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Ashley Maynard



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    MA11 - Biomarkers of IO Response (ID 912)

    • Event: WCLC 2018
    • Type: Mini Oral Abstract Session
    • Track: Immunooncology
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/25/2018, 10:30 - 12:00, Room 203 BD
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      MA11.09 - Single-Cell Characterization of the Immunologic Microenvironment in Advanced-Stage, Oncogene-Driven NSCLC (ID 12122)

      11:30 - 11:35  |  Author(s): Ashley Maynard

      • Abstract
      • Presentation
      • Slides

      Background

      The immunologic microenvironment in oncogene-driven non-small cell lung cancer (NSCLC) is poorly understood. Despite high initial response rates to tyrosine kinase inhibitors (TKIs) in patients with oncogene-driven NSCLC, responses are incomplete and transient. Furthermore, response rates to subsequent checkpoint inhibitor immunotherapies are very low. Understanding the immunologic microenvironment may facilitate understanding treatment resistance in this population.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      From October 2016 to March 2018 we performed single-cell sequencing on 35 tissue samples from 28 patients with NSCLC. Fresh tissue samples were obtained at time of standard of care biopsies and as research sample collections. Single-cell level whole transcriptome RNA sequencing was performed using SmartSeq2. Cells were clustered into distinct cell states on a multi-dimensional gene expression space and visualized using t-distributed stochastic neighbor embedding (t-SNE) for further dimensionality reduction. Cellular identities for each cluster were established by examining the enrichment of known cell-type specific genes across all distinct clusters.

      4c3880bb027f159e801041b1021e88e8 Result

      Tumor samples were obtained from predominantly stage IV lung adenocarcinoma (90.6%) harboring an oncogenic driver (EGFR-mutant 50%, ALK-rearranged 21.9%, BRAF V600E 9.4%, ROS1-rearranged 9.4%, MET exon 14 skipping 6.3%, and KRAS-mutant 3.1%). Samples were collected prior to treatment (21.9%), during treatment (46.9%), and at disease progression on therapy (31.3%). All patients with a targetable oncogenic driver received a standard of care TKI and the KRAS-mutant patient received pembrolizumab monotherapy. A total of 6048 cells were isolated, including 3457 immune cells, with an average of one million reads and 2500 genes per cell. The immunologic microenvironment (average 108 immune cells/sample) included macrophages/monocytes (33% of cells), T cells (31.9%), and B cells (11.6%), as well as a smaller fraction (<10%) of dendritic cells, Langerhans cells, mast cells, neutrophils, and NK cells. Unbiased gene expression-based subclustering of T cells identified 7 distinct T cell populations, including naïve (22.6%), cytotoxic and/or memory T cells (44.1%), and T regulatory cells (5.6%), as well as 6 tumor-associated macrophage populations with distinct gene expression patterns.

      8eea62084ca7e541d918e823422bd82e Conclusion

      Single-cell RNA sequencing to identify immune cell populations is feasible in advanced-stage NSCLC biopsy specimens across multiple time points during treatment. Here, we describe the heterogeneity of infiltrating immune cell phenotypes including T cell and macrophage subtypes. An improved understanding of the immunologic microenvironment in oncogene-driven NSCLC may facilitate patient selection for immunotherapy treatment and aid in the rational design of alternative or combination immunotherapy strategies for a patient population rarely responsive to current immunotherapeutic agents.

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