Breast cancer patient-derived microtumors resemble tumor heterogeneity and enable protein-based stratification and functional validation of individualized drug treatment.

Despite tremendous progress in deciphering breast cancer at the genomic level, the pronounced intra- and intertumoral heterogeneity remains a major obstacle to the advancement of novel and more effective treatment approaches. Frequent treatment failure and the development of treatment resistance highlight the need for patient-derived tumor models that reflect the individual tumors of breast cancer patients and allow a comprehensive analyses and parallel functional validation of individualized and therapeutically targetable vulnerabilities in protein signal transduction pathways. Here, we introduce the generation and application of breast cancer patient-derived 3D microtumors (BC-PDMs).

Protein Profiling of Breast Carcinomas Reveals Expression of Immune-Suppressive Factors and Signatures Relevant for Patient Outcome.

In cancer, the complex interplay between tumor cells and the tumor microenvironment results in the modulation of signaling processes. By assessing the expression of a multitude of proteins and protein variants in cancer tissue, wide-ranging information on signaling pathway activation and the status of the immunological landscape is obtainable and may provide viable information on the treatment response. Archived breast cancer tissues from a cohort of 84 patients (no adjuvant therapy) were analyzed by high-throughput Western blotting, and the expression of 150 proteins covering central cancer pathways and immune cell markers was examined.

web-rMKL: a web server for dimensionality reduction and sample clustering of multi-view data based on unsupervised multiple kernel learning.

More and more affordable high-throughput techniques for measuring molecular features of biomedical samples have led to a huge increase in availability and size of different types of multi-omic datasets, containing, for example, genetic or histone modification data. Due to the multi-view characteristic of the data, established approaches for exploratory analysis are not directly applicable. Here we present web-rMKL, a web server that provides an integrative dimensionality reduction with subsequent clustering of samples based on data from multiple inputs.