(T1530-01-02) Maximizing Reproducibility and Sensitivity in qPCR for Detecting Transcripts Over a Broad Dynamic Range in Response to Anti-PD-1 Therapy

Purpose: Quantitative PCR (qPCR) is an essential technique for assessing gene expression, particularly in the context of immunotherapy response monitoring. With the rise of immune checkpoint inhibitors like anti-PD-1 therapy, precise measurement of immune-related gene expression has become crucial for evaluating therapeutic effectiveness and personalizing treatment. This study focuses on optimizing qPCR protocols to improve reproducibility and sensitivity for detecting immune-related gene expression in patients undergoing anti-PD-1 therapy. By refining assay conditions, and using robust normalization techniques, we enhance the accuracy of measuring key biomarkers, aiding in the identification of patients most likely to benefit from immunotherapy and advancing personalized treatment strategies.

Methods: Biological Material: The biological materials used in this study include formalin-fixed, paraffin-embedded (FFPE) tissue sections from human NSCLC lung cancer samples. These tissue blocks, originating from NSCLC patient biopsies, were chosen based on known responses to pembrolizumab immunotherapy. qPCR: RNA was extracted from FFPE tissues using the MagMax kit, involving xylene treatment, ethanol washes, proteinase K digestion, DNase treatment, and elution from magnetic beads. Purity and concentration were verified with Nanodrop, while RNA integrity was assessed with Bioanalyzer 2100. Using the High-Capacity cDNA Kit, 500 ng of RNA was reverse-transcribed into cDNA. qPCR was performed for PD-1, PD-L1, and CTLA-4 gene expression, normalized to GAPDH, on a Quant Studio 7 Pro with controls for no template and no RT. Data were analyzed by the 2^−ΔΔCq method, with Mann-Whitney tests indicating significance at p < 0.05.

Results: The sensitivity analysis results indicate that reliable amplification of PD-1, PD-L1, and CTLA-4 can be achieved across a large range of cDNA input quantities. Optimal sensitivity was observed at input levels of 10 ng and above, where consistent expression levels were detected with minimal variability. A comparative gene expression analysis of immune markers PD-1, PD-L1, and CTLA-4 was performed on two lung cancer tissue samples: pembrolizumab-responsive and non-responsive. Responder showed an approximate 4.5-fold increase in PD-1, a 52-fold increase in PD-L1, and a 7-fold increase in CTLA-4, indicating significant expression differences that may correlate with treatment responsiveness.

Conclusion: This work successfully optimized qPCR conditions for the reproducible and sensitive quantification of immune markers PD-1, PD-L1, and CTLA-4 in lung cancer tissue. By establishing reliable assay conditions, we ensured consistent detection across different tissue types and identified the optimal cDNA input to balance sensitivity with minimal sample usage. These findings provide a robust framework for using qPCR to assess immune marker expression, offering insights that could enhance the evaluation of patient responses to immunotherapy.


Comparative analysis of immune marker gene expression (PD-1, PD-L1, and CTLA4) in NSCLC lung cancer samples with known responses to pembrolizumab immunotherapy.