Landscape survey of non-small-cell lung carcinoma (NSCLC) for immuno-oncology (IO)

Overview

Immuno-oncology (IO) has significantly transformed the treatment landscape for non-small-cell lung carcinoma (NSCLC), with therapies targeting immune checkpoints such as PD-1 and PD-L1 demonstrating promising clinical outcomes. However, identifying reliable biomarkers and understanding treatment efficacy across different therapy lines requires extensive analysis of clinical trial data and scientific literature.

Excelra partnered with a large pharmaceutical company to perform a comprehensive NSCLC immuno-oncology landscape survey, analyzing clinical studies and biomarker data to support clinical trial strategy. Leveraging advanced data curation services, bioinformatics solutions, and scientific informatics, Excelra developed a structured dataset from a wide range of clinical studies to uncover biomarker insights and treatment trends.

Our client

The client is a large pharmaceutical company based in the United States focused on developing new therapeutic strategies for non-small-cell lung cancer (NSCLC). Their research team aimed to identify biomarkers that could support the design of effective immuno-oncology clinical trials and improve patient outcomes.

Client’s challenge

Identifying suitable biomarkers for NSCLC immunotherapy requires extensive analysis of existing clinical trial data and scientific literature.

The client faced several challenges:

• Extracting NSCLC clinical trial data from large volumes of published literature
• Structuring heterogeneous datasets for meaningful analysis
• Identifying correlations between immune response biomarkers and clinical outcomes
• Evaluating checkpoint inhibitors targeting PD-1 and PD-L1 pathways
• Understanding treatment efficacy across multiple lines of therapy

Conducting this analysis manually would require significant time and resources, prompting the client to collaborate with Excelra for a comprehensive data analysis solution.

Client’s goals

The client sought to achieve the following objectives:

• Conduct a comprehensive landscape survey of NSCLC clinical trials
• Identify biomarkers that could guide immuno-oncology clinical trial design
• Evaluate checkpoint inhibitors targeting PD-1 and PD-L1
• Assess treatment outcomes across multiple therapy lines
• Gain insights into immune response mechanisms and tumor-infiltrating lymphocytes (TILs)

Our approach

Literature identification using text mining

Excelra developed a text-mining algorithm to identify relevant literature and clinical studies related to NSCLC immunotherapies. This automated approach enabled rapid extraction of relevant publications from large scientific databases.

Data curation and annotation

Once relevant studies were identified, Excelra’s scientific experts manually curated and annotated the data. According to the curated dataset, 82 relevant clinical studies were analyzed across multiple parameters including therapy lines, immune profiles, and biomarker correlations.

Key curated data points included:

• Line of therapy
• Circulating immune cell profile
• Tumor-infiltrating lymphocytes (TILs)
• PD-L1 expression assays
• Baseline mutation status
• Correlation between clinical response and immune markers

Clinical trial landscape analysis

Excelra’s analysis focused on checkpoint inhibitors targeting PD-1 and PD-L1, evaluating their efficacy across four lines of therapy:

• Naïve patients
• First-line therapy
• Second-line therapy
• Third-line therapy

The compiled landscape dataset included detailed information about drugs, treatment regimens, and efficacy-related endpoints.

Comparative immunotherapy analysis

Excelra conducted comparative analyses between anti-PD-1 and anti-PD-L1 therapies, identifying similarities and unique efficacy endpoints across treatments. The analysis revealed several correlations between PD-L1 expression levels, immune cell profiles, and clinical response rates.

Figure 1: Summary of the compiled NSCLC landscape for pembrolizumab

Table: Treatment regimen for NSCLC and the measured clinical response

Our solution

Excelra delivered a comprehensive NSCLC immuno-oncology landscape dataset that enabled the client to make informed decisions regarding biomarker selection and clinical trial design.

Key outcomes

• Curated dataset of 82 NSCLC clinical studies
• Identification of biomarkers associated with immune response and treatment outcomes
• Comparative analysis of anti-PD-1 and anti-PD-L1 therapies
• Insights into tumor-infiltrating lymphocyte (TIL) profiles and immune cell dynamics
• Structured analysis of treatment regimens across therapy lines

The analysis also highlighted trends in PD-L1 assays and tumor proportion score (TPS) correlations with clinical response across multiple therapy stages.

Key benefits

• Accelerated biomarker discovery
  The curated NSCLC dataset enabled faster identification of biomarkers relevant to immunotherapy clinical trials.
• Improved clinical trial strategy
  Comprehensive analysis of therapy lines and treatment regimens supported optimized trial design.
• High-Quality Analysis-Ready data
  Excelra delivered structured datasets ready for downstream analysis and research applications.

Figure 3: Anti-PD-1 and anti-PD-L1 treatment and efficacy data points. Blue data points are common
for both anti-PD-1 and anti-PD-L1. Orange data points are unique to anti-PD-1

Figure 4: Comparative analysis of efficacy endpoints between different lines of therapy

Figure 5: PD-L1 assays and association with the clinical response across different lines of therapy