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 hMENA11a 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. METHODS: Methods involved RNA-SEQ on NSCLC cells with depleted hMENA isoforms. A retrospective observational study assessed tissues from surgically treated N0 patients with NSCLC, using immunohistochemistry for tumoral and stromal hMENA isoforms, fibronectin, and TLS presence. ICB-treated patient tumors were analyzed using Nanostring nCounter and GeoMx spatial transcriptomics. Multiparametric flow cytometry characterized B cells and tissue-resident memory T cells (TRM). Survival and ICB response were estimated in the cohort and validated using bioinformatics pipelines in different datasets. FINDINGS: Findings indicate that hMENA11a in NSCLC cells upregulates the TLS regulator LTßR, decreases fibronectin, and favors CXCL13 production by TRM. Conversely, hMENAΔv6 in CAFs inhibits LTßR-related NF-kB pathway, reduces CXCL13 secretion, and promotes fibronectin production. These patterns are validated in N0 NSCLC tumors, where hMENA11ahigh expression, CAF hMENAΔv6low, and stromal fibronectinlow are associated with intratumoral TLS, linked to memory B cells and predictive of longer survival. The hMENA isoform pattern, fibronectin, and LTßR expression broadly predict ICB response in tumors where TLS indicates an anti-tumor immune response. INTERPRETATION: This study uncovers hMENA alternative splicing as an unexplored contributor to TLS-related Tumor Immune Microenvironment (TIME) and a promising biomarker for clinical outcomes and likely ICB responsiveness in N0 patients with NSCLC. FUNDING: This work is supported by AIRC (IG 19822), ACC (RCR-2019-23669120), CAL.HUB.RIA Ministero Salute PNRR-POS T4, "Ricerca Corrente" granted by the Italian Ministry of Health.

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.

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). An AI-based approach was used on hematoxylin and eosin (H&E) sections from 158 NSCLC specimens to accurately identify tumor cells, both adenocarcinoma and squamous carcinoma cells, and to generate a classifier of tumor cell spatial clustering. Consecutive tissue sections were stained with metal-labeled antibodies and processed through the IMC workflow, allowing quantitative detection of 24 markers related to tumor cells, tissue architecture, CD45+ myeloid and lymphoid cells, and immune activation. IMC identified 11 macrophage clusters that mainly localized in the stroma, except for S100A8+ cells, which infiltrated tumor nests. T cells were preferentially localized in peritumor areas or in tumor nests, the latter being associated with better prognosis, and they were more abundant in highly clustered tumors. Integrated tumor and immune classifiers were validated as prognostic on whole slides. In conclusion, integration of AI-powered H&E and multiparametric IMC allows investigation of spatial patterns and reveals tissue relevant features with clinical relevance. SIGNIFICANCE: Leveraging artificial intelligence-powered H&E analysis integrated with hi-plex imaging mass cytometry provides insights into the tumor ecosystem and can translate tumor features into classifiers to predict prognosis, genotype, and therapy response.

Time interval from last visit to imaging diagnosis influences outcome in pancreatic adenocarcinoma: A regional population-based study on linked medico-administrative and clinical data.

Background: Excessive waiting time intervals for the diagnosis and treatment of patients with pancreatic cancer can influence their prognosis but they remain unclear. The objective was to describe time intervals from the medical visit to diagnostic imaging and to treatment and their prognostic impact in pancreatic cancer in one French region. Methods: This retrospective observational multicentre study included all patients with pancreatic cancer seen for the first time in 2017 in multidisciplinary team meetings (MTMs), where clinical data were collected. A probabilistic matching with the medico-administrative data from the French national healthcare database (Système National des Données de Santé) was performed to define the care pathway from clinical presentation to the beginning of treatment. Median key time intervals were estimated for both resected and unresected tumours. Factors associated with 1-year survival were studied using Cox model. Results: A total of 324 patients (88% of total patients with MTM presentation) were matched and included: male 54%, mean age 72 years ±9.2, Eastern Cooperative Oncology Group (ECOG) PS > 1 19.5%, metastatic disease at diagnosis 47.4%, tumour resection 16%. At 1 year, 57% had died (65% in the unresected group and 17% in the resected group). The median time interval from the medical visit to diagnostic imaging was 15 days [Q1-Q3: 8-44]. After imaging, median time intervals to definite diagnosis and to first treatment were 11 and 20 days, respectively. Significant prognostic factors associated with the risk of death at 1 year were ECOG PS > 1 (hazard ratio (HR) 2.1 [1.4-3.0]), metastasis (HR 2.7 [1.9-3.9]), no tumour resection (HR 2.7 [1.3-5.6]) and time interval between the medical visit and diagnostic imaging ⩾25 days (HR 1.7 [1.2-2.3]). Conclusion: Delay in access to diagnostic imaging impacted survival in patients with pancreatic cancer, regardless of whether tumour resection had been performed.

Microenvironmental Factors in Oral Cavity Squamous Cell Carcinoma Undergoing Surgery: Correlation with Diffusion Kurtosis Imaging and Dynamic Contrast-Enhanced MRI.

BACKGROUND: In this prospective study, we hypothesized that magnetic resonance imaging (MRI) may represent not only the tumor but also the microenvironment, reflecting the heterogeneity and microstructural complexity of neoplasms. We investigated the correlation between both diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced (DCE)-MRI with the pathological factors in oral cavity squamous cell carcinomas (OSCCs). METHODS: A total of 37 patients with newly diagnosed OSCCs underwent an MR examination on a 3T system. The diffusion coefficient (D), the kurtosis parameter (K), the transfer constants Ktrans and Kep and the volume of extravascular extracellular space ve were quantified. A histogram-based approach was proposed to investigate the associations between the imaging and the pathological factors based on the histology and immunochemistry. RESULTS: Significant differences in the DCE-MRI and DKI parameters were found in relation to the inflammatory infiltrate, tumor grading, keratinization and desmoplastic reaction. Relevant relationships emerged between tumor-infiltrating lymphocytes (TILs) and DKI, with lower D and higher K values being associated with increased TILs. CONCLUSION: Although a further investigation is needed, these findings provide a more comprehensive biological characterization of OSCCs and may contribute to a better understanding of DKI-derived parameters, whose biophysical meaning is still not well-defined.

Photons detected in the active nerve by photographic technique.

The nervous system is one of the most complex expressions of biological evolution. Its high performance mostly relies on the basic principle of the action potential, a sequential activation of local ionic currents along the neural fiber. The implications of this essentially electrical phenomenon subsequently emerged in a more comprehensive electromagnetic perspective of neurotransmission. Several studies focused on the possible role of photons in neural communication and provided evidence of the transfer of photons through myelinated axons. A hypothesis is that myelin sheath would behave as an optical waveguide, although the source of photons is controversial. In a previous work, we proposed a model describing how photons would arise at the node of Ranvier. In this study we experimentally detected photons in the node of Ranvier by Ag+ photoreduction measurement technique, during electrically induced nerve activity. Our results suggest that in association to the action potential a photonic radiation takes place in the node.