Selective Adhesion and Switchable Release of Breast Cancer Cells via Hyaluronic Acid Functionalized Dual Stimuli-Responsive Microgel Films.

The detection of tumor cells from liquid biopsy samples is of critical importance for early cancer diagnosis, malignancy assessment, and treatment. In this work, coatings of hyaluronic acid (HA)-functionalized dual-stimuli responsive poly(N-isopropylacrylamide) (PNIPAM) microgels are used to study the specificity of breast cancer cell binding and to assess cell friendly release mechanisms for further diagnostic procedures. The microgels are established by straightforward precipitation polymerization with amine bearing comonomers and postfunctionalization with a UV-labile linker that covalently binds HA to the microgel network. Well-defined microgel coatings for cell binding are established via simple physisorption and annealing. The HA-presenting PNIPAM microgel films are shown to specifically adhere CD44 expressing breast cancer cell lines (MDA-MB-231 and MCF-7), where an increase in adhesion correlates with higher CD44 expression and HA functionalization. Upon cooling below the lower critical solution temperature of PNIPAM microgels, the cells could be released; however, 10-30% of the cells still remained on the surface even after prolonged cooling and mild mechanical agitation. A complete cell release is achieved after applying the light stimulus by short UV treatment cleaving HA units from the microgels. Owing to the comparatively straightforward preparation procedures, such dual-responsive microgel films could be considered for the effective capture, release, and diagnostics of tumor cells.

Progression-specific genes identified by expression profiling of matched ductal carcinomas in situ and invasive breast tumors, combining laser capture microdissection and oligonucleotide microarray analysis.

Becoming invasive is a crucial step in breast cancer oncogenesis. At this point, a lesion carries the potential for spreading and metastasis--a process, whose molecular characteristics still remain poorly understood. In this article, we describe a matched-pair analysis of ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) of nine breast ductal carcinomas to identify novel molecular markers characterizing the transition from DCIS to IDC. The purpose of this study was to better understand the molecular biology of this transition and to identify candidate genes whose products might serve as prognostic markers and/or as molecular targets for treatment. To obtain cellular-based gene expression profiles from epithelial tumor cells, we combined laser capture microdissection with a T7-based two-round RNA amplification and Affymetrix oligonucleotide microarray analysis. Altogether, a set of 24 tumor samples was analyzed, comprised of nine matched DCIS/IDC and replicate DCIS/IDC preparations from three of the nine tumors. Cluster analysis on expression data shows the robustness and reproducibility of the techniques we established. Using multiple statistical methods, 546 significantly differentially expressed probe sets were identified. Eighteen candidate genes were evaluated by RT-PCR. Examples of genes already known to be associated with breast cancer invasion are BPAG1, LRRC15, MMP11, and PLAU. The expression of BPAG1, DACT1, GREM1, MEF2C, SART2, and TNFAIP6 was localized to epithelial tumor cells by in situ hybridization and/or immunohistochemistry, confirming the accuracy of laser capture microdissection sampling and microarray analysis.

Progression-specific genes identified in microdissected formalin-fixed and paraffin-embedded tissue containing matched ductal carcinoma in situ and invasive ductal breast cancers.

BACKGROUND: The transition from ductal carcinoma in situ (DCIS) to invasive breast carcinoma (IBC) is an important step during breast carcinogenesis. Understanding its molecular changes may help to identify high-risk DCIS that progress to IBC. Here, we describe a transcriptomic profiling analysis of matched formalin-fixed and paraffin-embedded (FFPE) DCIS and IBC components of individual breast tumours, containing both tumour compartments. The study was performed to validate progression-associated transcripts detected in an earlier gene profiling project using fresh frozen breast cancer tissue. In addition, FFPE tissues from patients with pure DCIS (pDCIS) were analysed to identify candidate transcripts characterizing DCIS with a high or low risk of progressing to IBC. METHODS: Fifteen laser microdissected pairs of DCIS and IBC were profiled by Illumina DASL technology and used for expression validation by qPCR. Differential expression was independently validated using further 25 laser microdissected DCIS/IBC sample pairs. Additionally, laser microdissected epithelial cells from 31 pDCIS were investigated for expression of candidate transcripts using qPCR. RESULTS: Multiple statistical calculation methods revealed 1784 mRNAs which are differentially expressed between DCIS and IBC (P < 0.05), of which 124 have also been identified in the gene profiling project using fresh frozen breast cancer tissue. Nine mRNAs that had been selected from the gene list obtained using fresh frozen tissues by applying pathway and network analysis (MMP11, GREM1, PLEKHC1, SULF1, THBS2, CSPG2, COL10A1, COL11A1, KRT14) were investigated in tissues from the same 15 microdissected specimens and the 25 independent tissue samples by qPCR. All selected transcripts were also detected in tumour cells from pDCIS. Expression of MMP11 and COL10A1 increased significantly from pDCIS to DCIS of DCIS/IBC mixed tumours. CONCLUSION: We confirm differential expression of progression-associated transcripts in FFPE breast cancer samples which might mediate the transition from DCIS to IBC. MMP11 and COL10A1 may characterize pure DCIS with a high risk developing IDC.

Metabolomics approach for predicting response to neoadjuvant chemotherapy for breast cancer.

Breast cancer is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. As an example, only some women will benefit from chemotherapy. Identifying patients who will respond to chemotherapy and thereby improve their long-term survival has important implications to treatment protocols and outcomes, while identifying non responders may enable these patients to avail themselves of other investigational approaches or other potentially effective treatments. In this study, serum metabolite profiling was performed to identify potential biomarker candidates that can predict response to neoadjuvant chemotherapy for breast cancer. Metabolic profiles of serum from patients with complete (n = 8), partial (n = 14) and no response (n = 6) to chemotherapy were studied using a combination of nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography-mass spectrometry (LC-MS) and statistical analysis methods. The concentrations of four metabolites, three (threonine, isoleucine, glutamine) from NMR and one (linolenic acid) from LC-MS were significantly different when comparing response to chemotherapy. A prediction model developed by combining NMR and MS derived metabolites correctly identified 80% of the patients whose tumors did not show complete response to chemotherapy. These results show promise for larger studies that could result in more personalized treatment protocols for breast cancer patients.