Quantitative proteomics of breast tumors: Tissue quality assessment to clinical biomarkers

An integrated modelling approach for targeted degradation: insights on optimization, data requirements and PKPD predictions from semi- or fully-mechanistic models and exact steady state solutions

The value of an integrated mathematical modelling approach for protein degraders which combines the benefits of traditional turnover models and fully mechanistic models is presented. Firstly, we show how exact solutions of the mechanistic models of monovalent and bivalent degraders can provide insight on the role of each system parameter in driving the pharmacological response. We show how on/off binding rates and degradation rates are related to potency and maximal effect of monovalent degraders, and how such relationship can be used to suggest a compound optimization strategy. Even convoluted exact steady state solutions for bivalent degraders provide insight on the type of observations required to ensure the predictive capacity of a mechanistic approach. Specifically for PROTACs, the structure of the exact steady state solution suggests that the total remaining target at steady state, which is easily accessible experimentally, is insufficient to reconstruct the state of the whole system at equilibrium and observations on different species (such as binary/ternary complexes) are necessary. Secondly, global sensitivity analysis of fully mechanistic models for PROTACs suggests that both target and ligase baselines (actually, their ratio) are the major sources of variability in the response of non-cooperative systems, which speaks to the importance of characterizing their distribution in the target patient population. Finally, we propose a pragmatic modelling approach which incorporates the insights generated with fully mechanistic models into simpler turnover models to improve their predictive ability, hence enabling acceleration of drug discovery programs and increased probability of success in the clinic.

Deciphering Phenotypes from Protein Biomarkers for Translational Research with PIPER

Liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM) has widespread clinical use for detection of inborn errors of metabolism, therapeutic drug monitoring, and numerous other applications. This technique detects proteolytic peptides as surrogates for protein biomarker expression, mutation, and post-translational modification in individual clinical assays and in cancer research with highly multiplexed quantitation across biological pathways. LC-MRM for protein biomarkers must be translated from multiplexed research-grade panels to clinical use. LC-MRM panels provide the capability to quantify clinical biomarkers and emerging protein markers to establish the context of tumor phenotypes that provide highly relevant supporting information. An application to visualize and communicate targeted proteomics data will empower translational researchers to move protein biomarker panels from discovery to clinical use. Therefore, we have developed a web-based tool for targeted proteomics that provides pathway-level evaluations of key biological drivers (e.g., EGFR signaling), signature scores (representing phenotypes) (e.g., EMT), and the ability to quantify specific drug targets across a sample cohort. This tool represents a framework for integrating summary information, decision algorithms, and risk scores to support Physician-Interpretable Phenotypic Evaluation in R (PIPER) that can be reused or repurposed by other labs to communicate and interpret their own biomarker panels.

Proteomic Technology "Lens" for Epithelial-Mesenchymal Transition Process Identification in Oncology

The epithelial-mesenchymal transition (EMT) is a complex transformation process that induces local and distant progression of many malignant tumours. Due to its complex array of proteins that are dynamically over-/underexpressed during this process, proteomic technologies gained their place in the EMT research in the last years. Proteomics has identified new molecular pathways of this process and brought important insights to develop new therapy targets. Various proteomic tools and multiple combinations were developed in this area. Out of the proteomic technology armentarium, mass spectrometry and array technologies are the most used approaches. The main characteristics of the proteomic technology used in this domain are high throughput and detection of minute concentration in small samples. We present herein, using various proteomic technologies, the identification in cancer cell lines and in tumour tissue EMT-related proteins, proteins that are involved in the activation of different cellular pathways. Proteomics has brought besides standard EMT markers (e.g., cell-cell adhesion proteins and transcription factors) other future potential markers for improving diagnosis, monitoring evolution, and developing new therapy targets. Future will increase the proteomic role in clinical investigation and validation of EMT-related biomarkers.

Proteomics for cancer drug design

Introduction: Signal transduction cascades drive cellular proliferation, apoptosis, immune, and survival pathways. Proteins have emerged as actionable drug targets because they are often dysregulated in cancer, due to underlying genetic mutations, or dysregulated signaling pathways. Cancer drug development relies on proteomic technologies to identify potential biomarkers, mechanisms-of-action, and to identify protein binding hot spots. Areas covered: Brief summaries of proteomic technologies for drug discovery include mass spectrometry, reverse phase protein arrays, chemoproteomics, and fragment based screening. Protein-protein interface mapping is presented as a promising method for peptide therapeutic development. The topic of biosimilar therapeutics is presented as an opportunity to apply proteomic technologies to this new class of cancer drug. Expert opinion: Proteomic technologies are indispensable for drug discovery. A suite of technologies including mass spectrometry, reverse phase protein arrays, and protein-protein interaction mapping provide complimentary information for drug development. These assays have matured into well controlled, robust technologies. Recent regulatory approval of biosimilar therapeutics provides another opportunity to decipher the molecular nuances of their unique mechanisms of action. The ability to identify previously hidden protein hot spots is expanding the gamut of potential drug targets. Proteomic profiling permits lead compound evaluation beyond the one drug, one target paradigm.

Development of parallel reaction monitoring (PRM)-based quantitative proteomics applied to HER2-Positive breast cancer

Introduction

treatments targeting the Human Epidermal Growth Factor Receptor 2 (HER2/ERBB2) have improved the natural history of HER2-positive breast cancer. However, except HER2 protein expression and gene amplification, there is no predictive biomarker to guide the HER2-targeted therapies. We developed Parallel reaction monitoring (PRM) a powerful approach, to quantify and evaluate key proteins involved in the HER2 pathway and/or anti-HER2 treatment sensitivity.

Results

in BCLs, PRM measurements correlated with western blot immunocytochemistry and transcriptomic data. At baseline, higher expression of HER2, EGFR, PTEN and HER3 but lower expression of phospho-HER2 correlated with trastuzumab sensitivity. Under trastuzumab, PRM demonstrated a decrease in HER2 and an increase in phospho-HER2, which correlated with drug sensitivity. The opposite was observed under lapatinib. HER2 quantification was also correlated with immunohistochemistry in PDXs and clinical breast cancer samples.

Discussion

in conclusion, PRM-based assay, developed to quantify proteins of the HER2 pathway in breast cancer samples revealed a large magnitude of expression, which may have relevance in terms of treatment sensitivity.

Materials and Methods

we first evaluated PRM in term of sensitivity, linearity and reproducibility. PRM was then applied to breast cancer cell lines (BCLs) including BCLs exposed to anti-HER2 agents, patient-derived xenografts (PDXs) and frozen breast cancer samples.

Quantification of Breast Cancer Protein Biomarkers at Different Expression Levels in Human Tumors

Liquid chromatography-selected reaction monitoring (LC-SRM) mass spectrometry has developed into a versatile tool for quantification of proteins with a wide range of applications in basic science, translational research, and clinical patient assessment. This strategy uniquely complements traditional pathology approaches, like hematoxylin and eosin (H&E) staining and immunohistochemistry (IHC). The multiplexing capabilities offered by mass spectrometry are currently unmatched by other techniques. However, quantification of biomarkers in tissue specimens without the other data obtained from H&E-stained slides or IHC, including tumor cellularity or percentage of positively stained cells inter alia, may not provide as much information that is needed to fully understand tumor biology or properly assess the patient. Therefore, additional characterization of the tissue proteome is needed, which in turn requires the ability to assess protein markers across a wide range of expression levels from a single sample. This protocol provides an example of multiplexed analysis in breast tumor tissue quantifying specific biomarkers, specifically estrogen receptor, progesterone receptor, and the HER2 receptor tyrosine kinase, in combination with other proteins that can report on tissue content and other aspects of tumor biology.