicity to patients. Herein, we describe a series of functional genomic analysis with a unique isogenic cell line panel to accurately identify targetable differences between mutations within the same gene. Using an isogenic cell line model bearing two distinct hotspot PIK3CA mutations found in breast cancer, we were able to identify many gene expression and chromatin accessibility differences. These findings allowed us to identify mutation specific molecular targets, specifically AREG as well as a proximal gene regulatory region, that may provide clinically relevant targets. When disrupted, these targets induce a mutation-specific decrease in proliferation and survival in vivo. These findings suggest new mutation-specific modes of treatment for PIK3CA mutant breast cancer and provide a means with which to find mutation-specific targets for the treatment of other oncogenic mutations.

2023-12-01 | GSE247819 | GEO

A functional genomics process for systematic dissection and mutation-specific target discovery in breast cancer PIK3CA hotspot mutations [ATAC-Seq]

2023-12-01 | GSE247821 | GEO

Genomics insights of SARS-CoV-2 (COVID-19) into target-based drug discovery.

Project description:Coronavirus disease (COVID-19) pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 is a global health emergency and no clinically approved vaccines or antiviral drugs available to date. Intensive research on SARS-CoV-2 is urgently warranted to understand its pathogenesis and virulence mechanisms and to discover target-based antiviral therapeutics. Among various research logics, current bioinformatics highlights novel testable hypotheses for systematic drug repositioning and designing against COVID-19. A total of 121 articles related to bioinformatics facets of this virus were collected from the PubMed Central. The content of each investigation was comprehensively reviewed, manually curated, and included herein. Interestingly, 109 COVID-19-related literature published in 2020 (January-June) were included in this review. The present article emphasizes novel resource development on its genome structure, evolution, therapeutic targets, drug designing, and drug repurposing strategies. Genome organization, the function of coding genes, origin, and evolution of SARS-CoV-2 is described in detail. Genomic insights into understanding the structure-function relationships of drug targets including spike, main protease, and RNA-dependent RNA polymerase of SARS-CoV-2 are discussed intensively. Several molecular docking and systems pharmacology approaches have been investigated some promising antiviral drugs against SARS-CoV-2 based on its genomic characteristics, pathogenesis mechanism, and host specificity. Perhaps, the present genomic insights of this virus will provide a lead to the researchers to design or repurpose of antiviral drugs soon and future directions to control the spread of COVID-19.

| S-EPMC7394272 | biostudies-literature

Heparanase: a target for drug discovery in cancer and inflammation.

Project description:The remodelling of the extracellular matrix (ECM) has been shown to be highly upregulated in cancer and inflammation and is critically linked to the processes of invasion and metastasis. One of the key enzymes involved in specifically degrading the heparan sulphate (HS) component of the ECM is the endo-beta-glucuronidase enzyme heparanase. Processing of HS by heparanase releases both a host of bioactive growth factors anchored within the mesh of the ECM as well as defined fragments of HS capable of promoting cellular proliferation. The finding that heparanase is elevated in a wide variety of tumor types and is subsequently linked to the development of pathological processes has led to an explosion of therapeutic strategies to inhibit its enzyme activity. So far only one compound, the sulphated oligosaccharide PI88, which both inhibits heparanase activity and has effects on growth factor binding has reached clinical trials where it has shown to have promising efficacy. The scene has clearly been set however for a new generation of compounds, either specific to the enzyme or with dual roles, to emerge from the lab and enter the clinic. The aim of this review is to describe the current drug discovery status of small molecule, sugar and neutralising antibody inhibitors of heparanase enzyme activity. Potential strategies will also be discussed on the selection of suitable biomarker strategies for specific monitoring of in vivo heparanase inhibition which will be crucial for both animal model and clinical trial testing.

| S-EPMC2012981 | biostudies-other

An integrative genomics approach to biomarker discovery in breast cancer.

Project description:Genome-wide association studies (GWAS) have successfully identified genetic variants associated with risk for breast cancer. However, the molecular mechanisms through which the identified variants confer risk or influence phenotypic expression remains poorly understood. Here, we present a novel integrative genomics approach that combines GWAS information with gene expression data to assess the combined contribution of multiple genetic variants acting within genes and putative biological pathways, and to identify novel genes and biological pathways that could not be identified using traditional GWAS. The results show that genes containing SNPs associated with risk for breast cancer are functionally related and interact with each other in biological pathways relevant to breast cancer. Additionally, we identified novel genes that are co-expressed and interact with genes containing SNPs associated with breast cancer. Integrative analysis combining GWAS information with gene expression data provides functional bridges between GWAS findings and biological pathways involved in breast cancer.

| S-EPMC3153161 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data