Protein biomarkers for subtyping breast cancer and implications for future research: a 2024 update.

INTRODUCTION: Breast cancer subtyping is used clinically for diagnosis, prognosis, and treatment decisions. Subtypes are categorized by cell of origin, histomorphology, gene expression signatures, hormone receptor status, and/or protein levels. Categorizing breast cancer based on gene expression signatures aids in assessing a patient's recurrence risk. Protein biomarkers, on the other hand, provide functional data for selecting therapies for primary and recurrent tumors. We provide an update on protein biomarkers in breast cancer subtypes and their application in prognosis and therapy selection. AREAS COVERED: Protein pathways in breast cancer subtypes are reviewed in the context of current protein-targeted treatment options. PubMed, Science Direct, Scopus, and Cochrane Library were searched for relevant studies between 2017 and 17 August 2024. EXPERT OPINION: Post-translationally modified proteins and their unmodified counterparts have become clinically useful biomarkers for defining breast cancer subtypes from a therapy perspective. Tissue heterogeneity influences treatment outcomes and disease recurrence. Spatial profiling has revealed complex cellular subpopulations within the breast tumor microenvironment. Deciphering the functional relationships between and within tumor clonal cell populations will further aid in defining breast cancer subtypes and create new treatment paradigms for recurrent, drug resistant, and metastatic disease.

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.

Combining the "Sibling Technologies" of Laser Capture Microdissection and Reverse Phase Protein Microarrays.

Reverse phase protein microarrays (RPPA) and laser capture microdissection (LCM) are "sibling" technologies that originated from the same laboratory to overcome the challenge of quantifying low-abundance proteins in heterogeneous tissues. Combining both technologies provides both unique opportunities and unique challenges. Enabling the unprecedented resolution of the activation state of labile biomarkers, such as phosphorylated cell signaling proteins, has had a substantial impact on our understanding of diseases and is playing a significant role in clinical trials. At the same time, quantifying proteins at this sensitivity in very small amounts of material requires cognizance of pre-analytical variability and the limits of downstream detection technologies. Here, we discuss both the potential that the combination of both technologies presents and the potential pitfalls that must be navigated.

Protein biomarkers for subtyping breast cancer and implications for future research.

INTRODUCTION: Breast cancer subtypes are currently defined by a combination of morphologic, genomic, and proteomic characteristics. These subtypes provide a molecular portrait of the tumor that aids diagnosis, prognosis, and treatment escalation/de-escalation options. Gene expression signatures describing intrinsic breast cancer subtypes for predicting risk of recurrence have been rapidly adopted in the clinic. Despite the use of subtype classifications, many patients develop drug resistance, breast cancer recurrence, or therapy failure. Areas covered: This review provides a summary of immunohistochemistry, reverse phase protein array, mass spectrometry, and integrative studies that are revealing differences in biological functions within and between breast cancer subtypes. We conclude with a discussion of rigor and reproducibility for proteomic-based biomarker discovery. Expert commentary: Innovations in proteomics, including implementation of assay guidelines and standards, are facilitating refinement of breast cancer subtypes. Proteomic and phosphoproteomic information distinguish biologically functional subtypes, are predictive of recurrence, and indicate likelihood of drug resistance. Actionable, activated signal transduction pathways can now be quantified and characterized. Proteomic biomarker validation in large, well-designed studies should become a public health priority to capitalize on the wealth of information gleaned from the proteome.

Proteomics based selection achieves complete response to HER2 therapy in HER2 IHC 0 breast cancer.

Recent trials have shown the efficacy of trastuzumab deruxtecan (T-DXd) in HER2-negative patients, but there is not yet a way to identify which patients will best respond, especially with the inability of current HER2 IHC and FISH assays to accurately determine HER2 expression in the unamplified setting. Here, we present a heavily pre-treated patient with triple-negative breast cancer (HER2 IHC 0 who had a complete response to T-DXd. In this case, we used a CLIA-certified reverse-phase protein array-based proteomic assay (RPPA) to determine that the patient had moderate HER2 protein expression (HER2Total 2+, 42%) and activation (HER2Y1248 1+, 23%). Using these results, we determined that the patient may benefit from T-Dxd despite being traditionally qualified as HER2 IHC 0. These findings highlight the potential for proteomics-based assays that may more accurately quantitate HER2 and (its activation) in the HER2 unamplified/IHC 0 setting to better select patients whose tumors are classically molecularly defined as HER2 IHC 0, but still could respond to HER2-directed therapy, and give patients access to therapies which for which they otherwise would not be eligible.

Reverse Phase Protein Arrays.

Reverse phase protein arrays (RPPA) are used to quantify proteins and protein posttranslational modifications in cellular lysates and body fluids. RPPA technology is suitable for biomarker discovery, protein pathway profiling, functional phenotype analysis, and drug discovery mechanism of action. The principles of RPPA technology are (a) immobilizing protein-containing specimens on a coated slide in discrete spots, (b) antibody recognition of proteins, (c) amplification chemistries to detect the protein-antibody complex, and (d) quantifying spot intensity. Construction of a RPPA begins with the robotic liquid transfer of protein-containing specimens from microtiter plates onto nitrocellulose-coated slides. The robotic arrayer deposits each sample as discrete spots in an array format. Specimens, controls, and calibrators are printed on each array, thus providing a complete calibrated assay on a single slide. Each RPPA slide is subsequently probed with catalyzed signal amplification chemistries and a single primary antibody, a secondary antibody, and either fluorescent or colorimetric dyes. The focus of this chapter is to describe RPPA detection and imaging using a colorimetric (diaminobenzidine (DAB)) detection strategy.

A new model isolates glioblastoma clonal interactions and reveals unexpected modes for regulating motility, proliferation, and drug resistance.

Tumor clonal heterogeneity drives treatment resistance. But robust models are lacking that permit eavesdropping on the basic interaction network of tumor clones. We developed an in vitro, functional model of clonal cooperation using U87MG glioblastoma cells, which isolates fundamental clonal interactions. In this model pre-labeled clones are co-cultured to track changes in their individual motility, growth, and drug resistance behavior while mixed. This highly reproducible system allowed us to address a new class of fundamental questions about clonal interactions. We demonstrate that (i) a single clone can switch off the motility of the entire multiclonal U87MG cell line in 3D culture, (ii) maintenance of clonal heterogeneity is an intrinsic and influential cancer cell property, where clones coordinate growth rates to protect slow growing clones, and (iii) two drug sensitive clones can develop resistance de novo when cooperating. Furthermore, clonal communication for these specific types of interaction did not require diffusible factors, but appears to depend on cell-cell contact. This model constitutes a straightforward but highly reliable tool for isolating the complex clonal interactions that make up the fundamental "hive mind" of the tumor. It uniquely exposes clonal interactions for future pharmacological and biochemical studies.

Induction of nerve growth factor by phorbol 12-myristate 13-acetate is dependent upon the mitogen activated protein kinase pathway.

Several small molecules have been identified that induce glial cells to synthesize and secrete nerve growth factor (NGF), a critical neurotrophin that supports neuronal growth and survival, and as such show promise in the development of drugs for the chemoprevention of Alzheimer's disease. To map the signal transduction cascade leading to NGF synthesis and secretion, cultured human glial cells were stimulated by phorbol 12-myristate 13-acetate (PMA), an agonist of Protein Kinase C. Changes in intracellular protein phosphorylation states were evaluated by reverse phase protein microarrays (RPPA), selectively screening over 130 protein endpoints. Of these, 55 proteins showed statistically significant changes in phosphorylation state due to cellular exposure to PMA. A critical signal transduction pathway was identified, and subsequent validation by ELISA and qPCR revealed that the signaling proteins Raf, MEK, ERK, and the signal transduction factor CREB are all essential to the upregulation of NGF gene expression by PMA. Additionally, members of the RSK family of kinases appear to be involved in glial secretion (exocytosis) of the NGF protein. Furthermore, through RPPA, the effects of PMA on apoptosis signaling events and cell proliferation were differentiated from the pathway to NGF upregulation. Overall, this study reveals potential protein targets for the rational design of Alzheimer's therapeutics.

ELISA for Monitoring Nerve Growth Factor.

Nerve growth factor (NGF), a neurotrophin critical to neuronal viability, has become a popular research focus for the treatment of neurodegenerative diseases. Enzyme-linked immunosorbent assays (ELISA) allow the quantification of cellular proteins, such as NGF, secreted into conditioned culture media. However, off-the-shelf reagents and kit components may require optimization depending on the specific specimens and/or antibodies that will be utilized with the ELISA assay. Herein we describe a protocol for developing a sandwich ELISA, incorporating NGF-specific primary and secondary antibodies capable of detecting NGF at concentrations as low as 10 pg/mL.