Person:

Almendro, Vanessa

Molecular analysis has revealed extensive intra-tumor heterogeneity in human cancer samples, but cannot identify cell-to-cell variations within the tissue microenvironment. In contrast, in situ analysis can identify genetic aberrations in phenotypically defined cell subpopulations while preserving tissue-context specificity. GoIFISH is a widely applicable, user-friendly system tailored for the objective and semi-automated visualization, detection and quantification of genomic alterations and protein expression obtained from fluorescence in situ analysis. In a sample set of HER2-positive breast cancers GoIFISH is highly robust in visual analysis and its accuracy compares favorably to other leading image analysis methods. GoIFISH is freely available at www.sourceforge.net/projects/goifish/. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0442-y) contains supplementary material, which is available to authorized users.

SUMMARY Cancers arise through a process of somatic evolution that can result in substantial sub-clonal heterogeneity within tumors. The mechanisms responsible for the coexistence of distinct sub-clones and the biological consequences of this coexistence remain poorly understood. Here we used a mouse xenograft model to investigate the impact of sub-clonal heterogeneity on tumor phenotypes and the competitive expansion of individual clones. We found that tumor growth can be driven by a minor cell subpopulation, which enhances the proliferation of all cells within a tumor by overcoming environmental constraints and yet can be outcompeted by faster proliferating competitors, resulting in tumor collapse. We then developed a mathematical modeling framework to identify the rules underlying the generation of intratumor clonal heterogeneity. We found that non-cell autonomous driving, together with clonal interference, stabilizes sub-clonal heterogeneity, thereby enabling inter-clonal interactions that can lead to new phenotypic traits.

Two lineages, epithelial, and myoepithelial cells are the main cell populations in the normal mammary gland and in breast cancer. Traditionally, cancer research has been performed using commercial cell lines, but primary cell cultures obtained from fresh breast tissue are a powerful tool to study more reliably new aspects of mammary gland biology, including normal and pathological conditions. Nevertheless, the methods described to date have some technical problems in terms of cell viability and yield, which hamper work with primary mammary cells. Therefore, there is a need to optimize technology for the proper isolation of epithelial and myoepithelial cells. For this reason, we compared four methods in an effort to improve the isolation and primary cell culture of different cell populations of human mammary epithelium. The samples were obtained from healthy tissue of patients who had undergone mammoplasty or mastectomy surgery. We based our approaches on previously described methods, and incorporated additional steps to ameliorate technical efficiency and increase cell survival. We determined cell growth and viability by phase-contrast images, growth curve analysis and cell yield, and identified cell-lineage specific markers by flow cytometry and immunofluorescence in 3D cell cultures. These techniques allowed us to better evaluate the functional capabilities of these two main mammary lineages, using CD227/K19 (epithelial cells) and CD10/K14 (myoepithelial cells) antigens. Our results show that slow digestion at low enzymatic concentration combined with the differential centrifugation technique is the method that best fits the main goal of the present study: protocol efficiency and cell survival yield. In summary, we propose some guidelines to establish primary mammary epithelial cell lines more efficiently and to provide us with a strong research instrument to better understand the role of different epithelial cell types in the origin of breast cancer.

The nervous system is now recognized to be a relevant component of the tumor microenvironment. Receptors for neuropeptides and neurotransmitters have been identified in breast cancer. However, very little is known about the role of neurogenes in regulating breast cancer progression. Our purpose was to identify neurogenes associated with breast cancer tumorigenesis with a potential to be used as biomarker and/or targets for treatment. We used three databases of human genes: GeneGo, GeneCards and Eugenes to generate a list of 1266 relevant neurogenes. Then we used bioinformatics tools to interrogate two published breast cancer databases SAGE and MicMa (n=96) and generated a list of 7 neurogenes that are differentially express among breast cancer subtypes. The clinical potential was further investigated using the GOBO database (n=1881). We identified 6 neurogenes that are differentially expressed among breast cancer subtypes and whose expression correlates with prognosis. Histamine receptor1 (HRH1), neuropilin2 (NRP2), ephrin-B1 (EFNB1), neural growth factor receptor (NGFR) and amyloid precursor protein (APP) were differentially overexpressed in basal and HER2-enriched tumor samples and syntaxin 1A (STX1A) was overexpressed in HER2-enriched and luminal B tumors. Analysis of HRH1, NRP2, and STX1A expression using the GOBO database showed that their expression significantly correlated with a shorter overall survival (p < 0.0001) and distant metastasis-free survival (p < 0.0001). In contrast, elevated co-expression of NGFR, EFNB1 and APP was associated with longer overall (p < 0.0001) and metastasis-free survival (p < 0.0001). We propose that HRH1, NRP2, and STX1A can be used as prognostic biomarkers and therapeutic targets for basal and HER2-enriched breast cancer subtypes.

Detection of minor genetically distinct subpopulations within tumors is a key challenge in cancer genomics. Here we report STAR-FISH (Specific-To-Allele PCR – FISH), a novel method for the combined detection of single nucleotide and copy number alterations in single cells in intact archived tissues. Using this method, we assessed the clinical impact of changes in the frequency and topology of PIK3CA mutation and HER2/ERBB2 amplification within HER2+ breast cancer during neoadjuvant therapy. We found that the two genetic events are not always present within the same cell. Chemotherapy selects for PIK3CA mutant cells, a minor subpopulation in nearly all treatment-naïve samples, and modulates genetic diversity within tumors. Treatment-associated changes in spatial distribution of cellular genetic diversity correlated with poor long-term outcome following adjuvant trastuzumab therapy. Our findings support the use of in situ single-cell based methods in cancer genomics and imply that chemotherapy before HER2-targeted therapy may promote treatment resistance.