Antibody-based cancer immunotherapy has revolutionized oncology. The first successful therapeutic antibodies relied on eliciting immune-mediated cytotoxicity (rituximab) or modulation of intracellular signaling (trastuzumab). Further attempts to enhance the antitumor effects led to the development of immunoconjugates with radioactive or cytotoxic compounds (tositumomab, brentuximab vedotin). Another line of research led to the bispecific antibodies that can enhance the formation of immunological synapse between cancer and cytotoxic T cells (blinatumomab). Despite the constant advances in design and production, the application of monoclonal antibodies in cancer treatment remains limited by the presence of specific cell surface markers. A rational approach to target intracellular cancer antigens was proposed almost two decades ago by the development of anti-peptide human leukocyte antigen (HLA) complex (T cell receptor-like/mimic) antibodies. They could recognize specifically cancer neoantigens expressed in the context of specific HLA molecules theoretically providing unprecedented specificity. Furthermore, they can be developed in a semigeneric format, that is, to target common neoantigens expressed in the context of common HLA molecules. It is rationale to expect that the development of such antibodies in the format of bispecific antibody constructs can bring together the power of specific antibody-based recognition and that of T cell-mediated lysis. There are already some preliminary reports that suggest such constructs would be an achievable goal. In this brief review, we discuss some of the successful preclinical developments in the field and the major challenges that are yet to be addressed.

Background: Malignant pleural mesothelioma (MPM) is a rare cancer type with an increasing incidence worldwide. We aimed to develop a rational gene expression-based prognostic score in MPM using publicly available datasets. Methods: We developed and validated a two-gene prognostic score (2-PS) using three independent publicly available gene expression datasets. The 2-PS was built using the Robust Likelihood-Based Survival Modeling with Microarray Data method. Results: We narrowed down the model building to the analysis of 179 genes, which have been shown previously to be of importance to MPM development. Our statistical approach showed that a model including two genes (GOLT1B and MAD2L1) was the best predictor for overall survival (OS) (p < 0.0001). The binary score based on the median of the continuous score stratified the patients into low and high score groups and also showed statistical significance in uni- and multivariate models. The 2-PS was validated using two independent transcriptomic datasets. Furthermore, gene set enrichment analysis using training and validation datasets showed that high score patients had distinct gene expression profiles. Our 2-PS also showed a correlation with the estimated infiltration by some immune cell fractions such as CD8+ T cells and M1/2 macrophages. Finally, 2-PS correlated with sensitivity or resistance to some commonly used chemotherapeutic drugs. Conclusion: This is the first study to demonstrate good performance of only two-gene expression-based prognostic scores in MPM. Our initial approach for features selection allowed for an increased likelihood for the predictive value of the developed score, which we were also able to demonstrate.

Phenotypic divergence between parental forms is important for the varietal improvement. The present study aimed to find the degree of phenotypic diversity among 22 chickpea collection cultivars and to identify accessions with valuable breeding traits. The experiment was conducted during the period 2023-2025 at the Institute of Forage Crops (Pleven). Genetic distance, heritability coefficient, genetic advance, and genotypic and phenotypic variation coefficients were determined. The highest PCV and GCV were found for the traits of seeds number (49.37%, 34.05%) and pods number/plant (36.53%, 28.99%). Estimates for broad-sense heritability ranged from 5.73% for 1000-seed mass to 88.28% for plant height. High heritability coupled with high genetic advance was established for plant height (H² = 88.28%, GA = 9.49) and number of pods per plant (H² = 62.77%, GA = 13.01), indicating the predominance of additive gene action and the potential for successful direct phenotypic selection. Hierarchical cluster analysis and principal component analysis classified the cultivars into distinct groups based on their agro-morphological profiles. The genotypes Garbanzo 4, Alfa, Caqui, Garbanzo 1, and Blanco Lechoso exhibited the highest genetic divergence, marking them as valuable parental components for hybridization programs aimed at improving chickpea productivity and environmental adaptability.