Blood biomarkers associated to complete pathologicalresponse on NSCLC patients treated with neoadjuvantchemoimmunotherapy included in NADIM clinical trial

Received: 13 April 2021 Revised: 17 June 2021 Accepted: 21 June 2021 Published online: 14 July 2021 DOI: 10.1002/ctm2.491 RESEARCH ARTICLE Blood biomarkers associated to complete pathological response on NSCLC patients treated with neoadjuvant chemoimmunotherapy included in NADIM clinical trial Raquel Laza-Briviesca1,2Alberto Cruz-Bermúdez1Ernest Nadal3 Amelia Insa4María del Rosario García-Campelo5Gerardo Huidobro6 Manuel Dómine7Margarita Majem8Delvys Rodríguez-Abreu9 Alex Martínez-Martí10 Javier De Castro Carpeño11 Manuel Cobo12 Guillermo López Vivanco13 Edel Del Barco14 Reyes Bernabé Caro15 Nuria Viñolas16 Isidoro Barneto Aranda17 Santiago Viteri18 Bartomeu Massuti19 Marta Casarrubios1,2Belén Sierra-Rodero1,2 Carlos Tarín20 Aránzazu García-Grande21 Cara Haymaker22 Ignacio I. Wistuba22 Atocha Romero1Fernando Franco1 Mariano Provencio1 1Servicio de Oncología Médica, Instituto de Investigación Sanitaria Puerta de Hierro-Segovia de Arana (IDIPHISA), Hospital Universitario Puerta de Hierro-Majadahonda, Madrid, Spain 2Doctoral School, Universidad Autónoma de Madrid, Madrid, Spain 3Institut Català d’Oncologia, L’Hospitalet De Llobregat, Barcelona, Spain 4Fundación INCLIVA, Hospital Clínico Universitario de Valencia, Valencia, Spain 5Hospital Universitario A Coruña, A Coruña, Spain 6Hospital Universitario de Vigo, Pontevedra, Spain 7Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain 8Hospital de la Santa Creu i Sant Pau, Barcelona, Spain 9Hospital Insular de Gran Canaria, Las Palmas, Spain 10 Hospital Universitario e Instituto de Oncología Vall dťHebron (VHIO), Barcelona, Spain 11 Hospital Universitario La Paz, Madrid, Spain 12 Hospital Universitario Regional de Málaga, Málaga, Spain 13 Hospital Universitario Cruces, Barakaldo, Spain 14 Hospital Universitario de Salamanca, Salamanca, Spain 15 Hospital Universitario Virgen del Rocio, Seville, Spain 16 Hospital Clínic, Barcelona, Spain 17 Hospital Universitario Reina Sofia, Córdoba, Spain 18 Instituto Oncológico Dr. Rosell, Hospital Universitario Quiron Dexeus, Grupo QuironSalud, Barcelona, Spain 19 Hospital General de Alicante, Alicante, Spain 20 Bioinformatics Unit, Instituto de Investigación Sanitaria Puerta de Hierro-Segovia de Arana, Madrid, Spain 21 Flow Cytometry Core Facility, Instituto de Investigación Sanitaria Puerta de Hierro-Segovia de Arana (IDIPHISA), Madrid, Spain 22 Departments of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA Clin. Transl. Med. 2021;11:e491. wileyonlinelibrary.com/journal/ctm2 https://doi.org/10.1002/ctm2.491 20011326, 2021, 7, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ctm2.491 by Universidad De Sevilla, Wiley Online Library on [05/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License LAZA-BRIVIESCA et al. Correspondence MarianoProvencioandAlbertoCruz Bermudez,MedicalOncologyDepartment,PuertadeHierroHospital,Manuel deFallaStreet#1,28222Majadahonda, Madrid,Spain. Email:[email protected]; [email protected] Graphical Abstract 1. Non-small cell lung cancer (NSCLC) patients achieving complete pathologic response (CPR) after neoadjuvant chemoimmunotherapy seem to have a distinctive peripheral blood immune status at diagnosis and surgery. 2. At diagnosis, CPR patients are characterized by a stronger previously induced immune response with a higher cytotoxic profile and lower levels of inhibitory cytokines and cells. 3. This exploratory analysis of the NADIM study supports the use of blood as a valid source for response biomarkers and may serve as a first step to elucidate response mechanisms to chemoimmunotherapy in NSCLC. 20011326, 2021, 7, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ctm2.491 by Universidad De Sevilla, Wiley Online Library on [05/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Received: 13 April 2021 Revised: 17 June 2021 Accepted: 21 June 2021 Published online: 14 July 2021 DOI: 10.1002/ctm2.491 RESEARCH ARTICLE Blood biomarkers associated to complete pathological response on NSCLC patients treated with neoadjuvant chemoimmunotherapy included in NADIM clinical trial Raquel Laza-Briviesca1,2Alberto Cruz-Bermúdez1Ernest Nadal3 Amelia Insa4María del Rosario García-Campelo5Gerardo Huidobro6 Manuel Dómine7Margarita Majem8Delvys Rodríguez-Abreu9 Alex Martínez-Martí10 Javier De Castro Carpeño11 Manuel Cobo12 Guillermo López Vivanco13 Edel Del Barco14 Reyes Bernabé Caro15 Nuria Viñolas16 Isidoro Barneto Aranda17 Santiago Viteri18 Bartomeu Massuti19 Marta Casarrubios1,2Belén Sierra-Rodero1,2 Carlos Tarín20 Aránzazu García-Grande21 Cara Haymaker22 Ignacio I. Wistuba22 Atocha Romero1Fernando Franco1 Mariano Provencio1 1Servicio de Oncología Médica, Instituto de Investigación Sanitaria Puerta de Hierro-Segovia de Arana (IDIPHISA), Hospital Universitario Puerta de Hierro-Majadahonda, Madrid, Spain 2Doctoral School, Universidad Autónoma de Madrid, Madrid, Spain 3Institut Català d’Oncologia, L’Hospitalet De Llobregat, Barcelona, Spain 4Fundación INCLIVA, Hospital Clínico Universitario de Valencia, Valencia, Spain 5Hospital Universitario A Coruña, A Coruña, Spain 6Hospital Universitario de Vigo, Pontevedra, Spain 7Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain 8Hospital de la Santa Creu i Sant Pau, Barcelona, Spain 9Hospital Insular de Gran Canaria, Las Palmas, Spain 10 Hospital Universitario e Instituto de Oncología Vall dťHebron (VHIO), Barcelona, Spain 11 Hospital Universitario La Paz, Madrid, Spain 12 Hospital Universitario Regional de Málaga, Málaga, Spain 13 Hospital Universitario Cruces, Barakaldo, Spain 14 Hospital Universitario de Salamanca, Salamanca, Spain 15 Hospital Universitario Virgen del Rocio, Seville, Spain 16 Hospital Clínic, Barcelona, Spain 17 Hospital Universitario Reina Sofia, Córdoba, Spain 18 Instituto Oncológico Dr. Rosell, Hospital Universitario Quiron Dexeus, Grupo QuironSalud, Barcelona, Spain 19 Hospital General de Alicante, Alicante, Spain 20 Bioinformatics Unit, Instituto de Investigación Sanitaria Puerta de Hierro-Segovia de Arana, Madrid, Spain This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2021 The Authors. Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics Clin. Transl. Med. 2021;11:e491. wileyonlinelibrary.com/journal/ctm2 1of16 https://doi.org/10.1002/ctm2.491 20011326, 2021, 7, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ctm2.491 by Universidad De Sevilla, Wiley Online Library on [05/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2of16 LAZA-BRIVIESCA et al. 21 Flow Cytometry Core Facility, Instituto de Investigación Sanitaria Puerta de Hierro-Segovia de Arana (IDIPHISA), Madrid, Spain 22 Departments of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA Correspondence MarianoProvencioandAlbertoCruz Bermudez,MedicalOncologyDepartment,PuertadeHierroHospital,Manuel deFallaStreet#1,28222Majadahonda, Madrid,Spain. Email:[email protected]; [email protected] RaquelLaza-BriviescaandAlbertoCruz- Bermúdezcontributedequally. Presentaddress CarlosTarín,BasicMedicalSciences, FacultyofMedicine,UniversidadCEUSan Pablo,Madrid,Spain. Fundinginformation EuropeanSocialFund,Grant/Award Numbers:PEJ16/MED/AI-1972,PEJD- 2018-PRE/SAL-8641,PEJD-2019- PRE/BMD-17006;SpanishLungCancerGroup;Bristol-MyersSquibb;Horizon2020FrameworkProgramme, Grant/AwardNumber:CLARIFY875160; MinisteriodeCienciaeInnovación, Grant/AwardNumbers:RTC2017- 6502-1,RTC2019-007359-1;Institutode SaludCarlosIII,Grant/AwardNumbers:CD19/00170, PI19/01652;European RegionalDevelopmentFund(ERDF), Grant/AwardNumbers:CD19/00170, PI19/01652 Abstract Background: Immunotherapy is being tested in early-stage non-small cell lung cancer (NSCLC), and achieving higher rates of complete pathological responses (CPR) as compared to standard of care. Early identification of CPR patients has vital clinical implications. In this study, we focused on basal peripheral immune cells and their treatment-related changes to find biomarkers associated to CPR. Methods: Blood from 29 stage IIIA NSCLC patients participating in the NADIM trial (NCT03081689) was collected at diagnosis and post neoadjuvant treatment. More than 400 parameters of peripheral blood mononuclear cells (PBMCs) phenotype and plasma soluble factors were analyzed. Results: Neoadjuvant chemoimmunotherapy altered more than 150 immune parameters. At diagnosis, 11 biomarkers associated to CPR were described, with an area under the ROC curve >0.70 and p-value <.05. CPR patients had significantly higher levels of CD4+PD-1+cells, NKG2D, and CD56 expression on T CD56 cells, intensity of CD25 expression on CD4+CD25hi+cells and CD69 expression on intermediate monocytes; but lower levels of CD3+CD56–CTLA- 4+cells, CD14++CD16+CTLA-4+cells, CTLA-4 expression on T CD56 cells and lower levels of b-NGF, NT-3, and VEGF-D in plasma compared to non-CPR. Post treatment, CPR patients had significantly higher levels of CD19 expression on B cells, BCMA, 4-1BB, MCSF, and PARC and lower levels of MPIF-1 and Flt-3L in plasma compared to non-CPR. Conclusions: Patients achieving CPR seem to have a distinctive peripheral blood immune status at diagnosis, even showing different immune response to treatment.Theseresultsreinforcethedifferent biology behindCPR andnon-CPR responses. KEYWORDS biomarkers, chemoimmunotherapy, immune cells, neoadjuvant, non-small cell lung cancer 1 INTRODUCTION Non-small cell lung cancer (NSCLC) accounts for 80%–85% of all lung cancer cases, being the primary cause of cancerrelated death worldwide and the second by incidence.1 Over 30% of NSCLC patients are diagnosed at locally advanced stage known as stage III.2Specifically, patients with resectable stage III disease, which are potentially curable, are usually treated with multimodal neoadjuvant treatment, based on chemotherapy and surgery.3How- ever, clinical outcomes of current treatments based on chemotherapy are still discouraging, with complete pathological responses (CPR) near 4%,4and 2-year overall survival (OS) rates of 60%.5 Cancer immunology knowledge has remarkably advanced with the discovery of immune checkpoint proteins. The immune checkpoint PD-1 in lymphocytes6 and myeloid cells7binds to PD-L1 in the tumor cells, leading to immune cell inactivation and tumor escape.8 This has led to anti-PD-1 or anti-PD-L1 antibodies use against cancer, bringing a new era of treatments known as immunotherapies.9The adaptive immune response lead by T cells plays a major role on antitumor responses10; however, macrophages,11 natural killer (NK) cells,12 B 20011326, 2021, 7, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ctm2.491 by Universidad De Sevilla, Wiley Online Library on [05/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License LAZA-BRIVIESCA et al. 3of16 cells,13,14 or NK-T cells15 are also involved in antitumor responses. Immunotherapy treatment has become the standard of treatment for advanced stages and is now being studied for early and locally advanced stages.16 Recently, the combination of nivolumab (anti-PD-1) and platinum-based chemotherapy as neoadjuvant treatment for resectable stage III (NADIM clinical trial, NCT03081689) has shown 63% of CPR for resected patients, and 2-year OS of 90%.17 These results open the possibility of transforming locally advanced NSCLC to a curable disease in a substantial percentage of patients, as CPR may be predictive of long-term survival following neoadjuvant therapy.18,19 Consequently, there is a clinical need for predictive biomarkers to identify such patients achieving CPR, which could potentially be free of disease after neoadjuvant treatment. Current biomarkers such as PD-L1 tumor proportion score (TPS) and Tumor mutational burden (TMB) remain controversial in clinical practice for anti-PD-1 monotherapies.20,21 Moreover, their value for combined chemoimmunotherapy is more limited,22 and its power to identify patients achieving CPR insufficient.17,23 Thus, additional predictive biomarkers beyond PD-L1 and TMB are required. In this way, an increasing number of investigations are focusing on the host antitumor immune response. Accordingly, patients achieving CPR showed a distinct tumor infiltrating lymphocytes (TILs) component at surgery evaluation.17 Additionally, an association between TILs24 or peripheral blood mononuclear cells (PBMCs) characteristics,25,26 with clinical benefit of lung cancer patients under immunotherapy, has been described. These differences seem to indicate that CPR may also be reflected in patient’s immune system at the peripheral level. This would allow a noninvasive alternative to tissue biopsy in predicting and monitoring treatment outcomes. Here, we present the first research study focused on the composition and phenotypic characteristics of different peripheral immune cell subpopulations and plasma factors in NSCLC patients receiving chemoimmune neoadjuvant therapy, in order to characterize treatment effectiveness and identify biomarkers of CPR. 2 MATERIALS AND METHODS 2.1 Patients and samples Patients were selected from NADIM clinical trial: neoadjuvant chemotherapy plus nivolumab in resectable stage IIIA non-small-cell lung cancer, an open-label, multicenter, single-arm, phase 2 trial.17 All patients with available peripheral blood sample were included. Peripheral blood samples from 30 patients at baseline (pretreatment) and 34 patients after three cycles of neoadjuvant treatment with nivolumab plus carboplatin (posttreatment) were obtained. Blood samples at both time points were available for 29 patients. Informed consent for the collection of research samples and study protocol were approved by the clinical research ethics committee of Hospital Puerta de Hierro and the Spanish Lung Cancer Group (SLCG) Board in accordance with the International Conference on Harmonization Guidelines on Good Clinical Practice and the Declaration of Helsinki. Clinical characteristics of patients with blood sample available for this study are described in Table S1. Hospitals and patients recruited are listed in Table S2. Pathologic response evaluation was carried out as previously described following international guidelines recommendations.17,27 We classified patients in complete pathological response (CPR, 0% of viable tumor cells in tumor bed or any lymph node tested) and non-CPR (patients with any percentage of viable tumor cells in resection specimens). Patients that did not undergo surgery were excluded from the pathological response analysis; however, they were included in comparisons of treatment effect and correlation analysis. Additionally, PD-L1 TPS and TMB were retrieved from previous report17 and correlated with blood parameters analyzed at diagnosis from this study. 2.2 Cell and plasma isolation Peripheral blood was diluted 1:1 in 1640 RPMI (Corning, NY, USA), and mononuclear cells (PBMCs) were isolated by Lymphoprep (Stemcell, Vancouver, Canada) density gradient centrifugation and cryopreserved using freezing medium (1:1 RPMI/FBS, Linus, Spain) containing 10% DMSO (Carl Roth, Germany) until use. Plasma fraction was collected after density gradient centrifugation was performed and stored at −80◦Cuntiluse. 2.3 Immunophenotyping of PBMCs Cryopreserved pretreatment and posttreatment PBMCs were thawed in parallel, washed with 5% FBS in 1×PBS, and surface stained with CD3-PerCP (clone BW264/56), CD4-Viogreen (clone REA623), CD8-APCVio770 (clone BW135/80), CD14-APC (clone Tük4), CD16-Viogreen (clone REA423), CD19-FITC (clone LT19), CD56- APCVio770 (clone REA196), CD69-FITC (clone REA824), CD107a-FITC (clone H4A3), CTLA4-PE (clone BNI3), PD1-PEVio770 (clone PD1.3.1.3), NKG2D-PE (clone REA797), NKp44-PEVio770 (clone 2.29) from Miltenyi 20011326, 2021, 7, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ctm2.491 by Universidad De Sevilla, Wiley Online Library on [05/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 4of16 LAZA-BRIVIESCA et al. Biotec (Germany) and CD25-PeCy7 (clone M-A251) from BD Bioscience (NJ, USA). Detection of PD-1 using anti- PD-1 clone PD1.3.1.3 was almost technically abrogated in posttreatment samples due to PD-1 binding of nivolumab as previously described.28 However, percentage of PD-1+ cells and PD-1 MFI in PD-1+cells was still analyzed in posttreatment samples to discard possible differences between groups. Cells were acquired on a MACS Quant 10 cytometer and DAPI (ThermoFisher, CA, USA) staining prior acquisition was used to exclude dead cells during analysis on FlowJo V10 software. The median number of viable cells after freezing, thawing, and staining was 93.6%, IQR 91.2–95.2. A minimum of 100,000 lymphocyte events were recorded. Expression of surface markers was evaluated by percentage of positive cells and median fluorescence intensity (MFI) using fluorescence minus one (FMO) controls. Gating strategy for all panels is shown in Figure S1A–F.Flow cytometry analysis was performed only in paired samples (29 patients). Immunophenotyping of circulating NK cells (CD3–CD56+), T cells (CD3+CD56–), cytotoxic T cells (CD3+CD8+), helper T cells (CD3+CD4+), T CD56 cells (CD3+CD56+), B cells (CD3–CD19+), and monocytes (CD14+) was determined. 2.4 Immunoassays for detecting cytokines and soluble factors in plasma Sixteen key soluble factors from plasma were measured in duplicate using the human immuno-oncology checkpoint protein panel (Cat. #HCKPMAG-11K, Millipore, MA, USA) following manufacturer’s instructions. Plasma levels of 200 cytokines were measured on the same samples using G-Series Human Cytokine Antibody Array 4000 (RayBiotech, GA, USA) following manufacturer’s instructions. Cytokine array and soluble factor analysis were performed on all samples. 2.5 Statistical analysis Raw data generated from flow cytometry and cytokine analysis is available in Table S3. Nonparametric Wilcoxon signed rank test was used for analysis of treatment variation and Mann–Whitney U-test was performed to determine differences between CPR and non-CPR patients. Pearson’s chi-square test was used for association between categorical groups (for baseline characteristics with pathological response). We considered area under the curve (AUC) >0.70 with significant p-value as relevant predictive biomarker using receiver operating characteristic (ROC) curve analysis. Relationship between variables was done by Spearmanťs correlation considering R>±0.80 as strong linear correlation with significant p-value. For all statistical tests, p-value <.05 was considered statistically significant; *, **, and *** indicated p-values <.05, <.01, and <.001, respectively. For p-values between .05 and .1, the numerical value is included. Because the research was designed as a discovery study, p-values were not adjusted in order to maximize the finding of new biomarkers and the generation of new hypothesis. IBM SPSS Statistics 25 was used for all statistical analyses. All authors had access to the data and certify the accuracy of the results presented. 3RESULTS 3.1 Patient characteristics No statistical differences on age, sex, smoking status, histology, and lymph node involvement were observed according to the degree of pathological response (Table 1). Patients included in this study showed similar clinical characteristics to NADIM trial complete cohort (Table S1). At baseline and prior to surgery, similar proportions of circulating NK cells (p-value .864), T cells (p-value .845), CD56+T cells (p-value .306), B cells (p-value .262), and monocytes (p-value .124) were found between CPR and non-CPR patients (Figure S2A). However, deepening into their phenotype and activation levels, we found significant differences described below. 3.2 Association of immunocheckpoints expressed in PBMCs and pathological response 3.2.1 PD-1 expression on lymphocytes PD-1 expression and percentages of PD-1+cells on different lymphocytes subsets, as T cells, CD4+T cells, CD8+ T cells, CD56+T cells, and NK cells were measured. At diagnosis, CPR patients showed a higher percentage of PD- 1+cells compared to non-CPR patients, for all lymphocyte subsets, reaching to statistically significant differences for CD4+PD-1+cells (p-value .045). PD-1+cells were almost technically undetectable after initiating the treatment with chemotherapy plus nivolumab (Figure 1A). The percentage of CD3+CD4+PD-1+cells at diagnosis had a predictive value to distinguish CPR and non-CPR patients, with an AUC of 0.728 (p-value .045) (Figure 1B). No differences between pathologic response groups were found on MFI PD-1 expression neither at diagnosis nor after neoadjuvant treatment (Figure S2B). 20011326, 2021, 7, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ctm2.491 by Universidad De Sevilla, Wiley Online Library on [05/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License LAZA-BRIVIESCA et al. 5of16 0 5 10 15 20 25 30 35 Percentage of lymphocytes subpopulations PD-1 + Percentage of PD-1+ cells T-NK like CD3+CD56+PD1+ ns * PRE POST PRE POST PRE POST PRE POST PRE POST T cells CD3+CD56-PD1+ CD4 T cells CD3+CD4+PD1+ CD8 T cells CD3+CD8+PD1+ NK cells CD3-CD56+PD1+ 0.07 ns ns ns ns ns ns ns 0 150 300 450 600 750 900 1050 1200 1350 MFI NKG2D on T lymphocytes and NK cells MFI of NKG2D+ ns * PRE POST PRE POST PRE POST PRE POST PRE POST 0.06 ns ns ns ns ns T-NK like CD3+CD56+NKG2D+ T cells CD3+CD56-NKG2D+ CD4 T cells CD3+CD4+NKG2D+ CD8 T cells CD3+CD8+NKG2D+ NK cells CD3-CD56+NKG2D+ * 0.05 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 ROC curve of MFI NKG2D on CD3+CD56+NKG2D+ 1 - Specificity Sensitivity AUC ROC 0.786 p-value 0.012 Cut-off > 541 Sensitivity 60% Specifity 92% 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 ROC curve of b-NGF, NT-3 and VEGF-D 1 - Specificity Sensitivity VEGF-D NT-3 b-NGF 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 Neurotrophins and angiogenesis factors Levels of soluble expression *** ns ns ns PRE POST PRE POST PRE POST NT-3 b-NGF VEGF-D 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 ROC curve of CD3+CD4+PD-1+ 1 - Specificity Sensitivity AUC ROC 0.728 p-value 0.045 Cut-off >5.31 Sensitivity 67% Specificity 92% CPR Non-CPR Diagnosis Post-treatment A C B D EF Differences between CPR and non-CPR at diagnosis and post neoadjuvant treatment FIGURE 1 PD-1 and NKG2D immunocheckpoints and pathological response. Differences between CPR and non-CPR patients at diagnosis and post neoadjuvant treatment. (A) Percentage of PD-1+cells on T cells (CD3+CD56–PD-1+), CD4 T cells (CD3+CD4+PD-1+), CD8 T cells (CD3+CD8+PD-1+), T-NK like (CD3+CD56+PD-1+) cells, and NK cells (CD3–CD56+PD-1+). (B) AUC ROC curve for CD3+CD4+PD-1+. (C) MFI of NKG2D on T cells (CD3+CD56–NKG2D+), CD4 T cells (CD3+CD4+NKG2D+), CD8 T cells (CD3+CD8+NKG2D+), T-NK like (CD3+CD56+NKG2D+) cells, and NK cells (CD3–CD56+NKG2D+). (D) AUC ROC curve for CD3+CD56+NKG2D+. (E) Levels of b-NGF, NT-3, and VEGF-D in plasma. AUC ROC curve for b-NGF, NT-3, VEGF-D. Legend: Diagnosis (PRE, grey dots), prior to surgery (POST, black dots), CPR patients (yellow bars), and non-CPR patients (purple bar). Nonparametric Mann–Whitney test was used for comparisons (ns, not significant; *p-value <.05; **p-value <.01; ***p-value <.001) and receiver operating characteristic (ROC) curve analysis showing AUC ROC curve, p-value, and optimum cut-off 20011326, 2021, 7, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ctm2.491 by Universidad De Sevilla, Wiley Online Library on [05/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 6of16 LAZA-BRIVIESCA et al. TABLE 1 Baseline characteristics of patients according to pathological response Total (N=27) CPR (N=15) Non-CPR (N=12) p-Value (CPR vs. non-CPR) Age (years)a67 (59–72) 69 (60–70) 64 (57.5–72.5) .695 Sexb.756 Male 21 (77.8) 12 (80) 9(75) Female 6(22.2) 3(20) 3(25) Cigarette-smoking historyb.795 Former smoker (≥1 year) 15 (55.6) 8 (53.3) 7 (58.3) Current smoker 12 (44.4) 7 (46.7) 5 (41.7) Histologyb.115 Adenocarcinoma 15 (55.6) 9 (60) 6(50) Squamous 9(33.3) 3(20) 6 (50) NOS/undifferenciated 3(11.1) 3(20) 0 Nodesb.984 N0 7 (25.9) 4 (26.7) 3 (25) N1 2 (7.4) 1 (6.7) 1 (8.3) N2 18 (66.7) 10 (66.7) 8 (66.7) Note: Age, sex, smoking status, histology, and affected lymph nodes comparison between pathological responses groups (CPR, complete pathological response) and non-CPR using Pearsonťs chi-square test. p-Value of chi-square or Kruskall–Wallis when adequate. aMedian and interquartile range (IQR). bN(%). 3.2.2 NKG2D expression on T lymphocytes and NK cells When analyzing NKG2D expression, we found that at diagnosis, NKG2D expression was higher in CD56+T cells in CPR patients compared to non-CPR patients (p-value .012) and there was a trend for CD8+T cells (p-value .051) (Figure 1C). Post neoadjuvant treatment, higher expression of NKG2D on CD4+T cells was found in CPR patients (pvalue .017) (Figure 1C). NKG2D expression at diagnosis on CD3+CD56+NKG2D+cells showed an AUC of 0.786 (pvalue .012) (Figure 1D). On the contrary, no significant differences at diagnosis or post treatment in the percentage of NKG2D+cells for T cells, CD56+T cells, and NK cells between CPR and non-CPR patients were obtained. However, a trend toward higher percentage of CD8+NKG2D+cells was observed in CPR patients (p-value .07) (Figure S2C). Furthermore, we noticed that NKG2D expression inversely correlate with plasma levels of neurotrophin 3 (NT-3) on CD8 T cells and T CD56 cells, as well as with vascular endothelial growth factor D levels (VEGF-D) on T CD56 cells (Figure S3A–D). Lower levels of these factors and the nerve growth factor beta (b-NGF) were found on plasma from CPR patients at diagnosis (Figure 1E). Moreover, these soluble factors can be used as biomarkers associated to CPR at diagnosis, shown by an AUC of 0.750 (p-value .028) for b-NGF, 0.778 (p-value .015) for NT-3, and 0.728 (p-value .045) for VEGF-D (Figure 1F and Figure S3F–H). 3.2.3 CTLA-4 expression on lymphocytes We analyzed CTLA-4+surface expression on T cells, CD56+T cells, NK cells, B cells, and monocytes and, despite of the scarcity of CTLA-4+cells in peripheral blood, significant differences were found on NK cells and monocytes (Figure 2). We observed lower pretreatment percentages of CTLA-4+NK cells (CD3–CD56+CTLA- 4+) in patients with CPR (Figure 2A). Similarly, lower percentages of CTLA-4+classical monocytes (p-value .026) and nonclassical monocytes (p-value .071) were determined in CPR patients (Figure 2C). At diagnosis, higher expression of CTLA-4 was found on CD56+ T cells (CD3+CD56+CTLA-4+), but not on T cells (CD3+CD56–CTLA-4+) in non-CPR patients (Figure 2E). These three subsets of cells showed an AUC ROC of 0.750 (p-value .028), 0.753 (p-value .026), and 0.844 (p-value .011), respectively (Figure 2B,D,F). Summarizing these initial results, our study describes the relevance of PD-1, CTLA-4, and NKG2D immunocheckpoints on immune cells subsets and, even greater, their value as biomarkers associated to complete pathological response to neoadjuvant chemoimmunotherapy in lung cancer patients. 20011326, 2021, 7, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ctm2.491 by Universidad De Sevilla, Wiley Online Library on [05/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License LAZA-BRIVIESCA et al. 7of16 0.0 0.2 0.4 0.6 0.8 1.0 1 4 7 10 13 16 Percentage of lymphocytes subpopulations CTLA-4 + Percentage of CTLA-4 + cells T-NK like CD3+CD56+CTLA-4+ PRE POST PRE POST PRE POST PRE POST PRE POST T cells CD3+CD56-CTLA-4+ CD4 T cells CD3+CD4+CTLA-4+ CD8 T cells CD3+CD8+CTLA-4+ NK cells CD3-CD56+CTLA-4+ ns ns ns ns ns ns ns ns PRE POST B cells CD3-CD19+CTLA-4+ ns ns ns * 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 ROC curve of CD3-CD56+CTLA-4+ 1 - Specificity Sensitivity AUC ROC 0.750 p-value 0.028 Cut-off < 0.195 Sensitivity 67% Specifity 92% Percentage of monocytes subpopulations CTLA-4 + Percentage ofC TLA-4+cells 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 ROC curve of CD14++CD16+CTLA4+ 1 - Specificity Sensitivity AUC ROC 0.753 p-value 0.026 Cut-off < 0.115 Sensitivity 60% Specificity 100% 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 ROC curve of MFI CTLA4 on CD3+CD56+CTLA4+ 1 - Specificity Sensitivity AUC ROC 0.844 p-value 0.011 Cut-off < 234 Sensitivity 70% Specifity 100% 0 100 200 300 400 500 500 2000 3500 5000 6500 MFI CTLA-4 on CD3 + cells MFIofCTLA-4+ PRE POST PRE POST * ns nsns T-NK like CD3+CD56+CTLA-4+ NK cells CD3-CD56+CTLA-4+ CPR Non-CPR Diagnosis Post-treatment A C B D EF Differences between CPR and non-CPR at diagnosis and post neoadjuvant treatment 0.0 0.5 1.0 1 4 7 10 10 40 70 100 * PRE POST PRE POST PRE POST Intermediate Non-classicalClassical ns 0.07 ns ns ns FIGURE 2 CTLA-4 immunocheckpoint and pathological response. Differences between CPR and non-CPR patients at diagnosis and post neoadjuvant treatment. (A) Percentage of CTLA-4+cells on T cells (CD3+CD56–CTLA-4+), CD4 T cells (CD3+CD4+CTLA-4+), CD8 T cells (CD3+CD8+CTLA-4+), T-NK like (CD3+CD56+CTLA-4+) cells, NK cells (CD3–CD56+CTLA-4+), and B cells (CD3–CD19+CTLA-4+). (B) AUC ROC curve for CD3–CD56+CTLA-4+. (C) Percentage of CTLA-4+cells on classical monocytes (CD14++CD16–CTLA-4+), intermediate monocytes (CD14++CD16+CTLA-4+), and nonclassical monocytes (CD14+CD16+CTLA-4+). (D) AUC ROC curve for CD14++CD16–CTLA-4+. (E) MFI of CTLA-4+on T-NK like cells (CD3+CD56+CTLA-4+), and T cells (CD3+CD56–CTLA-4+). (F) AUC ROC curve for CD3+CD56+CTLA-4+. Legend: Diagnosis (PRE, grey dots), prior to surgery (POST, black dots), CPR patients (yellow bars), and non-CPR patients (purple bar). Nonparametric Mann–Whitney test was used for comparisons (ns, not significant; *p-value <.05; **p-value <.01; ***p-value <.001) and receiver operating characteristic (ROC) curve analysis showing AUC ROC curve, p-value, and optimum cut-off 20011326, 2021, 7, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ctm2.491 by Universidad De Sevilla, Wiley Online Library on [05/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 14 of 16 LAZA-BRIVIESCA et al. AUTHOR CONTRIBUTIONS Alberto Cruz-Bermúdez and Mariano Provencio conceived and designed the study. Ernest Nadal, Amelia Insa, María del Rosario García Campelo, Gerardo Huidobro, Manuel Dómine, Margarita Majem, Delvys Rodríguez-Abreu, Alex Martínez-Martí, Javier De Castro Carpeño, Manuel Cobo, Guillermo López Vivanco, Edel Del Barco, Reyes Bernabé Caro, Nuria Viñolas, Isidoro Barneto Aranda, Santiago Viteri, Bartomeu Massuti, Fernando Franco, and Mariano Provencio recruited and treated patients. Raquel Laza-Briviesca, Marta Casarrubios, Belén Sierra-Rodero, Alberto Cruz-Bermúdez, Carlos Tarín, Atocha Romero and Aránzazu García-Grande carried out the experiments and analyzed the data. Raquel Laza-Briviesca, Alberto Cruz- Bermúdez, Marta Casarrubios, Belén Sierra-Rodero, Cara Haymaker, Ignacio I. Wistuba, Atocha Romero, and Mariano Provencio interpreted the data. All the authors have read and contributed to the final version of the manuscript and approved its submission for publication. DATA AVAILABILITY STATEMENT The data that support the findings of this study are available in the supplementary material of this article. ORCID Raquel Laza-Briviesca https://orcid.org/0000-0003- 2369-567X AlbertoCruz-Bermúdez https://orcid.org/0000-0001- 5136-7011 Ernest Nadal https://orcid.org/0000-0002-9674-5554 AmeliaInsa https://orcid.org/0000-0002-3438-6170 Maríadel RosarioGarcía-Campelo https://orcid.org/ 0000-0003-2113-1504 ManuelDómine https://orcid.org/0000-0003-1634-9832 MargaritaMajem https://orcid.org/0000-0002-9919- 7485 DelvysRodríguez-Abreu https://orcid.org/0000-0003- 0506-1366 AlexMartínez-Martí https://orcid.org/0000-0002-6714- 9526 JavierDeCastroCarpeño https://orcid.org/0000-0002- 3622-6306 ManuelCobo https://orcid.org/0000-0003-3402-1144 GuillermoLópez Vivanco https://orcid.org/0000-0003- 3800-6224 EdelDel Barco https://orcid.org/0000-0001-6443-0585 ReyesBernabé Caro https://orcid.org/0000-0003-0312- 9195 NuriaViñolas https://orcid.org/0000-0002-8287-2739 IsidoroBarnetoAranda https://orcid.org/0000-0001- 6302-1034 Santiago Viteri https://orcid.org/0000-0002-7572-1123 BartomeuMassuti https://orcid.org/0000-0002-6247- 4493 MartaCasarrubios https://orcid.org/0000-0002-2872- 9552 BelénSierra-Rodero https://orcid.org/0000-0003-0276- 6088 CarlosTarín https://orcid.org/0000-0002-1056-4024 Aránzazu García-Grande https://orcid.org/0000-0001- 5737-8918 CaraHaymaker https://orcid.org/0000-0002-1317-9287 IgnacioI. 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Blood biomarkers associated to complete pathological response on NSCLC patients treated with neoadjuvant chemoimmunotherapy included in NADIM clinical trial. Clin Transl Med. 2021;11:e491. https://doi.org/10.1002/ctm2.491 20011326, 2021, 7, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ctm2.491 by Universidad De Sevilla, Wiley Online Library on [05/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License