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Carbohydrate-based vaccines with a glycolipid adjuvant for breast cancer.

ABSTRACT: Globo H (GH) is a hexasaccharide specifically overexpressed on a variety of cancer cells and therefore, a good candidate for cancer vaccine development. To identify the optimal carrier and adjuvant combination, we chemically synthesized and linked GH to a carrier protein, including keyhole limpet hemocyanion, diphtheria toxoid cross-reactive material (CRM) 197 (DT), tetanus toxoid, and BSA, and combined with an adjuvant, and it was administered to mice for the study of immune response. Glycan microarray analysis of the antiserum obtained indicated that the combination of GH-DT adjuvanted with the α-galactosylceramide C34 has the highest enhancement of anti-GH IgG. Compared with the phase III clinical trial vaccine, GH-keyhole limpet hemocyanion/QS21, the GH-DT/C34 vaccine elicited more IgG antibodies, which are more selective for GH and the GH-related epitopes, stage-specific embryonic antigen 3 (SSEA3) and SSEA4, all of which were specifically overexpressed on breast cancer cells and breast cancer stem cells with SSEA4 at the highest level (>90%). We, therefore, further developed SSEA4-DT/C34 as a vaccine candidate, and after immunization, it was found that the elicited antibodies are also IgG-dominant and very specific for SSEA4.

SUBMITTER: Huang YL

PROVIDER: S-EPMC3574961 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Cancer vaccines and carbohydrate epitopes.

Project description:Tumor-associated carbohydrate antigens (TACA) result from the aberrant glycosylation that is seen with transformation to a tumor cell. The carbohydrate antigens that have been found to be tumor-associated include the mucin related Tn, Sialyl Tn, and Thomsen-Friedenreich antigens, the blood group Lewis related Lewis(Y), Sialyl Lewis(X) and Sialyl Lewis(A), and Lewis(X) (also known as stage-specific embryonic antigen-1, SSEA-1), the glycosphingolipids Globo H and stage-specific embryonic antigen-3 (SSEA-3), the sialic acid containing glycosphingolipids, the gangliosides GD2, GD3, GM2, fucosyl GM1, and Neu5GcGM3, and polysialic acid. Recent developments have furthered our understanding of the T-independent type II response that is seen in response to carbohydrate antigens. The selection of a vaccine target antigen is based on not only the presence of the antigen in a variety of tumor tissues but also on the role this antigen plays in tumor growth and metastasis. These roles for TACAs are being elucidated. Newly acquired knowledge in understanding the T-independent immune response and in understanding the key roles that carbohydrates play in metastasis are being applied in attempts to develop an effective vaccine response to TACAs. The role of each of the above mentioned carbohydrate antigens in cancer growth and metastasis and vaccine attempts using these antigens will be described.

| S-EPMC3208265 | biostudies-literature

Glycolipid-peptide conjugate vaccines elicit CD8<sup>+</sup> T-cell responses and prevent breast cancer metastasis.

Project description:ObjectivesMetastasis is the principal cause of breast cancer mortality. Vaccines targeting breast cancer antigens have yet to demonstrate clinical efficacy, and there remains an unmet need for safe and effective treatment to reduce the risk of metastasis, particularly for people with triple-negative breast cancer (TNBC). Certain glycolipids can act as vaccine adjuvants by specifically stimulating natural killer T (NKT) cells to provide a universal form of T-cell help.MethodsWe designed and made a series of conjugate vaccines comprising a prodrug of the NKT cell-activating glycolipid α-galactosylceramide covalently linked to tumor-expressed peptides, and assessed these using E0771- and 4T1-based breast cancer models in vivo. We employed peptides from the model antigen ovalbumin and from clinically relevant breast cancer antigens HER2 and NY-ESO-1.ResultsGlycolipid-peptide conjugate vaccines that activate NKT cells led to antigen-presenting cell activation, induced inflammatory cytokines, and, compared with peptide alone or admixed peptide and α-galactosylceramide, specifically enhanced CD8+ T-cell responses against tumor-associated peptides. Primary tumor growth was delayed by vaccination in all tumor models. Using 4T1-based cell lines expressing HER2 or NY-ESO-1, a single administration of the relevant conjugate vaccine prevented tumor colonisation of the lung following intravenous inoculation of tumor cells or spontaneous metastasis from breast, respectively.ConclusionGlycolipid-peptide conjugate vaccines that activate NKT cells prevent lung metastasis in breast cancer models and warrant investigation as adjuvant therapies for high-risk breast cancer.

| S-EPMC9250805 | biostudies-literature

Synthetic carbohydrate-based vaccines: challenges and opportunities.

Project description:Glycoconjugate vaccines based on bacterial capsular polysaccharides (CPS) have been extremely successful in preventing bacterial infections. The glycan antigens for the preparation of CPS based glycoconjugate vaccines are mainly obtained from bacterial fermentation, the quality and length of glycans are always inconsistent. Such kind of situation make the CMC of glycoconjugate vaccines are difficult to well control. Thanks to the advantage of synthetic methods for carbohydrates syntheses. The well controlled glycan antigens are more easily to obtain, and them are conjugated to carrier protein to from the so-call homogeneous fully synthetic glycoconjugate vaccines. Several fully glycoconjugate vaccines are in different phases of clinical trial for bacteria or cancers. The review will introduce the recent development of fully synthetic glycoconjugate vaccine.

| S-EPMC6941340 | biostudies-literature

Personalized Breast Cancer Vaccines Based on Genome Sequencing

Project description:Personalized Breast Cancer Vaccines Based on Genome Sequencing

| PRJNA801392 | ENA

Personalized Breast Cancer Vaccines Based on Genome Sequencing

Project description:Personalized Breast Cancer Vaccines Based on Genome Sequencing

| PRJNA801393 | ENA

Carbohydrate based meningococcal vaccines: past and present overview.

Project description:Neisseria meningitidis is a major cause of bacterial meningitidis worldwide. Children less than five years and adolescents are particularly affected. Nearly all invasive strains are surrounded by a polysaccharide capsule, based on which, 12 N. meningitidis serogroups are differentiated. Six of them, A, B, C, W, X, and Y, cause the vast majority of infections in humans. Mono- and multi-valent carbohydrate-based vaccines against meningococcal infections have been licensed or are currently in clinical development. In this mini-review, an overview of the past and present approaches for producing meningococcal glycoconjugate vaccines is provided.

| S-EPMC8076658 | biostudies-literature

Therapeutic Cancer Vaccines-Antigen Discovery and Adjuvant Delivery Platforms.

Project description:For decades, vaccines have played a significant role in protecting public and personal health against infectious diseases and proved their great potential in battling cancers as well. This review focused on the current progress of therapeutic subunit vaccines for cancer immunotherapy. Antigens and adjuvants are key components of vaccine formulations. We summarized several classes of tumor antigens and bioinformatic approaches of identification of tumor neoantigens. Pattern recognition receptor (PRR)-targeting adjuvants and their targeted delivery platforms have been extensively discussed. In addition, we emphasized the interplay between multiple adjuvants and their combined delivery for cancer immunotherapy.

| S-EPMC9325128 | biostudies-literature

Recent progress in adjuvant discovery for peptide-based subunit vaccines.

Project description:Peptide-based subunit vaccines are of great interest in modern immunotherapy as they are safe, easy to produce and well defined. However, peptide antigens produce a relatively weak immune response, and thus require the use of immunostimulants (adjuvants) for optimal efficacy. Developing a safe and effective adjuvant remains a challenge for peptide-based vaccine design. Recent advances in immunology have allowed researchers to have a better understanding of the immunological implication of related diseases, which facilitates more rational design of adjuvant systems. Understanding the molecular structure of the adjuvants allows the establishment of their structure-activity relationships which is useful for the development of next-generation adjuvants. This review summarizes the current state of adjuvants development in the field of synthetic peptide-based vaccines. The structural, chemical and biological properties of adjuvants associated with their immunomodulatory effects are discussed.

| S-EPMC4130256 | biostudies-literature

Breast Cancer Vaccines: New Insights.

Project description:Breast cancer (BC) is a persistent global challenge for its high frequency in women (although it seldom occurs in men), due to the large diffusion of risk factors and gene mutations, and for its peculiar biology and microenvironment. To date, BC can benefit from different therapeutic strategies involving surgery, ablation, chemotherapy, radiotherapy, and more specific approaches such as hormone therapy and the administration of various substances impairing cancer growth, aggressivity, and recurrence with different modalities. Despite these relatively wide chances, also used in combinatory protocols, relevant mortality and relapse rates, often associated with resistant phenotypes, stress the need for a personalized-medicine based on prompting the patient's immune system (IS) against cancer cells. BC immunogenicity was latterly proven, so the whole immunotherapy field for BC is still at a very early stage. This immunotherapeutic approach exploits both the high specificity of adaptive immune response and the immunological memory. This review is focused on some of the majorly relevant BC vaccines available (NeuVax, AVX901, and INO-1400), providing a description of the more promising clinical trials. The efficacy of cancer vaccines highly depends on the patient's IS, and a wide optimization is needed in terms of targets' selection, drug design and combinations, dose finding, protocol structuring, and patients' recruitment; moreover, new standards are being discussed for the outcome evaluation. However, early-phases excellent results suggest that the manipulation of the IS via specific vaccines is a highly attractive approach for BC.

| S-EPMC5645504 | biostudies-literature

Deep Learning-Based Prediction Model for Breast Cancer Recurrence Using Adjuvant Breast Cancer Cohort in Tertiary Cancer Center Registry.

Project description:Several prognosis prediction models have been developed for breast cancer (BC) patients with curative surgery, but there is still an unmet need to precisely determine BC prognosis for individual BC patients in real time. This is a retrospectively collected data analysis from adjuvant BC registry at Samsung Medical Center between January 2000 and December 2016. The initial data set contained 325 clinical data elements: baseline characteristics with demographics, clinical and pathologic information, and follow-up clinical information including laboratory and imaging data during surveillance. Weibull Time To Event Recurrent Neural Network (WTTE-RNN) by Martinsson was implemented for machine learning. We searched for the optimal window size as time-stamped inputs. To develop the prediction model, data from 13,117 patients were split into training (60%), validation (20%), and test (20%) sets. The median follow-up duration was 4.7 years and the median number of visits was 8.4. We identified 32 features related to BC recurrence and considered them in further analyses. Performance at a point of statistics was calculated using Harrell's C-index and area under the curve (AUC) at each 2-, 5-, and 7-year points. After 200 training epochs with a batch size of 100, the C-index reached 0.92 for the training data set and 0.89 for the validation and test data sets. The AUC values were 0.90 at 2-year point, 0.91 at 5-year point, and 0.91 at 7-year point. The deep learning-based final model outperformed three other machine learning-based models. In terms of pathologic characteristics, the median absolute error (MAE) and weighted mean absolute error (wMAE) showed great results of as little as 3.5%. This BC prognosis model to determine the probability of BC recurrence in real time was developed using information from the time of BC diagnosis and the follow-up period in RNN machine learning model.

| S-EPMC8129587 | biostudies-literature

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