A multivalent CD44 glycoconjugate vaccine candidate for cancer immunotherapy

CD44 and Its Role in Solid Cancers - A Review: From Tumor Progression to Prognosis and Targeted Therapy

Cluster of differentiation 44 (CD44) is a transmembrane protein expressed in normal cells but overexpressed in several types of cancer. CD44 plays a major role in tumor progression, both locally and systemically, by direct interaction with the extracellular matrix, inducing tissue remodeling, activation of different cellular pathways, such as Akt or mechanistic target of rapamycin (mTOR), and stimulation of angiogenesis. As a prognostic marker, CD44 has been identified as a major player in cancer stem cells (CSCs). CSCs with a CD44 phenotype are associated with chemoresistance, alone or in combination with other CSC markers, such as CD24 or aldehyde dehydrogenase 1 (ALDH1), and may be used for patient stratification. In the therapy setting, CD44 has been explored as a viable target, directly or indirectly. It has revealed promising potential, paving the way for its future use in the clinical setting. Immunohistochemistry effectively detects CD44 overexpression, enabling patients to be accurately selected for surgery and targeted anti-CD44 therapies. In this review, we highlight the properties of CD44, its expression in normal and tumoral tissues through immunohistochemistry and potential treatment options. We also discuss the clinical significance of this marker and its added value in therapeutic decision-making.

E-selectin affinity glycoproteomics reveals neuroendocrine proteins and the secretin receptor as a poor-prognosis signature in colorectal cancer

Colorectal cancer (CRC) cells express sialylated Lewis antigens (sLe), crucial for metastasis via E-selectin binding. However, these glycoepitopes lack cancer specificity, and E-selectin-targeted glycoproteins remain largely unknown. Here, we established a framework for identifying metastasis-linked glycoproteoforms. More than 70% of CRC tumors exhibited overexpression of sLeA/X, yet without discernible associations with metastasis or survival. However, The Cancer Genome Atlas (TCGA) analysis unveiled differing expression patterns of sLeA/X-related glycogenes correlating with disease severity, indicating context-dependent regulation by distinct glycosyltransferases. Deeper exploration of metastatic tumor sialoglycoproteome identified nearly 600 glycoproteins, greatly expanding our understanding of the metastasis-related glycoproteome. These glycoproteins were linked to cell adhesion, oncogenic pathways, and neuroendocrine functions. Using an in-house algorithm, the secretin receptor (SCTR) emerged as a top-ranked targetable glycoprotein. Tumor screening confirmed SCTR's association with poor prognosis and metastasis, with N-glycosylation adding cancer specificity to this glycoprotein. Prognostic links were reinforced by TCGA-based investigations. In summary, SCTR, a relatively unknown CRC glycoprotein, holds potential as a biomarker of poor prognosis and as an E-selectin ligand, suggesting an unforeseen role in disease dissemination. Future investigations should focus on this glycoprotein's biological implications for clinical applications.

Next-generation chemotherapy treatments based on black hole algorithms: From cancer remission to chronic disease management

PROBLEM

Therapeutic planning strategies have been developed to enhance the effectiveness of cancer drugs. Nevertheless, their performance is highly limited by the inefficient biological representativeness of predictive tumor growth models, which hinders their translation to clinical practice.

OBJECTIVE

This study proposes a disruptive approach to oncology based on nature-inspired control using realistic Black Hole physical laws, in which tumor masses are trapped to experience attraction dynamics on their path to complete remission or to become a chronic disease. This control method is designed to operate independently of individual patient idiosyncrasies, including high tumor heterogeneities and highly uncertain tumor dynamics, making it a promising avenue for advancing beyond the limitations of the traditional survival probabilistic paradigm.

DESIGN

Here, we provide a multifaceted study of chemotherapy therapeutic planning that includes: (1) the design of a pioneering controller algorithm based on physical laws found in the Black Holes; (2) investigation of the ability of this controller algorithm to ensure stable equilibrium treatments; and (3) simulation tests concerning tumor volume dynamics using drugs with significantly different pharmacokinetics (Cyclophosphamide and Atezolizumab), tumor volumes (200 mm3 and 12 732 mm3) and modeling characterizations (Gompertzian and Logistic tumor growth models).

RESULTS

Our results highlight the ability of this new astrophysical-inspired control algorithm to perform effective chemotherapy treatments for multiple tumor-treatment scenarios, including tumor resistance to chemotherapy, clinical scenarios modelled by time-dependent parameters, and highly uncertain tumor dynamics.

CONCLUSIONS

Our findings provide strong evidence that cancer therapy inspired by phenomena found in black holes can emerge as a disruptive paradigm. This opens new high-impacting research directions, exploring synergies between astrophysical-inspired control algorithms and Artificial Intelligence applied to advanced personalized cancer therapeutics.

Targeted and Self-Adjuvated Nanoglycovaccine Candidate for Cancer Immunotherapy

Advanced-stage solid primary tumors and metastases often express mucin 16 (MUC16), carrying immature glycans such as the Tn antigen, resulting in specific glycoproteoforms not found in healthy human tissues. This presents a valuable approach for designing targeted therapeutics, including cancer glycovaccines, which could potentially promote antigen recognition and foster the immune response to control disease spread and prevent relapse. In this study, we describe an adjuvant-free poly(lactic-co-glycolic acid) (PLGA)-based nanoglycoantigen delivery approach that outperforms conventional methods by eliminating the need for protein carriers while exhibiting targeted and adjuvant properties. To achieve this, we synthesized a library of MUC16-Tn glycoepitopes through single-pot enzymatic glycosylation, which were then stably engrafted onto the surface of PLGA nanoparticles, generating multivalent constructs that better represent cancer molecular heterogeneity. These glycoconstructs demonstrated affinity for Macrophage Galactose-type Lectin (MGL) receptor, known to be highly expressed by immature antigen-presenting cells, enabling precise targeting of immune cells. Moreover, the glycopeptide-grafted nanovaccine candidate displayed minimal cytotoxicity and induced the activation of dendritic cells in vitro, even in the absence of an adjuvant. In vivo, the formulated nanovaccine candidate was also nontoxic and elicited the production of IgG specifically targeting MUC16 and MUC16-Tn glycoproteoforms in cancer cells and tumors, offering potential for precise cancer targeting, including targeted immunotherapies.