Analysis of GAGE, NY-ESO-1 and SP17 cancer/testis antigen expression in early stage non-small cell lung carcinoma

BACKGROUND: The unique expression pattern and immunogenic properties of cancer/testis antigens make them ideal targets for immunotherapy of cancer. The MAGE-A3 cancer/testis antigen is frequently expressed in non-small cell lung cancer (NSCLC) and vaccination with MAGE-A3 in patients with MAGE-A3-positive NSCLC has shown promising results. However, little is known about the expression of other cancer/testis antigens in NSCLC. In the present study the expression of cancer/testis antigens GAGE, NY-ESO-1 and SP17 was investigated in patients with completely resected, early stage, primary NSCLC. METHODS: Tumor biopsies from normal lung tissue and from a large cohort (n = 169) of NSCLC patients were examined for GAGE, NY-ESO-1 and SP17 protein expression by immunohistochemical analysis. The expression of these antigens was further matched to clinical and pathological features using univariate cox regression analysis. RESULTS: GAGE and NY-ESO-1 cancer/testis antigens were not expressed in normal lung tissue, while SP17 was expressed in ciliated lung epithelia. The frequency of GAGE, NY-ESO-1 and SP17 expression in NSCLC tumors were 26.0% (44/169), 11.8% (20/169) and 4.7% (8/169), respectively, and 33.1% (56/169) of the tumors expressed at least one of these antigens. In general, the expression of GAGE, NY-ESO-1 and SP17 was not significantly associated with a specific histotype (adenocarcinoma vs. squamous cell carcinoma), but high-level GAGE expression (>50%) was more frequent in squamous cell carcinoma (p = 0.02). Furthermore, the frequency of GAGE expression was demonstrated to be significantly higher in stage II-IIIa than stage I NSCLC (17.0% vs. 35.8%; p = 0.02). Analysis of the relation between tumor expression of GAGE and NY-ESO-1 and survival endpoints revealed no significant associations. CONCLUSION: Our study demonstrates that GAGE, NY-ESO-1 and SP17 cancer/testis antigens are candidate targets for immunotherapy of NSCLC and further suggest that multi-antigen vaccines may be beneficial

De novo pathway-based biomarker identification

Gene expression profiles have been extensively discussed as an aid to guide the therapy by predicting disease outcome for the patients suffering from complex diseases, such as cancer. However, prediction models built upon single-gene (SG) features show poor stability and performance on independent datasets. Attempts to mitigate these drawbacks have led to the development of network-based approaches that integrate pathway information to produce meta-gene (MG) features. Also, MG approaches have only dealt with the two-class problem of good versus poor outcome prediction. Stratifying patients based on their molecular subtypes can provide a detailed view of the disease and lead to more personalized therapies. We propose and discuss a novel MG approach based on de novo pathways, which for the first time have been used as features in a multi-class setting to predict cancer subtypes. Comprehensive evaluation in a large cohort of breast cancer samples from The Cancer Genome Atlas (TCGA) revealed that MGs are considerably more stable than SG models, while also providing valuable insight into the cancer hallmarks that drive them. In addition, when tested on an independent benchmark non-TCGA dataset, MG features consistently outperformed SG models. We provide an easy-touse web service at http:// pathclass. compbio. sdu. dk where users can upload their own gene expression datasets from breast cancer studies and obtain the subtype predictions from all the classifiers

Global MicroRNA Expression Profiling of High-Risk ER+ Breast Cancers from Patients Receiving Adjuvant Tamoxifen Mono-Therapy: A DBCG Study

PURPOSE: Despite the benefits of estrogen receptor (ER)-targeted endocrine therapies in breast cancer, many tumors develop resistance. MicroRNAs (miRNAs) have been suggested as promising biomarkers and we here evaluated whether a miRNA profile could be identified, sub-grouping ER+ breast cancer patients treated with adjuvant Tamoxifen with regards to probability of recurrence. EXPERIMENTAL DESIGN: Global miRNA analysis was performed on 152 ER+ primary tumors from high-risk breast cancer patients with an initial discovery set of 52 patients, followed by two independent test sets (N = 60 and N = 40). All patients had received adjuvant Tamoxifen as mono-therapy (median clinical follow-up: 4.6 years) and half had developed distant recurrence (median time-to-recurrence: 3.5 years). MiRNA expression was examined by unsupervised hierarchical clustering and supervised analysis, including clinical parameters as co-variables. RESULTS: The discovery set identified 10 highly significant miRNAs that discriminated between the patient samples according to outcome. However, the subsequent two independent test sets did not confirm the predictive potential of these miRNAs. A significant correlation was identified between miR-7 and the tumor grade. Investigation of the microRNAs with the most variable expression between patients in different runs yielded a list of 31 microRNAs, eight of which are associated with stem cell characteristics. CONCLUSIONS: Based on the large sample size, our data strongly suggests that there is no single miRNA profile predictive of outcome following adjuvant Tamoxifen treatment in a broad cohort of ER+ breast cancer patients. We identified a sub-group of Tamoxifen-treated breast cancer patients with miRNA-expressing tumors associated with cancer stem cell characteristics

Development of a specific affinity-matured exosite inhibitor to MT1-MMP that efficiently inhibits tumor cell invasion in vitro and metastasis in vivo.

This is the final version of the article. It first appeared from Impact Journals via https://doi.org/10.18632/oncotarget.7780The membrane-associated matrix metalloproteinase-14, MT1-MMP, has been implicated in pericellular proteolysis with an important role in cellular invasion of collagenous tissues. It is substantially upregulated in various cancers and rheumatoid arthritis, and has been considered as a potential therapeutic target. Here, we report the identification of antibody fragments to MT1-MMP that potently and specifically inhibit its cell surface functions. Lead antibody clones displayed inhibitory activity towards pro-MMP-2 activation, collagen-film degradation and gelatin-film degradation, and were shown to bind to the MT1-MMP catalytic domain outside the active site cleft, inhibiting binding to triple helical collagen. Affinity maturation using CDR3 randomization created a second generation of antibody fragments with dissociation constants down to 0.11 nM, corresponding to an improved affinity of 332-fold with the ability to interfere with cell-surface MT1-MMP functions, displaying IC50 values down to 5 nM. Importantly, the new inhibitors were able to inhibit collagen invasion by tumor-cells in vitro and in vivo primary tumor growth and metastasis of MDA-MB-231 cells in a mouse orthotopic xenograft model. Herein is the first demonstration that an inhibitory antibody targeting sites outside the catalytic cleft of MT1-MMP can effectively abrogate its in vivo activity during tumorigenesis and metastasis.KAB was supported by a grant from the Danish Cancer Society (R40-A1838). HJD was supported in part by grants from the Danish Cancer Society, The Danish Research Council. HFK and GM were supported by Cancer Research UK and Hutchison Whampoa Ltd. HFK was also supported in part by grants from the University of Macau Start-Up Research Grant (SRG2014-00006-FHS) and Multi-Year Research Grant (MYRG2015-00065-FHS)

Integrative analysis of miRNA and gene expression reveals regulatory networks in tamoxifen-resistant breast cancer

Tamoxifen is an effective anti-estrogen treatment for patients with estrogen receptor-positive (ER+) breast cancer, however, tamoxifen resistance is frequently observed. To elucidate the underlying molecular mechanisms of tamoxifen resistance, we performed a systematic analysis of miRNA-mediated gene regulation in three clinically-relevant tamoxifen-resistant breast cancer cell lines (TamRs) compared to their parental tamoxifen-sensitive cell line. Alterations in the expression of 131 miRNAs in tamoxifen-resistant vs. parental cell lines were identified, 22 of which were common to all TamRs using both sequencing and LNA-based quantitative PCR technologies. Although the target genes affected by the altered miRNA in the three TamRs differed, good agreement in terms of affected molecular pathways was observed. Moreover, we found evidence of miRNA-mediated regulation of ESR1, PGR1, FOXM1 and 14-3-3 family genes. Integrating the inferred miRNA-target relationships, we investigated the functional importance of 2 central genes, SNAI2 and FYN, which showed increased expression in TamR cells, while their corresponding regulatory miRNA were downregulated. Using specific chemical inhibitors and siRNA-mediated gene knockdown, we showed that both SNAI2 and FYN significantly affect the growth of TamR cell lines. Finally, we show that a combination of 2 miRNAs (miR-190b and miR-516a-5p) exhibiting altered expression in TamR cell lines were predictive of treatment outcome in a cohort of ER+ breast cancer patients receiving adjuvant tamoxifen mono-therapy. Our results provide new insight into the molecular mechanisms of tamoxifen resistance and may form the basis for future medical intervention for the large number of women with tamoxifen-resistant ER+ breast cancer

Intrinsic Differences in Spatiotemporal Organization and Stromal Cell Interactions Between Isogenic Lung Cancer Cells of Epithelial and Mesenchymal Phenotypes Revealed by High-Dimensional Single-Cell Analysis of Heterotypic 3D Spheroid Models

The lack of inadequate preclinical models remains a limitation for cancer drug development and is a primary contributor to anti-cancer drug failures in clinical trials. Heterotypic multicellular spheroids are three-dimensional (3D) spherical structures generated by self-assembly from aggregates of two or more cell types. Compared to traditional monolayer cell culture models, the organization of cells into a 3D tissue-like structure favors relevant physiological conditions with chemical and physical gradients as well as cell-cell and cell-extracellular matrix (ECM) interactions that recapitulate many of the hallmarks of cancer in situ. Epidermal growth factor receptor (EGFR) mutations are prevalent in non-small cell lung cancer (NSCLC), yet various mechanisms of acquired resistance, including epithelial-to-mesenchymal transition (EMT), limit the clinical benefit of EGFR tyrosine kinase inhibitors (EGFRi). Improved preclinical models that incorporate the complexity induced by epithelial-to-mesenchymal plasticity (EMP) are urgently needed to advance new therapeutics for clinical NSCLC management. This study was designed to provide a thorough characterization of multicellular spheroids of isogenic cancer cells of various phenotypes and demonstrate proof-of-principle for the applicability of the presented spheroid model to evaluate the impact of cancer cell phenotype in drug screening experiments through high-dimensional and spatially resolved imaging mass cytometry (IMC) analyses. First, we developed and characterized 3D homotypic and heterotypic spheroid models comprising EGFRi-sensitive or EGFRi-resistant NSCLC cells. We observed that the degree of EMT correlated with the spheroid generation efficiency in monocultures. In-depth characterization of the multicellular heterotypic spheroids using immunohistochemistry and high-dimensional single-cell analyses by IMC revealed intrinsic differences between epithelial and mesenchymal-like cancer cells with respect to self-sorting, spatiotemporal organization, and stromal cell interactions when co-cultured with fibroblasts. While the carcinoma cells harboring an epithelial phenotype self-organized into a barrier sheet surrounding the fibroblasts, mesenchymal-like carcinoma cells localized to the central hypoxic and collagen-rich areas of the compact heterotypic spheroids. Further, deep-learning-based single-cell segmentation of IMC images and application of dimensionality reduction algorithms allowed a detailed visualization and multiparametric analysis of marker expression across the different cell subsets. We observed a high level of heterogeneity in the expression of EMT markers in both the carcinoma cell populations and the fibroblasts. Our study supports further application of these models in pre-clinical drug testing combined with complementary high-dimensional single-cell analyses, which in turn can advance our understanding of the impact of cancer-stroma interactions and epithelial phenotypic plasticity on innate and acquired therapy resistance in NSCLC.publishedVersio