Dataset Information

OVERDRIVE: overcoming cancer cell resistance to drugs by supplementary nontoxic molecule predicted by a machine learning tool

ABSTRACT: Here we aim to test the notion that overcoming cancer cells’ acquired resistance to chemotherapy drugs may be achieved by administrating together with the main drug a molecule that is not toxic by itself but enhances the drug efficacy in killing the resistant cells. Finding such a supplementary molecule is a nontrivial task. Our algorithmic approach, named OVERcoming Drug Resistance by In Vitro cell Exposure (OVERDRIVE), involves exposing drug-sensitive cancer cells to increasing drug concentrations for several weeks followed by a comprehensive proteomic analysis of both the exposed and sensitive cells. Applying a machine learning tool Genome Enhancer to the proteomic data reveals the protein network accounting for the differences between the exposed and naïve cells and predicts molecules that may shift the exposed cell phenotypes to the sensitive one. Experimental validation of the top predicted nontoxic molecules confirmed that the combined treatment of the experimental drug LTCA2940 that targets redox pathways with identified supplementary molecule restored the sensitivity of LTCA2940-resistant cells. The OVERDRIVE approach may pave a way for a strategy to overcoming anticancer resistance to chemotherapy in clinic.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Culture

SUBMITTER: Hezheng Lyu

LAB HEAD: Roman Zubarev

PROVIDER: PXD071434 | Pride | 2026-06-08

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	OxidoResist_5FU_HCT116_Frac01.raw	Raw
	OxidoResist_5FU_HCT116_Frac02.raw	Raw
	OxidoResist_5FU_HCT116_Frac03.raw	Raw
	OxidoResist_5FU_HCT116_Frac04.raw	Raw
	OxidoResist_5FU_HCT116_Frac05.raw	Raw

Items per page:

1 - 5 of 105

Publications

Peptide-to-Protein Data Aggregation Using Fisher's Method Improves Target Identification in Chemical Proteomics.

Lyu Hezheng H Gharibi Hassan H Meng Zhaowei Z Sokolova Bohdana B Lundström Susanna S Zhang Xuepei X Zubarev Roman A RA

Analytical chemistry 20260422 17

Protein-level statistical tests in proteomics, aimed at obtaining p-values, are conventionally made on protein abundances aggregated from peptide data. This integral approach overlooks peptide-level heterogeneity and ignores important information coded in individual peptide data, while protein p-values can also be obtained by Fisher's method of combining peptide p-values using chi-square statistics. Here, we test this latter approach across diverse chemical proteomics data sets based on assessme ...[more]

PMID: 42017605

Similar Datasets

Project description:A summary of the work associated to these microarrays is the following: MicroRNAs (miRNAs) are small non-coding RNAs involved in RNA silencing that play a role in many biological processes. They are involved in the development of many diseases, including cancer. Extensive experimental data show that they play a role in the pathogenesis of cancer as well as the development of drug resistance during treatment. MiRNA microarrays of sensitive and MTX-resistant HT29 colon cancer cells were performed. The results were analyzed using the GeneSpring GX11.5 software. Differentially expressed microRNAs in resistant cells were identified and miR-224, which was greatly underexpressed and displayed robust raw signal values, was selected for further studies. Putative targets were predicted using TargetScan 5.1 software and intersected with the data from expression microarrays previously performed. This approach allowed us to identify miR-224 targets that were differentially expressed more than 2-fold in resistant cells. Among them, ARL3, CDS2, DCP2, HSPC159, MYST3 and SLC4A4 were validated at the mRNA level by qRT-PCR. Functional assays using an anti-miR against miR-224 desensitized the cells toward MTX, mimicking the resistant phenotype. On the other hand, siRNA treatment against SLC4A4 or incubation of Poly Purine Reverse Hoogsteen (PPRH) hairpins against CDS2 or HSPC159 increased sensitivity to MTX. These results revealed a role for miR-224 and its targets in MTX resistance in HT29 colon cancer cells. KEYWORDS Methotrexate, miRNAs, drug resistance, DHFR Two cell lines are compared in the study, which are HT29 colon cancer cells sensitive to methotrexate and HT29 cells resistant to 10e-5M MTX. Six samples are provided which correspond to 3 samples for the control condition and 3 samples for the resistant condition. Data files from miRNA and mRNA (previously submitted to GEO as GSE11440) microarrays were analyzed with GeneSpring GX11.5 software (Agilent Technologies) to find differentially expressed miRNAs and their cellular target genes in the resistant cell lines compared to their sensitive counterparts.

Project description:Background: Current protocols for the treatment of ovarian cancer include combination chemotherapy with a platinating agent and a taxane. However, many patients experience relapse of their cancer and the development of drug resistance is not uncommon, making successful second line therapy difficult to achieve. The objective of this study was to develop a cell line resistant to both carboplatin and docetaxel (dual drug resistant ovarian cell line A2780CBNDXL), along with single agent resistant lines (docetaxel resistant A2780DXL and carboplatin resistant A2780CBN), to investigate the mechanisms which underlie the development of dual drug resistance. Methods: The A2780 epithelial ovarian cancer cell line was used to select for isogenic carboplatin, docetaxel and dual drug resistant cell lines. A selection method of gradually increasing drug doses was implemented to avoid clonal selection. Resistance was confirmed using a clonogenic assay. Changes in gene expression associated with the development of drug resistance were determined by microarray analysis compared to parental co-culture control A2780CC. Changes in selected genes were validated by QPCR and immunoblotting. Results: Three isogenic cell lines were developed and resistance to each drug or the combination of drugs was confirmed. Development of resistance was accompanied by a reduced growth rate. The microarray and QPCR analyses showed that unique changes in gene expression occurred in the dual drug resistant cell line and that genes known to be involved in resistance could be identified in all cell lines. Conclusions: Novel changes in gene expression can occur in the development of dual drug resistance, indicating that dual drug resistance is not a simple combination of the changes occurring in single agent resistance Carboplatin, docetaxel (GSE26129) and carboplatin/docetaxel dual resistant cell lines of ovarian A2780 were generated for gene expression profilling. Two colour microarray of Agilent whole human genome nucleotide arrays was conducted with two replicate of both forward and reverse labellings for each cell line. Four arrays were used for this experiments. And it was four replicate of both forward and reverse labellings for A2780 cells. Eight arrays were used for this experiments

Project description:The mTOR (mammalian Target of Rapamycin) pathway is constitutively activated in Diffuse Large B-Cell Lymphoma (DLBCL). mTOR inhibition has been shown to have clinical activity in patients with DLBCL, although overall response rates remain low. We therefore evaluated differences in the transcriptome between DLBCL cell lines with differential sensitivity to the mTOR inhibitor Rapamycin, to (A) identify gene-expression patterns(GEP) capable of identifying sensitivity to Rapamycin, (B) understand the underlying mechanisms of resistance to Rapamycin in DLBCL and (C) identify bioactive molecules likely to synergize with mTOR inhibitors. Using Affymetrix HuGene ST 1.0 microarrays, we were able to identify a gene expression signature capable of accurately predicting sensitivity and resistance to Rapamycin in DLBCL cell lines. Pathway analysis identified the serine/threonine kinase Akt as central to the differentially-expressed gene network. Connectivity mapping of our datasets identified compounds targeting the AKT pathway with a high likelihood of reversing the GEP associated with resistance to Rapamycin. Specifically, we evaluated the HIV protease inhibitor (PI) Nelfinavir, which is known to have anti-cancer and Akt-inhibitory properties, as well as the small molecule Akt inhibitor MK-2206, for their potential to synergize with to Rapamycin in DLBCL. Nelfinavir and MK-2206 caused profound inhibition of cell viability in combination with Rapamycin in DLBCL cell lines. Low nanomolar concentrations of Rapamycin inhibited phosphorylation of Akt and also downstream targets of activated mTOR when used in combination with these Akt inhibitors. These findings have the potential to significantly improve patient selection for mTOR inhibitor therapy, and to improve rates and depths of response. More broadly, they support the use of global RNA expression and connectivity mapping to improve patient selection and identify synergistic drug combinations for cancer therapy. DLBCL cell lines were tested for Rapamycin sensitivity and classified as "sensitive" or "resistant." Genome-wide analysis of all cell lines were performed using the Affymetrix HuGene ST 1.0 Array Platform. Genes with differential expression between sensitive and resistant cell lines were analyzed using Statistical Analysis of Microarrays (SAM) software, and a signature of genes determnined. This signature was found to accurately predict sensitivity or resistance of other DLBCL cell lines, and to identify the protein kinase Akt as central to resistance.