Harnessing transcriptomics and immune cell biology to predict response to checkpoint blockade
The landscape of cancer treatment has been significantly transformed by immunotherapy, particularly through the use of programmed cell death protein 1 (PD-1) blockade. However, despite the promise of this approach, it remains effective for only a minority of patients with advanced-stage non-small cell lung cancer (NSCLC). The study by Hummelink et al., published in Clinical Cancer Research, addresses this critical issue by developing a robust, clinically applicable biomarker to predict nonresponse to PD-1 blockade (1).
The study’s primary goal was to create a reliable RNA signature reflecting a tumour’s PD-1T tumor-infiltrating lymphocyte (TIL) status. PD-1T TILs are a dysfunctional yet tumor-reactive subset of T cells whose presence correlates with a poor response to PD-1 blockade. The researchers aimed to translate the detection of these TILs into a routine clinical diagnostic setting using the NanoString nCounter platform.
Baseline tumor samples from 41 patients treated with nivolumab were analysed to develop the predictive gene signature. This training cohort was followed by independent validation in a second cohort of 42 patients. The study’s primary outcome was disease control at 12 months (DC12m), with secondary outcomes including progression-free survival (PFS) and overall survival (OS).
The study successfully identified a 12-gene RNA signature (STAT1, OAS1, TAP1, HEY1, CXCL13, IFIT2, IL6, TDO2, CD6, CTLA4, CD274, LAG3) that could distinguish between PD-1T immunohistochemistry (IHC)-high tumors from patients with DC12m and PD-1T IHC-low tumors from those with progressive disease (PD). In the validation cohort, the signature correctly classified all 6 patients with DC12m and 23 out of 36 patients with PD, achieving a negative predictive value (NPV) of 100% and a positive predictive value (PPV) of 32%. The RNA signature’s high sensitivity and NPV were comparable to those obtained through digital IHC quantification of PD-1T TIL.
The study’s findings have significant implications for clinical practice. The PD-1T mRNA signature offers a streamlined, less technically demanding alternative to IHC quantification, facilitating its integration into routine diagnostics. This advancement means that clinicians can more accurately identify patients unlikely to benefit from PD-1 blockade, thereby sparing them from ineffective treatments and associated adverse effects.
While the study presents promising results, several limitations warrant consideration. The relatively small sample size, especially in the validation cohort, limits the generalizability of the findings. Larger studies are needed to confirm the RNA signature’s predictive power across diverse patient populations. The cohorts used in the study were specific to patients treated with nivolumab. It remains to be seen if the RNA signature will be equally predictive for patients treated with other PD-1 inhibitors or combination therapies. Although the NanoString platform simplifies RNA signature analysis compared to traditional methods, integrating this technology into all clinical settings may still present logistical and technical challenges, particularly in resource-limited environments. While the signature’s NPV is impressive, the PPV remains relatively low at 32%. This indicates that while the test is excellent at ruling out non-responders, it is less effective at positively identifying those who will benefit from PD-1 blockade.
The development of a PD-1T mRNA expression signature represents a significant step forward in the precision medicine approach to NSCLC. By enabling routine clinical diagnostics to predict nonresponse to PD-1 blockade, this biomarker paves the way for more personalized and effective treatment strategies. The reality of the situation however is that there is a plethora of data now published looking at multi-omic, orthogonal ways in which to determine response to checkpoint blockade. With the development of artificial intelligence and radiomics platforms, groups are describing ways in which to do this purely non-invasively. He et al. (2) have demonstrated that by combining deep learning technology and computed tomography (CT) images, an individual non-invasive biomarker was able to distinguish high-tumour mutational burden (TMB) from low-TMB, which might inform decisions on the use of immune checkpoint inhibitors (ICIs) in patients with advanced NSCLC. Currently, programmed death ligand 1 (PD-L1) expression, high TMB and mismatch repair (MMR) deficiency stand as the most robust predictive biomarkers of response to PD-1 pathway inhibition and have been approved for clinical use (3). However, these biomarkers are limited by the presence of intra-tumour heterogeneity and the lack of a standardised threshold so there is certainly a need to develop more complex, individualised prediction tools. The requirement for tissue given the complexity of lung cancer evolution and genomic diversity, is high and multiple biopsies are often needed which in a clinical setting can be challenging. The future is likely to be centred around a plethora of different prediction tools to form an accurate representation of an individual’s tumour microenvironment, immune milieu and genomic repertoire/instability. Many groups focus on response to treatment in terms of efficacy but perhaps focusing on how patients will respond in terms of immune-related adverse events is just as if not more important as it will identify patients who will not benefit at all from checkpoint blockade which comes with the added healthcare economic benefits.
The limitations highlighted underscore the need for further research. Larger, multicentre studies encompassing diverse patient demographics and treatment regimens are essential to validate the RNA signature’s utility and ensure its broad applicability. Combining the PD-1T mRNA signature with other emerging biomarkers could enhance predictive accuracy and offer a more comprehensive approach to personalizing immunotherapy. Efforts to simplify and standardize the NanoString nCounter platform for broader clinical use will be crucial for widespread adoption.
This study exemplifies the ongoing efforts to refine cancer immunotherapy, ensuring that patients receive the most appropriate and beneficial treatments based on their individual tumor biology. By complementing existing biomarkers such as PD-L1 expression and the tumor inflammation signature (TIS), the PD-1T RNA signature provides a novel, clinically actionable tool to refine patient selection for immunotherapy. Despite its current limitations, it represents a crucial advancement towards achieving this goal. Future research and technological innovations will be key to translating these findings into routine clinical practice, ultimately improving outcomes for patients with NSCLC enhancing the lives of patients worldwide.
Acknowledgments
Funding: None.
Footnote
Provenance and Peer Review: This article was commissioned by the editorial office, Translational Cancer Research. The article has undergone external peer review.
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References
- Hummelink K, Tissier R, Bosch LJW, et al. A Dysfunctional T-cell Gene Signature for Predicting Nonresponse to PD-1 Blockade in Non-small Cell Lung Cancer That Is Suitable for Routine Clinical Diagnostics. Clin Cancer Res 2024;30:814-23. [Crossref] [PubMed]
- He B, Dong D, She Y, et al. Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker. J Immunother Cancer 2020;8:e000550. [Crossref] [PubMed]
- Li H, van der Merwe PA, Sivakumar S. Biomarkers of response to PD-1 pathway blockade. Br J Cancer 2022;126:1663-75. [Crossref] [PubMed]
