Dataset Information

Selection of aptamers against triple negative breast cancer cells using high throughput sequencing.

ABSTRACT: Triple-negative breast cancer is the most aggressive subtype of invasive breast cancer with a poor prognosis and no approved targeted therapy. Hence, the identification of new and specific ligands is essential to develop novel targeted therapies. In this study, we aimed to identify new aptamers that bind to highly metastatic breast cancer MDA-MB-231 cells using the cell-SELEX technology aided by high throughput sequencing. After 8 cycles of selection, the aptamer pool was sequenced and the 25 most frequent sequences were aligned for homology within their variable core region, plotted according to their free energy and the key nucleotides possibly involved in the target binding site were analyzed. Two aptamer candidates, Apt1 and Apt2, binding specifically to the target cells with [Formula: see text] values of 44.3 ± 13.3 nM and 17.7 ± 2.7 nM, respectively, were further validated. The binding analysis clearly showed their specificity to MDA-MB-231 cells and suggested the targeting of cell surface receptors. Additionally, Apt2 revealed no toxicity in vitro and showed potential translational application due to its affinity to breast cancer tissue sections. Overall, the results suggest that Apt2 is a promising candidate to be used in triple-negative breast cancer treatment and/or diagnosis.

SUBMITTER: Ferreira D

PROVIDER: S-EPMC8060331 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:While high-throughput drug screening offers possibilities to profile phenotypic responses of hundreds of compounds, elucidation of the cell context-specific mechanisms of drug action requires additional analyses. To that end, we developed a computational target deconvolution pipeline that identifies the key target dependencies based on collective drug response patterns in each cell line separately. The pipeline combines quantitative drug-cell line responses with drug-target interaction networks among both intended on- and potent off-targets to identify pharmaceutically actionable and selective therapeutic targets. To demonstrate its performance, the target deconvolution pipeline was applied to 310 small molecules tested on 20 genetically and phenotypically heterogeneous triple-negative breast cancer (TNBC) cell lines to identify cell line-specific target mechanisms in terms of cytotoxic and cytostatic drug target vulnerabilities. The functional essentiality of each protein target was quantified with a target addiction score (TAS), as a measure of dependency of the cell line on the therapeutic target. The target dependency profiling was shown to capture inhibitory information that is complementary to that obtained from the structure or sensitivity of the drugs. Comparison of the TAS profiles and gene essentiality scores from CRISPR-Cas9 knockout screens revealed that certain proteins with low gene essentiality showed high target addictions, suggesting that they might be functioning as protein groups, and therefore be resistant to single gene knock-out. The comparative analysis discovered protein groups of potential multi-target synthetic lethal interactions, for instance, among histone deacetylases (HDACs). Our integrated approach also recovered a number of well-established TNBC cell line-specific drivers and known TNBC therapeutic targets, such as HDACs and cyclin-dependent kinases (CDKs). The present work provides novel insights into druggable vulnerabilities for TNBC, and opportunities to identify multi-target synthetic lethal interactions for further studies.

Project description:PurposeTriple-negative breast cancer (TNBC) does not have defined therapeutic targets and is currently treated with chemotherapy only. Kinase dysregulation triggers cancer cell proliferation and metastasis and is a crucial therapeutic target for cancer. In this study, targeted kinome sequencing of TNBC tumors was performed to assess the association between kinome gene alterations and disease outcomes in TNBC.MethodsA kinome gene panel consisting of 612 genes was used for the targeted sequencing of 166 TNBC samples and matched normal tissues. Analyses of the significantly mutated genes were performed. Genomic differences between Asian and non-Asian patients with TNBC were evaluated using two Asian TNBC datasets (from Seoul National University Hospital [SNUH] and Fudan University Shanghai Cancer Center [FUSCC]) and three non-Asian TNBC datasets (The Cancer Genome Atlas [TCGA], METABRIC, and Gustave Roussy). The prognostic value of kinome gene mutations was evaluated using tumor mutational burden (TMB) and oncogenic pathway analyses. Mutational profiles from the TCGA were used for validation.ResultsThe significantly mutated genes included TP53 (60% of patients), PIK3CA (21%), BRCA2 (8%), and ATM (8%). Compared with data from non-Asian public databases, the mutation rates of PIK3CA p.H1047R/Q were significantly higher in the SNUH cohort (p = 0.003, 0.048, and 0.032, respectively). This was verified using the FUSCC dataset (p = 0.003, 0.078, and 0.05, respectively). The TMB-high group showed a trend toward longer progression-free survival in our cohort and the TCGA TNBC cohort (p = 0.041 and 0.195, respectively). Kinome gene alterations in the Wnt pathway in patients with TNBC were associated with poor survival in both datasets (p = 0.002 and 0.003, respectively).ConclusionComprehensive analyses of kinome gene alterations in TNBC revealed genomic alterations that offer therapeutic targets and should help identify high-risk patients more precisely in future studies.

Dataset Information

Selection of aptamers against triple negative breast cancer cells using high throughput sequencing.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets