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Deciphering immunotherapy response in NSCLC with spatial biology

Deciphering Immunotherapy Response in NSCLC with Spatial Biology

Lung cancer is a formidable foe. Globally, it is the leading cause of cancer-related deaths in men and women, with non-small lung cancer (NSCLC) representing more than 80% of all lung cancers. While NSCLC has historically had a poor prognosis, the treatment landscape has evolved rapidly in recent years with the introduction of immune checkpoint inhibitors (ICI). Today, anti-PD-1/PD-L1 antibodies have taken their place as first-line therapies, demonstrating durable efficacy in a subset of patients with NSCLC.

The key word in that sentence is “subset”.

While the number of patients with long-term survival after ICI treatment is significantly higher compared to conventional chemotherapy, these cases are limited. The need for biomarkers that accurately stratify responders and non-responders is thus crucial to reveal the underlying biology and to enable a more personalized approach to immunotherapy.

The need for better biomarkers

PD-L1 expression and tumor mutation burden (TMB) are the current companion diagnostic assays for ICI treatment. These are incomplete biomarkers, however, suffering from relatively poor performance in stratifying responders from non-responders. It is likely, therefore, that ICI efficacy is not solely dependent on PD-L1-directed immune evasion or tumor antigenicity, but rather influenced by other phenotypic properties of the tissue.

“Spatial localization of immune cells within tumors is key to understanding intercellular communications that can dictate clinical outcomes,” said Dr. Arutha Kulasinghe, Scientific Director of the new Queensland Spatial Biology Centre (QSBC) at the Wesley Research Institute, and Group Leader at the University of Queensland, in Brisbane. In a study led by Dr. Kulasinghe and James Monkman, a team from the QSBC, Akoya collaborators, and Enable Medicine derived new spatial insights into ICI response in NSCLC using multiplexed tissue imaging.

“In this study, we used multiplexed single-cell resolved spatial technologies for deep characterization of the cellular organization that occurs in the tumor microenvironment in NSCLC,” said Dr. Kulasinghe. “Our goal was to advance the understanding of biomarkers and cellular phenotypes that may be more predictive of response to ICI therapy.”

Spatial analysis of tissue from responders and non-responders

For the study, 42 pre-treatment NSCLC tissues were profiled from patients who were subsequently treated with ICI. Pathologists reviewed whole sections prior to scoring representative tumor regions that avoided benign tissue, and included stroma and tumor around invasive margins.

Clinicopathological treatment, ICI response and survival parameters were available; clinical endpoints included ICI response according to RECIST 1.1 criteria and overall survival. A total of 39% of patients were classified as responsive, while 61% were non-responsive. Anti-PD-1 therapies Nivolumab and Pembrolizumab comprised 93% of treatments. The percentage of patients who were alive at follow up was 93% and 38%, for responsive and non-responsive groups, respectively.

Tissue staining and imaging were performed using an Akoya PhenoCycler-Fusion system. Cellular phenotypes and spatial metrics were analyzed for differences between ICI response groups and patient survival times.

Insights relevant to clinical decisions

“The richness of the multiplex imaging data that was developed in this study has increased our understanding of tissue architecture in NSCLC and will provide valuable information relevant to clinical decisions,” said Dr. Kulasinghe. “We were able to highlight the importance of spatial biomarkers for lung cancer immunotherapy by creating a full analytical pipeline from cell segmentation, clustering, cell annotations, and cellular neighborhoods, to spatial associations.”

In this study, spatial data enabled novel observation of a number of phenotypic properties of immune cells that associate with outcome of ICI therapy, and are aligned with current principles of immunology.

Key findings can be summarized as follows:

  • Regulatory T cells (Tregs) were enriched in non-responding patients, and was consistent with their localization in stromal and peripheral tumor margins.
  • Proximity-based interactions between Tregs and monocytes and CD8+ T cells were more frequently found in non-responding patients, while macrophages were more frequently located in proximity to HLADR+ tumor cells within responding patients.
  • Cellular neighborhood analysis indicated that the presence of macrophages and effector CD4+ T cells in mixed tumor neighborhoods, as well as CD8+ T cells in HLADR+ tumor neighborhoods were associated with favorable clinical response.
  • Macrophages exhibited an immunosuppressive phenotype against both CD4+ and CD8+ T cells and this association scored more highly in ICI refractory patients.

“Our study is a compelling example of the power of spatial biology to delineate tumor and immune cell phenotypes, infer their functional properties, and measure how their changes can associate with patient outcomes,” noted Dr. Kulasinghe. “This is an unparalleled approach to understand how to better manage therapeutic strategies, and aid clinical decision-making and diagnostic pathology.

To learn more:

  • View the publication in the Journal of Translational Medicine
  • Connect with us to discuss how Akoya’s spatial biology solutions can help you advance your research.
  • Attend our Spotlight Theater at AACR 2024 to hear from Dr. Arutha Kulasinghe

[1] Ganti AK, Klein AB, Cotarla I, Seal B, Chou E. Update of Incidence, Prevalence, Survival, and Initial Treatment in Patients With Non–Small Cell Lung Cancer in the US. JAMA Oncol. 2021;7(12):1824–1832. doi:10.1001/jamaoncol.2021.4932

[2] Grant, M.J., Herbst, R.S. & Goldberg, S.B. Selecting the optimal immunotherapy regimen in driver-negative metastatic NSCLC. Nat Rev Clin Oncol 18, 625–644 (2021). https://doi.org/10.1038/s41571-021-00520-1

[3] Onoi K, Chihara Y, Uchino J, Shimamoto T, Morimoto Y, Iwasaku M, Kaneko Y, Yamada T, Takayama K. Immune Checkpoint Inhibitors for Lung Cancer Treatment: A Review. J Clin Med. 2020 May 6;9(5):1362. doi: 10.3390/jcm9051362. PMID: 32384677; PMCID: PMC7290914.

[4] Jardim DL, et al. The challenges of tumor mutational burden as an immunotherapy
biomarker. Cancer Cell. 2021;39(2):154–73.

[5] Aguiar PN Jr, et al. The role of PD-L1 expression as a predictive biomarker in advanced non-small-cell lung cancer: a network meta-analysis. Immunotherapy. 2016;8(4):479–88.

[6] Monkman J, Moradi A, Yunis J, Ivison G, Mayer A, Ladwa R, O’Byrne K, Kulasinghe A. Spatial insights into immunotherapy response in non-small cell lung cancer (NSCLC) by multiplexed tissue imaging. J Transl Med. 2024 Mar 4;22(1):239. doi: 10.1186/s12967-024-05035-8. PMID: 38439077; PMCID: PMC10910756.

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