Dr Helen McGuire

Dr Helen McGuire is a Research Officer at the Ramaciotti Facility for Human Systems Biology, an initiative established to support mass cytometry and systems biology analysis across Sydney and wider collaborative links. Her research interest lies in the clinical application of immunological studies to a range of human diseases, including cancer.

Abstract

Predictive immune signature analysis for clinical cancer application

Helen M McGuire1,2, Clara Choi2, Barbara Fazekas de St Groth1,2

1 Ramaciotti Facility for Human Systems Biology, The University of Sydney, Camperdown, NSW, Australia
2 The University of Sydney, Camperdown, NSW, Australia

Mass cytometry, or Cytometry by Time-Of-Flight (CyTOF), is a powerful platform for high-dimensional single-cell analysis of the immune system. It enables the simultaneous measurement of over 40 markers on individual cells through the use of monoclonal antibodies conjugated to rare-earth heavy metal isotopes. Coupled with our already extensive immunological knowledge of canonical immune subsets and an ability to delve into and describe subtle populations, mass cytometry presents an opportunity to investigate cumulative subtle changes across many specific immune subsets in a range of clinical cohorts.

We have concentrated on analysis of peripheral blood mononuclear cells, which are readily available as a reproducible source of tissue from patients and healthy subjects. Our aim is to develop analysis pipelines with clinical utility, for example to provide predictive tests that could inform clinical management.

Based on our previous studies in several autoimmune states, which revealed remarkably stable changes in the size of multiple peripheral blood cell subsets, we conducted a study of cell subsets in melanoma and lung cancer patients before and after therapy with the checkpoint inhibitor, anti-PD-1.

In line with multiple published studies, we found many therapy-dependent changes in expression of molecules either directly targeted by therapy, or closely associated with checkpoint pathways. These changes did not correlate with clinical response to therapy. However many of the T cell subsets previously identified in autoimmune patients, including those defined by expression of receptors responsible for tissue localisation and chemokine response, were differentially represented in baseline (pre-therapy) samples from cancer patients who failed to respond to anti-PD-1 therapy. We used a data analysis approach originally developed to analyse gene expression signatures in highly multiparametric datasets to analyse the cell subset distribution within samples.

We identified an immune signature in baseline blood samples that robustly identified patients who would subsequently make clinical responses to cancer treating anti-PD-1 therapy. Such an approach is well suited to machine learning, which will be used in future application of the predictive signature in clinical settings.