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125 Single cell spatial analysis and biomarker discovery in hodgkin lymphoma
  1. Alexander Xu1,
  2. Aixiang Jiang2,
  3. Tomohiro Aoki2,
  4. Alicia Gamboa3,
  5. Lauren Chong2,
  6. Anthony Colombo3,
  7. Yifan Yin2,
  8. Joseph Lownik3,
  9. Katsuyoshi Takata2,
  10. Monirath Hav3,
  11. Christian Steidl2 and
  12. Akil Merchant3
  1. 1Cedars Sinai Medical Center, Valley Village, CA, USA
  2. 2BC Cancer, Vancouver, BC, Canada
  3. 3Cedars-Sinai Medical Center, Los Angeles, CA, USA
  • Journal for ImmunoTherapy of Cancer (JITC) preprint. The copyright holder for this preprint are the authors/funders, who have granted JITC permission to display the preprint. All rights reserved. No reuse allowed without permission.


Background The tumor microenvironment (TME) is the complex milieu of cells and molecules surrounding the tumor. Single cell methods and systems biology have been used to identify cells with pro- or anti-tumor properties, and selectively modulating these has great therapeutic benefits in immunotherapy. However, there is limited information on how the spatial organization of the TME determines cells’ signaling and their therapeutic potential. Single cell resolved spatial computational analysis is needed to describe the complex interactions of the TME and their effect on patient outcomes. Hodgkin’s Lymphoma presents a unique spatial TME due to the sparse tumor distribution of Hodgkin’s Reed-Sternberg tumor cells. Hodgkin’s is highly receptive to checkpoint inhibitors and insights from the Hodgkin’s TME could better inform immunotherapies in general.

Methods Here we apply Imaging Mass Cytometry (IMC), a technology to perform ~40-plex protein analysis with 1 micron resolution in tissue (figure 1A), to study a cohort of 260 matched samples at diagnosis and after relapse from 91 patients with relapsed/refractory Hodgkin’s Lymphoma. We developed a computational pipeline to describe tumor architecture and propose putative biomarkers of Hodgkin’s clinical response and relapse. New methods are used to define biomarkers based on localized ligand receptor signaling driving tumor-immune clustering (rosetting) unique to Hodgkin’s.

Results We analyzed over 7 million cells with IMC to describe spatial features of the tumor – cell subtypes and their positioning – that correlate to clinical outcomes. In brief, we found signatures of spatial reorganization from diagnosis to the relapsed tumor. We validated existing biomarkers such as CD68+ macrophage proportion (p=0.018, likelihood ratio test). We used spatial metrics to perform ‘digital biopsies’ to derive novel, spatially-dependent biomarkers such as LAG3+Tregs (p=0.022 for all cells, p=0.66 near tumor) (figure 1B,C). We quantified cell rosetting, a recruitment of immune cells that is characteristic of Hodgkin’s (figure 1D), by defining ligand-receptor associations that are unique to different stages of disease severity. We proposed novel ligand-receptor biomarkers involving macrophages (TIM3/Galectin9, CD80, PDL1) and CD4 T cells (LAG3/HLAII, VISTA/Galectin9, PD1/PDL1) (p<0.05) (figure 1E).

Conclusions Spatial analysis of the HL microenvironment revealed composite features of the TME that predict clinical outcomes (figure 1F). These features cannot be described using single cell tools or low-plexed imaging, and represent a truer picture of HL biology. The pipeline developed here can be universally applied to other spatial protein data for biomarker discovery and analysis, and the novel spatial biomarkers proposed here have been validated with multi-color IHC in an independent cohort.

Abstract 125 Figure 1

IMC analysis of Hodgkin’s Lymphoma. (A) IMC generatesa 40-plex protein image that can be segmented into single cells. (B) (C) Single cell data can be processed to obtain immune phenotypes, shown by heatmap and UMAP. (D) Immune cells aggregate to form rosettes around tumor cells. We find specific ligand-receptor interactions associated with rosetting. (E) Tumor/Treg ICOSL/ICOS expression is highly correlated with Treg rosettes, while Tumor/CD4 LAG3/HLADPDQDR expression is negatively correlated with CD4 rosettes. (F) IMC-generated biomarkers predict overall survival.

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