AI drives the assessment of lung cancer microenvironment composition.

Purpose: The abundance and distribution of tumor-infiltrating lymphocytes (TILs) as well as that of other components of the tumor microenvironment is of particular importance for predicting response to immunotherapy in lung cancer (LC). We describe here a pilot study employing artificial intelligence (AI) in the assessment of TILs and other cell populations, intending to reduce the inter- or intra-observer variability that commonly characterizes this evaluation.

Design: We developed a machine learning-based classifier to detect tumor, immune, and stromal cells on hematoxylin and eosin-stained sections, using the open-source framework .

ADA/CD26 axis increases intra-tumor PD-1CD28CD8 T-cell fitness and affects NSCLC prognosis and response to ICB.

Improving cancer immunotherapy efficacy hinges on identifying key T-cell populations critical for tumor control and response to Immune Checkpoint Blockade (ICB). We have recently reported that while the co-expression of PD-1 and CD28 is associated with impaired functionality in peripheral blood, it significantly enhances T-cell fitness in the tumor site of non-small cell lung cancer (NSCLC) patients. To uncover the underlying mechanisms, we explored the role of CD26, a key player in T-cell activation through its interaction with adenosine deaminase (ADA), a crucial intra/extracellular enzyme able to neutralize local adenosine (ADO).

Tumoral and stromal hMENA isoforms impact tertiary lymphoid structure localization in lung cancer and predict immune checkpoint blockade response in patients with cancer.

Background: Tertiary Lymphoid Structures (TLS) correlate with positive outcomes in patients with NSCLC and the efficacy of immune checkpoint blockade (ICB) in cancer. The actin regulatory protein hMENA undergoes tissue-specific splicing, producing the epithelial hMENA linked to favorable prognosis in early NSCLC, and the mesenchymal hMENAΔv6 found in invasive cancer cells and pro-tumoral cancer-associated fibroblasts (CAFs). This study investigates how hMENA isoforms in tumor cells and CAFs relate to TLS presence, localization and impact on patient outcomes and ICB response.

Integrating AI-Powered Digital Pathology and Imaging Mass Cytometry Identifies Key Classifiers of Tumor Cells, Stroma, and Immune Cells in Non-Small Cell Lung Cancer.

Unlabelled: Artificial intelligence (AI)-powered approaches are becoming increasingly used as histopathologic tools to extract subvisual features and improve diagnostic workflows. On the other hand, hi-plex approaches are widely adopted to analyze the immune ecosystem in tumor specimens. Here, we aimed at combining AI-aided histopathology and imaging mass cytometry (IMC) to analyze the ecosystem of non-small cell lung cancer (NSCLC).