In an innovative breakthrough study published in BMC Cancer, researchers have unveiled that systemic inflammation biomarkers may hold the key to identifying high tumor mutation burden (TMB) in lung adenocarcinoma patients. This revelation stands to revolutionize the way clinicians approach the assessment of TMB, a crucial biomarker for immunotherapy efficacy in non-small cell lung cancer (NSCLC). Traditionally, determining TMB has demanded the costly and complex application of whole-exome sequencing (WES), which is often hindered by stringent sample requirements and limited clinical accessibility. This new research offers a promising alternative, focusing on readily measurable systemic inflammation markers to predict TMB status, potentially transforming patient outcomes and personalized medicine practices.
Tumor mutation burden quantifies the number of somatic mutations within a tumor genome and has been firmly established as a predictor of response to immune checkpoint inhibitors, which have gained traction in recent years as a frontline therapeutic modality for NSCLC. However, the reliance on WES to evaluate TMB limits its application, particularly in resource-constrained settings. Motivated to bridge this gap, the study involved comprehensive genomic profiling of tumor tissues and matched peripheral blood samples from 72 lung adenocarcinoma patients. The investigation aimed to delineate mutation landscapes across patients with varying TMB levels, while concurrently profiling systemic inflammatory markers such as neutrophil-to-lymphocyte ratio (NLR), derived NLR (dNLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR).
Through meticulous analysis, the researchers confirmed that missense mutations predominate this cancer subtype, with single nucleotide variants (SNVs) constituting the bulk of these alterations. Among the frequently mutated genes, EGFR, TP53, and TTN emerged as the most prominent players, occurring in 35%, 33%, and 24% of cases respectively. Strikingly, patients with high TMB demonstrated a distinct genetic signature characterized by a higher prevalence of C > A transversions and significantly elevated mutation frequencies in TP53 and TTN compared to their low TMB counterparts. These genetic disparities underline the heterogeneity within lung adenocarcinoma and hint at the diverse mutational processes driving tumorigenesis.
Further advancing the understanding of mutational processes, the study identified five de novo mutational signatures, each variably contributing to different TMB strata. This nuance offers vital insight into the etiological factors underpinning genomic instability and mutation accumulation within tumors, which may influence both disease progression and treatment response. By capturing these signatures, researchers can better appreciate the complex interplay between environmental insults, endogenous mechanisms, and immune responses in shaping tumor genomes.
Central to this research was the evaluation of systemic inflammatory markers as surrogate predictors for TMB. Inflammatory mediators circulating in the peripheral blood have garnered attention for their role in cancer biology, particularly due to their interaction with the tumor microenvironment and immune modulation. Employing multivariate generalized linear models, the team uncovered significant associations between elevated NLR and PLR values and high TMB, while lower LMR was also linked to increased mutation burden. These findings suggest that inflammatory status, accessible through routine blood work, might reflect underlying tumor genomic complexity.
The utilization of restricted cubic spline (RCS) plots further illuminated the nature of these relationships, revealing non-linear associations between TMB and the inflammatory indices NLR and PLR. This indicates that the relationship is not simply a direct proportional increase but instead involves more complex dynamics that could reflect threshold effects or nonlinear biological responses. Such insights are critical in refining predictive models and tailoring clinical decision-making strategies.
Recognizing the multifactorial dimensions influencing TMB, the study harnessed the machine learning capabilities of the XGBoost model to evaluate variable importance in TMB prediction. This quantitative assessment underscored the predominant influence of tumor staging (T stage), LMR, and body mass index (BMI) in forecasting mutation burden. Notably, the significant involvement of T stage aligns with the understanding that tumor size and local invasion impact genomic alterations and immune landscape, while systemic factors reflected by BMI and inflammatory profiles play contributory roles.
The integration of systemic inflammatory markers into predictive frameworks for TMB assessment promises tangible benefits in clinical oncology. By circumventing the limitations posed by WES, oncologists may deploy less invasive, cost-effective blood-based biomarkers to identify candidates likely to benefit from immunotherapies, streamlining patient stratification and treatment planning. This approach aligns with the burgeoning paradigm of liquid biopsy, emphasizing minimally invasive diagnostics and real-time monitoring of tumor evolution.
Moreover, the study’s exploration into the distinct mutational features among Chinese lung adenocarcinoma patients broadens the demographic scope of precision oncology research. Genetic and environmental factors influencing mutation spectra and systemic inflammation may vary across populations, necessitating diverse cohort studies to ensure predictive models are universally applicable or properly tailored to genetic ancestries. The comprehensive analysis here thus contributes valuable genomic and clinical data, enriching the global cancer research repository.
Equally important is the potential impact on health economics and clinical workflows. Should systemic inflammation markers validate as robust predictors of TMB, routine pre-treatment blood tests could reduce diagnostic turnaround times and healthcare expenditure related to genomic testing. This would democratize access to immunotherapy indicators, especially in healthcare settings where WES is not readily available, ultimately enhancing equitable cancer care delivery.
However, challenges remain in fully operationalizing inflammation markers as standalone surrogates for TMB. The inflammatory milieu is influenced by myriad factors including infections, comorbidities, and medications, which could confound biomarker specificity. Therefore, ongoing research will be pivotal in refining algorithms, incorporating additional variables, and validating findings across larger, multi-institutional cohorts to bolster reliability and clinical utility.
The pioneering work by Fang, Li, Xu, and colleagues represents a critical step towards integrating systemic inflammatory biomarkers into the diagnostic toolkit for lung adenocarcinoma. By bridging genomic insights with accessible clinical parameters, this research heralds a new era of precision immuno-oncology, where blood-based inflammation indices complement genetic profiling to identify patients most likely to benefit from novel therapies. As immunotherapy continues to reshape lung cancer treatment paradigms, such advancements portend improved survival outcomes and optimized personalized care in one of the world’s deadliest malignancies.
In summary, this landmark study elucidates the intricate relationship between systemic inflammation and tumor genomic characteristics, supporting the feasibility of using easily measurable peripheral blood markers to predict high TMB status in lung adenocarcinoma. It underscores the relevance of inflammation as both a biomarker and a biological modulator in cancer progression, offering a cost-effective, minimally invasive approach to patient stratification. The incorporation of machine learning further enhances predictive accuracy, underscoring the power of integrative analytic methods in modern oncology research. Collectively, these findings pave the way for innovative diagnostic strategies and highlight the immense potential of combining genomic and immunological data to personalize cancer treatment.
Subject of Research: Identification of high tumor mutation burden in lung adenocarcinoma using systemic inflammation biomarkers.
Article Title: Systemic inflammation biomarkers can identify high tumor mutation burden in lung adenocarcinoma.
Article References:
Fang, J., Li, Q., Xu, N. et al. Systemic inflammation biomarkers can identify high tumor mutation burden in lung adenocarcinoma. BMC Cancer 25, 1543 (2025). https://doi.org/10.1186/s12885-025-14894-3
Image Credits: Scienmag.com
DOI: https://doi.org/10.1186/s12885-025-14894-3
Tags: genomic profiling in lung cancerhigh tumor mutation burden identificationimmunotherapy efficacy indicatorsinnovative cancer research breakthroughslung adenocarcinoma researchlung cancer biomarkersnon-small cell lung cancer treatmentpersonalized medicine in oncologypredictive biomarkers for cancer therapysystemic inflammation indicatorstumor mutation burden assessmentwhole-exome sequencing limitations
