Recurrent (R) or metastatic (M) head and neck squamous cell carcinoma (HNSCC) is associated with a poor prognosis. For many years, the standard-of-care first-line systemic treatment was the EXTREME regimen, consisting of a platinum-based chemotherapy regimen and cetuximab, an anti-EGFR monoclonal antibody (Van Cutsem et al., 2009). The KEYNOTE-048 trial in 2019 changed the treatment paradigm for these patients by incorporating pembrolizumab, an immune checkpoint inhibitor, into the first-line setting (Burtness et al., 2019). However, the EXTREME regimen remains a first-line standard-of-care for a substantial number of patients, specifically those with programmed death ligand 1 (PD-L1) negative tumours or those with contraindications to the use of anti-PD1 immunotherapy.

While effective, these regimens are associated with significant toxicities. One of the key challenges for the treating physician is to identify the patients who would benefit from either of these treatment regimens. A predictive biomarker signature for patients with advanced HNSCC will help individualize discussions with patients regarding the risk-benefit balance of treatment and may guide patients who are likely to perform poorly towards alternative therapy regimens or clinical trials.

The absence of predictive biomarkers in this patient cohort represents a significant clinical unmet need. Until the development of PD-L1 as a biomarker for immunotherapy, efforts to generate biomarkers in HNSCC have focused on gene expression profiles, which are dependent on the availability of tumour tissue and are only performed on pre-treatment samples (Bossi et al., 2016; You et al., 2019). Signatures based on a single biological modality and taken at a single timepoint may be insufficient to predict outcomes, as response to therapy relies on a dynamic interplay between cancer genomics, immune profile, tumour microenvironment, and clinicopathological characteristics of the patient receiving treatment (Cheerla and Gevaert, 2019; Huang et al., 2019).

Efforts to develop a machine learning model to stratify survival risk by combining genetic and clinicopathological characteristics have revealed some success in advanced oral squamous cell carcinoma (Tseng et al., 2020). We hypothesize that a multimodal analysis, taking into account both clinicopathological and laboratory-based biological covariates at different timepoints, would provide better predictive value. Furthermore, by only including covariates that can be obtained from a blood biopsy, patients could be easily screened for discovered biomarkers.

We prospectively collected peripheral blood samples from a Phase 2 trial in R/M HNSCC (NTC02633800) (Forster et al., 2019), which utilized cetuximab with platinum therapy as a backbone, and conducted a parallel exploratory analysis with the aim of generating a biomarker signature which would predict outcomes to treatment. We hypothesized that the detailed definition of a broad immune cell signature could contribute to the development of assays employing liquid biopsies to predict clinical outcomes. We also incorporated the analysis of two circulating microRNAs (miRNAs) from exosomes: miR-21-5p and miR-142-3p, which have previously demonstrated prognostic and predictive utility (Summerer et al., 2015; Vahabi et al., 2021). As the trial investigated the efficacy of an anti-ErbB3 antibody, patritumab, administered alongside an anti-EGFR antibody, we simultaneously analysed EGFR-ErbB3 dimerization using Förster resonance energy transfer (FRET) and included it in our analysis.

By extracting information from patient samples at baseline and after the first cycle of treatment within this trial, we aimed to generate a multimodal predictive signature for the systemic therapy based on a novel Bayesian multivariate model. The immune populations driving the risk signature were then prospectively validated in an independent cohort of patients from a Phase 2 trial evaluating avelumab in combination with cetuximab in R/M HNSCC (the EACH trial; NCT03494322). Blood samples were collected at baseline and post first dose of cetuximab before administration of avelumab. Validation was performed against the best overall response, as assessed by iRECIST, showing strong correlation between biomarkers identified in the risk signature and therapeutic response despite changes in treatment regimen. We then used imaging mass cytometry driven by the risk signature to identify correlate changes in systemic immune populations with intra-tumoural immune subsets that colocalize at the tumoural level and appear to be key to response. This risk signature can serve as a non-invasive risk stratification for patients with R/M HNSCC using only peripheral blood, guiding the clinician toward the likelihood of success early during the treatment course.

There is a clinical unmet need to identify predictive biomarkers for treatment in head and neck cancer. Gene expression profiling has revealed promising initial results in this domain but have been limited to HPV-positive HNSCC which inherently have better prognoses. The ratio of neutrophils to lymphocytes (NLR) in the peripheral blood of HNSCC patients prior to treatment has been extensively investigated for its ability to predict disease outcome. Two recent meta-analyses on the subject have identified that a high pre-treatment NLR correlated with worse OS with respective HRs of 1.69 (95% CI: 1.47–1.93; p<0.001) and HR of 1.78 (95% CI: 1.53–2.07; p<0.0001) (Mascarella et al., 2018; Takenaka et al., 2018). Using our combined risk signature, we were able to predict disease outcome more accurately across two separate treatment modalities. Other recent biomarker research has focused on predicting response to immune checkpoint blockade by analysing tissue-based biomarkers, such as PD-L1 expression levels, but when used in isolation have not been sufficiently predictive at identifying patients who would benefit (Burtness et al., 2019).

While unimodal biomarkers may offer some predictive value, the biology of HNSCC and likelihood of response to treatment is likely to be dictated by an interplay between tumour immunity, genomic signatures, and a host of clinicopathological characteristics which can only be assessed by a multivariate analysis. There has been an increased interest in peripheral blood-based liquid biopsies in recent years, particularly in the context of PBMC analysis (Nixon et al., 2019). The ability to extract predictive biomarkers from a blood-based liquid biopsy mitigates certain limitations posed by tissue biopsies – particularly the tissue accessibility, technical expertise to obtain the biopsy, patient frailty, and the amount of tissue available. The ease of obtaining liquid biopsies also facilitates longitudinal monitoring of response to treatment. Moreover, PBMCs offer a much more systemic snapshot of the immune response which overcomes intra-tumoural heterogeneity that results in non-uniform distribution of biomarkers. The potential of PBMCs to unravel the tumoural immune landscape is slowly being realized, thanks to advancements in the fields of high-dimensional flow cytometry, single-cell RNA sequencing, and cytometry by time of flight. However, such techniques generate an excessive number of variables which require powerful statistical analysis tools to overcome pitfalls such as overfitting (Shalabi et al., 2018).

To our knowledge, the current study is one of the first examples of integrating multiple biological covariates derived from peripheral blood and patient clinical data to generate a signature which predicts treatment response and that has been validated across multiple therapeutic modalities.

By employing cross-validation iterations to estimate training and validation errors, implementing advanced overfitting correlation protocols, using built-in corrections for informative data missingness, and probabilistic covariate removal, we were able to derive a robust optimal covariate set which correlates with PFS. This combination of analyses has been shown to produce robust signatures that do generalize to uncaptured data (Barber et al., 2020).

The biological components of the predictive model warrant discussion. The only clinical covariate to feature in the combined risk signature was the hypopharyngeal SCC sub-site, which was adversely correlated with PFS. This corroborates previous findings that the 5-year relative survival of patients with hypopharyngeal SCC is consistently the worst amongst different anatomical HNSCC sub-sites (Gatta et al., 2017; Machiels et al., 2020). The propensity of hypopharyngeal tumours to present at the de novo advanced stage (Cadoni et al., 2017) and the density of submucosal lymphatics in this anatomical region translates into these patients inherently performing worse – lending support to the robust nature of our predictive signature. The notable absence of patritumab (denoted as ‘Drug’) in our predictive signatures is also consistent with the outcome of the Phase 2 clinical trial where the addition of this investigational medicinal product did not produce any benefit to PFS (Forster et al., 2019).

Another key discovery in this study is the significance of analysis of sequential samples even at early stages of therapy. This, we believe, is important for the analysis of lab-based biological variables. There is a high level of inter-individual heterogeneity in the relative abundance of PBMC subpopulations even between healthy donors. This inherently weakens the predictive power of covariates based on raw PBMC populations. We observe this phenomenon in our dataset as the combined risk signature had higher predictive power than the baseline one. By opting to present the biological data obtained from the first post-treatment biopsy as change relative to the baseline biopsy, we obtained a much more predictive risk signature in our combined model. This is highlighted in the post-treatment change in CD8CM percentage relative to baseline value which is not only the strongest predictor of PFS in the combined model, but also has a greater univariate predictive value than the whole baseline signature. This validates the potential of our approach has in deconvoluting observed biological phenomena due to patient heterogeneity from that induced by drug treatment, both of which are key to biomarker discovery.

We also validated our risk signature in a second cohort thus allowing us to successfully identify predictive biological biomarkers for therapy across different treatment modalities. The fact that the first trial involved targeted therapies and the second a combination of anti-EGFR and anti-PD-L1 therapy, yet the same blood kinetic change can still distinguish between responders and non-responders is novel and has not been seen before according to our knowledge. The limitation is that the independent validation cohort is small despite the fact that the result we obtained with the present number of validation samples did reach significance (Kandall tau = 0.725, with p-value 0.018). This further highlights the power of multivariate analysis as none of the univariates identified in the test cohort independently predicted treatment outcome in the validation cohort, yet the risk signature strongly correlated with a poorer outcome. We believe that understanding the interplay of key immune subsets between the tumoural microenvironment and the circulation will be key to biomarker development. We focused on two covariates from the risk signature based on their predictive power in both cohorts. Analysis of immune populations at the tissue level allowed us to identify tissue counterparts of peripheral CD33⁺HLADR⁺ monocytes and CD8CM that not only colocalized at the tissue level but also were favourable to a good outcome. Hence, we hypothesize to have uncovered a two-checkpoint system for predicting response to systemic therapy which merits further investigation.

Firstly, a favourable therapeutic outcome requires the presence of high pre-treatment levels of CD14⁺CD16⁺CD33⁺HLADR^high monocytes. These correlated with a similar population of CD74⁺CD68⁺ population of tumoural myeloid cells. Myeloid cells are plastic yet given the similarity in the marker expression patterns between the peripheral CD33⁺CD14⁺CD16⁺HLADR^high monocyte population and the CD14⁺CD33⁺CD74⁺CD68⁺ tissue macrophages, as well as the strong correlation between their corresponding blood and tissue abundance (Figure 6D), we hypothesize that they represent the same group of cells that gain CD68 expression as they enter the tissue. This monocyte population matches the population of intermediate monocytes described in the literature shown to have a key role in antigen presentation (Cravens et al., 2007; Zawada et al., 2011; Wong et al., 2011). Given the correlation between tissue levels CD8⁺ memory T cell, peripheral intermediate monocytes, and the tissue colocalization of the CD8⁺ memory T cells with the CD74⁺ macrophages, we can imagine a role for our myeloid population in inducing post-treatment expansion of CD8CM potentially via a mechanism involving cross-presentation of antigens released by drug-induced cancer cell death (Colbert et al., 2020). Indeed, cetuximab treatment can be a source of tumour-derived antigens as it has been shown to increase TCR diversity in peripheral T cells as well as cross-presentation by antigen-presenting cells (Kansy et al., 2018; Srivastava et al., 2013). Ideally, we would require analysis of the draining lymph node to fully understand how these cells move between the tumour and the circulation and their contribution to the expansion of the CD8CM, a role generally accomplished by dendritic cells at the level of the lymph node. Nevertheless, monocyte-derived macrophages have been shown to activate and expand tissue resident memory T cells (Trm) (Snyder et al., 2021; Low et al., 2020; Muntjewerff et al., 2020; Chu et al., 2020).

An on-treatment increase in the level of circulating CD8CM significantly correlated with a worse disease outcome in patients who received two regimens of systemic treatments within two trials. Interestingly, high levels of pre-treatment CD8CM cells correlating with a favourable treatment outcome in HNSCC has been previously and independently reported by the Whiteside group indirectly validating our results (Czystowska et al., 2013). While an increase in CD8CM correlating with a poorer outcome in both our datasets seems counter-intuitive, it is key to realize that this increase is only seen at the levels of peripheral CD8CM which inversely correlate with tissue Trm (Figure 6D). Using single-cell sequencing and mapping of T cell receptor clonality at the levels of TILs and peripheral T cells, researchers have definitively proven that tumour reactive T cell clones can be found in the periphery (Fairfax et al., 2020; Kok et al., 2020; Pauken et al., 2021; Padrón et al., 2022). The movement of these cells between the tumour and draining lymph node was recently investigated using photoactivable T cells in mice which showed that there is continuous recruitment of memory T cells from the periphery into the tumour which replenishes the pool of tissue Trm identified by CD103 and TCF1 expression. However, this recruitment is usually matched by an egress of Trm cells out of the tumour to the draining lymph nodes which is accompanied by decreased expression CXCR6 (Li et al., 2022). The peripheral origin of Trm cells is still under investigation with recent data suggesting that CD8CM are not only capable of homing into the inflamed tissue but also undergoing transcriptional reprogramming to a stable Trm phenotype (Matos et al., 2022). We hypothesize that the predictive power of the population of peripheral CD8CM stems from its ability to home to the tumour and contribute to the Trm population which has recently been identified as key player in anti-tumour immunity in HNSCC as well as other cancers mainly in the context of immunotherapy but also in chemo/radiotherapy (Han et al., 2021; Ida et al., 2021; Savas et al., 2018; Djenidi et al., 2015; Luoma et al., 2022). Unfortunately, one key shortcoming of our study is the lack of Trm-specific markers such as CD103, CD69, and TCF1 from the imaging mass cytometry panel of antibodies which we believe contributed to this anti-correlation not being stronger.

The accumulation of clonally expanded memory T cells in the periphery in poor responders is the potential result of tumour-induced T cell exclusion which we can infer from the negative correlation observed between peripheral and tumour infiltrating memory T cells (Jerby-Arnon et al., 2018). In this model CD8⁺ memory T cells are being generated only in patients having a large pre-treatment pool of competent APCs yet these anti-tumour CD8⁺ memory cells cannot enter the tumour from the periphery hence accumulating in circulation. Our group is now focused on understanding the different aspects of this model mainly with regards to the specific tumour exclusion of CD8CM T cells.

Another limitation of our work to discuss is the absence of overall survival (OS) data within our current dataset. It would have been interesting to assess whether the immune markers predict survival in the longer term. However, the accuracy of the predictive signature for OS is often diluted by a variety of subsequent treatment regimens.

The present study shows that the combination of biomarkers established prospectively by liquid biopsies early in the treatment course offers potential for the provision of personalized treatments to patients (Nenclares et al., 2021). The risk signature drove the discovery of synergies between the systemic and tumoural immune response. Identifying such complex biological interplay as having a role in disease outcome is impossible without multivariate analysis. The post-stratification survival curves in our study demonstrate markedly different PFS outcomes as a testament to this robust statistical model and could represent an invaluable guide to clinicians during the initial stages of treatment.

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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Decision letter after peer review:

Thank you for submitting your article "Predicting Progression Free Survival after Systemic Therapy in Advanced Head and Neck Cancer: Bayesian regression and Model development" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Wafik El-Deiry as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Reviewer #1 (Recommendations for the authors):

Thinking in terms of what would be clinically tractable, I suggest the authors use ML techniques to reduce the assay panel size to a more parsimonious number of covariates, to train this in their existing dataset, and to then validate this in an independent cohort.

Reviewer #2 (Recommendations for the authors):

Barber et al. present a manuscript discussing predictive factors for chemotherapy efficacy in head and neck squamous cancer (HNSCC). The paper is well written, , and its style/formatting are optimal. The baseline signature moderately predicted outcome, and the data after one cycle further improved the algorithm, though this decreases its utility as a pure predictive tool. It is interesting that a subpopulation of monocytes, a subset of white peripheral cells long suspected to correlate with outcomes in HNSCC was one of the key drivers of the algorithm. However the overall impact in the field of this work seems limited.

Comments:

– The authors focused on immune cell subpopulations and exosomes, which narrows the scope (no cytokines or other biomarkers were included).

– The signatures were not prospectively validated on an independent cohort.

– Unfortunately this algorithm predicts outcome for a first-line therapy that is no longer considered to be the standard of care for HNSCC.

– The outcome measure is PFS, which is appropriate for therapy effect but not the standard in first line therapy (OS would be).

– The conclusions of the manuscript are supported by the data, but some of the caveats (such as the lack of a validation cohort, key in predictive biomarker development), are not addressed.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting the paper entitled "Predicting Progression Free Survival after Systemic Therapy in Advanced Head and Neck Cancer: Bayesian regression and Model development" for further consideration by eLife. Your revised article has been evaluated by a Senior Editor and a Reviewing Editor. We are sorry to say that we have decided that this submission will not be considered further for publication by eLife.

Reviewer #1 (Recommendations for the authors):

Thank you for the opportunity to review this revised manuscript. I feel that the authors have made a good-faith effort to answer all of my prior comments. I, unfortunately, feel that the prior comments have not been very completely addressed.

First is the issue of unmet clinical need. Several reviewers asked whether a predictive biomarker for chemotherapy is relevant, in the era of ICB. I do in fact agree with the authors that there is still clinical value to predicting response to regimens such as EXTREME which do in fact still have a role in the treatment of HNSCC, and are understudied.

However, I did raise the question as to whether this fairly labor-intensive, difficult-to-scale, and expensive assay would add value to the fairly inexpensive and widely available (although admittedly not widely used) biomarkers we already have. I gave the example of NLR but also alluded to simple nomograms that include clinical factors such as age, performance status, tumor stage, etc. It seems important to more convincingly make the case that this model outperforms the widely available, inexpensive data we already have. Looking at the c-indices for this model, I am not sure this is the case. I again would advise this be shown rigorously with direct comparisons, to make the case that this model adds value.

The validation set is very important to the conclusion that this assay has potential clinical use. I fully recognize that a validation set is hard to come by. But this is the "acid test" for such an assay. The validation data here is promising, and again, I understand how hard it is to gather such data, but it is not very convincing. Only 8 patients, with no PFS data (iRECIST is used, with a 50% objective response rate, which seems very high). To support the claim that such an assay has clinical value, a much broader set of validation data are needed.

I raised the question earlier about the large number of patients excluded due to a lack of biospecimens and am concerned that this may limit the results, as this is akin to unequal censoring in a clinical trial. It would be important to compare the patients being studied and those not being studied. This was not addressed.

As with the other reviewers, the lack of OS data is a weakness here. This is a more minor point, as I do understand that the reviewers tried and were unable to obtain this data from the industry sponsor. I want to stress this is a minor weakness compared to some of the above points. However, multiple co-authors have very significant industry COI (co-authors each receiving personal payments from 15-20 companies) and I am concerned that the withholding of OS data (which has already been collected and is in fact a component of PFS) may reflect other motives on the part of the industry sponsor with whom some authors may be conflicted. Nevertheless, if the data are unavailable, a suggestion: if OS data are not available, perhaps the authors could show rigorously that PFS is a very good surrogate marker of OS in this disease.

Reviewer #2 (Recommendations for the authors):

Barber et al. present a manuscript discussing predictive factors for chemotherapy efficacy in head and neck squamous cancer (HNSCC). The paper is well written, , and its style/formatting are optimal. The baseline signature moderately predicted outcome, and the data after one cycle further improved the algorithm, though this decreases its utility as a pure predictive tool. It is interesting that a subpopulation of monocytes, a subset of white peripheral cells long suspected to correlate with outcomes in HNSCC was one of the key drivers of the algorithm. However the overall impact in the field of this work seems limited.

Comments:

– The authors focused on immune cell subpopulations and exosomes, which narrows the scope (no cytokines or other biomarkers were included).

Thank you. We selected a finite number of covariates based on a few factors – (a) published literature, (b) previous data generated by the group and (c) the applicability of the findings to the clinic. Instead of an exploratory article in which we could generate an infinite number of covariates by a technique similar to RNA sequencing, we opted for a select set of covariates. This hypothesis-driven approach generated a strong signature that is now validated across two trials. The focus on immune population is driven by our hypothesis that systemic changes in the PBMCs are indicative and reflective of the status of the intra-tumoral immune response. In the revised manuscript we used a custom immune focused imaging mass cytometry antibody panel to probe tissue sections from 9 patients. We now show that the key populations driving the predictive model in the periphery are not only reflected at the tumoral level, but these disparate immune cell subpopulations also interact. Figure 6 from the manuscript is shown below, in which we use a machine learning approach to segment cells and assign them to distinct immunological subpopulations. We found that the peripheral monocyte population strongly correlated with a tumoral macrophage population having a similar marker expression pattern. We found that the peripheral central memory CD8 T cells inversely correlated with tissue resident memory T cells. The tissue presence of both these cells correlated positively with outcome. Most strikingly, these two populations were most likely to co-localize with each other at the tissue level at a frequency of almost double the second highest co-localization. Data on the nature of the interplay between peripheral systemic immunity and intra-tumoral immunity is novel and rarely exists in the literature outside the scope of in-vivo animal models. Here we describe these interactions using human patient samples treated with a clinically relevant therapy.

Given the limited amount of patient sera collected in the trial we opted to perform exosome analysis on markers known to impact the response to the anti-EGFR/HER3 treatment/immune responses. This was in line with our labs work to use exosome FRET-FLIM as a surrogate for tissue FRET-FLIM which we originally used to discover a potential dimer dependent mechanism for anti-EGFR treatment resistance in neoadjuvant breast cancer patients⁹; and more recently published on a colorectal patient sample cohort from the COIN study ¹⁰. While exosome EGFR-HER3 heterodimer failed to reach significance in our risk signature, it was close as depicted in the Kaplan-Meier curve from Figure 3C shown below. We of course acknowledge the potential added benefit of having serum cytokine array analysis. While that was not feasible for this study our group now aims at ensuring that extra patient serum samples are bio-banked for such analysis from ongoing and future trials.

– The signatures were not prospectively validated on an independent cohort.

We completely agree with the reviewer regarding the need to obtain a validation set. Obtaining patient samples from a similar cohort was difficult but we managed to validate the signature on a set of patients treated with an anti-PDL1 monoclonal antibody in combination with Cetuximab. Furthermore, the validation was performed using a limited numbers of covariates that were identified in the risk signature by the Bayesian model. These immune populations can be obtained by running a limited set of markers on flow cytometry. We were very happy to see that these limited immune based covariates strongly correlated with a worst disease response in an independent cohort using a different treatment modality. This furthers our hypothesis that changes in the immune populations are key to understanding response to systemic therapy. Fueled with the data from the validation cohort we furthered our analysis of the tissue from a total of 9 patients from the test cohort. Using imaging mass cytometry, we were able to identify how immune populations are mirrored at the tumoral level opening the horizon for new research. The data for the validation set is copied into this letter in response to point 2 of the public evaluation summary.

– Unfortunately this algorithm predicts outcome for a first-line therapy that is no longer considered to be the standard of care for HNSCC.

The EXTREME regimen (platinum/5-FU/cetuximab) remains a first-line standard of care treatment in the UK and European countries for HNSCC patients with negative PDL1 status (CPS score <1) which account for around 15% of all HNSCC patients ⁷. While the US Food and Drug Administration (FDA) approved pembrolizumab in combination with chemotherapy as first-line treatment regardless of PD-L1 expression and pembrolizumab alone for patients with PD-L1-expressing tumours (CPS ≥1), the European Medicines Agency (EMA) approved pembrolizumab with or without chemotherapy only for patients with a CPS ≥1, and this has been highlighted in the European Society for Medical Oncology (ESMO) and the UK National Institute for Health and Care Excellence (NICE) guidelines ⁸ and (https://www.nice.org.uk/guidance/ta661/chapter/1-Recommendations).

Furthermore, chemotherapy with EXTREME regimen is standard of care for patients with contraindications to immune checkpoint inhibitors such as autoimmune disease ⁸. It can also be considered as second-line treatment in patients who only received pembrolizumab monotherapy in the first line setting.

– The outcome measure is PFS, which is appropriate for therapy effect but not the standard in first line therapy (OS would be).

Thank you. We admit that the absence of overall survival is an inherent limitation of the study. In the process of submitting this revision, we have once again requested this dataset from the sponsoring pharmaceutical company but were informed that they are unable to provide it. This is because reorganization of funding priorities within the company precludes them opening datasets from an already-published clinical trial. We are equally disappointed to not be able to obtain this data, but firmly believe that the ability of the signature to predict PFS (the primary endpoint of the trial, untainted by subsequent lines of treatment), as well as cross-validation against the contemporary EACH trial, is a testament to the signature’s strength.

– The conclusions of the manuscript are supported by the data, but some of the caveats (such as the lack of a validation cohort, key in predictive biomarker development), are not addressed.

We hope that the reviewer accepts the revised manuscript in which we believe we have addressed all his comments.

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14. Sano, D. et al. Real-world Treatment Outcomes of the EXTREME Regimen as First-line Therapy for Recurrent/Metastatic Squamous Cell Carcinoma of the Head and Neck: A Multi-center Retrospective Cohort Study in Japan. Anticancer Res 39, 6819-6827, doi:10.21873/anticanres.13898 (2019).

15. Guigay, J. et al. Observational, prospective, phase 4 study in patients with first-line recurrent and/or metastatic squamous cell carcinoma of the head and neck treated with cetuximab and platinum-based therapy: DIRECT. Cancer Rep (Hoboken) 5, e1467, doi:10.1002/cnr2.1467 (2022).

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Reviewer #1 (Recommendations for the authors):

Thank you for the opportunity to review this revised manuscript. I feel that the authors have made a good-faith effort to answer all of my prior comments. I, unfortunately, feel that the prior comments have not been very completely addressed.

We would like to thank the reviewer for taking the time to go through our revisions and the detailed rebuttal letter. However, we are sorry that he felt that our revision did not address all his concerns. We were confident and reasonably hopeful that the inclusion of a difficult to obtain validation cohort and the significant expansion of our immunological data would be sufficient to satisfy the previous reviewers’ comments. The fact that the first trial involved targeted therapies and the second a combination of anti-EGFR and anti-PDL1 therapy, yet the same blood kinetic change can still distinguish between responders and non-responders is very novel and unprecedented according to our knowledge and reinforced the originality of the research. We also regret that the immune cell biology findings from the deep profiling of our two Phase 2 studies were largely ignored despite their implications for the development of future immunotherapeutic. For instance, the additional imaging CyTOF data (in Figure 6) showed a very novel finding that the two most significant immune cell subpopulations (CD33+CD14+HLADRhigh monocytes and CD8 central memory T cells) were most likely to co-localize with each other at the tissue level at a frequency of almost double the second highest co-localization. This suggests that in-situ antigen presentation is a key driver for therapy induced response across multiple therapeutic modalities (e.g. standard of care chemotherapy and targeted therapies). These results once published will stimulate the immune-oncology community to further test this specific cell-cell interaction mechanism in preclinical mouse model treated e.g. with immune checkpoint therapeutics.

First is the issue of unmet clinical need. Several reviewers asked whether a predictive biomarker for chemotherapy is relevant, in the era of ICB. I do in fact agree with the authors that there is still clinical value to predicting response to regimens such as EXTREME which do in fact still have a role in the treatment of HNSCC, and are understudied.

However, I did raise the question as to whether this fairly labor-intensive, difficult-to-scale, and expensive assay would add value to the fairly inexpensive and widely available (although admittedly not widely used) biomarkers we already have. I gave the example of NLR but also alluded to simple nomograms that include clinical factors such as age, performance status, tumor stage, etc. It seems important to more convincingly make the case that this model outperforms the widely available, inexpensive data we already have. Looking at the c-indices for this model, I am not sure this is the case. I again would advise this be shown rigorously with direct comparisons, to make the case that this model adds value.

We strongly disagree with the “no added value” comment. As a translational research group, we have been passionate about applying modern omic technology coupled with mathematical modelling and machine learning to Phase 1/Phase 2/Phase 3 clinical trial samples (Barber, Weitsman et al., 2020), with a goal of gaining a deeper understanding of the scientific mechanisms behind heterogeneity of response between patients to targeted and/or immune-based therapeutics. Also I sincerely believe that this early phase trial related translational work, underpinned by deep profiling techniques (rather than the crude neutrophil:lymphocyte ratio (NLR)) has shown us the way to find hidden subpopulations of patients who may be able to respond better; and for those the models predict will be resistant to treatment, we can in the future offer them new treatments based on the molecular immunological information about the individual patients arising from these deep profiling techniques.

As an illustration, our results echo the recent finding that treatment-induced expansion of emergent T cell clones in tumors and the systemic circulation that were undetectable prior to immune checkpoint blockade therapy (Luoma, Suo et al., 2022). The latter requires looking at multiple time points and is similar to our findings that it is the analysis of the blood kinetic changes between pretreatment and (early) post treatment time points that reveals the memory CD8⁺ T cell expansion which predict response. These memory T cells are now believed to be bona fide responders to PD-1/PD-L1 blockade according to recent results obtained from preclinical models (Huang, Wu et al., 2022).

It was precisely the deployment of deep profiling (Spectral flow cytometry analysis (Luoma et al., 2022)) that leads to the conclusion of this recent translational trial paper (which unlike ours does not have an independent phase 2 trial to confirm the findings). We therefore strongly disagree with the reviewer’s criticism on the basis of the lack of need for expensive assays (compared to NLR) as a reason to reject our revised work.

We also believe that the value of this work should not be hindered by its ability to be readily implemented in the clinic by directly comparing it to clinical nomograms. While we appreciate the relevance and importance of generic clinical nomograms, they often lack the specific type of data required for discovery and development of next generation precision and personalized medicine, by identifying future targets for overcoming resistance, for instance.

Summing up, our work is the first of its kind where the derived deep profiling signature was cross validated in a separate independent cohort, and the drug induced changes in immune populations were assessed at the tumoral and systemic level furthering our understanding of immune cells interaction and trafficking.

The validation set is very important to the conclusion that this assay has potential clinical use. I fully recognize that a validation set is hard to come by. But this is the "acid test" for such an assay. The validation data here is promising, and again, I understand how hard it is to gather such data, but it is not very convincing. Only 8 patients, with no PFS data (iRECIST is used, with a 50% objective response rate, which seems very high). To support the claim that such an assay has clinical value, a much broader set of validation data are needed.

Regarding the small sample size criticism, our collaborator Prof. Ton Coolen is one of the few statistical physics/machine learning experts (https://www.ru.nl/en/people/coolen-a) who was invaluable in helping us to review a recent ML paper https://doi.org/10.7554/eLife.80150, which would have been difficult to review without his specialist opinion. The following is his response to this specific Reviewer’s comment on our paper:

“Regarding the small number of patients in the validation cohort; there was an a priori chance that this cohort would not have demonstrated the correlation of the risk score with treatment response, and that more patients would have been required. However, the result we obtained with the present number of validation samples (Kandall tau = 0.725, with p-value 0.018) indicates that the number of validation samples suffices. The p-value, by definition, already takes into account the number of patients in a cohort — for smaller data sets it is easier to observe correlations as a result of fluke, and p-values are typically larger. Our present result states that the likelihood of achieving for the validation data presently used a correlation value of 0.725 or higher by chance, i.e. under the null hypothesis of statistical independence, is 0.018. This being below the standard confidence level of 0.05 for statistical significance, one must conclude with confidence that our validation data is correlated with the risk score, and hence no further patients are necessary to support this claim.”

Hence obtaining a significant correlation despite the small sample size in fact empowers our model.

The 50% objective response rate in this validation cohort is rather a reflection of improved response and efficacy of combining the anti-EGFR and anti-PDL1 in this setting. This is based on the preliminary finding of the trial which was presented at the European Society for Medical Oncology (ESMO) Annual Congress in 2020 and published as an abstract (M. Forster, 2020).

I raised the question earlier about the large number of patients excluded due to a lack of biospecimens and am concerned that this may limit the results, as this is akin to unequal censoring in a clinical trial. It would be important to compare the patients being studied and those not being studied. This was not addressed.

We understand the reviewer’s concern with regards to a potential skewing of outcome data due to some patient exclusions. While we attempted to address this concern by showing no difference in PFS when comparing our study to the literature, we accept that this answer did not meet the reviewer’s expectations. Hence in this revised manuscript we have amended supplementary file 1 to compare the patient characteristics between the discovery sub-cohort assessed in this study with the original study cohort (Dillon, Grove et al., 2019). The table clearly shows that the clinical characteristics of the included sub-cohort were similar to those in the published full cohort. Hopefully this comparison will convince the reviewer that the data is robust and not skewed by patient subsampling due to availability of biological specimens.

As with the other reviewers, the lack of OS data is a weakness here. This is a more minor point, as I do understand that the reviewers tried and were unable to obtain this data from the industry sponsor. I want to stress this is a minor weakness compared to some of the above points. However, multiple co-authors have very significant industry COI (co-authors each receiving personal payments from 15-20 companies) and I am concerned that the withholding of OS data (which has already been collected and is in fact a component of PFS) may reflect other motives on the part of the industry sponsor with whom some authors may be conflicted. Nevertheless, if the data are unavailable, a suggestion: if OS data are not available, perhaps the authors could show rigorously that PFS is a very good surrogate marker of OS in this disease.

We do regret that the OS data is not available, and we thank the reviewer for acknowledging that this is a minor point. We have previously explained the reason for the lack of OS data in this work due to the reorganization of funding priorities within the sponsor which precluded them from accessing datasets from an already-published clinical trials. We are equally disappointed to not be able to obtain this data, but firmly believe that the ability of the signature to predict PFS (the primary endpoint of the trial, untainted by subsequent lines of treatment), as well as cross-validation against the contemporary EACH trial, is a testament to the signature’s strength.

The PFS has been extensively studied across many cancer types as a surrogate for overall survival in oncology trials (cite: https://www.nature.com/articles/s41416-020-0805-y) and it was a valid surrogate end point for OS to assess treatment effect of chemotherapy in loco-regionally advanced nasopharyngeal carcinoma (PFS was strongly correlated with OS at the individual level (ρ = 0.93, 95% confidence interval [CI] = 0.93 to 0.94)) and at the trial level (R2 = 0.95, 95% CI = 0.47 to 1.00) (cite: https://academic.oup.com/jnci/article/109/4/djw239/2605763?login=false). A similar high correlation between PFS and OS was also observed in HNSCC in response to PD1/PDL1 immune checkpoint inhibitors (in the ITT population (rho = 0.76) and PD-L1-positive population (rho = 0.74)) (Ye, Ji et al., 2020).

References

Barber PR, Weitsman G, Lawler K, Barrett JE, Rowley M, Rodriguez-Justo M, Fisher D, Gao F, Tullis IDC, Deng J, Brown L, Kaplan R, Hochhauser D, Adams R, Maughan TS, Vojnovic B, Coolen ACC, Ng T (2020) HER2-HER3 Heterodimer Quantification by FRET-FLIM and Patient Subclass Analysis of the COIN Colorectal Trial. J Natl Cancer Inst 112: 944-954

Dillon MT, Grove L, Newbold KL, Shaw H, Brown NF, Mendell J, Chen S, Beckman RA, Jennings A, Ricamara M, Greenberg J, Forster M, Harrington KJ (2019) Patritumab with Cetuximab plus Platinum-Containing Therapy in Recurrent or Metastatic Squamous Cell Carcinoma of the Head and Neck: An Open-Label, Phase Ib Study. Clin Cancer Res 25: 487-495

Huang Q, Wu X, Wang Z, Chen X, Wang L, Lu Y, Xiong D, Liu Q, Tian Y, Lin H, Guo J, Wen S, Dong W, Yang X, Yuan Y, Yue Z, Lei S, Wu Q, Ran L, Xie L et al. (2022) The primordial differentiation of tumor-specific memory CD8(+) T cells as bona fide responders to PD-1/PD-L1 blockade in draining lymph nodes. Cell 185: 4049-4066 e25

Luoma AM, Suo S, Wang Y, Gunasti L, Porter CBM, Nabilsi N, Tadros J, Ferretti AP, Liao S, Gurer C, Chen YH, Criscitiello S, Ricker CA, Dionne D, Rozenblatt-Rosen O, Uppaluri R, Haddad RI, Ashenberg O, Regev A, Van Allen EM et al. (2022) Tissue-resident memory and circulating T cells are early responders to pre-surgical cancer immunotherapy. Cell 185: 2918-2935 e29

M. Forster RM, J. Sacco, A. Kong, G. Wheeler, S. Forsyth, R. Bhat, K. Blair, J. Ward, H. Lowe, V. Spanswick, L. Ensell, J. Hartley, L. White (2020) 922P – EACH: A phase II study evaluating the safety and anti-tumour activity of avelumab and cetuximab in recurrent/metastatic squamous cell carcinomas. In ESMO

Ye J, Ji X, Dennis PA, Abdullah H, Mukhopadhyay P (2020) Relationship Between Progression-Free Survival, Objective Response Rate, and Overall Survival in Clinical Trials of PD-1/PD-L1 Immune Checkpoint Blockade: A Meta-Analysis. Clin Pharmacol Ther 108: 1274-1288

Author details

1. Paul R Barber

   1. UCL Cancer Institute, Paul O'Gorman Building, University College London, London, United Kingdom
   2. Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, King’s College London, London, United Kingdom

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Rami Mustapha, Fabian Flores-Borja, Giovanna Alfano and Kenrick Ng

   Competing interests

   is a shareholder of Nano Clinical Ltd

   "This ORCID iD identifies the author of this article:" 0000-0002-8595-1141

2. Rami Mustapha

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

   Contribution

   Conceptualization, Formal analysis, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Paul R Barber, Fabian Flores-Borja, Giovanna Alfano and Kenrick Ng

   Competing interests

   No competing interests declared

3. Fabian Flores-Borja

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

   Present address

   Centre for Oral Immunobiology and Regenerative Medicine, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom

   Contribution

   Data curation, Formal analysis, Investigation, Methodology

   Contributed equally with

   Paul R Barber, Rami Mustapha, Giovanna Alfano and Kenrick Ng

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0881-8822

4. Giovanna Alfano

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

   Contribution

   Data curation, Formal analysis, Investigation

   Contributed equally with

   Paul R Barber, Rami Mustapha, Fabian Flores-Borja and Kenrick Ng

   Competing interests

   No competing interests declared

5. Kenrick Ng

   UCL Cancer Institute, Paul O'Gorman Building, University College London, London, United Kingdom

   Contribution

   Formal analysis, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Paul R Barber, Rami Mustapha, Fabian Flores-Borja and Giovanna Alfano

   Competing interests

   has received honoraria from Pfizer, GSK/Tesaro and Boheringer Ingleheim, and has had travel/accommodation/expenses paid for by Tesaro

6. Gregory Weitsman

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

   Contribution

   Formal analysis, Investigation

   Competing interests

   No competing interests declared

7. Luigi Dolcetti

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

   Contribution

   Formal analysis, Investigation

   Competing interests

   No competing interests declared

8. Ali Abdulnabi Suwaidan

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

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

9. Felix Wong

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

   Contribution

   Formal analysis, Investigation

   Competing interests

   No competing interests declared

10. Jose M Vicencio

    1. UCL Cancer Institute, Paul O'Gorman Building, University College London, London, United Kingdom
    2. Richard Dimbleby Laboratory of Cancer Research, School of Cancer & Pharmaceutical Sciences, King's College London, London, United Kingdom

    Contribution

    Formal analysis, Investigation, Methodology

    Competing interests

    No competing interests declared

11. Myria Galazi

    UCL Cancer Institute, Paul O'Gorman Building, University College London, London, United Kingdom

    Contribution

    Data curation, Investigation

    Competing interests

    No competing interests declared

12. James W Opzoomer

    Tumor Immunology Group, School of Cancer & Pharmaceutical Sciences, King’s College London, London, United Kingdom

    Contribution

    Data curation, Formal analysis, Investigation

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6842-756X

13. James N Arnold

    Tumor Immunology Group, School of Cancer & Pharmaceutical Sciences, King’s College London, London, United Kingdom

    Contribution

    Supervision, Writing – review and editing

    Competing interests

    No competing interests declared

14. Selvam Thavaraj

    Centre for Clinical, Oral & Translational Science, King’s College London, London, United Kingdom

    Contribution

    Methodology

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-5720-7422

15. Shahram Kordasti

    Systems Cancer Immunology, School of Cancer & Pharmaceutical Sciences, King’s College London, London, United Kingdom

    Contribution

    Formal analysis, Supervision

    Competing interests

    has received research funding in the form of a grant from Novartis and Celgene

    "This ORCID iD identifies the author of this article:" 0000-0002-0347-4207

16. Jana Doyle

    Daiichi Sankyo Incorporated, Newark, United States

    Contribution

    Resources, Supervision, Funding acquisition, Project administration

    Competing interests

    is in employment with Daichii Sankyo, and has stock and other ownership interests, research funding within Daichii Sankyo and has had travel/accommodation/expenses paid for by Daichii Sankyo

17. Jon Greenberg

    Daiichi Sankyo Incorporated, Newark, United States

    Contribution

    Resources, Funding acquisition, Project administration, Writing – review and editing

    Competing interests

    is in employment with Daichii Sankyo, and has stock and other ownership interests, research funding within Daichii Sankyo and has had travel/accommodation/expenses paid for by Daichii Sankyo

18. Magnus T Dillon

    The Institute of Cancer Research, London, United Kingdom

    Contribution

    Methodology, Project administration

    Competing interests

    No competing interests declared

19. Kevin J Harrington

    The Institute of Cancer Research, London, United Kingdom

    Contribution

    Supervision, Methodology, Project administration, Writing – review and editing

    Competing interests

    has received honoraria from Amgen; Arch Oncology; AstraZeneca; Boehringer-Ingelheim; Bristol-Myers Squibb; Codiak; Inzen; Merck; MSD; Pfizer; Replimune and is on a speakers' bureau for Amgen, AstraZeneca; Bristol-Myers Squibb; Merck, MSD; Pfizer. KH has also received research funding from AstraZeneca, Boehringer-Ingelheim, MSD and Replimune

20. Martin Forster

    UCL Cancer Institute, Paul O'Gorman Building, University College London, London, United Kingdom

    Contribution

    Conceptualization, Supervision, Methodology, Writing – review and editing

    Competing interests

    has received institutional research funding from AstraZeneca, Boehringer-Ingelheim, Merck and MSD and serves in a consulting or advisory role to Achilles, Astrazeneca, Bayer, Bristol-Myers Squibb, Celgene, Guardant Health, Merck, MSD, Nanobiotix, Novartis, Oxford VacMedix, Pfizer, Roche, Takeda, UltraHuman

21. Anthony CC Coolen

    1. Institute for Mathematical and Molecular Biomedicine, King’s College London, London, United Kingdom
    2. Saddle Point Science Ltd, London, United Kingdom

    Contribution

    Conceptualization, Data curation, Formal analysis, Supervision, Methodology

    Competing interests

    has stock and other ownership interests with Saddle Point Science Limited

    "This ORCID iD identifies the author of this article:" 0000-0002-6976-5875

22. Tony Ng

    1. UCL Cancer Institute, Paul O'Gorman Building, University College London, London, United Kingdom
    2. Richard Dimbleby Laboratory of Cancer Research, School of Cancer & Pharmaceutical Sciences, King's College London, London, United Kingdom
    3. Breast Cancer Now Research Unit, School of Cancer & Pharmaceutical Sciences, King’s College London, London, United Kingdom

    Contribution

    Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Writing – review and editing

    For correspondence

    tony.ng@kcl.ac.uk

    Competing interests

    has received research funding from Astrazeneca and Daichii Sankyo. TN is a founder and shareholder in Nano Clinical Ltd, and PRB is a shareholder

    "This ORCID iD identifies the author of this article:" 0000-0003-3894-5619

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