A Review of Biomarkers and Their Clinical Impact in Resected Early-Stage Non-Small-Cell Lung Cancer

Variation of peripheral blood-based biomarkers for response of anti-PD-1 immunotherapy in non-small-cell lung cancer

PURPOSE

Immune checkpoint inhibitors (ICIs) for non-small-cell lung cancer (NSCLC) are on the rise, but unfortunately, only a small percentage of patients benefit from them in the long term. Thus, it is crucial to identify biomarkers that can forecast the efficacy of immunotherapy.

METHODS

We retrospectively studied 224 patients with NSCLC who underwent anti-PD-1 therapy. The role of biomarkers and clinical characteristics were assessed in a prognostic model.

RESULTS

Only 14.3% of patients had both programmed death ligand 1 (PD-L1) and tumor mutational burden (TMB) outcomes, highlighting the need to investigate more available biomarkers. Our analysis found a correlation between histological PD-L1 TPS and hematological PD-1 expression. Analysis of hematological biomarkers revealed that elevated expression of CD4/CD8 and LYM% are positively associated with effective immunotherapy, while PD-1+ on T cells, NLR, and MLR have a negative impact. Moreover, high level of ΔCEA%, CYFRA21-1 and LDH may suggest ineffective ICIs. We also observed that disparate immunotherapy drugs didn't significantly impact prognosis. Lastly, by comparing squamous carcinoma and adenocarcinoma cohorts, ΔCEA%, CD3+PD-1+, CD4+PD-1+, and CD4/CD8 are more important in predicting the prognosis of adenocarcinoma patients, while age is more significant for squamous carcinoma patients.

CONCLUSION

Our research has yielded encouraging results in identifying a correlation between immunotherapy's response and clinical characteristics, peripheral immune cell subsets, and biochemical and immunological biomarkers. The screened hematological detection panel could be used to forecast an NSCLC patient's response to anti-PD-1 immunotherapy with an accuracy rate of 76.3%, which could help customize suitable therapeutic decision-making.

Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients

Deep-learning tools that extract prognostic factors derived from multi-omics data have recently contributed to individualized predictions of survival outcomes. However, the limited size of integrated omics-imaging-clinical datasets poses challenges. Here, we propose two biologically interpretable and robust deep-learning architectures for survival prediction of non-small cell lung cancer (NSCLC) patients, learning simultaneously from computed tomography (CT) scan images, gene expression data, and clinical information. The proposed models integrate patient-specific clinical, transcriptomic, and imaging data and incorporate Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathway information, adding biological knowledge within the learning process to extract prognostic gene biomarkers and molecular pathways. While both models accurately stratify patients in high- and low-risk groups when trained on a dataset of only 130 patients, introducing a cross-attention mechanism in a sparse autoencoder significantly improves the performance, highlighting tumor regions and NSCLC-related genes as potential biomarkers and thus offering a significant methodological advancement when learning from small imaging-omics-clinical samples.

The role of immunotherapy in early-stage and metastatic NSCLC

In the past decade we have seen new advances and thus remarkable progress in the therapeutic options for non-small cell lung cancer (NSCLC). Among cytostatic therapies with new approaches in molecularly targeted therapies, we see new developments in a wide range of applications for immunotherapies. In this review we discuss the new potential modalities for the use of immune checkpoint inhibitors (ICIs) in the frontlines, including in early-stage (perioperative) and metastatic settings. The perioperative use of ICIs in both neoadjuvant and adjuvant settings may show benefits for patients. In early-stage NSCLC (from stage IIB and above) a multimodality approach is recommended as the gold standard for the treatment. After surgical resection platinum-based adjuvant chemotherapy has been the standard of care for many years. Based on the benefit of disease-free survival, the approval of adjuvant atezolizumab and adjuvant pembrolizumab was a significant breakthrough. In the metastatic setting, the use of immune checkpoint inhibitors with chemotherapy, regardless of PD-L1 expression or ICI alone (PD-L1 expression equal to or greater than 50%) also improves overall survival and progression-free survival.

Identification and Application of Emerging Biomarkers in Treatment of Non-Small-Cell Lung Cancer: Systematic Review

Non-small-cell lung cancer (NSCLC) comprises approximately 85% of all lung cancer cases, often diagnosed at advanced stages, which diminishes the effective treatment options and survival rates. This systematic review assesses the utility of emerging biomarkers-circulating tumor DNA (ctDNA), microRNAs (miRNAs), and the blood tumor mutational burden (bTMB)-enhanced by next-generation sequencing (NGS) to improve the diagnostic accuracy, prognostic evaluation, and treatment strategies in NSCLC. Analyzing data from 37 studies involving 10,332 patients from 2020 to 2024, the review highlights how biomarkers like ctDNA and PD-L1 expression critically inform the selection of personalized therapies, particularly beneficial in the advanced stages of NSCLC. These biomarkers are critical for prognostic assessments and in dynamically adapting treatment plans, where high PD-L1 expression and specific genetic mutations (e.g., ALK fusions, EGFR mutations) significantly guide the use of targeted therapies and immunotherapies. The findings recommend integrating these biomarkers into standardized clinical pathways to maximize their potential in enhancing the treatment precision, ultimately fostering significant advancements in oncology and improving patient outcomes and quality of life. This review substantiates the prognostic and predictive value of these biomarkers and emphasizes the need for ongoing innovation in biomarker research.

Dynamics of a diffusive model for cancer stem cells with time delay in microRNA-differentiated cancer cell interactions and radiotherapy effects

Understand the dynamics of cancer stem cells (CSCs), prevent the non-recurrence of cancers and develop therapeutic strategies to destroy both cancer cells and CSCs remain a challenge topic. In this paper, we study both analytically and numerically the dynamics of CSCs under radiotherapy effects. The dynamical model takes into account the diffusion of cells, the de-differentiation (or plasticity) mechanism of differentiated cancer cells (DCs) and the time delay on the interaction between microRNAs molecules (microRNAs) with DCs. The stability of the model system is studied by using a Hopf bifurcation analysis. We mainly investigate on the critical time delay τ c , that represents the time for DCs to transform into CSCs after the interaction of microRNAs with DCs. Using the system parameters, we calculate the value of τ c for prostate, lung and breast cancers. To confirm the analytical predictions, the numerical simulations are performed and show the formation of spatiotemporal circular patterns. Such patterns have been found as promising diagnostic and therapeutic value in management of cancer and various diseases. The radiotherapy is applied in the particular case of prostate model. We calculate the optimum dose of radiation and determine the probability of avoiding local cancer recurrence after radiotherapy treatment. We find numerically a complete eradication of patterns when the radiotherapy is applied before a time t < τ c . This scenario induces microRNAs to act as suppressors as experimentally observed in prostate cancer. The results obtained in this paper will provide a better concept for the clinicians and oncologists to understand the complex dynamics of CSCs and to design more efficacious therapeutic strategies to prevent the non-recurrence of cancers.

Cell-Free DNA 5-Hydroxymethylcytosine Signatures for Lung Cancer Prognosis

Accurate prognostic markers are essential for guiding effective lung cancer treatment strategies. The level of 5-hydroxymethylcytosine (5hmC) in tissue is independently associated with overall survival (OS) in lung cancer patients. We explored the prognostic value of cell-free DNA (cfDNA) 5hmC through genome-wide analysis of 5hmC in plasma samples from 97 lung cancer patients. In both training and validation sets, we discovered a cfDNA 5hmC signature significantly associated with OS in lung cancer patients. We built a 5hmC prognostic model and calculated the weighted predictive scores (wp-score) for each sample. Low wp-scores were significantly associated with longer OS compared to high wp-scores in the training [median 22.9 versus 8.2 months; p = 1.30 × 10-10; hazard ratio (HR) 0.04; 95% confidence interval (CI), 0.00-0.16] and validation (median 18.8 versus 5.2 months; p = 0.00059; HR 0.22; 95% CI: 0.09-0.57) sets. The 5hmC signature independently predicted prognosis and outperformed age, sex, smoking, and TNM stage for predicting lung cancer outcomes. Our findings reveal critical genes and signaling pathways with aberrant 5hmC levels, enhancing our understanding of lung cancer pathophysiology. The study underscores the potential of cfDNA 5hmC as a superior prognostic tool for guiding more personalized therapeutic strategies for lung cancer patients.

What's new in thoracic oncology

Many changes have occurred in the field of thoracic surgery over the last several years. In this review, we will discuss new diagnostic techniques for lung cancer, innovations in surgery, and major updates on latest treatment options including immunotherapy. All these have significantly started to change our approach toward the management of lung cancer and have great potential to improve the lives of our patients afflicted with this disease.

The DC-T cell axis is an effective target for the treatment of non-small cell lung cancer

The dendritic cell (DC)-T cell axis is a bridge that connects innate and adaptive immunities. The initial immune response against tumors is mainly induced by mature antigen-presenting DCs. Enhancing the crosstalk between DCs and T cells may be an effective approach to improve the immune response to non-small cell lung cancer (NSCLC). In this article, a review was made of the interaction between DCs and T cells in the treatment of NSCLC and how this interaction affects the treatment outcome.