AIMC Topic: Lymphocytes, Tumor-Infiltrating

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Artificial Intelligence and Multimodality Data Integration Decipher Tertiary Lymphoid Structure Maturity in Gastric Cancer.

Cancer research
UNLABELLED: Tertiary lymphoid structures (TLS) are critical components of the tumor microenvironment in gastric cancer, but clinical assessment of TLSs is challenging. The development of automated annotation tools for histopathologic slide analysis c...

A machine learning approach to risk-stratification of gastric cancer based on tumour-infiltrating immune cell profiles.

Annals of medicine
BACKGROUND: Gastric cancer (GC) is a highly heterogeneous disease, and the response of patients to clinical treatment varies substantially. There is no satisfactory strategy for predicting curative effects to date. We aimed to explore a new method fo...

Associations between Calcium Intake and T-cell Infiltration in Colorectal Tumors.

Cancer prevention research (Philadelphia, Pa.)
UNLABELLED: Higher T-cell infiltration in colorectal tumors has been associated with better prognosis. Evidence indicates that calcium signaling is essential for T-cell functioning. However, as it is unknown whether calcium intake influences T-cell i...

Tumor-Intrinsic and Microenvironmental Determinants of Impaired Antitumor Immunity in Chromophobe Renal Cell Carcinoma.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology
PURPOSE: While immune checkpoint inhibition (ICI) has transformed the management of many advanced renal cell carcinomas (RCCs), the determinants of effective antitumor immunity for chromophobe RCC (ChRCC) and renal oncocytic tumors remain an unmet cl...

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...

Deep Learning Model for Predicting Immunotherapy Response in Advanced Non-Small Cell Lung Cancer.

JAMA oncology
IMPORTANCE: Only a small fraction of patients with advanced non-small cell lung cancer (NSCLC) respond to immune checkpoint inhibitor (ICI) treatment. For optimal personalized NSCLC care, it is imperative to identify patients who are most likely to b...

Tumor-infiltrating immune cell signature score reveals prognostic biomarkers and therapeutic targets for colorectal cancer.

Frontiers in immunology
BACKGROUND: Colorectal cancer (CRC) is one of the leading contributors to cancer-related deaths worldwide, with more than 900,000 new diagnoses and related deaths each year. This study aims to explore the prognostic value of tumor-infiltrating immune...

Personalized treatment decision-making using a machine learning-derived lactylation signature for breast cancer prognosis.

Frontiers in immunology
BACKGROUND: Breast cancer is a heterogeneous malignancy with complex molecular characteristics, making accurate prognostication and treatment stratification particularly challenging. Emerging evidence suggests that lactylation, a novel post-translati...

Development of an immune-related gene signature applying Ridge method for improving immunotherapy responses and clinical outcomes in lung adenocarcinoma.

PeerJ
BACKGROUND: Lung adenocarcinoma (LUAD) is a major cause of cancer mortality. Considering the critical role of tumor infiltrating lymphocytes in effective immunotherapy, this study was designed to screen molecular markers related to tumor infiltrating...