Introduction
The application of PD-1/PD-L1 inhibitors has brought new opportunities in the treatment of advanced non-small cell lung cancer (NSCLC). Results from multiple studies indicate that PD-1/PD-L1 inhibitors can significantly improve overall survival in NSCLC patients. However, in unselected NSCLC patients, the current response rate is only about 20%. Therefore, understanding predictive factors for whether patients will respond to treatment can help identify the patient population most likely to benefit. Nevertheless, there remains a lack of large-scale, multi-omics cohort studies targeting specific NSCLC patients undergoing immunotherapy, hindering the identification and integration of relevant predictive factors.
Recently, the Massachusetts General Hospital Cancer Center, Broad Institute, and AstraZeneca teamsNature GeneticsPublished an article titled "Genomic and transcriptomic analysis of checkpoint blockade response in advanced non-small cell lung cancer." In this study, to deepen the understanding of the molecular characteristics of immune checkpoint inhibitor response in NSCLC,The research team conducted the first joint analysis of the Stand Up To Cancer-Mark Foundation (SU2C-MARK) cohort and identified multiple molecular features associated with patient response outcomes, including favorable (ATM mutations) and unfavorable (TERT amplifications) genomic subgroups, the association between the expression of immunoproteasome-inducible components and response, as well as the impact of tumor intrinsic subtypes.The study findings highlight the complexity of the biological determinants behind immunotherapy and the potential for comprehensive analysis in large, specific cancer cohorts.
The article was published inNature Genetics
The research team analyzed tumor samples collected prior to first-line treatment with PD-1/PD-L1 drugs in NSCLC patients. These samples were obtained from 393 advanced NSCLC patients across nine cancer centers. Both tumor and matched normal samples underwent whole-exome sequencing (WES). Additionally, the researchers conducted whole-transcriptome sequencing (RNA-seq) analysis on tumor tissues from a subset of patients. Overall,The study included a total of 309 WES and 152 RNA-seq samples for analysis and quantification.
To better understand the relationship between mutation drivers and disease response, researchers evaluated the prevalence of known lung cancer drivers across three response categories: partial or complete response (PR/CR), stable disease (SD), and progressive disease (PD). Consistent with previous reports, non-synonymous TMB was associated with response categories, with a median TMB of 14.0 mut/MB in PR/CR patients, 9.0 mut/MB in SD patients, and 7.4 mut/MB in PD patients.
At the same time, the research team analyzed the relationship between 49 known lung cancer drivers and treatment response, among which six genes showed statistical significance or were close to significance. For example,ATM mutations appear to be most favorable for checkpoint inhibition therapy., correlation analysis between ATM and responsiveness was also conducted in another independent cohort, revealingATM Mutation Associated with Improved Overall Survival After Immune Checkpoint Inhibition TreatmentTherefore, in addition to the TMB index, individual driver events may also define favorable and unfavorable factors for immune checkpoint inhibitor therapy.
In addition, the research team also conducted the identification of transcriptional response predictors.Whole-genome analysis of differentially expressed genes between responders (PR/CR) and non-responders (SD/PD) preliminarily identified three related genes: PSME1, PSME2, and PSMB9.Given the significant enrichment of these three genes in the components of the proteasome/immunoproteasome system responsible for peptide generation, the researchers expanded their exploration of antigen presentation pathway-specific genes and discoveredImmunoproteasome subunits appear to be very important predictors of treatment response., even in the broader IFN-γ-induced transcription.
Figure 1. Overview of the SU2C-MARK cohort and initial genomic features, Source:Nature Genetics
Microenvironment Expression Characteristics
To identify microenvironment (M) features associated with immunotherapy response beyond individual cell types, the researchers applied Bayesian non-negative matrix factorization (B-NMF) to the top 770 differentially expressed genes, generating three distinct M signatures: M-1, M-2, and M-3. Since these signature genes were derived from sequencing data of mixed cell populations, they are expected to reflect comprehensive microenvironment characteristics, including both tumor and non-tumor (i.e., immune and stromal) origins.
Gene set enrichment analysis results showed that,M-1 is associated with epithelial-mesenchymal transition; M-2 is related to allograft rejection/IFN-γ response, consistent with the inflammatory immune environment; M-3 shows a weak correlation with E2F targets involved in the cell cycle.Notably, the response rates to immune checkpoint inhibitors vary among these subtypes, with M-1 and M-3 showing differences.M-2 response rate significantly increased。
Figure 2. Identification of microenvironment subtypes and their relationship with immune checkpoint inhibitor treatment response, Source:Nature Genetics
Comprehensive Analysis of the Study Cohort
After evaluating a wide range of clinical, genomic, and transcriptomic features associated with NSCLC checkpoint blockade responses, the researchers conducted a comprehensive analysis of the relationships between these predictors.
Overall, the research team identified three strongly correlated modules. The first correlated module (C1) appears to reflect a typical "wound-healing" microenvironment, including immunosuppressive myeloid cells and stromal features. The second correlated module (C2) reflects a more classical cytokine and immune environment associated with "immune activation/exhaustion," including infiltrating immune signatures and proteasome subunits. The third correlated module (C3) consists of features related to mutational burden, likely representing neoantigen abundance and subsequently enhanced immune recognition. The remaining nine features show some degree of discrete correlation, enriching as a fourth group (C4) with monogenic alterations and diverse immunobiological characteristics. Notably, this module includes EGFR mutations, although the correlation between EGFR mutations and immune features is weak, there is a moderate correlation with mutational load features, suggesting that the intrinsic resistance of this subtype may primarily be driven by insufficient neoantigens.
Figure 3. Integrated analysis of clinical, genomic, and transcriptomic features, source:Nature Genetics
To further understand the specific cellular components that may drive response and resistance, the research team evaluated the expression of 13 cancer-related markers in published single-cell sequencing data of NSCLC, revealing their direct association with different tumor subtypes. The study showed that EMT and TGF-b signaling mainly reflect fibroblasts and endothelial cells within tumor cells rather than the mesenchymal epigenetic state itself; some major single-gene transcription predictors, such as AUTS2 and TCF7L1, exhibited substantial tumor-intrinsic expression. These findings suggestThe body contains a rich, interacting ecosystem, which may serve as the broad basis for the effectiveness of immune checkpoint inhibitors. It also provides a series of specific signaling pathways and cell types, which could represent potential targets for effective intervention in the future.
Figure 4. Analysis of Transcriptomic Features in Single-Cell Data, Source:Nature Genetics
In summary, the study performed a joint analysis of the SU2C-MARK cohort in NSCLC patients, identifying predictors at different levels associated with immunotherapy response.Through comprehensive analysis of genomic features and existing characteristics related to immunology and tumor biology, the complex interactions between different signaling pathways and various cell types were discovered, further unveiling the multidimensional interactions underlying immune checkpoint inhibitor therapy.The researchers expressed hope that the SU2C-MARK cohort could continue to serve as a rich resource for multiple research teams to conduct in-depth investigations, further unravel the complex structure of relevant genomic predictors, and provide deeper insights into the biology of anti-tumor immunity.
Ravi, A., Hellmann, M.D., Arniella, M.B. et al. Genomic and transcriptomic analysis of checkpoint blockade response in advanced non-small cell lung cancer. Nat Genet (2023).
https://www.nature.com/articles/s41588-023-01355-5
