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Background: We aim to implement an immune cell score model in routine clinical practice for resected non-small-cell lung cancer (NSCLC) patients (NCT03299478). Molecular and genomic features associated with immune phenotypes in NSCLC have not been explored in detail., Patients and Methods: We developed a machine learning (ML)-based model to classify tumors into one of three categories: inflamed, altered, and desert, based on the spatial distribution of CD8+ T cells in two prospective (n = 453; TNM-I trial) and retrospective (n = 481) stage I-IIIA NSCLC surgical cohorts. NanoString assays and targeted gene panel sequencing were used to evaluate the association of gene expression and mutations with immune phenotypes., Results: Among the total of 934 patients, 24.4% of tumors were classified as inflamed, 51.3% as altered, and 24.3% as desert. There were significant associations between ML-derived immune phenotypes and adaptive immunity gene expression signatures. We identified a strong association of the nuclear factor-κB pathway and CD8+ T-cell exclusion through a positive enrichment in the desert phenotype. KEAP1 [odds ratio (OR) 0.27, Q = 0.02] and STK11 (OR 0.39, Q = 0.04) were significantly co-mutated in non-inflamed lung adenocarcinoma (LUAD) compared to the inflamed phenotype. In the retrospective cohort, the inflamed phenotype was an independent prognostic factor for prolonged disease-specific survival and time to recurrence (hazard ratio 0.61, P = 0.01 and 0.65, P = 0.02, respectively)., Conclusions: ML-based immune phenotyping by spatial distribution of T cells in resected NSCLC is able to identify patients at greater risk of disease recurrence after surgical resection. LUADs with concurrent KEAP1 and STK11 mutations are enriched for altered and desert immune phenotypes., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Immunoscore is a prognostic tool defined to quantify in situ immune cell infiltrates and appears highly promising as a supplement to the tumor-node-metastasis (TNM) classification of various tumors. In colorectal cancer, an international task force has initiated prospective multicenter studies aiming to implement TNM-Immunoscore (TNM-I) in a routine clinical setting. In breast cancer, recommendations for the evaluation of tumor-infiltrating lymphocytes (TILs) have been proposed by an international working group. Regardless of promising results, there are potential obstacles related to implementing TNM-I into the clinic. Diverse methods may be needed for different malignancies and even within each cancer entity. Nevertheless, a uniform approach across malignancies would be advantageous. In nonsmall-cell lung cancer (NSCLC), there are several previous reports indicating an apparent prognostic importance of TILs, but studies on TILs in a TNM-I setting are sparse and no general recommendations are made. However, recently published data is promising, evoking a realistic hope of a clinical useful NSCLC TNM-I. This review will focus on the TNM-I potential in NSCLC and propose strategies for clinical implementation of a TNM-I in resected NSCLC., (© The Author 2015. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Platelet-derived growth factors (PDGFs) and vascular endothelial growth factors and their receptors [platelet-derived growth factor receptors (PDGFRs) and vascular endothelial growth factor receptors (VEGFRs)] are related to both angiogenesis and lymphangiogenesis and are important targets in new cancer treatment strategies. We aimed to study the PDGFs/PDGFRs and correlations with lymph node metastasis (LNM) and investigate the prognostic impact of the co-expression of PDGF-B and VEGFR-3 and its correlation with LNM., Patients and Methods: Tumor tissue samples from 335 resected patients with stage I-IIIA non-small-cell lung cancer (NSCLC) were obtained and tissue microarrays were constructed from duplicate cores of tumor cells and tumor-related stroma from each specimen. Immunohistochemistry was used to evaluate the expression of the molecular markers PDGF-A, PDGF-B, PDGF-C, PDGF-D, PDGFR-alpha, PDGFR-beta, VEGFR-3 and D2-40., Results: There were 232 N0 and 103 N+ patients (76 N1 and 27 N2). In multivariate analyses, high tumor cell PDGF-A expression (P = 0.017) correlated with LNM. Tumor cell co-expression of VEGFR-3 and PDGF-B correlated with nodal metastasis and was an independent indicator of poor prognosis (hazard ratio 4.8, confidence interval 95% 2.80-8.31, P < 0.001)., Conclusion: Tumor cell PDGF-A expression correlates with LNM, and the co-expression of PDGF-B and VEGFR-3 is strongly associated with poor survival in NSCLC patients.