Development of anti DLL4 Nanobody fused to truncated form of Pseudomonas exotoxin: As a novel immunotoxin to inhibit of cell proliferation and neovascularization

Nanobody-based immunotoxins: A precision tool in the treatment of solid tumors

Solid tumors, the main cause of cancer-related death, represent a significant therapeutic challenge due to the high-density microenvironment and intolerance to conventional treatments. Nanobody-based immunotoxins (NbITs) are an exciting candidate, combining the ultimate specificity of nanobodies (single-domain antibody fragments of camelid antibodies) and detrimental effects of the toxin. These nanobodies are small (one-tenth of conventional antibody size), thermostable with high specificity, high antigen binding affinity which give it the ability to penetrate into solid tumors. Specific delivery to tumor cells is achieved through conjugating nanobodies with cytotoxic agents of bacterial origin or synthetic drugs. This phenomenon is initially attracted to the cells by the antigen-antibody interaction that is further enhanced by receptor-mediated internalization and cytotoxic payload release that subdues essential cellular processes and, as a consequence, damages the cells. This review discusses the mechanisms that underlie the effectiveness of NbITs, such as tumor antigen recognition, toxin release, and cellular signaling pathways elicited by the internalized toxins. We also discuss the application of NbITs in treating cancers such as HER2-positive breast cancer and EGFR-overexpressing lung cancer, and other cancers, highlighting their ability to address limitations of conventional therapies. Key challenges in NbIT development, including stability, immunogenicity, and efficient delivery, are critically evaluated. Current advances such as the creation of bispecific nanobody constructs, optimization of linker strategies, as well as the incorporation of nanoparticle-based delivery systems are maximizing the therapeutic potential of these molecules. This review synthesizes recent progress and addresses current obstacles in NbIT development, showcasing their transformative potential as a targeted therapeutic approach for solid tumors. It also covers future opportunities to develop and advance this emerging treatment strategy.

Anti-tumor activity of an αPD-L1-PE38 immunotoxin delivered by engineered Nissle 1917

Although immune checkpoint inhibitors specifically targeting the PD-1/PD-L1 axis have exhibited remarkable clinical success, they are not uniformly effective across all patient cohorts. Immunotoxins, a novel class of cancer therapeutics, offering a promising alternative. PD-L1, which is also present in certain normal tissues, limits its suitability as an ideal target for immunotoxins. The probiotic strain of E. coli Nissle 1917 (EcN) could target and colonize to solid tumors, which positions it as a promising candidate for tumor tissue-specific delivery of anti-tumor proteins. In this study, we constructed a PD-L1-targeted immunotoxin, designated as αPD-L1-PE38, by fusing an anti-PD-L1 nanobody and a clinically validated PE38 toxin. This immunotoxin exhibited potent cytotoxic activity against tumor cells while showed slightly cytotoxic activity against normal cells. To effectively deliver the αPD-L1-PE38 to tumor tissues, we engineered the EcN strain to release the immunotoxin induced by L-arabinose. Upon induction, the immunotoxin was efficiently secreted, and exhibited robust anti-tumor activity mainly by inducing cell apoptosis both in vitro and in vivo. Furthermore, we enhanced the immunotoxin's affinity for PD-L1 by optimizing the linker between the nanobody and PE38 toxin. The engineered EcN expressing the optimized immunotoxin, achieved superior anti-tumor activity. Collectively, our study suggests that the delivery of immunotoxins through live bacteria to improve safety and efficacy is a promising option in cancer therapeutics.

The Prediction of DLL4 as a Prognostic Biomarker in Patients with Gastric Cancer Using Anti-DLL4 Nanobody

BACKGROUND

Angiogenesis and cancer metastasis depend on the DLL4/Notch signaling pathway. A new approach to treating angiogenesis could inhibit or block this pathway. In the present study, we investigated DLL4 expression as a biomarker capable of predicting survival outcomes in gastric cancer patients using a novel anti-DLL4 Nanobody.

PATIENTS AND METHODS

By using a recently developed anti-DLL4 Nanobody, the expression of DLL4 was evaluated in tissue samples from 135 gastric cancer patients. It was evaluated whether DLL4 expression is related to clinicopathological factors, overall survival (OS), and recurrence-free survival (RFS).

RESULTS

Sixty-five (48%) gastric cancer patients had a positive expression of DLL4 within the tumor tissue. Based on both the univariate and multivariate regression analyses, the expression of DLL4 was strongly associated with RFS (HR, 1.94; p = 0.008) and OS (HR, 2.06; p = 0.004). Moreover, the survival analysis demonstrated that DLL4 expression was a significant independent factor of unfavorable OS (HR, 2.7; p = 0.01) and RFS (HR, 2.3; p = 0.02) in gastric cancer patients.

CONCLUSION

DLL4 expression in gastric cancer patients may predict poor prognosis and survival. Furthermore, the current data demonstrate the potential of Nanobody for detecting DLL4, and it may lead to develop novel therapies and diagnostics for tumors.

Assessing angiogenesis factors as prognostic biomarkers in breast cancer patients and their association with clinicopathological factors

INTRODUCTION

Angiogenesis is fundamental for tumor growth and metastasis across many solid malignancies. Considerable interest has focused on the molecular regulation of tumor angiogenesis as a means to predict disease outcomes and guide therapeutic decisions.

METHODS

In the present study, we investigated the prognostic value of transforming growth factor beta (TGF-β), epidermal growth factor (EGF), fibroblast growth factor (FGF), delta-like ligand 4 (DLL4), and vascular endothelial growth factor (VEGF) in the serum of 120 women diagnosed with breast cancer using ELISA as well as examined their associations with clinical parameters and the outcome of the disease.

RESULTS

Our results demonstrated that the serum concentration of TGF-β and EGF were remarkably higher in patients with higher tumor size, end stages of the disease, and positive lymph node involvement compared to patients with lower tumor size, early stages of the disease, and negative lymph node involvement. In addition, we found a significant correlation between the serum concentration of VEGF and the level of EGF, FGF, and DLL4 in patients with breast cancer. Furthermore, both univariate and multivariate analyses showed that TGF-β and EGF can be used as end-stage predictors.

DISCUSSION/CONCLUSION

Based on our findings, increasing the level of angiogenesis factors is significantly associated with higher tumor size and late stages of the disease in patients with breast cancer. Moreover, measuring the level of angiogenesis factors could lead to better prediction of disease outcomes and choosing the best treatments for patients.

Promising Diagnostic and Therapeutic Approaches Based on VHHs for Cancer Management

The discovery of the distinctive structure of heavy chain-only antibodies in species belonging to the Camelidae family has elicited significant interest in their variable antigen binding domain (VHH) and gained attention for various applications, such as cancer diagnosis and treatment. This article presents an overview of the characteristics, advantages, and disadvantages of VHHs as compared to conventional antibodies, and their usage in diverse applications. The singular properties of VHHs are explained, and several strategies that can augment their utility are outlined. The preclinical studies illustrating the diagnostic and therapeutic efficacy of distinct VHHs in diverse formats against solid cancers are summarized, and an overview of the clinical trials assessing VHH-based agents in oncology is provided. These investigations demonstrate the enormous potential of VHHs for medical research and healthcare.

Dynamic NIR Fluorescence Imaging and Machine Learning Framework for Stratifying High vs. Low Notch-Dll4 Expressing Host Microenvironment in Triple-Negative Breast Cancer

Delta like canonical notch ligand 4 (Dll4) expression levels in tumors are known to affect the efficacy of cancer therapies. This study aimed to develop a model to predict Dll4 expression levels in tumors using dynamic enhanced near-infrared (NIR) imaging with indocyanine green (ICG). Two rat-based consomic xenograft (CXM) strains of breast cancer with different Dll4 expression levels and eight congenic xenograft strains were studied. Principal component analysis (PCA) was used to visualize and segment tumors, and modified PCA techniques identified and analyzed tumor and normal regions of interest (ROIs). The average NIR intensity for each ROI was calculated from pixel brightness at each time interval, yielding easily interpretable features including the slope of initial ICG uptake, time to peak perfusion, and rate of ICG intensity change after reaching half-maximum intensity. Machine learning algorithms were applied to select discriminative features for classification, and model performance was evaluated with a confusion matrix, receiver operating characteristic curve, and area under the curve. The selected machine learning methods accurately identified host Dll4 expression alterations with sensitivity and specificity above 90%. This may enable stratification of patients for Dll4 targeted therapies. NIR imaging with ICG can noninvasively assess Dll4 expression levels in tumors and aid in effective decision making for cancer therapy.