PD-L1 Over-Expression Varies in Different Subtypes of Lung Cancer: Will This Affect Future Therapies?

Management of patients with extensive small-cell lung cancer in the immunotherapy era: An Italian consensus through a Delphi approach

BACKGROUND

Immunotherapy represented a turning point for treating extensive small-cell lung cancer (ES-SCLC). Although, many issues remain debated.

METHODS

A group of Italian medical and radiation oncologists with expertise in managing patients with ES-SCLC developed a list of statements divided in six areas of interest. The Delphi method was used to assess the consensus on the defined list of statements.

RESULTS

32 statements were included in the final list to be voted by the Delphi panel, and 26 reached a consensus on the agreement. A prompt involvement of a multidisciplinary team is a priority to provide an integrated treatment strategy. First-line recommended treatment is immunotherapy in combination with platinum-based chemotherapy and etoposide for four cycles followed by maintenance immunotherapy.

CONCLUSIONS

While awaiting new data from clinical trials and real-world studies, these recommendations can represent a useful tool to guide the management of ES-SCLC patients in daily practice.

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.

Exploring histological predictive biomarkers for immune checkpoint inhibitor therapy response in non-small cell lung cancer

Treatment challenges persist in advanced lung cancer despite the development of therapies beyond the traditional platinum-based chemotherapy. The early 2000s marked a shift to tyrosine kinase inhibitors targeting epidermal growth factor receptor, ushering in personalized genetic-based treatment. A further significant advance was the development of immune checkpoint inhibitors (ICIs), especially for non-small cell lung cancer. These target programmed death-ligand 1 (PD-L1) and cytotoxic T lymphocyte antigen 4, which enhanced the immune response against tumor cells. However, not all patients respond, and immune-related toxicities arise. This review emphasizes identifying biomarkers for ICI response prediction. While PD-L1 is a widely used, validated biomarker, its predictive accuracy is imperfect. Investigating tumor-infiltrating lymphocytes, tertiary lymphoid structure, and emerging biomarkers such as high endothelial venule, Human leukocyte antigen class I, T-cell immunoreceptors with Ig and ITIM domains, and lymphocyte activation gene-3 counts is promising. Understanding and exploring additional predictive biomarkers for ICI response are crucial for enhancing patient stratification and overall care in lung cancer treatment.

[Construction of A Nomogram Prediction Model for PD-L1 Expression in Non-small Cell Lung Cancer Based on 18F-FDG PET/CT Metabolic Parameters]

BACKGROUND

In recent years, immunotherapy represented by programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) immunosuppressants has greatly changed the status of non-small cell lung cancer (NSCLC) treatment. PD-L1 has become an important biomarker for screening NSCLC immunotherapy beneficiaries, but how to easily and accurately detect whether PD-L1 is expressed in NSCLC patients is a difficult problem for clinicians. The aim of this study was to construct a Nomogram prediction model of PD-L1 expression in NSCLC patients based on 18F-fluorodeoxy glucose (18F-FDG) positron emission tomography/conputed tomography (PET/CT) metabolic parameters and to evaluate its predictive value.

METHODS

Retrospective collection of 18F-FDG PET/CT metabolic parameters, clinicopathological information and PD-L1 test results of 155 NSCLC patients from Inner Mongolia People's Hospital between September 2016 and July 2021. The patients were divided into the training group (n=117) and the internal validation group (n=38), and another 51 cases of NSCLC patients in our hospital between August 2021 and July 2022 were collected as the external validation group according to the same criteria. Then all of them were categorized according to the results of PD-L1 assay into PD-L1+ group and PD-L1- group. The metabolic parameters and clinicopathological information of patients in the training group were analyzed by univariate and binary Logistic regression, and a Nomogram prediction model was constructed based on the screened independent influencing factors. The effect of the model was evaluated by receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) in both the training group and the internal and external validation groups.

RESULTS

Binary Logistic regression analysis showed that metabolic tumor volume (MTV), gender and tumor diameter were independent influences on PD-L1 expression. Then a Nomogram prediction model was constructed based on the above independent influences. The ROC curve for the model in the training group shows an area under the curve (AUC) of 0.769 (95%CI: 0.683-0.856) with an optimal cutoff value of 0.538. The AUC was 0.775 (95%CI: 0.614-0.936) in the internal validation group and 0.752 (95%CI: 0.612-0.893) in the external validation group. The calibration curves were tested by the Hosmer-Lemeshow test and showed that the training group (χ2=0.040, P=0.979), the internal validation group (χ2=2.605, P=0.271), and the external validation group (χ2=0.396, P=0.820) were well calibrated. The DCA curves show that the model provides clinical benefit to patients over a wide range of thresholds (training group: 0.00-0.72, internal validation group: 0.00-0.87, external validation group: 0.00-0.66).

CONCLUSIONS

The Nomogram prediction model constructed on the basis of 18F-FDG PET/CT metabolic parameters has greater application value in predicting PD-L1 expression in NSCLC patients.

Hepatic cavernous hemangioma developed in non-small cell lung cancer patients after receiving Camrelizumab treatment: two case reports

Purpose

To report two cases of hepatic cavernous hemangioma, a rare complication, in patients with locally advanced and advanced non-squamous non-small cell lung cancer (NSCLC) treated with PD-1 inhibitors. Additionally, to share clinical experiences related to the management of this condition.

Methods

Two patients with locally advanced and advanced non-squamous non-small cell lung cancer (NSCLC) were enrolled in our hospital. Following the NCCN guidelines and expert consensus, both patients received standard treatment with Camrelizumab (PD-1 inhibitor). Subsequent abdominal CT scans revealed hepatic focal lesions that did not exhibit typical characteristics of metastatic tumors. Therefore, further systematic investigation was conducted to study the hepatic focal lesions.

Results

(1) Ultrasound-guided percutaneous biopsy confirmed the diagnosis of hepatic cavernous hemangioma. A multidisciplinary consultation concluded that it was an adverse drug reaction to Camrelizumab. (2) Ten-gene testing for both patients did not reveal any driver gene mutations associated with lung cancer. Apart from the occurrence of hepatic cavernous hemangioma, there were no signs of disease progression or worsening. (3) Both patients had resolution of hepatic cavernous hemangioma after switching to alternative PD-1 inhibitors or discontinuing PD-1 inhibitor treatment. One patient experienced hemorrhage related to the hepatic hemangioma, which was managed with hemostasis and symptomatic treatment, resulting in improvement. (4) Clinical outcomes: The first patient achieved a progression-free survival (PFS) of 33 months in first-line treatment and had not reached the PFS endpoint in second-line treatment, with an overall survival exceeding 56 months. The second patient had not reached the PFS endpoint in first-line treatment, with an overall survival exceeding 31 months.

Conclusion

Hepatic cavernous hemangioma is a rare and serious adverse reaction associated with PD-1 inhibitors. Camrelizumab may interact with the PD-1 molecule in a different manner compared to other PD-1 inhibitors, affecting the regulation of the VEGFR/ULBP2 signaling pathway. In future studies, next-generation sequencing may provide detailed molecular pathology information, which could help explain individual differences and provide a basis for the prevention or intervention of hepatic cavernous hemangioma.

PATZ1 in Non-Small Cell Lung Cancer: A New Biomarker That Negatively Correlates with PD-L1 Expression and Suppresses the Malignant Phenotype

Non-small cell lung cancer (NSCLC), the leading cause of cancer death worldwide, is still an unmet medical problem due to the lack of both effective therapies against advanced stages and markers to allow a diagnosis of the disease at early stages before its progression. Immunotherapy targeting the PD-1/PD-L1 checkpoint is promising for many cancers, including NSCLC, but its success depends on the tumor expression of PD-L1. PATZ1 is an emerging cancer-related transcriptional regulator and diagnostic/prognostic biomarker in different malignant tumors, but its role in lung cancer is still obscure. Here we investigated expression and role of PATZ1 in NSCLC, in correlation with NSCLC subtypes and PD-L1 expression. A cohort of 104 NSCLCs, including lung squamous cell carcinomas (LUSCs) and adenocarcinomas (LUADs), was retrospectively analyzed by immunohistochemistry for the expression of PATZ1 and PD-L1. The results were correlated with each other and with the clinical characteristics, showing on the one hand a positive correlation between the high expression of PATZ1 and the LUSC subtype and, on the other hand, a negative correlation between PATZ1 and PD-L1, validated at the mRNA level in independent NSCLC datasets. Consistently, two NSCLC cell lines transfected with a PATZ1-overexpressing plasmid showed PD-L1 downregulation, suggesting a role for PATZ1 in the negative regulation of PD-L1. We also showed that PATZ1 overexpression inhibits NSCLC cell proliferation, migration, and invasion, and that Patz1-knockout mice develop LUAD. Overall, this suggests that PATZ1 may act as a tumor suppressor in NSCLC.

Predicting PD-L1 expression status in patients with non-small cell lung cancer using [18F]FDG PET/CT radiomics

BACKGROUND

In recent years, immune checkpoint inhibitor (ICI) therapy has greatly changed the treatment prospects of patients with non-small cell lung cancer (NSCLC). Among the available ICI therapy strategies, programmed death-1 (PD-1)/programmed death ligand-1 (PD-L1) inhibitors are the most widely used worldwide. At present, immunohistochemistry (IHC) is the main method to detect PD-L1 expression levels in clinical practice. However, given that IHC is invasive and cannot reflect the expression of PD-L1 dynamically and in real time, it is of great clinical significance to develop a new noninvasive, accurate radiomics method to evaluate PD-L1 expression levels and predict and filter patients who will benefit from immunotherapy. Therefore, the aim of our study was to assess the predictive power of pretherapy [¹⁸F]-fluorodeoxyglucose ([¹⁸F]FDG) positron emission tomography/computed tomography (PET/CT)-based radiomics features for PD-L1 expression status in patients with NSCLC.

METHODS

A total of 334 patients with NSCLC who underwent [¹⁸F]FDG PET/CT imaging prior to treatment were analyzed retrospectively from September 2016 to July 2021. The LIFEx7.0.0 package was applied to extract 63 PET and 61 CT radiomics features. In the training group, the least absolute shrinkage and selection operator (LASSO) regression model was employed to select the most predictive radiomics features. We constructed and validated a radiomics model, clinical model and combined model. Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were used to evaluate the predictive performance of the three models in the training group and validation group. In addition, a radiomics nomogram to predict PD-L1 expression status was established based on the optimal predictive model.

RESULTS

Patients were randomly assigned to a training group (n = 233) and a validation group (n = 101). Two radiomics features were selected to construct the radiomics signature model. Multivariate analysis showed that the clinical stage (odds ratio [OR] 1.579, 95% confidence interval [CI] 0.220-0.703, P < 0.001) was a significant predictor of different PD-L1 expression statuses. The AUC of the radiomics model was higher than that of the clinical model in the training group (0.706 vs. 0.638) and the validation group (0.761 vs. 0.640). The AUCs in the training group and validation group of the combined model were 0.718 and 0.769, respectively.

CONCLUSION

PET/CT-based radiomics features demonstrated strong potential in predicting PD-L1 expression status and thus could be used to preselect patients who may benefit from PD-1/PD-L1-based immunotherapy.

Lung Cancer Immunotherapy: Beyond Common Immune Checkpoints Inhibitors

Immunotherapy is an ever-expanding field in lung cancer treatment research. Over the past two decades, there has been significant progress in identifying immunotherapy targets and creating specific therapeutic agents, leading to a major paradigm shift in lung cancer treatment. However, despite the great success achieved with programmed death protein 1/ligand 1 (PD-1/PD-L1) monoclonal antibodies and with anti-PD-1/PD-L1 plus anti-cytotoxic T-lymphocyte antigen 4 (CTLA-4), only a minority of lung cancer patients respond to treatment, and of these many subsequently experience disease progression. In addition, immune-related adverse events sometimes can be life-threatening, especially when anti-CTLA-4 and anti-PD-1 are used in combination. All of this prompted researchers to identify novel immune checkpoints targets to overcome these limitations. Lymphocyte activation gene-3 (LAG-3), T cell immunoglobulin (Ig) and Immunoreceptor Tyrosine-Based Inhibitory Motif (ITIM) domain (TIGIT), T cell immunoglobulin and mucin-domain containing-3 (TIM-3) are promising molecules now under investigation. This review aims to outline the current role of immunotherapy in lung cancer and to examine efficacy and future applications of the new immune regulating molecules.