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). Residual fresh tumor tissue specimens were collected from n = 102 patients diagnosed with breast cancer and subjected to BC-PDM isolation. BC-PDMs retained histopathological characteristics, and extracellular matrix (ECM) components together with key protein signaling pathway signatures of the corresponding primary tumor tissue. Accordingly, BC-PDMs reflect the inter- and intratumoral heterogeneity of breast cancer and its key signal transduction properties. DigiWest®-based protein expression profiling of identified treatment responder and non-responder BC-PDMs enabled the identification of potential resistance and sensitivity markers of individual drug treatments, including markers previously associated with treatment response and yet undescribed proteins. The combination of individualized drug testing with comprehensive protein profiling analyses of BC-PDMs may provide a valuable complement for personalized treatment stratification and response prediction for breast cancer.

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. By assessing CD8α, CD11c, CD16 and CD68 expression, immune cell infiltration was determined and revealed a strong correlation between event-free patient survival and the infiltration of immune cells. The presence of tumor-infiltrating lymphocytes was linked to the pronounced activation of the Jak/Stat signaling pathway and apoptotic processes. The elevated phosphorylation of PPARγ (pS112) in non-immune-infiltrated tumors suggests a novel immune evasion mechanism in breast cancer characterized by increased PPARγ phosphorylation. Multiplexed immune cell marker assessment and the protein profiling of tumor tissue provide functional signaling data facilitating breast cancer patient stratification.

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. The underlying machine learning method rMKL-LPP performed best for clinical enrichment in a recent benchmark of state-of-the-art multi-view clustering algorithms. The method was introduced for a multi-omic cancer subtype discovery setting, however, it is not limited to this application scenario as exemplified by a presented use case for stem cell differentiation. web-rMKL offers an intuitive interface for uploading data and setting the parameters. rMKL-LPP runs on the back end and the user may receive notifications once the results are available. We also introduce a preprocessing tool for generating kernel matrices from tables containing numerical feature values. This program can be used to generate admissible input if no precomputed kernel matrices are available. The web server is freely available at web-rMKL.org.