Peripheral Blood Autoantibodies Against to Tumor-Associated Antigen Predict Clinical Outcome to Immune Checkpoint Inhibitor-Based Treatment in Advanced Non-Small Cell Lung Cancer

Nomogram for Predicting Efficacy and Prognosis After Chemotherapy for Advanced NSCLC

PURPOSE

One major issue is the therapeutic effect following chemotherapy for non-small cell lung cancer (NSCLC). Although numerous risk factors have been identified and novel therapies have been developed, improving patient overall survival (OS) remains a crucial postoperative issue. This study aimed to develop a nomogram for accurately predicting the OS of patients with Stage III-IV NSCLC treated with chemotherapy.

METHODS

The Department of Respiration at Tangdu Hospital, Air Force Medical University, prospectively collected data on 321 patients between January 2018 and December 2023. A week before treatment, the platelet-to-lymphocyte ratio (PLR), the neutrophil-to-lymphocyte ratio (NLR), and seven autoantibodies were measured using Youden's index, which was obtained using the ROC curve. The formula was used to compute the values of PLR and NLR. After using multifactor Cox regression analysis to identify risk factors, a nomogram was produced regarding the therapeutic effect following chemotherapy. The performance of the nomogram was assessed using a bootstrapped-concordance index and calibration plots.

RESULT

It was determined that NLR, sex-determining region Y-box 2 (SOX2), adenosine triphosphate binding RNA deconjugase 4-5 (GBU4-5), and MAGE family member A1 (MAGEA1) were significantly associated factors that could be combined to accurately predict the therapeutic effect following chemotherapy. Utilizing these risk indicators, we were able to develop a nomogram that predicted the patients' survival at 1, 3, and 5 years. At 3 years, the area under the curve representing the expected survival probability was 0.762 (95% confidence interval 0.66-0.87). With a bootstrapped-concordance index of 0.762, the nomogram demonstrated good calibration.

CONCLUSIONS

Our nomogram proved to be a valuable instrument in accurately predicting the overall survival of patients.

An analysis of the influencing factors of false negative autoantibodies in patients with non-small cell lung cancer

Objectives

To analyze the clinical significance of seven autoantibodies (P53, PGP9.5, SOX2, GAGE7, GBU4-5, MAGE, and CAGE) in patients with non-small cell lung cancer (NSCLC) and the factors that influence false-negative results.

Methods

Seven autoantibodies were measured in the serum of 502 patients with non-small cell lung cancer (NSCLC) using ELISA, and their correlations with age, sex, smoking history, pathological type, clinical stage, and PD-L1 gene expression were analyzed. The clinicopathological data of the false-negative and positive groups for the seven autoantibodies were compared to determine the influencing factors.

Results

P53 antibody expression level was correlated with lobulation sign, PGP9.5 antibody expression level with sex and vascular convergence; SOX2 antibody expression level with pathological type, clinical stage, and enlarged lymph nodes; and MAGE antibody expression level with the pathological type (P<0.05). False-negative autoantibodies are prone to occur in lung cancer patients with ground-glass nodules, no enlarged lymph nodes, no vascular convergence, and PD-L1 gene expression <1% (P <0.05).

Conclusion

Detection of seven autoantibodies was clinically significant in patients with NSCLC. However, poor sensitivity should be considered in clinical diagnoses to prevent missed diagnoses.

Anti‑PD‑1/PD‑L1 and anti‑CTLA‑4 associated checkpoint inhibitor pneumonitis in non‑small cell lung cancer: Occurrence, pathogenesis and risk factors (Review)

Immune checkpoint inhibitors (ICIs) play a significant anti‑tumor role in the management of non‑small cell lung cancer. The most broadly used ICIs are anti‑programmed death 1 (PD‑1), anti‑programmed cell death‑ligand 1, and anti‑cytotoxic T lymphocyte‑associated antigen‑4 monoclonal antibody. Compared with traditional chemotherapy, ICIs have the advantages of greater efficiency and more specific targeting. However, the resulting immune‑related adverse events limit the clinical application of ICIs, especially checkpoint inhibitor pneumonitis (CIP). CIP chiefly occurs within 6 months of administration of ICIs. Excessive activation and amplification of cytotoxic T lymphocytes, helper T cells, downregulation of regulatory T cells, and over‑secretion of pro‑inflammatory cytokines are the dominant mechanisms underlying the pathophysiology of CIP. The dysregulation of innate immune cells, such as an increase in inflammatory monocytes, dendritic cells, neutrophils and M1 polarization of macrophages, an increase in IL‑10 and IL‑35, and a decrease in eosinophils, may underlie CIP. Although contested, several factors may accelerate CIP, such as a history of previous respiratory disease, radiotherapy, chemotherapy, administration of epidermal growth factor receptor tyrosine kinase inhibitors, PD‑1 blockers, first‑line application of ICIs, and combined immunotherapy. Interestingly, first‑line ICIs plus chemotherapy may reduce CIP. Steroid hormones remain the primary treatment strategy against grade ≥2 CIP, although cytokine blockers are promising therapeutic agents. Herein, the current research on CIP occurrence, clinical and radiological characteristics, pathogenesis, risk factors, and management is summarized to further expand our understanding, clarify the prognosis, and guide treatment.

Common methodological pitfalls in ICI pneumonitis risk prediction studies

Background

Pneumonitis is one of the most common adverse events induced by the use of immune checkpoint inhibitors (ICI), accounting for a 20% of all ICI-associated deaths. Despite numerous efforts to identify risk factors and develop predictive models, there is no clinically deployed risk prediction model for patient risk stratification or for guiding subsequent monitoring. We believe this is due to systemic suboptimal approaches in study designs and methodologies in the literature. The nature and prevalence of different methodological approaches has not been thoroughly examined in prior systematic reviews.

Methods

The PubMed, medRxiv and bioRxiv databases were used to identify studies that aimed at risk factor discovery and/or risk prediction model development for ICI-induced pneumonitis (ICI pneumonitis). Studies were then analysed to identify common methodological pitfalls and their contribution to the risk of bias, assessed using the QUIPS and PROBAST tools.

Results

There were 51 manuscripts eligible for the review, with Japan-based studies over-represented, being nearly half (24/51) of all papers considered. Only 2/51 studies had a low risk of bias overall. Common bias-inducing practices included unclear diagnostic method or potential misdiagnosis, lack of multiple testing correction, the use of univariate analysis for selecting features for multivariable analysis, discretization of continuous variables, and inappropriate handling of missing values. Results from the risk model development studies were also likely to have been overoptimistic due to lack of holdout sets.

Conclusions

Studies with low risk of bias in their methodology are lacking in the existing literature. High-quality risk factor identification and risk model development studies are urgently required by the community to give the best chance of them progressing into a clinically deployable risk prediction model. Recommendations and alternative approaches for reducing the risk of bias were also discussed to guide future studies.

Targeting tumor microenvironment for non-small cell lung cancer immunotherapy

The tumor microenvironment (TME) is composed of different cellular and non-cellular elements. Constant interactions between tumor cells and the TME are responsible for tumor initiation, tumor progression, and responses to therapies. Immune cells in the TME can be classified into two broad categories, namely adaptive and innate immunity. Targeting these immune cells has attracted substantial research and clinical interest. Current research focuses on identifying key molecular players and developing targeted therapies. These approaches may offer more efficient ways of treating different cancers. In this review, we explore the heterogeneity of the TME in non-small cell lung cancer, summarize progress made in targeting the TME in preclinical and clinical studies, discuss the potential predictive value of the TME in immunotherapy, and highlight the promising effects of bispecific antibodies in the era of immunotherapy.

Chinese expert consensus on the multidisciplinary management of pneumonitis associated with immune checkpoint inhibitor

Immune checkpoint inhibitors (ICIs) have successfully treated a number of different types of cancer, which is of great significance for cancer treatment. With the widespread use of ICIs in clinical practice, the increasing checkpoint inhibitor pneumonia (CIP) will be a challenge to clinicians. To guide the diagnosis and treatment of CIP, we conducted in-depth discussions based on the latest evidence, forming a consensus among Chinese experts on the multidisciplinary management of CIP.

Fractional exhaled nitric oxide in checkpoint inhibitor pneumonitis: a case report and literature review

Checkpoint inhibitor pneumonitis (CIP) is a relatively rare adverse event and a potential cause of death in patients treated with immune checkpoint inhibitors (ICIs). Because the symptoms and signs are nonspecific, the diagnosis of CIP is challenging. Additionally, compared with the biomarkers that can monitor the effect of ICIs, there is less research evaluating markers to monitor CIP. We report a case of CIP induced by camrelizumab in a patient with advanced non-small-cell lung cancer, in which the fractional exhaled nitric oxide levels showed obvious increases. Fractional exhaled nitric oxide may have the potential to monitor the condition of airway inflammation in patients using ICIs.

[Clinical Value of Autoantibody Prognostic Markers in Tumor Immune Checkpoint Inhibitor Therapy]

Serum autoantibody markers have the advantages of easy specimen acquisition, simple detection technology and dynamic real-time monitoring. With the wide application of immune checkpoint inhibitors in the treatment of malignant tumors, autoantibody markers in predicting tumor immune checkpoint inhibitors efficacy and forecasting irAEs (immune related adverse events) show good prediction of potential. This review mainly focused on the progress of autoantibody markers in the prediction of therapeutic effect and the monitoring of irAE in tumor immunotherapy.
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'Know thyself' - host factors influencing cancer response to immune checkpoint inhibitors

Immune checkpoint inhibitors (ICIs) have revolutionised oncology and are now standard-of-care for the treatment of a wide variety of solid neoplasms. However, tumour responses remain unpredictable, experienced by only a minority of ICI recipients across malignancy types. Therefore, there is an urgent need for better predictive biomarkers to identify a priori the patients most likely to benefit from these therapies. Despite considerable efforts, only three such biomarkers are FDA-approved for clinical use, and all rely on the availability of tumour tissue for immunohistochemical staining or genomic assays. There is emerging evidence that host factors - for example, genetic, metabolic, and immune factors, as well as the composition of one's gut microbiota - influence the response of a patient's cancer to ICIs. Tantalisingly, some of these factors are modifiable, paving the way for co-therapies that may enhance the therapeutic index of these treatments. Herein, we review key host factors that are of potential biomarker value for response to ICI therapy, with a particular focus on the proposed mechanisms for these influences. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Melanoma-specific antigen-associated antitumor antibody reactivity as an immune-related biomarker for targeted immunotherapies

Background:

Immunotherapies, including cancer vaccines and immune checkpoint inhibitors have transformed the management of many cancers. However, a large number of patients show resistance to these immunotherapies and current research has provided limited findings for predicting response to precision immunotherapy treatments.

Methods:

Here, we applied the next generation phage display mimotope variation analysis (MVA) to profile antibody response and dissect the role of humoral immunity in targeted cancer therapies, namely anti-tumor dendritic cell vaccine (MelCancerVac^®) and immunotherapy with anti-PD-1 monoclonal antibodies (pembrolizumab).

Results:

Analysis of the antibody immune response led to the characterization of epitopes that were linked to melanoma-associated and cancer-testis antigens (CTA) whose antibody response was induced upon MelCancerVac® treatments of lung cancer. Several of these epitopes aligned to antigens with strong immune response in patients with unresectable metastatic melanoma receiving anti-PD-1 therapy.

Conclusions:

This study provides insights into the differences and similarities in tumor-specific immunogenicity related to targeted immune treatments. The antibody epitopes as biomarkers reflect melanoma-associated features of immune response, and also provide insights into the molecular pathways contributing to the pathogenesis of cancer. Concluding, antibody epitope response can be useful in predicting anti-cancer immunity elicited by immunotherapy.

Immunotherapy treatments, which utilize the patient’s own immune system to fight cancer, have become a standard treatment of cancer. However, for many patients’ immunotherapy does not work. During the immune response the body produces proteins called antibodies. This study characterized the antibodies produced following treatment with two different types of immunotherapies that treat skin cancer, to gain insights into how the immune system responds in different individuals. Our results demonstrate that multiple proteins that are present in patients with skin cancer are specifically targeted by the immune system during skin cancer specific immunotherapy. Our results should help further anti-cancer drug development.

Rähni et al profile antibody response in patients with varied response to cancer immunotherapies. They identify antibody epitope responses that predict anti-cancer immunity elicited by immunotherapy.

Detection of leucine-rich alpha-2-glycoprotein 1-containing immunocomplexes in the plasma of lung cancer patients with epitope-specific mAbs

BACKGROUND

Lung cancer is the leading cause of cancer-related deaths worldwide. With the expectation of improved survival, tremendous efforts and resources have been invested in the discovery of specific biomarkers for early detection of the disease. Several investigators have reported the presence of cancer-associated autoantibodies in the plasma or serum of lung cancer patients. Previously, we used a monoclonal-antibody proteomics technology platform for the discovery of novel lung cancer-associated proteins.

OBJECTIVE

The identification of specific protein epitopes associated with various cancers is a promising method in biomarker discovery. Here, in a preliminary study, we aimed to detect autoantibody-leucine-rich alpha-2-glycoprotein 1 (LRG1) immunocomplexes using epitope-specific monoclonal antibodies (mAbs).

METHODS

We performed sandwich ELISA assays using the LRG1 epitope-specific capture mAbs, Bsi0352 and Bsi0392, and an IgG-specific polyclonal antibody coupled to a reporter system as the detection reagent. We tested the plasma of lung-cancer patients and apparently healthy controls.

RESULTS

Depending on the epitope specificity of the capture monoclonal mAb, we were either unable to distinguish the control from LC-groups or showed a higher level of LRG1 and IgG autoantibody containing immunocomplexes in the plasma of non-small cell lung cancer and small cell lung cancer subgroups of lung cancer patients than in the plasma of control subjects.

CONCLUSIONS

Our findings underline the importance of protein epitope-specific antibody targeted approaches in biomarker research, as this may increase the accuracy of previously described tests, which will need further validation in large clinical cohorts.