Dataset Information

Characterization of Cell-Type-Specific Drug Transport and Resistance of Breast Cancers Using Tumor-Microenvironment-on-Chip.

ABSTRACT: Heterogeneous response and resistance of cancer cells to chemotherapeutic drugs pose a significant challenge for successful cancer treatments. In this study, an integrated experimental and theoretical analysis of cellular drug transport was developed. The experimental platform, called tumor-microenvironment-on-chip (T-MOC), is a microfluidic platform where cancer cells were cultured within a three-dimensional extracellular matrix perfused with interstitial fluid. Three types of human breast cancer cell lines (MCF-7, MDA-MB-231, and SUM-159PT) were cultured on this T-MOC platform, and their drug response and resistance to doxorubicin were characterized by time-lapse quantitative fluorescence microscopy. To study the effects of nanoparticle-mediated drug delivery, the transport and action of doxorubicin encapsulated nanoparticles were also examined. Based on the experimental data obtained, a theoretical model was developed to quantify and ultimately predict the cellular transport processes of drugs cell-type specifically. The results demonstrate that the cellular drug transport can be cell-type-specifically quantified by rate constants representing the uptake and efflux of doxorubicin across the cellular membrane.

SUBMITTER: Shin K

PROVIDER: S-EPMC5032827 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Characterization of Cell-Type-Specific Drug Transport and Resistance of Breast Cancers Using Tumor-Microenvironment-on-Chip.

Shin Kyeonggon K Klosterhoff Brett S BS Han Bumsoo B

Molecular pharmaceutics 20160610 7

Heterogeneous response and resistance of cancer cells to chemotherapeutic drugs pose a significant challenge for successful cancer treatments. In this study, an integrated experimental and theoretical analysis of cellular drug transport was developed. The experimental platform, called tumor-microenvironment-on-chip (T-MOC), is a microfluidic platform where cancer cells were cultured within a three-dimensional extracellular matrix perfused with interstitial fluid. Three types of human breast canc ...[more]

PMID: 27228477

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Project description:Breast cancer is one of the most common female malignancies worldwide. Due to its early metastases formation and a high degree of malignancy, the 10 year-survival rate of metastatic breast cancer does not exceed 30%. Thus, more precise biomarkers are urgently needed. In our study, we first estimated the tumor microenvironment (TME) infiltration using the xCell algorithm. Based on TME infiltration, the three main TME clusters were identified using consensus clustering. Our results showed that the three main TME clusters cause significant differences in survival rates and TME infiltration patterns (log-rank test, p = 0.006). Then, multiple machine learning algorithms were used to develop a nine-pathway-based TME-related risk model to predict the prognosis of breast cancer (BRCA) patients (the immune-related pathway-based risk score, defined as IPRS). Based on the IPRS, BRCA patients were divided into two subgroups, and patients in the IPRS-low group presented significantly better overall survival (OS) rates than the IPRS-high group (log-rank test, p < 0.0001). Correlation analysis revealed that the IPRS-low group was characterized by increases in immune-related scores (cytolytic activity (CYT), major histocompatibility complex (MHC), T cell-inflamed immune gene expression profile (GEP), ESTIMATE, immune, and stromal scores) while exhibiting decreases in tumor purity, suggesting IPRS-low patients may have a strong immune response. Additionally, the gene-set enrichment analysis (GSEA) result confirmed that the IPRS-low patients were significantly enriched in several immune-associated signaling pathways. Furthermore, multivariate Cox analysis revealed that the IPRS was an independent prognostic biomarker after adjustment by clinicopathologic characteristics. The prognostic value of the IPRS model was further validated in three external validation cohorts. Altogether, our findings demonstrated that the IPRS was a powerful predictor to screen out certain populations with better prognosis in breast cancer and may serve as a potential biomarker guiding clinical treatment decisions.

Project description:Background: Immunotherapy has been a key option for the treatment of many types of cancer. A positive response to immunotherapy is heavily dependent on tumor microenvironment (TME) interaction. However, in pancreatic adenocarcinoma (PAAD), the association between TME mode of action and immune cell infiltration and immunotherapy, clinical outcome remained unknown. Methods: We systematically evaluated 29 TME genes in PAAD signature. Molecular subtypes of distinct TME signatures in PAAD were characterized by consensus clustering. After this, we comprehensively analyzed their clinical features, prognosis, and immunotherapy/chemotherapy response using correlation analysis, Kaplan-Meier curves analysis, ssGSEA analysis. 12 programmed cell death (PCD) patterns were acquired from previous study. Differentially expressed genes (DEGs) were acquired based on differential analysis. Key genes affecting overall survival (OS) of PAAD were screened by COX regression analysis and used to develop a RiskScore evaluation model. Finally, we assessed the value of RiskScore in predicting prognosis and treatment response in PAAD. Results: We identified 3 patterns of TME-associated molecular subtypes (C1, C2, C3), and observed that clinicopathological characteristics, prognosis, pathway features and immune features, immunotherapy/chemosensitivity of patients were correlated with the TME related subtypes. C1 subtype was more sensitive to the four chemotherapeutic drugs. PCD patterns were more likely to occur at C2 or C3. At the same time, we also detected 6 key genes that could affect the prognosis of PAAD, and 5 genes expressions were closely associated to methylation level. Low-risk patients with high immunocompetence had favorable prognostic results and high immunotherapy benefit. Patients in the high-risk group were more sensitive to chemotherapeutic drugs. RiskScore related to TME was an independent prognostic factor for PAAD. Conclusion: Collectively, we identified a prognostic signature of TME in PAAD patients, which could help elucidate the specific mechanism of action of TME in tumors and help to explore more effective immunotherapy strategies.

Dataset Information

Characterization of Cell-Type-Specific Drug Transport and Resistance of Breast Cancers Using Tumor-Microenvironment-on-Chip.

Publications

Characterization of Cell-Type-Specific Drug Transport and Resistance of Breast Cancers Using Tumor-Microenvironment-on-Chip.

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