Efficacy of immune checkpoint inhibitors in non-small cell lung cancer: A systematic review and meta-analysis

Single-cell analysis and machine learning-based integration develop an immune-responsive signature of antigen-presenting cancer-associated fibroblasts in lung adenocarcinoma

Background

Cancer-associated fibroblasts (CAFs) are pivotal regulators of the tumor immune microenvironment, shaping immune responses and influencing therapeutic outcomes. While previous studies have predominantly focused on CAF subpopulations that impair responses to immune checkpoint inhibitors (ICIs), CAF subsets associated with favorable ICIs responses in lung adenocarcinoma (LUAD) remain underexplored. In this study, we integrated bulk RNA and single-cell RNA sequencing data from LUAD samples to identify CAF subpopulations relevant to ICIs efficacy.

Methods

Using a machine learning-driven approach, we developed a robust immune response signature based on this antigen-presenting CAFs (apCAFs) subset to predict ICIs responses.

Results

We uncovered a novel subset of apCAFs exhibiting macrophage-like features, characterized by the expression of major histocompatibility complex (MHC) class II, CD74, and costimulatory molecules (CD80, CD86, CD83, and CD40). This subset, distinct from classic apCAFs described in other cancer types, is strongly associated with favorable ICIs responses across multiple datasets. Notably, these macrophage-like apCAFs are present in LUAD samples prior to treatment, although their abundance varies among individuals. Patients classified as high-risk using signature calculated by a machine learning-driven approach exhibited lower overall survival rates and diminished immune cell infiltration following ICIs therapy.

Conclusions

Collectively, our findings establish a critical link between macrophage-like apCAFs and ICIs efficacy, offering a clinically applicable signature for patient stratification and guiding therapeutic strategies targeting the tumor microenvironment.

Utilizing a Proximity Dependent Labeling Strategy to Study Cancer-Immune Intercellular Interactions In Vitro and In Vivo

Immune cells play a critical role in surveilling and defending against cancer, emphasizing the importance of understanding how they interact and communicate with cancer cells to determine cancer status, treatment response, and the formation of the tumor microenvironment (TME). To this end, we conducted a study demonstrating the effectiveness of an enzyme-mediated intercellular proximity labeling (EXCELL) method, which utilizes a modified version of the sortase A enzyme known as mgSrtA, in detecting and characterizing immune-tumor cell interactions. The mgSrtA enzyme is expressed on the membrane of tumor cells, which is able to label immune cells that interact with tumor cells in a proximity-dependent manner. Our research indicates that the EXCELL technique can detect and characterize immune-tumor cell interactions in a time- and concentration-dependent manner, both in vitro and in vivo, without requiring pre-engineering of the immune cells. We also highlight its ability to detect various types of immune cell subpopulations in vivo that have migrated out of the tumor into the spleen, providing insights into the role of peripheral T-cell recruitment in tumor progression. Overall, our findings suggest that the EXCELL method has great potential for improving our understanding of immune cell dynamics within the TME, ultimately leading to more potent pharmacological effects and cancer immunotherapy strategies. SIGNIFICANCE STATEMENT: The enzyme-mediated intercellular proximity labeling method holds promise for detecting immune cell interactions with cancer cells, both in vitro and in vivo. It has important implications for studying immune tumor cell dynamics and potentially uncovering novel subtypes of immune cells within the tumor microenvironment, both prior to and during immunotherapeutic interventions.

Adoptive T cell therapy for solid tumors: current landscape and future challenges

Adoptive cell therapy (ACT) comprises different strategies to enhance the activity of T lymphocytes and other effector cells that orchestrate the antitumor immune response, including chimeric antigen receptor (CAR) T-cell therapy, T-cell receptor (TCR) gene-modified T cells, and therapy with tumor-infiltrating lymphocytes (TILs). The outstanding results of CAR-T cells in some hematologic malignancies have launched the investigation of ACT in patients with refractory solid malignancies. However, certain characteristics of solid tumors, such as their antigenic heterogeneity and immunosuppressive microenvironment, hamper the efficacy of antigen-targeted treatments. Other ACT modalities, such as TIL therapy, have emerged as promising new strategies. TIL therapy has shown safety and promising activity in certain immunogenic cancers, mainly advanced melanoma, with an exciting rationale for its combination with immune checkpoint inhibitors. However, the implementation of TIL therapy in clinical practice is hindered by several biological, logistic, and economic challenges. In this review, we aim to summarize the current knowledge, available clinical results, and potential areas of future research regarding the use of T cell therapy in patients with solid tumors.

Radiomics and Delta-Radiomics Signatures to Predict Response and Survival in Patients with Non-Small-Cell Lung Cancer Treated with Immune Checkpoint Inhibitors

The aim of our study was to determine the potential role of CT-based radiomics in predicting treatment response and survival in patients with advanced NSCLC treated with immune checkpoint inhibitors. We retrospectively included 188 patients with NSCLC treated with PD-1/PD-L1 inhibitors from two independent centers. Radiomics analysis was performed on pre-treatment contrast-enhanced CT. A delta-radiomics analysis was also conducted on a subset of 160 patients who underwent a follow-up contrast-enhanced CT after 2 to 4 treatment cycles. Linear and random forest (RF) models were tested to predict response at 6 months and overall survival. Models based on clinical parameters only and combined clinical and radiomics models were also tested and compared to the radiomics and delta-radiomics models. The RF delta-radiomics model showed the best performance for response prediction with an AUC of 0.8 (95% CI: 0.65−0.95) on the external test dataset. The Cox regression delta-radiomics model was the most accurate at predicting survival with a concordance index of 0.68 (95% CI: 0.56−0.80) (p = 0.02). The baseline CT radiomics signatures did not show any significant results for treatment response prediction or survival. In conclusion, our results demonstrated the ability of a CT-based delta-radiomics signature to identify early on patients with NSCLC who were more likely to benefit from immunotherapy.

M-Health in lung cancer: A literature review

The internet and digital technology have become an important resource for patients with cancer. Mobile health strategies permit patients and clinicians to interact via different tools to enrich the supplements to routine hospital visits or out-patient attendance. In this work, we have reviewed different mobile health platforms to support lung cancer patients in different areas: pre-surgery; post-surgery and on systemic treatment. We have also reviewed different digital tools used by long-term lung cancer survivors as well as the impact of these tools on the quality of life, and we tried to analyse according to literature the potential efficiency of these platforms in health system administration.