Coupling of Response Biomarkers Between Tumor and Peripheral Blood in Chemoimmunotherapy

Background Introduction

This study focuses on malignant pleural mesothelioma (MPM), a rare and highly lethal cancer strongly associated with asbestos exposure. Most patients are diagnosed at advanced stages, with a survival time of only 12 to 15 months, and traditional treatments revolve around platinum-based chemotherapy. However, treatment paradigms are undergoing significant transformation with the introduction of immune checkpoint inhibitors (ICIs) and chemoimmunotherapy combinations. While these therapies demonstrate remarkable clinical outcomes in some patients, their efficacy varies widely among individuals. Currently, the lack of predictive biomarkers to guide treatment decisions presents a challenge for clinicians in identifying which patients will benefit from the treatment and in balancing its potential therapeutic efficacy against immune-related adverse effects. This study explores potential predictive biomarkers in the tumor microenvironment (TME) and peripheral blood immune features to enhance the prediction of patient responses to treatment.

Source of the Paper

This research was conducted by a team of scientists from several Australian institutions, with key authors including Wee Loong Chin, Alistair M. Cook, and Anna K. Nowak. The study was led by the National Centre for Asbestos Related Diseases and the University of Western Australia, and it was published in the journal Cell Reports Medicine on January 21, 2025. The DOI for the article is 10.1016/j.xcrm.2024.101882.

Detailed Research Process

This study is based on the single-arm, phase-2 DREAM clinical trial (ID: ACTRN12616001170415), involving 54 patients with previously untreated, advanced, or unresectable pleural mesothelioma. All patients received a combined treatment protocol comprising six cycles of platinum-based chemotherapy with pemetrexed and maintenance therapy using the PD-L1 inhibitor durvalumab.

Methodology

Sample Collection and Grouping
  • Sample Sources: The study included patients’ peripheral blood samples and diagnostic tumor tissue samples.
  • Timepoints: Peripheral blood samples were collected at pre-treatment (T0), and during the third week (T1) and sixth week (T2) of treatment.
  • Experimental Design: Bulk RNA sequencing (bulk RNA-seq), single-cell RNA sequencing (single-cell RNA-seq), and T cell receptor sequencing (TCR-seq) were utilized to analyze peripheral blood mononuclear cells (PBMCs) and tumor transcriptomics data.
Data Processing and Analysis
  1. Peripheral Blood Transcriptomics Analysis:
    Bulk RNA-seq was performed on peripheral blood samples from 40 patients to explore differentially expressed genes between responders and non-responders.

  2. Single-Cell Transcriptomics and TCR-seq:
    Single-cell RNA sequencing was conducted on samples from 35 patients to identify cell subpopulations. TCR-seq was used to analyze T cell clonal expansion and immune diversity.

  3. Tumor Tissue Analysis:
    Nanostring nCounter was applied to analyze gene expression profiles in tumor tissue samples from 46 patients, focusing on immune-related gene expression.

Key Technologies and Tools

  • SCCODA (single-cell compositional data analysis) was used for statistical modeling of immune cell proportions.
  • DIABLO (Data Integration Analysis for Biomarker discovery using Latent cOmponents) was employed to identify predictive biomarkers by integrating tumor and peripheral blood multiomics data.
  • UMAP (Uniform Manifold Approximation and Projection) dimensionality reduction was used to construct the immune cell clustering map.

Detailed Results

  1. Peripheral Blood Gene Expression Features:

    • Differential expression analysis showed that CD8+ T cell-related genes were significantly upregulated in responders from T0 to T1.
    • GO enrichment analysis revealed that the “CD8 positive alpha-beta T cell differentiation” gene set was significantly enriched in responders.
  2. Activated CD8+ T Effector Memory Cells Identified by Single-Cell Analysis:

    • CD8+ T effector memory (TEM) cells were significantly expanded in responders, exhibiting a progenitor-exhausted-like phenotype, linked with enhanced stem-like properties.
    • TCR sequencing confirmed that CD8+ TEM cells in responders showed higher levels of clonal expansion, with these clones displaying greater persistence.
  3. Synergistic Interaction Between Tumor and Peripheral Blood:

    • Tumor transcriptomics analysis indicated that responders exhibited a more immunologically supportive tumor microenvironment (permissive TME). Conversely, non-responders showed gene enrichment related to cell division.
  4. Predictive Model and Biomarker Integration:

    • DIABLO analysis that integrated tumor and peripheral blood transcriptomics data identified 50 highly predictive gene features.
    • Kaplan-Meier survival analysis confirmed the predictive utility of stem-like CD8+ TEM-specific gene signatures, showing significant correlations with longer progression-free survival (PFS).

Value and Implications of the Study

This study integrates peripheral blood biomarkers with tumor microenvironment features to provide new insights into predicting patient responses to chemoimmunotherapy for pleural mesothelioma. Its main contributions include:
- Scientific Value: The study validated the critical role of stem-like CD8+ TEM cells in treatment response, further elucidating the dynamic interactions between the tumor and the immune system.
- Clinical Application: This dual-integrated predictive model can help clinicians anticipate treatment responses at an early stage, optimizing treatment decisions and reducing unnecessary side effects.
- Methodological Innovation: By combining single-cell sequencing and multiomics analysis with traditional bulk RNA-seq, the study significantly improved the precision and predictive power of biomarker identification.

Research Highlights

  • This is the first study to link the progenitor-exhausted-like and stem-like characteristics of CD8+ TEM cells in responders’ peripheral blood with tumor behavior.
  • It proposes a novel predictive strategy that integrates tumor microenvironment features with peripheral blood traits, enhancing the reliability of the predictive model.
  • The innovative multiomics analytical framework and tools set a pathway for future biomarker discovery in cancer research.

Limitations and Future Directions

Despite its promising results, the study’s sample size was relatively small, and complementary mechanisms of treatment response require further validation. The ongoing multi-center DREAM3R trial is expected to provide larger statistical power to validate these findings and develop biomarkers broadly applicable to clinical practice.

Conclusion

Through comprehensive multiomics analysis and biomarker integration, this study paves the way for improving the ability to predict the efficacy of chemoimmunotherapy in pleural mesothelioma patients. This achievement not only provides foundational data and analytical frameworks for future research but also holds the potential to transform clinical therapeutic practices.