Immunotherapy drug target identification using machine learning and patient-derived tumour explant validation DOI Creative Commons
Kevin Litchfield,

Marcellus Augustine,

Nuno R. Nené

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 5, 2024

Abstract Immunotherapy has revolutionised cancer treatment, yet few patients respond clinically, necessitating alternative strategies that can benefit these patients. Novel immune-oncology targets achieve this through bypassing resistance mechanisms to standard therapies. To address this, we introduce MIDAS, a multimodal graph neural network system for target discovery leverages gene interactions, multi-omic patient profiles, immune cell biology, antigen processing, disease associations, and phenotypic consequences of genetic perturbations. MIDAS generalises time-sliced data, outcompetes existing methods, including OpenTargets, distinguishes approved from prospective targets. Moreover, recovers immunotherapy response-associated genes in unseen trials, thus capturing tumour-immune dynamics within human tumours. Interpretability analyses reveal reliance on autoimmunity, regulatory networks, relevant biological pathways. Functionally perturbing the OSM-OSMR axis, proposed target, TRACERx melanoma patient-derived explants yielded reduced dysfunctional CD8+ T cells, which associate with response. Our results present machine learning framework analysing data discovery.

Language: Английский

Characteristics and clinical significance of immune cells in omental milky spots of patients with gastric cancer DOI Creative Commons
Yasunobu Mano, Yuka Igarashi,

KEISUKE KOMORI

et al.

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 16

Published: Jan. 30, 2025

The omentum is a common site of peritoneal metastasis in various cancers, including gastric cancer. It contains immune cell aggregates known as milky spots, which provide microenvironment for immunity by regulating innate and adaptive responses. In this study, we investigated gene expression profiles cells from omental spots patients with cancer (n = 37) RNA sequencing analysis classified the into four groups (G1-4). Notably, significant differences were observed between terms macroscopic type, lymphatic invasion, venous pathological stage (pStage). G3, was enriched genes related to acquired immunity, showed earlier tumor stages (macroscopic type 0, Ly0, V0, pStage I) better prognosis. contrast, G4 enrichment neutrophils immunity; G1 G2 no or immune-related genes, suggesting an desert microenvironment. Cytometric revealed significantly more T B fewer G3. Accordingly, may vary depending on progression. When univariate Cox proportional hazards regression models used search prognostically relevant specific 23 potential prognostic identified associated relapse-free survival overall survival. addition, multivariate model using these clinicopathological information that combining marker CD19 Ly had high predictive accuracy Based study’s results, it possible progression, such and/or infiltration cells, affect composition proportions help predict

Language: Английский

Citations

0
Identification of the B7-H3 Interaction Partners Using a Proximity Labeling Strategy DOI Open Access

Shutsung Liao,

Jiamin Huang, Cecylia S. Lupala

et al.

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(4), P. 1731 - 1731

Published: Feb. 18, 2025

B7 homolog 3 (B7-H3) has emerged as a promising target for cancer therapy due to its high expression in various types of cells. It not only regulates the activity immune cells but also modulates signal transduction and metabolism However, specific interaction partners B7-H3 still remain unclear, limiting comprehensive understanding precise role progression. In this study, we report that can bind resting Raji cells, stimulated THP-1 even PC3 prostate through IgV domain alone. Furthermore, identify potential on these adopted an ascorbate peroxidase 2 (APEX2)-based proximity labeling strategy, which revealed about 10 key partners. Interestingly, our results suggest CD45 could be putative receptor while epidermal growth factor (EGFR) closely interact with Based further computational structure modeling studies, show (EGF) binding pocket EGFR-surprisingly, stronger affinity than EGF itself. Overall, study provides effective approach identifying both cell lines.

Language: Английский

Citations

0
Single-cell transcriptomics analysis reveals that the tumor-infiltrating B cells determine the indolent fate of papillary thyroid carcinoma DOI Creative Commons
Chunmei Li, Pei Wang, Zhizhong Dong

et al.

Journal of Experimental & Clinical Cancer Research, Journal Year: 2025, Volume and Issue: 44(1)

Published: March 11, 2025

Abstract Objective Active surveillance (AS) offers a viable alternative to surgical intervention for the management of indolent papillary thyroid carcinoma (PTC), helping minimize incidence unnecessary treatment. However, broader adoption AS is hindered by need more reliable diagnostic markers. This study aimed identify differences between and progressive PTC find new targets biomarker development therapeutic strategies. Methods We used single-cell RNA sequencing (scRNA-seq) analyze cellular in 10 early-stage tumors. Findings were validated an additional 25 tumors using cell co-culture, migration assays, immunofluorescence staining, flow cytometry, analysis data from The Cancer Genome Atlas (TCGA). Results Tumor-infiltrating B cells (TIL-B), particularly germinal center (GC-B), abundant PTC. These suppressed proliferation both cases, though had higher capacity recruit peripheral cells. In TIL-B showed increased formed clusters within tertiary lymphoid structures (TLS). PTPRC-CD22 interactions identified as potential drivers proliferation. Markers linked GC-B cells, such LMO2 , highlighted prognostic indicators Conclusion provides insights into landscape PTC, revealing distinct tumor immune microenvironment features cases. findings advance understanding biology support biomarkers.

Language: Английский

Citations

0
The evolving role of B cells in malignancies DOI
Samik Bindu,

Roshni Bibi,

Roshini Pradeep

et al.

Human Immunology, Journal Year: 2025, Volume and Issue: 86(3), P. 111301 - 111301

Published: March 25, 2025

Language: Английский

Citations

0
Identification of B cell antigens in solid cancer: initial insights and functional implications DOI Creative Commons
Jung‐In Yang, Philip Moresco, Douglas T. Fearon

et al.

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 16

Published: April 28, 2025

Cancer antigen discovery has mostly focused on T cell antigens, while antigens driving B responses have been largely overlooked despite representing another important branch of adaptive immune in cancer. Traditional cancer studied using serological approaches analyzing polyclonal antibodies serum. With recent technological advances single-cell sequencing, a few studies begun to investigate single specificity the tumor microenvironment immunoglobulin recombinant monoclonal antibody production, binding screening, and identification. In this review, we highlight initial insights into directed categorize them cancer-associated viral non-viral with latter featuring autoantigens. We will further discuss functions cells context their specificity, effector function, activation, secretion. Lastly, provide perspectives challenges opportunities identification new translational potential.

Language: Английский

Citations

0
Integrating scRNA-seq and Visium HD for the analysis of the tumor microenvironment in the progression of colorectal cancer DOI
Chun Wang, Mengying Lu,

Cuimin Chen

et al.

International Immunopharmacology, Journal Year: 2024, Volume and Issue: 145, P. 113752 - 113752

Published: Dec. 6, 2024

Language: Английский

Citations

2
B cell heterogeneity in cancer comes of age DOI

Colleen Sturdevant,

Yuliya Pylayeva‐Gupta

Cancer Cell, Journal Year: 2024, Volume and Issue: 42(10), P. 1650 - 1652

Published: Oct. 1, 2024

Language: Английский

Citations

0
Machine Learning Integration with Single-Cell Transcriptome Sequencing Datasets Reveals the Impact of Tumor-Associated Neutrophils on the Immune Microenvironment and Immunotherapy Outcomes in Gastric Cancer DOI Open Access
Jingcheng Zhang, Mingsi Zhang,

J. Lou

et al.

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(23), P. 12715 - 12715

Published: Nov. 26, 2024

The characteristics of neutrophils play a crucial role in defining the tumor inflammatory environment. However, function tumor-associated (TANs) immunity and their response to immune checkpoint inhibitors (ICIs) remains incompletely understood. By analyzing single-cell RNA sequencing data from over 600,000 cells gastric cancer (GSE163558 GSE183904), colorectal (GSE205506), lung (GSE207422), we identified neutrophil subsets primary that are associated with treatment ICIs. Specifically, focused on high expression CD44 (CD44_NEU), which abundant during progression exert significant influence microenvironment. Machine learning analysis revealed 22 core genes CD44_NEU, impacting inflammation, proliferation, migration, oxidative stress. In addition, multiple immunofluorescence staining spatial transcriptome (GSE203612) showed correlation between CD44_NEU T-cell infiltration tissues. A risk score model derived seven essential (AQP9, BASP1, BCL2A1, PLEK, PDE4B, PROK2, ACSL1) better predictive capability for patient survival compared clinical features alone, integrating these scores variables resulted prognostic nomogram. Overall, this study highlights heterogeneity TANs, particularly critical immunotherapy outcomes, paving way personalized strategies.

Language: Английский

Citations

0
Immunotherapy drug target identification using machine learning and patient-derived tumour explant validation DOI Creative Commons
Kevin Litchfield,

Marcellus Augustine,

Nuno R. Nené

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 5, 2024

Abstract Immunotherapy has revolutionised cancer treatment, yet few patients respond clinically, necessitating alternative strategies that can benefit these patients. Novel immune-oncology targets achieve this through bypassing resistance mechanisms to standard therapies. To address this, we introduce MIDAS, a multimodal graph neural network system for target discovery leverages gene interactions, multi-omic patient profiles, immune cell biology, antigen processing, disease associations, and phenotypic consequences of genetic perturbations. MIDAS generalises time-sliced data, outcompetes existing methods, including OpenTargets, distinguishes approved from prospective targets. Moreover, recovers immunotherapy response-associated genes in unseen trials, thus capturing tumour-immune dynamics within human tumours. Interpretability analyses reveal reliance on autoimmunity, regulatory networks, relevant biological pathways. Functionally perturbing the OSM-OSMR axis, proposed target, TRACERx melanoma patient-derived explants yielded reduced dysfunctional CD8+ T cells, which associate with response. Our results present machine learning framework analysing data discovery.

Language: Английский

Citations

0