Molecular alterations and clinical prognostic factors in resectable non-small cell lung cancer

Emerging molecular testing paradigms in non-small cell lung cancer management-current perspectives and recommendations

Advances in molecular testing and precision oncology have transformed the clinical management of lung cancer, especially non-small cell lung cancer, enhancing diagnosis, treatment, and outcomes. Practical guidelines offer insights into selecting appropriate biomarkers and assays, emphasizing the importance of comprehensive testing. However, real-world data reveal the underutilization of biomarker testing and consequently targeted therapies. Molecular testing often occurs late in diagnosis or not at all in clinical practice, leading to delayed or inadequate treatment. Enhancing precision requires adherence to best practices by all health care professionals involved, which can ultimately improve lung cancer patient outcomes. The future of precision oncology for lung cancer will likely involve a more personalized approach, starting increasingly from earlier disease settings, with novel and more complex targeted therapies, immunotherapies, and combination regimens, and relying on liquid biopsies, muti-detection advanced genomic technologies and data integration, with artificial intelligence as a central orchestrator. This review presents the currently known actionable mutations in lung cancer and new upcoming ones that are likely to enter clinical practice soon and provides an overview of established and emerging concepts in testing methodologies. Challenges are discussed and best practice recommendations are made that are relevant today, will continue to be relevant in the future, and are likely to be relevant for other cancer types too.

Stage-dependent spatial distribution and prognostic value of CD8+ tissue-resident memory T cells in NSCLC

CD8+ tissue-resident memory T cells (CD8+TRM), expressing PD-1 and/or TIM-3, are linked to immunological surveillance in non-small cell lung cancer (NSCLC). However, their prognostic value across activation states and spatial distributions in NSCLC stages is unclear. We analyzed 271 NSCLC patients' primary tumors and lymph nodes, using multiplex immunohistochemistry and inForm software for cell identification. Statistical analyses included the Mann-Whitney U test and Cox survival analysis. Findings showed CD8+TRM were categorized into four activation states. In locally advanced NSCLC, PD-1-TIM-3+CD8+TRM3, and PD-1+TIM-3+CD8+TRM4 densities were notably higher at invasive margins. Fewer interactions between CD8+TRM and tumor cells were observed in advanced lesions. Decreased PD-1+TIM-3-CD8+TRM2 interactions with tumor cells and increased PD-1+TIM-3+CD8+TRM4 interactions with tumor cells were independently associated with recurrence in patients with early lung adenocarcinoma and squamous carcinoma, respectively. These results suggest that CD8+TRM activation state and distribution are linked to recurrence risk in early-stage NSCLC, emphasizing the importance of CD8+TRM spatial dynamics in prognosis.

Potential predictive biomarkers in antitumor immunotherapy: navigating the future of antitumor treatment and immune checkpoint inhibitor efficacy

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment modality, offering promising outcomes for various malignancies. However, the efficacy of ICIs varies among patients, highlighting the essential need of accurate predictive biomarkers. This review synthesizes the current understanding of biomarkers for ICI therapy, and discusses the clinical utility and limitations of these biomarkers in predicting treatment outcomes. It discusses three US Food and Drug Administration (FDA)-approved biomarkers, programmed cell death ligand 1 (PD-L1) expression, tumor mutational burden (TMB), and microsatellite instability (MSI), and explores other potential biomarkers, including tumor immune microenvironment (TIME)-related signatures, human leukocyte antigen (HLA) diversity, non-invasive biomarkers such as circulating tumor DNA (ctDNA), and combination biomarker strategies. The review also addresses multivariable predictive models integrating multiple features of patients, tumors, and TIME, which could be a promising approach to enhance predictive accuracy. The existing challenges are also pointed out, such as the tumor heterogeneity, the inconstant nature of TIME, nonuniformed thresholds and standardization approaches. The review concludes by emphasizing the importance of biomarker research in realizing the potential of personalized immunotherapy, with the goal of improving patient selection, treatment strategies, and overall outcomes in cancer treatment.