The research, conducted by a team led by Andrea De Giglio of the University of Bologna and published in Cancer Immunology, Immunotherapy, followed 40 patients with advanced non-small cell lung cancer who were beginning first-line treatment with immune checkpoint inhibitors, either alone or in combination with chemotherapy. Rather than focusing exclusively on conventional tumor-side markers such as PD-L1 expression or tumor mutational burden, the investigators tracked a panel of host-derived immunometabolic biomarkers in the blood: soluble receptor for advanced glycation end products, better known as sRAGE, fibroblast growth factor 21, or FGF21, growth differentiation factor 15, or GDF15, and cell-surface-associated perilipin 2, or cPLIN2. In parallel, they used computed tomography scans — which these patients undergo routinely for disease monitoring — to quantify body composition parameters, including measures of lean mass and fat distribution.

The rationale behind this approach is rooted in a growing recognition that cancer immunotherapy is not simply a dialogue between a drug and a tumor. Checkpoint inhibitors work by unleashing T cells, and T cells are exquisitely sensitive to the metabolic environment in which they operate. Systemic inflammation, insulin resistance, sarcopenia and obesity-related metabolic signaling can all reshape that environment. FGF21 and GDF15, for example, are stress-responsive endocrine factors that rise during mitochondrial dysfunction, cellular stress and cachexia, and they have been implicated in reprogramming immune cell behavior. sRAGE, meanwhile, serves as a decoy receptor that mops up inflammatory damage-associated molecular patterns, and its circulating levels are increasingly viewed as a readout of chronic low-grade inflammation and lung epithelial health.

The findings were striking. In the exploratory cohort, patients with high baseline levels of sRAGE experienced significantly longer progression-free survival and overall survival than those with lower levels. Even more intriguingly, the trajectory of sRAGE during treatment carried information: patients whose sRAGE levels increased while on immunotherapy were more likely to respond to the treatment, suggesting that this biomarker might serve not only as a static prognostic factor but as a dynamic indicator of an unfolding, favorable immune response. This kind of longitudinal signal is particularly valuable in a clinical setting where early assessment of treatment efficacy can determine whether to continue, intensify or switch therapy long before radiographic changes become apparent.

FGF21 told a very different story. Elevated circulating levels of this hormone were associated with worse overall survival in the exploratory cohort, with a hazard ratio of 1.11 per unit increase and a p-value of 0.026. FGF21 levels were also inversely correlated with lean mass, linking the biomarker to the loss of skeletal muscle — a hallmark of cancer cachexia and a well-established adverse prognostic factor in lung cancer. This connection between a metabolic stress hormone and body composition underscores a central theme of the study: the metabolic state of the patient’s body as a whole may help determine whether the immune system, once released from checkpoint inhibition, can mount and sustain an effective anti-tumor campaign.

To guard against the risk that these associations were statistical accidents in a small cohort, the team validated the two most promising markers — FGF21 and sRAGE — in an independent group of 76 patients with advanced non-small cell lung cancer. The result was a partial replication that sharpened the biological picture. FGF21 remained an independent predictor of outcome, with higher levels associated with both shorter progression-free survival and shorter overall survival. sRAGE, however, lost its association with survival in the validation cohort, a sobering reminder of the reproducibility challenges that plague biomarker research, particularly for inflammatory markers that fluctuate with comorbidities, medications and subclinical disease activity. The authors are appropriately cautious, framing sRAGE as a hypothesis-generating signal that warrants further study rather than a validated clinical tool.

The study went beyond circulating proteins to interrogate the tumor microenvironment itself. Using in silico transcriptomic analyses of tumor gene expression data, the researchers explored how the genes related to these biomarkers associate with immune signatures within tumors. The results revealed two distinct patterns. Tumor expression of PLIN2 and sRAGE was linked to immune-inflammatory phenotypes — tumors characterized by an active, infiltrated immune landscape where checkpoint blockade is most likely to succeed. In contrast, FGF21 and GDF15 were associated with immune-desert phenotypes, tumors that lack meaningful immune cell infiltration and are notoriously resistant to immunotherapy regardless of how the drug is delivered.

This convergence of clinical and transcriptomic evidence gives the study its conceptual weight. It suggests that the same immunometabolic axes measurable in a patient’s blood may mirror, or even shape, the immune architecture of the tumor. A patient whose blood chemistry reflects chronic metabolic stress — elevated FGF21, dwindling lean mass — may also harbor a tumor microenvironment depleted of T cells, rendering checkpoint inhibitors pharmacologically futile. Conversely, a favorable inflammatory-metabolic profile, signaled by higher sRAGE and rising levels during therapy, may coincide with an inflamed tumor that is primed to respond. The blood biomarkers, in other words, could act as accessible windows into the tumor’s immunological personality.

The clinical implications are significant, though the investigators and outside experts alike emphasize that the work is preliminary. A blood test measuring four circulating proteins, combined with body composition data already embedded in routine CT scans, could one day help stratify patients before or early during immunotherapy — identifying those who need closer monitoring, metabolic interventions such as nutritional support and exercise to preserve muscle, or alternative treatment strategies altogether. Prospective cohorts of 40 and 76 patients cannot support immediate changes to clinical practice, and neither biomarker has yet demonstrated the predictive — as opposed to purely prognostic — performance needed to guide individual treatment decisions. Larger, multi-center validation studies will be essential, ideally with standardized assay protocols and serial sampling designed to test whether early changes in FGF21 or sRAGE genuinely anticipate radiographic response.

Still, the SARC-LUNG study adds to a compelling and fast-moving body of evidence that the success of cancer immunotherapy depends on the whole organism, not just the tumor. As immunometabolism moves from the laboratory bench toward the oncology clinic, biomarkers like FGF21, GDF15, sRAGE and PLIN2 may help clinicians see what conventional tumor profiling has missed: the metabolic soil in which the immune response must grow. For patients with advanced lung cancer — a population in which cachexia, inflammation and metabolic derangement are common — that broader view could eventually mean smarter treatment selection, earlier detection of failure, and new opportunities to intervene on the host side of the cancer-immunity equation.

One notable aspect of the SARC-LUNG design is its prospective structure. Unlike most biomarker studies in lung cancer, which are assembled retrospectively from banked samples of uncertain provenance, this cohort enrolled patients at the moment treatment began and collected blood specimens and imaging at defined time points. That prospective framework reduces the risk of selection artifacts and makes the longitudinal observations — such as the rise in sRAGE among responders — considerably more meaningful than a single cross-sectional measurement would allow.

The study also reflects a broader shift in how biomarker research is being organized in Italy and across Europe. The work received support through national research programs, including funding linked to the HEAL ITALIA initiative and projects addressing aging in an aging society, and it was carried out jointly by the University of Bologna and collaborating centers in Genoa and Rome. This multi-institutional arrangement matters because host-derived biomarkers are sensitive to pre-analytical conditions — how blood is drawn, processed and stored — and harmonized procedures across sites strengthen the credibility of the measurements.

It is worth emphasizing what distinguishes prognostic from predictive information in this setting. A prognostic marker tells us something about the likely course of disease regardless of the therapy given, whereas a predictive marker identifies patients who will specifically benefit from a particular treatment. The associations reported in SARC-LUNG, including the independent relationship between FGF21 and survival in the validation cohort, are best understood as prognostic signals. Demonstrating true predictive value would require comparing biomarker-defined subgroups across randomized treatment arms, something a single-arm observational study cannot do.

The biological plausibility of the FGF21 finding deserves attention as well. FGF21 is produced primarily by the liver in response to mitochondrial stress and nutrient deprivation, and chronically elevated levels are observed in conditions ranging from obesity-related metabolic dysfunction to cardiac cachexia. Its inverse correlation with lean mass in this cohort fits a model in which rising FGF21 reflects an emerging catabolic state — one that may deplete the energy reserves and muscle-derived substrates that a robust anti-tumor immune response demands.

Finally, the publication appears as an open-access article shared ahead of final copyediting, a format that accelerates access to accepted peer-reviewed findings while the Version of Record is being prepared. For a field where biomarker hypotheses often circulate slowly through conference presentations, this early availability allows other research groups to begin designing the larger validation studies that will determine whether these immunometabolic signatures can ultimately earn a place in clinical decision-making.

Subject of Research: Host-derived immunometabolic biomarkers associated with prognosis and immune microenvironment profiles in advanced non-small cell lung cancer treated with immune checkpoint inhibitors.

Article Title: Host-derived immunometabolic biomarkers identify prognostic profiles under immunotherapy in advanced NSCLC: the SARC-LUNG study

Article References: De Giglio, A., Conte, M., Coco, S., Galuppi, F., Santamaria, S., Rosa, A., Lo Bianco, F., Naddeo, M., Ricciotti, I., Di Federico, A., Favorito, V., Trofarello, L., Mantuano, F., Sperandi, F., Gelsomino, F., Brocchi, S., Mosconi, C., Genova, C., Salvioli, S., & Ardizzoni, A. (2026). Host-derived immunometabolic biomarkers identify prognostic profiles under immunotherapy in advanced NSCLC: the SARC-LUNG study. Cancer Immunology, Immunotherapy. https://doi.org/10.1007/s00262-026-04565-y

Image Credits: AI Generated

DOI: 10.1007/s00262-026-04565-y

Keywords: non-small cell lung cancer, immunotherapy, immune checkpoint inhibitors, biomarkers, FGF21, sRAGE, GDF15, immunometabolism, body composition, cachexia, tumor microenvironment, prognosis

Non-small cell lung cancer remains the leading cause of cancer mortality worldwide, largely due to late diagnosis and heterogeneous responses to existing therapies. Immune checkpoint inhibitors, targeting proteins such as PD-1 and CTLA-4, have revolutionized treatment paradigms by reactivating cytotoxic T lymphocytes against tumor cells. However, the variability in patient response poses a formidable obstacle in clinical practice, underscoring the urgent need for predictive biomarkers that can preemptively identify which individuals will benefit from these costly and potentially toxic interventions.

The team’s meticulous investigation harnessed advanced flow cytometry and single-cell RNA sequencing to interrogate the functional and phenotypic landscape of T cells circulating in the peripheral blood of NSCLC patients prior to and during ICI treatment. Their analyses revealed that the abundance and activation states of a specific subset of tumor-reactive T cells correlate strongly with therapeutic outcomes. These CTRTs exhibited distinct surface marker signatures indicating an effector memory phenotype coupled with high expression of exhaustion markers, suggesting a poised but dysfunctional state that ICIs can robustly reinvigorate.

Further molecular dissection highlighted key transcriptional programs governing CTRT activation and exhaustion, driven by complex interplay between chronic antigen stimulation and immunosuppressive tumor microenvironmental signals. Notably, enriched expression of genes such as TOX, NR4A, and PDCD1 delineated CTRTs from other T cell populations, underscoring the nuanced balance between immune exhaustion and reinvigoration potential. This duality appears to shape clinical responses and offers a window into patient stratification based on immune dynamics.

Intriguingly, longitudinal monitoring revealed that patients with a higher baseline proportion of these tumor-reactive, yet partially exhausted T cells were far more likely to experience durable clinical benefit from ICIs. Conversely, patients with low CTRT levels or skewed toward terminally differentiated, non-responsive T cells exhibited poorer outcomes, elucidating a critical mechanistic underpinning for therapeutic resistance. This suggests that the mere presence of T cell infiltration within the tumor is insufficient; rather, precise functional states govern anti-tumor efficacy.

The implications of these findings extend beyond biomarker development. This research incites a paradigm shift in how immunologists and oncologists conceptualize T cell dynamics in cancer immunotherapy. It challenges the binary classification of T cells as simply “active” or “exhausted” and prompts a more sophisticated appreciation of phenotypic plasticity within tumor-reactive T cells. Consequently, it opens avenues for combinatorial approaches aimed at modulating these cellular states to heighten ICI responsiveness.

Importantly, the study also highlights the practicality of liquid biopsy approaches leveraging peripheral blood samples to monitor tumor-specific immune activity without invasive tissue biopsies. This noninvasive snapshot of systemic antitumor immunity may enable real-time treatment monitoring and early intervention strategies to enhance patient survival. It heralds a transformative clinical tool that could democratize precision oncology by providing accessible and dynamic biomarkers.

In addition to predicting outcomes, the researchers posit that characterizing CTRTs could inform the design of personalized immunotherapeutic modalities. For instance, adoptive cell transfer therapies might be optimized by selectively expanding tumor-reactive T cells with favorable phenotypic profiles identified through this approach. Moreover, co-targeting pathways implicated in exhaustion and activation could recalibrate the immune response towards a more effective and sustained anti-tumor attack.

Detailed mechanistic explorations into the signaling pathways modulating CTRT fate uncovered roles for metabolic regulators and epigenetic modifiers that tune T cell exhaustion thresholds. These insights align with emerging evidence that metabolic reprogramming is indispensable for T cell function in tumors, suggesting potential adjunct targets to synergize with checkpoint blockade. Exploration of these pathways could yield novel pharmacological agents enhancing immune competence.

The rigorous clinical correlations presented in this paper were bolstered by extensive cohorts spanning multiple NSCLC stages and treatment histories, enhancing the robustness and generalizability of the conclusions. This comprehensive framework integrates immunophenotyping and transcriptomics with patient outcome data, exemplifying a model for future translational immuno-oncology research striving to bridge basic science with real-world clinical impact.

While the study advances our understanding substantially, the authors acknowledge the complexity inherent in tumor-immune interactions and propose future avenues for refining predictive models by incorporating additional immune subsets, tumor mutational burden, and microbiome influences. Multimodal data integration coupled with machine learning techniques may further enhance predictive precision, ultimately facilitating truly individualized immunotherapy.

In conclusion, the identification of circulating tumor-reactive T cell phenotypes as predictors of immune checkpoint inhibitor response delineates a critical biomarker axis with profound clinical relevance. This work represents a milestone in NSCLC immunotherapy, offering a beacon of hope for patients and clinicians grappling with therapeutic uncertainty. By illuminating the subtle immunological intricacies underlying treatment success, this study equips the medical community with vital tools to tailor cancer immunotherapy and improve patient survival in a field marked by remarkable yet variable progress.

As immune-oncology continues to evolve at a rapid pace, integrating these novel biomarkers into clinical workflows promises to enhance the precision and efficacy of therapeutic interventions. The pioneering efforts of Ito, Iida, Hirano, and their team underscore the indispensable value of deep immunophenotyping in conquering cancer’s adaptive resilience, heralding a new era of personalized medicine where immune profiling guides treatment decisions. Their findings, published in the prestigious journal Nature Communications, are likely to catalyze major shifts in research and clinical practice, shining a spotlight on the power of the immune system in combating lethal malignancies.

Subject of Research: The immunophenotypic characterization of circulating tumor-reactive T cells as a predictive biomarker for immune checkpoint inhibitor response in non-small cell lung cancer.

Article Title: Phenotype of circulating tumor-reactive T cells predicts immune checkpoint inhibitor response in non-small cell lung cancer.

Article References:
Ito, K., Iida, K., Hirano, T. et al. Phenotype of circulating tumor-reactive T cells predicts immune checkpoint inhibitor response in non-small cell lung cancer. Nat Commun (2026). https://doi.org/10.1038/s41467-026-69680-x

Image Credits: AI Generated