PD-L1 and T-cell Activity Biomarkers in Non-small Cell Lung Cancer Patient Prognosis and Response to Immune Checkpoint Inhibitors


Student thesis: Doctoral Thesis

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Awarding Institution
  • Zongli ZHENG (Supervisor)
  • Robert WEISS (External person) (External Co-Supervisor)
  • Kristy L. RICHARDS (External person) (External Co-Supervisor)
Award date9 Sep 2022


Lung cancer is the most common cancer type and the leading cause of cancer death worldwide. Non-small cell lung cancer (NSCLC) accounts for about 85% of lung cancer. The discovery of cancer immune checkpoints and subsequent developments of immune checkpoint inhibitors (ICIs) have prolonged the survival of cancer patients. However, only about 20-30% of unselected cancer patients could benefit from ICI therapy. Recently, the isoforms of the immune checkpoint PD-L1, including secreted and 3’ untranslated region (UTR) truncated isoforms, were shown to mediate resistance to ICI therapies in animal models, but their clinical significance is unknown. Furthermore, T-cell reactivity factors such as granzyme, perforin, and reactive T-cells, may also play important roles in ICI therapy, however, little data is available. Immunohistochemistry (IHC) is the current standard assay for PD-L1 expression detection to triage patients for ICI therapies. However, various PD-L1 detection antibodies raised against different epitopes of PD-L1 make the cut-offs for PD-L1 positivity have little standardization, which leads to the complexity of PD-L1 expression interpretation. Inability to distinguish PD-L1 isoforms, and difficulties in multiplexing with other immune-related factors and genetic alterations will limit the predictive role of immunohistochemistry. Thus, a more efficient method to classify NSCLC patients is in urgent need.

To this end, we first developed a new method, ALPHA-RNA, to quantify mRNA expression of CD274 (encoding PD-L1) with various splicing variants and multiplex with T-cell activity factors and other genetic drivers by Next Generation Sequencing (NGS). Anaplastic lymphoma kinase (ALK) mRNA expression was quantified by ALPHA-RNA to validate the technical accuracy of ALPHA-RNA. The results showed that ALK mRNA expression, as measured by our new method, was highest among those cases with ALK fusions and no expression in ALK-fusion negative cases, which is consistent with other publications. One retrospective clinical cohort was recruited to quantify multiple gene expressions including CD274 and the protein level of PD-L1 was detected by immunohistochemistry. The positive correlation between PD-L1 protein level detected by immunohistochemistry and exon level quantified by ALPHA-RNA indicates the potential of ALPHA-RNA as a companion diagnostic. We also evaluated the associations between CD274 mRNA expression, with isoforms, and the prognosis of NSCLC patients. Interestingly, we found that patients with high expression of CD274 exon6 and low expression of exon4 (presumably resembling secreted isoform) had the worst overall survival when compared to patients with other CD274 isoforms or patients with low CD274 expression (P value < 0.01).

A second retrospective NSCLC cohort consisting of patients who received anti-PD-1 therapy was used to study whether CD274 expression and T-cell reactivity factors can predict patients’ response to ICI therapy. We found that patients with both high expression of CD274 and GZMA (encoding granzyme A) had a better response to ICIs compared with patients with high expression of CD274 and low expression of GZMA, suggesting that the combination of CD274 and GZMA has the potential to predict ICI therapy response. Taken together, these results demonstrate that ALPHA-RNA is a highly accurate NGS method to quantify multiple gene expressions even with low-quality clinical samples; PD-L1 isoforms and GZMA expressions may predict patient survival and response to ICI immunotherapy in NSCLC patients.

    Research areas

  • Next generation sequencing, non-small cell lung cancer, immune checkpoint inhibitors, PD-L1, GZMA