Predicting progression free survival after systemic therapy in advanced head and neck cancer: Bayesian regression and model development
- University College London, United Kingdom
- King's College London, United Kingdom
- Daichii Sankyo Incorporated, United States
- Institute of Cancer Research, United Kingdom
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
-
Head and Neck Cancer Multivariate Blood DataUCL Research Data Repository, doi:10.5522/04/16566207.v1.
Article and author information
Author details
Fabian Flores-Borja
Anthony C C Coolen
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.
Metrics
-
- 1,253
- views
-
- 212
- downloads
-
- 3
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Predicting progression free survival after systemic therapy in advanced head and neck cancer: Bayesian regression and model development
eLife 11:e73288.
https://doi.org/10.7554/eLife.73288
Further reading
-
- Cancer Biology
Most human pancreatic ductal adenocarcinoma (PDAC) are not infiltrated with cytotoxic T cells and are highly resistant to immunotherapy. Over 90% of PDAC have oncogenic KRAS mutations, and phosphoinositide 3-kinases (PI3Ks) are direct effectors of KRAS. Our previous study demonstrated that ablation of Pik3ca in KPC (KrasG12D; Trp53R172H; Pdx1-Cre) pancreatic cancer cells induced host T cells to infiltrate and completely eliminate the tumors in a syngeneic orthotopic implantation mouse model. Now, we show that implantation of Pik3ca−/− KPC (named αKO) cancer cells induces clonal enrichment of cytotoxic T cells infiltrating the pancreatic tumors. To identify potential molecules that can regulate the activity of these anti-tumor T cells, we conducted an in vivo genome-wide gene-deletion screen using αKO cells implanted in the mouse pancreas. The result shows that deletion of propionyl-CoA carboxylase subunit B gene (Pccb) in αKO cells (named p-αKO) leads to immune evasion, tumor progression, and death of host mice. Surprisingly, p-αKO tumors are still infiltrated with clonally enriched CD8+ T cells but they are inactive against tumor cells. However, blockade of PD-L1/PD1 interaction reactivated these clonally enriched T cells infiltrating p-αKO tumors, leading to slower tumor progression and improve survival of host mice. These results indicate that Pccb can modulate the activity of cytotoxic T cells infiltrating some pancreatic cancers and this understanding may lead to improvement in immunotherapy for this difficult-to-treat cancer.
-
- Cancer Biology
- Immunology and Inflammation
In this study, we present a proof-of-concept classical vaccination experiment that validates the in silico identification of tumor neoantigens (TNAs) using a machine learning-based platform called NAP-CNB. Unlike other TNA predictors, NAP-CNB leverages RNA-seq data to consider the relative expression of neoantigens in tumors. Our experiments show the efficacy of NAP-CNB. Predicted TNAs elicited potent antitumor responses in mice following classical vaccination protocols. Notably, optimal antitumor activity was observed when targeting the antigen with higher expression in the tumor, which was not the most immunogenic. Additionally, the vaccination combining different neoantigens resulted in vastly improved responses compared to each one individually, showing the worth of multiantigen-based approaches. These findings validate NAP-CNB as an innovative TNA identification platform and make a substantial contribution to advancing the next generation of personalized immunotherapies.
