Skip to main page content (VSports app下载)
U.S. flag

An official website of the United States government

Dot gov

The . gov means it’s official. Federal government websites often end in . gov or . mil. Before sharing sensitive information, make sure you’re on a federal government site VSports app下载. .

Https

The site is secure V体育官网. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely. .

. 2017 Feb 1:8:14381.
doi: 10.1038/ncomms14381.

VSports - Neutrophils dominate the immune cell composition in non-small cell lung cancer

Julia Kargl  1   2 , Stephanie E Busch  1 , Grace H Y Yang  1 , Kyoung-Hee Kim  1 , Mark L Hanke  1 , Heather E Metz  1 , Jesse J Hubbard  1 , Sylvia M Lee  1 , David K Madtes  1   3 , Martin W McIntosh  4 , A McGarry Houghton  1   3   5
Affiliations

Neutrophils dominate the immune cell composition in non-small cell lung cancer

Julia Kargl et al. Nat Commun. .

Abstract

The response rate to immune checkpoint inhibitor therapy for non-small-cell lung cancer (NSCLC) is just 20% VSports手机版. To improve this figure, several early phase clinical trials combining novel immunotherapeutics with immune checkpoint blockade have been initiated. Unfortunately, these trials have been designed without a strong foundational knowledge of the immune landscape present in NSCLC. Here, we use a flow cytometry panel capable of measuring 51 immune cell populations to comprehensively identify the immune cell composition and function in NSCLC. The results show that the immune cell composition is fundamentally different in lung adenocarcinoma as compared with lung squamous cell carcinoma, and that neutrophils are the most prevalent immune cell type. Using T-cell receptor-β sequencing and tumour reactivity assays, we predict that tumour reactive T cells are frequently present in NSCLC. These results should help to guide the design of clinical trials and the direction of future research in this area. .

PubMed Disclaimer

Conflict of interest statement (V体育平台登录)

The authors declare no competing financial interests.

Figures

Figure 1
Figure 1. Expansion and specificity of TCR-β repertoire in NSCLC.
(a) Frequency of TCR-β Top 10 Clones (mean=dotted line, L-SCCA (red) versus L-ADCA (blue), *P=0.0133) were analysed using Adaptive Biotechnologies TCR sequencing platform (N=56). Frequency of Top 10 clones is comprised of 10 most expanded clones from each specimen (represented as stacked bar graph). (b) Productive clonality score (L-ADCA versus L-SCCA, *P=0.0226, N=60) and (c) linear correlation of productive clonality score and smoking consumption (pack year) (P=0.0821, R2=0.0540, N=58). (d) Representative scatter plots for matched lung-tumour specimen with no expanded tumour TAC (left) and expanded TAC (right). TACs were defined as clones only found in tumour specimen (red) but not in matched lung specimen (green). (e) Clonality score was calculated for TAC (L-ADCA versus L-SCCA, *P=0.0004, N=34). (f) Linear correlation of productive clonality score and TIL IFNγ production (P=0.6684, r2=0.0241, N=10) and (g) linear correlation of maximum (max) TAC and TIL IFNγ production (P=0.0035, r2=0.6357, N=10). (h) Patients with TAC for NSCLC, L-ADCA and L-SCCA. TACs were defined as clones only present in tumour expanded more than ≥0.1% (top row) and ≥0.5% (bottom row). Student's tests were performed to compare two groups (a,b,e) and Pearson correlation calculations were performed for linear correlations (c,f,g). Each data point represents one patient sample and data are presented as mean±s.e.m., *P<0.05.
Figure 2
Figure 2. Robust immune cell infiltration in NSCLC.
(a) Representative polychromatic dot plots demonstrating the gating strategy employed to identify immune cell content in NSCLC. Starting at the top left, initial two gates are to eliminate doublets from the analysis followed by gating on live (FVD) and CD45+ cells. For lymphocyte analysis, a size gate was applied followed by CD3 to identify: NK cells (CD3−CD56+), B cells (CD3−CD19+CD20+), γδT-cells (CD3+γδTCR+) and CD3+CD8+ and CD3+CD4+ T cells. T-cell subsets are displayed including CD8+INFγ+, Tregs (CD4+CD25+CD127lo), Th17 (CD4+IL17A+), Th22 (CD4+IL22+), Th1 (CD4+INFγ+) and γδTCR+IL17A+. Myeloid lineage cells were gated on CD45+ cells: neutrophils (CD66b+), monocytes (CD14+CD33+) and macrophages (CD68hi). Tumour cells were defined as CD45−EpCAM+. (b) Tabulation of immune cell content from non-adjacent lung (white bars) and NSCLC (green bars) flow cytometry data. Data presented as CD45+ cells per mm3, % CD45+, % CD4+, or % parent, as indicated. (c) Representative CD45 IHC depicting immune cell distribution in L-SCCA and L-ADCA specimens. Scale bar, 500 μm (d) Tabulation of CD45 content in L-ADCA and L-SCCA measured by flow cytometry (N=73, left) and IHC (N=46, right). Student's tests were performed to compare two groups (b,d). Data are presented as mean±s.e.m., *P<0.05.
Figure 3
Figure 3. Differential CD4+ subset composition for L-ADCA versus L-SCCA.
(a) CD4+ cells displayed as % CD45+ cells, (b) Treg (L-ADCA versus L-SCCA, *P<0.0001, N=63), (c) Th17 (L-ADCA versus L-SCCA, *P=0.0353, N=63), and (d) Th1 cells (L-ADCA versus L-SCCA, *P=0.0471, N=63) shown as per cent of CD4+ cells. Linear correlations of tumour size (cm) and (e) CD4+ cells (P=0.1113, R2=0.0369, N=53), (f) Treg (*P=0.0402, R2=0.0642, N=50), (g) Th17 (P=0.9848, R2=0.0000, N=48) and (h) Th1 cells (*P=0.0286, R2=0.0727, N=48) in L-ADCA specimen. Student's tests were performed to compare two groups (a–d) and Pearson correlation calculations were performed for linear correlations (e–h). Each dot represents an independent data point as determined by flow cytometry. Data are presented as mean±s.e.m., *P<0.05.
Figure 4
Figure 4. CD4+ and CD8+ function segregate by NSCLC subtype.
(a) CD8+ cells shown as % CD45+ and (b) CD8+IFNγ+ shown as per cent of CD8+. (c) Representative dot plots showing CD8+ and CD4+ cell subtypes (CCR7−CD45RA− effector memory, CCR7−CD45RA+ effector memory RA, CCR7+CD45RA− central memory, CCR7+CD45RA+ naïve). Statistical analyses of (d) CD8+EM (L-ADCA versus L-SCCA, *P=0.0123, N=54) and (e) CD8+EMRA (L-ADCA versus L-SCCA, *P=0.0064, N=54). (f) Representative histograms of low (top) and high expression of inhibitory receptors (bottom) on CD8+ cells determined by expression (MFI) of PD1 (left) and TIM3 (right). Matched normal lung-tumour pairs for (g) CD8+CD69+ (left, *P=0.0003, N=55). CD8+PD1+ (middle, *P<0.0001, N=56) and CD8+TIM3+ (right, *P<0.0001, N=56) content. (h) CD8+PD1−TIM3− (*P=0.0156, N=53), (i) CD8+PD1+TIM3− (*P=0.0188, N=53), (j) CD8+PD1−TIM3+, (k) CD8+PD1+TIM3+ (l) CD4+PD1−TIM3− (*P=0.027, N=51), (m) CD4+PD1+TIM3− (*P=0.0195, N=51), (n) CD4+PD1−TIM3+ and (o) CD4+PD1+TIM3+ flow cytometry data displayed as %CD8 or %CD4 for L-ADCA and L-SCCA. Associations of CD4+PD1 and (p) tumor size (linear correlation, P=0.0001, R2=0.2386, N=43), (q) clinical stage (ANOVA, stage 1a versus 3/4, P=0.0277, N=43) and associations of CD8+PD1 and (r) tumour size (linear correlation, P=0.0598, R2=0.0629, N=43) and (s) clinical stage (ANOVA, stage 1a versus 3/4, P=0.1798, N=43). Student's tests were performed to compare two groups (all except, c,f,p–s), Pearson correlation calculations were performed for linear correlations (p,r) and one-way ANOVA analysis was performed for multiple comparisons (q,s). Each dot represents an independent data point as determined by flow cytometry. Data are presented as mean±s.e.m. *P<0.05. ANOVA, analysis of variance.
Figure 5
Figure 5. Neutrophils are the dominant immune cell type in NSCLC.
(a) Macrophages (L-ADCA versus L-SCCA, *P=0.0198, N=64), (b) monocytes and (c) neutrophils (L-ADCA versus L-SCCA, *P=0.0328, N=65) shown as % CD45+. (d) Representative CD66b IHC depicting neutrophil distribution in stromal (Str) areas in NSCLC. Scale bar, 100 μm. Linear correlation between (e) neutrophils and CD8+ (L-ADCA (blue): *P=0.0013, R2=0.2000; L-SCCA (red): *P=0.0008, R2=0.5916, N=64, lung (inset): P=0.2285, R2=0.02189, N=68), (f) neutrophils and CD4+ (L-ADCA (blue): *P<0.0001, R2=0.3174; L-SCCA (red): *P=0.0077, R2=0.4592, N=63), (g) neutrophils and Th1 (NSCLC: *P=0.0079, R2=0.1050, N=66), (h) neutrophils and Th17 (NSCLC: *P=0.0068, R2=0.1088, N=66), (i) neutrophils and Tregs (NSCLC: P=0.0623, R2=0.0549, N=64), (j) monocytes and CD8+ (L-ADCA (blue): P=0.2403, R2=0.0327; L-SCCA (red): P=0.9069, R2=0.0011, N=59) and (k) monocytes and CD4+ (L-ADCA (blue): *P=0.0172, R2=0.1279; L-SCCA (red): P=0.5558, R2=0.02970, N=58). Linear correlations of tumour size and (l) neutrophils (*P=0.0001, R2=0.2034, N=67) and (m) monocytes (*P=0.0498, R2=0.05878, N=66) for NSCLC specimen measured by flow cytometry. Student's tests were performed to compare two groups (a–c) and Pearson correlation calculations were performed for linear correlations (e–m). All data determined by flow cytometry and each dot represents independent data point. Data are presented as mean±s.e.m., *P<0.05 unless otherwise indicated.
Figure 6
Figure 6. TCR clonality is associated with CD8 immunity.
Linear correlation for NSCLC specimens between productive clonality and (a) CD4+ (P=0.1300, R2=0.0405, N=58), (b) Treg (*P<0.0001, R2=0.2641, N=57), (c) CD8+ (*P=0.0021, R2=0.1567, N=58), (d) CD8+EM (*P=0.0022, R2=0.1657, N=54), (e) CD8+EMRA (*P=0.0036, R2=0.1520, N=54), (f) CD8+PD1+ (*P=0.0004, R2=0.2182, N=53) and (g) CD8+TIM3+ (*P=0.0011, R2=0.1910, N=53). Each dot represents an independent data point as determined by flow cytometry. Pearson correlation calculations were performed for linear correlations (a–g). Data are presented as mean±s.e.m., *P<0.05.
See this image and copyright information in PMC

"VSports app下载" Comment in

V体育平台登录 - References

    1. Topalian S. L. et al.. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N. Engl. J. Med 366, 2443–2454 (2012). - PMC - PubMed
    1. Brahmer J. R. et al.. Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. N. Engl. J. Med 366, 2455–2465 (2012). - PMC - PubMed
    1. Coussens L. M. & Werb Z. Inflammation and cancer. Nature 420, 860–867 (2002). - "VSports app下载" PMC - PubMed
    1. de Visser K. E., Eichten A. & Coussens L. M. Paradoxical roles of the immune system during cancer development. Nat. Rev. Cancer 6, 24–37 (2006). - PubMed
    1. Marx J. Cancer immunology. Cancer's bulwark against immune attack: MDS cells. Science 319, 154–156 (2008). - PubMed

V体育安卓版 - Publication types

MeSH terms (VSports在线直播)