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E-Poster Display

1948P - Characterising the peripheral myeloid response to immune checkpoint blockade

Date

17 Sep 2020

Session

E-Poster Display

Topics

Translational Research

Tumour Site

Presenters

Rosalin Cooper

Citation

Annals of Oncology (2020) 31 (suppl_4): S1034-S1051. 10.1016/annonc/annonc294

Authors

R.A. Cooper1, I. Nassiri1, R.A. Watson1, C. Taylor1, E. Mahé1, S. Danielli2, B.P. Fairfax1

Author affiliations

  • 1 The Mrc Weatherall Institute Of Molecular Medicine, University of Oxford, OX3 9DS - Oxford/GB
  • 2 University Of Oxford & Oxford Cancer Centre, NIHR Oxford Biomedical Research Centre,, OX42PG - Oxford/GB

Resources

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Abstract 1948P

Background

Immune checkpoint blockade can offer survival benefit for those with advanced or metastatic melanoma. However, only a proportion of patients will achieve clinical response and there is risk of side effect-associated morbidity. The circulating myeloid population is associated with a poor prognosis in the context of melanoma and CTLA4 blockade. Recent evidence has shown a key role for PD-1 on myeloid cells in inhibiting the anti-tumour immune response. Despite this, the myeloid response to checkpoint inhibitor therapy has not been well-characterised.

Methods

We have characterised transcriptomic expression using bulk RNA-seq in CD14+ cells in response to checkpoint inhibitor therapy across 97 patients. To further characterise the response at a single cell level we performed single-cell sequencing (scRNA-seq) of peripheral monocytes across eight patients prior to and after treatment.

Results

Transcripts differentially expressed in response to single versus combination immune checkpoint blockade are identified, and a distinct transcriptomic profile is characterised in patients versus healthy controls. Notably, a number of expression associations are made with clinical outcome in terms of treatment associated side effects, confirming the importance of monocytes in predicting clinical response. scRNA-seq reveals distinct monocyte subsets, of which one is characterised by over-expression of human leukocyte antigen (HLA) and interferon-induced genes, is specific to the post-treatment state. We use deconvolution to identify these monocyte subsets in bulk RNA sequencing (RNA-seq) data across a large patient cohort.

Conclusions

In summary, characterisation of both bulk RNA-seq and scRNA-seq reveals novel insights into inter-individual variation within the peripheral monocyte population in response to checkpoint inhibitor therapy and has potential ramifications for early identification of non-responding patients. Application of these subsets to bulk RNA-seq data may provide opportunities to explore association between monocyte subsets with clinical outcome.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Cancer Research UK, Wellcome Trust, UK Medical Research Council.

Disclosure

All authors have declared no conflicts of interest.

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