Blood biomarkers associated to complete pathological response on NSCLC patients treated with neoadjuvant chemoimmunotherapy included in NADIM clinical trial

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. These results reinforce the different biology behind CPR and non-CPR responses.

Keywords: biomarkers; chemoimmunotherapy; immune cells; neoadjuvant; non-small cell lung cancer.

Conflict of interest statement

Ernest Nadal reports personal fees from Bristol Myers Squibb, Merck Sharpe & Dohme, AstraZeneca, Lilly, Amgen, and Boehringer Ingelheim, and grants and personal fees from Roche and Pfizer, outside the submitted work. Amelia Insa reports personal fees from Bristol, BOERINGHER, MSD, PFIZER, Roche, and ASTRA ZENECA, outside the submitted work. María del Rosario García Campelo reports personal fees from BMS, MSD, Roche, Pfizer, and AstraZeneca, outside the submitted work. Manuel Dómine reports personal fees from Astra‐Zeneca, BMS, Boehringer Ingelheim, MSD, Pfizer, and Roche, outside the submitted work. Margarita Majem reports grants and personal fees from BMS, personal fees and nonfinancial support from MSD, BOEHRINGER INGELHEIM, ASTRA ZENENCA, ROCHE, and personal fees from KYOWA KYRIN and PIERRE FABRE, outside the submitted work. Delvys Rodríguez‐Abreu reports grants and personal fees from Bristol Myers Squibb, personal fees from GENENTECH/ROCHE, MSD, ASTRA ZENECA, BOEHRINGER INGELHEIM, Novartis, and Lilly, outside the submitted work. Alex Martínez‐Martí reports personal fees and nonfinancial support from Bristol Myers Squibb, F. Hoffmann La Roche AG, Merck Sharp & Dohme, Pfizer, Boehringer Ingelheim, MSD Oncology, and AstraZeneca, outside the submitted work. Javier De Castro Carpeño reports personal fees from Astra Zeneca, Boehringer Ingelheim, Merck Sharp and Dohme, Hoffmann‐la Roche, Bristol Myers Squibb, Takeda, Pfizer, and Novartis, outside the submitted work. Edel Del Barco reports nonfinancial support from ROCHE, BMS, PFIZER, ASTRA‐ZENECA, and MERCK, during the conduct of the study. Isidoro Barneto Aranda reports consulting or advisory board for Bristol Myers, Takeda, Roche, Astra Zeneca, and Behringer Inngelheim. Santiago Viteri reports personal fees and nonfinancial support from BMS, ROCHE, personal fees from MSD and ABBVIE, and nonfinancial support from OSE IMMUNOTHERAPEUTICS and MERCK SERONO, outside the submitted work. Bartomeu Massuti reports grants and personal fees from Roche, and personal fees and other from BMS, Takeda, MSD, and Boehringer, outside the submitted work. Cara Haymaker participated in scientific advisory board from Briacell. Ignacio I. Wistuba reports grants and personal fees from Genentech/Roche, Bayer, Bristol Myers Squibb, AstraZeneca/Medimmune, Pfizer, HTG Molecular, Merck, Guardant Health, and personal fees from GlaxoSmithKline, MSD, and Oncocyte, and grants from Oncoplex, DepArray, Adaptive, Adaptimmune, EMD Serono, Takeda, Amgen, Johnson & Johnson, Karus, Iovance, 4D, Novartis, and Akoya, outside the submitted work. Atocha Romero reports personal fees from Boehringer Ingelheim, outside the submitted work. Mariano Provencio reports grants, personal fees, and nonfinancial support from BMS, ROCHE, ASTRAZENECA, and personal fees from MSD and TAKEDA, outside the submitted work. Raquel Laza‐Briviesca, Alberto Cruz‐Bermúdez, Gerardo Huidobro, Manuel Cobo, Guillermo López Vivanco, Reyes Bernabé Caro, Nuria Viñolas, Marta Casarrubios, Belén Sierra‐Rodero, Carlos Tarín, Aránzazu García‐Grande, and Fernando Franco declare no conflict of interest.

© 2021 The Authors. Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.

Figures

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

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

FIGURE 3

Immunophenotyping associated to pathological response. (A1) MFI of CD56+ cells on T‐NK like (CD3+CD56+) cells and NK cells (CD3–CD56+). (A2) AUC ROC curve of CD56 MFI on CD3+CD56+. (B1) MFI of CD25 on CD3+CD4+CD25hi. (B2) AUC ROC curve of CD25 MFI on CD3+CD4+CD25hi. (C1) MFI of CD19 on CD3–CD19+ and CD3–CD19hi. (D1) BCMA levels of expression. 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

FIGURE 4

Monocytes and macrophages activation. (A1) MFI of CD69 on classical monocytes (CD14++CD16–CD69+), intermediate monocytes (CD14++CD16+CD69+), and nonclassical monocytes (CD14+CD16+CD69+). (A2) AUC ROC curve of CD69 MFI on CD14++CD16+CD69+. (B) 4‐1BB levels of expression. (C) MCSF levels of expression. (D) PARC levels of expression. (E) Flt‐3L levels of expression. (F) MPIF levels of expression. 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

FIGURE 5

Neoadjuvant treatment influence on immune cells and plasma factors. (A) Percentage of immune cells from peripheral blood and MFI on immune cells from peripheral blood. (B) Soluble factors secretion in plasma. (C) Cytokine secretion on plasma. Legend: Fold‐change (POST/PRE) from 0 to 2, 0 indicating (blue) a decrease after treatment, 1 (pink) indicates no changes, 2 (red) indicates increase, and >2 (dark red) indicates more than double of increase, and black squares (no data). Nonparametric Wilcoxon test was used for comparisons, (empty, not significant; *p‐value <.05; **p‐value <.01; ***p‐value <.001)