The research team searched PubMed and the Cochrane Library up to December 18, 2025, for studies that measured plasma proteins in patients before they started immune checkpoint inhibitor therapy and then tracked clinical outcomes such as overall survival, progression-free survival, objective response rate, or durable clinical benefit. After applying eligibility criteria, the investigators included 49 studies in their analysis. The majority of these focused on lung cancer, with 21 studies, and melanoma, with 13 studies, reflecting the central place of these two malignancies in the modern immunotherapy era. The remaining studies covered a broader spectrum of solid tumors, giving the review its pan-cancer scope.

Technologically, the field is dominated by two complementary approaches. Twenty-three of the included studies used Olink proximity extension assay panels, which allow simultaneous quantification of hundreds of proteins from small plasma volumes with high sensitivity and reproducibility. Fifteen studies employed mass spectrometry platforms, including liquid chromatography-tandem mass spectrometry workflows with data-dependent, data-independent, and SWATH acquisition modes, as well as tandem mass tag labeling and high-resolution isoelectric focusing fractionation. Additional studies used enzyme-linked immunosorbent assays, multi-analyte profiling on the Luminex platform, and Meso Scale Discovery electrochemiluminescence assays. This diversity of technologies, while a strength in terms of coverage, also introduces heterogeneity, because different platforms measure different protein subsets with different dynamic ranges and analytical precision.

To synthesize findings across this heterogeneous literature, the authors used a vote-counting approach, tallying how often each protein was reported to associate with favorable or unfavorable outcomes across independent studies and cancer types. They also assessed the methodological quality of each study using the QUIPS tool, the Quality In Prognosis Studies instrument, which evaluates risk of bias across domains such as study participation, confounding, statistical analysis, and outcome measurement. This dual strategy allowed the team to distinguish proteins with reproducible, cross-cohort signals from those reported only once or in isolated contexts.

The results reveal a strikingly coherent biological pattern. High pre-treatment circulating levels of a cluster of inflammatory proteins were consistently associated with poorer outcomes in patients treated with checkpoint inhibitors. This adverse signature includes interleukin-6, the chemokine CXCL8, also known as interleukin-8, the angiopoietin ANGPT2, C-reactive protein, the chemokine CCL3, colony-stimulating factor 1, the interferon-inducible chemokine CXCL10, the TNF family ligand TNFSF14, and the acute-phase proteins serum amyloid A1 and A2. Elevated levels of these molecules collectively point to a state of chronic systemic inflammation, which is increasingly understood to undermine the antitumor immune activity that checkpoint blockade is designed to unleash.

The mechanistic logic is compelling. Interleukin-6 and C-reactive protein are hallmarks of inflammatory activation, and their elevation has been linked in prior work to myeloid cell expansion, immunosuppressive cytokine milieus, and T-cell dysfunction. CXCL8 recruits neutrophils and promotes angiogenesis, while ANGPT2 contributes to vascular remodeling and abnormal tumor vasculature that can impede immune cell infiltration. Colony-stimulating factor 1 drives the differentiation of macrophages that often adopt immunosuppressive phenotypes within the tumor microenvironment. CXCL10, although it can participate in antitumor chemotaxis, has also been associated with exhausted T-cell states in chronic inflammation. The convergence of these independent strands into a single adverse axis suggests that the systemic inflammatory state of a patient, readable from a simple blood draw, shapes the probability that checkpoint inhibition will succeed.

On the favorable side of the ledger, the review identified a distinct set of proteins whose higher pre-treatment levels reproducibly correlated with improved treatment outcomes. These include the soluble T-cell ligands ICOSLG, TNFSF10, and FASLG, together with the chemokine CXCL5. ICOSLG is the ligand for the ICOS co-stimulatory receptor on T cells and signals productive T-cell priming and activation. TNFSF10, better known as TRAIL, and FASLG, the Fas ligand, participate in apoptosis signaling pathways that can contribute to immune-mediated tumor cell killing. CXCL5 is a chemokine implicated in granulocyte recruitment, and its association with benefit in these cohorts may reflect contexts in which myeloid or epithelial signaling supports effective immune engagement. The consistent direction of these associations across studies suggests that markers of an activated, functional T-cell compartment in the circulation may signal a tumor-immune configuration that responds well to checkpoint release.

The clinical implications of this synthesis are substantial. If validated prospectively, a pre-treatment blood panel combining inflammatory markers and T-cell activation ligands could help oncologists stratify patients before therapy begins, identifying individuals likely to benefit from checkpoint inhibitors alone and those who might need combination strategies, such as dual blockade therapy or pairing with tyrosine kinase inhibitors, to overcome an inflammatory or immunosuppressive baseline. Such a panel would be minimally invasive, relatively inexpensive compared with tissue biopsy-based approaches, and repeatable over time, allowing dynamic monitoring of the immune state during the early phases of treatment. The review’s pan-cancer framing also raises the possibility that core proteomic signatures of response transcend tumor histology, though cancer-type-specific nuances certainly remain.

The authors are careful to acknowledge the limitations of the current evidence base. Heterogeneity across proteomic platforms complicates direct comparison of effect sizes, and variability in how outcomes are reported, from overall survival to progression-free survival to objective response rate and immune-related response criteria, makes unified synthesis challenging. Risk of bias assessments using the QUIPS tool revealed methodological variability among the included studies, and vote counting, while useful for identifying reproducible directions of association, does not quantify effect magnitude. The work was supported by the European Union under Project 101136926-MULTIR, and the corresponding author is Antonia Vlahou of the Biomedical Research Foundation of the Academy of Athens. One co-author, Maria Frantzi, is an employee of Mosaiques Diagnostics.

Nevertheless, the consistent patterns that emerge from this comprehensive review represent a meaningful step toward clinically actionable immunotherapy biomarkers. The dichotomy between systemic inflammation, which predicts poor response, and T-cell activation signaling, which predicts benefit, provides a biologically grounded framework for designing prospective validation studies and for building multi-protein predictive models. As proteomic technologies continue to mature and as more pre-treatment plasma cohorts become available through ongoing immunotherapy trials, the prospect of a routine blood test that guides checkpoint inhibitor decisions moves closer to reality, promising a future in which the right patients receive the right immunotherapy at the right time.

Subject of Research: Circulating proteomic biomarkers of immune checkpoint inhibitor outcomes: a comprehensive pan-cancer review of pre-treatment cohorts

Article Title: Circulating proteomic biomarkers of immune checkpoint inhibitor outcomes: a comprehensive pan-cancer review of pre-treatment cohorts

Article References: Circulating proteomic biomarkers of immune checkpoint inhibitor outcomes: a comprehensive pan-cancer review of pre-treatment cohorts. (n.d.). https://doi.org/10.1186/s12014-026-09642-z

Image Credits: AI Generated

DOI: 10.1186/s12014-026-09642-z

Keywords: immune checkpoint inhibitors, proteomic biomarkers, immunotherapy, blood biomarkers, IL-6, CXCL8, melanoma, lung cancer, Olink, mass spectrometry, systematic review, cancer prognosis