Nodal immune flare mimics nodal disease progression following neoadjuvant immune checkpoint inhibitors in non-small cell lung cancer

Abstract

Radiographic imaging is the standard approach for evaluating the disease involvement of lymph nodes in patients with operable NSCLC although the impact of neoadjuvant immune checkpoint inhibitors (ICIs) on lymph nodes has not yet been characterized. Herein, we present an ad hoc analysis of the NEOSTAR trial (NCT03158129) where we observed a phenomenon we refer to as "nodal immune flare" (NIF) in which patients treated with neoadjuvant ICIs demonstrate radiologically abnormal nodes post-therapy that upon pathological evaluation are devoid of cancer and demonstrate de novo non-caseating granulomas. Abnormal lymph nodes are analyzed by computed tomography and 18F-fluorodeoxyglucose positron emission tomography/computer tomography to evaluate the size and the maximum standard uptake value post- and pre-therapy in NEOSTAR and an independent neoadjuvant chemotherapy cohort. NIF occurs in 16% (7/44) of patients treated with ICIs but in 0% (0/28) of patients after neoadjuvant chemotherapy. NIF is associated with an inflamed nodal immune microenvironment and with fecal abundance of genera belonging to the family Coriobacteriaceae of phylum Actinobacteria, but not with tumor responses or treatment-related toxicity. Our findings suggest that this apparent radiological cancer progression in lymph nodes may occur due to an inflammatory response after neoadjuvant immunotherapy, and such cases should be evaluated by pathological examination to distinguish NIF from true nodal progression and to ensure appropriate clinical treatment planning.

Conflict of interest statement

T. Cascone has received speaker’s fees from the Society for Immunotherapy of Cancer, Bristol Myers Squibb and Roche; reports consultant/advisory role fees from MedImmune/AstraZeneca, Bristol Myers Squibb, EMD Serono, Merck & Co., Genentech and Arrowhead Pharmaceuticals; and reports clinical research funding to The University of Texas MD Anderson Cancer Center from Boehringer Ingelheim, MedImmune/AstraZeneca, Bristol Myers Squibb, and EMD Serono. M.C.B. Godoy has received research funding from Siemens Healthcare. W.N. William Jr. has received honoraria/speaker’s fees and/or participated in advisory boards from Roche/Genentech, Bristol Myers Squibb, Eli Lilly, Merck, AstraZeneca, and Pfizer. C. Haymaker reports personal fees from Nanobiotix and science advisory board fees from Briacell. H. Kadara has received funding from Johnson and Johnson. I.I. Wistuba has provided consulting or advisory roles for AstraZeneca/MedImmune, Asuragen, Bayer, Bristol Myers Squibb, Genentech/Roche, GlaxoSmithKline, Guardant Health, HTG Molecular Diagnostics, Merck, MSD Oncology, OncoCyte, Novartis, Flame Inc., and Pfizer; has received grants and personal fees from Asuragen, Genentech/Roche, Bristol Myers Squibb, AstraZeneca/MedImmune, HTG Molecular, Merck, and Guardant Health; has received personal fees from GlaxoSmithKline, OncoCyte, Daiichi-Sankyo, Roche, AstraZeneca, Pfizer, and Bayer; has received research funding to his institution from 4D Molecular Therapeutics, Adaptimmune, Adaptive Biotechnologies, Akoya Biosciences, Amgen, Bayer, EMD Serono, Genentech, Guardant Health, HTG Molecular Diagnostics, Iovance Biotherapeutics, Johnson & Johnson, Karus Therapeutics, MedImmune, Merck, Novartis, OncoPlex Diagnostics, Pfizer, Silicon Biosystems, Takeda, and Novartis. P. Sharma reports consulting, advisory roles, and/or stocks/ownership for Achelois, Apricity Health, BioAlta, Codiak BioSciences, Constellation, Dragonfly Therapeutics, Forty-Seven Inc., Hummingbird, ImaginAb, Jounce Therapeutics, Lava Therapeutics, Lytix Biopharma, Marker Therapeutics, Oncolytics, Infinity Pharma, BioNTech, Adaptive Biotechnologies, and Polaris; and owns a patent licensed to Jounce Therapeutics (61/247,438; “Combination Immunotherapy for the Treatment of Cancer”). J.P. Allison reports consulting, advisory roles, and/or stocks/ownership for Achelois, Apricity Health, BioAtla, Codiak BioSciences, Dragonfly Therapeutics, Forty-Seven Inc., Hummingbird, ImaginAb, Jounce Therapeutics, Lava Therapeutics, Lytix Biopharma, Marker Therapeutics, Polaris, BioNTech, and Adaptive Biotechnologies; and owns a patent licensed to Jounce Therapeutics (61/247,438; “Combination Immunotherapy for the Treatment of Cancer”). J.A. Wargo is an inventor on a US patent application PCT/US17/53,717, “Methods for enhancing immune checkpoint blockade therapy by modulating the microbiome”, submitted by the University of Texas MD Anderson Cancer Center and on a patent “Targeting B Cells To Enhance Response To Immune Checkpoint Blockade” UTSC.P1412US.P1 - MDA19-023. J.A. Wargo reports honoraria from Imedex, Dava Oncology, Omniprex, Illumina, Gilead, PeerView, Physician Education Resource, MedImmune, and Bristol-Myers Squibb. J. Wargo serves as a consultant/advisory board member for Roche/Genentech, Novartis, AstraZeneca, GlaxoSmithKline, Bristol-Myers Squibb, Merck, Diversigen, Micronoma, and Ella Therapeutics. R.R. Jenq receives consultant role fees from Merck, Karius and Microbiome DX, advisory member role fees from Seres and Kaleido and patent licensing fees from Seres (US20170258854A1; “Intestinal microbiota and gvhd”). D.L. Gibbons has served on scientific advisory committees for AstraZeneca, GlaxoSmithKline, Sanofi, Eli Lilly, and Janssen; has received research support from Janssen, Takeda, Ribon Therapeutics, Astellas, and AstraZeneca. S.G. Swisher has participated in advisory committees for Ethicon and for the Peter MacCallum Cancer Center. J.V. Heymach has received research support from AstraZeneca, GlaxoSmithKline, and Spectrum; participated in advisory committees for AstraZeneca, Boehringer Ingelheim, Catalyst, Genentech, GlaxoSmithKline, Guardant Health, Foundation Medicine, Hengrui Therapeutics, Eli Lilly, Novartis, Specrtum, EMD Serono, Sanofi, Takeda, Mirati Therapeutics, Bristol Myers Squibb, BrightPath Biotherapeutics, Janssen Global Services, Nexus Health System, Pneuma Respiratory, Kairos Venture Investments, Roche, and Leads Biolabs; and received royalties and/or licensing fees from Spectrum. B. Sepesi receives consultant/advisory role fees from Bristol Myers Squibb. No potential conflicts of interest are disclosed by the other authors.

© 2021. The Author(s).

Figures

Fig. 1. Radiological and histopathological features of abnormal nodes following neoadjuvant ICIs.

a–d Axial contrast enhanced CT (a), and 18F-FDG PET/CT (b) images of the mediastinum showing normal nodes prior to neoadjuvant treatment with ICIs on NEOSTAR study in a patient with NSCLC (metastasis to station 7; stations 4 R, 4 L, and 11 L negative after invasive baseline mediastinal staging with EBUS). 18F-FDG uptake in the mediastinum is due to esophagitis. Restaging axial CT (c) and 18F-FDG PET/CT (d) images post-neoadjuvant ICIs show marked increase in nodal size and FDG uptake at bilateral mediastinal regions, suspicious for nodal progression. Mediastinoscopy post-neoadjuvant ICIs did not demonstrate carcinoma in lower paratracheal stations (4 L and 4 R). e, f FNA image of paratracheal nodal station pre-therapy (e) demonstrating lack of tumor cells and normal composition (Papanicolaou, x20), and resected station 4 R lymph node post-therapy (f) revealing absence of cancer and evidence of necrotizing non-caseating granulomatous inflammation (hematoxylin and eosin, x10). g–j Axial contrast enhanced CT (g), and 18F-FDG PET/CT (h) images of the mediastinum show nodal enlargement and abnormal 18F-FDG uptake in the right hilum and right mediastinum prior to neoadjuvant ICIs on NEOSTAR study in a patient with NSCLC (baseline invasive mediastinal staging with mediastinoscopy revealed metastasis to station 4 R). Restaging axial contrast enhanced CT (i) and 18F-FDG PET/CT (j) images show increase in size and increase in FDG uptake at right hilar, right mediastinal (4 R) and prevascular nodes, consistent with progression of nodal metastasis. Abnormal nodes were also present at mediastinal 1 R, 2 R and 7 stations post-therapy, which were previously normal at baseline. Subsequent biopsy confirmed carcinoma in the right paratracheal (2 R and 4 R) and subcarinal stations. k FNA image of post-ICI abnormal node (station 7 pictured) revealed the presence of malignancy with disease progression (Papanicolaou, x20). Analyses related to the presented images and micrographs were conducted once. NIF, nodal immune flare; CT, computed tomography; FDG, fluorodeoxyglucose; FNA, fine needle aspiration; PET, positron emission tomography; PD, progressive disease.

Fig. 2. Histopathological features of nodal specimens pre- and post-neoadjuvant therapy in NEOSTAR and ICON patients.

a Illustrative FNA image from preoperative mediastinal staging by EBUS in NEOSTAR NIF patient did not demonstrate granulomatous inflammation within examined nodes (station 4 L pictured; Papanicolaou, x20). b Resected nodal specimen in NEOSTAR NIF patient following ICIs demonstrating a diffuse non-caseating granulomatous inflammatory reaction (station 11 R pictured; hematoxylin and eosin, x10). c Illustrative FNA image from preoperative mediastinal staging by EBUS in ICON No-NIF patient did not demonstrate granulomatous inflammation within examined nodes (station 7 pictured; Papanicolaou, x20). d Resected nodal specimen following neoadjuvant chemotherapy in a patient with No-NIF from ICON cohort with the absence of diffuse non-caseating granulomatous inflammatory reaction (station 7 pictured; hematoxylin and eosin, x4). e Proportions of patients with NIF, characterized by abnormal nodes on imaging that are devoid of cancer and contain de novo non-caseating granulomas in NEOSTAR (n = 44) and ICON (n = 28) patient cohorts. The red bars depict the proportions of patients with NIF. Analyses related to the presented micrographs were conducted once. NIF, nodal immune flare; ICIs, immune checkpoint inhibitors; ICON, ImmunogenomiC prOfiling in NSCLC; EBUS, endobronchial ultrasound; FNA, fine needle aspiration. Source data for panel (e) are provided as a Source Data file.

Fig. 3. Changes in node size and SUVmax in ICON and NEOSTAR patients with abnormal nodes post-therapy.

a Mean node size (cm) of abnormal nodes post-neoadjuvant chemotherapy as compared to pre-therapy in ICON patients. Data are shown as mean node size in cm ±SD. Two-sided P value is from linear mixed-effects model. N1 = 13 nodes analyzed in nine patients. N2 = 13 nodes analyzed in nine patients. b Mean node SUVmax of abnormal nodes post-neoadjuvant chemotherapy as compared to pre-therapy in ICON patients. Data are shown as mean node SUVmax ±SD. Two-sided P value is from linear mixed-effects model. N1 = 6 nodes analyzed in three patients. N2 = 6 nodes analyzed in three patients. c, d Mean node size (c) and SUVmax (d) of abnormal nodes post-neoadjuvant ICIs as compared to pre-therapy in NEOSTAR patients with NIF. Data are shown as mean node size in cm ±SD in panel (c) and mean SUVmax ±SD in panel (d). N1 = 38 nodes analyzed in seven patients. N2 = 38 nodes analyzed in seven patients. Two-sided P value is from linear mixed-effects model. The red circles and squares depict the node size and SUVmax collected from pre-therapy and post-therapy, respectively, in the NIF group. e, f Mean node size (e) and SUVmax (f) of abnormal nodes post-neoadjuvant ICIs as compared to pre-therapy in NEOSTAR patients with No-NIF. Data are shown as mean node size in cm ±SD in panel (e) and mean SUVmax ±SD in panel (f). N1 = 40 nodes analyzed in 17 patients (e); 34 nodes analyzed in 15 patients (f) with available scans/images. N2 = 40 nodes analyzed in 17 patients (e); 34 nodes analyzed in 15 patients (f) with available scans/images. Two-sided P value is from linear mixed-effects model. The blue circles and squares depict the node size and SUVmax collected from pre-therapy and post-therapy, respectively, in the No-NIF group. g Difference in mean size of abnormal nodes between post- and pre-therapy in NEOSTAR patients with NIF as compared with those with No-NIF. Data are shown as change in mean node size in cm ±SE. N1 = 38 nodes analyzed in seven patients. N2 = 40 nodes analyzed in 17 patients. Two-sided P value is from linear mixed-effects model. The red circles depict the change of node size in NIF group, and the blue squares depict the change of node size in No-NIF group. h Difference in mean SUVmax of abnormal nodes between post- and pre-therapy in NEOSTAR patients with NIF as compared with those with No-NIF. Data are shown as change in mean node SUVmax ±SE. N1 = 38 nodes analyzed in seven patients. N2 = 34 nodes analyzed in 15 patients. Two-sided P value is from linear mixed-effects model. The red circles depict the change of node SUVmax in NIF group, and the blue squares depict the change of node SUVmax in No-NIF group. ICON, ImmunogenomiC prOfiling in NSCLC; NIF, nodal immune flare; ICIs, immune checkpoint inhibitors; SUV, standardized uptake value; SD, standard deviation; SE, standard error. Source data are provided as a Source Data file.

Fig. 4. Composition of nodal immune infiltrates of NIF/non-caseating granulomas and No-NIF NEOSTAR patients.

NanoString gene expression analysis was performed in tumor-free nodes from patients with NIF (n = 8) and No-NIF (n = 29). The NIF group for these analyses includes patients with available nodal samples after neoadjuvant therapy that were cancer-free and contained non-caseating granulomas with available nodal NanoString expression data. a–f Violin plots show the distribution of immune scores (log2 normalized counts) in nodes resected from NIF and No-NIF patients in the NEOSTAR study: immune cells expressing CD45 (a), macrophages (b), dendritic cells (DCs) (c), cytotoxic cells (d), Th1 cells (e), and exhausted CD8 T cells (f). The log2 normalized counts are presented as median with minima, lower and upper quartiles, and maxima. The dashed line indicates the median; the dotted lines indicate the lower quartile and upper quartile values; top and bottom of the violin plots indicate the maxima and minima. The red circles depict data from NIF group, and the blue squares depict data from No-NIF group. g Differential expression of genes between NIF and No-NIF nodal samples are illustrated as a volcano plot. Red dots depict significantly upregulated genes in NIF compared to No-NIF nodes and blue dots represent significantly upregulated genes in nodes of No-NIF compared to nodes of NIF patients. h Bar plots showing differentially expressed pathways between nodes of NIF and No-NIF patients, computed by GSEA analysis. Red bars indicate pathways that are upregulated while blue bars indicate pathways that are downregulated in nodes of NIF compared to nodes of No-NIF patients. Two-sided P value is from Wilcoxon rank-sum test in panels (a–f). Two-sided P values are from Welch’s t-test in panel (g). P values (FDR-adjusted < 0.2) are from GSEA algorithm in panel (h). NIF, nodal immune flare; DCs, dendritic cells; Th1, T helper cells 1. NES, normalized enrichment score. Source data are provided as a Source Data file and Supplementary Data file (Supplementary Data 1).

Fig. 5. Analysis of gut microbiome diversity and composition conducted by sequencing V4 region of 16 S rRNA gene in NIF and No-NIF NEOSTAR patients.

a Inverse Simpson measuring alpha diversity of fecal microbiome in NIF (n = 7) and No-NIF (n = 29) patients. Data are presented as median with minima, lower and upper quartiles, and maxima. The ends of the box are the upper and lower quartiles (75th and 25th percentiles), the median is the horizontal line inside the box. The whiskers are the two lines outside the box that extend to the maxima and minima. Two-sided P value is from Mann-Whitney U test. The red circles depict data from the NIF group, and the blue squares depict data from the No-NIF group. b Ordination plot based on the principal coordinate analysis (PCoA) using weighted UniFrac demonstrating taxonomic similarities between NIF and No-NIF patients. The two axes of the ordination plot explained 49.48% variation in the dataset. Analysis of similarity (ANOSIM) test was used to test whether there is a significant difference between these two groups with 1000 permutations (r = 0.20; P = 0.06). c Linear discriminant analysis (LDA) scores (log10) calculated for differentially abundant bacterial taxa at the genus level in the fecal microbiomes of NIF and No-NIF patients using LDA cutoff of 2 and two-sided P value cutoff of 0.05. d–g Few most differentially abundant bacterial taxa present in fecal samples of NIF (n = 7) and No-NIF (n = 29) patients. Relative abundance comparisons of (d) Actinobacteria (phylum), (e) Coriobacteriaceae (family), (f) Collinsella (genus), and (g) Adlercreutzia (genus) between NIF and No-NIF patients. Data are presented as median with minima, lower and upper quartiles, and maxima. The ends of the box are the upper and lower quartiles (75th and 25th percentiles), the median is the horizontal line inside the box. The whiskers are the two lines outside the box that extend to the maxima and minima. Two-sided P value is from Mann-Whitney U test. The red circles depict data from the NIF group, and the blue squares depict data from the No-NIF group. Source data are provided as a Source Data file.