Abstract
Immune checkpoint inhibitors (ICIs) targeting PD-1 improve outcomes in advanced non-small cell lung cancer (NSCLC), but responses are heterogeneous and tissue biomarkers are imperfect. Circulating immunoglobulins integrate B- and T-cell function and may reflect systemic immune competence. We investigated whether plasma immunoglobulin levels during ICI therapy are associated with clinical outcomes in stage IV NSCLC. In this single-center retrospective study, 55 patients received anti-PD-1-based regimens: anti-PD-1 monotherapy (n = 31), chemotherapy plus anti-PD-1 (n = 18), or ipilimumab plus nivolumab (n = 6). Plasma IgG, IgA, and IgM were measured at baseline and after 1–4 cycles, and associations with objective response, progression-free survival (PFS), and overall survival (OS) were evaluated. In the chemotherapy plus anti-PD-1 group, IgG decreased significantly on treatment (p = 0.0030), whereas no significant changes in any isotype were observed with anti-PD-1 monotherapy. In the monotherapy cohort, post-treatment IgG was higher in responders than in non-responders (p = 0.0262) and was associated with longer PFS (p = 0.0218) and OS (p = 0.0166). In multivariable Cox models in the monotherapy cohort, higher post-treatment IgG remained independently associated with longer PFS (adjusted HR 0.28, 95% CI 0.11–0.75; p = 0.011) and OS (adjusted HR 0.29, 95% CI 0.09–0.87; p = 0.028). Sensitivity analyses incorporating PD-L1 status and 6-week landmark analyses showed similar trends. In exploratory pooled analyses, no significant association between post-treatment IgG and PFS or OS was observed across all 55 patients. IgA and IgM were not significantly associated with outcomes. Higher early on-treatment plasma IgG may serve as a non-invasive biomarker of favorable outcome, particularly in the anti-PD-1 monotherapy setting, warranting prospective validation and mechanistic studies.
Supplementary Information
The online version contains supplementary material available at 10.1038/s41598-026-46923-x.
Keywords: Non-small cell lung cancer, Immune checkpoint inhibitor, Immunoglobulin G, PD-1 blockade, Biomarker, Humoral immunity
Subject terms: Biomarkers, Cancer, Immunology, Oncology
Introduction
Immune checkpoint inhibitors (ICIs) targeting programmed cell death protein 1 (PD-1) or its ligand (PD-L1) have transformed the treatment landscape for advanced non-small-cell lung cancer (NSCLC), yielding durable remission in a subset of patients. Among patients with high PD-L1 expression, first-line pembrolizumab monotherapy achieves approximately 30% 5-year survival1. Nevertheless, many patients fail to respond or eventually relapse, and the overall 5-year survival for metastatic NSCLC remains approximately 15%, highlighting the need for more reliable predictive and prognostic biomarkers2. Tumor PD-L1 expression is currently the primary biomarker used to guide ICI therapy, yet its predictive accuracy is limited3,4. Consequently, additional biomarkers are needed to refine patient selection and optimize the clinical application of ICIs.
Blood-derived biomarkers are particularly appealing because they can be sampled repeatedly and provide real-time information about the systemic immune environment. Our group has previously conducted several studies examining soluble and cellular immune-related factors in circulation and has proposed novel blood-based biomarkers associated with ICI efficacy and prognosis across multiple cancers5–12. Circulating immunoglobulins (antibodies) produced by B-cells are logical candidates because immunoglobulin G (IgG) synthesis requires coordinated activity among antigen-presenting cells, CD4+ T-helper cells, and B-cells. Thus, total IgG levels may serve as an indicator of global immunocompetence.
Clinical data from other malignancies support the role of humoral immunity in ICI responsiveness. In metastatic melanoma, higher baseline total IgG and IgG subclass levels are associated with significantly longer progression-free survival (PFS) and overall survival (OS) during checkpoint inhibitor therapy13. Histopathological studies also indicate that B-cell-rich tertiary lymphoid structures (TLS) within tumors correlate with improved prognosis and enhanced ICI responses across cancer types14. In NSCLC, tumors enriched with intratumoral plasma cells and B-cells have been associated with prolonged OS following anti-PD-L1 therapy15. Collectively, these findings suggest that humoral immunity complements T-cell-mediated antitumor responses.
However, the clinical significance of circulating immunoglobulins in lung cancer immunotherapy remains uncertain, and existing evidence indicates context-dependent effects. In hepatocellular carcinoma, early treatment-related changes in IgG levels, rather than baseline levels, are associated with outcomes during ICI therapy16. Conversely, in metastatic renal cell carcinoma treated with ICI + tyrosine kinase inhibitor (TKI) combinations, increases in serum IgG after 3 months are paradoxically associated with worse PFS and OS17. These observations suggest that IgG dynamics may reflect either beneficial immune activation or detrimental inflammation, depending on tumor type and treatment context.
This study aimed to clarify the relationship between ICI therapy and immunoglobulin (Ig) levels in patients with advanced NSCLC. We analyzed patients with stage IV NSCLC treated with anti-PD-1-based regimens and measured plasma IgG, IgA, and IgM levels at baseline and early during treatment. We evaluated associations between immunoglobulin levels and objective response, PFS, and OS, with particular attention to differences between anti-PD-1 monotherapy and chemo-immunotherapy. We also performed exploratory pooled and sensitivity analyses to address treatment heterogeneity, clinically relevant covariates, and variability in the timing of post-treatment sampling.
Materials and methods
Study design and patients
This was a single-center retrospective study of patients with advanced NSCLC who received anti-PD-1-based therapy at Showa Medical University. Patients who initiated ICI treatment at our institution during or after May 2016 and had paired pre-treatment and on-treatment plasma samples were consecutively enrolled. Of 55 patients with stage IV NSCLC, 31 received anti-PD-1 monotherapy (nivolumab or pembrolizumab), 18 received anti-PD-1 therapy in combination with platinum-based chemotherapy, and 6 received the combination of an anti-CTLA-4 antibody (ipilimumab) plus nivolumab. The data cut-off date for follow-up was February 2025.
Clinical and pathological data, including age, sex, smoking history, tumor histology, disease status (de novo metastatic vs. recurrent), prior treatments, PD-L1 tumor proportion score, gene alteration status, Eastern Cooperative Oncology Group (ECOG) performance status (PS), line of ICI therapy, and occurrence of immune-related adverse events (irAEs), were collected from medical records. Adverse events were assessed and graded by the treating physicians according to the Common Terminology Criteria for Adverse Events (CTCAE), version 5.0. Tumor responses were evaluated by the treating physicians in accordance with the Response Evaluation Criteria in Solid Tumors (RECIST), version 1.1.
Sample collection and measurement of immunoglobulins
Blood samples were collected at two time points: immediately before the initiation of ICI therapy (pre-treatment; pre) and after 1–4 cycles of therapy (on-treatment; post). For most patients, the post-sampling coincided with the first radiological evaluation, 6–8 weeks after treatment initiation (after 2 or 3 cycles). Plasma was separated and sent to an external laboratory (BML Inc., Tokyo, Japan) for the quantification of IgG, IgA, and IgM concentrations using immunoturbidimetric assays on standard automated analyzers. The results were reported in mg/dL. All measurements were performed in a blinded manner using appropriate internal quality control procedures.
Clinical endpoints
The tumor response was assessed using RECIST version 1.1. Patients achieving complete response (CR) or partial response (PR) on imaging were classified as responders, and those with stable disease or progressive disease as non-responders. The objective response rate was defined as the proportion of patients with CR or PR. PFS was defined as the time from ICI initiation to radiographic or clinical disease progression or death from any cause, whichever occurred first. OS was defined as the time from ICI treatment initiation to death due to any cause. For response analyses, only patients with evaluable post-baseline radiologic response assessments according to RECIST version 1.1 were included. Patients who discontinued therapy early or lacked evaluable imaging were excluded from the corresponding analyses, as appropriate.
Statistical analysis
Nonparametric tests were used to compare immunoglobulin levels and group differences. Within each treatment group (anti-PD-1 monotherapy and chemotherapy plus anti-PD-1), paired pre- and post-treatment immunoglobulin levels were compared using the Wilcoxon signed-rank test. Differences in immunoglobulin levels between responders (CR + PR) and non-responders (SD + PD) were assessed using the Mann–Whitney U test. For time-to-event endpoints, the Kaplan–Meier method was used to estimate PFS and OS, and survival curves were compared using log-rank tests. Patients were stratified into high and low groups for each immunoglobulin isotype (IgG, IgA, and IgM) using the median value of the analyzed cohort.
As concurrent cytotoxic chemotherapy could influence circulating immunoglobulin levels, the primary Cox proportional hazards analyses for PFS and OS were performed in the anti-PD-1 monotherapy cohort. Univariate Cox models were first fitted for each candidate clinical variable, including age, sex, ECOG PS, line of ICI therapy, history of adjuvant chemotherapy, PD-L1 tumor proportion score, presence of gene alterations, and occurrence of irAEs. As the baseline (pre) and treatment (post) levels of each immunoglobulin isotype are intrinsically and strongly correlated, including pre- and post-values of the same marker in a single multivariable model would be expected to induce multicollinearity and yield unstable hazard ratio (HR) estimates18,19. Therefore, pre- and post-immunoglobulin variables were not simultaneously entered into the same multivariate model. Instead, separate pre- and post-based multivariable Cox models were constructed in which all three immunoglobulin isotypes measured at the relevant time point were entered together with the selected clinical covariates. HRs and 95% confidence intervals (CIs) were calculated.
To assess the robustness of the post-IgG association, we performed an additional sensitivity Cox analysis in the anti-PD-1 monotherapy cohort incorporating post-IgG and key clinical variables, including PD-L1 status. We also performed exploratory pooled analyses across all 55 patients. As the post-treatment sample was obtained after 1–4 cycles, a 6-week landmark analysis was performed as a sensitivity analysis for PFS and OS in the chemotherapy plus anti-PD-1 and anti-PD-1 monotherapy cohorts. All statistical tests were two-sided, and p-values < 0.05 were considered significant.
Ethical considerations
The study was conducted in accordance with the principles of the Declaration of Helsinki. Given the retrospective design and the use of de-identified data, formal approval was obtained from the Ethics Committee of Showa Medical University School of Medicine, Tokyo, Japan (approval numbers M2165 and M2253). Written informed consent was obtained from all participants. Patient data were anonymized, and appropriate measures were taken to protect privacy and confidentiality during data collection, storage, and analysis.
Results
Patient characteristics
Baseline characteristics of the 55 patients with advanced NSCLC are summarized in Table 1. The mean age was 70.06 years (standard deviation [SD] 8.64), and 42 patients were male. Adenocarcinoma was the most common histologic subtype (37 patients), followed by squamous cell carcinoma (14 patients). Thirty-six patients had de novo metastatic disease and 19 had recurrent disease after prior surgical resection. Eleven patients had received adjuvant chemotherapy. At the time of ICI initiation, ECOG PS was 0 in 8 patients, 1 in 43, and 2 in 4. PD-L1 expression was distributed across the cohort, and PD-L1 data were unavailable in a small subset of patients. Gene alterations were detected in 17 patients (EGFR in 13, KRAS in 3, and ROS1 in 1) and were absent in 38. Regarding treatment regimen, 31 patients received anti-PD-1 monotherapy, 18 received chemotherapy plus anti-PD-1, and 6 received ipilimumab plus nivolumab. The median number of ICI cycles administered was nine (range, 1–98). As only six patients received ipilimumab plus nivolumab, this subset was treated as a descriptive reference group.
Table 1.
Baseline clinicopathological characteristics of the study population.
| Characteristics | |
|---|---|
| Age (years) (mean ± SD) | 70.06 ± 8.64 |
| Sex (n) | |
| Male | 42 |
| Female | 13 |
| Histological types (n) | |
| Adenocarcinoma | 37 |
| Squamous cell carcinoma | 14 |
| Non-small cell carcinoma | 3 |
| Pleomorphic carcinoma | 1 |
| Metastatic or recurrent disease (n) | |
| Metastatic | 36 |
| Recurrent after surgical resection | 19 |
| History of adjuvant chemotherapy (n) | |
| Yes | 11 |
| No | 44 |
| Performance status at the initiation of ICI therapy(n) | |
| 0 | 8 |
| 1 | 43 |
| 2 | 4 |
| PD-L1 expression on tumor tissue (n) | |
| >=50% | 21 |
| 1–49% | 15 |
| < 1% | 13 |
| no data | 6 |
| Gene alteration (n) | |
| EGFR | 13 |
| KRAS | 3 |
| ROS1 | 1 |
| No mutation | 38 |
| Regimen of ICI therapy (n) | |
| Anti-PD-1 antibody monotherapy | 31 |
| Combination therapy of chemo and ICI | 18 |
| Combination of anti-CTLA-4 and anti-PD-1 | 6 |
| Number of ICI administration cycles (median) | 1–98 (9) |
SD, Standard deviation; ICI, Immune checkpoint inhibitor PFS, Progression-free survival; OS, Overall survival.
Longitudinal changes in immunoglobulin levels
Longitudinal changes in plasma immunoglobulin levels (IgG, IgA, and IgM) before and after treatment are shown in Fig. 1. In the chemotherapy plus anti-PD-1 group (n = 18), IgG levels decreased significantly from pre- to post-treatment (p = 0.0030), whereas IgA and IgM levels did not change significantly (p = 0.0739 and 0.4874, respectively).
Fig. 1.
Longitudinal changes in plasma immunoglobulins between pre- and post-treatment. Individual patient trajectories (spaghetti plots) of plasma IgG, IgA, and IgM (mg/dL) are shown for pre-treatment (Pre; immediately before ICI initiation) and on-treatment (Post; after 1–4 cycles). (a) Chemo + anti-PD-1 group (n = 18): IgG decreased significantly from Pre to Post (p = 0.0030), whereas IgA and IgM showed no significant change (p = 0.0739 and 0.4874, respectively). (b) Anti-PD-1 monotherapy group (n = 35): no significant pre–post changes were observed for IgG (p = 0.7690), IgA (p = 0.7994), or IgM (p = 0.4516). Wilcoxon matched-pairs signed-rank tests were used.
In the anti-PD-1 monotherapy group (n = 31), there were no significant differences between baseline and on-treatment levels for any immunoglobulin isotype (IgG, p = 0.7690; IgA, p = 0.7994; IgM, p = 0.4516). In the ipilimumab plus nivolumab group (n = 6), no significant pre-post differences were observed based on Wilcoxon matched-pair signed-rank tests (IgG, p = 0.1875; IgA, p = 0.3125; IgM, p = 0.8125; Supplementary Fig. 1A). These results indicate that anti-PD-1 monotherapy did not markedly alter circulating immunoglobulin levels during the early treatment period, whereas the addition of chemotherapy was associated with a significant reduction in IgG levels.
Immunoglobulin levels and objective response
Next, we evaluated whether immunoglobulin levels at baseline or during treatment were associated with the objective response to ICI therapy. The distributions of IgG, IgA, and IgM concentrations in responders versus non-responders at both time points stratified by treatment group are shown in Fig. 2.
Fig. 2.
Baseline and on-treatment immunoglobulins according to objective response. Box-and-whisker plots comparing plasma IgG, IgA, and IgM (mg/dL) between non-responders (PD/SD) and responders (CR/PR) at pre- and post-time points. (a) Chemo + anti-PD-1 group: no significant differences in IgG, IgA, or IgM at either time point. (b) Anti-PD-1 monotherapy group: post-IgG was significantly higher in responders than in non-responders (p = 0.0262), and pre-IgG showed a trend toward higher values in responders (p = 0.0722). IgA and IgM did not significantly differ between response groups. Mann–Whitney U tests were used.
In the Chemo + anti-PD-1 group, no significant differences in IgG, IgA, or IgM levels were observed between the responders and non-responders at baseline or during treatment (Fig. 2A). Formal statistical comparisons were not performed for the ipilimumab + nivolumab group because of the small sample size (n = 6; Supplementary Fig. 1B). In contrast, in the anti-PD-1 monotherapy cohort, responders exhibited higher IgG levels than non-responders. Post-treatment IgG was significantly higher in responders than in non-responders (p = 0.0262), and baseline IgG showed a non-significant trend toward higher values in responders (p = 0.0722; Fig. 2B). For IgA and IgM, no significant differences were detected between responders and non-responders at either time point in the monotherapy cohort (Fig. 2B).
We also examined the relative changes in immunoglobulin levels (post/pre-ratios) according to the response status (Supplementary Fig. 2A, 2B). In both the Chemo + anti-PD-1 (Supplementary Fig. 2A) and monotherapy (Supplementary Fig. 2B) cohorts, the post/pre ratios for IgG, IgA, and IgM did not significantly differ between responders and non-responders, suggesting that the magnitude of immunoglobulin change from baseline was not strongly linked to the objective response.
Immunoglobulin levels and PFS
The relationship between immunoglobulin levels and PFS is shown in Fig. 3. Kaplan-Meier curves for PFS were generated after stratifying patients by high versus low immunoglobulin levels (median cut-off) at both pre- and post-treatment time points in the chemotherapy plus anti-PD-1 (Fig. 3A) and anti-PD-1 monotherapy (Fig. 3B) groups.
Fig. 3.
Kaplan–Meier analysis of PFS according to immunoglobulin levels. Kaplan–Meier curves for PFS are shown for patients stratified by high versus low (median cut-off) pre- and post-immunoglobulin levels. (a) Chemo + anti-PD-1 group: no significant associations between pre- or post-IgG, IgA, or IgM and PFS. (b) Anti-PD-1 monotherapy group: high post-IgG was associated with longer PFS (HR 0.31, 95% CI 0.12–0.84; p = 0.0218), whereas pre-IgG and IgA/IgM were not significantly associated (post-IgA p = 0.0870).
In the chemotherapy plus anti-PD-1 cohort, PFS did not significantly differ according to high versus low IgG, IgA, or IgM levels at either time point. No significant HRs or log-rank p-values were observed for any isotype (Fig. 3A). In contrast, in the anti-PD-1 monotherapy cohort, high on-treatment IgG was significantly associated with longer PFS. Patients with post-IgG above the median had longer PFS than those with low post-IgG (HR 0.31, 95% CI 0.12–0.84; p = 0.0218; Fig. 3B). Baseline IgG levels were not significantly associated with PFS, and no significant association was identified with IgA or IgM. Post-treatment IgA levels showed a non-significant trend (p = 0.0870; Fig. 3B). The ipilimumab plus nivolumab group did not demonstrate any notable PFS differences according to immunoglobulin levels. Formal statistical analyses were not performed because of the small sample size.
Changes in immunoglobulin levels were not significantly associated with PFS. In both the chemotherapy plus anti-PD-1 (Supplementary Fig. 3A) and anti-PD-1 monotherapy groups (Supplementary Fig. 3B), PFS did not significantly differ between patients with an increase in immunoglobulin levels (post/pre ratio > 1.0) and those without an increase (post/pre ≤ 1.0) for any isotype (all log-rank p-values > 0.05).
The correlation and subgroup analyses supported these findings (Supplementary Fig. 4A, 4B). In the anti-PD-1 monotherapy group, post-IgG levels showed a moderate positive correlation with PFS (Spearman r = 0.5417, p = 0.004; Supplementary Fig. 4B). In contrast, no significant correlations between PFS and any immunoglobulin parameter were observed in the chemotherapy plus anti-PD-1 group (Supplementary Fig. 4A). Similarly, PFS was not significantly correlated with the post/pre ratios of any isotype in either cohort (Supplementary Fig. 5A, 5B).
Immunoglobulin levels and OS
We evaluated the association between immunoglobulin levels and OS (Fig. 4). In the chemotherapy plus anti-PD-1 cohort, OS did not differ significantly according to high versus low levels of IgG, IgA, or IgM at either baseline or on-treatment (Fig. 4A). In contrast, in the anti-PD-1 monotherapy cohort, high on-treatment IgG was significantly associated with longer OS. Patients with post-IgG above the median had prolonged OS compared with those with low post-IgG (HR 0.24, 95% CI 0.074–0.77; p = 0.0166; Fig. 4B). Baseline IgG, IgA, and IgM levels were not significantly associated with OS in this group in Kaplan–Meier analyses.
Fig. 4.
Kaplan–Meier analysis of overall survival (OS) according to immunoglobulin levels. Kaplan–Meier curves for OS by high versus low (median cut-off) pre- and post-immunoglobulin levels. (a) Chemo + anti-PD-1 group: no significant associations between pre- or post-IgG, IgA, or IgM and OS. (b) Anti-PD-1 monotherapy group: high post-IgG was associated with longer OS (HR 0.24, 95% CI 0.074–0.77; p = 0.0166), whereas other immunoglobulin parameters were not significantly associated with OS.
Changes in immunoglobulin levels were not significantly associated with OS. In both treatment groups, OS did not significantly differ between patients with increased versus non-increased immunoglobulin levels (post/pre > 1.0 vs. ≤ 1.0) for any isotype (Supplementary Fig. 6A, 6B). In the anti-PD-1 monotherapy cohort, there was a trend toward longer OS in patients with an IgG increase (post-/pre-IgG > 1.0) compared with those without an increase (HR 0.37, 95% CI 0.13–1.03; p = 0.0565), but this did not reach statistical significance (Supplementary Fig. 6B).
The correlation analyses were consistent with these results (Supplementary Fig. 7A, 7B). In the anti-PD-1 monotherapy group, on-treatment IgG levels positively correlated with OS (Spearman r = 0.5441, p = 0.003; Supplementary Fig. 7B). No significant correlations between OS and any immunoglobulin measure were detected in the chemotherapy plus anti-PD-1 group (Supplementary Fig. 7A). Similarly, OS was not significantly correlated with the post/pre ratios of any immunoglobulin in either cohort (Supplementary Fig. 8A, 8B).
Cox proportional hazards analysis
As concurrent cytotoxic chemotherapy could influence circulating immunoglobulin levels, primary Cox regression analyses were performed to determine PFS and OS in the anti-PD-1 monotherapy cohort (Tables 2 and 3). In the multivariable model including pre-treatment immunoglobulin variables, higher pre-IgG was associated with longer OS (adjusted HR 0.30, 95% CI 0.09–0.96; p = 0.042), whereas the association with PFS did not reach significance (adjusted HR 0.44, 95% CI 0.19–1.03; p = 0.058). In the multivariable model including on-treatment immunoglobulin variables, higher post-IgG emerged as an independent favorable factor for both PFS (adjusted HR 0.28, 95% CI 0.11–0.75; p = 0.011) and OS (adjusted HR 0.29, 95% CI 0.09–0.87; p = 0.028). Poorer PS (2 vs. PS 0–1) was independently associated with shorter PFS and OS.
Table 2.
Cox proportional hazards analyses for progression-free survival in patients treated with anti-PD-1 monotherapy.
| Clinical factors | Univariate analysis | Multivariate analysis for Pre-Immunoglobulin |
Multivariate analysis for Post-Immunoglobulin |
||||||
|---|---|---|---|---|---|---|---|---|---|
| HR | 95%CI | P value | HR | 95%CI | P value | HR | 95%CI | P value | |
| Age (> 70/≤70) (yr) | 1.24 | 0.57–2.72 | 0.59 | ||||||
| Gender (Male/Female) | 0.86 | 0.32–2.31 | 0.77 | ||||||
|
Adjuvant chemotherapy (Present/Absent) |
0.83 | 0.34–2.02 | 0.69 | ||||||
| Treatment line (3+/1–2) | 1.27 | 0.50–3.23 | 0.62 | ||||||
| Performance Status (PS 2/PS 0–1) | 3.04 | 0.97–9.52 | 0.056 | 3.93 | 1.13–13.66 | 0.032* | 11.57 | 2.62–51.0.62.0 | 0.0012** |
| irAE (> G1/None) | 0.99 | 0.44–2.22 | 0.99 | ||||||
| PD-L1 (≥ 50%/≤49%) | 0.55 | 0.24–1.25 | 0.15 | ||||||
| Gene alteration (Present/Absent) | 2.08 | 0.74–5.88 | 0.17 | ||||||
| Pre-IgG (High/Low) | 0.60 | 0.77–3.58 | 0.19 | 0.44 | 0.19–1.03 | 0.058 | |||
| Pre-IgA (High/Low) | 0.71 | 0.33–1.55 | 0.39 | 0.60 | 0.26–1.36 | 0.78 | |||
| Pre-IgM (High/Low) | 0.73 | 0.34–1.58 | 0.42 | 0.44 | 0.18–1.04 | 0.062 | |||
| Post-IgG (High/Low) | 0.37 | 0.16–0.89 | 0.026* | 0.28 | 0.11–0.75 | 0.011* | |||
| Post-IgA (High/Low) | 0.45 | 0.18–1.14 | 0.093 | 0.46 | 0.16–1.30 | 0.14 | |||
| Post-IgM (High/Low) | 0.70 | 0.31–1.61 | 0.41 | 0.40 | 0.15–1.07 | 0.068 | |||
Table 3.
Cox proportional hazards analyses for overall survival in patients treated with anti-PD-1 monotherapy.
| Clinical factors | Univariate analysis | Multivariate analysis for Pre-Immunoglobulin |
Multivariate analysis for Post-Immunoglobulin |
||||||
|---|---|---|---|---|---|---|---|---|---|
| HR | 95%CI | P value | HR | 95%CI | P value | HR | 95%CI | P value | |
| Age (> 70/≤70) (yr) | 1.45 | 0.59–3.53 | 0.42 | ||||||
| Gender (Male/Female) | 0.52 | 0.18–1.46 | 0.21 | ||||||
| Adjuvant chemotherapy (Present/Absent) | 0.76 | 0.28–2.02 | 0.58 | ||||||
| Treatment line (3+/1–2) | 1.09 | 0.36–3.31 | 0.88 | ||||||
| Performance Status (PS 2/PS 0–1) | 3.15 | 0.81–12.2 | 0.097 | 1.67 | 0.46–6.12 | 0.44 | 7.02 | 1.36–36.2 | 0.020* |
| irAE (> G1/None) | 0.97 | 0.40–2.35 | 0.94 | ||||||
| PD-L1 (≥ 50%/≤49%) | 0.44 | 0.16–1.20 | 0.11 | ||||||
| Gene alteration (Present/Absent) | 1.36 | 0.49–4.10 | 0.59 | ||||||
| Pre-IgG (High/Low) | 0.51 | 0.21–1.91 | 0.13 | 0.30 | 0.09–0.96 | 0.028* | |||
| Pre-IgA (High/Low) | 0.77 | 0.31–1.92 | 0.58 | 0.39 | 0.25–1.74 | 0.40 | |||
| Pre-IgM (High/Low) | 0.84 | 0.35–1.99 | 0.69 | 0.50 | 0.17–1.44 | 0.20 | |||
| Post-IgG (High/Low) | 0.30 | 0.10–0.84 | 0.023* | 0.29 | 0.09–0.87 | 0.028* | |||
| Post-IgA (High/Low) | 0.72 | 0.27–1.95 | 0.52 | 0.86 | 0.29–2.59 | 0.80 | |||
| Post-IgM (High/Low) | 0.90 | 0.34–2.42 | 0.84 | 0.63 | 0.21–1.91 | 0.42 | |||
To assess whether the association with post-treatment IgG was independent of tumor PD-L1 expression, we performed sensitivity Cox analysis for the anti-PD-1 monotherapy cohort, incorporating post-IgG and key clinical variables, including PD-L1 status. In this analysis, higher post-IgG remained significantly associated with longer PFS (adjusted HR 0.31, 95% CI 0.12–0.81; p = 0.016) and OS (adjusted HR 0.25, 95% CI 0.08–0.72; p = 0.017) (Supplementary Table 1).
Exploratory pooled analyses and landmark sensitivity analyses
To address treatment heterogeneity, we performed exploratory pooled analyses across all 55 patients. In the pooled cohort, there were no significant pre- to post-treatment changes in IgG, IgA, or IgM, and no significant differences in pre-treatment, on-treatment, or relative-change immunoglobulin measures between responders and non-responders (Supplementary Fig. 9). Likewise, pooled Kaplan–Meier analyses and multivariable Cox models did not show significant associations between high post-IgG and PFS or OS (Supplementary Fig. 10; Supplementary Tables 2 and 3). In Spearman analyses, post-IgG showed weak positive correlation with OS, but not with PFS, in the pooled cohort (Supplementary Fig. 11).
As the post-treatment sample was obtained after 1–4 cycles, we also performed 6-week landmark analyses. In the anti-PD-1 monotherapy cohort, higher post-IgG remained associated with favorable outcomes, with a similar trend for PFS (HR 0.38, 95% CI 0.13–1.09; p = 0.072) and a significant association with longer OS (HR 0.26, 95% CI 0.077–0.90; p = 0.0328) (Supplementary Figs. 12 and 13). No analogous association was observed in the chemotherapy plus anti-PD-1 cohort.
Discussion
In this retrospective study of stage IV NSCLC, higher early on-treatment plasma IgG was associated with response and longer PFS and OS in the anti-PD-1 monotherapy cohort. By contrast, the post/pre ratio itself was not significantly associated with objective response, PFS, or OS. The association between higher post-treatment IgG and favorable survival remained significant in multivariable Cox analyses and a sensitivity model incorporating PD-L1 status. Exploratory pooled analyses across all anti-PD-1–based regimens were not significant, indicating that the observed signal was most evident in the monotherapy setting.
These findings align with those of studies that reported that B-cells and humoral immunity contribute to responses to ICIs. In metastatic melanoma, higher baseline total IgG and IgG1-3 subclasses are linked to longer PFS and OS with ICI therapy13. Our results suggest that, in NSCLC, the absolute on-treatment IgG level may be the most consistent marker, although higher baseline IgG also showed a favorable association with OS in the pre-treatment multivariable model. The importance of humoral immunity is further supported by histological data showing that tumors enriched with B-cells, plasma cells, and tertiary lymphoid structures are associated with improved outcomes during ICI therapy14,15.
Although PD-1 blockade primarily acts on T-cells, PD-1 is also expressed on activated B-cells where it negatively regulates B-cell activation20. Engagement of PD-1 in B-cells inhibits their proliferation and antibody production, whereas PD-1 blockade enhances B-cell proliferation and immunoglobulin secretion in vitro20,21. Accordingly, patients with higher on-treatment IgG may represent those in whom PD-1 blockade is accompanied by more robust systemic immune activation. In our dataset, the absolute on-treatment IgG level, rather than the relative increase from baseline, appeared to be the more informative measure.
IgG did not exhibit prognostic value in patients receiving chemotherapy plus anti-PD-1, and IgG levels decreased significantly in this group, likely because of the immunosuppressive effects of cytotoxic chemotherapy on B-cells and plasma cells. Chemotherapy-induced reductions in immunoglobulin levels, particularly IgG, are well recognized22, and our results are consistent with this phenomenon. The exploratory pooled analyses likewise did not demonstrate a significant association between post-IgG and PFS or OS across regimens. Together, these findings suggest that the clinical relevance of plasma IgG is regimen-dependent and most evident when PD-1 blockade is given without concomitant cytotoxic chemotherapy.
Our results also highlight that the clinical meaning of immunoglobulin changes may not be uniform across cancers or treatment contexts. In metastatic renal cell carcinoma treated with ICI + TKI, an increase in serum IgG levels correlates with worse outcomes17, potentially reflecting an unfavorable inflammatory state. Thus, the prognostic significance of circulating IgG likely depends on tumor type, treatment regimen, and underlying immune landscape, including IgG subclass distribution and antigen specificity. Total IgG measurements cannot distinguish between tumor-specific antibodies and antibodies targeting unrelated antigens, and this heterogeneity may partly explain the divergent findings across diseases.
Robust humoral activation may lead to immune-related toxicity in some cases. A case report on NSCLC described progressive increases in serum IgG and IgA levels preceding the onset of severe pneumonitis during PD-1 inhibitor therapy, suggesting that immunoglobulin monitoring may help anticipate immune-related adverse events23. Therefore, any biomarker reflecting immune activation, including IgG, should be interpreted in a comprehensive clinical context that balances potential efficacy gains against the risk of toxicity.
This study has several limitations. First, its retrospective, single-center design limits generalizability and introduces the possibility of selection bias. Second, the cohort was heterogeneous with respect to treatment regimen. We attempted to minimize confounding by restricting the primary Cox analyses to the anti-PD-1 monotherapy cohort and by performing exploratory pooled analyses, but residual confounding cannot be excluded. Third, the post-sampling window (1–4 cycles) was not standardized. Although most samples were obtained near the first radiological evaluation and the 6-week landmark analyses showed similar results in that direction, timing-related bias cannot be fully excluded. Fourth, objective response analyses were limited to patients with evaluable radiologic response assessments. Fifth, we measured only total IgG, IgA, and IgM levels without assessing IgG subclasses or antigen specificity, and we did not perform detailed B-cell phenotyping. Consequently, we could not determine whether the observed association reflected tumor-specific responses, bystander activation, or broader inflammatory processes. Finally, the sample size-particularly in the chemotherapy plus anti-PD-1 and ipilimumab plus nivolumab subsets-was modest, limiting statistical power for subgroup analyses.
Despite these limitations, our results suggest that higher early on-treatment IgG is associated with favorable response and survival in patients with advanced NSCLC receiving anti-PD-1 monotherapy and may serve as a simple, non-invasive biomarker of a favorable immune state. Future prospective studies with larger cohorts should validate these findings and explore the mechanistic links between B-cell responses, IgG subclasses, tumor-specific antibodies, and clinical outcomes, ideally integrating tumor microenvironment profiling and longitudinal immune monitoring.
Conclusions
In patients with advanced NSCLC, higher early on-treatment plasma IgG was associated with response and longer PFS and OS, particularly in those receiving anti-PD-1 monotherapy. This association was not observed in exploratory pooled analyses across all anti-PD-1-based regimens, supporting a regimen-specific interpretation. Monitoring on-treatment IgG may provide a practical, non-invasive biomarker in the monotherapy setting, but prospective validation and mechanistic studies are needed before clinical application.
Supplementary Information
Below is the link to the electronic supplementary material.
Supplementary material 10 (JPG 456.7 kb)
Supplementary material 11 (JPG 309.1 kb)
Supplementary material 12 (JPG 301.2 kb)
Supplementary material 13 (JPG 392.6 kb)
Supplementary material 14 (JPG 305.8 kb)
Supplementary material 15 (DOCX 18.5 kb)
Author contributions
Conceptualization: RO, SW; Methodology: RO, NO, MW, JJ, and SW; Investigation: RO, HA, MS, GI, TI, RS, TT, TK, KY, TT, HA, and SW; Formal analysis: RO, NO, MW, JJ, and SW; Validation: RO, SW; Writing – original draft: RO; Writing – review & editing: RO, SK, TT, AH, and SW; Supervision: SK, TT, AH, and SW. All authors have read and approved the final version of the manuscript.
Funding
There was no specific funding.
Data availability
Available from the corresponding author on reasonable request.
Declarations
Competing interests
The authors declare no competing interests.
Ethics approval
This retrospective study was approved by the Ethics Committee of the Showa Medical University School of Medicine, Tokyo, Japan (approval numbers M2165 and M2253).
Consent to participate
Written informed consent for the participation and use of clinical data was obtained from all patients, and all data were anonymized in accordance with institutional and ethical guidelines.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary material 10 (JPG 456.7 kb)
Supplementary material 11 (JPG 309.1 kb)
Supplementary material 12 (JPG 301.2 kb)
Supplementary material 13 (JPG 392.6 kb)
Supplementary material 14 (JPG 305.8 kb)
Supplementary material 15 (DOCX 18.5 kb)
Data Availability Statement
Available from the corresponding author on reasonable request.