AIMC Topic: Tumor Microenvironment

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Learnable prototype-guided multiple instance learning for detecting tertiary lymphoid structures in multi-cancer whole-slide pathological images.

Medical image analysis
Tertiary lymphoid structures (TLS) are ectopic lymphoid aggregates that form under specific pathological conditions, such as chronic inflammation and malignancies. Their presence within the tumor microenvironment (TME) is strongly correlated with pat...

Multi-omics identifies OSM-OSMR as a key receptor-ligand in the tumor environment of endometrial adenocarcinoma.

International immunopharmacology
Endometrial adenocarcinoma carries a bleak prognosis, and the molecular markers that evaluate the progression of endometrial adenocarcinoma to advanced stages remain uncertain. Cell-cell communication plays a crucial role in the tumor microenvironmen...

Droplet microfluidics integrated with machine learning reveals how adipose-derived stem cells modulate endocrine response and tumor heterogeneity in ER breast cancer.

Lab on a chip
Approximately 70% of breast cancer (BC) diagnoses are estrogen receptor positive (ER) with ∼40% of ER BC patients presenting resistance to endocrine therapy (ET). Recent studies identify the tumor microenvironment (TME) as having a key role in endoc...

Pan-Cancer Spatial Profiling Reveals Conserved Subtypes and Niches of Cancer-Associated Fibroblasts.

Cancer research
Solid cancers are complex "ecosystems" comprised of diverse cell types and extracellular molecules, in which heterotypic interactions significantly influence disease etiology and therapeutic response. However, our current understanding of tumor micro...

Dissecting Exosomal-Tumoral-Vascular Interactions of Single Tumor Cells and Clusters Using a Tumoral-Transendothelial Migration Chip.

ACS nano
The complex interplay between tumor cells and clusters with endothelial tissues during metastasis, in particular with regard to the exosomes in mediating intercellular communication, is still not well understood. Here, we develop a tumoral-transendot...

Cancer-on-a-chip for precision cancer medicine.

Lab on a chip
Many cancer therapies fail in clinical trials despite showing potent efficacy in preclinical studies. One of the key reasons is the adopted preclinical models cannot recapitulate the complex tumor microenvironment (TME) and reflect the heterogeneity ...

Leveraging Artificial Intelligence for Neoantigen Prediction.

Cancer research
Neoantigens represent a class of antigens within tumor microenvironments that arise from diverse somatic mutations and aberrations specific to tumorigenesis, holding substantial promise for advancing tumor immunotherapy. However, only a subset of neo...

Leveraging deep learning to discover interpretable cellular spatial biomarkers for prognostic predictions based on hepatocellular carcinoma histology.

The journal of pathology. Clinical research
The spatial structure of various cell types in the tumour microenvironment (TME) can provide valuable insights into disease progression. However, identifying the spatial organization of diverse cell types that significantly correlates with patient pr...

Integrating multi-omics data with artificial intelligence to decipher the role of tumor-infiltrating lymphocytes in tumor immunotherapy.

Pathology, research and practice
Tumor-infiltrating lymphocytes (TILs) are capable of recognizing tumor antigens, impacting tumor prognosis, predicting the efficacy of neoadjuvant therapies, contributing to the development of new cell-based immunotherapies, studying the tumor immune...

Advancing T-cell immunotherapy for cellular senescence and disease: Mechanisms, challenges, and clinical prospects.

Ageing research reviews
Cellular senescence is a complex biological process with a dual role in tissue homeostasis and aging-related pathologies. Accumulation of senescent cells promotes chronic inflammation, tissue dysfunction, age-related diseases, and tumor suppression. ...