Letter to the Editor: Autoantibody profiling in gastric cancer immunotherapy - dynamic monitoring, toxicity-benefit trade-off, and immune endotypes

Abstract

A study by Zheng et al published in the World Journal of Gastroenterology has reported that antinuclear antibody (ANA) and extractable nuclear antigen (ENA) positivity, together with carcinoembryonic antigen and tumor-node-metastasis stage, independently predict outcomes in gastric cancer patients receiving anti-programmed death 1/programmed death-ligand 1 based immunotherapy. However, although the findings of these authors support the clinical relevance of host autoimmunity, we believe that the implications can be further extended in three directions. Firstly, dependence on a single baseline ANA/ENA measurement overlooks the longitudinal dynamics that may distinguish pre-existing autoimmunity from treatment-induced seroconversion, which could be differentially associated with a durable response and acquired resistance. Secondly, interpreting ANA/ENA purely as a favorable prognostic marker neglects their potential value in signaling a heightened risk of immune-related adverse events, thereby indicating a need to evaluate the net clinical benefit rather than survival alone. Thirdly, the binary definition of ANA/ENA positivity ignores immunofluorescence patterns and specific ENA components that might define distinct “autoantibody immune endotypes” with different tumor-immune microenvironments. To refine immunotherapy-oriented risk stratification in gastric cancer, we propose prospective studies that integrate dynamic autoantibody profiling, detailed patterns, and toxicity data within composite host-tumor models.

Key Words: Gastric cancer; Antinuclear antibody; Extractable nuclear antigen; Immunotherapy; Immune endotype

Core Tip: Baseline antinuclear antibody/extractable nuclear antigen status may serve as a potential predictor of immunotherapy outcomes in gastric cancer. However, static indicators fail to capture dynamic autoantibody changes or the underlying differences in immune endotypes. We propose to build a composite host-tumor model that integrates longitudinal antibody profiles, detailed staining patterns, and safety data, and apply calibration curves and decision curve analysis to evaluate its net clinical benefit, thereby promoting the establishment of more refined risk stratification strategies and facilitating the practice of personalized immuno-oncology.

TO THE EDITOR

We carefully read the original article by Zheng et al[1], entitled “Clinical significance of autoantibody profiling and systemic inflammation in predicting outcomes of gastric cancer patients undergoing immunotherapy”. The study demonstrated that, among patients with gastric cancer receiving anti- programmed death 1/programmed death-ligand 1 immunotherapy, baseline positivity for antinuclear antibodies (ANA) and extractable nuclear antigens (ENA), together with carcinoembryonic antigen levels and tumor-node-metastasis stage, constituted independent prognostic predictors. By integrating host autoimmune status with conventional clinical indicators, this work deepens our understanding of how host-tumor interactions shape immunotherapy outcomes.

LONGITUDINAL SEROLOGICAL MONITORING OF AUTOANTIBODY PROFILES

Sole dependence on a single baseline ANA or ENA test may fail to encompass the biological heterogeneity reflected in the dynamic evolution of autoantibody responses. Although baseline testing provides important prognostic information for the initial assessment of a patient’s immune status, longitudinal serological monitoring may provide more relevant insights into the temporal dynamics of autoantibody profiles. In particular, it may contribute to distinguishing between pre-existing autoimmunity and treatment-induced seroconversion, which could have different implications for treatment response and disease progression. Consequently, it is important to establish a systematic framework for longitudinal serum monitoring.

Previous studies have shown[2] that autoantibody levels are characterized by substantial fluctuations over time and that the frequency of such variations tends to increase over longer monitoring periods. Although the emergence of new ENA specificities or increases in antibody titers may indicate enhanced immune activity, the clinical significance of these fluctuations remains poorly understood in the context of specific diseases, particularly in patients undergoing cancer immunotherapy. Furthermore, from the perspective of clinical laboratory practice, standardized monitoring intervals during clinically stable phases are required to accurately determine the actual clinical relevance of antibody titer variability.

Given the considerable inter-individual heterogeneity in autoantibody profiles, the HuProt human proteome microarray has emerged as an effective platform for large-scale autoantibody profiling[3]. By analyzing longitudinal serum samples, this technology can simultaneously profile antibody responses against thousands of human proteins, thereby enabling high-throughput multiplex comparisons of antibody responses. This approach accordingly facilitates the comprehensive characterization of individualized immune signatures and may contribute to enhancing our understanding of host immune heterogeneity during immunotherapy.

In conclusion, these findings highlight the need for large-scale prospective studies to further evaluate the clinical utility of the longitudinal monitoring of autoantibodies. Integrating dynamic serological data within a predictive framework may contribute to the identification of treatment-responsive patients and prediction of the risk of recurrence, as well as guiding individualized therapeutic strategies for gastric cancer immunotherapy.

TOXICITY-BENEFIT TRADE-OFF

Defining ANA/ENA positivity alone as an independent predictor of immunotherapy outcomes in gastric cancer may also indicate an increased risk of immune-related adverse events (irAEs). Although immune checkpoint inhibitors have therapeutic effects by enhancing the antitumor immune response, excessive immune activation can also trigger autoimmune toxicity, resulting in a clinically relevant efficacy-toxicity trade-off[4]. Consequently, a sole dependence on survival endpoints, such as overall or progression-free survival, may fail fully reflect the overall clinical value of ANA/ENA positivity and related predictive models.

To improve the precision and clinical relevance of model evaluation, the analytical framework can be expanded to incorporate calibration curves, which be used to assess the consistency between predicted and observed outcomes, and thereby enable an evaluation of model accuracy[5]. Decision curve analysis can be used to quantify the net clinical benefit of predictive models for a range of clinically meaningful threshold probabilities[6], and by balancing the benefits of true-positive predictions against the potential harm associated with false-positive results at different decision thresholds, can provide a more rigorous assessment of the practical value of models in clinical decision making.

Integrating efficacy endpoints with irAE risks within a composite predictive model, validated through calibration curves and decision curve analysis, facilitates a more nuanced assessment of individualized toxicity-benefit. This integrated approach reflects the inherent complexity of gastric cancer immunotherapy, in which therapeutic gains and safety risks are inextricably linked, thereby overcoming the limitations of single survival-based endpoints. Furthermore, incorporating irAEs as biomarkers linked to ANA/ENA status provides a robust evidence base for refining risk stratification strategies and tailoring immune monitoring protocols to the specific needs of individual patients.

IMMUNE ENDOTYPES

Rather than serving as a simple binary variable, ANA positivity should be viewed as a biological continuum[7]. In this regard, The International Consensus on ANA Patterns emphasizes that HEp-2 cell-based indirect immunofluorescence is the gold standard for ANA detection, which facilitates the visualization of distinct staining patterns[8]. Consequently, instead of being limited to determining whether ANA is positive or negative, clinical evaluation should also incorporate specific immunofluorescence staining patterns and ENA subtypes within the assessment framework. These qualitative features may collectively contribute to defining distinct “autoantibody immune endotypes” which may reflect markedly different tumor-immune microenvironment states and underlying biological mechanisms.

Large-scale studies have suggested that different ANA immunofluorescence patterns may be associated with varying cancer risks[9]. For example, the nucleolar pattern has been linked to an increased risk of malignancy, whereas homogeneous and speckled patterns are more frequently observed in individuals without cancer. These findings suggest that distinct ANA patterns may reflect different systemic immune states or tumor-associated immune environments. In addition, studies have shown[10] that elevated ANA titers and a speckled ANA pattern, particularly in patients receiving combination immune checkpoint inhibitor therapy, are significantly associated with an increased risk of grade ≥ 2 irAEs.

At the ENA level, different ENA subtypes are likewise associated with distinct immune activation profiles in systemic autoimmune diseases. For instance, anti-Sjögren’s-syndrome-related antigen A/Ro autoantibodies have been associated with distinct patterns of immune activation. Studies have shown[11] that anti-Ro antibodies are among the most frequently detected ENA subtypes, and the presence of anti-Ro-60 antibodies is associated with a characteristic inflammatory signature that appears to be consistent across multiple autoimmune diseases.

Taken together, combined assessment of ANA and ENA is crucial for evaluating the nature and severity of autoimmune responses. In clinical practice, even when IIF-ANA results are negative, further ENA profiling is recommended if there remains a strong clinical suspicion of autoimmune activity, in order to minimize missed diagnoses[12]. Future studies should further validate the reproducibility of these proposed autoantibody immune endotypes in independent patient cohorts or prospective immunotherapy studies, and assess their associations with responses to immune checkpoint inhibitors and the risk of irAEs.

CONCLUSION

The study conducted by Zheng et al[1] has made a significant contribution in this field. Future research should focus on large-scale prospective cohort studies that integrate longitudinal autoantibody profiling, refined immunofluorescence pattern classification, and detailed clinical toxicity data for the construction of comprehensive host-tumor predictive models. Given this basis, calibration and decision curve analyses should be applied to evaluate the net clinical benefit, with the goal of establishing a multidimensional risk-benefit assessment framework for immunotherapy. Using static baseline serological markers for dynamic system-level immune phenotyping, an approach of this nature would contribute to advancing the field, thereby providing stronger evidence to support precision immunotherapy for gastric cancer and further optimization of immunotherapy-oriented risk stratification strategies.