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. 2024 Aug 19;16(12):829–840. doi: 10.1080/1750743X.2024.2379230

Peripheral blood cytokines and outcomes with immune checkpoint blockade: a systematic review and meta-analysis

Alexander B Karol a,*, Yu Fujiwara b,c, Tyler D'Ovidio d, Elena Baldwin a, Himanshu Joshi e,f,g, Deborah B Doroshow e, Matthew D Galsky e
PMCID: PMC11457654  PMID: 39155854

Abstract

Background: Tumor-promoting inflammation and inflammatory cytokines are linked to immune checkpoint blockade (ICB) resistance.

Methods: We assessed the associations between pre-treatment Interleukin-6 (IL-6), Interleukin-8 (IL-8) levels and on-treatment changes in IL-6, IL-8 and C-reactive protein (CRP) with ICB trial end points.

Results: 27 studies representing 6,719 patients were included. Low pre-treatment IL-6 levels were associated with improved objective response rate (ORR) (odds ratio (OR) = 0.31 [0.18–0.55]) and better progression-free survival (PFS) (hazard ratio (HR) = 0.59 [0.48–0.72]) and overall survival (OS) [95% confidence interval (CI)] (HR = 0.42 [0.35–0.50]). Low pre-treatment IL-8 levels were associated with improved ORR (OR = 0.47 [0.36–0.61]) and better PFS (HR = 0.65 [0.58–0.74]) and OS (HR = 0.44 [0.39–0.51]). On-treatment decline in CRP was associated with improved ORR (OR = 0.18 [0.11–0.20]), PFS (HR = 0.40 [0.31–0.91]) and OS (HR = 0.48 [0.40–0.58]).

Conclusion: Peripheral blood cytokines warrant further evaluation as enrichment and pharmacodynamic biomarkers for strategies targeting tumor-promoting inflammation.

Keywords: : checkpoint inhibitors, clinical immunology, cytokines and cell signaling, immunotherapy, molecular immunology

Plain Language Summary

Measuring a substance called C-reactive protein (CRP) in the blood can help predict if cancer treatments that boost the immune system, like immune checkpoint blockers (ICB), will work. CRP levels are increased when there is inflammation in the body, helping cancer cells grow. IL-6 and IL-8 are related blood markers that are more specific to cancer cells and may improve our ability to predict if ICB will effectivity destroy cancer cells. Our study found that having lower levels of IL-6 and IL-8 before treatment and low levels of CRP during treatment might mean patients live longer and respond better to ICB treatments. Measuring IL-6 and IL-8 before treatment and CRP during treatment could help improve how doctors use ICB to treat cancer by managing inflammation that helps cancer grow.

Tweetable Abstract

Our meta-analysis of 27 studies & 6,719 patients finds lower pre-treatment levels of IL-6/IL-8 and declines in C-reactive protein (CRP) levels during immune checkpoint blockade (ICB) predict better response to ICB therapy in solid tumors. Targeting inflammation may help overcome ICB resistance.

Plain language summary

Article highlights.

Introduction

  • Meta-analyses have established C-reactive protein (CRP) as a prognostic biomarker for solid tumor patients treated with immune checkpoint blockade (ICB).

  • Inflammatory cytokines levels of IL-6 and IL-8 in the tumor microenvironment have been linked to poor outcomes with ICB and Interleukin-6 (IL-6) leads to CRP release from the liver.

Methods

  • Our systematic review included 27 studies involving 6719 patients to evaluate the associations of both pre-treatment IL-6 and/or IL-8 levels and on-treatment changes in IL-6, Interleukin-8 (IL-8) and CRP levels with response to ICB.

Results

  • Low pre-treatment levels of IL-6 and IL-8 and declines in CRP levels during treatment were linked to better objective response rates and survival outcomes.

  • Meta-regression analysis did not observe a correlation between IL-6, IL-8 and CRP cutoff values and survival.

  • Our meta-analysis did not identify publication bias, included all high-quality studies, and identified sources of heterogeneity.

Discussion

  • Pretreatment IL-6 and IL-8 and on-treatment CRP levels may be attractive enrichment, or pharmacodynamic, biomarkers for strategies targeting tumor promoting inflammation to overcome ICB resistance.

  • Integrating cytokine levels with other known biomarkers of ICB response may enhance predictive power and provide a more comprehensive biomarker panel for clinical use.

Conclusions

  • Strategies targeting tumor-promoting inflammation are needed to extend the benefits of ICB to larger patient populations.

1. Introduction

Immune checkpoint blockade (ICB) has transformed the treatment landscape for multiple tumor types. However, while ICB can induce durable responses even in the context of metastatic disease, most patients do not respond to treatment. Hence, there have been extensive efforts to identify pre-treatment biomarkers associated with response and resistance to ICB to both refine selection of patients for treatment and generate mechanistic insights regarding potential therapeutic targets that might be exploited to overcome resistance.

A challenge to the development of clinically tractable biomarkers of response and resistance to ICB is the difficulty in sampling the dynamic and heterogeneous tumor microenvironment (TME) in real time, particularly in patients with metastatic solid tumors. Analytes measurable in the peripheral blood may overcome such challenges but often suffer from a limited understanding of the relationship between the biology measured in circulation and that in the TME, limiting mechanistic insights and complicating dissecting causal relationships. Among the most well studied and routinely collected peripheral blood analytes associated with poor outcomes in patients with advanced solid tumors is C-reactive protein (CRP). CRP is an acute phase reactant released from the liver in response to cytokines such as interleukin-6 (IL-6). Cytokines, in particular IL-6 and IL-8, are produced by cancer cells, stromal cells, and immune cells in the TME and are upregulated by NF-κß signaling which is central to the phenomenon of “tumor-promoting inflammation”, a hallmark of cancer pathogenesis (Figure 1).

Figure 1.

Figure 1.

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Illustration of Interleukin-6 (IL-6) and Interleukin-8 (IL-8) in the tumor-microenvironment in the setting of tumor-promoting inflammation.

Tumor-promoting inflammation, involving a TME shaped by activated fibroblasts, endothelial cells, and innate immune cells, has been linked to poor prognosis and resistance to ICB. Mechanistically, CRP can impair T-cell function and antigen presentation, whereas IL-6 promotes the expansion and IL-8 the recruitment of myeloid-derived suppressor cells (MDSCs) in the TME. MDSCs release inhibitory cytokines that dampen the anti-cancer immune response and mediate ICB resistance. [1,2] Further, pre-treatment CRP has been correlated with resistance to ICB in several studies and meta-analyses [3–5]. However, whether on-treatment changes in CRP associate with improved ICB outcomes, and whether upstream cytokines such as IL-6 and IL-8 also provide prognostic information in patients with metastatic solid tumors treated with ICB, has been underexplored. Here, we performed a systematic review and meta-analysis of the associations between IL-6 and IL-8 and ICB outcomes and of the association between on-treatment changes in these analytes, CRP, and response to ICB.

2. Materials & methods

2.1. Data sources & searches

This meta-analysis was conducted as per the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. PubMed and Embase were searched for available articles up to 08/24/2022. The search strategy is described in the appendix. The research protocol was registered in PROSPERO (CRD42023412348).

2.2. Selection criteria

The study aimed to evaluate the associations between IL-6 and or IL-8 and ICB outcomes in adults with solid tumors being treated with ICB therapy. The study further sought to evaluate whether short term on-treatment changes in IL-6, IL-8, and CRP were associated with response to ICB. Given prior meta-analyses of pre-treatment CRP and ICB outcomes have been published, we did not evaluate that relationship. Cohort studies or clinical trials meeting the following inclusion criteria were included: (1) studies exploring ICB in patients with solid tumors regardless of clinical disease state, (2) studies that evaluated ICB treatment outcomes according to baseline and/or on-treatment changes in IL-6, IL-8, and/or CRP ; (3) studies that reported objective response rate (ORR) or hazard ratio (HR) for progression-free survival (PFS) or overall survival (OS); (4) studies including >10 patients. The meta-analysis excluded: (1) reviews, notes, letters, editorials, comments, meeting abstracts, and case reports; (2) duplicated publications; (3) studies not including primary outcome data; (4) studies not written in English language.

2.3. Data extraction & risk of bias assessment

Data were extracted from eligible studies by four investigators (A.K, T.O, E.F, and Y.F). A.K and Y.F screened titles and abstracts identified from the literature search. T.O and E.F independently extracted data from included studies which included: study name, study design, cancer type, treatment type, cytokine(s) assessed, cytokine cutoff level, time of cytokine assessment, number of patients in high and low cytokine groups, median follow-up duration, objective response rate (ORR), hazard ratio (HR) of PFS or OS, and their 95% confidence intervals (95% CI). The Newcastle Ottawa Scale (NOS) and the Cochrane Risk of Bias Tool was used to evaluate the risk of bias for non-RCTs and RCTs respectively [6,7]. Cytokine cutoff values were determined according to study-specific cutoff values. Cytokine “responder” and “non-responders” were defined as patients below and above these cytokine cutoff values at study specific time points.

2.4. Data synthesis & statistical analysis

The odds ratio for ORR was calculated with 95% confidence intervals (95% CI). OS and PFS were evaluated by using univariate hazards ratios with 95% confidence intervals. Review manager 5.4 software [8] and R [9] were used to perform data analysis. Egger's test and visual inspection of funnel plots was used to evaluate publication bias. Egger's test result was the primary indicator, and a symmetric funnel plot with a p-value ≥0.05 was considered insignificant publication bias. Significance for equivalence hypothesis testing used the two-tailed 0.05 level was employed to identify the source of heterogeneity via ICB therapy modality and cancer type. Meta-regression analysis was conducted to assess if the cytokine cutoff value affects the HR of OS and PFS. Heterogeneity among the studies was evaluated using the I2 test. I2 >50% suggests significant statistical heterogeneity. The two-tailed alpha 0.10 level was set for significance for statistical heterogeneity.

3. Results

3.1. Study selection & characteristics

The study extraction process is shown in Supplementary Figure S1. We identified 824 studies (Supplementary Tables S1 & S2) matching our search parameters; 28 studies (N = 7,110 patients) were included in the review [10–38]. All studies were published between 2014–2022. All the studies meeting our inclusion criteria had NOS scores of 5 or higher (Supplementary Figure S2 & Supplementary Table S3). Characteristics of included studies are shown in Supplementary Table S4. The number of studies informing each analysis included: (1) on-treatment changes in CRP (n = 14 studies), (2) pre-treatment IL-6 (n = 11 studies), (3) on-treatment IL-6 (n = 3 studies), (4) pre-treatment IL-8 (n = 7 studies), (5) on-treatment IL-8 (n = 3 studies). ICB monotherapy and dual ICB therapy were assessed in 24 and 9 studies, respectively.

3.2. Pre-treatment IL-6 & IL-8

Lower versus higher pre-treatment IL-6 levels were associated with better ORR (OR = 0.31 [0.18–0.55], p < 0.001), PFS (HR = 0.59 [0.48–0.72], p < 0.001) and OS (HR = 0.42 [0.35–0.50], p < 0.001) (Table 1 & Figure 2). Sensitivity analyses indicated that the outcome of study Carril-Ajuria et al. [39] was underpowered and led to significant bias in the pooled HR and 95% CI. Therefore, this study was excluded. In meta-regression analysis, a correlation between specific pre-treatment IL-6 cutoff values and PFS (N = 8) and OS (N = 15) HR was not observed (Regression coefficient = 0.19 [-1.97–2.35], p = 0.83 and Regression coefficient = -0.04 [-0.07–0.004], p = 0.07 respectively; Figure 3). Visual inspection of funnel plots did not suggest publication bias for ORR, PFS, and OS (Supplementary Figure S3). Egger's test did not detect obvious publication bias for ORR, PFS, or OS (p = 0.44, p = 0.11 and p = 0.50, respectively).

Table 1.

Pooled OR of ORR [95%CI] and HR [95%CI] of PFS and OS with heterogeneity assessment according to pre-treatment IL-6 and IL-8 levels.

  Pre-tx IL-6 Pre-tx IL-8
  ORR OS PFS ORR OS PFS
HR/OR 0.31 [0.18–0.55] 0.42 [0.35–0.50] 0.59 [0.48–0.72] 0.47 [0.36–0.61] 0.44 [0.39–0.51] 0.65 [0.58–0.74]
p-value <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
I2 0% 27% 0% 0% 0% 3%
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95% CI: 95% confidence interval; HR: Hazard ratio; ; IL-6: Interleukin-6; IL-8: Interleukin-8; OR: Odds ratio; ORR: Objective response rate; OS: Overall survival; PFS: Progression-free survival; Pre-tx: Pre-treatment.

Figure 2.

Figure 2.

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Forest plot of C-reactive protein response according to (A) overall survival, (B) progression-free survival (C) and objective response rate. Egger's Test for (A) did not detect publication bias (p = 0.17).

Figure 3.

Forest plot of pre-treatment Interleukin-6 (IL-6) and Interleukin-8 (IL-8) respectively according to (A & D) overall survival, (B & E) progression-free survival (C & F) and objective response rate. Egger’s Test for (A), (B), (D) and (E) did not detect publication bias (p = 0.92, 0.054, 0.93 and 0.32), respectively.

graphic file with name IIMY_A_2379230_F0003A_C.jpg

graphic file with name IIMY_A_2379230_F0003B_C.jpg

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Lower versus higher pre-treatment IL-8 levels were associated with better ORR (OR = 0.47 [0.36–0.61], p < 0.001), PFS (HR = 0.65 [0.58–0.74], p < 0.001) and OS (HR = 0.44 [0.39–0.51], p < 0.001; Table 1 & Figure 2). In meta-regression analysis, the correlation between specific pre-treatment IL-8 cutoff values and PFS (n = 10) and OS (n = 9) HR was not observed (Regression coefficient = -0.01 [-0.05–0.02], p = 0.39 and Regression coefficient = -0.01 [-0.04–0.02], p = 0.57, respectively; Figure 3). Visual inspection of funnel plots did not suggest publication bias for ORR, PFS and OS (Supplementary Figure S3). Egger's test did not detect obvious publication bias for ORR, PFS and OS (p = 0.86, p = 0.85 and p = 0.64, respectively).

3.2. On-treatment changes in CRP, IL-6 & IL-8

On-treatment decline in CRP was associated with improved ORR (OR = 0.18 [0.11–0.20], p < 0.001), PFS (HR = 0.40 [0.31–0.91], p < 0.001) and OS (HR = 0.48 [0.40–0.58], p < 0.001; Table 2 & Figure 4). To assess significant heterogeneity in the OS analysis, a subgroup analysis was performed according to the site of primary malignancy and use of single agent versus dual ICB (Figure 5). In both subgroup analyses, high heterogeneity in the RCC (renal cell carcinoma) [I2 = 57%, p = 0.07] and ICB monotherapy group [I2 = 59%, p = 0.009] was observed, and heterogeneity among subgroups was also observed for primary malignancy [I2 = 63%, p = 0.003] and for dual vs mono ICB therapy [I2 = 63%, p = 0.003]. In a sensitivity analysis, omitting both subgroups yielded a reduced OS HR (HR = 0.42 [0.34–0.52], p < 0.001). In meta-regression analysis, the correlation between specific CRP cutoff values and OS (n = 8) HR was not observed (Regression coefficient = 0.21 [-1.31–1.73], p = 0.75) (Figure 3). Visual inspection of funnel plots did not suggest publication bias for ORR, PFS, and OS (Supplementary Figure S3). Egger's test did not detect obvious publication bias (p = 0.07, p = 0.16 and p = 0.15, respectively).

Table 2.

Pooled OR of ORR [95% CI] and HR [95% CI] of PFS and OS with heterogeneity assessment according to on-treatment CRP levels.

  ORR OS PFS
HR/OR 0.18 [0.11–0.29] 0.48 [0.40–0.58] 0.40 [0.31–0.91]
p-value <0.001 <0.001 <0.001
I2 30% 57% 33%
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95% CI: 95% Confidence interval; CRP: C-reactive protein; HR: Hazard ratio; OR: Odds ratio; ORR: Objective response rate; OS: Overall survival; PFS: Progression-free survival.

Figure 4.

Figure 4.

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Subgroup analysis for C-reactive protein response according to overall survival. (A) Stratified by primary malignancy. (B) Stratified according to immune checkpoint blockade dual versus mono therapy. NSCLC: Non-small cell lung cancer; RCC: Renal cell carcinoma.

Figure 5.

Figure 5.

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Meta-regression analysis of (A) C-reactive protein (CRP) studies evaluating overall survival (OS) and hazard ratio (HR). X axis: CRP cutoff value (mg/dl), Y axis: Log HR of OS. N = 8, cutoff values did not affect the extracted HR of OS (p = 0.74) of (B & C) interleukin-6 (IL-6) studies evaluating HR. X axis: IL-6 cutoff value (pg/ml), Y-axis: Log HR of OS (B) and progression-free survival (PFS) (C). (B) N = 15. IL-6 cutoff values did not affect the extracted HR of OS (p = 0.68). (C) N = 8. IL-6 cutoff values did not affect the extracted HR of PFS (p = 0.07). Meta regression of Interleukin-8 (IL-8) studies evaluating HR (D & E). X-axis: IL-6 cutoff value (pg/ml), Y-axis: Log HR of OS (D) and PFS (E). (D) N = 9 studies. IL-8 cutoff values did not affect the extracted HR of OS (p = 0.40). (E) N = 10. IL-8 cutoff values did not affect the extracted HR of PFS (p = 0.58).

Five studies assessed the association between on-treatment changes in IL-6 or IL-8 and ICB outcomes. OS was the only outcome measure available for analysis. On-treatment declines in IL-6 and IL-8 levels were associated with improved OS (HR = 0.49 [0.26–0.93], p = 0.03; HR = 0.37 [0.22–0.63], p < 0.001, respectively) (Supplementary Figure S4); however, there were insufficient studies to perform subgroup analysis, meta-regression or assess for publication bias.

4. Discussion

There are few features that can be currently measured routinely, reliably and repeatedly in the clinic that link to well established TME biology implicated in cancer pathogenesis and impairment of antitumor immunity. Among such analytes, peripheral blood CRP is perhaps the most well studied and has been associated with poor outcomes and ICB resistance across multiple prior studies. However, the availability of this readily measurable “biomarker” that confers robust prognostic information has not yet translated into our ability to select patients with elevated CRP and employ specific treatment regimens to modulate underlying tumor promoting inflammation. While there are several reasons for this translational gap, some barriers include uncertainties related to: (a) optimal CRP cut-points, (b) the causal relationship of CRP to disease pathogenesis versus simply representing a surrogate for “upstream” immunobiology biology and (c) the relationship between on-treatment modulation and outcomes. To address at least some of these knowledge gaps, we pursued a systemic review and meta-analysis to assess the associations between pre-treatment and/or on-treatment changes in CRP, IL-6 and IL-8 with clinical outcomes in patients with solid tumors treated with ICB. Indeed, elevated pre-treatment IL-6 and IL-8 were associated with significantly lower likelihoods of treatment response and with shorter PFS and OS while on-ICB treatment declines in IL-6, IL-8 and CRP were associated with more favorable outcomes. These relationships were observed irrespective of specific cut point values and without significant heterogeneity or meta-biases.

Strengths of our meta-analysis include the lack of identifiable publication bias, the inclusion of high-quality studies (all NOS ≥5), and identifiable sources of heterogeneity. A significant limitation of our study is that it only included patients with solid tumors. Our study would also benefit from validation in diverse populations, including different cancer types and stages, to ensure that our findings are universally applicable. Furthermore, different TMEs and genetic backgrounds may influence cytokine levels and their impact on ICB therapy. Furthermore, baseline CRP levels may be elevated for a variety of cancer and non-cancer related reasons [4]. Although our analysis permitted cutoffs as defined in the primary studies, the median CRP cutoff (1 mg/dl [SD = 0.44]) is consistent with prior literature [40,41]. Our meta-regression could not identify an optimal on-treatment decline associated with more favorable outcomes. However, it is important to note that the standard deviation and sample size were small, and most studies did not report rate outcomes at a specific timepoint.

Approaches to target IL-6 and IL-8 in combination with ICB have shown promise in pre-clinical and clinical studies. For example, anti-IL-6 combined with ICB reduces tumor progression in pre-clinical models and clinical trials are on-going [42,43]. The majority of ongoing clinical studies do not select patients based on IL-6 or IL-8 values, highlighting both (a) the challenges of developing “fit for purpose” enrichment biomarkers with optimal cut-points in the setting of incomplete knowledge regarding biomarker-response relationships with novel therapies and (b) the risk of missing signals of activity by developing novel strategies in populations that do not harbor tumors with the relevant biology. Future directions include integrating cytokine expression patterns with other known biomarkers of ICB response, such as PDL-1, tumor mutational burden and immune cell infiltration, to improve predictive power and potentially improve the identification of resistance mechanisms.

5. Conclusion

Low on treatment CRP levels and baseline IL-6/IL-8 levels are associated with improved ORR, PFS and OS in patients with solid tumors receiving ICB therapy. These peripheral blood measures warrant further evaluation as patient enrichment and pharmacodynamic biomarkers for strategies targeting tumor-promoting inflammation.

Supplementary Material

Supplementary Figures S1-S4 and Tables S1-S4
IIMY_A_2379230_SM0001.zip (1.3MB, zip)

Supplemental material

Supplemental data for this article can be accessed at https://doi.org/10.1080/1750743X.2024.2379230

Author contributions

AB Karol developed the concept and protocol, implemented protocol, analyzed data and wrote the manuscript. Y Fujiwarab developed protocol, implemented protocol, curated and analyzed data and revised manuscript. T D'Ovidiod and E Baldwina implemented protocol. H Joshie developed protocol. DB Doroshowe helped prepare manuscript. MD Galsky developed the concept and protocol, wrote the manuscript and supervised work. AB Karol is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Financial disclosure

The authors have no financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Competing interests disclosure

DB Doroshowe: Advisory Board/Consultant: Mirati, AstraZeneca, Summit Therapeutics, G1 Therapeutics, Sonata Therapeutics, Sanofi. MD Galsky: Research funding: Bristol Myers Squibb, Novartis, Dendreon, Astra Zeneca, Merck, Genentech; Advisory Board/Consultant: Bristol Myers Squibb, Merck, Genentech, AstraZeneca, Pfizer, EMD Serono, SeaGen, Janssen, Numab, Dragonfly, GlaxoSmithKline, Basilea, UroGen, Rappta Therapeutics, Alligator, Silverback, Fujifilm, Curis, Gilead, Bicycle, Asieris, Abbvie and Analog Devices. The authors have no other competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript apart from those disclosed.

Writing disclosure

No writing assistance was utilized in the production of this manuscript.

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Supplementary Materials

Supplementary Figures S1-S4 and Tables S1-S4
IIMY_A_2379230_SM0001.zip (1.3MB, zip)

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