Proteomic Biomarkers for Predicting Immunotherapy Outcomes: A Comprehensive Pan-Cancer Review

Proteomic Biomarkers for Predicting Immunotherapy Outcomes: A Comprehensive Pan-Cancer Review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Proteomic Biomarkers for Predicting Immunotherapy Outcomes: A Comprehensive Pan-Cancer Review Theodoros Margelos, Aggeliki Tserga, Maria Frantzi, Antonia Vlahou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9704067/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Immune checkpoint inhibitors (ICIs) have revolutionized cancer management; nevertheless, a large number of patients fail to respond to treatment. Despite extensive research efforts, there is a lack of accurate non-invasive biomarkers for prognosis and treatment outcome prediction. In this comprehensive review, we aim to record and evaluate studies investigating blood-circulating protein biomarkers and their association with ICIs outcomes. A literature search of PubMed and the Cochrane Library (on December 18, 2025) was performed to identify relevant studies. Results were synthesized across cancer types and proteomic technologies, based on a vote-counting approach. The QUIPS (Quality In Prognosis Studies) tool was used to assess the risk of bias. A total of 49 studies meeting the eligibility criteria were included in the analysis. The majority of these studies focused on lung cancer (n = 21) and melanoma (n = 13) and commonly employed proteomic platforms such as Olink proximity extension assays (n = 23) and mass spectrometry (n = 15). Synthesis of the results revealed high pre-treatment systemic levels of IL-6, CXCL8, ANGPT2, CRP, CCL3, CSF1, CXCL10, TNFSF14, and SAA1/2 were associated with resistance to ICIs, while elevated levels of soluble T-cell ligands (ICOSLG, TNFSF10 and FASLG and CXCL5 were reproducibly correlated with improved treatment outcomes. To conclude, despite limitations linked to the heterogeneity of proteomic platforms and variability in outcome reporting across studies, consistent patterns emerged: markers of systemic inflammation were associated with poor response, while proteins involved in T-cell activation and signaling were linked to better outcomes. Cancer Biology Blood biomarkers Immune Checkpoint Inhibitors Immunotherapy Melanoma Lung Cancer IL-6 Figures Figure 1 Figure 2 Figure 3 Highlights Pan-cancer review of pre-treatment blood proteomic biomarkers for ICI response. Analysis of 49 studies reveals patterns across diverse proteomic technologies. Systemic inflammation (IL-6, CXCL8) consistently predicts ICI resistance. Elevated soluble T-cell ligands correlate with improved clinical outcomes. 1. Introduction One mechanism by which the human body limits tumor progression is immunosurveillance, whereby immune cells recognize tumor-associated antigens and eliminate malignant cells by inducing apoptosis [1]. Immunotherapy relies on this intrinsic ability of the immune system, particularly in the tumor microenvironment (TME) to control cancer cell growth, and prevent cancer spread [2]. However, tumors can evade immune detection by suppressing immune cell function. A key mechanism of immune evasion involves the expression of coinhibitory ligands, such as the programmed death-ligand 1 (PD-L1) by tumor cells, which bind to inhibitory checkpoint receptors on T cells, most notably cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and programmed death 1 (PD-1), thereby delivering signals that suppress T cell activity [3,4] . On this basis, immune checkpoint inhibitors (ICIs) block inhibitory signals or receptors to restore or enhance anti-tumor immune response by inducing immunosurveillance. Several humanized monoclonal blocking antibodies targeting CTLA-4 (ipilimumab), PD-1 (pembrolizumab, nivolumab, and cemiplimab), or PD-L1 (atezolizumab, durvalumab, and avelumab) have been approved by the FDA for clinical use, either as monotherapies or in combination with chemotherapy for the treatment of multiple tumor types. These therapies have revolutionized cancer treatment, particularly for solid tumors, significantly improving survival outcomes and expanding therapeutic options in advanced states across a wide range of malignancies [5–10]. However, despite improved outcomes with immunotherapy, only an estimated 20–50% of patients respond to these treatments (depending on the disease and study), while many others develop immune-related adverse events [11,12]. Considerable efforts to associate molecular profiles with response to immunotherapy have been made in recent years. In this context, the expression of tumor PD-L1 has been studied for its effectiveness in predicting response [13] to anti-PD-1 immunotherapy. However, results from major clinical studies demonstrate variability and often suboptimal overall response rates (ORR) in PD-L1-positive patients, ranging from 43% to 58% in melanoma [14] and 18% to 78% in urothelial cancer [15]. Studies have also shown that genomic instability via microsatellite instability and higher tumor mutational burden (TMB) correlates with improved responses to ICI blockade. Nevertheless, similar to PD-L1 detection, their predictive value is far from optimal, with the reported ORR of TMB-high patients in a meta-analysis from melanoma and non-small cell lung cancer (NSCLC) patients limited to 58% [16]. To fill these gaps, multiple studies have investigated blood-based biomarkers associated with response to immunotherapy. These include the circulating tumor DNA [17], the neutrophil-to-lymphocyte ratio [18], and the soluble levels of PD-L1 [19]. In parallel, protein quantification analyses of plasma and serum have been increasingly applied to identify immunotherapy response biomarkers non-invasively, driven by the recent advancements in proteomic technologies [20]. In this comprehensive review, we aim to define the current state of the art regarding blood-based proteomic biomarkers associated with immunotherapy response. Given their high clinical relevance for patient stratification, we focus on biomarkers’ levels prior to treatment (baseline biomarkers). To the best of our knowledge, no prior study has systematically synthesized evidence on pre-treatment circulating proteomic markers across malignancies in this context. By integrating the findings from the existing literature, we aim to identify promising candidates that consistently correlate with improved response to ICIs, while highlighting critical limitations and challenges that warrant further investigation. Specifically, this review addresses the following questions: ● Question 1 : Which technologies (e.g., ELISA- Enzyme-Linked Immunosorbent Assay, mass spectrometry) have been used for the analysis of baseline/pre-treatment plasma protein biomarkers for ICI? ● Question 2 : How is response to ICI defined and assessed across tumors/studies? ● Question 3 : What are the most consistent biomarker findings among different studies per cancer type and across different tumor types? ● Question 4 : When consistent biomarker proteins emerge across studies, what biological insights can be inferred regarding their roles in circulation? 2. Methods This comprehensive review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines [21]. Synthesis was performed following the Synthesis Without Meta-analysis (SWIM) guidelines [22]. Due to the significant heterogeneity in protein measurement platforms (e.g., ELISA vs. Mass Spectrometry), cancer types, and outcome definitions across the included studies, a quantitative meta-analysis was not feasible. Accordingly, a narrative synthesis was conducted to summarize associations between pretreatment blood proteomic profiles and immunotherapy outcomes. 2.1 Eligibility Criteria Studies were included if they met all of the following criteria: (1) they involve human cancer patients treated with immunotherapy, specifically immune checkpoint inhibitors targeting PD-1, PD-L1, CTLA-4, or dual blockade regimens; (2) they present proteomic analyses or quantification of specific protein levels on plasma or serum samples; (3) biospecimens are collected at baseline/pre-treatment, defined as prior to the initiation of immunotherapy, with analyses reporting predictive associations based on these pre-treatment proteomic profiles; (4) they report associations with treatment response or clinical outcomes, including ORR, progression-free survival (PFS) and/or overall survival (OS); and (5) studies conducted in patients with all cancer types that can be treated with immunotherapeutic regimens. Exclusion criteria were as follows: (1) metabolomics-focused studies; (2) studies examining proteomic changes during or after treatment rather than baseline/pre-treatment profiles; (3) single-cell proteomics analyses, including single-cell ReapSeq; (4) studies performing proteomic analyses of circulating tumor cells (CTCs) or extracellular vesicles (EVs), as these represent distinct cellular / biological compartments and were outside the scope of soluble plasma/serum proteomics; (5) studies involving dendritic cell–based therapies; (6) studies evaluating targeted or non- ICI therapies, including tyrosine kinase inhibitors or diphencyprone; (7) studies assessing only treatment-related adverse events without reporting clinical efficacy outcomes; (8) autoantibody profiling studies; (9) vaccine-based therapeutic interventions; (10) studies focusing exclusively on mass spectrometry spectral features without providing definite protein identities (such as studies involving the application of MALDI technologies) or methodological MS spectra characterization rather than protein-level quantification or biomarker discovery; (11) in vitro studies; (12) animal models; (13) review articles; and (14) meta-analyses. 2.2 Search Strategy A comprehensive literature search was conducted in MEDLINE and COCHRANE library on December 18, 2025. The used Boolean query combined terms for proteomics, samples collection time, ICI, clinical outcomes, and eligible cancers is provided in the Additional File. No language or date restrictions were applied. 2.3 Data Collection Publications’ titles, abstracts, and full texts were screened independently by two reviewers (1: TM and 2: AT) according to the predefined eligibility criteria. Discrepancies were resolved through a panel discussion and the involvement of MF. From the studies included, sample size, cancer type, ICI regimen, proteomic methods used, and the assessment metric of the outcome were extracted (Additional File 1 , Table 1 ). Table 1 Summary of clinical studies that identify pretreatment plasma/serum biomarkers associated with response to Immune Checkpoint Inhibitors (ICIs) in patients with Lung Cancer. (Anti-CTLA-4: Anti-cytotoxic T-lymphocyte-associated protein 4; Anti-PD-1: Anti-programmed cell death protein 1; Anti-PD-L1: Anti-programmed death-ligand 1; AUC: Area under the curve; CI: Confidence interval; CR: Complete response; DCB: Durable clinical benefit; DIA: Data-independent acquisition; ELISA: Enzyme-linked immunosorbent assay; FDR: False discovery rate; HR: Hazard ratio; ICI: Immune checkpoint inhibition; LC-MS/MS: Liquid chromatography-mass spectrometry; LTR: Long-term responder; NDB: Non-durable benefit; NR: Non-responders; OS: Overall survival; PD: Progressive disease; PEA: Proximity extension assay; PFS: Progression-free survival; PR: Partial response; QUIPS: Quality in Prognostic Studies; R: Responders; RECIST: Response Evaluation Criteria in Solid Tumors; SD: Stable disease; STR: Short-term responder; TMT: Tandem mass tag). Author Technology Sample Size Treatment Regimen Response Assessment Results QUIPS Risk of Bias Bessede et al. [105] Olink Explore 1536 (PEA) 74 Anti-PD-L1 Monotherapy OS & PFS High levels of TROP2 are significantly associated with shorter PFS (2.4 vs 10.9 months), and shorter OS (8.5 vs 24.6 months). Moderate Chao et al. [106] LC-MS/MS (DIA) and ELISA 15 (Discovery Cohort R: 12, NR: 3) and 22 (Validation Cohort R: 22, NR: 4) Anti-PD-1 monotherapy DCB vs NR ELISA validation confirmed that S100A9, S100A8, SAA1, and SAA2 were at higher abundance in the NR group compared to R. Moderate Eltahir et al. [38] Olink Immuno-Oncology panel (92 markers) 43 Anti-PD-1 OR Anti-PD-L1 OS Multivariate survival analysis (split by median) showed CD244 (HR: 4.7 x 10^3, 95% CI: 1.1 x 10^1–2.0 x 10^6, p = 0.006), IL18 (HR: 1.5 x 10^4, 95% CI: 1.1 x 10^2–2.1 x 10^6, p < 0.001), MUC16 (HR: 1.8 x 10^1, 95% CI: 3.2–1.0 x 10^2, p = 0.001), and ANGPT2 (HR: 4.8 x 10^3, 95% CI: 3.5 x 10^1–6.6 x 10^5, p < 0.001) were associated with shorter OS. FASLG (HR: 6.1 x 10^-3, 95% CI: 3.3 x 10^-4–1.1 x 10^-1, p < 0.001) was associated with prolonged OS. High Fan et al. [31] LC-MS/MS (DIA) 17 (R: 4, NR: 13) ICI - No treatment regimen reported RECIST 1.1; R: (CR&PR), NR: (SD&PD) Significantly decreased levels of CXCL8 were observed in the NR group compared to the R group. Note: Analysis mixed baseline (n = 17) and during-treatment samples (n = 30). High Gao et al. [40] Olink Immuno-oncology panel 64 (Cohort 1 R: 13, NR: 21; Cohort 2 n = 30, R: 14 NR: 16) Anti-PD-1 OR Anti-PD-L1 Monotherapy or in combination to Chemotherapy RECIST 1.1; R: (CR&PR), NR: (SD&PD) In Cohort 1, IL18, TNF, TNFRSF9, and GZMH levels were higher in R, while LAMP3, IL2, ANGPT1, TNFSF14, and CXCL10 were at higher levels in NR. In Cohort 2, TNF, TNFRSF9 levels were higher in R whereas ANGPT1, CXCL10, and TNFSF14 levels higher in NR. Moderate Harel et al. [32] Quantibody multiplex ELISA workflow 143 (R: 70, NR: 73) Anti-PD-1 OR Anti-PD-L1 +/- Chemotherapy RECIST 1.1; R: (CR&PR&SD), NR: (PD) Identified 49 differentially abundant proteins between R and NR. Only CXCL8, found at decreased levels in R, remained significant after FDR correction (< 0.1). Moderate Harutani et al. [41] MAP antibody assay 102 (DB: 33, NDB: 69) Anti-PD-1 OR Anti-PD-L1 RECIST 1.1; DB: (CR&PR), NDB: (SD&PD) & OS In the non-durable benefit (NDB) subset (n = 69), elevated CRP (HR 2.59, 95% CI 1.33–5.04, p = 0.005) and FST (HR 2.29, 95% CI 1.12–4.69, p = 0.023) were significantly associated with poor survival in multivariate analysis. Moderate Hong et al. [107] Olink Immuno-Oncology assay 76 (R: 14, NR: 62) Anti-PD-1 OR Anti-PD-L1 RECIST 1.1; R: (CR&PR&SD), NR: (PD) & PFS No proteins were identified as significant predictors of response or PFS in multivariate analysis. Higher levels of CXCL9 and IL-2 were associated with unfavorable outcomes in univariate PFS analysis but were not significant after adjustment for confounders. Moderate Hu et al. [36] 27-plex cytokine assay 51 (R: 28, NR: 23) Anti-PD-1 OR Anti-PD-L1 RECIST 1.1; R: (CR&PR&SD), NR: (PD) Pretreatment levels of IL-6 were significantly decreased in NR patients. Levels of IL-5 and IL-4 were significantly higher in the R group. Moderate Jie et al. [108] LC-MS/MS (DIA) and ELISA 118 (Discovery n = 42), Internal Validation (n = 40), External Validation (n = 36) Anti-PDL-1 + Chemo R vs NR based on PFS A 3-protein model (VASN, PARD3, PTGES3) achieved an AUC of 0.846 (training) and 0.821 (internal validation). ELISA quantification in an external cohort (n = 36) reported an AUC of 0.859. VASN and PARD3 were at higher levels in R, while PTGES3 was at higher levels in NR. Moderate Kauffman-Guerrero et al. [33] 9-plex cytokine assay 29 (R: 8, NR: 21) Anti-PD-1 RECIST 1.1; R: (CR&PR&SD), NR: (PD) & PFS IL-6 and IL-8 levels were elevated in NR patients. IFNG levels were higher in R and identified as an independent predictor of PFS (HR 0.92, 95% CI 0.86–0.97, p = 0.004). Moderate Mao et al. [109] Olink Explore (1472 markers) 66 (R: 49, NR: 17) Anti-PD-1 OR Anti-PD-L1 Monotherapy RECIST 1.1; R: (CR&PR), NR: (SD&PD) AKT1S1 plasma levels were higher in the NR group compared to R. Moderate Mondelo-Macía et al. [110] LC-MS (DIA) 48 (R: 30, NR: 18) Anti-PD-1 +/- Chemo RECIST 1.1; R: (CR&PR&SD), NR: (PD) A 7-protein model was constructed: ATG9A, DCDC2, LZTL1, FIL1L, and HPS5 were at higher levels in NR, while SPTN2 and PGTA were at higher levels in R. Moderate Oyanagi et al. [34] 3 human Milliplex MAP assay panels 38 (R: 19, NR: 19) Anti-PD-1 RECIST 1.1; R: (CR&PR&SD), NR: (PD) & PFS Elevated levels of BMP-9 and TNF-alpha were higher within R, while FST, IL-8, and IP-10 levels were higher in NR patients. Multivariate Cox analysis identified FST and IP-10 as independent predictors of poorer PFS. Low Park et al. [111] LC-MS/MS (TMT labeling) and ELISA 63 (R: 29, NR: 34) and 53 (External Validation Cohort) Anti-PD-1 OR Anti-PD-L1 RECIST 1.1; R: (CR&PR& SD w/ PFS > 6 months), NR: (SD w/ PFS < 6 months &PD) Complement component C7 levels were significantly elevated in NR. Validation via ELISA showed an AUC of 0.724 in the primary cohort and 0.649 in an external validation cohort Low Parra et al. [37] Olink Immuno-oncology panel 160 (R: 31, NR: 129) Anti-PD-1 Monotherapy OR Anti-PD-1 + Anti-CTLA-4 RECIST 1.1; R: (CR&PR), NR: (SD&PD) Dendritic cell marker LAMP3 displayed significantly higher levels in R compared to NR. Conversely, cytokines CCL23, CXCL13, TNFSF14, and IL-6 were observed in higher abundance in NR versus R. Low Rocha et al. [112] Olink Explore 384 panel 49 (LTR: 21, STR: 29) Anti-PD-1 OR Anti-PD-L1 Monotherapy Long-Term vs Short-Term Response 35 proteins were at significantly higher levels in LTRs compared to STRs including those related to apoptosis (CASP8, PRKRA, CHAC2, CIAPIN1), cell cycle (CDKN2D, DCTN1, TACC3, SIRT2, USO1, SUGT1) while STRs showed higher levels of ADAMTS8, ADAMTS15, CD5, and CD33 compared to LTRs. Moderate Stensgaard et al. [39] Olink Immuno-Oncology panel 42 Anti-PD-1 Monotherapy PFS & OS ICOSLG was significantly associated with longer PFS (HR: 0.21, 95% CI: 0.06–0.79, p = 0.022) and OS (HR: 0.23, 95% CI: 0.06–0.87, p = 0.030). FASLG levels also correlated with improved PFS (HR: 0.42, 95% CI: 0.22–0.82, p = 0.009) and OS (HR: 0.38, 95% CI: 0.19–0.76, p = 0.005). Low Tan et al. [43] Antibody microarrays, LC-MS/MS (DIA) and ELISA 17 (Discovery Cohort R:5, NR: 12) and 76 (Validation Cohort; R: 33, NR: 43) Anti-PD-1 monotherapy RECIST 1.1; R: (CR&PR&SD), NR: (PD) & PFS Validation confirmed KIT and TNFSF12 were at significantly higher levels in R, while CCL3 was at higher levels in NR. Multivariate analysis showed low levels of KIT (HR 2.01, p = 0.038) and low levels of TNFSF12 (HR 3.67, p = 0.013) associated with disease progression. Low Wu et al. [113] LC-MS/MS (DIA) 47 (R: 35, NR: 12) No treatment regimen reported RECIST 1.1; R: (CR&PR&SD), NR: (PD) BMP15, HSP9, and HEG1 were significantly more abundant in R compared to NR. Conversely, 20 proteins including FKBP1A, TUBB1, and UBA1 were significantly less abundant in R. No further validation was reported. High Xie et al [35] Olink Immuno-Oncology panel 43; DCB: 23, NDB: 20 Anti-PD-1 and Chemotherapy DCB (PFS > 6 months), NDB (PFS < 6 months) Patients with DCB exhibited higher levels of CD83, CD244, IL-12, and CD70, whereas those with NDB had higher levels of CSF-1, CCL3, CCL4, and IL-8. Moderate 2.4 Risk of Bias Assessment Risk of bias (RoB) was independently assessed by reviewers TM and AT using the Quality in Prognosis Studies (QUIPS) tool [23], which evaluates six domains: study participation, study attrition, prognostic factor measurement, outcome measurement, study confounding, and statistical analysis/reporting, labeling them as high or low risk. Studies were overall rated as low if all categories were of low risk, moderate if 1 or 2 categories were of high risk, and high risk when 3 or more categories were assessed as high risk (Additional File 1 , Table 2 ). Table 2 Summary of clinical studies that identify pretreatment plasma/serum biomarkers associated with response to Immune Checkpoint Inhibitors (ICIs) in patients with melanoma. (Anti-CTLA-4: Anti-cytotoxic T-lymphocyte-associated protein 4; Anti-PD-1: Anti-programmed cell death protein 1; CI: Confidence interval; CR: Complete response; DBT: Dual blockade therapy; DDA: Data-dependent acquisition; DIA: Data-independent acquisition; EDT: Early during therapy; EFS: Event-free survival; ELISA: Enzyme-linked immunosorbent assay; HR: Hazard ratio; LC-MS/MS: Liquid chromatography-mass spectrometry; NR: Non-responder; OS: Overall survival; PD: Progressive disease; PFS: Progression-free survival; PR: Partial response; QUIPS: Quality in Prognostic Studies; PB-NET: Porphyrin Based-Network/Net (Glycoproteomics tool); R: Responder; RECIST: Response Evaluation Criteria in Solid Tumors; SD: Stable disease). Author Technology Sample Size Treatment Regimen Response Assessment Key Results Risk of Bias Babačić et al. [114] High-Resolution Isoelectric Focusing LC-MS/MS 18 Anti-PD-1 or Anti-CTLA-4 OS Proteins associated with shorter OS included CD40 (HR = 9.9, 95% CI: 1.1–86), GZMB (HR = 3.7, 95% CI: 1.2–12), IL-10 (HR = 2.3, 95% CI: 1.1–4.7), ADA (HR = 5.3, 95% CI: 1.3–22), CCL2 (HR = 3.1, 95% CI: 1.2–8.2), CCL3 (HR = 5.3, 95% CI: 1.1–25), and CCL4 (HR = 6.2, 95% CI: 1.4–27). In contrast, soluble CD274 (HR = 0.096, 95% CI: 0.013–0.72) and CCL19 (HR = 0.35, 95% CI: 0.13–0.95) were associated with longer OS. High Hannani et al. [115] ELISA (CD25 and LAG3) 262 Anti-CTLA-4 OS Higher soluble CD25 levels (based on median) were significantly associated with poor OS (HR = 1.26, 95% CI: 1.04–1.54, p < 0.0165), even after adjustment for LDH levels, tumor staging, and immune response criteria. Low Hoefsmit et al. [116] LC-MS/MS (DIA/DDA) and Olink Cohort 1 n = 85 (R: 64, NR: 21); Cohort 2 n = 49 Anti-PD-1 + Anti-CTLA-4 EFS Levels of CFB and A1BG were higher in NR in cohort 1 but were tested and not validated in cohort 2. Higher levels of LRG1 were associated with poor EFS, a finding which was validated in the independent cohort. High Karlsson et al. [117] TMT LC-MS/MS 63 (R: 41, NR: 22) Anti-PD-1 Monotherapy OR Anti-PD-1 + Anti-CTLA-4 OR Anti-CTLA-4 RECIST 1.1; R: (CR&PR&SD), NR: (PD) & OS Levels of APOA1 and APOC1 were significantly higher in R. Inflammation markers (CRP, SAA1, SAA2, and SAA2-SAA4) were at significantly lower levels in patients with longer OS. High Koguchi et al. [118] Luminex and ELISA 124 (Discovery Cohort) and 48 (Internal Validation Cohort) Anti-CTLA-4 OS Multivariate analysis showed higher CXCL11 levels (HR = 1.88, p = 0.014) and higher sMICA levels (HR = 1.75, p = 0.0420) correlated with poor OS. Both CXCL11 (HR = 3.74) and sMICA (HR = 2.06) were validated as negative predictors of OS via ELISA. Low Lim et al. [119] SOMAscan and Luminex 65-plex 23 (R: 12, NR: 11) Anti-PD-1 Monotherapy OR Anti-PD-1 + Anti-CTLA-4 OR Anti-CTLA-4 Monotherapy RECIST 1.1; R: (CR&PR), NR: (SD&PD) No significant differences in protein expression between R and NR were observed. Moderate Lim et al. [120] 65-plex Chemokine Assay 98 [Anti-PD1(R/NR: 16/24); DBT (R/NR: 47/11)] Anti-PD-1 + Anti-CTLA-4 RECIST 1.1; R: (CR&PR), NR: (SD&PD) & OS In monotherapy (n=.40): High levels of TRAIL (HR = 0.318) and IL-2 (HR = 0.447) are associated with longer OS; high levels of MCP-1 predicted shorter OS (HR = 2.696). IL-2, MCP-4, TARC, and ENA-78 were higher in R. In DBT (n = 58): High levels of TNFa, IL-8, and IP-10 predicted poorer survival. Moderate Machiraju et al. [121] ELISA 90 [Anti-PD1(R/NR: 28/20); DBT (R/NR: 26/16)] Anti-PD-1 Monotherapy OR Anti-PD-1 + Anti-CTLA-4 RECIST 1.1; R: (CR&PR&SD), NR: (PD) & PFS In anti-PD-1 monotherapy, sLAG-3 was more abundant in resistant patients. In DBT, sPD-1 levels were higher in NR. Multivariate analysis showed higher sLAG-3 (> 148 pg/ml; HR = 0.39, p = 0.016) and higher sPD-1 (> 167 pg/ml; HR = 0.43, p = 0.037) levels were associated with longer PFS. Moderate Pedersen et al. [122] MSD immunoassay 16 Anti-PD-1 Monotherapy OR Anti-PD-1 + Anti-CTLA-4 PFS Higher levels (above median) of TNFa (HR = 0.081, 95% CI: 0.0098–0.66, p = 0.019) and MCP-1 (CCL2) (HR = 0.073, 95% CI: 0.0088–0.61, p = 0.016) were linked with longer PFS. High Pickering et al. [123] Glycoproteomics (LC-MS/MS with PB-NET) 202 Discovery Cohort (Limited Benefit n = 40; Sustained Control n = 56) and 27 (Validation) Anti-PD-1 Monotherapy OR Anti-PD-1 + Anti-CTLA-4 Clinical benefit based on OS Patients with limited clinical benefit had higher levels of AAT, AACT, LRG1, and B2M. Findings were not replicated in the external cohort (n = 27). Moderate Rossi et al. [124] Olink Inflammation and ELISA 87 (R: 46, NR: 41) Anti-PD-1 Monotherapy OR Anti-PD-1 + Anti-CTLA-4 OR Anti-CTLA-4 monotherapy RECIST 1.1; R: (CR&PR&SD), NR: (PD) & OS IL-6, HGF, and MCP-2 (CCL2) were at higher levels in NR. These three markers were validated via a second targeted ELISA assay. Continuous IL-6 levels were associated with poorer OS (HR = 1.90, 95% CI: 1.32–2.73, p = 5.46 x 10^-4). Low Yang et al. [44] Olink Explore 384 assay 39 (R: 27, NR: 12) Anti-PD-1 + Anti-CTLA-4 Pathological Response based on viable tumor cells R showed increased levels of CXCL8, CCL8, CCL7, IL17F, IL-7, CXCL9, ADM, HAVCR1, and GZMB. NR showed increased abundance of ICA1, SCG3, TNFAIP8, PPP1R12A, and SH2B3. Moderate Zila et al. [125] SWATH LC-MS/MS (DIA) and Shotgun (DDA) 56 Discovery Cohort (R: 36, NR: 20); Validation Cohort A (n = 80), Cohort B (n = 12), Cohort C (n = 17) Anti-PD-1 Monotherapy RECIST 1.1; R: (CR&PR&SD), NR: (PD) CRP, LDHB, S100A8, SAA2, SAA1, LYVE1, and CFHR3 levels were significantly elevated in NR in the discovery cohort and validated in at least one independent cohort. Low 2.5 Data Synthesis The reviewed studies were grouped by cancer type (e.g., lung cancer, melanoma, and other cancers) according to SWiM reporting recommendations. To ensure data consistency across studies, all proteins identified for the synthesis were manually mapped to their official HUGO Gene Nomenclature Committee (HGNC)-approved gene symbols. The proteins reported were converted to their Hugo Nomenclature gene symbol to harmonize them before synthesis. Due to substantial heterogeneity in proteomic platforms, study design, and outcome definitions, a quantitative meta-analysis was not considered appropriate. Therefore, findings were synthesized using a vote-counting approach based on the direction of effect, classifying biomarkers according to whether their baseline levels were positively or negatively associated with treatment outcomes. Consistency of findings was defined as a biomarker demonstrating a similar direction of association without significant contradictory evidence. Heterogeneity was explored across different types of cancer, proteomic technologies, treatment regimen, and response assessments. The certainty of evidence was assessed based on the consistency of a biomarker across different studies (Additional File 1 , Table 3 ) . For the final cohort, reported proteins had to show a consistent trend of abundance (in regard to the outcome) in at least three studies and not be reported with a significant opposite trend in other studies. Sensitivity analysis was conducted by removing high RoB studies. Visualization plots (pie plot, bar chart) were generated using R (version 4.3.0) and ggplot2 (version 3.4.0). Table 3 Summary of clinical studies that identify pretreatment plasma/serum biomarkers associated with response to Immune Checkpoint Inhibitors (ICIs), on cancer types different than melanoma or lung cancer. (Anti-PD-1: Anti-programmed cell death protein 1; Anti-PD-L1: Anti-programmed death-ligand 1; Anti-VEGF: Anti-vascular endothelial growth factor; AUC: Area under the curve; CR: Complete response; DFS: Disease-free survival; DIA: Data-independent acquisition; ELISA: Enzyme-linked immunosorbent assay; FDR: False discovery rate; HR: Hazard ratio; iRECIST: Immune-modified Response Evaluation Criteria in Solid Tumors; LC-MS/MS: Liquid chromatography-mass spectrometry; TIMS: Trapped Ion Mobility Spectrometry; TOF: Time-of-Flight; mRECIST: Modified Response Evaluation Criteria in Solid Tumors; MSD: Meso Scale Discovery; NR: Non-Responders; OS: Overall survival; pCR: Pathologic complete response; PD: Progressive disease; PFS: Progression-free survival; PR: Partial response; R: Responders; RECIST: Response Evaluation Criteria in Solid Tumors; SBRT: Stereotactic body radiation therapy; SD: Stable disease; TKI: Tyrosine kinase inhibitor; TMT: Tandem mass tag). Author Cancer Type Technology Sample Size Treatment Regimen Response Assessment Results Risk of Bias Simonetti et al. [126] Renal Cell Carcinoma Antibody Array & ELISA 16 Discovery Cohort (7R, 9NR); 15 (Validation Cohort) Anti-PD-1 (Nivolumab) RECIST 1.1; R: (CR&PR&SD), NR: (PD) & PFS & OS RANKL levels were significantly higher in NR patients after FDR correction; also; validated via ELISA. Moderate Rini et al. [127] Renal Cell Carcinoma Olink Explore 384 61 Anti-PD-L1 (Atezolizumab) DFS High serum KIM-1 levels associated with worse DFS (HR = 1.68 ). Low Carril-Ajuria et al. [128] Renal Cell Carcinoma MSD Immunoassay 353 (40 Discovery Cohort; 313 Validation Cohort) Anti-PD-1 (Nivolumab) OS High plasma BAFF (HR: 4.39), BCA-1/CXCL13 (HR: 4.74), and IL-6 (HR: 4.41) associated with worse OS in discovery. Validated in larger cohort (BAFF HR: 1.73; CXCL13 HR: 1.52; IL-6 HR: 2.53). Low Xiao et al. [129] Triple-Negative Breast Cancer Olink 96 Immuno-Oncology 134 (83R, 51NR) Anti-PD-1 + Chemo RECIST 1.1; R: (CR&PR), NR: (SD&PD) & PFS ARG1 and CD28 had higher levels in R while NOS3 decreased levels in R vs NR. Additionally, higher levels of IL-6, NOS3, VEGFA, KLRD1, and CSF-1 were associated with poor prognosis and shorter PFS. Low Liu et al. [130] Triple-Negative Breast Cancer Olink 96 Immuno-Oncology 34 (23R, 11NR) Anti-PD-1 + TKI + Chemo RECIST 1.1, (R: CR ) NR: (PR&SD&PD) High levels of IL-18 were associated with total pCR. IL-18 was the only protein consistently elevated in total pCR patients before treatment. Low Li Y et al. [131] Triple-Negative Breast Cancer LC-MS/MS (TMT) 10 (4R, 6NR) Anti-PD-L1 + Paclitaxel R vs NR according to CT imaging FAP and COMP levels were significantly increased in NR while LRG1 and LBP levels were increased in R. High Tognetti et al. [132] Pancreatic Cancer LC-MS/MS (DIA) 30 Anti-PD-1 + Chemo 1-Year Survival After adjusting for age and sex, ACE levels were significantly higher in patients who survived past one year. Low Christensen et al. [133] Pancreatic Cancer Olink Target 96 Immuno-Oncology 78 (22R, 48NR) Anti-PD-1 + SBRT RECIST 1.1; R: (CR&PR&SD), NR: (PD) & PFS & OS R had higher levels of FASLG and Galectin-1 (Gal-1), whereas NR had higher levels of CCL4. Gal-1 independently predicted longer PFS (HR = 0.25). Conversely, high levels of ANGPT2 (HR = 1.64), CCL17 (HR = 1.45), and MUC-16 (HR = 1.32) correlated with shorter OS. Low Cui et al. [134] Biliary Tract Cancer Olink Target 96 Immuno-Oncology 37 (21R, 16NR) Anti-PD-1 + Chemo RECIST 1.1; R: (CR&PR), NR: (SD&PD) Higher levels of HO-1 and CXCL1 were observed in NR versus R patients. High Gao et al. [42] Esophageal Cancer Olink 92 Immuno-Oncology 89 (24R, 65NR) Anti-PD-1 +/- Anti-CTLA-4 / Anti-angiogenic iRECIST & PFS & OS Increased levels of IL-8, TIE2, and HGF correlated with shorter PFS (IL-8 HR = 1.761, TIE2 HR = 2.326, HGF HR = 2.010) and OS. Increased levels of TNFRSF12A, CD83, ICOSLG, CD5, TRAIL, TNFRSF21, and DCN were associated with increased OS. Moderate Roudko et al. [135] Endometrial Cancer Olink 92 Immuno-Oncology 52 Anti-PD-1 +/- TKI inhibitor OS & PFS Higher levels of CSF-1, CCL23, PGF, TNFRSF12A, IL-10, ADGRG1, CCL20, and CAIX were associated with shorter OS, Elevated levels of CSF-1, CCL23, ANGPT2, IL-10, and CCL20 were associated with poorer PFS. Low Xu et al. [136] Colorectal Cancer Olink 96 Immuno-Oncology 32 (26R, 6NR) Anti-PD-1 + Cetuximab + Irinotecan RECIST 1.1; R: (CR&PR), NR: (SD&PD) IL-6 levels decreased in R compared to NR, while the levels of CD40-L, EGF, PGF, MCP-1, TRAIL, MUC-16, CD4, VEGFR-2, LAP TGF-beta, TWEAK, GZMB, and ICOSLG were increased in R compared to NR. Moderate Li ZC et al. [137] Hepatocellular Carcinoma TIMS-TOF LC-MS/MS 64 (34R, 30NR) Anti-PD-1 + Lenvatinib mRECIST; R: (CR&PR), NR: (SD&PD) & PFS Increased levels of complement membrane attack complex components (C5-C9), regulatory complement proteins (CFB, CFHR1, SERPIND1, CFI), lectin pathway proteins (FCN2, FCN3, MASP2), and TTN, CRTAC1, PLXDC2 were detected in R compared to NR. Low Li J et al. [138] Cervical Cancer Olink 92 Immuno-Oncology & ELISA 38 (Discovery Cohort: 17; Validation Cohort: 21) Anti-PD-1 +/- Chemo / Anti-VEGF R: PR, NR: PD A five-protein signature (ITGB5, TGF-α, TLR3, WIF-1, and ERBB3) effectively discriminated Rs from NRs (AUC 0.9227. ITGB5, TGF-α, TLR3, and ERBB3 were significantly higher in R, while WIF-1 was higher in NR. All five proteins were validated by ELISA, yielding a final model AUC of 0.9537. Moderate Zhang et al. [139] Head and Neck Cancer Olink 92 Immuno-Oncology 42 (27R, 15NR) Anti-PD-1 + Chemo Tumor Viability IL-5 and IL-13 levels had significantly higher abundance in R compared to NR, whereas CCL3, CCL4, and MMP7 were at increased levels in NR compared to R. Moderate 3. Results The initial MEDLINE search yielded 267 records, and the Cochrane Library (CENTRAL) search yielded 28 records. After removing 5 duplicates, 290 unique records were screened. During the title and abstract screening, review articles, meta-analyses, and non-human studies were excluded, leaving 199 publications, out of which 177 were retrieved for full-text eligibility assessment. Following the application of inclusion and exclusion criteria, 36 studies were selected. Notable exclusions included the studies by Lyu et al. [24] and Keegan et al. [25] which initially appeared to meet the criteria but were ultimately excluded because their primary outcomes were based on longitudinal proteomic changes during treatment rather than baseline pre-treatment profiles. Additionally, 13 studies were identified through manual searches of reference lists and included after verifying they met all eligibility criteria. This resulted in a final total of 49 included studies, as detailed in the PRISMA flow diagram ( Fig. 1 ) . 3.1 Risk of Bias The RoB assessment using the QUIPS tool revealed that approximately 34.7% (n = 17) of the included studies were classified with an overall low RoB, while 46.9% (n = 23) had a moderate and 18.4% (n = 9) had a high risk. The most common methodological concern was related to study confounding, as statistical assessment or adjusting for covariates was not reported in 42.9% of the studies. Additionally, high RoB based on statistical analysis was observed in 36.7% of the studies, mainly due to selective reporting of results and limited information regarding their significance (e.g., specific p-value or hazard ratio) ( Additional File 1 , Table 2 ). These findings highlight the limited adjustment for confounding factors and variability in statistical rigor across studies, which may affect the reliability of reported biomarker associations. 3.2 Diseases In the 49 included studies, lung cancer (n = 21) and melanoma (n = 13) were overrepresented, with all other cancer types accounting for 15 studies ( Fig. 2 A ) . Among the latter, triple-negative breast cancer and renal cell carcinoma were investigated in 3 studies, while all other cancer types were investigated in fewer than 3 studies ( Table 1 ) . This distribution partly reflects the pace at which such therapies have been implemented across different cancer types, with melanoma and lung cancers among the first to receive FDA approval [26]; while also highlighting the expanding application of exploratory proteomics applications in additional malignancies such as cervical or hepatocellular cancer. This imbalance may also influence the generalizability of findings across cancer types. 3.3 Treatment Regimens Immunotherapy treatments varied across different studies, particularly across cancer types. Importantly, all cohorts received ICI-based treatment regimens, with their combination with non-immunotherapy treatments appearing in approximately one third of the studies (n = 16). Among these 16 studies, most included chemotherapy (n = 11), followed by tyrosine kinase inhibitors (TKIs, n = 4). As shown in Table 2 , all studies including melanoma patients evaluated either ICI monotherapy or dual checkpoint blockade therapies (DBT), whereas among the cohorts with lung cancer patients (n = 21), only 4 included combinations with chemotherapy, with the rest being treated with immune checkpoint blockade (ICB) monotherapy or DBT. In contrast, among the remaining studies that included patients across other cancer types (n = 15), 11 cohorts reported the combination of ICI treatments with other treatment modalities, such as chemotherapy, radiotherapy, or targeted therapies (e.g., TKIs, anti-vascular endothelial growth factor - VEGF). Regarding specific treatment regimens, anti-PD-1 blockade was the most frequently applied strategy: A total of 29 studies included patients receiving anti-PD-1 monotherapy, predominantly pembrolizumab or nivolumab. In addition, 9 studies involved administration of anti-PD-1-based regimens in combination with other non-immunotherapy treatment approaches, specifically chemotherapy, TKIs (e.g., cabozantinib), radiotherapy, and other targeted therapies, including anti-VEGF and anti-angiogenic agents. Dual ICI with anti-PD-1 plus anti-CTLA-4 was also reported in 11 studies, while anti-CTLA-4 monotherapy (mainly in the form of ipilimumab) was used in 6 studies. Furthermore, 13 studies included anti-PD-L1-based regimens, primarily involving atezolizumab or durvalumab. Also, 2 studies reported the use of ICI without specifying the exact pharmaceutical regimen administered. Collectively, anti-PD-1 was evaluated either as monotherapy or in combination treatments in 41 of the 47 studies that reported the type of ICI used, while no studies involving anti-LAG3 treatment were retrieved. 3.4 Technologies used for Biomarkers assessment The techniques employed for the identification and quantification of proteins in plasma varied between the studies, ranging from targeted approaches to liquid chromatography-tandem mass spectrometry (LC-MS/MS). The technologies used can be categorized into 3 main types: targeted single- or low level-plex immunoassays, targeted multiplex affinity-based microarrays, and mass-spectrometry-based approaches ( Fig. 2 B ) . 3.4.1 Targeted Low-level Immunoassays Targeted immunoassays were used either as standalone analytical methods or as validation tools following discovery-based analyses. Specifically, 3 studies used an ELISA-targeted approach to quantify soluble levels of individual proteins (e.g., Soluble CD25-sCD25, Soluble lymphocyte activation gene-3 - sLAG3, Soluble interleukin-2 - sIL2) at the discovery stage. ELISA kits for identification of single proteins were also implemented in studies to validate proteins identified through MS or multiplex platforms. Commercial or custom-made ELISA kits from various vendors were used, such as the Human Cytokine-Inflammation (9-plex, 1 study) or Meso Scale Discovery (MSD) panels of 10 and 14 proteins (2 studies), respectively. 3.4.2 Affinity-Based Multiplex Platforms Affinity-based proteomic technologies represented the most frequently used analytical approach across the curated studies. These technologies use binding probes, such as aptamers or antibodies, in order to detect proteins of interest. Among them, the Olink proximity extension assay (PEA) platforms were by far the most used technology, appearing in a substantial number of studies (n = 23). Multiple generations and Olink panel configurations were used, with the most frequent being the Olink Immuno-Oncology 96-plex panel. Angiogenesis and Inflammation panels, as well as higher-level panels such as Olink Explore 384 and Olink Explore 1536, were also implemented. Other affinity-based multiplex platforms included SomaScan, a DNA aptamer-based proteomic platform, which was implemented in one study, as well as other multiplex antibody microarrays (n = 6). As with single-plex ELISA, multiplex arrays were used in some studies to validate LC–MS-based measurements across platforms. 3.4.3 Mass Spectrometry–Based Proteomics Mass spectrometry-based proteomics constituted the second major class of technologies, as it was implemented in 15 studies and was used in both discovery-driven and targeted analytical contexts. A wide range of MS acquisition strategies were applied; of those reported, Data Independent Acquisition (DIA, n = 7 studies) methods were prominent, and 2 more studies used either DIA or Data Dependent Acquisition (DDA) to quantify a portion of their samples, respectively. Additionally, Tandem Mass Tag MS (TMT) was applied in 4 studies, while DDA and an LC-MS workflow for glycoproteomics identification using Porphyrin-Based Network/Net (PB-NET) technology were applied in one case each. 3.5 Outcome assessment Outcome assessment across the included studies aligned with the pre-defined eligibility criteria for this review. Specifically, the clinical outcomes were reported either as binary response classifications, typically distinguishing non-responders (NR) from responders (R) or non-clinical benefit (NCB) from durable benefit (DCB), or as time-to-event endpoints, including PFS and OS, as can be seen in Fig. 2 C. Assessment of treatment response followed established clinical oncology standards, most commonly the Response Evaluation Criteria in Solid Tumors (RECIST). RECIST classifies the treatment response of cancer patients into four categories: complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD) [27]. Most curated studies (n = 28) assessed ICI response using the updated RECIST version 1.1 [28]. Additionally, one study applied immune-related RECIST (iRECIST), which was specifically developed to identify tumor response patterns observed with immunotherapy [29], while one study in hepatocellular carcinoma employed RECIST tailored to this disease context (modified RECIST) [30]. Despite the widespread use of RECIST-based frameworks (in 30 out of 49 studies), substantial heterogeneity is evident in how the four response categories were dichotomized for downstream analyses. Notably, 18 studies classified patients with SD as R or clinical beneficiaries, and 11 studies categorized them as NR or non-beneficiaries, while one study compared patients labeled CR against all other response categories. Additionally, 8 studies did not use binary response classifications but instead assessed biomarker performance using survival endpoints, mainly PFS and OS (6 studies), with the remaining two reporting event-free survival (EFS) or disease-free survival (DFS). In several cases, survival analyses complemented the response-based classifications. The remaining studies (n = 11) applied binary outcome classification without explicitly referencing RECIST criteria, instead relying on alternative assessment strategies such as quantification of viable tumor cells after treatment or dichotomization based on survival or PFS (e.g., early failure compared to sustained control based on survival in specific time points or PFS in 6 months). 3.6 Biomarkers in each Cancer Type Tables 1 – 3 provide a comprehensive summary of the reported biomarkers identified in each study, categorized by malignancy: lung cancer (Table 1 ) , melanoma ( Table 2 ) , and various other cancer types (Table 3 ) . Information about the treatment regimen, the proteomic platform utilized, and the reported biomarkers, which, at baseline, were found to associate with clinical response or survival, is provided, reflecting the abovementioned inter-study variability. Notably, sample sizes varied widely (from 10 to 353 samples) and differences in the study design -including the presence/absence of external validation cohorts- may influence statistical power and reproducibility. A vote-counting approach was implemented for the synthesis of the findings across studies, based on the direction of the reported statistically significant associations (p < 0.05 in comparison of R and NR or significant associations with time-to-event endpoints such as PFS or OS) in independent studies. Comparing the findings from the 20 lung cancer studies ( Table 1 ) , a small number of consistent findings may be highlighted: Interleukin 8 (CXCL8), which was reported in 5 studies [31–35] correlated with inferior response and clinical outcomes. Additionally, elevated circulating levels of Interleukin 6 (IL-6) were associated with inferior outcomes in three studies [33,36,37]. Several markers showed consistent associations with treatment response across two studies each: Fas Ligand (FASLG) [38,39], and Tumor Necrosis Factor (TNF) [34,40] associated positively, while. Conversely, Follistatin (FST) [34,41], TNF Superfamily Member 14 (TNFSF14) [37,42], and C-C Motif Chemokine Ligand 3 (CCL3 or MIP-1α) [35,43] associated negatively with response. In the 13 reviewed melanoma studies ( Table 2 ) , C-reactive protein (CRP), Serum Amyloid A1 (SAA1), and Serum Amyloid A2 (SAA2) demonstrated reproducible correlations with clinical outcomes in two studies, with increased levels of these proteins being consistently linked to NR and poor OS. Overall, the melanoma evidence is characterized by limited reproducibility and frequent lack of external validation. Pan-Cancer synthesis From the synthesis of the 49 studies across all cancer types, we observed that a number of proteins showed correlations with outcomes across all cancer types (Fig. 3 ) . Characteristically, the inflammation marker IL-6 was the most consistently reported negative biomarker (defined as being observed at higher levels in NR or linked to shorter OS or PFS in at least 3 studies), as it was identified as a negative predictor in 8 studies across 6 different types of cancer (melanoma, lung cancer, breast cancer, renal cell carcinoma, esophageal cancer, and colorectal cancer). Similarly, the chemokine C-X-C Motif Chemokine Ligand 8 (Interleukin 8) (CXCL8 or IL-8) was consistently associated with poorer outcomes in 7 studies from 3 different cancer types (lung cancer, melanoma, and esophageal cancer). However, one study [44] reported a contradictory finding, as higher CXCL8 levels were observed in melanoma patients who responded to therapy. This discrepancy could be explained by their alternative response assessment, based on the number of viable tumor cells as an endpoint [44]. Another notable negative biomarker is Angiopoietin-2 (ANGPT2), which was associated with inferior outcomes in 4 studies across 4 distinct cancer types: endometrial, esophageal, lung, and pancreatic cancer. Similarly, CCL3 and Colony Stimulating Factor 1 (CSF1) were linked to poorer outcomes in 4 studies each. CCL3 was reported in head and neck carcinoma, lung cancer, and melanoma, whereas CSF1 was identified in breast, esophageal, endometrial, and lung cancers. Additionally, CRP, C-X-C Motif Chemokine Ligand 10 (CXCL10/IP-10), SAA1, and SAA2, which are acute-phase proteins involved in inflammation and immune modulation, demonstrated negative associations with response or survival in 3 studies involving lung cancer and melanoma. TNFSF14 also showed a negative association in 3 studies from lung and esophageal cancer. In contrast, among positive biomarkers (defined as those with significantly higher abundances in R or significantly associated with longer PFS or OS in at least 3 studies), T-cell ligand proteins were the most replicated findings. Specifically, Inducible T Cell Costimulator Ligand (ICOSLG) and FASLG were associated with R and prolonged survival in 4 studies across 3 (lung, pancreatic, and endometrial cancer) and 4 (endometrial, esophageal, colorectal, and lung cancer) cancer types, respectively. Likewise, Tumor Necrosis Factor (Ligand) Family, Member 10 (TNFSF10) was also identified as a positive biomarker in 3 studies from 3 different cancer types (colorectal, melanoma, and esophageal cancer). Lastly, the chemokine C-X-C Motif Chemokine Ligand 5 (CXCL5) was also found to be a positive marker in 3 studies from endometrial, colorectal, and melanoma cancers. To increase the reliability of the synthesis, a sensitivity analysis was performed by investigating the consistent findings after excluding studies labeled with a high RoB (n = 9). The association between elevated baseline levels of IL-6 and ICI resistance remained the most robust finding, with CXCL8, ANGPT2, CSF1, CCL3, and TNFSF14 also remaining negative biomarkers in at least 3 studies. Along similar lines, none of the positive biomarkers (ICOSLG, FASLG, TNFSF10, CXCL5) were removed during sensitivity analysis. A dependency on specific proteomic platforms for biomarker detection may frequently be observed, as summarized in Table 4 . The Olink Immuno-Oncology panel was the sole technology supporting the association of FASLG, ICOSLG, ANGPT2, TNFSF10, TNFSF14, and CSF1 with response, while IL-6 findings were frequently supported by both Olink and various multiplex bead-based assays. In contrast, the detection of SAA1, SAA2, and CRP relied almost exclusively on LC-MS/MS, while CCL3 and CXCL8 were identified using LC-MS/MS but also PEA-based panels. Table 4 Frequency and association of the replicated baseline proteomic biomarkers across the studies. The table includes biomarkers identified in at least 3 independent studies, with identical trends in respect to outcome in all studies shown. Olink IO means Olink ImmunoOncology panel. Positive means association with response or longer survival or improved progression free survival while negative means the opposite. Biomarker Association No. of Studies Panels Sample Type Cancer Types Reported Studies IL-6 Negative 8 Olink IO (n = 4), Olink Inflammation (n = 1), Bio-Plex Cytokines Grp 27-plex Panel (n = 1), BioVendor Human Cytokine-Inflammation 9-plex kit (n = 1), MSD Immunoassay (n = 1) Plasma (n = 4), Serum (n = 4) Melanoma, Lung, Breast, RCC, Esophageal, Colorectal [33,36,37,42,124,128,129,136] CXCL8 (IL-8) Negative 7 Olink IO (n = 2), Olink Explore 384 (n = 1), ELISA-based multiplex Antibody Arrays(n = 1), Multiplex MAP assay (n = 1), Multiplex Bead Array (65-plex Chemokine Assay (n = 1), LC-MS/MS (n = 1) Plasma (n = 3), Serum (n = 4) Lung, Melanoma, Esophageal [31–35,42,120] ANGPT2 Negative 4 Olink IO (n = 4) Plasma (n = 2), Serum (n = 2) Endometrial, Esophageal, Lung, Pancreatic [38,42,133,135] SAA1 / SAA2 Negative 3 LC-MS/MS (n = 3) Plasma (n = 2), Serum (n = 1) Lung, Melanoma [106,117,125] CRP Negative 3 LC-MS/MS (n = 2), Multiplex Bead Assay (n = 1) Plasma (n = 1), Serum (n = 2) Lung, Melanoma [32,41,125] CCL3 (MIP-1α) Negative 4 LC-MS/MS (n = 2), Olink IO (n = 2) Plasma (n = 4) Head & Neck, Lung, Melanoma [35,43,114,139] CSF1 Negative 4 Olink IO (n = 4) Plasma (n = 3), Serum (n = 1) Breast, Esophageal, Endometrial, Lung [35,42,129,135] TNFSF14 Negative 3 Olink IO (n = 3) Plasma (n = 1), Serum (n = 2) Lung, Esophageal [37,40,42] CXCL10 Negative 3 Olink IO (n = 1), Multiplex MAP assay (n = 1), Multiplex Bead Array 65-plex Chemokine Assay (n = 1) Plasma (n = 2), Serum (n = 1) Lung, Melanoma [34,40,120] FASLG Positive 4 Olink IO (n = 4) Plasma (n = 3), Serum (n = 1) Endometrial, Pancreatic, Lung, Colorectal [38,39,133,135] ICOSLG Positive 4 Olink IO (n = 4) Plasma (n = 2), Serum (n = 2) Lung, Pancreatic, Endometrial, Esophageal [39,42,132,135] TNFSF10 Positive 3 Olink IO (n = 2), Multiplex Bead Array (65-plex Chemokine Assay (n = 1) Plasma (n = 2), Serum (n = 1) Colorectal, Melanoma, Esophageal [42,120,136] CXCL5 Positive 3 Olink IO (n = 2), Multiplex Bead Array (65-plex Chemokine Assay (n = 1) Plasma (n = 3) Endometrial, Colorectal, Melanoma [120,135,136] 4. Discussion The synthesis of the 49 studies indicated that circulating proteins in plasma related to systemic inflammation and vascular dysfunction are predominant hallmarks of resistance to ICI, while soluble T-cell ligands were markers for improved outcomes. Characteristically, IL-6, which is a pleiotropic chemokine often induced during acute stress [45] and a driver of systemic inflammation [46] emerged as the most frequent and consistent negative correlator of response. High circulating concentrations of IL-6 have been observed in various pathological states [47] and participate in inflammatory signaling mechanisms that promote tumor progression [48]. Additionally, IL-6 has been linked to immunosuppressive functions either via the recruitment and activation of myeloid-derived suppressor cells [49] or via the induction of T-cell exhaustion, both of which lead to immune evasion [50]. Preclinical evidence on murine models has also shown the negative impact of IL-6 on immunotherapy. Specifically, in pancreatic cancer, the dual blockade of IL-6 and PD-L1 suppresses tumor growth [51], while in models of B-cell lymphoma, its absence has been associated with increased efficiency of anti-PD-L1 blockade [52]. These findings suggest that high baseline levels of IL-6 may reflect a broader state of systemic dysregulation. Similarly, CXCL8 (IL-8) is an inflammatory chemokine that activates neutrophils and is associated with cancer cell growth and epithelial-to-mesenchymal transition [53,54]. In addition to its proangiogenic functions, it has been linked with increased infiltration of myeloid suppressor cells and reduced NK cell activity in the TME [55,56]. In this manner, its blockade using neutralizing antibodies is currently being evaluated in combination with ICIs for cancer treatment [57]. Notably, in a meta-analysis of 1334 patients involving a targeted analysis, increased serum CXCL8 levels were predictors of poor response to ICI [58]. Collectively, the increased systemic levels of IL-6 and CXCL8 are linked to immunosuppressive and pro-tumorigenic functions [59], hence contributing to ICI resistance. Other inflammation markers associated with inferior outcomes in immunotherapy include serum antigens (SAA1, SAA2) and CRP. These proteins are synthesized in the liver in response to chemokine (including IL-6) stimulation, and thus their serum concentrations are highly intercorrelated [60–62]. They are linked to immune evasion and metastasis in cancer diseases [63,64], either by inducing immunosuppressive properties of T cells or neutrophils [65,66], or by promoting angiogenesis via chronic inflammation [67,68]. The negative prognostic role of vascularization proteins is also evident with ANGPT2. ANGPT2 is a pro-angiogenic factor, produced by endothelial cells, that antagonizes Ang1–Tie2 signaling and promotes vascular instability [69–71]. Elevated expression of ANGPT2 has been associated with tumor progression and poor prognosis in a variety of cancers, including melanoma [72]. Soluble levels of proteins that regulate myeloid cells (CXCL10, CCL3, and CSF1) were also consistently associated with an inferior response to ICI. CXCL10 is a chemoattractant cytokine of myeloid cells induced by interferon γ, with reported associations with the regulation of suppressive populations of T cells and neutrophils [73,74]. It has a dual role in cancer, linked to the pro- and anti-tumor functions of the 2 different splice variants of its receptor, C-X-C Motif Chemokine Receptor 3 (CXCR3), with the one variant suppressing tumor growth while the other one induces cell proliferation [75]. Additionally, CCL3 is a pro-inflammatory chemokine that mediates immune cell trafficking and can recruit cytotoxic immune cells through C-C motif chemokine receptor 5 (CCR5) signaling [76]. Elevated baseline CCL3 levels have been associated with expansion of tumor-associated macrophages (TAMs) and angiogenesis [77,78], while also resulting in unfavorable clinical outcomes, including worse OS in melanoma cohorts [79–81]. CSF1 is a cytokine that regulates TAMs’ survival and proliferation [82,83]. Elevated CSF1 levels have also been associated with disease progression, resistance to ICIs, and shorter PFS and OS in cancer patients [84–86]. TNFSF14 also emerged as a marker of worse prognosis despite its antitumor properties in preclinical models [87]. This may be linked to its role as a marker of broader systemic stress, evidenced by its correlation with CXCL8 levels and cardiovascular risk [88,89]. T-cell ligand proteins, on the other hand, were the most replicated positive biomarkers. Specifically, FASLG is a transmembrane ligand that binds to the FAS receptor mainly on T-cells and triggers their apoptosis [90], while its deficiency is often linked to better survival outcomes [91]. However, compared to the membrane-bound form of FASLG, its soluble form lacks the apoptosis-inducing activity and instead influences cytokine secretion while posing a protective role in its receptor-expressing cells [92,93], which could explain its positive association to ICI response. On the other hand, ICOSLG binds to the ICOS receptor on the surface of T cells, activating and enabling them to attack tumor cells [94]. Therapies based on ICOS agonists are being developed and tested for the improvement of cancer outcomes [95], suggesting that secreted ICOSLG could reflect a "hot" tumor setting. Similarly, TNFSF10, once bound to its cell receptors, including on tumor cells, promotes cell death via caspase activation [96,97]. These cytotoxic properties are also retained in the soluble form of TNFSF10, which may underlie the observed positive associations [98]. Lastly, soluble levels of CXCL5, a chemokine that attracts tumor-associated neutrophils to the TME [99], were associated with better prognosis in 3 studies. This apparent contradiction may be explained by the correlation of CXCL5 expression with increased tumor PD-L1 levels [100,101], which are typically associated with higher ICI efficacy [102]. Despite these findings, several limitations should be acknowledged. The heavy reliance on the Olink Immuno-Oncology panel restricted the proteome coverage and limited cross-platform validation to the 92 proteins of the panel, thus minimizing overlaps with LC-MS/MS approaches. In addition, response assessment varied across studies; to maximize the number of eligible studies, we considered both ORR and time-to-event endpoints (such as PFS and OS) as acceptable endpoints. Although a clear relationship between radiological response and survival outcomes has been demonstrated in several oncology contexts [103], this association is not universally generalized across all studies or settings [104]. These factors introduce heterogeneity and potentially limit the power of the presented conclusions. To adjust to this limitation, a count voting approach was used when integrating the findings, which, however, does not account for effect size. Therefore, the findings should be interpreted as exploratory rather than quantitative estimates of association. Despite these limitations, this comprehensive review, conducted in a systematic manner, highlights a consistent association between systemic inflammatory and angiogenic markers and resistance to ICIs, as well as a link between soluble T-cell-related proteins and improved outcomes. Notably, these biomarkers appear to reflect systemic immune states rather than tumor-specific mechanisms and are reproducible across multiple cancer types. 5. Conclusion In this comprehensive review, we aimed to synthesize findings from studies profiling the pre-treatment blood proteome and to investigate the potential of specific biomarkers as non-invasive tools for predicting outcomes in patients receiving ICI. The literature revealed a lack of standardized profiling protocols, resulting in significant heterogeneity across technologies and response assessments. While lung cancer and melanoma were the most prominently studied diseases, the most reproducible findings were mostly not disease-specific but instead were found across diverse cancer types. Based on the reproducible findings, a systemic environment characterized by elevated levels of the proteins IL-6, CXCL8, ANGPT2, CRP, and SAA1/2 was predictive of ICI resistance. These markers were reflective of a state of chronic systemic inflammation and vascular dysregulation that potentially induced an inhospitable environment for tumor immunosurveillance, hindering the efficacy of immunotherapy, while simultaneously promoting tumor growth. In contrast, the presence of soluble T-cell ligands, such as ICOSLG, TNFSF10, and FASLG, signaled a more immune-active TME that favored therapeutic success. Future research should focus on prospective validation of these biomarkers and the development of standardized analytical workflows, including the definition and application of validated cut-offs. Such efforts are essential to enable the clinical translation of circulating proteomic biomarkers into clinical practice and enable patient stratification based on outcome prediction. Abbreviations AUC Area Under the Curve BTC Biliary Tract Cancer DBT Dual Blockade Therapy DCB Durable Clinical Benefit DDA Data Dependent Acquisition DFS Disease-Free Survival DIA Data Independent Acquisition EDT Early During Therapy EFS Event-Free Survival ELISA Enzyme-Linked Immunosorbent Assay HCC Hepatocellular Carcinoma HNSCC Head and Neck Squamous Cell Carcinoma HiRIEF High-Resolution Isoelectric Focusing ICB Immune Checkpoint Blockade ICI Immune Checkpoint Inhibitor iRECIST Immune-related RECIST LC-MS/MS Liquid Chromatography-Tandem Mass Spectrometry LTR Long-Term Response MAP Multi-Analyte Profiling mRECIST Modified RECIST MS Mass Spectrometry MSD Meso Scale Discovery NSCLC Non-Small Cell Lung Cancer ORR Objective Response Rate OS Overall Survival PB-NET Porphyrin Based-Network/Net pCR Pathologic Complete Response PD Progressive Disease PDAC Pancreatic Ductal Adenocarcinoma PEA Proximity Extension Assay PFS Progression-Free Survival PR Partial Response RCC Renal Cell Carcinoma RECIST Response Evaluation Criteria in Solid Tumors RoB Risk of Bias SD Stable Disease STR Short-Term Response SWATH-MS Sequential Window Acquisition of all Theoretical Mass Spectra TIMS-TOF Trapped Ion Mobility Spectrometry Time-of-Flight TKI Tyrosine Kinase Inhibitor TME Tumor Microenvironment TMT Tandem Mass Tag TNBC Triple-Negative Breast Cancer Declarations Authors' contributions TM performed the literature search, screened the records, did the RoB assessment, and wrote the manuscript. AT additionally screened records, performed the RoB assessment, and assisted in data synthesis. MF and AV supervised the project and critically revised the manuscript for important intellectual content. All authors read and approved of the final manuscript. Author Contributions (CRediT) Theodoros Margelos (TM): Conceptualization, investigation, methodology, and writing: original draft. Aggeliki Tserga (AT): Investigation; Writing: original draft. Maria Frantzi (MF): Supervision; Validation; Writing: review and editing. Antonia Vlahou (AV): Supervision, project administration, and writing review and editing. Data Statement The finalized list of included studies is included in the Additional file. The search strings used for the literature review are provided within it. Competing interests MF is an employee of Mosaiques Diagnostics (Hannover, Germany). Funding Sources Funded by the European Union (Project 101136926-MULTIR). 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Liu, Proteomic, single-cell and bulk transcriptomic analysis of plasma and tumor tissues unveil core proteins in response to anti-PD-L1 immunotherapy in triple negative breast cancer, Comput. Biol. Med. 176 (2024) 108537. https://doi.org/10.1016/j.compbiomed.2024.108537. M. Tognetti, L. Chatterjee, N. Beaton, K. Sklodowski, R. Bruderer, L. Reiter, C.B. Messner, Serum proteomics reveals survival-associated biomarkers in pancreatic cancer patients treated with chemoimmunotherapy, iScience 28 (2025) 112230. https://doi.org/10.1016/j.isci.2025.112230. T.D. Christensen, E. Maag, S. Theile, K. Madsen, S.C. Lindgaard, J.P. Hasselby, D.L. Nielsen, J.S. Johansen, I.M. Chen, Circulating immune-related proteins associated with immune checkpoint inhibitor efficacy in patients with pancreatic ductal adenocarcinoma, ESMO Open 9 (2024) 103489. https://doi.org/10.1016/j.esmoop.2024.103489. S. Cui, H. Zheng, Y. Xu, Q. Wu, W. Liu, Y. Cai, L. Fan, Y. Tian, H. Qian, Y. Ding, X. Zhang, J. Zhang, X. Wu, R. Wang, X. Li, X. Chen, Plasma proteomic biomarkers predict therapeutic responses in advanced biliary tract cancer patients receiving Camrelizumab plus the GEMOX treatment, NPJ Precis. Oncol. 9 (2025) 102. https://doi.org/10.1038/s41698-025-00879-9. V. Roudko, D.M. Del Valle, E. Radkevich, G. Kelly, X. Hui, M. Patel, E. Gonzalez-Kozlova, K. Tuballes, H. Streicher, S. Atale, L. Wang, B. CzinCzin, S. Kim-Schulze, I.I. Wistuba, C.L. Haymaker, G. Al-Atrash, G. Manyam, J. Zhang, R. Thompson, M. Suarez-Farinas, S. Lheureux, S. Gnjatic, Immunological biomarkers of response and resistance to treatment with cabozantinib and nivolumab in recurrent endometrial cancer, J. Immunother. Cancer 13 (2025) e010541. https://doi.org/10.1136/jitc-2024-010541. X. Xu, L. Ai, K. Hu, L. Liang, M. Lv, Y. Wang, Y. Cui, W. Li, Q. Li, S. Yu, Y. Feng, Q. Liu, Y. Yang, J. Zhang, F. Xu, Y. Yu, T. Liu, Tislelizumab plus cetuximab and irinotecan in refractory microsatellite stable and RAS wild-type metastatic colorectal cancer: a single-arm phase 2 study, Nat. Commun. 15 (2024) 7255. https://doi.org/10.1038/s41467-024-51536-x. Z.-C. Li, J. Wang, H.-B. Liu, Y.-M. Zheng, J.-H. Huang, J.-B. Cai, L. Zhang, X. Liu, L. Du, X.-T. Yang, X.-Q. Chai, Y.-H. Jiang, Z.-G. Ren, J. Zhou, J. Fan, D.-C. Yu, H.-C. Sun, C. Huang, F. Liu, Proteomic and metabolomic features in patients with HCC responding to lenvatinib and anti-PD1 therapy, Cell Rep. 43 (2024) 113877. https://doi.org/10.1016/j.celrep.2024.113877. X. Zhang, J. Li, L. Yang, Y. Zhu, R. Gao, T. Zhang, X. Chen, J. Fu, G. He, H. Shi, S. Peng, X. Wu, Targeted proteomics-determined multi-biomarker profiles developed classifier for prognosis and immunotherapy responses of advanced cervical cancer, Front. Immunol. 15 (2024) 1391524. https://doi.org/10.3389/fimmu.2024.1391524. H. Zhang, W. Wu, M. Wang, J. Zhang, C. Guo, G. Han, L. Wang, Integrated peripheral blood multi-omics profiling identifies immune signatures predictive of neoadjuvant PD-1 blockade efficacy in head and neck squamous cell carcinoma, J. Transl. Med. 23 (2025) 693. https://doi.org/10.1186/s12967-025-06770-2. Additional Declarations The authors declare potential competing interests as follows: MF is an employee of Mosaiques Diagnostics (Hannover, Germany). Supplementary Files AdditionalFile.xlsx Supplementary File (Search String, Supp. Tables 1-3) Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9704067","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":639737105,"identity":"58956090-796a-47d4-91b2-c0d05efb33a7","order_by":0,"name":"Theodoros Margelos","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYPACCwYG9h6GA2D2AQJqeSCUBJB1hmQtEjlQIUJa7MUOP/v4pUJCTn7m24MHPtTYyfMd706TYPh1D7ct0mnGs2XOSBgb3M5LODjjWLLhzDNnt0kw9hXj0ZJgzCzZJpG4QTrH4DAP2wHGDTdygVp6EvBoSf/MLPlPInH+zDNALf8O2G+4/5aQlhxjxo8NEokNN3gMDvO2HUjccIN3mwTDDzxabucUMzMcA/rlDNAvM/uSk2eeyd1skdiAWwv77PTNjD9qbOTk288e/vDhm51t3/GzG298+INbCwgw86AJsEgktuHVwcD4A92MDwx/8GsZBaNgFIyCEQUAQIZcGb6wWRcAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0009-1978-0370","institution":"Biomedical Research Foundation, Academy of Athens","correspondingAuthor":true,"prefix":"","firstName":"Theodoros","middleName":"","lastName":"Margelos","suffix":""},{"id":639737106,"identity":"325556b6-4d23-4875-8ad5-f77f39bb3f27","order_by":1,"name":"Aggeliki Tserga","email":"","orcid":"https://orcid.org/0000-0002-5531-2111","institution":"Biomedical Research Foundation, Academy of Athens","correspondingAuthor":false,"prefix":"","firstName":"Aggeliki","middleName":"","lastName":"Tserga","suffix":""},{"id":639737107,"identity":"fa31daa4-142b-4bd8-9b45-373ec758a1cf","order_by":2,"name":"Maria Frantzi","email":"","orcid":"https://orcid.org/0000-0003-0415-0316","institution":"Mosaiques Diagnostics GmbH","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Frantzi","suffix":""},{"id":639737108,"identity":"0b0925e0-0bcc-4d52-8ac6-a975e911653b","order_by":3,"name":"Antonia Vlahou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYDACdgY2ECVnwAzmMhOhhRmixZh0LYkbYFyCwOAw87MHH9vs0rezMx/88KPGWk53RgLz6wq8WtjMDWe2JefubGZLluw5lm5sdiOBzfIMHi2SzQxm0rxtzLkbDvMYSDOwHU7cBtRi2IBXC/s3oJb6dIPDPMa/Gf4drieohZ+ZB2TL4QSgFjNpRiAD6DDmhwS0lEnOOHfcEOiXNMvevnTDbWcetjHi08LG3r5N4kNZtbw5/+HDN358s5Y3O558+CM+LViAQGKbBGk6GPgPMH8gUcsoGAWjYBQMbwAAaY5Je/br6DQAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-3284-5713","institution":"Biomedical Research Foundation, Academy of Athens","correspondingAuthor":true,"prefix":"","firstName":"Antonia","middleName":"","lastName":"Vlahou","suffix":""}],"badges":[],"createdAt":"2026-05-13 12:40:50","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9704067/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9704067/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109286990,"identity":"b711558d-c580-46f5-8a66-9a89fb548882","added_by":"auto","created_at":"2026-05-15 02:38:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44403,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003eRISMA 2020 flow diagram for the review. From 295 initial records, 49 studies were included (36 from databases and 13 from manual search). Exclusions were based on article type (reviews/meta-analyses), non-human data, or lack of pre-treatment measurements.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9704067/v1/4fb3caa0e6e93b4a9a10f38a.png"},{"id":109286992,"identity":"cd533227-a5f1-4b40-ad00-6c1643e4e0af","added_by":"auto","created_at":"2026-05-15 02:38:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":125329,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological characteristics of the included studies. (A) Distribution of cancer types. (B) Analytical proteomic technologies. (C) Clinical outcome assessment.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9704067/v1/8e82d30e7c32bcdbbbb20bf8.png"},{"id":109286991,"identity":"4e1fe83d-ed7e-4236-800a-891d52b5b031","added_by":"auto","created_at":"2026-05-15 02:38:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":32684,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency and direction of baseline proteomic biomarkers across studies of different cancer types. Green color means association of the protein biomarker with positive immunotherapy outcomes, while red color means association with negative immunotherapy outcomes.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9704067/v1/4be82ca1e072ed8304dd9868.png"},{"id":109296514,"identity":"5b01bf2a-fe91-4644-be0d-27159875e0bc","added_by":"auto","created_at":"2026-05-15 08:47:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":733328,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9704067/v1/ab9c0939-16a4-406c-9ed2-dfc9f6a0886d.pdf"},{"id":109286993,"identity":"4ac37d79-6d63-4f53-9a41-ccea8e1c1a25","added_by":"auto","created_at":"2026-05-15 02:38:06","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32611,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary File (Search String, Supp. Tables 1-3)\u003c/p\u003e","description":"","filename":"AdditionalFile.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9704067/v1/63238676f714bc7a8363defa.xlsx"}],"financialInterests":"The authors declare potential competing interests as follows: MF is an employee of Mosaiques Diagnostics (Hannover, Germany).","formattedTitle":"\u003cp\u003e\u003cstrong\u003eProteomic Biomarkers for Predicting Immunotherapy Outcomes: A Comprehensive Pan-Cancer Review\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003ePan-cancer review of pre-treatment blood proteomic biomarkers for ICI response.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAnalysis of 49 studies reveals patterns across diverse proteomic technologies.\u003c/li\u003e\n \u003cli\u003eSystemic inflammation (IL-6, CXCL8) consistently predicts ICI resistance.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eElevated soluble T-cell ligands correlate with improved clinical outcomes.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eOne mechanism by which the human body limits tumor progression is immunosurveillance, whereby immune cells recognize tumor-associated antigens and eliminate malignant cells by inducing apoptosis [1]. Immunotherapy relies on this intrinsic ability of the immune system, particularly in the tumor microenvironment (TME) to control cancer cell growth, and prevent cancer spread [2]. However, tumors can evade immune detection by suppressing immune cell function. A key mechanism of immune evasion involves the expression of coinhibitory ligands, such as the programmed death-ligand 1 (PD-L1) by tumor cells, which bind to inhibitory checkpoint receptors on T cells, most notably cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and programmed death 1 (PD-1), thereby delivering signals that suppress T cell activity [3,4] .\u003c/p\u003e \u003cp\u003eOn this basis, immune checkpoint inhibitors (ICIs) block inhibitory signals or receptors to restore or enhance anti-tumor immune response by inducing immunosurveillance. Several humanized monoclonal blocking antibodies targeting CTLA-4 (ipilimumab), PD-1 (pembrolizumab, nivolumab, and cemiplimab), or PD-L1 (atezolizumab, durvalumab, and avelumab) have been approved by the FDA for clinical use, either as monotherapies or in combination with chemotherapy for the treatment of multiple tumor types. These therapies have revolutionized cancer treatment, particularly for solid tumors, significantly improving survival outcomes and expanding therapeutic options in advanced states across a wide range of malignancies [5\u0026ndash;10]. However, despite improved outcomes with immunotherapy, only an estimated 20\u0026ndash;50% of patients respond to these treatments (depending on the disease and study), while many others develop immune-related adverse events [11,12].\u003c/p\u003e \u003cp\u003eConsiderable efforts to associate molecular profiles with response to immunotherapy have been made in recent years. In this context, the expression of tumor PD-L1 has been studied for its effectiveness in predicting response [13] to anti-PD-1 immunotherapy. However, results from major clinical studies demonstrate variability and often suboptimal overall response rates (ORR) in PD-L1-positive patients, ranging from 43% to 58% in melanoma [14] and 18% to 78% in urothelial cancer [15]. Studies have also shown that genomic instability via microsatellite instability and higher tumor mutational burden (TMB) correlates with improved responses to ICI blockade. Nevertheless, similar to PD-L1 detection, their predictive value is far from optimal, with the reported ORR of TMB-high patients in a meta-analysis from melanoma and non-small cell lung cancer (NSCLC) patients limited to 58% [16]. To fill these gaps, multiple studies have investigated blood-based biomarkers associated with response to immunotherapy. These include the circulating tumor DNA [17], the neutrophil-to-lymphocyte ratio [18], and the soluble levels of PD-L1 [19]. In parallel, protein quantification analyses of plasma and serum have been increasingly applied to identify immunotherapy response biomarkers non-invasively, driven by the recent advancements in proteomic technologies [20].\u003c/p\u003e \u003cp\u003eIn this comprehensive review, we aim to define the current state of the art regarding blood-based proteomic biomarkers associated with immunotherapy response. Given their high clinical relevance for patient stratification, we focus on biomarkers\u0026rsquo; levels prior to treatment (baseline biomarkers). To the best of our knowledge, no prior study has systematically synthesized evidence on pre-treatment circulating proteomic markers across malignancies in this context. By integrating the findings from the existing literature, we aim to identify promising candidates that consistently correlate with improved response to ICIs, while highlighting critical limitations and challenges that warrant further investigation.\u003c/p\u003e \u003cp\u003eSpecifically, this review addresses the following questions:\u003c/p\u003e \u003cp\u003e● \u003cb\u003eQuestion 1\u003c/b\u003e: Which technologies (e.g., ELISA- Enzyme-Linked Immunosorbent Assay, mass spectrometry) have been used for the analysis of baseline/pre-treatment plasma protein biomarkers for ICI?\u003c/p\u003e \u003cp\u003e● \u003cb\u003eQuestion 2\u003c/b\u003e: How is response to ICI defined and assessed across tumors/studies?\u003c/p\u003e \u003cp\u003e● \u003cb\u003eQuestion 3\u003c/b\u003e: What are the most consistent biomarker findings among different studies per cancer type and across different tumor types?\u003c/p\u003e \u003cp\u003e● \u003cb\u003eQuestion 4\u003c/b\u003e: When consistent biomarker proteins emerge across studies, what biological insights can be inferred regarding their roles in circulation?\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThis comprehensive review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines [21]. Synthesis was performed following the Synthesis Without Meta-analysis (SWIM) guidelines [22]. Due to the significant heterogeneity in protein measurement platforms (e.g., ELISA vs. Mass Spectrometry), cancer types, and outcome definitions across the included studies, a quantitative meta-analysis was not feasible. Accordingly, a narrative synthesis was conducted to summarize associations between pretreatment blood proteomic profiles and immunotherapy outcomes.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Eligibility Criteria\u003c/h2\u003e \u003cp\u003eStudies were included if they met all of the following criteria: (1) they involve human cancer patients treated with immunotherapy, specifically immune checkpoint inhibitors targeting PD-1, PD-L1, CTLA-4, or dual blockade regimens; (2) they present proteomic analyses or quantification of specific protein levels on plasma or serum samples; (3) biospecimens are collected at baseline/pre-treatment, defined as prior to the initiation of immunotherapy, with analyses reporting predictive associations based on these pre-treatment proteomic profiles; (4) they report associations with treatment response or clinical outcomes, including ORR, progression-free survival (PFS) and/or overall survival (OS); and (5) studies conducted in patients with all cancer types that can be treated with immunotherapeutic regimens.\u003c/p\u003e \u003cp\u003eExclusion criteria were as follows: (1) metabolomics-focused studies; (2) studies examining proteomic changes during or after treatment rather than baseline/pre-treatment profiles; (3) single-cell proteomics analyses, including single-cell ReapSeq; (4) studies performing proteomic analyses of circulating tumor cells (CTCs) or extracellular vesicles (EVs), as these represent distinct cellular / biological compartments and were outside the scope of soluble plasma/serum proteomics; (5) studies involving dendritic cell\u0026ndash;based therapies; (6) studies evaluating targeted or non- ICI therapies, including tyrosine kinase inhibitors or diphencyprone; (7) studies assessing only treatment-related adverse events without reporting clinical efficacy outcomes; (8) autoantibody profiling studies; (9) vaccine-based therapeutic interventions; (10) studies focusing exclusively on mass spectrometry spectral features without providing definite protein identities (such as studies involving the application of MALDI technologies) or methodological MS spectra characterization rather than protein-level quantification or biomarker discovery; (11) in vitro studies; (12) animal models; (13) review articles; and (14) meta-analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Search Strategy\u003c/h2\u003e \u003cp\u003eA comprehensive literature search was conducted in MEDLINE and COCHRANE library on December 18, 2025. The used Boolean query combined terms for proteomics, samples collection time, ICI, clinical outcomes, and eligible cancers is provided in the Additional File.\u003c/p\u003e \u003cp\u003eNo language or date restrictions were applied.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Collection\u003c/h2\u003e \u003cp\u003ePublications\u0026rsquo; titles, abstracts, and full texts were screened independently by two reviewers (1: TM and 2: AT) according to the predefined eligibility criteria. Discrepancies were resolved through a panel discussion and the involvement of MF. From the studies included, sample size, cancer type, ICI regimen, proteomic methods used, and the assessment metric of the outcome were extracted \u003cb\u003e(Additional File 1\u003c/b\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of clinical studies that identify pretreatment plasma/serum biomarkers associated with response to Immune Checkpoint Inhibitors (ICIs) in patients with Lung Cancer. (Anti-CTLA-4: Anti-cytotoxic T-lymphocyte-associated protein 4; Anti-PD-1: Anti-programmed cell death protein 1; Anti-PD-L1: Anti-programmed death-ligand 1; AUC: Area under the curve; CI: Confidence interval; CR: Complete response; DCB: Durable clinical benefit; DIA: Data-independent acquisition; ELISA: Enzyme-linked immunosorbent assay; FDR: False discovery rate; HR: Hazard ratio; ICI: Immune checkpoint inhibition; LC-MS/MS: Liquid chromatography-mass spectrometry; LTR: Long-term responder; NDB: Non-durable benefit; NR: Non-responders; OS: Overall survival; PD: Progressive disease; PEA: Proximity extension assay; PFS: Progression-free survival; PR: Partial response; QUIPS: Quality in Prognostic Studies; R: Responders; RECIST: Response Evaluation Criteria in Solid Tumors; SD: Stable disease; STR: Short-term responder; TMT: Tandem mass tag).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTreatment Regimen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eResponse Assessment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eResults\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQUIPS\u003c/p\u003e \u003cp\u003eRisk of Bias\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBessede et al. [105]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlink Explore 1536 (PEA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-L1 Monotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOS \u0026amp; PFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh levels of TROP2 are significantly associated with shorter PFS (2.4 vs 10.9 months), and shorter OS (8.5 vs 24.6 months).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChao et al. [106]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC-MS/MS (DIA) and ELISA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (Discovery Cohort R: 12, NR: 3) and 22 (Validation Cohort R: 22, NR: 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1 monotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDCB vs NR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eELISA validation confirmed that S100A9, S100A8, SAA1, and SAA2 were at higher abundance in the NR group compared to R.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEltahir et al. [38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlink Immuno-Oncology panel (92 markers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1 OR Anti-PD-L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMultivariate survival analysis (split by median) showed CD244 (HR: 4.7 x 10^3, 95% CI: 1.1 x 10^1\u0026ndash;2.0 x 10^6, p\u0026thinsp;=\u0026thinsp;0.006), IL18 (HR: 1.5 x 10^4, 95% CI: 1.1 x 10^2\u0026ndash;2.1 x 10^6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), MUC16 (HR: 1.8 x 10^1, 95% CI: 3.2\u0026ndash;1.0 x 10^2, p\u0026thinsp;=\u0026thinsp;0.001), and ANGPT2 (HR: 4.8 x 10^3, 95% CI: 3.5 x 10^1\u0026ndash;6.6 x 10^5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were associated with shorter OS. FASLG (HR: 6.1 x 10^-3, 95% CI: 3.3 x 10^-4\u0026ndash;1.1 x 10^-1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was associated with prolonged OS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFan et al. [31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC-MS/MS (DIA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (R: 4, NR: 13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICI - No treatment regimen reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR), NR: (SD\u0026amp;PD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSignificantly decreased levels of CXCL8 were observed in the NR group compared to the R group. Note: Analysis mixed baseline (n\u0026thinsp;=\u0026thinsp;17) and during-treatment samples (n\u0026thinsp;=\u0026thinsp;30).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGao et al. [40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlink Immuno-oncology panel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (Cohort 1 R: 13, NR: 21; Cohort 2 n\u0026thinsp;=\u0026thinsp;30, R: 14 NR: 16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1 OR Anti-PD-L1 Monotherapy or in combination to Chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR), NR: (SD\u0026amp;PD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIn Cohort 1, IL18, TNF, TNFRSF9, and GZMH levels were higher in R, while LAMP3, IL2, ANGPT1, TNFSF14, and CXCL10 were at higher levels in NR. In Cohort 2, TNF, TNFRSF9 levels were higher in R whereas ANGPT1, CXCL10, and TNFSF14 levels higher in NR.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHarel et al. [32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuantibody multiplex ELISA workflow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143 (R: 70, NR: 73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1 OR Anti-PD-L1 +/- Chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR\u0026amp;SD), NR: (PD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIdentified 49 differentially abundant proteins between R and NR. Only CXCL8, found at decreased levels in R, remained significant after FDR correction (\u0026lt;\u0026thinsp;0.1).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHarutani et al. [41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMAP antibody assay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003cp\u003e(DB: 33, NDB: 69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1 OR Anti-PD-L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRECIST 1.1; DB: (CR\u0026amp;PR), NDB: (SD\u0026amp;PD) \u0026amp; OS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIn the non-durable benefit (NDB) subset (n\u0026thinsp;=\u0026thinsp;69), elevated CRP (HR 2.59, 95% CI 1.33\u0026ndash;5.04, p\u0026thinsp;=\u0026thinsp;0.005) and FST (HR 2.29, 95% CI 1.12\u0026ndash;4.69, p\u0026thinsp;=\u0026thinsp;0.023) were significantly associated with poor survival in multivariate analysis.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHong et al. [107]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlink Immuno-Oncology assay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (R: 14, NR: 62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1 OR Anti-PD-L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR\u0026amp;SD), NR: (PD) \u0026amp; PFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo proteins were identified as significant predictors of response or PFS in multivariate analysis. Higher levels of CXCL9 and IL-2 were associated with unfavorable outcomes in univariate PFS analysis but were not significant after adjustment for confounders.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHu et al. [36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27-plex cytokine assay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (R: 28, NR: 23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1 OR Anti-PD-L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR\u0026amp;SD), NR: (PD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePretreatment levels of IL-6 were significantly decreased in NR patients. Levels of IL-5 and IL-4 were significantly higher in the R group.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJie et al. [108]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC-MS/MS (DIA) and ELISA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118 (Discovery n\u0026thinsp;=\u0026thinsp;42), Internal Validation (n\u0026thinsp;=\u0026thinsp;40), External Validation (n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PDL-1\u0026thinsp;+\u0026thinsp;Chemo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR vs NR based on PFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA 3-protein model (VASN, PARD3, PTGES3) achieved an AUC of 0.846 (training) and 0.821 (internal validation). ELISA quantification in an external cohort (n\u0026thinsp;=\u0026thinsp;36) reported an AUC of 0.859. VASN and PARD3 were at higher levels in R, while PTGES3 was at higher levels in NR.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKauffman-Guerrero et al. [33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9-plex cytokine assay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (R: 8, NR: 21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR\u0026amp;SD), NR: (PD) \u0026amp; PFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIL-6 and IL-8 levels were elevated in NR patients. IFNG levels were higher in R and identified as an independent predictor of PFS (HR 0.92, 95% CI 0.86\u0026ndash;0.97, p\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMao et al. [109]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlink Explore (1472 markers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (R: 49, NR: 17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1 OR Anti-PD-L1 Monotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR), NR: (SD\u0026amp;PD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAKT1S1 plasma levels were higher in the NR group compared to R.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMondelo-Mac\u0026iacute;a et al. [110]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC-MS (DIA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (R: 30, NR: 18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1 +/- Chemo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR\u0026amp;SD), NR: (PD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA 7-protein model was constructed: ATG9A, DCDC2, LZTL1, FIL1L, and HPS5 were at higher levels in NR, while SPTN2 and PGTA were at higher levels in R.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOyanagi et al. [34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 human Milliplex MAP assay panels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (R: 19, NR: 19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR\u0026amp;SD), NR: (PD) \u0026amp; PFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eElevated levels of BMP-9 and TNF-alpha were higher within R, while FST, IL-8, and IP-10 levels were higher in NR patients. Multivariate Cox analysis identified FST and IP-10 as independent predictors of poorer PFS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePark et al. [111]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC-MS/MS (TMT labeling) and ELISA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (R: 29, NR: 34) and 53 (External Validation Cohort)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1 OR Anti-PD-L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR\u0026amp; SD w/ PFS\u0026thinsp;\u0026gt;\u0026thinsp;6 months), NR: (SD w/ PFS\u0026thinsp;\u0026lt;\u0026thinsp;6 months \u0026amp;PD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eComplement component C7 levels were significantly elevated in NR. Validation via ELISA showed an AUC of 0.724 in the primary cohort and 0.649 in an external validation cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParra et al. [37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlink Immuno-oncology panel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160 (R: 31, NR: 129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1 Monotherapy OR Anti-PD-1\u0026thinsp;+\u0026thinsp;Anti-CTLA-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR), NR: (SD\u0026amp;PD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDendritic cell marker LAMP3 displayed significantly higher levels in R compared to NR. Conversely, cytokines CCL23, CXCL13, TNFSF14, and IL-6 were observed in higher abundance in NR versus R.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRocha et al. [112]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlink Explore 384 panel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (LTR: 21, STR: 29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1 OR Anti-PD-L1 Monotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLong-Term vs Short-Term Response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35 proteins were at significantly higher levels in LTRs compared to STRs including those related to apoptosis (CASP8, PRKRA, CHAC2, CIAPIN1), cell cycle (CDKN2D, DCTN1, TACC3, SIRT2, USO1, SUGT1) while STRs showed higher levels of ADAMTS8, ADAMTS15, CD5, and CD33 compared to LTRs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStensgaard et al. [39]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlink Immuno-Oncology panel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1 Monotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePFS \u0026amp; OS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eICOSLG was significantly associated with longer PFS (HR: 0.21, 95% CI: 0.06\u0026ndash;0.79, p\u0026thinsp;=\u0026thinsp;0.022) and OS (HR: 0.23, 95% CI: 0.06\u0026ndash;0.87, p\u0026thinsp;=\u0026thinsp;0.030). FASLG levels also correlated with improved PFS (HR: 0.42, 95% CI: 0.22\u0026ndash;0.82, p\u0026thinsp;=\u0026thinsp;0.009) and OS (HR: 0.38, 95% CI: 0.19\u0026ndash;0.76, p\u0026thinsp;=\u0026thinsp;0.005).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTan et al. [43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAntibody microarrays, LC-MS/MS (DIA) and ELISA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (Discovery Cohort R:5, NR: 12) and 76 (Validation Cohort; R: 33, NR: 43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1 monotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR\u0026amp;SD), NR: (PD) \u0026amp; PFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eValidation confirmed KIT and TNFSF12 were at significantly higher levels in R, while CCL3 was at higher levels in NR. Multivariate analysis showed low levels of KIT (HR 2.01, p\u0026thinsp;=\u0026thinsp;0.038) and low levels of TNFSF12 (HR 3.67, p\u0026thinsp;=\u0026thinsp;0.013) associated with disease progression.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWu et al. [113]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC-MS/MS (DIA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (R: 35, NR: 12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo treatment regimen reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR\u0026amp;SD), NR: (PD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBMP15, HSP9, and HEG1 were significantly more abundant in R compared to NR. Conversely, 20 proteins including FKBP1A, TUBB1, and UBA1 were significantly less abundant in R. No further validation was reported.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXie et al [35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlink Immuno-Oncology panel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43; DCB: 23, NDB: 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1 and Chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDCB (PFS\u0026thinsp;\u0026gt;\u0026thinsp;6 months), NDB (PFS\u0026thinsp;\u0026lt;\u0026thinsp;6 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePatients with DCB exhibited higher levels of CD83, CD244, IL-12, and CD70, whereas those with NDB had higher levels of CSF-1, CCL3, CCL4, and IL-8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Risk of Bias Assessment\u003c/h2\u003e \u003cp\u003eRisk of bias (RoB) was independently assessed by reviewers TM and AT using the Quality in Prognosis Studies (QUIPS) tool [23], which evaluates six domains: study participation, study attrition, prognostic factor measurement, outcome measurement, study confounding, and statistical analysis/reporting, labeling them as high or low risk. Studies were overall rated as low if all categories were of low risk, moderate if 1 or 2 categories were of high risk, and high risk when 3 or more categories were assessed as high risk \u003cb\u003e(Additional File 1\u003c/b\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of clinical studies that identify pretreatment plasma/serum biomarkers associated with response to Immune Checkpoint Inhibitors (ICIs) in patients with melanoma. (Anti-CTLA-4: Anti-cytotoxic T-lymphocyte-associated protein 4; Anti-PD-1: Anti-programmed cell death protein 1; CI: Confidence interval; CR: Complete response; DBT: Dual blockade therapy; DDA: Data-dependent acquisition; DIA: Data-independent acquisition; EDT: Early during therapy; EFS: Event-free survival; ELISA: Enzyme-linked immunosorbent assay; HR: Hazard ratio; LC-MS/MS: Liquid chromatography-mass spectrometry; NR: Non-responder; OS: Overall survival; PD: Progressive disease; PFS: Progression-free survival; PR: Partial response; QUIPS: Quality in Prognostic Studies; PB-NET: Porphyrin Based-Network/Net (Glycoproteomics tool); R: Responder; RECIST: Response Evaluation Criteria in Solid Tumors; SD: Stable disease).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTreatment Regimen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eResponse Assessment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKey Results\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRisk of Bias\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBabačić et al. [114]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-Resolution Isoelectric Focusing LC-MS/MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1 or Anti-CTLA-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProteins associated with shorter OS included CD40 (HR\u0026thinsp;=\u0026thinsp;9.9, 95% CI: 1.1\u0026ndash;86), GZMB (HR\u0026thinsp;=\u0026thinsp;3.7, 95% CI: 1.2\u0026ndash;12), IL-10 (HR\u0026thinsp;=\u0026thinsp;2.3, 95% CI: 1.1\u0026ndash;4.7), ADA (HR\u0026thinsp;=\u0026thinsp;5.3, 95% CI: 1.3\u0026ndash;22), CCL2 (HR\u0026thinsp;=\u0026thinsp;3.1, 95% CI: 1.2\u0026ndash;8.2), CCL3 (HR\u0026thinsp;=\u0026thinsp;5.3, 95% CI: 1.1\u0026ndash;25), and CCL4 (HR\u0026thinsp;=\u0026thinsp;6.2, 95% CI: 1.4\u0026ndash;27). In contrast, soluble CD274 (HR\u0026thinsp;=\u0026thinsp;0.096, 95% CI: 0.013\u0026ndash;0.72) and CCL19 (HR\u0026thinsp;=\u0026thinsp;0.35, 95% CI: 0.13\u0026ndash;0.95) were associated with longer OS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHannani et al. [115]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eELISA (CD25 and LAG3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-CTLA-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigher soluble CD25 levels (based on median) were significantly associated with poor OS (HR\u0026thinsp;=\u0026thinsp;1.26, 95% CI: 1.04\u0026ndash;1.54, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0165), even after adjustment for LDH levels, tumor staging, and immune response criteria.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHoefsmit et al. [116]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC-MS/MS (DIA/DDA) and Olink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohort 1 n\u0026thinsp;=\u0026thinsp;85 (R: 64, NR: 21); Cohort 2 n\u0026thinsp;=\u0026thinsp;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1\u0026thinsp;+\u0026thinsp;Anti-CTLA-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLevels of CFB and A1BG were higher in NR in cohort 1 but were tested and not validated in cohort 2. Higher levels of LRG1 were associated with poor EFS, a finding which was validated in the independent cohort.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKarlsson et al. [117]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTMT LC-MS/MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (R: 41, NR: 22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1 Monotherapy OR Anti-PD-1\u0026thinsp;+\u0026thinsp;Anti-CTLA-4 OR Anti-CTLA-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR\u0026amp;SD), NR: (PD) \u0026amp; OS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLevels of APOA1 and APOC1 were significantly higher in R. Inflammation markers (CRP, SAA1, SAA2, and SAA2-SAA4) were at significantly lower levels in patients with longer OS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKoguchi et al. [118]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLuminex and ELISA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (Discovery Cohort) and 48 (Internal Validation Cohort)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-CTLA-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMultivariate analysis showed higher CXCL11 levels (HR\u0026thinsp;=\u0026thinsp;1.88, p\u0026thinsp;=\u0026thinsp;0.014) and higher sMICA levels (HR\u0026thinsp;=\u0026thinsp;1.75, p\u0026thinsp;=\u0026thinsp;0.0420) correlated with poor OS. Both CXCL11 (HR\u0026thinsp;=\u0026thinsp;3.74) and sMICA (HR\u0026thinsp;=\u0026thinsp;2.06) were validated as negative predictors of OS via ELISA.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLim et al. [119]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOMAscan and Luminex 65-plex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (R: 12, NR: 11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1 Monotherapy OR Anti-PD-1\u0026thinsp;+\u0026thinsp;Anti-CTLA-4 OR Anti-CTLA-4 Monotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR), NR: (SD\u0026amp;PD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo significant differences in protein expression between R and NR were observed.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLim et al. [120]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65-plex Chemokine Assay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 [Anti-PD1(R/NR: 16/24); DBT (R/NR: 47/11)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1\u0026thinsp;+\u0026thinsp;Anti-CTLA-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR), NR: (SD\u0026amp;PD) \u0026amp; OS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIn monotherapy (n=.40): High levels of TRAIL (HR\u0026thinsp;=\u0026thinsp;0.318) and IL-2 (HR\u0026thinsp;=\u0026thinsp;0.447) are associated with longer OS; high levels of MCP-1 predicted shorter OS (HR\u0026thinsp;=\u0026thinsp;2.696). IL-2, MCP-4, TARC, and ENA-78 were higher in R. In DBT (n\u0026thinsp;=\u0026thinsp;58): High levels of TNFa, IL-8, and IP-10 predicted poorer survival.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMachiraju et al. [121]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eELISA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 [Anti-PD1(R/NR: 28/20); DBT (R/NR: 26/16)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1 Monotherapy OR Anti-PD-1\u0026thinsp;+\u0026thinsp;Anti-CTLA-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR\u0026amp;SD), NR: (PD) \u0026amp; PFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIn anti-PD-1 monotherapy, sLAG-3 was more abundant in resistant patients. In DBT, sPD-1 levels were higher in NR. Multivariate analysis showed higher sLAG-3 (\u0026gt;\u0026thinsp;148 pg/ml; HR\u0026thinsp;=\u0026thinsp;0.39, p\u0026thinsp;=\u0026thinsp;0.016) and higher sPD-1 (\u0026gt;\u0026thinsp;167 pg/ml; HR\u0026thinsp;=\u0026thinsp;0.43, p\u0026thinsp;=\u0026thinsp;0.037) levels were associated with longer PFS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePedersen et al. [122]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMSD immunoassay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1 Monotherapy OR Anti-PD-1\u0026thinsp;+\u0026thinsp;Anti-CTLA-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigher levels (above median) of TNFa (HR\u0026thinsp;=\u0026thinsp;0.081, 95% CI: 0.0098\u0026ndash;0.66, p\u0026thinsp;=\u0026thinsp;0.019) and MCP-1 (CCL2) (HR\u0026thinsp;=\u0026thinsp;0.073, 95% CI: 0.0088\u0026ndash;0.61, p\u0026thinsp;=\u0026thinsp;0.016) were linked with longer PFS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePickering et al. [123]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlycoproteomics (LC-MS/MS with PB-NET)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202 Discovery Cohort (Limited Benefit n\u0026thinsp;=\u0026thinsp;40; Sustained Control n\u0026thinsp;=\u0026thinsp;56) and 27 (Validation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1 Monotherapy OR Anti-PD-1\u0026thinsp;+\u0026thinsp;Anti-CTLA-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClinical benefit based on OS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePatients with limited clinical benefit had higher levels of AAT, AACT, LRG1, and B2M. Findings were not replicated in the external cohort (n\u0026thinsp;=\u0026thinsp;27).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRossi et al. [124]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlink Inflammation and ELISA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (R: 46, NR: 41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1 Monotherapy OR Anti-PD-1\u0026thinsp;+\u0026thinsp;Anti-CTLA-4 OR Anti-CTLA-4 monotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR\u0026amp;SD), NR: (PD) \u0026amp; OS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIL-6, HGF, and MCP-2 (CCL2) were at higher levels in NR. These three markers were validated via a second targeted ELISA assay. Continuous IL-6 levels were associated with poorer OS (HR\u0026thinsp;=\u0026thinsp;1.90, 95% CI: 1.32\u0026ndash;2.73, p\u0026thinsp;=\u0026thinsp;5.46 x 10^-4).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYang et al. [44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlink Explore 384 assay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (R: 27, NR: 12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1\u0026thinsp;+\u0026thinsp;Anti-CTLA-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePathological Response based on viable tumor cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR showed increased levels of CXCL8, CCL8, CCL7, IL17F, IL-7, CXCL9, ADM, HAVCR1, and GZMB. NR showed increased abundance of ICA1, SCG3, TNFAIP8, PPP1R12A, and SH2B3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZila et al. [125]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSWATH LC-MS/MS (DIA) and Shotgun (DDA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 Discovery Cohort (R: 36, NR: 20); Validation Cohort A (n\u0026thinsp;=\u0026thinsp;80), Cohort B (n\u0026thinsp;=\u0026thinsp;12), Cohort C (n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-PD-1 Monotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR\u0026amp;SD), NR: (PD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCRP, LDHB, S100A8, SAA2, SAA1, LYVE1, and CFHR3 levels were significantly elevated in NR in the discovery cohort and validated in at least one independent cohort.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data Synthesis\u003c/h2\u003e \u003cp\u003eThe reviewed studies were grouped by cancer type (e.g., lung cancer, melanoma, and other cancers) according to SWiM reporting recommendations. To ensure data consistency across studies, all proteins identified for the synthesis were manually mapped to their official HUGO Gene Nomenclature Committee (HGNC)-approved gene symbols. The proteins reported were converted to their Hugo Nomenclature gene symbol to harmonize them before synthesis. Due to substantial heterogeneity in proteomic platforms, study design, and outcome definitions, a quantitative meta-analysis was not considered appropriate. Therefore, findings were synthesized using a vote-counting approach based on the direction of effect, classifying biomarkers according to whether their baseline levels were positively or negatively associated with treatment outcomes.\u003c/p\u003e \u003cp\u003eConsistency of findings was defined as a biomarker demonstrating a similar direction of association without significant contradictory evidence. Heterogeneity was explored across different types of cancer, proteomic technologies, treatment regimen, and response assessments. The certainty of evidence was assessed based on the consistency of a biomarker across different studies \u003cb\u003e(Additional File 1\u003c/b\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. For the final cohort, reported proteins had to show a consistent trend of abundance (in regard to the outcome) in at least three studies and not be reported with a significant opposite trend in other studies. Sensitivity analysis was conducted by removing high RoB studies. Visualization plots (pie plot, bar chart) were generated using R (version 4.3.0) and ggplot2 (version 3.4.0).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of clinical studies that identify pretreatment plasma/serum biomarkers associated with response to Immune Checkpoint Inhibitors (ICIs), on cancer types different than melanoma or lung cancer. (Anti-PD-1: Anti-programmed cell death protein 1; Anti-PD-L1: Anti-programmed death-ligand 1; Anti-VEGF: Anti-vascular endothelial growth factor; AUC: Area under the curve; CR: Complete response; DFS: Disease-free survival; DIA: Data-independent acquisition; ELISA: Enzyme-linked immunosorbent assay; FDR: False discovery rate; HR: Hazard ratio; iRECIST: Immune-modified Response Evaluation Criteria in Solid Tumors; LC-MS/MS: Liquid chromatography-mass spectrometry; TIMS: Trapped Ion Mobility Spectrometry; TOF: Time-of-Flight; mRECIST: Modified Response Evaluation Criteria in Solid Tumors; MSD: Meso Scale Discovery; NR: Non-Responders; OS: Overall survival; pCR: Pathologic complete response; PD: Progressive disease; PFS: Progression-free survival; PR: Partial response; R: Responders; RECIST: Response Evaluation Criteria in Solid Tumors; SBRT: Stereotactic body radiation therapy; SD: Stable disease; TKI: Tyrosine kinase inhibitor; TMT: Tandem mass tag).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCancer Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTechnology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTreatment Regimen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eResponse Assessment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eResults\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRisk of Bias\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimonetti et al. [126]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRenal Cell Carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAntibody Array \u0026amp; ELISA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 Discovery Cohort (7R, 9NR); 15 (Validation Cohort)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnti-PD-1 (Nivolumab)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR\u0026amp;SD), NR: (PD) \u0026amp; PFS \u0026amp; OS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRANKL levels were significantly higher in NR patients after FDR correction; also; validated via ELISA.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRini et al. [127]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRenal Cell Carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlink Explore 384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnti-PD-L1 (Atezolizumab)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh serum KIM-1 levels associated with worse DFS (HR\u0026thinsp;=\u0026thinsp;1.68 ).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarril-Ajuria et al. [128]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRenal Cell Carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMSD Immunoassay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e353 (40 Discovery Cohort; 313 Validation Cohort)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnti-PD-1 (Nivolumab)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh plasma BAFF (HR: 4.39), BCA-1/CXCL13 (HR: 4.74), and IL-6 (HR: 4.41) associated with worse OS in discovery. Validated in larger cohort (BAFF HR: 1.73; CXCL13 HR: 1.52; IL-6 HR: 2.53).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXiao et al. [129]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTriple-Negative Breast Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlink 96 Immuno-Oncology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e134 (83R, 51NR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnti-PD-1\u0026thinsp;+\u0026thinsp;Chemo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR), NR: (SD\u0026amp;PD) \u0026amp; PFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eARG1 and CD28 had higher levels in R while NOS3 decreased levels in R vs NR. Additionally, higher levels of IL-6, NOS3, VEGFA, KLRD1, and CSF-1 were associated with poor prognosis and shorter PFS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiu et al. [130]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTriple-Negative Breast Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlink 96 Immuno-Oncology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (23R, 11NR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnti-PD-1\u0026thinsp;+\u0026thinsp;TKI\u0026thinsp;+\u0026thinsp;Chemo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRECIST 1.1, (R: CR ) NR: (PR\u0026amp;SD\u0026amp;PD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh levels of IL-18 were associated with total pCR. IL-18 was the only protein consistently elevated in total pCR patients before treatment.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi Y et al. [131]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTriple-Negative Breast Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC-MS/MS (TMT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (4R, 6NR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnti-PD-L1\u0026thinsp;+\u0026thinsp;Paclitaxel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR vs NR according to CT imaging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFAP and COMP levels were significantly increased in NR while LRG1 and LBP levels were increased in R.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTognetti et al. [132]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePancreatic Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC-MS/MS (DIA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnti-PD-1\u0026thinsp;+\u0026thinsp;Chemo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1-Year Survival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAfter adjusting for age and sex, ACE levels were significantly higher in patients who survived past one year.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChristensen et al. [133]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePancreatic Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlink Target 96 Immuno-Oncology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78 (22R, 48NR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnti-PD-1\u0026thinsp;+\u0026thinsp;SBRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR\u0026amp;SD), NR: (PD) \u0026amp; PFS \u0026amp; OS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR had higher levels of FASLG and Galectin-1 (Gal-1), whereas NR had higher levels of CCL4. Gal-1 independently predicted longer PFS (HR\u0026thinsp;=\u0026thinsp;0.25). Conversely, high levels of ANGPT2 (HR\u0026thinsp;=\u0026thinsp;1.64), CCL17 (HR\u0026thinsp;=\u0026thinsp;1.45), and MUC-16 (HR\u0026thinsp;=\u0026thinsp;1.32) correlated with shorter OS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCui et al. [134]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiliary Tract Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlink Target 96 Immuno-Oncology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (21R, 16NR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnti-PD-1\u0026thinsp;+\u0026thinsp;Chemo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR), NR: (SD\u0026amp;PD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigher levels of HO-1 and CXCL1 were observed in NR versus R patients.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGao et al. [42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEsophageal Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlink 92 Immuno-Oncology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (24R, 65NR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnti-PD-1 +/- Anti-CTLA-4 / Anti-angiogenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eiRECIST \u0026amp; PFS \u0026amp; OS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIncreased levels of IL-8, TIE2, and HGF correlated with shorter PFS (IL-8 HR\u0026thinsp;=\u0026thinsp;1.761, TIE2 HR\u0026thinsp;=\u0026thinsp;2.326, HGF HR\u0026thinsp;=\u0026thinsp;2.010) and OS. Increased levels of TNFRSF12A, CD83, ICOSLG, CD5, TRAIL, TNFRSF21, and DCN were associated with increased OS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoudko et al. [135]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndometrial Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlink 92 Immuno-Oncology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnti-PD-1 +/- TKI inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOS \u0026amp; PFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigher levels of CSF-1, CCL23, PGF, TNFRSF12A, IL-10, ADGRG1, CCL20, and CAIX were associated with shorter OS, Elevated levels of CSF-1, CCL23, ANGPT2, IL-10, and CCL20 were associated with poorer PFS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXu et al. [136]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColorectal Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlink 96 Immuno-Oncology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (26R, 6NR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnti-PD-1\u0026thinsp;+\u0026thinsp;Cetuximab\u0026thinsp;+\u0026thinsp;Irinotecan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRECIST 1.1; R: (CR\u0026amp;PR), NR: (SD\u0026amp;PD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIL-6 levels decreased in R compared to NR, while the levels of CD40-L, EGF, PGF, MCP-1, TRAIL, MUC-16, CD4, VEGFR-2, LAP TGF-beta, TWEAK, GZMB, and ICOSLG were increased in R compared to NR.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi ZC et al. [137]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHepatocellular Carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTIMS-TOF LC-MS/MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64 (34R, 30NR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnti-PD-1\u0026thinsp;+\u0026thinsp;Lenvatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003emRECIST; R: (CR\u0026amp;PR), NR: (SD\u0026amp;PD) \u0026amp; PFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIncreased levels of complement membrane attack complex components (C5-C9), regulatory complement proteins (CFB, CFHR1, SERPIND1, CFI), lectin pathway proteins (FCN2, FCN3, MASP2), and TTN, CRTAC1, PLXDC2 were detected in R compared to NR.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi J et al. [138]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCervical Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlink 92 Immuno-Oncology \u0026amp; ELISA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (Discovery Cohort: 17; Validation Cohort: 21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnti-PD-1 +/- Chemo / Anti-VEGF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR: PR, NR: PD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA five-protein signature (ITGB5, TGF-α, TLR3, WIF-1, and ERBB3) effectively discriminated Rs from NRs (AUC 0.9227. ITGB5, TGF-α, TLR3, and ERBB3 were significantly higher in R, while WIF-1 was higher in NR. All five proteins were validated by ELISA, yielding a final model AUC of 0.9537.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhang et al. [139]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHead and Neck Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlink 92 Immuno-Oncology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (27R, 15NR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnti-PD-1\u0026thinsp;+\u0026thinsp;Chemo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTumor Viability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIL-5 and IL-13 levels had significantly higher abundance in R compared to NR, whereas CCL3, CCL4, and MMP7 were at increased levels in NR compared to R.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe initial MEDLINE search yielded 267 records, and the Cochrane Library (CENTRAL) search yielded 28 records. After removing 5 duplicates, 290 unique records were screened. During the title and abstract screening, review articles, meta-analyses, and non-human studies were excluded, leaving 199 publications, out of which 177 were retrieved for full-text eligibility assessment. Following the application of inclusion and exclusion criteria, 36 studies were selected. Notable exclusions included the studies by Lyu et al. [24] and Keegan et al. [25] which initially appeared to meet the criteria but were ultimately excluded because their primary outcomes were based on longitudinal proteomic changes during treatment rather than baseline pre-treatment profiles. Additionally, 13 studies were identified through manual searches of reference lists and included after verifying they met all eligibility criteria. This resulted in a final total of 49 included studies, as detailed in the PRISMA flow diagram \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Risk of Bias\u003c/h2\u003e \u003cp\u003eThe RoB assessment using the QUIPS tool revealed that approximately 34.7% (n\u0026thinsp;=\u0026thinsp;17) of the included studies were classified with an overall low RoB, while 46.9% (n\u0026thinsp;=\u0026thinsp;23) had a moderate and 18.4% (n\u0026thinsp;=\u0026thinsp;9) had a high risk. The most common methodological concern was related to study confounding, as statistical assessment or adjusting for covariates was not reported in 42.9% of the studies. Additionally, high RoB based on statistical analysis was observed in 36.7% of the studies, mainly due to selective reporting of results and limited information regarding their significance (e.g., specific p-value or hazard ratio) (\u003cb\u003eAdditional File 1\u003c/b\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These findings highlight the limited adjustment for confounding factors and variability in statistical rigor across studies, which may affect the reliability of reported biomarker associations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Diseases\u003c/h2\u003e \u003cp\u003eIn the 49 included studies, lung cancer (n\u0026thinsp;=\u0026thinsp;21) and melanoma (n\u0026thinsp;=\u0026thinsp;13) were overrepresented, with all other cancer types accounting for 15 studies \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Among the latter, triple-negative breast cancer and renal cell carcinoma were investigated in 3 studies, while all other cancer types were investigated in fewer than 3 studies \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. This distribution partly reflects the pace at which such therapies have been implemented across different cancer types, with melanoma and lung cancers among the first to receive FDA approval [26]; while also highlighting the expanding application of exploratory proteomics applications in additional malignancies such as cervical or hepatocellular cancer. This imbalance may also influence the generalizability of findings across cancer types.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Treatment Regimens\u003c/h2\u003e \u003cp\u003eImmunotherapy treatments varied across different studies, particularly across cancer types. Importantly, all cohorts received ICI-based treatment regimens, with their combination with non-immunotherapy treatments appearing in approximately one third of the studies (n\u0026thinsp;=\u0026thinsp;16). Among these 16 studies, most included chemotherapy (n\u0026thinsp;=\u0026thinsp;11), followed by tyrosine kinase inhibitors (TKIs, n\u0026thinsp;=\u0026thinsp;4). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, all studies including melanoma patients evaluated either ICI monotherapy or dual checkpoint blockade therapies (DBT), whereas among the cohorts with lung cancer patients (n\u0026thinsp;=\u0026thinsp;21), only 4 included combinations with chemotherapy, with the rest being treated with immune checkpoint blockade (ICB) monotherapy or DBT. In contrast, among the remaining studies that included patients across other cancer types (n\u0026thinsp;=\u0026thinsp;15), 11 cohorts reported the combination of ICI treatments with other treatment modalities, such as chemotherapy, radiotherapy, or targeted therapies (e.g., TKIs, anti-vascular endothelial growth factor - VEGF).\u003c/p\u003e \u003cp\u003eRegarding specific treatment regimens, anti-PD-1 blockade was the most frequently applied strategy: A total of 29 studies included patients receiving anti-PD-1 monotherapy, predominantly pembrolizumab or nivolumab. In addition, 9 studies involved administration of anti-PD-1-based regimens in combination with other non-immunotherapy treatment approaches, specifically chemotherapy, TKIs (e.g., cabozantinib), radiotherapy, and other targeted therapies, including anti-VEGF and anti-angiogenic agents. Dual ICI with anti-PD-1 plus anti-CTLA-4 was also reported in 11 studies, while anti-CTLA-4 monotherapy (mainly in the form of ipilimumab) was used in 6 studies. Furthermore, 13 studies included anti-PD-L1-based regimens, primarily involving atezolizumab or durvalumab. Also, 2 studies reported the use of ICI without specifying the exact pharmaceutical regimen administered.\u003c/p\u003e \u003cp\u003eCollectively, anti-PD-1 was evaluated either as monotherapy or in combination treatments in 41 of the 47 studies that reported the type of ICI used, while no studies involving anti-LAG3 treatment were retrieved.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.4 Technologies used for Biomarkers assessment\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe techniques employed for the identification and quantification of proteins in plasma varied between the studies, ranging from targeted approaches to liquid chromatography-tandem mass spectrometry (LC-MS/MS). The technologies used can be categorized into 3 main types: targeted single- or low level-plex immunoassays, targeted multiplex affinity-based microarrays, and mass-spectrometry-based approaches \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Targeted Low-level Immunoassays\u003c/h2\u003e \u003cp\u003eTargeted immunoassays were used either as standalone analytical methods or as validation tools following discovery-based analyses. Specifically, 3 studies used an ELISA-targeted approach to quantify soluble levels of individual proteins (e.g., Soluble CD25-sCD25, Soluble lymphocyte activation gene-3 - sLAG3, Soluble interleukin-2 - sIL2) at the discovery stage. ELISA kits for identification of single proteins were also implemented in studies to validate proteins identified through MS or multiplex platforms. Commercial or custom-made ELISA kits from various vendors were used, such as the Human Cytokine-Inflammation (9-plex, 1 study) or Meso Scale Discovery (MSD) panels of 10 and 14 proteins (2 studies), respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Affinity-Based Multiplex Platforms\u003c/h2\u003e \u003cp\u003eAffinity-based proteomic technologies represented the most frequently used analytical approach across the curated studies. These technologies use binding probes, such as aptamers or antibodies, in order to detect proteins of interest. Among them, the Olink proximity extension assay (PEA) platforms were by far the most used technology, appearing in a substantial number of studies (n\u0026thinsp;=\u0026thinsp;23). Multiple generations and Olink panel configurations were used, with the most frequent being the Olink Immuno-Oncology 96-plex panel. Angiogenesis and Inflammation panels, as well as higher-level panels such as Olink Explore 384 and Olink Explore 1536, were also implemented. Other affinity-based multiplex platforms included SomaScan, a DNA aptamer-based proteomic platform, which was implemented in one study, as well as other multiplex antibody microarrays (n\u0026thinsp;=\u0026thinsp;6). As with single-plex ELISA, multiplex arrays were used in some studies to validate LC\u0026ndash;MS-based measurements across platforms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.4.3 Mass Spectrometry\u0026ndash;Based Proteomics\u003c/h2\u003e \u003cp\u003eMass spectrometry-based proteomics constituted the second major class of technologies, as it was implemented in 15 studies and was used in both discovery-driven and targeted analytical contexts. A wide range of MS acquisition strategies were applied; of those reported, Data Independent Acquisition (DIA, n\u0026thinsp;=\u0026thinsp;7 studies) methods were prominent, and 2 more studies used either DIA or Data Dependent Acquisition (DDA) to quantify a portion of their samples, respectively. Additionally, Tandem Mass Tag MS (TMT) was applied in 4 studies, while DDA and an LC-MS workflow for glycoproteomics identification using Porphyrin-Based Network/Net (PB-NET) technology were applied in one case each.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Outcome assessment\u003c/h2\u003e \u003cp\u003eOutcome assessment across the included studies aligned with the pre-defined eligibility criteria for this review. Specifically, the clinical outcomes were reported either as binary response classifications, typically distinguishing non-responders (NR) from responders (R) or non-clinical benefit (NCB) from durable benefit (DCB), or as time-to-event endpoints, including PFS and OS, as can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC.\u003c/p\u003e \u003cp\u003eAssessment of treatment response followed established clinical oncology standards, most commonly the Response Evaluation Criteria in Solid Tumors (RECIST). RECIST classifies the treatment response of cancer patients into four categories: complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD) [27].\u003c/p\u003e \u003cp\u003eMost curated studies (n\u0026thinsp;=\u0026thinsp;28) assessed ICI response using the updated RECIST version 1.1 [28]. Additionally, one study applied immune-related RECIST (iRECIST), which was specifically developed to identify tumor response patterns observed with immunotherapy [29], while one study in hepatocellular carcinoma employed RECIST tailored to this disease context (modified RECIST) [30].\u003c/p\u003e \u003cp\u003eDespite the widespread use of RECIST-based frameworks (in 30 out of 49 studies), substantial heterogeneity is evident in how the four response categories were dichotomized for downstream analyses. Notably, 18 studies classified patients with SD as R or clinical beneficiaries, and 11 studies categorized them as NR or non-beneficiaries, while one study compared patients labeled CR against all other response categories.\u003c/p\u003e \u003cp\u003eAdditionally, 8 studies did not use binary response classifications but instead assessed biomarker performance using survival endpoints, mainly PFS and OS (6 studies), with the remaining two reporting event-free survival (EFS) or disease-free survival (DFS). In several cases, survival analyses complemented the response-based classifications.\u003c/p\u003e \u003cp\u003eThe remaining studies (n\u0026thinsp;=\u0026thinsp;11) applied binary outcome classification without explicitly referencing RECIST criteria, instead relying on alternative assessment strategies such as quantification of viable tumor cells after treatment or dichotomization based on survival or PFS (e.g., early failure compared to sustained control based on survival in specific time points or PFS in 6 months).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Biomarkers in each Cancer Type\u003c/h2\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provide a comprehensive summary of the reported biomarkers identified in each study, categorized by malignancy: lung cancer (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, melanoma \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, and various other cancer types (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Information about the treatment regimen, the proteomic platform utilized, and the reported biomarkers, which, at baseline, were found to associate with clinical response or survival, is provided, reflecting the abovementioned inter-study variability. Notably, sample sizes varied widely (from 10 to 353 samples) and differences in the study design -including the presence/absence of external validation cohorts- may influence statistical power and reproducibility.\u003c/p\u003e \u003cp\u003eA vote-counting approach was implemented for the synthesis of the findings across studies, based on the direction of the reported statistically significant associations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in comparison of R and NR or significant associations with time-to-event endpoints such as PFS or OS) in independent studies.\u003c/p\u003e \u003cp\u003eComparing the findings from the 20 lung cancer studies \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, a small number of consistent findings may be highlighted: Interleukin 8 (CXCL8), which was reported in 5 studies [31\u0026ndash;35] correlated with inferior response and clinical outcomes. Additionally, elevated circulating levels of Interleukin 6 (IL-6) were associated with inferior outcomes in three studies [33,36,37]. Several markers showed consistent associations with treatment response across two studies each: Fas Ligand (FASLG) [38,39], and Tumor Necrosis Factor (TNF) [34,40] associated positively, while. Conversely, Follistatin (FST) [34,41], TNF Superfamily Member 14 (TNFSF14) [37,42], and C-C Motif Chemokine Ligand 3 (CCL3 or MIP-1α) [35,43] associated negatively with response.\u003c/p\u003e \u003cp\u003eIn the 13 reviewed melanoma studies \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, C-reactive protein (CRP), Serum Amyloid A1 (SAA1), and Serum Amyloid A2 (SAA2) demonstrated reproducible correlations with clinical outcomes in two studies, with increased levels of these proteins being consistently linked to NR and poor OS. Overall, the melanoma evidence is characterized by limited reproducibility and frequent lack of external validation.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePan-Cancer synthesis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFrom the synthesis of the 49 studies across all cancer types, we observed that a number of proteins showed correlations with outcomes across all cancer types (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Characteristically, the inflammation marker IL-6 was the most consistently reported negative biomarker (defined as being observed at higher levels in NR or linked to shorter OS or PFS in at least 3 studies), as it was identified as a negative predictor in 8 studies across 6 different types of cancer (melanoma, lung cancer, breast cancer, renal cell carcinoma, esophageal cancer, and colorectal cancer). Similarly, the chemokine C-X-C Motif Chemokine Ligand 8 (Interleukin 8) (CXCL8 or IL-8) was consistently associated with poorer outcomes in 7 studies from 3 different cancer types (lung cancer, melanoma, and esophageal cancer). However, one study [44] reported a contradictory finding, as higher CXCL8 levels were observed in melanoma patients who responded to therapy. This discrepancy could be explained by their alternative response assessment, based on the number of viable tumor cells as an endpoint [44].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnother notable negative biomarker is Angiopoietin-2 (ANGPT2), which was associated with inferior outcomes in 4 studies across 4 distinct cancer types: endometrial, esophageal, lung, and pancreatic cancer. Similarly, CCL3 and Colony Stimulating Factor 1 (CSF1) were linked to poorer outcomes in 4 studies each. CCL3 was reported in head and neck carcinoma, lung cancer, and melanoma, whereas CSF1 was identified in breast, esophageal, endometrial, and lung cancers. Additionally, CRP, C-X-C Motif Chemokine Ligand 10 (CXCL10/IP-10), SAA1, and SAA2, which are acute-phase proteins involved in inflammation and immune modulation, demonstrated negative associations with response or survival in 3 studies involving lung cancer and melanoma. TNFSF14 also showed a negative association in 3 studies from lung and esophageal cancer.\u003c/p\u003e \u003cp\u003eIn contrast, among positive biomarkers (defined as those with significantly higher abundances in R or significantly associated with longer PFS or OS in at least 3 studies), T-cell ligand proteins were the most replicated findings. Specifically, Inducible T Cell Costimulator Ligand (ICOSLG) and FASLG were associated with R and prolonged survival in 4 studies across 3 (lung, pancreatic, and endometrial cancer) and 4 (endometrial, esophageal, colorectal, and lung cancer) cancer types, respectively. Likewise, Tumor Necrosis Factor (Ligand) Family, Member 10 (TNFSF10) was also identified as a positive biomarker in 3 studies from 3 different cancer types (colorectal, melanoma, and esophageal cancer). Lastly, the chemokine C-X-C Motif Chemokine Ligand 5 (CXCL5) was also found to be a positive marker in 3 studies from endometrial, colorectal, and melanoma cancers.\u003c/p\u003e \u003cp\u003eTo increase the reliability of the synthesis, a sensitivity analysis was performed by investigating the consistent findings after excluding studies labeled with a high RoB (n\u0026thinsp;=\u0026thinsp;9). The association between elevated baseline levels of IL-6 and ICI resistance remained the most robust finding, with CXCL8, ANGPT2, CSF1, CCL3, and TNFSF14 also remaining negative biomarkers in at least 3 studies. Along similar lines, none of the positive biomarkers (ICOSLG, FASLG, TNFSF10, CXCL5) were removed during sensitivity analysis.\u003c/p\u003e \u003cp\u003eA dependency on specific proteomic platforms for biomarker detection may frequently be observed, as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The Olink Immuno-Oncology panel was the sole technology supporting the association of FASLG, ICOSLG, ANGPT2, TNFSF10, TNFSF14, and CSF1 with response, while IL-6 findings were frequently supported by both Olink and various multiplex bead-based assays. In contrast, the detection of SAA1, SAA2, and CRP relied almost exclusively on LC-MS/MS, while CCL3 and CXCL8 were identified using LC-MS/MS but also PEA-based panels.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequency and association of the replicated baseline proteomic biomarkers across the studies. The table includes biomarkers identified in at least 3 independent studies, with identical trends in respect to outcome in all studies shown. Olink IO means Olink ImmunoOncology panel. Positive means association with response or longer survival or improved progression free survival while negative means the opposite.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiomarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. of Studies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePanels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSample Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCancer Types Reported\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStudies\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOlink IO (n\u0026thinsp;=\u0026thinsp;4), Olink Inflammation (n\u0026thinsp;=\u0026thinsp;1), Bio-Plex Cytokines Grp 27-plex Panel (n\u0026thinsp;=\u0026thinsp;1), BioVendor Human Cytokine-Inflammation 9-plex kit (n\u0026thinsp;=\u0026thinsp;1), MSD Immunoassay (n\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlasma (n\u0026thinsp;=\u0026thinsp;4), Serum (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMelanoma, Lung, Breast, RCC, Esophageal, Colorectal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[33,36,37,42,124,128,129,136]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL8 (IL-8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOlink IO (n\u0026thinsp;=\u0026thinsp;2), Olink Explore 384 (n\u0026thinsp;=\u0026thinsp;1), ELISA-based multiplex Antibody Arrays(n\u0026thinsp;=\u0026thinsp;1), Multiplex MAP assay (n\u0026thinsp;=\u0026thinsp;1), Multiplex Bead Array (65-plex Chemokine Assay (n\u0026thinsp;=\u0026thinsp;1), LC-MS/MS (n\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlasma (n\u0026thinsp;=\u0026thinsp;3), Serum (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLung, Melanoma, Esophageal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[31\u0026ndash;35,42,120]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANGPT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOlink IO (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlasma (n\u0026thinsp;=\u0026thinsp;2), Serum (n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEndometrial, Esophageal, Lung, Pancreatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[38,42,133,135]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAA1 / SAA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC-MS/MS (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlasma (n\u0026thinsp;=\u0026thinsp;2), Serum (n\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLung, Melanoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[106,117,125]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC-MS/MS (n\u0026thinsp;=\u0026thinsp;2), Multiplex Bead Assay (n\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlasma (n\u0026thinsp;=\u0026thinsp;1), Serum (n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLung, Melanoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[32,41,125]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL3 (MIP-1α)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC-MS/MS (n\u0026thinsp;=\u0026thinsp;2), Olink IO (n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlasma (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHead \u0026amp; Neck, Lung, Melanoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[35,43,114,139]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOlink IO (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlasma (n\u0026thinsp;=\u0026thinsp;3), Serum (n\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBreast, Esophageal, Endometrial, Lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[35,42,129,135]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNFSF14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOlink IO (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlasma (n\u0026thinsp;=\u0026thinsp;1), Serum (n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLung, Esophageal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[37,40,42]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOlink IO (n\u0026thinsp;=\u0026thinsp;1), Multiplex MAP assay (n\u0026thinsp;=\u0026thinsp;1), Multiplex Bead Array 65-plex Chemokine Assay (n\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlasma (n\u0026thinsp;=\u0026thinsp;2), Serum (n\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLung, Melanoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[34,40,120]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFASLG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOlink IO (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlasma (n\u0026thinsp;=\u0026thinsp;3), Serum (n\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEndometrial, Pancreatic, Lung, Colorectal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[38,39,133,135]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICOSLG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOlink IO (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlasma (n\u0026thinsp;=\u0026thinsp;2), Serum (n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLung, Pancreatic, Endometrial, Esophageal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[39,42,132,135]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNFSF10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOlink IO (n\u0026thinsp;=\u0026thinsp;2), Multiplex Bead Array (65-plex Chemokine Assay (n\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlasma (n\u0026thinsp;=\u0026thinsp;2), Serum (n\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eColorectal, Melanoma, Esophageal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[42,120,136]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOlink IO (n\u0026thinsp;=\u0026thinsp;2), Multiplex Bead Array (65-plex Chemokine Assay (n\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlasma (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEndometrial, Colorectal, Melanoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[120,135,136]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe synthesis of the 49 studies indicated that circulating proteins in plasma related to systemic inflammation and vascular dysfunction are predominant hallmarks of resistance to ICI, while soluble T-cell ligands were markers for improved outcomes. Characteristically, IL-6, which is a pleiotropic chemokine often induced during acute stress [45] and a driver of systemic inflammation [46] emerged as the most frequent and consistent negative correlator of response. High circulating concentrations of IL-6 have been observed in various pathological states [47] and participate in inflammatory signaling mechanisms that promote tumor progression [48]. Additionally, IL-6 has been linked to immunosuppressive functions either via the recruitment and activation of myeloid-derived suppressor cells [49] or via the induction of T-cell exhaustion, both of which lead to immune evasion [50]. Preclinical evidence on murine models has also shown the negative impact of IL-6 on immunotherapy. Specifically, in pancreatic cancer, the dual blockade of IL-6 and PD-L1 suppresses tumor growth [51], while in models of B-cell lymphoma, its absence has been associated with increased efficiency of anti-PD-L1 blockade [52]. These findings suggest that high baseline levels of IL-6 may reflect a broader state of systemic dysregulation.\u003c/p\u003e \u003cp\u003eSimilarly, CXCL8 (IL-8) is an inflammatory chemokine that activates neutrophils and is associated with cancer cell growth and epithelial-to-mesenchymal transition [53,54]. In addition to its proangiogenic functions, it has been linked with increased infiltration of myeloid suppressor cells and reduced NK cell activity in the TME [55,56]. In this manner, its blockade using neutralizing antibodies is currently being evaluated in combination with ICIs for cancer treatment [57]. Notably, in a meta-analysis of 1334 patients involving a targeted analysis, increased serum CXCL8 levels were predictors of poor response to ICI [58]. Collectively, the increased systemic levels of IL-6 and CXCL8 are linked to immunosuppressive and pro-tumorigenic functions [59], hence contributing to ICI resistance.\u003c/p\u003e \u003cp\u003eOther inflammation markers associated with inferior outcomes in immunotherapy include serum antigens (SAA1, SAA2) and CRP. These proteins are synthesized in the liver in response to chemokine (including IL-6) stimulation, and thus their serum concentrations are highly intercorrelated [60\u0026ndash;62]. They are linked to immune evasion and metastasis in cancer diseases [63,64], either by inducing immunosuppressive properties of T cells or neutrophils [65,66], or by promoting angiogenesis via chronic inflammation [67,68]. The negative prognostic role of vascularization proteins is also evident with ANGPT2. ANGPT2 is a pro-angiogenic factor, produced by endothelial cells, that antagonizes Ang1\u0026ndash;Tie2 signaling and promotes vascular instability [69\u0026ndash;71]. Elevated expression of ANGPT2 has been associated with tumor progression and poor prognosis in a variety of cancers, including melanoma [72].\u003c/p\u003e \u003cp\u003eSoluble levels of proteins that regulate myeloid cells (CXCL10, CCL3, and CSF1) were also consistently associated with an inferior response to ICI. CXCL10 is a chemoattractant cytokine of myeloid cells induced by interferon γ, with reported associations with the regulation of suppressive populations of T cells and neutrophils [73,74]. It has a dual role in cancer, linked to the pro- and anti-tumor functions of the 2 different splice variants of its receptor, C-X-C Motif Chemokine Receptor 3 (CXCR3), with the one variant suppressing tumor growth while the other one induces cell proliferation [75]. Additionally, CCL3 is a pro-inflammatory chemokine that mediates immune cell trafficking and can recruit cytotoxic immune cells through C-C motif chemokine receptor 5 (CCR5) signaling [76]. Elevated baseline CCL3 levels have been associated with expansion of tumor-associated macrophages (TAMs) and angiogenesis [77,78], while also resulting in unfavorable clinical outcomes, including worse OS in melanoma cohorts [79\u0026ndash;81]. CSF1 is a cytokine that regulates TAMs\u0026rsquo; survival and proliferation [82,83]. Elevated CSF1 levels have also been associated with disease progression, resistance to ICIs, and shorter PFS and OS in cancer patients [84\u0026ndash;86]. TNFSF14 also emerged as a marker of worse prognosis despite its antitumor properties in preclinical models [87]. This may be linked to its role as a marker of broader systemic stress, evidenced by its correlation with CXCL8 levels and cardiovascular risk [88,89].\u003c/p\u003e \u003cp\u003eT-cell ligand proteins, on the other hand, were the most replicated positive biomarkers. Specifically, FASLG is a transmembrane ligand that binds to the FAS receptor mainly on T-cells and triggers their apoptosis [90], while its deficiency is often linked to better survival outcomes [91]. However, compared to the membrane-bound form of FASLG, its soluble form lacks the apoptosis-inducing activity and instead influences cytokine secretion while posing a protective role in its receptor-expressing cells [92,93], which could explain its positive association to ICI response. On the other hand, ICOSLG binds to the ICOS receptor on the surface of T cells, activating and enabling them to attack tumor cells [94]. Therapies based on ICOS agonists are being developed and tested for the improvement of cancer outcomes [95], suggesting that secreted ICOSLG could reflect a \"hot\" tumor setting. Similarly, TNFSF10, once bound to its cell receptors, including on tumor cells, promotes cell death via caspase activation [96,97]. These cytotoxic properties are also retained in the soluble form of TNFSF10, which may underlie the observed positive associations [98]. Lastly, soluble levels of CXCL5, a chemokine that attracts tumor-associated neutrophils to the TME [99], were associated with better prognosis in 3 studies. This apparent contradiction may be explained by the correlation of CXCL5 expression with increased tumor PD-L1 levels [100,101], which are typically associated with higher ICI efficacy [102].\u003c/p\u003e \u003cp\u003eDespite these findings, several limitations should be acknowledged. The heavy reliance on the Olink Immuno-Oncology panel restricted the proteome coverage and limited cross-platform validation to the 92 proteins of the panel, thus minimizing overlaps with LC-MS/MS approaches. In addition, response assessment varied across studies; to maximize the number of eligible studies, we considered both ORR and time-to-event endpoints (such as PFS and OS) as acceptable endpoints. Although a clear relationship between radiological response and survival outcomes has been demonstrated in several oncology contexts [103], this association is not universally generalized across all studies or settings [104]. These factors introduce heterogeneity and potentially limit the power of the presented conclusions. To adjust to this limitation, a count voting approach was used when integrating the findings, which, however, does not account for effect size. Therefore, the findings should be interpreted as exploratory rather than quantitative estimates of association. Despite these limitations, this comprehensive review, conducted in a systematic manner, highlights a consistent association between systemic inflammatory and angiogenic markers and resistance to ICIs, as well as a link between soluble T-cell-related proteins and improved outcomes. Notably, these biomarkers appear to reflect systemic immune states rather than tumor-specific mechanisms and are reproducible across multiple cancer types.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this comprehensive review, we aimed to synthesize findings from studies profiling the pre-treatment blood proteome and to investigate the potential of specific biomarkers as non-invasive tools for predicting outcomes in patients receiving ICI. The literature revealed a lack of standardized profiling protocols, resulting in significant heterogeneity across technologies and response assessments. While lung cancer and melanoma were the most prominently studied diseases, the most reproducible findings were mostly not disease-specific but instead were found across diverse cancer types. Based on the reproducible findings, a systemic environment characterized by elevated levels of the proteins IL-6, CXCL8, ANGPT2, CRP, and SAA1/2 was predictive of ICI resistance. These markers were reflective of a state of chronic systemic inflammation and vascular dysregulation that potentially induced an inhospitable environment for tumor immunosurveillance, hindering the efficacy of immunotherapy, while simultaneously promoting tumor growth. In contrast, the presence of soluble T-cell ligands, such as ICOSLG, TNFSF10, and FASLG, signaled a more immune-active TME that favored therapeutic success.\u003c/p\u003e \u003cp\u003eFuture research should focus on prospective validation of these biomarkers and the development of standardized analytical workflows, including the definition and application of validated cut-offs. Such efforts are essential to enable the clinical translation of circulating proteomic biomarkers into clinical practice and enable patient stratification based on outcome prediction.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"495\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eArea Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eBiliary Tract Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eDBT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eDual Blockade Therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eDCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eDurable Clinical Benefit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eDDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eData Dependent Acquisition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eDisease-Free Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eDIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eData Independent Acquisition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eEDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eEarly During Therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eEFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eEvent-Free Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eEnzyme-Linked Immunosorbent Assay\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eHCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eHepatocellular Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eHNSCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eHead and Neck Squamous Cell Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eHiRIEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eHigh-Resolution Isoelectric Focusing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eICB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eImmune Checkpoint Blockade\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eICI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eImmune Checkpoint Inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eiRECIST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eImmune-related RECIST\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eLC-MS/MS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eLiquid Chromatography-Tandem Mass Spectrometry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eLTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eLong-Term Response\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eMAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eMulti-Analyte Profiling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003emRECIST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eModified RECIST\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eMass Spectrometry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eMSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eMeso Scale Discovery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eNSCLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eNon-Small Cell Lung Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eORR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eObjective Response Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eOverall Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003ePB-NET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003ePorphyrin Based-Network/Net\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003epCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003ePathologic Complete Response\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eProgressive Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003ePDAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003ePancreatic Ductal Adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003ePEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eProximity Extension Assay\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003ePFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eProgression-Free Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003ePartial Response\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eRCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eRenal Cell Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eRECIST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eResponse Evaluation Criteria in Solid Tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eRoB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eRisk of Bias\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eStable Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eShort-Term Response\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eSWATH-MS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eSequential Window Acquisition of all Theoretical Mass Spectra\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eTIMS-TOF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eTrapped Ion Mobility Spectrometry Time-of-Flight\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eTKI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eTyrosine Kinase Inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eTME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eTumor Microenvironment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eTMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eTandem Mass Tag\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 408px;\"\u003e\n \u003cp\u003eTriple-Negative Breast Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTM performed the literature search, screened the records, did the RoB assessment, and wrote the manuscript. AT additionally screened records, performed the RoB assessment, and assisted in data synthesis. MF and AV supervised the project and critically revised the manuscript for important intellectual content. All authors read and approved of the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions (CRediT)\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eTheodoros Margelos (TM):\u003c/strong\u003e Conceptualization, investigation, methodology, and writing: original draft.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAggeliki Tserga (AT):\u003c/strong\u003e Investigation; Writing: original draft.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMaria Frantzi (MF):\u003c/strong\u003e Supervision; Validation; Writing: review and editing.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAntonia Vlahou (AV):\u003c/strong\u003e Supervision, project administration, and writing review and editing.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eData Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe finalized list of included studies is included in the Additional file. The search strings used for the literature review are provided within it.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMF is an employee of Mosaiques Diagnostics (Hannover, Germany).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunded by the European Union (Project 101136926-MULTIR). Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or HADEA. Neither the European Union nor the granting authority can be held responsible for them.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eM. Tufail, C.-H. Jiang, N. Li, Immune evasion in cancer: mechanisms and cutting-edge therapeutic approaches, Signal Transduct. Target. Ther. 10 (2025) 227. https://doi.org/10.1038/s41392-025-02280-1.\u003c/li\u003e\n\u003cli\u003eK.J. Hiam-Galvez, B.M. Allen, M.H. 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Med. 23 (2025) 693. https://doi.org/10.1186/s12967-025-06770-2.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"34333be3-1e82-40ff-8e12-16cecef34922","identifier":"10.13039/100010661","name":"Horizon 2020 Framework Programme","awardNumber":"Project 101136926-MULTIR","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"BRFAA","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Blood biomarkers, Immune Checkpoint Inhibitors, Immunotherapy, Melanoma, Lung Cancer, IL-6","lastPublishedDoi":"10.21203/rs.3.rs-9704067/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9704067/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eImmune checkpoint inhibitors (ICIs) have revolutionized cancer management; nevertheless, a large number of patients fail to respond to treatment. Despite extensive research efforts, there is a lack of accurate non-invasive biomarkers for prognosis and treatment outcome prediction. In this comprehensive review, we aim to record and evaluate studies investigating blood-circulating protein biomarkers and their association with ICIs outcomes.\u003c/p\u003e \u003cp\u003eA literature search of PubMed and the Cochrane Library (on December 18, 2025) was performed to identify relevant studies. Results were synthesized across cancer types and proteomic technologies, based on a vote-counting approach. The QUIPS (Quality In Prognosis Studies) tool was used to assess the risk of bias.\u003c/p\u003e \u003cp\u003eA total of 49 studies meeting the eligibility criteria were included in the analysis. The majority of these studies focused on lung cancer (n\u0026thinsp;=\u0026thinsp;21) and melanoma (n\u0026thinsp;=\u0026thinsp;13) and commonly employed proteomic platforms such as Olink proximity extension assays (n\u0026thinsp;=\u0026thinsp;23) and mass spectrometry (n\u0026thinsp;=\u0026thinsp;15). Synthesis of the results revealed high pre-treatment systemic levels of IL-6, CXCL8, ANGPT2, CRP, CCL3, CSF1, CXCL10, TNFSF14, and SAA1/2 were associated with resistance to ICIs, while elevated levels of soluble T-cell ligands (ICOSLG, TNFSF10 and FASLG and CXCL5 were reproducibly correlated with improved treatment outcomes.\u003c/p\u003e \u003cp\u003eTo conclude, despite limitations linked to the heterogeneity of proteomic platforms and variability in outcome reporting across studies, consistent patterns emerged: markers of systemic inflammation were associated with poor response, while proteins involved in T-cell activation and signaling were linked to better outcomes.\u003c/p\u003e","manuscriptTitle":"Proteomic Biomarkers for Predicting Immunotherapy Outcomes: A Comprehensive Pan-Cancer Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 02:38:02","doi":"10.21203/rs.3.rs-9704067/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e67e4be1-f13c-4082-8e8e-9ca623c29cc8","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":68095626,"name":"Cancer Biology"}],"tags":[],"updatedAt":"2026-05-15T02:38:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 02:38:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9704067","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9704067","identity":"rs-9704067","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}