Proteomic Resistance Biomarkers for PI3K Inhibitor in Triple Negative Breast Cancer Patient-Derived Xenograft Models

The Power and Promise of Patient-Derived Xenografts of Human Breast Cancer

In 2016, a group of researchers engaged in the development of patient-derived xenografts (PDXs) of human breast cancer provided a comprehensive review of the state of the field. In that review, they summarized the clinical problem that PDXs might address, the technical approaches to their generation (including a discussion of host animals and transplant conditions tested), and presented transplantation success (take) rates across groups and across transplantation conditions. At the time, there were just over 500 unique PDX models created by these investigators representing all three clinically defined subtypes (ER+, HER2+, and TNBC). Today, many of these PDX resources have at least doubled in size, and several more PDX development groups now exist, such that there may be well upward of 1000 PDX models of human breast cancer in existence worldwide. They also presented a series of open questions for the field. Many of these questions have been addressed. However, several remain open, or only partially addressed. Herein, we revisit these questions, and recount the progress that has been made in a number of areas with respect to generation, characterization, and use of PDXs in translational research, and re-present questions that remain open. These open questions, and others, are now being addressed not only by individual investigators, but also large, well-funded consortia including the PDXNet program of the National Cancer Institute in the United States, and the EuroPDX Consortium, an organization of PDX developers across Europe. Finally, we discuss the new opportunities in PDX-based research.

Predictive, preventive, and personalized medicine in breast cancer: targeting the PI3K pathway

Breast cancer (BC) is a multifaceted disease characterized by distinct molecular subtypes and varying responses to treatment. In BC, the phosphatidylinositol 3-kinase (PI3K) pathway has emerged as a crucial contributor to the development, advancement, and resistance to treatment. This review article explores the implications of the PI3K pathway in predictive, preventive, and personalized medicine for BC. It emphasizes the identification of predictive biomarkers, such as PIK3CA mutations, and the utility of molecular profiling in guiding treatment decisions. The review also discusses the potential of targeting the PI3K pathway for preventive strategies and the customization of therapy based on tumor stage, molecular subtypes, and genetic alterations. Overcoming resistance to PI3K inhibitors and exploring combination therapies are addressed as important considerations. While this field holds promise in improving patient outcomes, further research and clinical trials are needed to validate these approaches and translate them into clinical practice.

Endorsement of TNBC Biomarkers in Precision Therapy by Nanotechnology

Breast cancer is a heterogeneous disease which accounts globally for approximately 1 million new cases annually, wherein more than 200,000 of these cases turn out to be cases of triple-negative breast cancer (TNBC). TNBC is an aggressive and rare breast cancer subtype that accounts for 10-15% of all breast cancer cases. Chemotherapy remains the only therapy regimen against TNBC. However, the emergence of innate or acquired chemoresistance has hindered the chemotherapy used to treat TNBC. The data obtained from molecular technologies have recognized TNBC with various gene profiling and mutation settings that have helped establish and develop targeted therapies. New therapeutic strategies based on the targeted delivery of therapeutics have relied on the application of biomarkers derived from the molecular profiling of TNBC patients. Several biomarkers have been found that are targets for the precision therapy in TNBC, such as EGFR, VGFR, TP53, interleukins, insulin-like growth factor binding proteins, c-MET, androgen receptor, BRCA1, glucocorticoid, PTEN, ALDH1, etc. This review discusses the various candidate biomarkers identified in the treatment of TNBC along with the evidence supporting their use. It was established that nanoparticles had been considered a multifunctional system for delivering therapeutics to target sites with increased precision. Here, we also discuss the role of biomarkers in nanotechnology translation in TNBC therapy and management.

Molecular Classification, Treatment, and Genetic Biomarkers in Triple-Negative Breast Cancer: A Review

Breast cancer is the most common malignancy and the second most common cause of cancer-related mortality in women. Triple-negative breast cancers do not express estrogen receptors, progesterone receptors, or human epidermal growth factor receptor 2 and have a higher recurrence rate, greater metastatic potential, and lower overall survival rate than those of other breast cancers. Treatment of triple-negative breast cancer is challenging; molecular-targeted therapies are largely ineffective and there is no standard treatment. In this review, we evaluate current attempts to classify triple-negative breast cancers based on their molecular features. We also describe promising treatment methods with different advantages and discuss genetic biomarkers and other prediction tools. Accurate molecular classification of triple-negative breast cancers is critical for patient risk categorization, treatment decisions, and surveillance. This review offers new ideas for more effective treatment of triple-negative breast cancer and identifies novel targets for drug development.

Proteomic characterization of post-translational modifications in drug discovery

Protein post-translational modifications (PTMs), which are usually enzymatically catalyzed, are major regulators of protein activity and involved in almost all celluar processes. Dysregulation of PTMs is associated with various types of diseases. Therefore, PTM regulatory enzymes represent as an attractive and important class of targets in drug research and development. Inhibitors against kinases, methyltransferases, deacetyltransferases, ubiquitin ligases have achieved remarkable success in clinical application. Mass spectrometry-based proteomics technologies serve as a powerful approach for system-wide characterization of PTMs, which facilitates the identification of drug targets, elucidation of the mechanisms of action of drugs, and discovery of biomakers in personalized therapy. In this review, we summarize recent advances of proteomics-based studies on PTM targeting drugs and discuss how proteomics strategies facilicate drug target identification, mechanism elucidation, and new therapy development in precision medicine.

Targeted Therapeutic Strategies for Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, which is characterized by the absence of estrogen receptor (ER) and progesterone receptor (PR) expression and the absence of human epidermal growth factor receptor 2 (HER2) expression/amplification. Conventional chemotherapy is the mainstay of systemic treatment for TNBC. However, lack of molecular targeted therapies and poor prognosis of TNBC patients have prompted a great effort to discover effective targets for improving the clinical outcomes. For now, poly (ADP-ribose) polymerase (PARP) inhibitors (PARPi's) and immune checkpoint inhibitors have been approved for the treatment of TNBC. Moreover, agents that target signal transduction, angiogenesis, epigenetic modifications, and cell cycle are under active preclinical or clinical investigations. In this review, we highlight the current major developments in targeted therapies of TNBC, with some descriptions about their (dis)advantages and future perspectives.

The Potential Prognostic Role of Oligosaccharide-Binding Fold-Containing Protein 2A (OBFC2A) in Triple-Negative Breast Cancer

BACKGROUND

Patients with triple-negative breast cancer (TNBC) have poor overall survival. The present study aimed to investigate the potential prognostics of TNBC by analyzing breast cancer proteomic and transcriptomic datasets.

METHODS

Candidate proteins selected from CPTAC (the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium) were validated using datasets from METABRIC (Molecular Taxonomy of Breast Cancer International Consortium). Kaplan-Meier analysis and ROC (receiver operating characteristic) curve analysis were performed to explore the prognosis of candidate genes. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis were performed on the suspected candidate genes. Single-cell RNA-seq (scRNA-seq) data from GSE118389 were used to analyze the cell clusters in which OBFC2A (Oligosaccharide-Binding Fold-Containing Protein 2A) was mainly distributed. TIMER (Tumor Immune Estimation Resource) was used to verify the correlation between OBFC2A expression and immune infiltration. Clone formation assays and wound healing assays were used to detect the role of OBFC2A expression on the proliferation, invasion, and migration of breast cancer cells. Flow cytometry was used to analyze the effects of silencing OBFC2A on breast cancer cell cycle and apoptosis.

RESULTS

Six candidate proteins were found to be differentially expressed in non-TNBC and TNBC groups from CPTAC. However, only OBFC2A was identified as an independently poor prognostic gene marker in METABRIC (HR=3.658, 1.881-7.114). And OBFC2A was associated with immune functions in breast cancer. Biological functional experiments showed that OBFC2A might promote the proliferation and migration of breast cancer cells. The inhibition of OBFC2A expression blocked the cell cycle in G1 phase and inhibited the transformation from G1 phase to S phase. Finally, downregulation of OBFC2A also increased the total apoptosis rate of cells.

CONCLUSION

On this basis, OBFC2A may be a potential prognostic biomarker for TNBC.

Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidatesfor targeted treatment

Development of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established the genomic landscapes of 536 patient-derived xenograft (PDX) models across 25 cancer types, together with mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared with human tumors, PDXs typically have higher purity and fit to investigate dynamic driver events and molecular properties via multiple time points from same case PDXs. Here, we report on dynamic genomic landscapes and pharmacogenomic associations, including associations between activating oncogenic events and drugs, correlations between whole-genome duplications and subclone events, and the potential PDX models for NCI-MATCH trials. Lastly, we provide a web portal having comprehensive pan-cancer PDX genomic profiles and source code to facilitate identification of more druggable events and further insights into PDXs' recapitulation of human tumors.