Resistance Studies in Lung Cancer
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ABSTRACT:
PROVIDER: PRJNA276487 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:In depth label free quantitative mass spectrometry based proteomics for identification of potential biomarkers of drug resistance in lung cancer.
2023-03-10 | PXD036996 | Pride
Project description:Lung cancer is the leading cause of cancer related deaths, worldwide. Fibroblast growth factor receptor 1 (FGFR1) gene amplification is one of the most prominent and potentially targetable genetic alterations in squamous cell lung cancer (SQCLC). Highly selective tyrosine kinase inhibitors have been developed to target FGFR1, however, resistance mechanisms originally existing in patients or acquired throughout treatment have limited treatment efficiency in clinical trials, so far. In this study, we performed a wide-scale phosphoproteomic mass spectrometry analysis to explore signaling pathways that lead to FGFR1 inhibition resistance in lung cancer cells with intrinsic, induced and mutational resistance. We identified a CD44/AKT signaling axis as a new and common mechanism of resistance to FGFR1 inhibition in lung cancer. Co-inhibition of AKT or CD44 synergistically sensitized intrinsic and induced resistant cells to FGFR1 inhibition. Furthermore, strong CD44 expression was significantly correlated to AKT activation in squamous cell lung cancer patients. Collectively, our phosphoproteomic analysis of FGFR1 inhibitor resistant lung cancer cells promotes a large data library of resistance associated phosphorylation patterns and proposes a common resistance pathway consisting of CD44 and AKT activation. Examination of CD44/AKT activation could help to predict response to FGFR1 inhibition and combination with AKT inhibitors might path the way for an effective therapy of FGFR1 dependent lung cancer patients in case of treatment resistance.
2022-08-12 | PXD025389 | Pride
Project description:<p>The purpose of the study was to compare gene expression profiles from a cohort of crizotinib-resistant ALK-rearranged lung tumors and a cohort of treatment-naive ALK-rearranged lung tumors. Expression profiles were generated by RNA-seq. In parallel, gene expression profiles were obtained from ALK-rearranged lung cancer cell lines in the presence or absence of the ALK inhibitor TAE684. Gene expression profiles were also obtained from ALK-rearranged cells ectopically expressing genes associated with ALK inhibitor resistance that were identified from a functional genetic study.</p>
| phs000855 | dbGaP
Project description:Purpose: To characterize microRNA signatures for tolerance, persistence and resistance to EGFR tyrosine kinase inhibitors (TKIs) in human lung cancer. Methods: microRNA profiles of gefitinib- and osimertinib-tolerant cells in PC9 and HCC827 cells were generated by deep microRNA sequencing using Illumina. In addition, microRNA profiles of PC9 subpopulations cells with characterizations of persistence and resistance to gefitinib were generated by deep microRNA sequencing. The mappable reads were aligned to the human genome and miRbase using Bowtie. Results: We identified a specific microRNA profile distinguishing tolerance, persistence and resistance to gefitinib or osimertinib from parental human lung cancer cells with mutated EGFR. The expressions of those microRNAs in lung cancer cells were validated by qRT-PCR. Functionally, knocking down top-upregulated microRNAs reduced the tolerance, persistence and resistance to gefitinib or osimertinib in those tolerant and resistant cells. Conversely, overexpression of those microRNAs enhanced the tolerance and resistance to EGFR inhibition in cells sensitive to gefitinib and osimertinib. Conclusions: Our work identifies a panel of microRNAs that mediate EGFR-TKI tolerance and resistance in lung cancer. Our study provides potential non-coding targets to improve the efficacy of EGFR-TKIs therapy in cancer pagtients.
2018-01-11 | GSE103352 | GEO
Project description:Mechanisms of drugs-resistance in small cell lung cancer
| PRJNA706414 | ENA
Project description:Anoikis (detachment-induced cell death) is a specific type of programmed cell death which occurs in response to the loss of the correct extracellular matrix connections. Anoikis resistance is an important mechanism in cancer invasiveness and metastatic behavior. Autophagy, on the other hand, involves the degradation of damaged organelles and the recycling of misfolded proteins and intracellular components. However, the intersection of these two cellular responses in lung cancer cells has not been extensively studied. Here, we identified that upon matrix deprivation, the lymphocyte lineage-specific Ets transcription factor SPIB was activated and directly enhanced SNAP47 transcription in certain lung cancer cells. Loss of attachment-induced autophagy significantly increased anoikis resistance by SPIB activation. Consistent with this function, SPIB depletion by short hairpin RNA abrogated SNAP47 transcriptional activation upon matrix deprivation. Therefore, these data delineate an important role of SPIB in autophagy-mediated anoikis resistance in lung cancer cells. Accordingly, these findings suggest that manipulating SPIB-regulated pathways in vivo and evaluating the impact of anoikis resistance warrant further investigation.
2019-03-11 | GSE125561 | GEO
Project description:The dismal lethality of lung cancer is due to late stage at diagnosis and inherent therapeutic resistance. The incorporation of targeted therapies has modestly improved clinical outcomes, but the identification of new targets could further improve clinical outcomes by guiding stratification of poor-risk early-stage patients and individualizing therapeutic choices. We hypothesized that a sequential, combined microarray approach would be valuable to identify and validate new targets in lung cancer. We profiled gene expression signatures during lung epithelial cell immortalization and transformation, and showed that genes involved in mitosis were progressively enhanced in carcinogenesis. 28 genes were validated by immunoblotting and 4 genes were further evaluated in non-small cell lung cancer tissue microarrays. Although CDK1 was highly expressed in tumor tissues, its loss from the cytoplasm unexpectedly predicted poor survival and conferred resistance to chemotherapy in multiple cell lines, especially microtubule-directed agents. An analysis of expression of CDK1 and CDK1-associated genes in the NCI60 cell line database confirmed the broad association of these genes with chemotherapeutic responsiveness. These results have implications for personalizing lung cancer therapy and highlight the potential of combined approaches for biomarker discovery. In these studies, we systematically profiled gene expression in normal (NHBE), immortalized (BEAS-2B) and fully transformed (NNK-BEAS-2B) human bronchial epithelial cells, as well as a non-small cell lung cancer (NSCLC) cell line (H157) from a smoker. Expression profiles that accompany the immortalization and/or transformation of bronchial epithelial cells were generated, and expression of 28 genes was validated by immunoblotting. 4 of them were further evaluated in immunohistochemical analyses of tissue microarrays that contain NSCLC specimens, surrounding non-diseased tissues and non-pulmonary normal tissues. Although all 4 genes were predominantly expressed in tumor tissues, loss of expression of cytoplasmic CDK1 was clinically important because it was associated with a poor prognosis for NSCLC patients. This poor prognostic value may be associated with therapeutic resistance because decreasing levels of cytoplasmic CDK1 in vitro increased resistance to standard chemotherapies used in the treatment of NSCLC, especially microtubule agents where resistance was almost complete. These studies illustrate how a combined microarray approach can facilitate the identification of new, relevant targets in cancer.
2011-03-31 | GSE28282 | GEO
Project description:Activating mutations of EGFR have been characterized as important mechanisms for carcinogenesis in a subset of EGFR-dependent non-small cell lung cancers (NSCLC). EGFR tyrosine kinase inhibitors (TKI), such as erlotinib and gefitinib, have dramatic clinical effects on EGFR-addicted lung cancers and are used as first-line therapy for EGFR-mutant tumors. However, eventually all tumors acquire secondary resistance to the drugs and progress. We established a model to better understand mechanisms of acquired resistance. NCI- HCC827 cells are EGFR-mutant and highly erlotinib-sensitive. In this study we exposed HCC827 cells to increasing concentrations of erlotinib and two highly erlotinib-resistant subclones were developed (ER3 and T15-2). In these subclones no acquired alterations of EGFR or MET were found. We hereby performed a gene expression microarray studies to understand changes that might explain mechanisms of resistance. Through these studies we demonstrated in one resistant clone (ER3) overexpression of AXL, a tyrosine kinase implicated in imatinib and lapatinib resistance. Gene expression profilings were measured in NSCLC cell line HCC827 and two erlotinib-resistant HCC827-originated sublines ER3 and T15-2.
2012-05-29 | E-GEOD-38310 | biostudies-arrayexpress
Project description:Cancer precision medicine largely relies on knowledge about genetic aberrations in tumors and next-generation-sequencing studies have shown a high mutational complexity in many cancers. Although a large number of the observed mutations is believed to be not causally linked with cancer, the functional effects of many rare mutations but also of combinations of driver mutations are often unknown. Here, we perform a systems analysis of a model of EGFR-mutated non-small cell lung cancer resistant to targeted therapy that integrates whole exome sequencing, global time-course discovery phosphoproteomics and computational modeling to identify functionally relevant molecular alterations. Our approach allows for a complexity reduction from over 2,000 genetic events potentially involved in mediating resistance to only 44 phosphoproteins and 35 topologically close genetic alterations. We perform single- and combination-drug testing against the predicted phosphoproteins and discovered that targeting of HSPB1, DBNL and AKT1 showed potent anti-proliferative effects overcoming resistance against EGFR-inhibitory therapy. Our approach may therefore be used to complement mutational profiling to identify functionally relevant molecular aberrations and propose combination therapies across cancers.
2018-07-02 | PXD009996 | Pride
Project description:The model is based on publication:
Mathematical analysis of gefitinib resistance of lung adenocarcinoma caused by MET amplification
Abstract:
Gefitinib, one of the tyrosine kinase inhibitors of epidermal growth factor receptor (EGFR), is effective for treating lung adenocarcinoma harboring EGFR mutation; but later, most cases acquire a resistance to gefitinib. One of the mechanisms conferring gefitinib resistance to lung adenocarcinoma is the amplification of the MET gene, which is observed in 5–22% of gefitinib-resistant tumors. A previous study suggested that MET amplification could cause gefitinib resistance by driving ErbB3-dependent activation of the PI3K pathway. In this study, we built a mathematical model of gefitinib resistance caused by MET amplification using lung adenocarcinoma HCC827-GR (gefitinib resistant) cells. The molecular reactions involved in gefitinib resistance consisted of dimerization and phosphorylation of three molecules, EGFR, ErbB3, and MET were described by a series of ordinary differential equations. To perform a computer simulation, we quantified each molecule on the cell surface using flow cytometry and estimated unknown parameters by dimensional analysis. Our simulation showed that the number of active ErbB3 molecules is around a hundred-fold smaller than that of active MET molecules. Limited contribution of ErbB3 in gefitinib resistance by MET amplification is also demonstrated using HCC827-GR cells in culture experiments. Our mathematical model provides a quantitative understanding of the molecular reactions underlying drug resistance.
2024-09-02 | BIOMD0000000827 | BioModels