Pretreatment Tissue TCR Repertoire Evenness Is Associated with Complete Pathologic Response in Patients with NSCLC Receiving Neoadjuvant Chemoimmunotherapy

Marta Casarrubios, Alberto Cruz-Bermúdez, Ernest Nadal, Amelia Insa, María Del Rosario García Campelo, Martín Lázaro, 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, Miguel Barquín, Raquel Laza-Briviesca, Belén Sierra-Rodero, Edwin R Parra, Beatriz Sanchez-Espiridion, Pedro Rocha, Humam Kadara, Ignacio I Wistuba, Atocha Romero, Virginia Calvo, Mariano Provencio, Marta Casarrubios, Alberto Cruz-Bermúdez, Ernest Nadal, Amelia Insa, María Del Rosario García Campelo, Martín Lázaro, 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, Miguel Barquín, Raquel Laza-Briviesca, Belén Sierra-Rodero, Edwin R Parra, Beatriz Sanchez-Espiridion, Pedro Rocha, Humam Kadara, Ignacio I Wistuba, Atocha Romero, Virginia Calvo, Mariano Provencio

Abstract

Purpose: Characterization of the T-cell receptor (TCR) repertoire may be a promising source for predictive biomarkers of pathologic response to immunotherapy in locally advanced non-small cell lung cancer (NSCLC).

Experimental design: In this study, next-generation TCR sequencing was performed in peripheral blood and tissue samples of 40 patients with NSCLC, before and after neoadjuvant chemoimmunotherapy (NADIM clinical trial, NCT03081689), considering their complete pathologic response (CPR) or non-CPR. Beyond TCR metrics, tissue clones were ranked by their frequency and spatiotemporal evolution of top 1% clones was determined.

Results: We have found a positive association between an uneven TCR repertoire in tissue samples at diagnosis and CPR at surgery. Moreover, TCR most frequently ranked clones (top 1%) present in diagnostic biopsies occupied greater frequency in the total clonal space of CPR patients, achieving an AUC ROC to identify CPR patients of 0.967 (95% confidence interval, 0.897-1.000; P = 0.001), and improving the results of PD-L1 tumor proportion score (TPS; AUC = 0.767; P = 0.026) or tumor mutational burden (TMB; AUC = 0.550; P = 0.687). Furthermore, tumors with high pretreatment top 1% clonal space showed similar immune cell populations but a higher immune reactive gene expression profile. Finally, the selective expansion of pretreatment tissue top 1% clones in peripheral blood of CPR patients suggests also a peripheral immunosurveillance, which could explain the high survival rate of these patients.

Conclusions: We have identified two parameters derived from TCR repertoire analysis that could outperform PD-L1 TPS and TMB as predictive biomarkers of CPR after neoadjuvant chemoimmunotherapy, and unraveled possible mechanisms of CPR involving enhanced tumor immunogenicity and peripheral immunosurveillance.

©2021 The Authors; Published by the American Association for Cancer Research.

Figures

Figure 1.
Figure 1.
Pretreatment tissue TCR evenness is associated with pathologic response to neoadjuvant chemoimmunotherapy. A, Differences in metrics derived from TCR repertoire analysis (such as number of clones, evenness, convergence, and Shannon's diversity index) in peripheral blood samples between CPR and non-CPR patients at pre- and post-neoadjuvant treatment. Pretreatment (pre-T) CPR patients, n = 15; pre-T non-CPR patients, n = 12; posttreatment (post-T) CPR patients, n = 19; post-T non-CPR patients, n = 13. B, Differences in metrics in tissue samples between CPR and non-CPR patients. Pre-T CPR patients, n = 10; pre-T non-CPR patients, n = 9; post-T CPR patients, n = 24; post-T non-CPR patients, n = 14. Comparisons were done between CPR and non-CPR patients and between pre- and posttreatment timepoints. C, Clonal space occupied by each of the percentage rank (top 1%, top 1–2%, top 2–5%, and top > 5%) of the total repertoire in tissue samples. Comparisons between CPR and non-CPR patients are shown for each rank. Pre-T CPR patients, n = 10; pre-T non-CPR patients, n = 9; post-T CPR patients, n = 24; post-T non-CPR patients, n = 14. Each patient is represented by a black symbol. P < 0.0125 was considered statistically significant after Bonferroni's correction for multiple tests. Only significant differences after Bonferroni's correction are shown.
Figure 2.
Figure 2.
Pretreatment tissue TCR evenness and top 1% could be better predictors of pathologic response than TMB and PD-L1. A, ROC curve analysis for TMB (square, n = 23), PD-L1 (asterisk, n = 25), evenness (triangle, n = 19), and top 1% (dot, n = 19) determined in pretreatment tissue samples. B, PFS and OS percent survival stratified by pretreatment top 1% clonal space high and low patients (n = 22). C, Correlation between clonal space occupied by top 1% clones and evenness in pretreatment tumor samples (n = 22; CPR, n = 10; non-CPR, n = 9; nonresected, n = 3). D, Correlation between clonal space occupied by the top 1% clones and PD-L1 in pre-T tissue samples (n = 21; CPR, n = 10; non-CPR, n = 8; nonresected, n = 3) and correlation between frequency of the top 1% clones and TMB in pretreatment tissue samples (n = 20; CPR, n = 9; non-CPR, n = 8; nonresected, n = 3). Each patient is represented by a dark gray (CPR), light gray (non-CPR), or white (nonresected) symbol. The black line indicates the linear regression line, and the dotted lines indicate the upper and lower boundaries of the 95% CI.
Figure 3.
Figure 3.
CPR patients showed a selective expansion of tissue top 1% clones in peripheral blood. A, Clonal space occupied by the top 1% tissue pre-T clones in pre- and posttreatment PBMC samples. Comparisons between CPR and non-CPR patients in pre- and posttreatment timepoints are shown (n = 13; CPR, n = 6; non-CPR, n = 7). B, Percentage of top 1% tissue pre-T clones that were peripherally expanded or contracted (known as dynamic clones). Comparisons between CPR and non-CPR patients are shown (n = 13; CPR, n = 6; non-CPR, n = 7). C, Clonal space occupied by dynamic clones in peripheral repertoire: PBMCs pre-T and PBMCs post-T. Expressed as frequency of clonal space occupied by the top 1% tissue pre-T clones and fold change between pre- and posttreatment timepoints. Comparisons between CPR and non-CPR patients are shown (n = 13; CPR, n = 6; non-CPR, n = 7). D, Median contribution of peripherally expanded (n = 13; CPR, n = 6; non-CPR, n = 7) or contracted clones (n = 12; CPR, n = 6; non-CPR, n = 6) in pre- and posttreatment peripheral blood. Comparisons between CPR and non-CPR patients are shown. E, Clonal space occupied by the top 1% tissue pre-T in pre- and posttreatment tissue samples. Comparisons between CPR and non-CPR patients in pre- and posttreatment timepoints are shown (n = 18; CPR, n = 10; non-CPR, n = 8). F, Percentage of top 1% tissue pre-T clones that were intratumorally expanded or contracted. Comparisons between CPR and non-CPR patients are shown (n = 18; CPR, n = 10; non-CPR, n = 8). G, Clonal space occupied by top 1% dynamic clones in tissue: tissue pre-T and tissue post-T. Expressed as frequency of clonal space occupied and fold change between pre- and post-timepoints. Comparisons between CPR and non-CPR patients are shown (n = 18; CPR, n = 10; non-CPR, n = 8). H, Median contribution of intratumorally expanded (n = 16; CPR, n = 10; non-CPR, n = 6) or contracted clones (n = 18; CPR, n = 10; non-CPR, n = 8) in pre- and posttreatment peripheral blood. Comparisons between CPR and non-CPR patients are shown. Each patient is represented by a symbol. P < 0.0125 was considered statistically significant after Bonferroni's correction for multiple tests. Only significant differences after Bonferroni's correction are shown.
Figure 4.
Figure 4.
Immune cells and gene expression analysis of tumors with high or low top 1% clonal space. A, Correlation between CD3+ tumor-infiltrating lymphocytes (cells per mm2) and top 1% clonal space in pretreatment tissue. Comparisons between high top 1% and low top 1% patients are shown. P < 0.001 was considered statistically significant after Bonferroni's correction for multiple tests. B, Hierarchical clustered heatmap showing the expression patterns of all genes analyzed across tumors with high (pink) and low (cyan) top 1% clonal space. The red boxes indicate the upregulated genes, and the blue boxes indicate downregulated genes. C, Volcano plot showing the log10 of adjusted P value and log2 fold change of all genes studied. Red (upregulated) and blue (downregulated) dots represent genes with log2 fold change >|1| and statistically significant (adjusted P value <0.05). D, Dot plots of top 25 enriched GO pathways for downregulated and upregulated genes in tumor with high top 1% clonal space versus tumors with low top 1% clonal space.
Figure 5.
Figure 5.
Composition of top 1% clones of surgical specimens. A, Relative top 1% posttreatment clonal space occupied by clones shared between pre- and posttreatment, belonging to the top 1% pre-T tissue or non–top 1%, and by NEC (n = 18; CPR, n = 10; non-CPR, n = 8). Comparisons between shared top 1% pre-T clones, shared non–top 1% pre-T clones, and NEC were done. B, Relative top 1% posttreatment clonal space occupied by clones shared between pre- and posttreatment, belonging to the top 1% pre-T tissue or non–top 1%, and by NEC stratified by response (n = 18; CPR, n = 10; non-CPR, n = 8). Comparisons between CPR and non-CPR patients were done. C, Comparison of the relative top 1% posttreatment clonal space occupied between shared top 1% clones and non–top 1% plus NEC (n = 18). D, Comparison of the relative top 1% posttreatment clonal space occupied between total shared clones and NEC (n = 18). Each patient is represented by a symbol. P < 0.05 was considered statistically significant. Only significant differences are shown.

References

    1. Provencio M, Nadal E, Insa A, García-Campelo MR, Casal-Rubio J, Dómine M, et al. . Neoadjuvant chemotherapy and nivolumab in resectable non-small-cell lung cancer (NADIM): an open-label, multicentre, single-arm, phase 2 trial. Lancet Oncol 2020;21:1413–22.
    1. Forde P, Spicer J, Lu S, Provencio M, Mitsudomi T, Awad M, et al. . Abstract CT003 - Nivolumab (NIVO) + platinum-doublet chemotherapy (chemo) vs chemo as neoadjuvant treatment (tx) for resectable (IB-IIIA) non-small cell lung cancer (NSCLC) in the phase 3 CheckMate 816 trial. In: Proceedings of the Annual Meeting of the American Association for Cancer Research; 2021Apr 10–15; Philadelphia (PA).
    1. Rizvi H, Sanchez-Vega F, La K, Chatila W, Jonsson P, Halpenny D, et al. . Molecular determinants of response to anti-programmed cell death (PD)-1 and anti-programmed death-ligand 1 (PD-L1) blockade in patients with non-small-cell lung cancer profiled with targeted next-generation sequencing. J Clin Oncol 2018;36:633–41.
    1. Ready N, Hellmann MD, Awad MM, Otterson GA, Gutierrez M, Gainor JF, et al. . First-line nivolumab plus ipilimumab in advanced non-small-cell lung cancer (CheckMate 568): Outcomes by programmed death ligand 1 and tumor mutational burden as biomarkers. J Clin Oncol 2019;37:992–1000.
    1. Borghaei H, Hellmann MD, Paz-Ares LG, Ramalingam SS, Reck M, O'Byrne KJ, et al. . Nivolumab (Nivo) + platinum-doublet chemotherapy (Chemo) vs chemo as first-line (1L) treatment (Tx) for advanced non-small cell lung cancer (NSCLC) with <1% tumor PD-L1 expression: Results from CheckMate 227. J Clin Oncol 2018;36:9001.
    1. Borghaei H, Langer CJ, Paz-Ares L, Rodríguez-Abreu D, Halmos B, Garassino MC, et al. . Pembrolizumab plus chemotherapy versus chemotherapy alone in patients with advanced non-small cell lung cancer without tumor PD-L1 expression: A pooled analysis of 3 randomized controlled trials. Cancer 2020;126:4867–77.
    1. Garassino M, Rodriguez-Abreu D, Gadgeel S, Esteban E, Felip E, Speranza G, et al. . OA04.06 evaluation of TMB in KEYNOTE-189: pembrolizumab plus chemotherapy vs placebo plus chemotherapy for nonsquamous NSCLC. J Thorac Oncol 2019;14:S216–7.
    1. Voong KR, Feliciano J, Becker D, Levy B. Beyond PD-L1 testing-emerging biomarkers for immunotherapy in non-small cell lung cancer. Ann Transl Med 2017;5:376.
    1. Li S, Zhang C, Pang G, Wang P. Emerging blood-based biomarkers for predicting response to checkpoint immunotherapy in non-small-cell lung cancer. Front Immunol 2020;11:1–11.
    1. Robins HS, Campregher PV, Srivastava SK, Wacher A, Turtle CJ, Kahsai O, et al. . Comprehensive assessment of T-cell receptor β-chain diversity in αβ T cells. Blood 2009;114:4099–107.
    1. Kidman J, Principe N, Watson M, Lassmann T, Holt RA, Nowak AK, et al. . Characteristics of TCR repertoire associated with successful immune checkpoint therapy responses. Front Immunol 2020;11:1–11.
    1. Poran A, Scherer J, Bushway ME, Besada R, Balogh KN, Wanamaker A, et al. . Combined TCR repertoire profiles and blood cell phenotypes predict melanoma patient response to personalized neoantigen therapy plus anti-PD-1. Cell Reports Med 2020;1: 100141.
    1. Cham J, Zhang L, Kwek S, Paciorek A, He T, Fong G, et al. . Combination immunotherapy induces distinct T-cell repertoire responses when administered to patients with different malignancies. J Immunother Cancer 2020;8:1–10.
    1. Cha E, Klinger M, Hou Y, Cummings C, Ribas A, Faham M, et al. . Improved survival with T cell clonotype stability after anti-CTLA-4 treatment in cancer patients. Sci Transl Med 2014;6:238ra70.
    1. Wu TD, Madireddi S, de Almeida PE, Banchereau R, Chen YJ, Chitre AS, et al. . Peripheral T cell expansion predicts tumour infiltration and clinical response. Nature 2020;579:274–8.
    1. Reuben A, Zhang J, Chiou S-H, Gittelman RM, Li J, Lee W-C, et al. . Comprehensive T cell repertoire characterization of non-small cell lung cancer. Nat Commun 2020;11:603.
    1. Anagnostou V, Forde PM, White JR, Niknafs N, Hruban C, Naidoo J, et al. . Dynamics of tumor and immune responses during immune checkpoint blockade in non–small cell lung cancer. Cancer Res 2019;79:1214–25.
    1. Han J, Duan J, Bai H, Wang Y, Wan R, Wang X, et al. . TCR repertoire diversity of peripheral PD-1 + CD8 + T cells predicts clinical outcomes after immunotherapy in patients with non–small cell lung cancer. Cancer Immunol Res 2020;8:146–54.
    1. Zhang J, Ji Z, Caushi JX, El Asmar M, Anagnostou V, Cottrell TR, et al. . Compartmental analysis of T-cell clonal dynamics as a function of pathologic response to neoadjuvant PD-1 blockade in resectable non-small cell lung cancer. Clin Cancer Res 2020;26:1327–37.
    1. Vroman H, Balzaretti G, Belderbos RA, Klarenbeek PL, Van Nimwegen M, Bezemer K, et al. . T cell receptor repertoire characteristics both before and following immunotherapy correlate with clinical response in mesothelioma. J Immunother Cancer 2020;8:1–7.
    1. Looney TJ, Topacio-Hall D, Lowman G, Conroy J, Morrison C, Oh D, et al. . TCR convergence in individuals treated with immune checkpoint inhibition for cancer. Front Immunol 2020;10.
    1. Parra ER, Uraoka N, Jiang M, Cook P, Gibbons D, Forget MA, et al. . Validation of multiplex immunofluorescence panels using multispectral microscopy for immune-profiling of formalin-fixed and paraffin-embedded human tumor tissues. Sci Rep 2017;7:1–11.
    1. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014;15:550.
    1. Steinbaugh MJ, Pantano L, Kirchner RD, Barrera V, Chapman BA, Piper ME, et al. . BcbioRNAseq: R package for bcbio RNA-seq analysis [version 2; peer review: 1 approved, 1 approved with reservations]. F1000Research. F1000 Research 2018;7:1976.
    1. Gudmundsdottir H, Wells AD, Turka LA. Dynamics and requirements of T cell clonal expansion in vivo at the single-cell level: effector function is linked to proliferative capacity. J Immunol 1999;162:5212–23.
    1. Binnewies M, Roberts EW, Kersten K, Chan V, Fearon DF, Merad M, et al. . Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med 2018;24:541–50.
    1. Goodman AM, Castro A, Pyke RM, Okamura R, Kato S, Riviere P, et al. . MHC-I genotype and tumor mutational burden predict response to immunotherapy. Genome Med 2020;12:45.
    1. Shim JH, Kim HS, Cha H, Kim S, Kim TM, Anagnostou V, et al. . HLA-corrected tumor mutation burden and homologous recombination deficiency for the prediction of response to PD-(L)1 blockade in advanced non-small-cell lung cancer patients. Ann Oncol 2020;31:902–11.
    1. O'Donnell JS, Teng MWL, Smyth MJ. Cancer immunoediting and resistance to T cell-based immunotherapy. Nat Rev Clin Oncol 2019;16:151–67.
    1. Joshi K, de Massy MR, Ismail M, Reading JL, Uddin I, Woolston A, et al. . Spatial heterogeneity of the T cell receptor repertoire reflects the mutational landscape in lung cancer. Nat Med 2019;25:1549–59.
    1. Danilova L, Anagnostou V, Caushi JX, Sidhom J-W, Guo H, Chan HY, et al. . The mutation-associated neoantigen functional expansion of specific T cells (MANAFEST) assay: a sensitive platform for monitoring antitumor immunity. Cancer Immunol Res 2018;6:888–99.

Source: PubMed

Sponzoři a spolupracovníci

Zdravotní podmínky

Drogové intervence

3
Předplatit