Predicting progression free survival after systemic therapy in advanced head and neck cancer: Bayesian regression and model development
Abstract
Background: Advanced Head and Neck Squamous Cell Cancer (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 Progression Free Survival (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 3 covariates and was strongly driven by baseline percentage of CD33+CD14+HLADRhigh monocytes. The combined signature has 6 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 9 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.
Data availability
The data generated in this study and used for multivariate modelling are available from the UCL repository: https://doi.org/10.5522/04/16566207.v1
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Head and Neck Cancer Multivariate Blood DataUCL Research Data Repository, doi:10.5522/04/16566207.v1.
Article and author information
Author details
Funding
Cancer Research UK (Early Detection Award C7675/A29313)
- Paul R Barber
Institute of Cancer Research
- Magnus T Dillon
- Kevin J Harrington
Guy's and St Thomas' NHS Foundation Trust
- Selvam Thavaraj
Cancer Research UK
- Rami Mustapha
- Gregory Weitsman
- Shahram Kordasti
Cancer Research UK (City of London Centre CTRQQR-2021\100004)
- Paul R Barber
- Tony Ng
Cancer Research UK (Clinical Fellowship Awards)
- Kenrick Ng
- Ali Abdulnabi Suwaidan
- Myria Galazi
Cancer Research UK (Early Detection and Diagnosis Committee Project grant)
- Giovanna Alfano
- Jose M Vicencio
Innovative Health Initiative (EU IMI2 IMMUCAN (Grant agreement number 821558))
- Luigi Dolcetti
Medical Research Council (MR/N013700/1)
- James W Opzoomer
Medical Research Council (MR/N013700/1)
- Felix Wong
Cancer Research UK (DCRPGF\100009)
- James N Arnold
Cancer Research Institute (Wade F.B. Thompson CLIP grant (CRI3645))
- James N Arnold
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: Written informed consent was obtained for all patients who participated in the Phase 2 clinical trial. Approval was obtained from ethics committees (Research Ethics Committee reference: 15/LO/1670). Approval to procure and process a separate cohort of blood samples from patients at risk of developing lung cancer was also obtained (IRAS ID: 261766).
Copyright
© 2022, Barber et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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