Original Research
Predicting immunotherapy outcomes under therapy in
patients with advanced NSCLC using dNLR and its early
dynamics
*
Laura Mezquita
a,b,c,1, Isabel Preeshagul
d,1, Edouard Auclin
e,
Diana Saravia
f, Lizza Hendriks
a,g, Hira Rizvi
d, Wungki Park
f,
Ernest Nadal
h, Patricia Martin-Romano
i, Jose C. Ruffinelli
h,
Santiago Ponce
j, Clarisse Audigier-Valette
k, Simona Carnio
l,
Felix Blanc-Durand
a, Paolo Bironzo
l, Fabrizio Tabbo`
l,
Maria Lucia Reale
l, Silvia Novello
l, Matthew D. Hellmann
d,
Peter Sawan
m, Jeffrey Girshman
m, Andrew J. Plodkowski
m,
Gerard Zalcman
n, Margarita Majem
o, Melinda Charrier
p,
Marie Naigeon
p, Caroline Rossoni
q, AnnaPaola Mariniello
l,
Luis Paz-Ares
j, Anne Marie Dingemans
q, David Planchard
a,
Nathalie Cozic
r, Lydie Cassard
p, Gilberto Lopes
f, Nathalie Chaput
p,s,
Kathryn Arbour
d,2, Benjamin Besse
a,t,*
,2a
Cancer Medicine Department, Gustave Roussy, Villejuif, France
bMedical Oncology Department, Hospital Clı´nic, Barcelona, Spain
cTranslational Genomics and Targeted Therapeutics in Solid Tumors, August Pi I Sunyer Biomedical Research Institute,
Barcelona, Spain
dDepartment of Medicine, Thoracic Oncology Service, Memorial Sloan Kettering Cancer Center NY, USA
*Previously presented at:
WCLC 2017, Minioral communication: L Mezquita, E Auclin, M Charrier, et al. MA 05.03 The Early Monitoring of Derived Neutrophil-To-Lymphocyte Ratio (dNLR) Could Be a Surrogate Marker of Benefit of Immunotherapy in NSCLC.https://doi.org/10.1016/j.jtho.2017.09.480
ESMO 2018, Poster: L Mezquita, K Arbour, E Auclin, et al. Derived neutrophil-to lymphocyte ratio (dNLR) change between baseline and cycle 2 is correlated with benefit during immune-checkpoint inhibitors (ICI) in advanced nonesmall-cell lung cancer (NSCLC) patients.https://doi. org/10.1093/annonc/mdy292.029
WCLC 2019, Minioral communication: L Mezquita, I Preeshagul, E Auclin et al. MA07.02 Early Change of dNLR Is Correlated with Out-comes in Advanced NSCLC Patients Treated with Immunotherapy.https://doi.org/10.1016/j.jtho.2019.08.548
WCLC 2019, Minioral communication: L Mezquita, P Martin-Romano, E Auclin et al. MA07.01 Circulating Immature Neutrophils, Tumour-Associated Neutrophils and dNLR for Identification of Fast Progressors to Immunotherapy in NSCLC.https://doi.org/10.1016/j.jtho.2019.08.547
* Corresponding author: Head of the Medical Oncology Department, Gustave Roussy, Villejuif, France 114, rue Edouard Vaillant, Villejuif, 94805, France. Fax:þ33 (0) 1 42 11 52 19.
E-mail address:[email protected](B. Besse).
LauraMezquitaMD(L. Mezquita)
1 Equally contributors (co-first position). 2 Equally contributors (co-last position).
https://doi.org/10.1016/j.ejca.2021.03.011
0959-8049/ª 2021 Elsevier Ltd. All rights reserved.
Available online atwww.sciencedirect.com
ScienceDirect
e
Medical and Gastrointestinal Oncology Department, Georges Pompidou Hospital, Paris, France
f
Medical Oncology Department Sylvester Comprehensive Cancer Center, University of Miami
gPulmonary Diseases GROW- School for Oncology and Biology, Maastricht UMC
þ, Maastricht, the Netherlands
hMedical Oncology Department, Catalan Institute of Oncology, L’Hospitalet, Barcelona Spain iEarly Drug Development Department, Gustave Roussy, Villejuif, France
jMedical Oncology Department, Hospital 12 Octubre, Madrid, Spain kCentre Hospitalier Sainte Musse, Pneumology Department, Toulon, France
lThoracic Oncology Unit, Department of Oncology, University of Turin, AOU San Luigi, Orbassano (TO) Italy mDepartment of Radiology, Memorial Sloan Kettering Cancer Center NY, USA
nThoracic Oncology Department, CIC1425/CLIP2 Paris-Nord, Hoˆpital Bichat- Claude Bernard, Paris, France oMedical Oncology Department, Hospital San Pau, Barcelona, Spain
pLaboratory of Immunomonitoring in Oncology, UMS3655 CNRS US 23 INSERM, Gustave Roussy, Villejuif, France q
Department Pulmonology, Erasmus MC, Rotterdam, the Netherlands
r
Biostatistics Unit, INSERM U1018, Villejuif, France
s
University Paris-Saclay, School of Pharmacy, France
t
University Paris-Saclay, School of Medicine, France
Received 7 December 2020; received in revised form 16 February 2021; accepted 1 March 2021 Available online 19 May 2021
KEYWORDS dNLR; Neutrophils; Immunotherapy; NSCLC; Biomarker
Abstract Background: dNLR at the baseline (B), defined by
neutrophils/[leucocytes-neutrophils], correlates with immune-checkpoint inhibitor (ICI) outcomes in advanced non esmall-cell lung cancer (aNSCLC). However, dNLR is dynamic under therapy and its longi-tudinal assessment may provide data predicting efficacy. We sought to examine the impact of dNLR dynamics on ICI efficacy and understand its biological significance.
Patients and methods: aNSCLC patients receiving ICI at 17 EU/US centres were included [Feb/13-Jun/18]. As chemotherapy-only group was evaluated (NCT02105168). dNLR was
determined at (B) and at cycle2 (C2) [dNLR3 Z low]. BþC2 dNLR were combined in
one score: goodZ low (BþC2), poor Z high (BþC2), intermediate Z other situations. In 57 patients, we prospectively explored the immunophenotype of circulating neutrophils, particularly the CD15þCD244-CD16lowcells (immature) by flow cytometry.
Results: About 1485 patients treatment with ICI were analysed. In ICI-treated patients, high dNLR (B) (~1/3rd) associated with worse progression-free (PFS)/overall survival (OS) (HR 1.56/HR 2.02, P< 0.0001) but not with chemotherapy alone (N Z 173). High dNLR at C2 was associated with worse PFS/OS (HR 1.64/HR 2.15, P< 0.0001). When dNLR at both time points were considered together, those with persistently high dNLR (23%) had poor survival (mOSZ 5 months (mo)), compared with high dNLR at one time point (22%; mOS Z 9.2mo) and persistently low dNLR (55%; mOSZ 18.6mo) (P < 0.0001). The dNLR impact remained significant after PD-L1 adjustment. By cytometry, high rate of immature neutrophils (B) (30/ 57) correlated with poor PFS/OS (PZ 0.04; P Z 0.0007), with a 12-week death rate of 49%. Conclusion: The dNLR (B) and its dynamics (C2) under ICI associate with ICI outcomes in aNSCLC. Persistently high dNLR (BþC2) correlated with early ICI failure. Immature neutro-phils may be a key subpopulation on ICI resistance.
ª 2021 Elsevier Ltd. All rights reserved.
1. Introduction
Immunotherapy has revolutionised the landscape of therapeutic modalities for nonesmall-cell lung cancer (NSCLC). Immune-checkpoint inhibitors (ICI) have demonstrated prolonged durable responses and survival
improvement in NSCLC, both as monotherapy [1e3]
and as a combination [4e6]. However, a limited pro-portion of patients benefit from ICI, and identification of this target population remains challenging.
A wide spectrum of tumour biomarkers is under investigation; however, new evidence is emerging on host-related biomarkers. Among them, ratios combining circulating immune blood cells, such as neutrophils/ [leucocytes-neutrophils] (dNLR) have been explored in advanced NSCLC patients treated with ICI therapy [7]. These parameters are simple and accessible worldwide, demonstrating in many retrospective studies to offer prognostic and in some potentially predictive value regarding response to checkpoint inhibition [8e10].
Neutrophils as modulators of the immune system in patients with cancer are emerging as potential bio-markers related to the immune context of the patient [11]. While the neutrophil population is heterogeneous, with pro- and anti-tumour properties, globally neutro-phils have been associated with cancer progression, with a key subpopulation, known as immature neutrophils, that may carry a potentially detrimental impact [12,13]. dNLR is a dynamic parameter that can change under therapy; therefore, its longitudinal assessment may provide additional data on predicting ICI efficacy. Only small retrospective cohorts have explored the impact of the monitoring of different parameters (lymphocytes, neutrophils, lactate dehydrogenase etc.) or ratios (e.g. neutrophil-to-lymphocyte ratio etc.) on ICI outcomes while under therapy [14,15]; however, the significance of dNLR and its dynamics remains unknown.
In this work, we conducted a large retrospective analysis of patients with advanced NSCLC receiving ICI therapy, specifically assessing the clinical relevance of dNLR at ICI baseline and its change at 2nd infusion. We additionally studied the clinical impact of dNLR in a control cohort treated exclusively with chemotherapy. As an exploratory analysis, we prospectively charac-terised the immunophenotyping of circulating neutro-phils, to identify potential key populations related to ICI resistance.
2. Patients and methods 2.1. Patients
Retrospective analysis of patients with advanced
NSCLC treated with ICI (nivolumab, pembrolizumab, atezolizumab etc.) between February 2013 and January 2018 in 17 EU/US centres (ICI cohort) (Table1S). Pa-tient’s characteristics and biological data were collected, at ICI baseline and before the 2nd infusion. Additional data in theSupplement.
To explore if dNLR had the same prognostic impact on outcomes with treatments other than ICI therapies, we evaluated dNLR in a historical chemotherapy-only control cohort. This study was approved by the Insti-tutional Review Board of the GR and informed consent was only required for the prospective cohort analysis. 2.2. dNLR score
dNLR was calculated as
neutrophils/[leucocytes-neutrophils] (high>3, as previously defined) [7]; it was collected at baseline (B) and before the 2nd infusion (C2). We built a dNLR score combining dNLR at each time point (B and C2) (goodZ low at both time points, poorZ high at both time points, intermediate Z change to high or low at C2).
2.3. Neutrophils immunophenotyping
Pretreatment immunophenotyping of granulocytes was prospectively performed by flow cytometry in an exploratory cohort of 57 patients. We defined an immature phenotype of neutrophils: CD15þCD244-CD16low(Supplement).
3. Results
A total of 1485 patients were identified in the immuno-cohort (Table 1 and Table 1S; Supplement) and 173 patients were included in the chemo-cohort (Table2S; Supplement).
3.1. dNLR at baseline and at C2 are both associated with ICI outcomes (Supplement)
3.1.1. dNLR early dynamics is correlated with outcomes under ICI
We evaluated the dNLR change between baseline and C2 (NZ 1424). dNLR changed in 310 patients (22%).
In the low-dNLR population at baseline (N Z 959),
155 patients changed (16%) to high-dNLR at C2. In high-dNLR population at baseline (NZ 482), in 155 patients changed (32%) to low-dNLR at C2. In the rest of the population dNLR remained stable: remained-low in 804 (56%) or remained-high in 327 (23%) (Fig. 1).
These dNLR variations impacted ICI outcomes. In the low-dNLR population (66%), those that exhibited a change to high-dNLR at C2 correlated with poorer outcomes compared to the population that remained low [HR for OS: 1.69 (95% CI, 1.34 to 2.14); P< 0.001]. In the group with high-dNLR at baseline (34%), the change to low-dNLR at C2 was associated with better outcomes vs. the population that remained high [HR for OS 0.54 (95% CI, 0.42 to 0.69); both P < 0.0001] (Fig. 2). Correlation with PFS data in Supplement. Distribution of dNLR values by each dNLR score group is described inTable 3S.
3.1.2. dNLR score is correlated with outcomes under ICI We categorised the dNLR and its early change in 3 separate dNLR cohorts based on both time points
(BþC2). The 3 risk groups were: good-group that
included 804 patients (if dNLR remained low; 55%), the intermediate-group with 310 patients (if dNLR changed to low at C2, 11%; or the dNLR changed to high at C2, 11%) and the poor-group with 327 patients if dNLR remained high; 23%).
The median OS was 18.6 months [95% CI, 16.9 to 21.0] in the good dNLR group vs. 9.2 months [8.0 to 13.9] in the intermediate vs. 5.0 months [4.1 to 6.0] in the poor group (P < 0.001). Similar findings were
observed in term of PFS (P < 0.001) (Fig. 3;
In the multivariate analysis, the dNLR score was an independent factor for PFS [HR of 1.84 for poor group]
and OS [HR of 2.56 for poor group]; both
P< 0.0001(Table4S). The prognostic value of the dNLR score was internally validated by a boot strap analysis showing its stability for OS and PFS prediction (P < 0.0001) (Table 2). Baseline characteristics by dNLR score subgroups is summarised in Table 6S and Supplement.
The discrimination of the baseline dNLR and dNLR score (BþC2) was acceptable, with a small statistically significant difference for the dNLR score (Table 7S).
3.1.3. dNLR score & PD-L1 expression
The dNLR and ICI outcomes by PD-L1 (NZ 497) are
shown inFig. 2andSupplement. The impact of dNLR score on OS remained significant after PD-L1 adjust-ment for the intermediate group (adjusted HR for OS 2.08; 1.02 to 4.21) and poor group (adjusted HR for OS 3.67; 1.85 to 7.28) vs. good group (both P < 0.001).
Similar findings were observed in terms of PFS (Supplement). In a sensibility analysis with multiple imputation of PD-L1 missing data, the results were similar (Table8S).
3.1.4. Early identification of fast-progressors based on dNLR score
Overall, the fast-PD/early death, defined as 12week-death rate since ICI start was 15%, significantly higher in the population with high-dNLR at baseline (27% vs. 9% in low-dNLR), with an OR of 3.6 [2.7 to 4.8] (P< 0.001).
Applying combined dNLR score, the 12week-death rate was 32% for the dNLR poor-group with an OR of 5.99 [4.2 to 8.5] (P< 0.0001) (Fig. 3S). In the interme-diate and good groups, the fast-PD rate was 14% [OR 1.9; 1.3 to 3.0] and 7% respectively (PZ 0.0001). 3.1.5. Neutrophils immunophenotype
Finally, we explored the immunophenotype of circu-lating neutrophils in 57 patients from the GR cohort (Fig. 4S). Baseline characteristics are described inTable 10S. Overall, the 12-week death rate was 31% [25.9 to 35.6].
The immature phenotype CD15þCD244-CD16low
was significantly associated with ICI outcomes. We stratified the population by into 2 groups, based on the % of pretreatment immature-neutrophils (B), according to the cutoff of >0.22% (logrank maximisation). High rate of immature-neutrophils (30/57; 53%), were
asso-ciated with poor PFS (logrank test, P Z 0.04), OS
(P Z 0.0007) and a 12wk-death rate of 49% [26.7%e
64.1%] (Fig. 4). 4. Discussion
Here we report, on one of the largest real-world cohort of patients with advanced NSCLC treated with ICI and the clinical relevance of analysing host circulating neu-trophils to improve the prediction of ICI outcomes. In our work, we demonstrated a strong association be-tween the early change of dNLR and outcomes for predicting benefit during ICI therapy, independent of other clinical factors and remaining significant after PD-L1 adjustment. We identified a refractory population (23%), with persistently high-dNLR under therapy with no benefit from ICI. In addition, we provided pre-liminary prospective data about the impact of an immature phenotype of circulating neutrophils, that could be related to fast-progression and resistance to ICI.
Among the heterogeneous population of neutrophils [12], displaying anti- and pro-tumour properties (13), neutrophils in cancer patients are generally associated with immune tolerance [16], pro-inflammatory response [17] and a detrimental impact on T cell activity [18].
Table 1
Baseline characteristics of the study population.
Overall population, NZ 1485 (%) Age, median, range 66 (21e93) Gender Female 605 (41%) Male 880 (59%) Smoking status Smoker 1291 (88%) Non-smoker 175 (12%) Missing 19 Histology Non-squamous 1094 (74%) Squamous 391 (26%) PD-L1 status <1% 162 (30%) 1% 379 (70%) Missing 944
Driver oncogene alterationa 99 (7%)
Performance status ECOG
0-1 1311 (89%) 2 171 (12%) Missing 3 Line of immunotherapy 2 1007 (68%) >2 472 (31.91%) Missing 6 N# metastatic sites 2 417 (54%) >2 358 (46%) Missing 710 Type of immunotherapy
Single agent PD(L)-1 inhibitor 1463 (99%) PD-L1þ CTLA-4 inhibitor 6 (<1%) PD-L1þ other inhibitor 2 (<1%)
a
EGFR mutation, ALK fusion, BRAFV600E mutation, ROS1 mutation.
In this work, we report that pretreatment high-dNLR, a neutrophils-based ratio, is associated with poor outcomes, which is in line with previous preclinical [16] and clinical works [7,10]. But no impact on chemotherapy outcomes was observed in the control cohort, raising the hypothesis that dNLR can be a more relevant biomarker in the context of ICI therapy. However, this data is based on the assessment in a small cohort and it is in contrast to other larger retrospective cohorts that observed an association between dNLR and clinical outcomes regardless of the cancer therapy [19,20].
dNLR was dynamic in 22% of patients and its early dynamic evolution significantly impacted ICI outcomes. A positive outcome was noted if dNLR changed to low and a negative outcome if it changed to high. Interest-ingly, we identified a poor-group (23%; persistently high-dNLR), associated with an early failure to ICI (PFS 2 months; OS 5 months). During cancer progression, the number of neutrophils usually increases, which favours a circulating inflammatory environment related to im-mune tolerance; in this situation, neutrophils can adapt their functions more likely to pro-tumour activities [21]. In this context, dNLR seems to be a good predictor of this peripheral inflammatory status, independently of other tissue or clinical factors. Theoretically, in an environment dominated by immune tolerance, the persistence of this “inflammation” (continued high-dNLR) or the emergence of a pro-inflammatory status after ICI beginning (change to high-dNLR) could explain the detrimental impact of these dNLR changes on prognosis.
Only a few prior studies have explored similar hy-pothesis, mainly assessing NLR in smaller cohorts, either as a unique parameter [22,23] combined with other parameters [24] or clinical variables [25]. However,
these reports did not include a controlled comparison cohort with chemotherapy only. Some similar publica-tions have also explored monitoring NLR ratio in small
cohorts, but with a heterogeneous methodology,
including different thresholds, time points (baseline, at C2, at 6 weeks, at C5, within the first 3 months, etc.) and strategies for monitoring (i.e. delta NLR, increased >20% NLR or longitudinal assessment of NLR com-bined to the sum of the longest diameters (SLD) of target lesions, etc.) [15,26e30]. Here, we have assessed dNLR and no other ratios, because this considers the neutrophils within the global context of the patient’s white blood cells populations (host), including in the denominator not only lymphocytes, but also monocytes and other granulocytes, which can play a role on the global immune response to ICI7.
With no previous studies evaluating dNLR and its early change on ICI outcomes, our study is the first and largest cohort reported to date, that uses a simple and «user-friendly» tool for early identification of the ICI benefit. Our work represents the clinical demonstration of how host immune cells, particularly neutrophils, could be major actors in ICI resistance. Further biomarker research should be focused on better characterising of neutrophils to identify accurate biomarkers.
Neutrophil phenotype is highly diverse resulting from functional plasticity and/or changes to granulopoiesis
[11]. Among them, immature neutrophils have an
altered functional capacity vs. mature neutrophils that may promote cancer progression. These immature forms can be significantly increased in the peripheral blood in cancer patients, derived from their early release from the bone marrow haematopoietic niche due to increased systemic chemokines produced by the tumour, or even induced by cancer therapies[13]. Also, their expansion
can induce the suppression of T cell-mediated immune responses, promoting an immunosuppressive environ-ment [18]. Thus, a high proportion of these circulating immature forms, that can be also identified as other myeloid cells (MDSCs), could help to explain the immunological mechanism of early failure to ICI [30,31].
In our prospective cohort, we identified a population with a high rate of circulating immature forms with a 12wk-death rate of 49%. Although only limited clinical data is available on circulating neutrophils; this prom-ising evidence warrants future research on the immu-nophenotype characterisation of neutrophils.
Hyperprogressive disease (HPD [31]) (~14% and fast progression/early death (6%) have been described as
aggressive progression patterns under ICI, associated with ICI resistance and high mortality rates in NSCLC [32].Biomarkers to predict these aggressive patterns are currently a major need for patients on ICI therapy. While, in lung cancer, high-dNLR at baseline was not initially associated with HPD [33], Kim et al. recently reported that high-dNLR (>4) was independently associated with HPD (differently defined). We observed, using dNLR score, that 12-week death rate was higher in high-dNLR (B) population (27% vs. 15% overall), and increased up to 32% if continued high-dNLR, supporting the biolog-ical hypothesis of neutrophil involvement on ICI resis-tance [12]. Similarly, other recent work have reported a correlation between dNLR and HPD [34,35]. Although these findings require confirmatory investigations, these
Fig. 2. A) Progression-free survival (PFS) and overall survival (OS) in the population with low-dNLR at baseline, as per the dNLR change at C2; B) PFS and OS in the population with high-dNLR at baseline, as per dNLR change at C2.
studies together with our work, warrant further studies to explore dNLR and its dynamic changes in regards to predicting Fast-PD or HPD.
Next, we identified a subset of patients with negative PD-L1 tumours but good dNLR score, with ICI benefit (PFS 5.4 months; OS 21.7 months), emphasising the importance of the host immune context in our patients to complement the information provided for the tumour-based biomarkers.
4.1. Limitations
Our study has several limitations derived from the retrospective nature of the multicentric cohort. In particular the sample size of the chemotherapy group was small; the lack of prognostic impact of dNLR should be investigated and repeated in large cohorts. In addition, some data was not available (e.g. PD-L1 was missing in 67%). dNLR was not available for C2 in the chemotherapy-cohort and therefore we are not able to
compare the dNLR score between the two therapies. The radiographic assessments were not standardised, give the various institutions involved, which, limits our PFS interpretation. Additionally, the immunopheno-typing was only performed on a small cohort with a limited panel of surface markers, derived from the exploratory aim of this cohort.
Despite these limitations, we have highlighted the clinical relevance of the integration of host-related bio-markers with others (e.g. PD-L1) to improve the pre-diction of ICI efficacy. Our study provides original evidence to monitor certain host immune cells to predict benefit to ICI therapy in a large real-life data cohort of NSCLC patients. It is also hypothesis-generating on the role of neutrophils in primary resistance to ICI, with preliminary clinical evidence on the immature neutro-phil population.
The dNLR score remains an attractive biomarker for risk stratification of patients with cancer that could also help guide treatment decisions. With ICI in combination
Table 2
Multivariate analysis for progression-free survival (PFS) and overall survival (OS), internally validated by bootstrapping (P< 0.001).
Boostrap analysis PFS OS
HR 95% CI P value HR 95% CI P value
Age
>65yo 0.95 0.83e1.07 0.40 1.04 0.89e1.14 0.60
Gender
Male 1.09 0.95e1.24 0.19 1.19 1.02e1.40 0.03
Smoking
Smoker 0.61 0.48e0.75 <0.0001 0.78 0.64e1.00 0.054
Histology
Squamous 0.95 0.82e1.11 0.48 1.01 0.85e1.20 0.93
Molecular status
Driver alt. 1.08 0.80e1.45 0.56 1.06 0.78e1.45 0.71
ICI line
>2 1.08 0.94e1.24 0.28 1.12 0.96e1.31 0.14
PS
2 1.69 1.40e2.04 <0.0001 2.11 1.73e2.25 <0.0001
dNLR score
Intermediate 1.32 1.12e1.55 <0.0001 1.49 1.23e1.80 <0.0001
Poor 1.84 1.58e2.15 2.56 2.15e3.05
Fig. 4. Progression-free survival (PFS) and overall survival (OS) of the population (NZ 57) according to the baseline high immature-neutrophils.
with chemotherapy as current standard of care in advanced NSCLC patients with<50% PD-L1 expression, dNLR score should also be evaluated in an additional study to explore if the chemotherapy can modify the impact of dNLR dynamics and its prediction on ICI outcomes.
5. Conclusion
The pretreatment neutrophils/[leucocytes-neutrophils] ratio (dNLR) (B) and its dynamics (C2) under immuno-therapy is associated with outcomes in advanced NSCLC patients. The dNLR score (BþC2) can improve the pre-diction of ICI outcomes compared to dNLR (B); a persistent high-dNLR was correlated with early ICI fail-ure. The dNLR score is a simple and accessible biomarker for risk stratification of patients and it should be pro-spectively validated in clinical trials. Immature neutro-phils may be a key subpopulation on ICI resistance. Author contributions
Conception and design: was contributed by Laura Mez-quita, Isabel Preeshagul, Edouard Auclin, Matthew D. Hellmann, Nathalie Chaput, Kathryn Arbour and Benjamin Besse.
All authors contributed to collection and assembly of data.
Data analysis and interpretation: was contributed by Laura Mezquita, Isabel Preeshagul, Edouard Auclin,
Lizza Hendriks, Wungki Park, Patricia Martin,
Matthew D. Hellmann, Gerard Zalcman, Marie Nai-geon, Caroline Rossoni, Anne Marie Dingemans, Nathalie Cozic, Lydie Cassard, Nathalie Chaput, Kathryn Arbour and Benjamin Besse.
Original Draft writing: was contributed by Laura Mezquita, Isabel Preeshagul, Edouard Auclin, Matthew D. Hellmann, Nathalie Chaput, Kathryn Arbour and Benjamin Besse.
All authors contributed to review, editing and final approval of manuscript.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Laura Mezquita received support from the IASLC Research Fellowship Award (2018), ESMO Translational Research Fellowship (2019) and SEOM retorno de Investigadores (2019).
Conflict of interest statement
LM: Sponsored Research: Amgen, Bristol-Myers Squibb, Boehringer Ingelheim. Consulting, advisory role: Roche Diagnostics, Takeda, Roche. Lectures and
educational activities: Bristol-Myers Squibb, Tecno-farma, Roche. Travel, Accommodations, Expenses: Bristol-Myers Squibb, Roche. Mentorship program with key opinion leaders: funded by AstraZeneca.
IP has served as a consultant for AstraZeneca, Pfizer and BluePrint Medicines.
EA: Travel expenses: Mundipharma. Lectures and educational activities: Sanofi Genzymes.
DS, HR, JR, SP, SC, FB, FT, MLR, PS, JG, AJP, MC, MN, CR, APM, LC and WPhad nothing to disclose.
LH: none related to the current article, outside of the current article: research funding Roche Genentech, Boehringer Ingelheim, AstraZeneca (all institution), advisory board: Boehringer, BMS, Eli Lilly, Roche Genentech, Pfizer, Takeda, MSD, Boehringer Ingel-heim, Amgen (all institution), speaker: MSD, travel/ conference reimbursement: Roche Genentech (self); mentorship program with key opinion leaders: funded by AstraZeneca; fees for educational webinars: Quadia (self), interview sessions funded by Roche Genentech (institution), local PI of clinical trials: AstraZeneca, Novartis, BMS, MSD/Merck, GSK, Takeda, Blueprint Medicines, Roche Genentech, Janssen Cilag.
EN: none related to the current article, outside of the current article: research support from Roche and Pfizer; advisory boards from Bristol Myers Squibb, Merck Sharpe & Dohme, Lilly, Roche, Pfizer, Takeda, Boeh-ringer Ingelheim, Amgen and AstraZeneca.
PM: Principal/subinvestigator of Clinical Trials for Abbvie, Adaptimmune, Aduro Biotech, Agios Phar-maceuticals, Amgen, Argen-X Bvba, Arno Therapeu-tics, Astex Pharmaceuticals, Astra Zeneca Ab, Aveo,
Basilea Pharmaceutica International Ltd, Bayer
Healthcare Ag, Bbb Technologies Bv, Beigene, Blue-print Medicines, Boehringer Ingelheim, Boston
Phar-maceuticals, Bristol Myers Squibb, Ca, Celgene
Corporation, Chugai Pharmaceutical Co, Clovis
Oncology, Cullinan-Apollo, Daiichi Sankyo, Debio-pharm, Eisai, Eisai Limited, Eli Lilly, Exelixis, Forma Tharapeutics, Gamamabs, Genentech, Glaxosmithkline, H3 Biomedicine, Hoffmann La Roche Ag, Imcheck Therapeutics, Innate Pharma, Institut De Recherche Pierre Fabre, Iris Servier, Janssen Cilag, Janssen Research Foundation, Kyowa Kirin Pharm. Dev, Lilly France, Loxo Oncology, Lytix Biopharma As, Medi-mmune, Menarini Ricerche, Merck Sharp & Dohme Chibret, Merrimack Pharmaceuticals, Merus, Millen-nium Pharmaceuticals, Molecular Partners Ag, Nano-biotix, Nektar Therapeutics, Novartis Pharma, Octimet Oncology Nv, Oncoethix, Oncopeptides, Orion Pharma, Ose Pharma, Pfizer, Pharma Mar, Pierre Fabre, Medi-cament, Roche, Sanofi Aventis, Sotio A.S, Syros Phar-maceuticals, Taiho Pharma, Tesaro, Xencor Research Grants from Astrazeneca, BMS, Boehringer Ingelheim, Janssen Cilag, Merck, Novartis, Pfizer, Roche, Sanofi
Astrazeneca, Bayer, BMS, Boringher Ingelheim, Medi-mmune, Merck, NH TherAGuiX, Pfizer, Roche.
CAV: Personal fees from Roche, AbbVie, MSD,
Bristol-Myers Squibb, Novartis, Astra Zeneca,
Takeda and Ipsen.
PB: Speaker bureau: AstraZeneca, BMS, Roche, MSD. Honoraria: Beigene.
SN: Speaker Bureau/Advisor: Amgen, AstraZeneca, Boehringer, Beigene, MSD, Roche, Takeda, Pfizer.
MDH receives research support from Bristol-Myers Squibb; has been a compensated consultant for Merck, Bristol-Myers Squibb, AstraZeneca, Genentech/Roche, Nektar, Syndax, Mirati, Shattuck Labs, Immunai, Blueprint Medicines, Achilles and Arcus; received travel support/honoraria from AstraZeneca, Eli Lilly and Bristol-Myers Squibb; has options from Shattuck Labs, Immunai and Arcus; has a patent filed by his institution related to the use of tumour mutation burden to predict response to immunotherapy (PCT/US2015/062,208), which has received licensing fees from PGDx.
GZ: Grants and personal fees from BMS, non-financial support from MSD, personal fees from Borhinger, non-financial support from Roche, personal fees from Astra-Zeneca, non-financial support from Abbvie, outside the submitted work.
MM: Grants and personal fees from BMS, personal fees and non-financial support from MSD, personal fees
and non-financial support from BOEHRINGER
INGELHEIM, personal fees, non-financial support and other from ASTRA ZENENCA, personal fees, non-financial support and other from ROCHE, personal fees from KYOWA KYRIN, personal fees from PIERRE FABRE, outside the submitted work.
LPA: Honoraria (self): Adacap, Amgen, AstraZe-neca, Bayer, Blueprint Medicines, Boehringer Ingel-heim, Bristol-Myers Squibb, Celgene, Eli Lilly, Incyte, Ipsen, Merck, Merck Sharp and Dohme, Novartis, Pfizer, Pharmamar, Roche, Sanofi, Servier, Sysmex, Takeda; Leadership role: Altum sequencing; Research
grant/Funding (self): AstraZeneca, Bristol-Myers
Squibb, Merck Sharp and Dohme, Pfizer; Officer/Board of Directors Geno´mica.
AMD: Consulting, advisory role or lectures: Roche, Eli Lilly, Boehringer Ingelheim, Astra Zeneca, Pfizer, BMS, Amgen, Novartis, MSD, Takeda, Pharmamar. Research support: Amgen, Abbvie, BMS.
DP: Consulting, advisory role or lectures: AstraZe-neca, Bristol-Myers Squibb, Boehringer; Ingelheim, Celgene, Daiichi Sankyo, Eli Lilly, Merck, Novartis, Pfizer, prIME Oncology, Peer.
CME, Roche. Honoraria: AstraZeneca, Bristol-Myers Squibb, Boehringer Ingelheim, Celgene.
Eli Lilly, Merck, Novartis, Pfizer, prIME Oncology, Peer CME, Roche. Clinical trials research: AstraZeneca, Bristol-Myers Squibb, Boehringer Ingelheim, Eli Lilly, Merck, Novartis, Pfizer, Roche, Medimmune, Sanofi-Aventis, Taiho Pharma, Novocure, Daiichi Sankyo.
Travel, Accommodations, Expenses: AstraZeneca,
Roche, Novartis, prIME Oncology, Pfizer.
NC: Sponsored Research at Gustave Roussy Cancer Center BMS fondation, GSK, Sanofi, advisory role and lectures: AstraZeneca. These are outside the scope of this work.
GL: Research support Merck serono, BMS, Pfizer, AstraZeneca, Blueprint medicines.
KA: Consultant for Astrazeneca and Iovance Bio-therapeutics. Her institution has received non-monetary research support from Takeda and Novartis on her behalf.
BB: Sponsored Research at Gustave Roussy Cancer Center Abbvie, Amgen, AstraZeneca, Biogen, Blueprint Medicines, BMS, Celgene, Eli Lilly, GSK, Ignyta, IPSEN, Merck KGaA, MSD, Nektar, Onxeo, Pfizer,
Pharma Mar, Sanofi, Spectrum Pharmaceuticals,
Takeda, Tiziana Pharma.
Acknowledgements
None.Appendix A. Supplementary data
Supplementary data to this article can be found online athttps://doi.org/10.1016/j.ejca.2021.03.011.
References
[1] Herbst RS, Baas P, Kim D-W, Felip E, Pe´rez-Gracia JL, Han J-Y, et al. Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEY-NOTE-010): a randomised controlled trial. Lancet 2016; 387(10027):1540e50.
[2] Brahmer J, Reckamp KL, Baas P, Crino` L, Eberhardt WEE, Poddubskaya E, et al. Nivolumab versus docetaxel in advanced squamous-cell non-small-cell lung cancer. N Engl J Med 2015; 373(2):123e35.
[3] Borghaei H, Paz-Ares L, Horn L, Spigel DR, Steins M, Ready NE, et al. Nivolumab versus docetaxel in advanced non-squamous nonesmall-cell lung cancer. N Engl J Med 2015; 373(17):1627e39.
[4] Gandhi L, Rodrı´guez-Abreu D, Gadgeel S, Esteban E, Felip E, De Angelis F, et al. Pembrolizumab plus chemotherapy in meta-static non-small-cell lung cancer. N Engl J Med 2018;378(22): 2078e92.
[5] Paz-Ares L, Luft A, Vicente D, Tafreshi A, Gu¨mu¨s‚ M, Mazie`res J, et al. Pembrolizumab plus chemotherapy for squa-mous non-small-cell lung cancer. N Engl J Med 2018;379(21): 2040e51.
[6] Socinski MA, Jotte RM, Cappuzzo F, Orlandi F, Stroyakovskiy D, Nogami N, et al. Atezolizumab for first-line treatment of metastatic nonsquamous NSCLC. N Engl J Med 2018;378(24):2288e301.
[7] Mezquita L, Auclin E, Ferrara R, Charrier M, Remon J, Planchard D, et al. Association of the lung immune prognostic index with immune checkpoint inhibitor outcomes in patients with advanced non-small cell lung cancer. JAMA Oncol 2018; 4(3):351e7.
[8] Prelaj A, Tay R, Ferrara R, Chaput N, Besse B, Califano R. Predictive biomarkers of response for immune checkpoint in-hibitors in non-small-cell lung cancer. Eur J Canc 2019;106: 144e59.
[9] Riudavets M, Auclin E, Mezquita L. Host circulating biomarkers for immune-checkpoint inhibitors: single-agent and combinations. Future Oncol 2020;16(23):1665e8.
[10] Benitez JC, Recondo G, Rassy E, Mezquita L. The LIPI score and inflammatory biomarkers for selection of patients with solid tumors treated with checkpoint inhibitors. Q J Nucl Med Mol Imaging 2020;64(2):162e74.
[11] Coffelt SB, Wellenstein MD, de Visser KE. Neutrophils in cancer: neutral no more. Nat Rev Canc 2016;16(7):431e46.
[12] Kargl J, Busch SE, Yang GHY, Kim K-H, Hanke ML, Metz HE, et al. Neutrophils dominate the immune cell composition in non-small cell lung cancer. Nat Commun 2017;8:14381.
[13] Mackey JBG, Coffelt SB, Carlin LM. Neutrophil maturity in cancer. Front Immunol 2019;10:1912.
[14] Soda H, Ogawara D, Fukuda Y, Tomono H, Okuno D, Koga S, et al. Dynamics of blood neutrophil-related indices during nivo-lumab treatment may be associated with response to salvage chemotherapy for non-small cell lung cancer: a hypothesis-generating study. Thorac Cancer 2019;10(2):341e6.
[15] Park W, Mezquita L, Okabe N, Chae YK, Kwon D, Saravia D, et al. Association of the prognostic model iSEND with PD-1/L1 monotherapy outcome in non-small-cell lung cancer. Br J Canc 2020;122(3):340e7.
[16] Kargl J, Zhu X, Zhang H, Yang GHY, Friesen TJ, Shipley M, et al. Neutrophil content predicts lymphocyte depletion and anti-PD1 treatment failure in NSCLC. JCI Insight 2019;4(24). [17] Charrier M, Mezquita L, Lueza B, Dupraz L, Planchard D,
Remon J, et al. Circulating innate immune markers and outcomes in treatment-naı¨ve advanced non-small cell lung cancer patients. Eur J Canc 2019;108:88e96.
[18] Aarts CEM, Kuijpers TW. Neutrophils as myeloid-derived sup-pressor cells. Eur J Clin Invest 2018 Nov;48(Suppl 2):e12989. [19] Kazandjian D, Gong Y, Keegan P, Pazdur R, Blumenthal GM.
Prognostic value of the lung immune prognostic index for patients treated for metastatic non-small cell lung cancer. JAMA Oncol 2019;5(10):1481e5.
[20] Sorich MJ, Rowland A, Karapetis CS, Hopkins AM. Evaluation of the lung immune prognostic index for prediction of survival and response in patients treated with atezolizumab for non-small cell lung cancer: pooled analysis of clinical trials. J Thorac Oncol 2019;14(8):1440e6.
[21] Giese MA, Hind LE, Huttenlocher A. Neutrophil plasticity in the tumor microenvironment. Blood 2019;133(20):2159e67. [22] Diem S, Schmid S, Krapf M, Flatz L, Born D, Jochum W, et al.
Neutrophil-to-Lymphocyte ratio (NLR) and Platelet-to-Lymphocyte ratio (PLR) as prognostic markers in patients with non-small cell lung cancer (NSCLC) treated with nivolumab. Lung Canc 2017;111:176e81.
[23] Russo A, Russano M, Franchina T, Migliorino MR, Aprile G, Mansueto G, et al. Neutrophil-to-Lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and outcomes with
nivolumab in pretreated non-small cell lung cancer (NSCLC): a large retrospective multicenter study. Adv Ther 2020;37(3): 1145e55.
[24] Liu J, Li S, Zhang S, Liu Y, Ma L, Zhu J, et al. Systemic immune-inflammation index, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio can predict clinical outcomes in patients with metastatic non-small-cell lung cancer treated with nivolumab. J Clin Lab Anal 2019;33(8):e22964.
[25] Prelaj A, Ferrara R, Rebuzzi SE, Proto C, Signorelli D, Galli G, et al. EPSILoN: a prognostic score for immunotherapy in advanced non-small-cell lung cancer: a validation cohort. Cancers 2019;11(12). 05.
[26] Mo¨ller M, Turzer S, Schu¨tte W, Seliger B, Riemann D. Blood immune cell biomarkers in patient with lung cancer undergoing treatment with checkpoint blockade. J Immunothery 2020;43(2): 57e66.
[27] Li M, Spakowicz D, Burkart J, Patel S, Husain M, He K, et al. Change in neutrophil to lymphocyte ratio during immunotherapy treatment is a non-linear predictor of patient outcomes in advanced cancers. J Canc Res Clin Oncol 2019;145(10):2541e6. [28] Dusselier M, Deluche E, Delacourt N, Ballouhey J, Egenod T,
Melloni B, et al. Neutrophil-to-lymphocyte ratio evolution is an independent predictor of early progression of second-line nivo-lumab-treated patients with advanced non-small-cell lung cancers. PloS One 2019;14(7):e0219060.
[29] Passiglia F, Galvano A, Castiglia M, Incorvaia L, Calo` V, Listı` A, et al. Monitoring blood biomarkers to predict nivolumab effec-tiveness in NSCLC patients. Ther Adv Med Oncol 2019;11. 1758835919839928.
[30] Gavrilov S, Zhudenkov K, Helmlinger G, Dunyak J, Peskov K, Aksenov S. Longitudinal tumor size and neutrophil-to-lymphocyte ratio are prognostic biomarkers for overall survival in patients with advanced non-small cell lung cancer treated with durvalumab. CPT Pharmacometrics Syst Pharmacol 2021;10(1):67e74. [31] Aarts CEM, Hiemstra IH, Tool ATJ, van den Berg TK, Mul E,
van Bruggen R, et al. Neutrophils as suppressors of T cell pro-liferation: does age matter? Front Immunol 2019;10:2144. [32] Kas B, Talbot H, Ferrara R, Richard C, Lamarque J-P,
Pitre-Champagnat S, et al. Clarification of definitions of hyper-progressive disease during immunotherapy for non-small cell lung cancer. JAMA Oncol 2020;6(7):1039e46.
[33] Ferrara R, Mezquita L, Texier M, et al. Comparison of fast-progression, hyperprogressive disease, and early deaths in advanced nonesmall-cell lung cancer treated with PD-1/PD-L1 inhibitors or chemotherapy. JCO Precision Oncology 2020;4: 829e40.
[34] Ferrara R, Mezquita L, Texier M, Lahmar J, Audigier-Valette C, Tessonnier L, et al. Hyperprogressive disease in patients with advanced non-small cell lung cancer treated with PD-1/PD-L1 inhibitors or with single-agent chemotherapy. JAMA Oncol 2018;4(11):1543e52.
[35] Castello A, Rossi S, Toschi L, Mazziotti E, Lopci E. Hyper-progressive disease in patients with non-small cell lung cancer treated with checkpoint inhibitors: the role of 18F-FDG PET/CT. J Nucl Med 2020;61(6):821e6.
