CAR-T therapy based on the genetic modification of T-lymphocyte receptors is currently a rapidly expanding modern method of treatment of hematological malignancies. In this chapter, the authors review the history of the development of this kind of therapy, principles of the CAR-T therapy product manufacturing, and indications for the use of registered CAR-T therapy products. They also review the perspectives for the expansion of indications using this treatment on the basis of analysis of approved clinical trials. The need for the establishment of effective logistics pathways and the benefits of cryopreservation at different manufacturing steps are reviewed as well. During their 2-year experience, the authors established a system of fluent cooperation between the local licensed Tissue Establishment (TE), CAR-T therapy product manufacturers, the Hospital Pharmacy, and a certified clinical CAR-T therapy centre. The leukapheresis of starting material, its processing, storage, and release for the manufacture took place in the authorized TE. The starting material is usually fresh mature peripheral blood mononuclear cell concentrate, which is sent to the manufacturing site in a chilled (or sometimes cryopreserved) state and the final registered product is sent back in a frozen state. Individual manufacturers use different cold chains. Sometimes the starting material is frozen by the manufacturer and the final manufacture is carried out before the actual administration to the patient as a fresh suspension. However, the most common variant used in registered products is the supply of the final product to the place of the administration in the cryopreserved state.Receipt of final products in the hospital cryobank attached to the TE takes place in cooperation between the TE staff with representatives of the Hospital Pharmacy and is followed by storage in a vapour phase of liquid nitrogen in a separate GMP-compliant container used exclusively for the storage of registered and investigational CAR-T therapy products at a temperature below -150 °C. Before the transport to the Clinical Department, the chain of recipient identity is checked, then the product is transported to the Clinical Department in a dry shipper at temperature below -150 °C. After the second check in the presence of the clinical hematologists, the product is thawed and immediately infused. In the case of the use of investigational products, special attention is paid to meeting specific genetic safety rules in the regimen of genetically modified organisms.

Atypical carcinoids (ACs) and large cell neuroendocrine carcinomas (LCNECs) are defined by the WHO as intermediate- and high-grade lung neuroendocrine neoplasms, respectively, based on morphological criteria; however, treatment strategies remain debated. Given the emerging role of the tumor microenvironment (TME) and tumor-infiltrating lymphocytes (TILs) in cancer prognosis and therapy response, this study aimed to characterize the immune landscape of ACs and LCNECs comprehensively. Immunohistochemistry for T-cell markers (CD3, CD8), immune checkpoints (PD-1, PD-L1), HLA molecules (HLA-DR, HLA-I), and fibroblasts (α-SMA) was performed on a re-evaluated cohort of 56 ACs and 104 LCNECs. Digital image analysis quantified intra-tumor (iTILs) and stromal (sTILs) CD3 and CD8 TILs in the whole slide and in specific tumor regions (invasive margin [IM] and central tumor [CT]). LCNECs exhibited significantly higher stromal T-cell infiltration, immune checkpoint expression, and HLA compared to ACs (p < 0.001), while α-SMA was more prominent in ACs. No ACs showed PD-L1 tumor expression. Digital quantification confirmed greater iTILs and sTILs in LCNECs across all regions, with moderate concordance to manual counts. Interestingly, TIL parameters were higher at the IM than in the CT (p < 0.001). Using Boruta feature selection algorithm, Principal Component Analysis and Hierarchical Clustering, three patient clusters were identified: Cluster 1 (mainly ACs, low TILs, favorable prognosis), Cluster 2 (mixed histology, intermediate TILs, moderate prognosis), and Cluster 3 (mostly LCNECs, high TILs, poor prognosis), with distinct TME marker profiles. PD-L1 tumor expression was strongly linked to Cluster 3. These findings suggest that ACs and LCNECs may be stratified into three distinct immune clusters, highlighting the heterogeneity of their tumor microenvironment and providing a rationale for further translational studies.

This review will summarize recent research into the diverse biological consequences of splicing factor mutations, and possible therapeutic vulnerabilities uncovered by such mutations, with a dedicated focus on chronic myelomonocytic leukemia.

Splicing factor mutations dysregulate alternative splicing transcriptome-wide. The global nature of such dysregulation has pleiotropic effects on cellular function. Splicing factor mutations can alter NF-κβ and IFN-γ signaling, alter malignant hematopoietic cell differentiation, degrade the function of epigenetic complexes, and predispose cells to DNA replicative stress. Therapeutic strategies to target the altered biology of clones harboring splicing mutations have had varying degrees of success. Because splicing factor mutations are highly prevalent in chronic myelomonocytic leukemia and many other hematologic malignancies, an understanding of their downstream effects and therapeutic vulnerabilities is of key interest in the field. This review highlights recent developments and opportunities for targeted therapies.

Cancer is a life-threatening disease that affects several human lives all over the world. The classification of cancer severities utilizing histopathological images is vital for effective and timely diagnosis. This always creates a demandable requirement for promising and automated computer-aided diagnosis (CAD) frameworks in clinical analysis. In this direction, the study introduces a deep learning (DL) framework aimed at classifying renal cell carcinoma (RCC) into five distinct grades, and the work is also extended to include histopathology images of breast and colon cancer. The proposed architecture, MobileDANet, integrates a MobileNetV2 backbone with a dynamic attention (DA) block (multi-head attention + MLP) to capture both long and long-range dependencies efficiently and employs Grad-CAM for interpretability. On RCC (KMC dataset) data, MobileDANet attains 90.71% accuracy (F1 score as 90.94%); on BreakHis, it achieves a recognition rate of 88.16%; and on CRCH, it reaches a weighted F1-score of 99.08%, outperforming recent baselines. In future work, the framework will be extended to larger multi-institutional datasets, pursue model compression with automated hyperparameter optimization, and explore integration with clinical-decision support systems.

Accurate segmentation of brain tumors from multi-modal MRI scans is critical for diagnosis, treatment planning, and disease monitoring. Tumor heterogeneity and inter-image variability across MRI sequences pose challenging problems to state-of-the-art segmentation models. This paper presents a novel Multi-Modal Multi-Scale Contextual Aggregation with Attention Fusion (MM-MSCA-AF) framework that leverages multi-modal MRI images (T1, T2, FLAIR, and T1-CE) to enhance segmentation performance. The model employs multi-scale contextual aggregation to obtain global and fine-grained spatial features, and gated attention fusion for selectively refining effective feature representations and discarding noise. Evaluated on the BRATS 2020 dataset, MM-MSCA-AF achieves a Dice value of 0.8158 for necrotic tumor regions and 0.8589 in total, outperforming state-of-the-art architectures such as U-Net, nnU-Net, and Attention U-Net. These results demonstrate the effectiveness of MM-MSCA-AF in handling complex tumor shapes and improving segmentation accuracy. The proposed approach has significant clinical value, offering a more accurate and automatic brain tumor segmentation solution in medical imaging.

Personalized neoantigen mRNA vaccine showed high potency to treat advanced melanoma and pancreatic cancer in recent clinical trials. Strategies to further increase its therapeutic efficacy are highly demanded. This study explored flagellin to increase model antigen ovalbumin (OVA) mRNA-induced cytotoxic T lymphocyte (CTL) responses and anti-tumor immunity in OVA-expressing B16F10 melanoma models. To minimize the potential negative impact of flagellin-induced signaling pathways on OVA mRNA translation, flagellin mRNA was used in our studies. We found flagellin-OVA mRNA (co-expression) but not flagellin mRNA/OVA mRNA (separate expression) significantly increased granzyme B, perforin, and interferon γ-secreting CD8⁺ and CD4⁺ T cell levels in spleen of tumor-bearing mice. Flagellin co-expression but not separate expression also significantly increased perforin⁺ CD8⁺ tumor-infiltrating lymphocytes (TILs) and perforin⁺ and granzyme B⁺ CD4⁺ TILs. To our surprise, flagellin co-expression and separate expression significantly reduced CD8⁺ TILs as compared to OVA mRNA alone. The ratio of CD8⁺ to CD4⁺ TILs was significantly increased by OVA mRNA vaccination alone or with flagellin co-expression but not with flagellin separate expression. The ratio of CD8⁺ to CD4⁺ TILs was significantly higher in flagellin co-expression than separate expression group. Collectively, flagellin co-expression more significantly reduced tumor growth rate than flagellin separate expression but only slightly reduced tumor growth rate as compared to OVA mRNA alone. In summary, these results support flagellin co-expression to enhance mRNA vaccine-induced CTL responses, yet strategies are demanded to promote tumor infiltration of elicited peripheral CTLs.

The prediction of Cervical Cancer (CC) remains a tough task due to diverse clinical variations and unbalanced data distribution, while good-quality data remains limited. Early CC signs tend to lack distinct characteristics, which makes their precise identification more challenging for medical professionals. Different methods of diagnosis integrated with the unique attributes of different datasets make it difficult to apply a uniform model to generalized patient populations. To address these issues, this paper introduces the Deformable Kernel Darknet-53 with Depth-Wise Separable Convolutional Neural Network (DK-D53-DWSCNNet). This framework initiates with image enhancement and quality adaptation for diverse heterogeneous morphological structures using Deformable Kernel Networks for Joint Image Filtering (DKNet-JIF). An ensemble Darknet-53 Convolutional Neural Network with a Contextual Attention Network (D53-CNN-CAN) is used to perform segmentation, which improves the representation of variable lesion patterns. A hybrid GAT-DWSCNNet, which merges geometric algebra transformers (GAT) with depth-wise separable CNNs, is used for feature extraction and classification, allowing the system to capture both edge and contextual features while reducing redundancy. The Hyperbolic Sine Optimizer (HSO) optimizes model training by providing the best trade-off between convergence speed and accuracy. With an accuracy of 99.9% and sensitivity of 99.8%, the experiments on the Herlev and SEER datasets depict remarkable generalization. These findings demonstrate the potential of DK-D53-DWSCNNet in improving early and robust CC prediction across different datasets, supporting the model to be embedded in clinical diagnostic procedures.

Oxaliplatin resistance remains a major challenge in colorectal cancer (CRC) treatment. We investigated the FBXL5/IREB2/TFRC axis in ferroptosis-mediated resistance reversal. Bioinformatics analysis identified IREB2 as co-expressed in oxaliplatin resistance and ferroptosis pathways. Clinical samples revealed elevated iron metabolism in resistant CRC tissues. In vitro, FBXL5 knockdown in oxaliplatin-resistant cells (HCT-116/OXA) upregulated IREB2/TFRC, increased Fe²⁺/MDA, and reduced viability/proliferation. Combining oxaliplatin with ferroptosis inducer Erastin enhanced cell death, reversed by ferroptosis inhibitor Ferrostatin-1. Our findings demonstrate that targeting FBXL5 disrupts iron homeostasis, triggers ferroptosis, and overcomes oxaliplatin resistance in CRC.

Secondary cancers (SCs) following radiotherapy (RT) represent a significant long-term risk of cancer survivors, necessitating accurate predictive models for early intervention. This study developed a machine learning (ML) model integrating clinical, pathological, and genomic data to predict SC incidence. The model leverages a dataset of 1,240 patients from population-based registries and clinical cohorts, incorporating features such as radiation dose, age at exposure, histology, and mutations (e.g., TP53, BRCA1/2). A Random Forest (RF) regression achieved perfect performance metrics (MSE = 0.002, [Formula: see text]-squared = 0.98), with radiation dose (Gini importance = 0.42) and age at exposure (Gini importance = 0.38) identified as the most critical predictors. Predicted incidence rates for new patients, such as 15.2 per 10,000 for breast-to-lung SCs, are consistent with epidemiological trends. The model's impressive performance highlights its potential for accurately predicting SC, underscoring its utility in clinical settings for early detection and predictions for new patients. This study highlights the potential of ML in personalized oncology while emphasizing caution in interpreting overly optimistic metrics.

Ewing sarcoma (ES) is an aggressive bone and soft tissue neoplasm characterized by EWSR::ETS rearrangements whose cellular origin remains unclear. EWS::FLI1 expression in human pediatric mesenchymal stem cells (MSCs) induces a transcriptional response distinct from that of human adult MSCs, but fails to form tumors. Here we show that EWS::FLI1 expression in human embryonic mesenchymal stem cells (heMSCs) results in the acquisition of an ES transcriptome, with the oncogene not preferentially binding to gene promoters, but to intronic and intergenic microsatellites. In heMSCs, EWS::FLI1 directly regulates the expression of the DNA repair protein BRCA1, although cells expressing EWS::FLI1 show DNA damage. Xenografting of EWS::FLI1-transduced heMSCs results in the formation of tumors expressing characteristic ES markers. In summary, we show that EWS::FLI1 enforces an aberrant transcriptome and solely is able to endow transforming capacity when expressed in undifferentiated, early heMSCs.