Triple-negative breast cancer (TNBC) is one of the most aggressive subtypes of breast cancer (BC), comprising approximately 20% of newly diagnosed BC cases. The poor prognosis, high recurrence rates, and inefficacy of hormone-based therapies make TNBC one of the greatest challenges in contemporary oncology. The unique immunological features of TNBC, including relatively high tumor mutational burden, abundance of tumor-infiltrating lymphocytes, and elevated PD-L1 expression, offer a wide range of opportunities for immunotherapeutic approaches, of which the most progressive and promising are neoantigen-driven ones. This review examines the current landscape of neoantigen-based therapeutic approaches in TNBC treatment, spanning from discovery methodologies to clinical applications. We provide a critical analysis of the tumor microenvironment (TME) in TNBC, highlighting the balance between its immunoactivating (CD8+ T-cells, dendritic cells) and immunosuppressive (regulatory T-cells, M2 macrophages) components as the key determinant of therapeutic success, as well as reviewing the emerging approaches to TME reprogramming and recruiting in favor of better outcomes. We also present state-of the-art methods in neoantigen identification and prioritization, covering the landscape of technological platforms and prediction algorithms, addressing the existing accuracy limitations along with emerging computational solutions, and comprehensively discussing the TNBC neoantigen spectrum. Our analysis shows the strong domination of patient-specific (“private”) neoantigens over shared variants in the TNBC, with TP53 as the only gene with recurrent variants. Finally, we extensively cover neoantigen-recruiting therapeutic modalities including adoptive cell therapies, personalized vaccine platforms (peptide-based, mRNA/DNA vaccines, dendritic cell vaccines), and oncolytic viruses-based approaches. Our study of current clinical trials demonstrates the substantial gap between early proof-of-concept experiments and further applicability of neoantigen-driven therapies. The major challenges hampering the success of such methods include neoantigen prediction inaccuracy rates, high manufacturing costs, and time consumption. Promising ways to overcome these difficulties include the development of combinational strategies, TME modeling and modifying, and improvement of the therapy delivery properties, along with the optimization of production workflows and cost-effectiveness of vaccine development.

BACKGROUND: Despite the retentive relevance of preeclampsia (PE) among the main causes of maternal morbidity and mortality, its etiology remains unclear. Despite gaps in its pathophysiology, highly effective methods of prognosis, prevention, and treatment are still not devised yet. In recent years, the use of microRNA molecules that epigenetically control the expression of target genes at the post-transcriptional level received great interest as are they of key importance in the proliferation, differentiation, invasion, migration, and apoptosis of trophoblast cells and regulation of angiogenesis, immune response, and other processes during pregnancy AIM: This study aimed to investigate the epigenetic mechanisms of PE development based on the evaluation of the expression of pathogenetically significant microRNAs in women’s blood plasma. MATERIALS AND METHODS: The study included 62 female patients divided into the main study group (n=42 with PE) and the control group (n = 20 healthy women with uncomplicated pregnancy, childbirth, and post-natal period). All patients have undergone general clinical, laboratory, and instrumental examinations. The expression levels of 15 microRNAs in the blood plasma were evaluated using a quantitative real-time polymerase chain reaction. DIANA miRPath v. 3.0 was used to evaluate the effect of differentially expressed microRNAs on the functioning of signaling pathways. Statistical data analyses were performed using Statistica 6.0. RESULTS: Multidirectional changes in the expression levels of 13 of 15 plasma microRNAs were found in the PE group compared with the control group; however, the expression levels of the following eight microRNAs decreased significantly: hsa-miR-146a-5p (p=0.011), hsa-miR-181a-5p (p=0.015), hsa-miR-210-3p (p=0.031), hsa-miR-517a-3p (p=0.004), hsa-miR-517c-3p (p=0.007), hsa-miR-574-3p (p=0.048), hsa-miR-574-5p (p=0.003), and hsa-miR-1304-5p (p=0.001). The expression levels of hsa-miR-20a-5p (FC=0.39; p=0.049) and hsa-miR-143-3p (FC=0.71, p=0.05) significantly decreased in pregnant women with PE and symptoms of fetal growth retardation (FGR) compared with the subgroup without FGR. No significant differences in the expression level of the analyzed microRNAs were found between the subgroups with moderate and severe PE and early and late PE. The functional evaluation of differentially expressed microRNAs among women with PE, considering the identification of their potential target genes, revealed the dysregulation of 40 signaling pathways and biological processes in which these molecules are involved. CONCLUSION: PE progresses alongside significant epigenetic changes accompanied by changes in the microRNA expression profile, which are associated with cardiovascular and cerebrovascular diseases and placental disorders. The detected differentially expressed microRNAs may be potential diagnostic markers of PE.

Next-generation sequencing (NGS) is increasingly used in laboratory diagnostics of solid tumors. The use of NGS allows for the detection of virtually all clinically significant molecular changes identifiable by standard molecular genetic investigation (MGI) methods. Data obtained through NGS on the prevalence of molecular genetic alterations necessitates updating existing MGI tests and their application for different patient groups. In some cases, nucleotide sequence variants detected by NGS tests must be validated by standard MGI methods. A patient’s mutational profile obtained through NGS can be used to develop individualized PCR-based test systems for monitoring minimal residual disease. The integration of NGS testing with standard MGI methods is a pressing task in modern laboratory diagnostics of oncological diseases. This article provides an overview of the most important examples of such integration.

Structurally similar catalytic subunits A of ricin (RTA) and viscumin (MLA) exhibit cytotoxic activity through ribosome inactivation. Ricin is more cytotoxic than viscumin, although the molecular mechanisms behind this difference are still poorly understood. To shed more light on this problem, we used a combined biochemical/molecular modeling approach to assess possible relationships between the activity of toxins and their structural/dynamic properties. Based on bioassay measurements, it was suggested that the differences in activity are associated with the ability of RTA and MLA to undergo structural/hydrophobic rearrangements during trafficking through the endoplasmic reticulum (ER) membrane. Molecular dynamics simulations and surface hydrophobicity mapping of both proteins in different media showed that RTA rearranges its structure in a membrane-like environment much more efficiently than MLA. Their refolded states also drastically differ in terms of hydrophobic organization. We assume that the higher conformational plasticity of RTA is favorable for the ER-mediated translocation pathway, which leads to a higher rate of toxin penetration into the cytoplasm.

Malignant neoplasms have possible specific genetic characteristics that may determine the effectiveness of various treatments. Pharmacogenomics in oncology represents an advanced approach aimed at identifying the genetic features of tumors to select the most optimal therapeutic strategies. This review focuses on the role of pharmacogenomic approaches in the implementation of personalized treatment for oncological diseases. Pharmacogenomics is a cutting-edge field that relies not only on advances in next-generation sequencing but also on modern methods and algorithms for processing large volumes of data. This review discusses the relationship between pharmacogenomics and next-generation sequencing technologies, machine learning, and artificial intelligence. It emphasizes the importance of accumulating real-world data to enable the proper interpretation of identified genetic mutations and the use of this information in treatment personalization. The review also covers existing pharmacogenomic platforms and characterizes a domestically developed pharmacogenomic platform for making clinical decisions in oncology.