TY - JOUR
T1 - Predicting progression-free survival after systemic therapy in advanced head and neck cancer
T2 - Bayesian regression and model development
AU - Barber, Paul R.
AU - Mustapha, Rami
AU - Flores-Borja, Fabian
AU - Alfano, Giovanna
AU - Ng, Kenrick
AU - Weitsman, Gregory
AU - Dolcetti, Luigi
AU - Suwaidan, Ali Abdulnabi
AU - Wong, Felix
AU - Vicencio, Jose M.
AU - Galazi, Myria
AU - Opzoomer, James W.
AU - Arnold, James N.
AU - Thavaraj, Selvam
AU - Kordasti, Shahram
AU - Doyle, Jana
AU - Greenberg, Jon
AU - Dillon, Magnus T.
AU - Harrington, Kevin J.
AU - Forster, Martin
AU - Coolen, Anthony C.C.
AU - Ng, Tony
N1 - Funding Information:
MDF is supported by the UCL/UCLH NIHR Biomedical Research Centre and runs early phase studies in the NIHR UCLH Clinical Research Facility supported by the UCL ECMC.
Funding Information:
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.
Funding Information:
JWO is supported by the UK Medical Research Council (MR/N013700/1) and is a KCL member of the MRC Doctoral Training Partnership in Biomedical Science. FW is also supported by the UK Medical Research Council (MR/N013700/1). JNA is funded by a grant from Cancer Research UK (DCRPGF\100009) and is the recipient of a Cancer Research Institute/Wade FB Thompson CLIP grant (CRI3645).
Funding Information:
The EACH trial was sponsored by University College London and managed by the CRUK and UCL Cancer Trials Centre.
Funding Information:
This research was funded/supported by the National Institute for Health and Care Research (NIHR) Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London and/or the NIHR Clinical Research Facility. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. FUNDING: This work was supported by a grant from Daiichi Sankyo Inc (‘Identification of Non-Invasive Treatment Stratification and Longitudinal Monitoring Markers for Patritumab/Cetuximab Combination Therapy’). This work was also supported by Cancer Research UK funding support to King’s College London – UCL Comprehensive Cancer Imaging Centre (CR-UK and EPSRC), Cancer Research UK King’s Health Partners Centre at King’s College London, and Cancer Research UK UCL Centre; University College London (PRB) – Early Detection Award (C7675/A29313); as well as CRUK City of London Centre (CTRQQR-2021\100004). MG, KN, and AAM are supported by Cancer Research UK Clinical Training Fellowships (Award numbers: 163011 for MG, 176885 for KN and 100179 for AAM). LD is supported by EU IMI2 IMMUCAN (Grant agreement number 821558). GA and JMV are supported by CRUK Early Detection and Diagnosis Committee Project grant.
Funding Information:
MTD and KH acknowledge funding support from The Institute of Cancer Research/Royal Marsden Hospital NIHR Biomedical Research Centre and ST acknowledges funding from Guy’s and St Thomas' NHS Foundation Trust.
Publisher Copyright:
© Barber, Mustapha, Flores-Borja et al.
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85145641490&partnerID=8YFLogxK
U2 - 10.7554/ELIFE.73288
DO - 10.7554/ELIFE.73288
M3 - Article
C2 - 36562609
AN - SCOPUS:85145641490
SN - 2050-084X
VL - 11
JO - eLife
JF - eLife
M1 - e73288
ER -
