Metabolic phenotype and gender may predict treatment outcomes of atezolizumab in extensive-stage small cell lung cancer

Introduction

Small cell lung cancer (SCLC) accounts for approximately 10–15% of lung cancer cases (1). It is characterized by an aggressive clinical course and poor survival rates, with nearly two-thirds of patients diagnosed in extensive-stage (ES). While immune checkpoint blockade (ICB) has been a cornerstone in the treatment of non-small cell lung cancer (NSCLC), its integration into frontline therapy for SCLC occurred later, notably with the approval of atezolizumab in late 2019 (2). The combination of the programmed death-ligand 1 (PD-L1) inhibitor with standard chemotherapy (platinum + etoposide) demonstrated improved overall survival (OS) rates in the IMpower133 trial and has since become the standard of care for ES-SCLC patients (3). Similar results in the CASPIAN trial led to the approval of durvalumab approximately one year later (4).

However, despite these advancements, median survival rates remain at only 12 months. Current efforts are focused on identifying patients who will benefit from immunotherapy and on establishing predictive markers, especially since ICB may cause potentially life-threatening adverse events. The IMpower133 study showed a weak correlation between a high blood-based tumor mutational burden and response with a hazard ratio (HR) of 0.58 vs. 0.79 (5). Furthermore, transcriptomic analyses have identified distinct subtypes of SCLC that differ in their response to ICB, which was most effective in patients harboring an inflamed-phenotype SCLC characterized by a higher number of tumor-infiltrating lymphocytes and increased expression of immune-related genes (6,7). However, useful biomarkers to predict response and potentially stratify treatment in ES-SCLC are lacking to date.

Interestingly, IMpower133 trial’s updated subgroup analysis revealed differences according to sex, with statistically significant improvements through atezolizumab seen only in female patients (HR 0.64), but not in male patients (HR 0.83) (5). This sex-specific trend has also been noted in real-world data (RWD), where one study found male sex to be a significant predictor of worse survival in univariate analysis, though this effect did not retain statistical significance after multivariate adjustment (8). No substantive explanations have yet been provided to account for these observed gender differences.

In this retrospective study, we aim to evaluate the role of gender and metabolic phenotypes on treatment responses to atezolizumab in ES-SCLC. Comparison with a historic cohort of chemotherapy-only patients helps to highlight the ICB-specific effects. In addition, thorough metabolomic analysis of selected patients reveals potential biomarkers for ICB response. We present this article in accordance with the STROBE reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-300/rc).

Methods

Study design and patients for retrospective cohort

The single-center retrospective cohort study with 70 patients was conducted at TUM University Clinic (Klinikum rechts der Isar), which is the university hospital of the Technical University of Munich (TUM), Germany. Between September 2019 and May 2024, 33 patients who received atezolizumab plus chemotherapy as first-line treatment for ES-SCLC were included. For comparison, a group of 37 patients who received chemotherapy alone as first-line treatment beginning from January 2014 to September 2019 was selected. Last follow-up date for all patients was December 31, 2024. Primary endpoints were OS, progression-free survival (PFS) and time to treatment failure (TTF).

Study design and patients for metabolome data

During a defined sample collection period (September 2021 to December 2023), serum samples were prospectively collected for an exploratory metabolome analysis from a subgroup of 12 patients undergoing atezolizumab-based treatment. The inclusion of these 12 patients represents a convenience sample, comprising individuals who provided specific written informed consent for research biobanking and from whom suitable baseline serum samples could be successfully obtained and stored. A total of 55 data points were collected (range: 1 to 19 samples per patient). Time points included pre-treatment, on-treatment and after progression. The subcohort was evenly balanced by sex and comprised six non-responders (PFS <6 months) and six responders (PFS >6 months), the latter of whom included two long-term responders (PFS >24 months).

Targeted metabolite analysis by nuclear magnetic resonance (NMR) spectroscopy

Serum samples were obtained from whole blood through centrifugation and stored at −80 ℃ until NMR analysis. Prior to analysis, 350 μL of serum was mixed with an aqueous buffer containing 0.1 g/L NaN₃, 0.067 mol/L Na₂HPO₄, and 0.033 mol/L NaH₂PO₄ (pH 7.15±0.05), along with 5% deuterium oxide (D₂O) as a field-lock substance. Pyrazine (6 mM) was used as an internal standard for quantification. NMR measurements were conducted on a Bruker AVANCE NEO 600 MHz spectrometer using the 1D ¹H noesygppr1d_d20 method [number of scans (NS) =16, temperature =310 K] with a measurement time of 6.5 minutes per sample. Following routine quality control, the spectra underwent Fourier transformation using TopSpin software (version 4.1.1 and 4.2.0, Bruker Biospin, Germany), were automatically phased (apk0.noe), and baseline-corrected (absn). The proprietary lifespin Profiler software (version 1.4_Blood) was used for metabolite quantification, generating a list of 73 metabolites with concentrations in mmol/L. The selection of these 73 metabolites was based on their intrinsic presence in blood serum samples at a concentration exceeding the detection limit of the NMR method (approximately 1 μM, metabolite-dependent). This group includes amino acids and their derivatives, organic acids, organic amines and amides, lipids, sugars, alcohols, ketone bodies, and other small metabolites. The quantification relied on a concentration adjustment against an internal standard, without further normalization.

Evaluation

The follow-up period was defined as the time span from the commencement of treatment to the predefined data cutoff. PFS and TTF were evaluated in accordance with the Response Evaluation Criteria in Solid Tumors (RECIST), version 1.1. PFS was defined as the interval from treatment initiation to either radiologically confirmed disease progression or death from any cause. OS was defined as the duration from the start of therapy to death. Patients who had not experienced disease progression and were still receiving treatment at the time of last follow-up were censored for PFS at that date. Similarly, individuals who were alive at the last follow-up were censored for OS.

Statistical analysis

Baseline patient characteristics were compared using Fisher’s exact tests for categorical variables and Mann-Whitney U tests for continuous variables, as appropriate. Univariate and multivariate analysis with Cox proportional hazard analysis were used to evaluate prognostic factors. HRs were calculated using the log-rank test and Cox regression analysis. Kaplan-Meier estimation was used to analyze PFS and OS data. Log-rank test was used to compare survival endpoints. P<0.05 was considered to indicate statistical significance. Corrected P values were calculated using the Bonferroni method. All statistical analyses were conducted using GraphPad Prism v10.

Ethics

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by TUM University Clinic, Munich, Germany ethics committee (code: 728/20 S-KK). Written informed consent for the metabolome profiling was obtained from all participants. Individual and written informed consent for the retrospective analysis was waived.

Results

Patient characteristics

The baseline characteristics of the two SCLC cohorts, the atezolizumab cohort (N=33) and the pre-atezolizumab cohort (N=37), were broadly similar and representative, but reveal some notable differences in demographic and clinical profiles. All patients’ clinical characteristics are summarized in Table 1. Median follow-up times were 10.8 months for the atezolizumab cohort and 12.2 months for the pre-atezolizumab cohort.

The median age was slightly higher in the atezolizumab cohort compared to the pre-atezolizumab cohort (68.2 vs. 63.4 years, P=0.04). Of note, a higher percentage of patients in the pre-atezolizumab group were under 65 years of age (56.8% vs. 30.3%). In the atezolizumab cohort, sex distribution was nearly even, with 48.5% males and 51.5% females, whereas the pre-atezolizumab cohort exhibited a higher proportion of males (62.2%).

Brain metastases at diagnosis were slightly more common in the atezolizumab group, with 13 patients (39.4%) compared to 8 patients (21.6%) in the pre-atezolizumab group, though this difference was not statistically significant (P=0.12). The rates of metastases to other common sites, including the liver, bone, and adrenal glands, were comparable between the cohorts. In terms of Eastern Cooperative Oncology Group (ECOG) performance status (PS), 23 patients (69.7%) in the atezolizumab group and 29 patients (78.4%) in the pre-atezolizumab group had a PS of 0–1. A PS of 2 was observed in 10 patients (30.3%) in the atezolizumab group and in 7 patients (18.9%) in the pre-atezolizumab group.

Regarding smoking history, the distribution was similar: 18 patients (54.6%) in the atezolizumab group and 19 patients (51.4%) in the pre-atezolizumab group were active smokers. Nine patients (27.3%) in the atezolizumab group and 10 patients (27.0%) in the pre-atezolizumab group were ex-smokers, whereas 6 patients (18.2%) in the atezolizumab group and 8 patients (21.6%) in the pre-atezolizumab group had never smoked.

Further analysis of clinical markers revealed no significant differences between the cohorts (Table S1). Median body mass index (BMI; 24.1 vs. 24.8 kg/m²) and uric acid levels (4.7 vs. 4.4 mg/dL) were comparable. Surrogate markers for disease aggressiveness, like C-reactive protein (CRP) and lactate dehydrogenase (LDH) values, were slightly higher in the pre-atezolizumab group; these differences were not statistically significant, but should be considered when interpreting outcomes.

When comparing baseline characteristics between male (N=16) and female (N=17) patients within the atezolizumab cohort, no statistically significant differences were observed across key demographic, clinical, or laboratory parameters (Table S2). We did observe a trend towards a worse ECOG PS (≥2: 47.1% in females vs. 12.5% in males, P=0.06) and lower median LDH levels (286 U/L in females vs. 365 U/L in males, P=0.07) in female patients, although these differences did not reach statistical significance at the P<0.05 threshold. These trends, however, are important to note.

Our cohort characteristics should also be interpreted within the broader context of SCLC epidemiology and the characteristics of the IMpower133 study. In the IMpower133 atezolizumab group, median age was 64 years, 64.2% were male and 8.5% had brain metastasis at enrollment (3). In summary, our atezolizumab cohort was slightly older, had a higher incidence of brain metastases and worse performance score compared to the pivotal clinical trial.

Survival

Results of our single-center retrospective real-world study resembled the published data, with 18-month OS at 29% vs. 16% (IMpower133 data: 34% vs. 21%) (Figure 1A). Interestingly, the chemotherapy-only cohort appears to perform slightly better in the first 12 month—most likely due to a younger patient selection and fewer patients with brain metastases at diagnosis. Survival curves cross at around 1 year, leading to the expected 3-year OS advantage of 21 versus 5% in favor of atezolizumab.

Figure 1 OS of atezolizumab versus pre-atezolizumab cohort. (A) Kaplan-Meier graph of OS in all patients. (B) Kaplan-Meier graph of OS in female patients only. OS, overall survival.

As described in the literature, the advantage of adding atezolizumab to first-line treatment is especially pronounced in female patients. In women, immunotherapy provides an OS benefit of almost 9 months (20.2 vs. 11.7 months) in our RWD cohort (Figure 1B). For male patients, we observed no significant difference in OS between the atezolizumab and pre-atezolizumab groups (P=0.22) (Figure S1).

In univariate analysis in Table 2, the role of gender in OS in ES-SCLC patients treated with atezolizumab was confirmed (HR 0.42, P=0.04). This was not found in the pre-atezolizumab group, where there was a slight trend towards better survival for male patients. In addition, the presence of liver metastases, LDH, and CRP has a significant impact on the atezolizumab cohort. Moreover, an inverse U-shaped relationship was observed between BMI and OS. The highest median OS, 26.6 months, occurred in the group with intermediate BMI values (20–25 kg/m²). Conversely, both cachexia (BMI <20 kg/m²) and obesity (BMI >25 kg/m²) were linked to worse survival, with median OS times of 6.7 and 10.3 months, respectively. A similar correlation was seen for uric acid levels, where normal values of 4–5.9 mg/dL yielded better OS values (20.2 months) than patients with either hypouricemic (12.0 months) or hyperuricemic (7.3 months) states. Remarkably, the effect of gender, BMI and uric acid levels was not found in the pre-atezolizumab cohort treated with chemotherapy alone, suggesting that these biomarkers could be atezolizumab-specific. The importance of normal-range BMI and uric acid levels was also seen in the TTF analysis (Figure 2A,2B).

Figure 2 TTF for atezolizumab cohort. (A) Kaplan-Meier graph of TTF by serum concentration of uric acid. (B) Kaplan-Meier graph of TTF by BMI. BMI, body mass index; TTF, time to treatment failure.

To control for potential confounding variables, a multivariate Cox proportional hazards analysis was conducted. After adjusting for age, ECOG performance status, baseline LDH, brain metastases, BMI, and uric acid levels, female sex remained an independent and statistically significant predictor of improved OS in the atezolizumab cohort (HR 0.36, P=0.045), as did lower LDH levels (P=0.02). Normal-range uric acid level (4.0–5.9 mg/dL) showed a trend towards better survival but did not reach statistical significance in this model (P=0.11). The full analysis is presented in Figure 3.

Figure 3 Forest plot of the multivariate Cox proportional hazards analysis for overall survival in the atezolizumab cohort (N=33). Hazard ratios (squares) and 95% confidence intervals are shown for each predictor variable. *P<0.05. BMI, body mass index; ECOG, Eastern Cooperative Oncology Group; LDH, lactate dehydrogenase.

NMR data

To further characterize metabolic alterations in patients receiving atezolizumab, we performed a detailed metabolome analysis on a subset of 12 patients. To assess for potential confounding, baseline clinical characteristics, including comorbidities, common medications, and liver and kidney function, were compared between the responder and non-responder groups. As shown in Table S3, these groups were broadly comparable across most of these parameters.

Metabolomic profiling revealed distinct alterations in amino acid metabolism, lipid metabolism, and energy homeostasis in patients receiving atezolizumab. Given the small sample size, we were not able to create a partial least squares discriminant analysis (PLS-DA) model due to an insufficient number of statistically significant predictive components (see Figure S2). The results are therefore preliminary and hypothesis-generating only.

Between the six responders and six non-responders, we could identify several statistically significant metabolites with corrected P values, as described in Table 3. At the level of amino acids, cystine and histidine concentrations were significantly higher in responders (PFS >6 months) than in non-responders (PFS <6 months). On the other hand, the concentration of phenylalanine was lower in patients with a good response. Notably, a step-wise pattern is observed for several metabolites, including albumin, creatine, lactic acid, phenylalanine, and pyruvic acid, with concentrations changing progressively across the non-responder, responder, and long-term responder groups (Figure 4). The longitudinal stability of creatine concentrations across different draws for the responder groups can be seen in Figure S3.

Figure 4 Box plots for concentration of selected metabolites by NMR measurement, by non-responder (N=13; unique donors: 6), responder (N=31; unique donors: 4) and long-term responder (N=11; unique donors: 2). NMR, nuclear magnetic resonance.

Taken together, this preliminary data supports the proposition of an ICB-supportive metabolomic profile in ES-SCLC patients, consisting of high concentrations of cystine, histidine and creatine and low concentrations of phenylalanine.

Discussion

Firstly, this monocentric RWD cohort successfully replicated the improved outcome of women with ES-SCLC treated with atezolizumab, as observed in the updated OS analysis of IMpower133 study (5). Our multivariate analysis provides new evidence that this survival advantage is not simply explained by differences in baseline characteristics such as disease burden or PS. After adjusting for these and other key prognostic factors, female sex remained a significant and independent predictor of improved survival in our atezolizumab-treated cohort. Interestingly, a similar trend was seen in the CASPIAN trial: in ES-SCLC patients, the addition of durvalumab resulted in improved OS HRs of 0.63 in women versus 0.76 in men, though differences were not significant (4).

These gender-specific results described here are echoed in other tumor entities. For example, in advanced or metastatic urothelial cancer, a tendency toward better outcomes for women with immunotherapy was observed. Like in SCLC patients, the antibody atezolizumab was associated with a significantly better objective response rate in women (9). On the other hand, a comprehensive meta-analysis of phase III randomized clinical trials with advanced cancers treated with ICBs, demonstrated that PFS benefits were more pronounced in men (HR 0.67) than in women (HR 0.77) (10). These findings make it difficult to say whether the better outcomes of women seen here are due to a distinct tumor biology of ES-SCLC and/or the gender-specific pharmacokinetics and -dynamics of atezolizumab. Possible explanations include hormonal or immunological factors. Further analyses are warranted.

The inverse U-shaped association of BMI, observed in the ICB-treated cohort but not in the chemotherapy-alone group, is a novel finding that has not been reported in other ICB-treated ES-SCLC cohorts. It suggests a relevant role of body composition and metabolism for ICB treatment response. Obesity is increasingly recognized not only as a risk factor for certain cancers but also for its impact on the response to anti-tumor treatments. In lung cancer, however, a high BMI is linearly associated both with decreased lung cancer incidence and mortality in several meta-analyses, a phenomenon commonly described as the ‘obesity paradox’ (11). Intriguingly, one study observed a non-linear, but U-shaped relationship between BMI and lung cancer survival, with both underweight and severely overweight patients experiencing worse outcomes (12). With the introduction of ICB into lung cancer therapy, several studies across multiple tumor entities described an enhanced treatment response in patients with higher BMI (13). This phenomenon may in part be attributed to obesity-related chronic inflammation, which could lead to increased expression of T-cell exhaustion markers—serving as targets for ICB (14). Yet obesity is insufficiently studied in SCLC patients receiving ICB, with only one Japanese study reporting non-association of BMI and treatment efficacy of atezolizumab plus chemotherapy (15). However, this study employed a binary BMI cutoff of 22 kg/m², which limited its ability to detect an inverse U-shaped relationship.

Similarly, the association with uric acid described here is novel and warrants further analysis. Uric acid is a product of purine metabolism and serum levels are influenced by dietary factors, certain medications and kidney function. In cancer patients, elevated levels of uric acid correspond to high tumor volume, proliferative capacity and apoptosis, with hyperuricemia defined as one of the hallmarks of tumor lysis syndrome. Although hyperuricemia has been associated with adverse outcomes in NSCLC as well as in hematologic malignancies, there is limited data about the role of uric acid in immunotherapy (16,17). One preclinical model showed impaired T-cell functionality in hyperuricemic mice, leading to decreased effectiveness of immunotherapy and increased melanoma growth (18). In primary liver cancer patients, a study identified elevated serum uric acid as a negative prognostic factor in patients treated with programmed cell death protein 1 (PD-1)-directed ICB (19). Interestingly, the authors also observed positive correlation of hyperuricemia with both BMI and clinical stage at diagnosis. In conclusion, uric acid might not only correlate with outcomes due to its physiological association with tumor aggressiveness but also actively modulate ICB treatment course.

Our data on the ICB-supportive metabolomic profile (high cystine, histidine, creatine and low phenylalanine) echoes preclinical findings about these metabolites. Cystine, the oxidized dimer form of cysteine, is vital for synthesizing proteins, glutathione, and coenzyme A. In immune cells, cystine uptake is crucial for T cell activation and proliferation. Antigen-presenting cells, such as dendritic cells, can export cysteine to support T cell proliferation. Conversely, myeloid-derived suppressor cells (MDSCs) can sequester extracellular cystine and cysteine, thereby inhibiting full T cell activation (20). Creatine plays a role in cellular energy homeostasis by replenishing adenosine triphosphate levels, which is crucial for rapidly proliferating cells, including activated T cells. While specific studies on creatine’s impact on ICB are limited, maintaining energy homeostasis is vital for effective immune responses. Therefore, elevated creatine levels could support the energy demands of activated T cells, potentially enhancing the efficacy of ICB therapies. On the other hand, phenylalanine is known to modulate T-cell immune responses by suppressing proliferation and impairing signaling (21). Low levels of phenylalanine could therefore support immunotherapy. Complementing our serum metabolome profile, Sun et al. recently identified distinct gut microbial taxa and their metabolites that correlate with immunotherapy efficacy in ES-SCLC, suggesting a potential gut-tumor axis in modulating ICB response (22).

The present study is subject to several limitations. The study’s monocentric, retrospective design with a small sample size reduces the statistical power and limit the ability to draw definitive conclusions. Furthermore, our patients treated with atezolizumab were significantly older than both the pre-atezolizumab control group and the patient population in the pivotal IMpower133 trial, which may influence direct outcome comparisons. Our atezolizumab cohort also included a higher, though not statistically significant, proportion of patients with brain metastases; however, emerging RWD indicates that this patient population derives a substantial survival benefit from atezolizumab-based therapy, potentially mitigating this baseline imbalance (23). Additionally, our study was limited to patients treated with atezolizumab; therefore, these findings, particularly the metabolic associations, may not be generalizable to patients receiving other ICBs such as durvalumab, and further research is needed to validate our observations across different ICB-based regimens. The exploratory metabolome analysis with only 12 patients is preliminary and hypothesis-generating only and requires validation in a larger cohort. While we found no major imbalances between responders and non-responders in terms of baseline comorbidities, organ function, and concomitant medications, we cannot rule out the influence of these or other unmeasured confounding factors on the observed metabolic profiles. Our study also lacked information on factors such as patients’ dietary habits, which could play a role in influencing BMI, metabolic health, and possibly the effectiveness of ICB treatment.

Nonetheless, the consistent gender-based effects and the identification of novel metabolomic biomarkers highlight the need for further research, with the potential to uncover interventions that could enhance ICB treatment response in this highly aggressive tumor entity. Gaining deeper insights into amino acid signaling and metabolic dynamics in PD-1/PD-L1 regulation could unlock new opportunities for enhancing adaptive immunity and advancing novel cancer therapies.

Conclusions

These findings support the hypothesis that both sex and metabolic factors modulate responses to ICB in ES-SCLC. Further research is warranted to validate these biomarkers and to explore potential interventions to enhance immunotherapy efficacy in this aggressive malignancy.

Acknowledgments

We would like to thank lifespin GmbH for their great support.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-300/rc

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-300/dss

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-300/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-300/coif). S.S., R.G., and S.H. are current employees of lifespin GmbH. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by TUM University Clinic, Munich, Germany ethics committee (code: 728/20 S-KK). Written informed consent for the metabolome profiling was obtained from all participants. Individual consent for the retrospective part of the analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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