Predicting progression-free survival after systemic therapy in advanced head and neck cancer: Bayesian regression and model development

Author:

Barber Paul R12ORCID,Mustapha Rami3,Flores-Borja Fabian4ORCID,Alfano Giovanna3,Ng Kenrick1,Weitsman Gregory3,Dolcetti Luigi3,Mohamed Ali Abdulnabi3,Wong Felix3,Vicencio Jose M13,Galazi Myria1,Opzoomer James W5ORCID,Arnold James N5,Thavaraj Selvam6ORCID,Kordasti Shahram7ORCID,Doyle Jana8,Greenberg Jon8,Dillon Magnus T9,Harrington Kevin J9,Forster Martin1,Coolen Anthony CC1011ORCID,Ng Tony134ORCID

Affiliation:

1. UCL Cancer Institute, Paul O'Gorman Building, University College London

2. Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, King’s College London

3. Richard Dimbleby Laboratory of Cancer Research, School of Cancer & Pharmaceutical Sciences, King's College London

4. Breast Cancer Now Research Unit, School of Cancer & Pharmaceutical Sciences, King’s College London

5. Tumor Immunology Group, School of Cancer & Pharmaceutical Sciences, King’s College London

6. Centre for Clinical, Oral & Translational Science, King’s College London

7. Systems Cancer Immunology, School of Cancer & Pharmaceutical Sciences, King’s College London

8. Daiichi Sankyo Incorporated

9. The Institute of Cancer Research

10. Institute for Mathematical and Molecular Biomedicine, King’s College London

11. Saddle Point Science Ltd

Abstract

Background:Advanced head and neck squamous cell carcinoma (HNSCC) is associated with a poor prognosis, and biomarkers that predict response to treatment are highly desirable. The primary aim was to predict progression-free survival (PFS) with a multivariate risk prediction model.Methods:Experimental covariates were derived from blood samples of 56 HNSCC patients which were prospectively obtained within a Phase 2 clinical trial (NCT02633800) at baseline and after the first treatment cycle of combined platinum-based chemotherapy with cetuximab treatment. Clinical and experimental covariates were selected by Bayesian multivariate regression to form risk scores to predict PFS.Results:A ‘baseline’ and a ‘combined’ risk prediction model were generated, each of which featuring clinical and experimental covariates. The baseline risk signature has three covariates and was strongly driven by baseline percentage of CD33+CD14+HLADRhigh monocytes. The combined signature has six covariates, also featuring baseline CD33+CD14+HLADRhigh monocytes but is strongly driven by on-treatment relative change of CD8+ central memory T cells percentages. The combined model has a higher predictive power than the baseline model and was successfully validated to predict therapeutic response in an independent cohort of nine patients from an additional Phase 2 trial (NCT03494322) assessing the addition of avelumab to cetuximab treatment in HNSCC. We identified tissue counterparts for the immune cells driving the models, using imaging mass cytometry, that specifically colocalized at the tissue level and correlated with outcome.Conclusions:This immune-based combined multimodality signature, obtained through longitudinal peripheral blood monitoring and validated in an independent cohort, presents a novel means of predicting response early on during the treatment course.Funding:Daiichi Sankyo Inc, Cancer Research UK, EU IMI2 IMMUCAN, UK Medical Research Council, European Research Council (335326), Merck Serono. Cancer Research Institute, National Institute for Health Research, Guy’s and St Thomas’ NHS Foundation Trust and The Institute of Cancer Research.Clinical trial number:NCT02633800.

Funder

Cancer Research UK

Innovative Health Initiative

Medical Research Council

Cancer Research Institute

Institute of Cancer Research

Guy's and St Thomas' NHS Foundation Trust

Publisher

eLife Sciences Publications, Ltd

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

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