A replicating LCMV-based vaccine for the treatment of solid tumors.

Harnessing the immune system to eradicate tumors requires identification and targeting of tumor antigens, including tumor-specific neoantigens and tumor-associated self-antigens. Tumor-associated antigens are subject to existing immune tolerance, which must be overcome by immunotherapies. Despite many novel immunotherapies reaching clinical trials, inducing self-antigen-specific immune responses remains challenging. Here, we systematically investigate viral-vector-based cancer vaccines encoding a tumor-associated self-antigen (TRP2) for the treatment of established melanomas in preclinical mouse models, alone or in combination with adoptive T cell therapy. We reveal that, unlike foreign antigens, tumor-associated antigens require replication of lymphocytic choriomeningitis virus (LCMV)-based vectors to break tolerance and induce effective antigen-specific CD8+ T cell responses. Immunization with a replicating LCMV vector leads to complete tumor rejection when combined with adoptive TRP2-specific T cell transfer. Importantly, immunization with replicating vectors leads to extended antigen persistence in secondary lymphoid organs, resulting in efficient T cell priming, which renders previously "cold" tumors open to immune infiltration and reprograms the tumor microenvironment to "hot." Our findings have important implications for the design of next-generation immunotherapies targeting solid cancers utilizing viral vectors and adoptive cell transfer.

Targeting CD276 for T cell-based immunotherapy of breast cancer.

BACKGROUND: Breast cancer (BC) is the most common malignancy in women. Immunotherapy has revolutionized treatment options in many malignancies, and the introduction of immune checkpoint inhibition yielded beneficial results also in BC. However, many BC patients are ineligible for this T cell-based therapy, others do not respond or only briefly. Thus, there remains a high medical need for new therapies, particularly for triple-negative BC. CD276 (B7-H3) is overexpressed in several tumors on both tumor cells and tumor vessels, constituting a promising target for immunotherapy. METHODS: We analyzed tumor samples of 25 patients using immunohistochemistry to assess CD276 levels. The potential of CC-3, a novel bispecific CD276xCD3 antibody, for BC treatment was evaluated using various functional in vitro assays. RESULTS: Pronounced expression of CD276 was observed in all analyzed tumor samples including triple negative BC. In analyses with BC cells, CC-3 induced profound T cell activation, proliferation, and T cell memory subset formation. Moreover, treatment with CC-3 induced cytokine secretion and potent tumor cell lysis. CONCLUSION: Our findings characterize CD276 as promising target and preclinically document the therapeutic potential of CC-3 for BC treatment, providing a strong rationale for evaluation of CC-3 in BC patients in a clinical trial for which the recruitment has recently started.

Collagen fleece grafting for surgical treatment of patients with mild to severe peyronie's curvatures.

PURPOSE: Collagen fleece grafting (CFG) is the recommended treatment for severe Peyronie's disease (PD) curvature (> 60°), but its efficacy in mild/moderate curvatures remains uncertain. This study evaluated CFG in patients with mild/moderate curvatures (< 60°) at risk of penile shortening or symptomatic plaque. METHODS: A retrospective review was conducted on patients who underwent surgical treatment for PD using plaque incision or partial plaque excision and CFG. Clinical parameters and complications were reviewed. Subgroup analysis was performed on patients with curvatures of > 60° and curvatures ≤ 60°. RESULTS: 89 patients with a median age of 59 years and a median curvature of 70 (20-90)° were identified. Dorsal curvature was predominant in 66% of cases, followed by lateral (16%), ventral (8%), and complex curvatures (10%). Partial plaque excision was performed in 98% of patients, with an average grafting area of 2.1 cm2; 71% had a singular penile plaque, while 29% presented two or more plaques. The comparison between patients with curvatures ≤ 60° and > 60° revealed no significant differences in mean operation time (86.3 vs. 94.4 min, p = 0.13) or in the incidence of postoperative complications, including glans necrosis, hypoesthesia, ecchymosis, bleeding, hematoma, infection, residual curvature, revision surgery, or pain. CONCLUSIONS: Early postoperative outcomes and complication rates following plaque incision or partial plaque excision and grafting with CFG were comparable in patients with mild/moderate and severe PD deformities. The technique may be a viable option with a similar risk profile for achieving penile straightening in selected PD cases, particularly when plication is not feasible.

High-multiplex tissue imaging in routine pathology-are we there yet?

High-multiplex tissue imaging (HMTI) approaches comprise several novel immunohistological methods that enable in-depth, spatial single-cell analysis. Over recent years, studies in tumor biology, infectious diseases, and autoimmune conditions have demonstrated the information gain accessible when mapping complex tissues with HMTI. Tumor biology has been a focus of innovative multiparametric approaches, as the tumor microenvironment (TME) contains great informative value for accurate diagnosis and targeted therapeutic approaches: unraveling the cellular composition and structural organization of the TME using sophisticated computational tools for spatial analysis has produced histopathologic biomarkers for outcomes in breast cancer, predictors of positive immunotherapy response in melanoma, and histological subgroups of colorectal carcinoma. Integration of HMTI technologies into existing clinical workflows such as molecular tumor boards will contribute to improve patient outcomes through personalized treatments tailored to the specific heterogeneous pathological fingerprint of cancer, autoimmunity, or infection. Here, we review the advantages and limitations of existing HMTI technologies and outline how spatial single-cell data can improve our understanding of pathological disease mechanisms and determinants of treatment success. We provide an overview of the analytic processing and interpretation and discuss how HMTI can improve future routine clinical diagnostic and therapeutic processes.

A review on deep learning applications in highly multiplexed tissue imaging data analysis.

Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of DL-based pipelines used in preprocessing highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients. Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of the DL-based pipelines used in preprocessing the highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients.

Corrigendum: A review on deep learning applications in highly multiplexed tissue imaging data analysis.

[This corrects the article DOI: 10.3389/fbinf.2023.1159381.].