Lung cancer, the leading cause of cancer-related deaths worldwide, is a heterogeneous disease comprising multiple histologic subtypes that harbor disparate mutational profiles. Immune-based therapies have shown initial promise in the treatment of lung cancer patients but are limited by low overall response rates. We sought to determine whether the host immune response to lung cancer is dictated, at least in part, by histologic and genetic differences, because such correlations would have important clinical ramifications. Using mouse models of lung cancer, we show that small cell lung cancer (SCLC) and lung adenocarcinoma (ADCA) exhibit unique immune cell composition of the tumor microenvironment. The total leukocyte content was markedly reduced in SCLC compared with lung ADCA, which was validated in human lung cancer specimens. We further identified key differences in immune cell content using three models of lung ADCA driven by mutations in Kras, p53, and Egfr. Although Egfr-mutant cancers displayed robust myeloid cell recruitment, they failed to mount a CD8+ immune response. In contrast, Kras-mutant tumors displayed significant expansion of multiple immune cell types, including CD8+ cells, regulatory T cells, IL-17A–producing lymphocytes, and myeloid cells. A human tissue microarray annotated for KRAS and EGFR mutations validated the finding of reduced CD8+ content in human lung ADCA. Taken together, these findings establish a strong foundational knowledge of the immune cell contexture of lung ADCA and SCLC and suggest that molecular and histological traits shape the host immune response to cancer.

Despite decades of research, small cell lung cancer (SCLC) and non–small cell lung cancer (NSCLC) remain among the world’s deadliest diseases (1). SCLC, in which RB1 and TP53 mutations are common (2), accounts for 10–20% of lung cancer diagnoses (3). More than half of NSCLC cases are classified as lung adenocarcinomas (ADCAs), in which KRAS, EGFR, and TP53 mutations are the predominant genetic drivers (4, 5). Although patients with EGFR mutations initially respond to targeted therapies, drug resistance typically develops within the first year (6). SCLC and KRAS-mutant ADCA have proven less tractable because scant progress has been made toward the development of targeted therapeutics for these patients (7). Novel immunotherapeutic strategies have offered new hope for the management of NSCLC (810) and SCLC (11), but the clinical success of immunomodulatory agents depends on a strong foundational knowledge of the cells that make up the lung tumor microenvironment (TME) in these molecularly and histologically distinct diseases.

Inflammation is a key attribute of neoplasia (12). The host immune response to cancer is an intricate web of pro- and antitumorigenic signals (13). CD8+ T cells, also known as CTLs, are the body’s main immunological barrier against cancer, because they are capable of recognizing and killing tumor cells. However, CTL activity is curbed by tumor cells expressing immune checkpoint ligands (e.g., PD-L1), as well as an influx of immune-suppressive cells (i.e., regulatory T cells [Tregs], macrophages, monocytes, and neutrophils) into the TME (14, 15). Newly developed immune checkpoint inhibitors (ICIs; e.g., ipilimumab and nivolumab) seek to reverse this suppression and unleash an antitumor response (16).

Although some lung cancer patients have experienced remarkable tumor regression upon commencing ICI therapy, overall response rates have peaked around 20% (9, 10). These disparate outcomes may be explained, in part, by findings that tumor immune cell composition and function vary among anatomical sites and histological origins (17). In lung cancer, for example, squamous cell carcinoma patients exhibited longer progression-free survival with ipilimumab treatment than did ADCA patients (8). However, even within histological subgroups, response rates vary widely, raising the question of what other tumor attributes might predict clinical outcome.

The oncogenic functions of mutant RAS and EGFR in cancer include the production of proinflammatory cytokines, such as IL-8, which help to shape the TME (1821). TP53 similarly demonstrated non–cell autonomous behaviors during tumorigenesis (22, 23). Nevertheless, the discrete impact of molecular signatures, such as KRAS, EGFR, TP53, and RB1 mutations, on the immune cell composition of lung cancer remains largely undefined. To address this question, we profiled the TME of three genetically engineered mouse (GEM) models of NSCLC (KrasLSL-G12D, KrasLSL-G12D;Trp53Fl/Fl, and EgfrL858R), as well as the Rb1Fl/Fl;Trp53Fl/Fl model of SCLC. In this article, we show that the molecular and histological subtypes of lung cancer predict immune cell composition and may, therefore, demand specific immunotherapeutic regimens.

All animal experiments used aged-matched mice and were conducted at the Fred Hutchinson Cancer Research Center using protocols approved by the Institutional Animal Care and Use Committee. TetO-EgfrL858R mice (24) were obtained from the Mouse Models of Human Cancer Consortium on a C57BL/6 background. Ccsp-rtTA mice (25) on an FVB background were provided by Jeff Whitsett (University of Cincinnati). KrasLSL-G12D (Kras) (26), Trp53Fl/Fl (p53) (27), RORγtGFP (28), and Tcrd−/− (29) mice were obtained from the Jackson Laboratory on a C57BL/6 background. TetO-EgfrL858R;Ccsp-rtTA (Egfr), KrasLSL-G12D;Trp53Fl/Fl (Kp53), KrasLSL-G12D;RORγtGFP (K.RORγt), and KrasLSL-G12D;Tcrd−/− (K.Tgd) mice were generated by simple cross-breeding. Rb1Fl/Fl mice (30) were crossed with p53 mice to generate Rb1Fl/Fl;Trp53Fl/Fl (Rbp53) mice on a mixed C57BL/6 × 129 background.

Egfr and single-transgene control mice (Ccsp-rtTA or TetO-EgfrL858R) were fed food impregnated with 200 mg/kg doxycycline (Harlan, Indianapolis, IN). Kras, Kp53, p53, and wild-type (wt) C57BL/6 animals received an intratracheal dose of 2.5 × 107 PFU of adenoviral Cre recombinase (AdCre; University of Iowa Viral Vector Core, Iowa City, IA), as described (31). Each cohort was studied for 6, 10, and 14 wk postinitiation of doxycycline or infection with AdCre. Additional cohorts of K.RORγt and K.Tgd animals were similarly subjected to AdCre infection (2.5 × 107 PFU) and examined 14 wk postinitiation or when moribund. Rbp53 mice received 1 × 108 PFU of AdCre; given the long latency period, these animals were studied 9 mo postinduction.

CTLA4 Ab, clone 9D9 (MedImmune) or isotype control (mIgG2b) was administered to an additional cohort of Kras mice twice weekly via i.p. injection for a total of 4 wk, starting at 8 wk post-AdCre, at a dose of 10 mg/kg.

Lung tissue specimens were collected and processed as described (32). Briefly, the left lung was ligated and snap-frozen for later analysis. The right lung was inflated with 10% neutral buffered formalin at 25 cm H2O pressure before fixing in neutral buffered formalin overnight. Five-micrometer paraffin-embedded sections were stained with H&E or immunostained for CD45 (BD Bioscience, San Diego, CA), Foxp3 (eBioscience, San Diego, CA), or CD3 (Serotec, Raleigh, NC) using 3, 3′-diaminobenzidine development and hematoxylin counterstaining. Global adjustments to white balance, brightness, and/or contrast were made to some photomicrographs using Photoshop (Adobe Systems, San Jose, CA).

Slides were examined with an Eclipse 80i microscope (Nikon Instruments, Melville, NY), excluding the whole-lobe images presented in Fig. 1A, which were acquired with an Aperio digital pathology slide scanner (Leica Biosystems, Buffalo Grove, IL). Total lung and tumor areas (mm2) were measured from H&E-stained slides using NIS-Elements Advanced Research software (Nikon Instruments). Results are expressed as the percentage of lung occupied by tumor [(area tumor ÷ area lung) × 100]. Each lung was also scored for tumor grade, as described (33). Foxp3- and CD3-stained lung lobes (n = 5 mice per genotype) were scored for the presence or absence of tumor-associated (TA) cells, tumor-infiltrating (TI) cells, and cells within a lymphoid aggregate (LA).

FIGURE 1.

Egfr, Kras, and Kp53 mice develop lung tumors and associated inflammation. (A and B) All models chronologically develop atypical alveolar hyperplasia, adenoma, and ADCA 6, 10, and 14 wk posttumor induction. Normal lung from a nontumor-bearing wt mouse is depicted in the lower right corner. H&E sections. Scale bars, 2 mm (A), 500 μm, with the exception of the wt panel (1 mm) (B). (C) Spectrum of disease in murine ADCA models. Data are presented as percentage of mice exhibiting at least one indicated lesion at each time point postinduction (n ≥ 5 mice per group). All genotypes exhibited hyperplasia at all time points examined (data not shown). Analysis of 14-wk KrasLSL/+;Trp53Fl/Fl mice was precluded by early mortality. (D) Percentage tumor area was calculated at the indicated time points in a minimum of at least three representative lungs from Egfr, Kras, and Kp53 mice. (E) Body mass of tumor-bearing female mice (n ≥ 5) compared with nontumor-bearing littermate controls (n ≥ 3) at each time point postinduction.

FIGURE 1.

Egfr, Kras, and Kp53 mice develop lung tumors and associated inflammation. (A and B) All models chronologically develop atypical alveolar hyperplasia, adenoma, and ADCA 6, 10, and 14 wk posttumor induction. Normal lung from a nontumor-bearing wt mouse is depicted in the lower right corner. H&E sections. Scale bars, 2 mm (A), 500 μm, with the exception of the wt panel (1 mm) (B). (C) Spectrum of disease in murine ADCA models. Data are presented as percentage of mice exhibiting at least one indicated lesion at each time point postinduction (n ≥ 5 mice per group). All genotypes exhibited hyperplasia at all time points examined (data not shown). Analysis of 14-wk KrasLSL/+;Trp53Fl/Fl mice was precluded by early mortality. (D) Percentage tumor area was calculated at the indicated time points in a minimum of at least three representative lungs from Egfr, Kras, and Kp53 mice. (E) Body mass of tumor-bearing female mice (n ≥ 5) compared with nontumor-bearing littermate controls (n ≥ 3) at each time point postinduction.

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Single-cell suspensions were generated from saline-perfused mouse lungs using mechanical disruption, followed by a 1-h digestion at 37°C in RPMI 1640 containing 10% FCS and penicillin/streptomycin, 80 U/ml DNase, 300 U/ml collagenase Type 1 (both from Worthington Biochemical, Lakewood, NJ), and 60 U/ml hyaluronidase (Sigma, St Louis, MO). Digested lungs were sheared through a 19-gauge needle, strained through 70-μm nylon mesh, centrifuged, lysed (RBCs), washed, strained through 40-μm mesh, centrifuged, and resuspended in Dulbecco’s PBS + 2% FCS. Cell viability was determined using trypan blue staining and a TC20 Automated Cell Counter (Bio-Rad, Hercules, CA). We obtained two SCLC and five ADCA surgical specimens, each with nonadjacent normal lung tissue, using the approved Institutional Review Board file number 6663 in association with Fred Hutchinson Cancer Research Center, University of Washington Medical Center, and Northwest BioTrust. Single-cell suspensions were generated using the above digestion protocol.

Single-cell suspensions were incubated with 1.0 μg of Mouse TruStain FcX or 1.0 μl of Human TruStain FcX (both from BioLegend, San Diego, CA) per 106 cells prior to immunostaining. Twenty-seven fluorochrome-labeled Abs were distributed among four multicolor panels for mouse specimens (all flow Abs are detailed in Supplemental Table I). Immunostaining was performed for 30 min on ice, protected from light. Dead cells were excluded with Fixable Viability Dye (FVD) eFluor 780 (eBioscience), per the manufacturer’s protocol. Stained cells were washed, fixed with IC Fixation Buffer (eBioscience), and stored at 4°C until analysis.

Intracellular cytokine production was assessed using PMA (25 ng/ml), ionomycin (1 μg/ml; both from Sigma), and monensin (1.5 μl/ml; BD Bioscience) stimulation for 5 h at 37°C, 5% CO2. An unstimulated sample was incubated in the absence of PMA and ionomycin. After stimulation, cells were washed, stained with FVD, fixed, and permeabilized with a Transcription Factor Buffer Set (BD) prior to immunostaining.

Samples were analyzed on an LSR II flow cytometer with FACSDiva software (BD), and ≥1 × 105 events were recorded per sample. Data were compensated and analyzed with FlowJo software (TreeStar, Ashland, OR). Gates were defined by fluorescence-minus-one samples and verified with appropriate isotype controls. The unstimulated control was used to define cytokine gates. Total cell content was calculated by multiplying the overall number of live cells recovered from each animal (i.e., the trypan blue–negative hemocytometer count) by the percentage of live cells for each gated parameter. Cytokine-producing T cell subsets were calculated by multiplying the percent parent gate with the previously determined parent population count. The median fluorescence intensity (Med.F.I.) of NK group 2, member D receptor (NKG2D)-BV421 and PD-L1–BV421 parameters was calculated in FlowJo, and Med.F.I. of the relevant fluorescence-minus-one control was subtracted from all experimental values for normalization.

Splenocytes from wt mice were labeled with 50 μM CFSE (Molecular Probes, Eugene, OR), per the manufacturer’s instructions. A total of 1 × 106 CFSE-labeled splenocytes was transferred to six-well tissue culture plates coated with anti-CD3/anti-CD28 Abs (BioLegend). Cells were incubated with 200 μg of homogenate, generated from 10-wk Egfr, Kras, or wt lungs, at 37°C, 5% CO2 for 4 d. Lymphocyte proliferation was determined by harvesting the cells, staining with CD8a-BV421 (BioLegend) and FVD, and measuring ≥1 × 103 live CD8+ cells on an LSR II flow cytometer.

Total RNA was isolated from frozen mouse lungs using TRIzol reagent (Life Technologies, Carlsbad, CA) and subsequently purified with an RNeasy Mini Kit (QIAGEN, Hilden, Germany). cDNA was generated from 2 μg of total RNA using SuperScript II Reverse Transcriptase and oligo(dT) (Life Technologies). The expression of indicated target genes was analyzed using a StepOnePlus Real-Time PCR System and TaqMan primer/probe sets (Applied Biosystems, Foster City, CA), with all reactions run in triplicate. Δ Cycle threshold values were calculated using Gapdh as the endogenous housekeeping gene.

A lung ADCA cohort on a tissue microarray (TMA), consisting of 135 cases, was obtained from the University of Pittsburgh Cancer Institute. Patient identifiers were removed; therefore, the study was considered not human subjects research (i.e., Institutional Review Board exempt). Each case was previously annotated as EGFR mutant (n = 31), KRAS mutant (n = 69), or wt for EGFR and KRAS (n = 35). Formalin-fixed paraffin-embedded sections were stained with an anti-human CD8 Ab (catalog number ab4055; Abcam, Cambridge, MA). Immunohistochemical (IHC)-stained TMA slides were scanned in brightfield with a 20× objective using a NanoZoomer Digital Pathology System (Hamamatsu, Hamamatsu City, Japan). Twenty TMA cases (n = 5 EGFR and 16 KRAS) were excluded as lost, noninformative (e.g., poorly stained or nontumorous), or exhibiting high interpunch variability (i.e., SEM ≥ 50% of mean). The number of CD8+ cells per TMA core was recorded blind to genotype and normalized to core area. Individual core counts from two or more replicates were available for most cases, and CD8+ cell counts per square millimeter were averaged across replicates. Cut-off values of low versus high CD8+ cell content were defined by the midpoint. Comparison of TMA cohorts was conducted using the Fisher exact test with a one-tailed p value.

Significant differences between experimental groups were determined in Prism 6 (GraphPad, La Jolla, CA) using unpaired t tests or, for comparing at least three groups, one-way ANOVA with the indicated post hoc test for correction of multiple comparisons. The incidence of CD3+ and/or Foxp3+ cells per lobe was compared between genotypes using the χ2 test. Unless indicated otherwise, data are presented as mean ± SEM. The p values < 0.05 were considered statistically significant.

Lung tumor development and associated inflammation were assessed in Egfr, Kras, and Kp53 mice. To allow for the dynamic assessment of TA immune responses, lung tumor–bearing animals and appropriate littermate controls were studied for 6, 10, and 14 wk postinitiation. Consistent with previous studies (24, 26, 34), Egfr, Kras, and Kp53 mice developed neoplastic lesions reminiscent of human disease, from benign hyperplasia and adenomas to malignant ADCAs (Fig. 1A–C). Hyperplasia was observed at all time points, although it was less prevalent in Egfr mice than in Kras-mutant mice (data not shown). The introduction of a secondary mutation in Trp53 increased ADCA formation and amplified tumor growth. Accordingly, tumor burden in 10-wk Kras mice was significantly less than observed in age-matched Kp53 mice (Fig. 1D, p = 0.0231). Analysis of Kp53 mice at the 14-wk time point was precluded by early mortality, but Kras mice remained viable and exhibited 38% lung tumor burden. Body mass measurements, used to noninvasively monitor lung TA morbidity, correlated with tumor burden for all genotypes (Fig. 1E).

Previous investigations of pulmonary inflammation were largely based on the assessment of bronchoalveolar lavage fluid (BALF). Although a well-accepted methodology, BALF studies confine analysis to the airway compartment and limit the number of immune cell types that can be identified. Therefore, to more thoroughly investigate the immune cell composition of the TME, we performed flow cytometric analyses on single-cell suspensions generated from whole-lung tissues. Using the gating strategy shown in Fig. 2A, we identified 13 unique leukocyte populations defined by 16 Ab markers (2Materials and Methods, Supplemental Table I). Kras-, Kp53-, and Egfr-mutant mice display a robust immune response, as evidenced by 3–5-fold increases in total CD45+ cell content compared with normal lung (Fig. 2B–G).

FIGURE 2.

Flow cytometric analysis of the inflammatory response to lung tumorigenesis. (A) Representative dot plots demonstrate the strategy used to characterize the mouse lung TME. Single-cell gates (data not shown) were initially applied to remove doublet cells. All subsequent gating used a viability marker, followed by gating on the CD45+ population. Ly6G identified neutrophils (polymorphonuclear cells [PMN]), whereas the remaining myeloid cells were classified as macrophages (Mac; SiglecF+CD11c+), eosinophils (Eos; SiglecF+CD11c) or monocytes (Mono; SiglecFloCD11bhiLy6C+) from the Ly6G population. A size gate was applied for lymphocyte analysis, followed by staining to identify B cells (CD3CD19+), T cells (CD3+CD19), and NK cells (CD3CD19NK1.1+). T cells were further classified into γδ T cells (γδTCR+), CD4 cells (CD4+CD8), and CD8 cells (CD8+CD4). T cell subtypes were identified as Th1 (CD4+IFNγ+), Tregs (CD4+CD25+Foxp3+), and IL-17A producing (CD3+IL17A+). Major lung immune cell populations in Egfr mice at 10 wk posttumor induction (n ≥ 6) (B), Egfr mice at 14 wk (n ≥ 4) (C), Kras mice at 10 wk (n = 14) (D), Kras mice at 14 wk (n = 11) (E), Kp53 mice at 6 wk (n = 8) (F), and Kp53 mice at 10 wk (n ≥ 8) (G) compared with nontumor-bearing control mice (white bars, n ≥ 3). Early and late time points for each genotype are depicted as gray and black bars, respectively. Data for each cell type are displayed as the total number of live cells present within the mouse lung. (H) NKG2D Med.F.I. on NK cells was examined in Egfr-, Kras-, and Kp53-mutant lungs compared with normal lung controls at the indicated time points (n ≥ 3 per group). *p < 0.05.

FIGURE 2.

Flow cytometric analysis of the inflammatory response to lung tumorigenesis. (A) Representative dot plots demonstrate the strategy used to characterize the mouse lung TME. Single-cell gates (data not shown) were initially applied to remove doublet cells. All subsequent gating used a viability marker, followed by gating on the CD45+ population. Ly6G identified neutrophils (polymorphonuclear cells [PMN]), whereas the remaining myeloid cells were classified as macrophages (Mac; SiglecF+CD11c+), eosinophils (Eos; SiglecF+CD11c) or monocytes (Mono; SiglecFloCD11bhiLy6C+) from the Ly6G population. A size gate was applied for lymphocyte analysis, followed by staining to identify B cells (CD3CD19+), T cells (CD3+CD19), and NK cells (CD3CD19NK1.1+). T cells were further classified into γδ T cells (γδTCR+), CD4 cells (CD4+CD8), and CD8 cells (CD8+CD4). T cell subtypes were identified as Th1 (CD4+IFNγ+), Tregs (CD4+CD25+Foxp3+), and IL-17A producing (CD3+IL17A+). Major lung immune cell populations in Egfr mice at 10 wk posttumor induction (n ≥ 6) (B), Egfr mice at 14 wk (n ≥ 4) (C), Kras mice at 10 wk (n = 14) (D), Kras mice at 14 wk (n = 11) (E), Kp53 mice at 6 wk (n = 8) (F), and Kp53 mice at 10 wk (n ≥ 8) (G) compared with nontumor-bearing control mice (white bars, n ≥ 3). Early and late time points for each genotype are depicted as gray and black bars, respectively. Data for each cell type are displayed as the total number of live cells present within the mouse lung. (H) NKG2D Med.F.I. on NK cells was examined in Egfr-, Kras-, and Kp53-mutant lungs compared with normal lung controls at the indicated time points (n ≥ 3 per group). *p < 0.05.

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Macrophages were by far the most prevalent immune cell type in the lungs of the three murine ADCA models. Ten weeks after starting doxycycline, Egfr mice exhibited 11-fold increases in macrophage content compared with controls (Fig. 2B). Similarly, by 6 and 10 wk post-Cre induction in Kp53 and Kras animals, respectively, total macrophage cell counts had increased 14- and 17-fold (Fig. 2D, 2F). Of note, macrophage content increased with time only in Kras and Kp53 animals (Supplemental Table II). Because myeloid-derived suppressor cells (MDSC) are composed of monocytic MDSC and granulocytic MDSC subsets, we simply defined these cells as monocytes (CD11b+Ly6C+) and neutrophils (CD11b+Ly6G+). We observed small, but significant, differences in neutrophil content in 10- and 14-wk Egfr tumor-bearing lungs (Fig. 2B, 2C), as well as in 14-wk Kras tumor-bearing lungs (Fig. 2E). By 6 and 10 wk, TA neutrophil content increased 2- and 5-fold, respectively, in Kp53 animals compared with control (Fig. 2F, 2G). Indeed, neutrophil counts in Kp53 lungs were statistically significantly increased compared with all other genotypes at the 10-wk time point (Supplemental Table III). However, this neutrophil signature may merely reflect overall tumor burden, because statistical analysis of cohorts with approximately matched tumor area (i.e., 6-wk Kp53, 10-wk Kras, and 14-wk Egfr) failed to identify significant differences in pulmonary polymorphonuclear cell content.

NK cells are lymphocytes of the innate immune system that play an important role in the host defense against inhaled pathogens (35). NK cell counts in nontumor-bearing lungs were comparable to those of macrophages and granulocytes (Fig. 2). However, unlike the myeloid cell expansion observed with ADCA development, NK populations remained largely unaltered in tumor-bearing lungs. When significant increases were identified (Fig. 2B, 2D–G), the fold changes were small, and NK cell counts decreased over time in Kras animals (Supplemental Table II). NK cells are required for effective tumor immunosurveillance (36), but cancer cells have developed multiple strategies to escape NK cell–mediated cytotoxicity, including downregulation of NKG2D (also known as CD314) (37, 38). Surface expression of NKG2D on NK cells was decreased significantly in Kras- and Kp53-tumor–bearing animals at all time points but exhibited no change in Egfr mice (Fig. 2H). Thus, these murine models of Kras-driven lung ADCA recapitulate an immune-escape mechanism previously described in human lung cancer (39).

The majority of immunotherapeutic approaches are based on the ability of the adaptive immune system to infiltrate tumors and identify tumor-specific Ags. Therefore, we comprehensively surveyed the lymphocyte subpopulations present within the TME. B cell populations remained unaltered in tumor-bearing Egfr mice (Fig. 2B, 2C) but increased ≥2-fold in Kras and Kp53 mice at all time points (Fig. 2D–G). CD3+ populations were significantly increased in multiple groups (Fig. 2B, 2D–H), but T cell counts were demonstrably greater in Kras and Kp53 mice compared with Egfr mice (Supplemental Table III), even after controlling for tumor burden.

CD4+ Th cell expansion was observed in both Kras-driven tumor models but was limited in Egfr mice (Fig. 3A–F). This pattern was reflected in Treg content, which was significantly lower in tumor-bearing lungs from Egfr mice compared with Kras mice (Supplemental Table III). Interestingly, Treg content increased over time in Kras and Kp53 animals (Supplemental Table II), the only cell type other than macrophages and neutrophils to exhibit such dynamic behavior. Expression of IFN-γ by CD4+ T cells (Th1 cells) and IL-17A by CD3+ T cells was also significantly upregulated in Kras and Kp53 animals compared with control at early and late time points (Fig. 3C–F) but exhibited little to no increase in Egfr animals (Fig. 3A, 3B). Direct comparison of tumor-bearing lungs from all genotypes found higher Th1 and CD3+IL17A+ cell counts in both Kras-driven models compared with Egfr mice (Supplemental Table III). Despite repeated attempts, we were unable to identify IL4+ Th2 cells in our animals (Supplemental Fig. 1); it remains unclear whether this deficiency reflects a true biological absence or, more likely, a technical barrier.

FIGURE 3.

Oncogenic drivers dictate lymphocyte recruitment into the ADCA microenvironment. CD3+ T lymphocyte subpopulations in Egfr mice at 10 wk posttumor induction (n ≥ 6) (A), Egfr mice at 14 wk (n = 6) (B), Kras mice at 10 wk (n ≥ 10) (C), Kras mice at 14 wk (n = 11) (D), Kp53 mice at 6 wk (n = 8) (E), and Kp53 mice at 10 wk (n ≥ 6) (F) compared with nontumor-bearing control mice (white bars, n ≥ 4). Data for each cell type are displayed as the total number of live cells present within the mouse lung. (G) CD3 immunostaining revealed that TA T cells (arrowheads) were commonly located at the edges of neoplastic lesions. Scale bar, 250 μm. (H) Infiltration of CD3+ T cells into the tumor mass is indicated by arrows; note that large clusters of TA CD3+ cells were also present in the periphery of this lesion. (I) CD3+ LAs formed within the vicinity of a tumor (dashed line, Tu) and were typically associated with airways and/or blood vessels. (J) Although Foxp3+ Tregs were seldom observed infiltrating tumor masses, they constituted a significant portion of cells present in LAs. (K and L) Quantification of immune cell localization in matched tumor burden 14-wk Egfr (n = 6), 10-wk Kras (n = 5), and 6-wk Kp53 (n = 5) mice. Results are expressed as the percentage of lung lobes that contained at least one occurrence of TA CD3+ cells (left panel), TI CD3+ cells (middle panel), or CD3+ cells (right panel) in LAs (K) or TA Foxp3+ cells (left panel), TI Foxp3+ cells (middle panel), or Foxp3+ cells (right panel) in LAs (L). (M) Expression of cytokine and chemokine genes in tumor-bearing lungs from 14-wk Egfr and 10-wk Kras mice (n = 4 per genotype). Data are mean 1/Δ cycle threshold with 95% confidence interval. *p < 0.05.

FIGURE 3.

Oncogenic drivers dictate lymphocyte recruitment into the ADCA microenvironment. CD3+ T lymphocyte subpopulations in Egfr mice at 10 wk posttumor induction (n ≥ 6) (A), Egfr mice at 14 wk (n = 6) (B), Kras mice at 10 wk (n ≥ 10) (C), Kras mice at 14 wk (n = 11) (D), Kp53 mice at 6 wk (n = 8) (E), and Kp53 mice at 10 wk (n ≥ 6) (F) compared with nontumor-bearing control mice (white bars, n ≥ 4). Data for each cell type are displayed as the total number of live cells present within the mouse lung. (G) CD3 immunostaining revealed that TA T cells (arrowheads) were commonly located at the edges of neoplastic lesions. Scale bar, 250 μm. (H) Infiltration of CD3+ T cells into the tumor mass is indicated by arrows; note that large clusters of TA CD3+ cells were also present in the periphery of this lesion. (I) CD3+ LAs formed within the vicinity of a tumor (dashed line, Tu) and were typically associated with airways and/or blood vessels. (J) Although Foxp3+ Tregs were seldom observed infiltrating tumor masses, they constituted a significant portion of cells present in LAs. (K and L) Quantification of immune cell localization in matched tumor burden 14-wk Egfr (n = 6), 10-wk Kras (n = 5), and 6-wk Kp53 (n = 5) mice. Results are expressed as the percentage of lung lobes that contained at least one occurrence of TA CD3+ cells (left panel), TI CD3+ cells (middle panel), or CD3+ cells (right panel) in LAs (K) or TA Foxp3+ cells (left panel), TI Foxp3+ cells (middle panel), or Foxp3+ cells (right panel) in LAs (L). (M) Expression of cytokine and chemokine genes in tumor-bearing lungs from 14-wk Egfr and 10-wk Kras mice (n = 4 per genotype). Data are mean 1/Δ cycle threshold with 95% confidence interval. *p < 0.05.

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To assess the spatial relationship between lymphocytes and tumor cells, we performed IHC staining for CD3 and Foxp3 on Egfr-, Kras-, and Kp53-tumor–bearing mice. For each model of lung ADCA, immune cell location was identified as TA (or peripheral), TI, or within a neighboring LA. Examples of each are provided in Fig. 3G–J. Staining for CD3 illustrated key differences by genotype, because Egfr-mutant mice displayed markedly fewer TA CD3+ T cells than Kras and Kp53 specimens (Fig. 3K, left panel). TI CD3+ cells were identified in all ADCA models, although they were more prevalent in Kp53 mice (Fig. 3K, middle panel). Similarly, CD3+ cells were present in all LAs, but LAs were significantly less common in Egfr mice but were uniformly present in the other genotypes (Fig. 3K, right panel). Approximately 15% of lobes from Egfr mice contained TA Tregs compared with ∼40% for Kras mice (Fig. 3L, left panel). The primary location of Tregs in all genotypes was within LA structures (Fig. 3L, right panel), with essentially no tumor infiltration observed (Fig. 3L, middle panel). Taken together, the IHC studies confirm the flow cytometry data showing that Egfr mice contain fewer Tregs than the other genotypes and, moreover, demonstrate a paucity of TI lymphocytes, especially compared with Kp53 tumors.

CD8+ T cells are capable of detecting and discriminately eliminating tumor cells (40). Notably, CD8+ cell content differed significantly between models driven by mutant Kras versus Egfr. Expansion of CD8+ cells was observed at all time points in Kras and Kp53 mice but did not occur in the Egfr-mutant cohort (Fig. 3A–F). Even after controlling for tumor burden, Kras-mutant mice displayed greater CD8+ cell content than did Egfr mice (Supplemental Table III). Therefore, we carried out a number of experiments in an attempt to determine the mechanistic basis for this finding and to translate this observation to human disease. Initially, we performed quantitative real-time PCR for key CC and CXC chemokines known to impact immune cell recruitment. Notably, we identified an increase in Cxcl-9 and Cxcl-10 in Kras-mutant lungs compared with Egfr (Fig. 3M), which may explain, at least in part, the differences in lymphocyte content seen between the two lung ADCA models.

To translate these findings to human disease, we performed IHC staining for CD8 on a lung ADCA TMA annotated for KRAS (n = 53 cases) and EGFR (n = 26) mutational status. The frequency of KRAS and EGFR mutations in the cases displaying high versus low CD8 content (the top 50% and bottom 50% of cases, respectively) was assessed, and EGFR mutations were found to be significantly overrepresented in the CD8-low cohort (Fig. 4A). Specifically, 65.4% of EGFR-mutant cases were scored as CD8-low versus 41.5% of KRAS-mutant cases. Thus, similar to the findings in the GEM models presented above, EGFR-mutant lung ADCAs exhibit reduced CD8+ lymphocyte infiltration in human lung cancers compared with KRAS-mutant lung ADCA.

FIGURE 4.

CD8+ cell content and function correlate with lung ADCA subtype. (A) The number of CD8+ cells per square millimeter was tabulated for each core section present on a TMA of lung ADCA cases annotated for EGFR and KRAS mutational status. Cases were ranked from lowest to highest CD8 content prior to unblinding for genotype. Shown are representative images of EGFR-mutant (left panels) and KRAS-mutant (right panels) ADCA. Scale bar, 100 μm. A total of 65.4% (17/26) of EGFR-mutant cases were scored as CD8-low versus 41.5% (22/53) of KRAS-mutant cases. *p = 0.0392, Fisher exact test. (B) PD-L1 Med.F.I. was assessed on pulmonary EpCAM+ epithelial cells and macrophages from 10-wk Egfr mice (n = 5) and 14-wk Kras mice (n = 4). Expression compared with normal lung (n ≥ 4) is shown (lower panels). (C) Splenocytes from nontumor-bearing wt mice were labeled with CFSE and incubated with protein homogenate generated from wt normal lung (NL) or tumor-bearing lung from 10-wk Kras or Egfr mice or with media alone. The cells were subsequently stained with anti-CD8 and a viability marker and analyzed for CFSE intensity; representative plots are shown. For each genotype (n ≥ 3), the percentage of proliferating CD8+ T cells was determined after normalization to the media control. Statistical differences were assessed by one-way ANOVA with the Tukey posttest. (D) Flow cytometric analysis of T cell function in Egfr mice compared with wt control, gated from single live CD45+CD3+ parent population. Lymphocytes were gated as CD62L+CD44 (i.e., Naive), CD62L+CD44+ (TCM, central memory), and CD62LCD44+ (TEM/Eff, effector memory/effector). PD1 expression was assessed on CD8+ T cells only. CD8+ and CD4+ T cell populations were examined at 10 (E and F) and 14 wk (G and H) postinduction of mutant Egfr (n = 6 tumor-bearing lungs and n ≥ 3 controls per group), respectively. (I) Percentage lung tumor area of Kras mice treated with anti-CTLA4 (n = 7) or isotype (n = 6) (right panel) and representative H&E sections (left and middle panel). Scale bar, 500 μm. (J and K) Flow cytometric analysis of CD8+ and CD4+ T cell populations in anti-CTLA4–treated and isotype-treated Kras mice (n = 5 per group). *p < 0.05.

FIGURE 4.

CD8+ cell content and function correlate with lung ADCA subtype. (A) The number of CD8+ cells per square millimeter was tabulated for each core section present on a TMA of lung ADCA cases annotated for EGFR and KRAS mutational status. Cases were ranked from lowest to highest CD8 content prior to unblinding for genotype. Shown are representative images of EGFR-mutant (left panels) and KRAS-mutant (right panels) ADCA. Scale bar, 100 μm. A total of 65.4% (17/26) of EGFR-mutant cases were scored as CD8-low versus 41.5% (22/53) of KRAS-mutant cases. *p = 0.0392, Fisher exact test. (B) PD-L1 Med.F.I. was assessed on pulmonary EpCAM+ epithelial cells and macrophages from 10-wk Egfr mice (n = 5) and 14-wk Kras mice (n = 4). Expression compared with normal lung (n ≥ 4) is shown (lower panels). (C) Splenocytes from nontumor-bearing wt mice were labeled with CFSE and incubated with protein homogenate generated from wt normal lung (NL) or tumor-bearing lung from 10-wk Kras or Egfr mice or with media alone. The cells were subsequently stained with anti-CD8 and a viability marker and analyzed for CFSE intensity; representative plots are shown. For each genotype (n ≥ 3), the percentage of proliferating CD8+ T cells was determined after normalization to the media control. Statistical differences were assessed by one-way ANOVA with the Tukey posttest. (D) Flow cytometric analysis of T cell function in Egfr mice compared with wt control, gated from single live CD45+CD3+ parent population. Lymphocytes were gated as CD62L+CD44 (i.e., Naive), CD62L+CD44+ (TCM, central memory), and CD62LCD44+ (TEM/Eff, effector memory/effector). PD1 expression was assessed on CD8+ T cells only. CD8+ and CD4+ T cell populations were examined at 10 (E and F) and 14 wk (G and H) postinduction of mutant Egfr (n = 6 tumor-bearing lungs and n ≥ 3 controls per group), respectively. (I) Percentage lung tumor area of Kras mice treated with anti-CTLA4 (n = 7) or isotype (n = 6) (right panel) and representative H&E sections (left and middle panel). Scale bar, 500 μm. (J and K) Flow cytometric analysis of CD8+ and CD4+ T cell populations in anti-CTLA4–treated and isotype-treated Kras mice (n = 5 per group). *p < 0.05.

Close modal

Because CD8+ responses can be blunted by immune checkpoint ligands, we measured PD-L1 expression on macrophages and tumor cells (EpCAM+) by flow cytometry. Interestingly, although PD-L1 expression was decreased in tumor-bearing versus control lungs for Egfr and Kras mice, there was no difference in PD-L1 expression between oncogenic subtypes (Fig. 4B). Because other tumor microenvironmental factors can perturb lymphocyte function, we assessed whether the TME of Egfr mice was more suppressive to lymphocyte proliferation than that found in Kras mice. Egfr and Kras tumor homogenates reduced CD8+ T cell proliferation using CFSE-labeled lymphocytes, but the Egfr homogenates were not more suppressive than Kras (Fig. 4C).

Because the intriguing lack of CD8+ cell expansion in Egfr-mutant tumors suggests a failure of CD8+ cell activation in Egfr mice, we performed a detailed assessment of T cell effector and memory status at the 10- and 14-wk time points using the markers CD62L, CD44, and PD1 (Fig. 4D). No evidence of CD8+ T cell activation was observed, as reflected by the lack of an increase in CD8+PD1+ cells in tumor-bearing Egfr mice (Fig. 4E, 4G). Additionally, the proportion of central memory (CD62L+CD44+) and effector/effector memory (CD62LCD44+) CD8+ T cells was unchanged, with the majority of these cells still falling into the naive (CD62L+CD44) category in Egfr mice. In contrast, and consistent with the small, but significant, increase in Th cells shown in Fig. 3A and 3B, CD4+ T cells demonstrated a significant increase in effector/effector memory populations in 10- and 14-wk Egfr mice (Fig. 4F, 4H).

Given the robust increase in CD8+ T cell content observed in tumor-bearing lungs from Kras mice compared with control, we elected to assess the status and functionality of the lymphocyte populations in Kras mice using a CTLA4 mIgG2b (clone 9D9) Ab (MedImmune). Although administration of anti-CTLA4 increased the proportion of CD8+ (Fig. 4J) and CD4+ (Fig. 4K) effector/effector memory cells, this cellular phenotype failed to translate into an altered tumor burden in Kras mice (Fig. 4I). Thus, despite an activated CD8+ T cell response in Kras-mutant mice, tumor progression continued unabated.

Given the robust expansion of IL-17A+ T cells in Kras-mutant tumor–bearing mice and the known protumor role of Th17 cells in lung ADCA (41), we elected to examine the cellular sources of IL-17A in the lungs of our Kras-mutant mouse models. Surprisingly, the predominant source of IL-17A was found to be γδ T cells rather than CD4+ Th17 cells (Fig. 5A, 5B). An attempt to interrogate the role of IL-17A in lung tumorigenesis by crossing Kras-mutant mice to mice lacking the transcription factor for IL-17A (i.e., RORγt) (42) was stymied by the frequent occurrence of lymphoid neoplasms in these animals. The spontaneously arising lymphomas exhibited thymic (Fig. 5C) and splenic (Fig. 5D) involvement, as well as diffuse infiltration of the liver (Fig. 5E) and lungs (Fig. 5F). Previous studies of a related mouse model of RORγt deficiency (43) similarly identified a high incidence of lymphoma but did not detect pulmonary metastases that, unfortunately, preclude the use of this model in lung tumorigenesis studies. Further efforts to interrogate the specific role of γδ T cells in Kras-mutant lung ADCA revealed that deletion of γδ T cells did not impact tumor burden (Fig. 5G) or the immune cell composition of the TME (Fig. 5H).

FIGURE 5.

IL-17A cytokine production and impact in Kras-mutant lung ADCA. (A) Representative dot plot demonstrating the relative production of IL-17A by γδ T and non-γδ T cells; gated from single, live, CD45+CD3+ parent population. (B) Quantification of IL-17A cytokine’s cellular source in 14-wk Kras tumor–bearing lungs (n = 5). Spontaneously arising lymphomas occurred at high frequency in K.RORγt animals, commonly impacting thymus (C), spleen (D), liver (E), and lung (F) tissues. H&E stain. Scale bars, 250 μm (C–E), 500 μm (F). (G) Representative H&E images from 14-wk Kras and K.Tgd mice. Scale bar, 500 μm. (H) Flow cytometric analysis of lungs from tumor-bearing Kras and K.Tgd mice (n = 6 each) at 14 wk postinduction. Data for each cell type are displayed as the total number of live cells present within the mouse lung. *p < 0.05. n.s., not significant.

FIGURE 5.

IL-17A cytokine production and impact in Kras-mutant lung ADCA. (A) Representative dot plot demonstrating the relative production of IL-17A by γδ T and non-γδ T cells; gated from single, live, CD45+CD3+ parent population. (B) Quantification of IL-17A cytokine’s cellular source in 14-wk Kras tumor–bearing lungs (n = 5). Spontaneously arising lymphomas occurred at high frequency in K.RORγt animals, commonly impacting thymus (C), spleen (D), liver (E), and lung (F) tissues. H&E stain. Scale bars, 250 μm (C–E), 500 μm (F). (G) Representative H&E images from 14-wk Kras and K.Tgd mice. Scale bar, 500 μm. (H) Flow cytometric analysis of lungs from tumor-bearing Kras and K.Tgd mice (n = 6 each) at 14 wk postinduction. Data for each cell type are displayed as the total number of live cells present within the mouse lung. *p < 0.05. n.s., not significant.

Close modal

The immune cell composition of the SCLC TME has not been investigated to any extent. Therefore, we profiled the immune content of SCLC using cohorts of Rbp53 mice infected with AdCre. As described previously (44), lung tumors of mainly neuroendocrine histology arose within 40–50 wk of AdCre administration (Fig. 6A). Flow cytometric analysis of SCLC tumor–bearing lungs (gated as shown in Fig. 2) identified a small, but noteworthy, inflammatory presence in Rbp53 mice compared with control (Fig. 6B). The total number of CD45+ leukocytes was increased 2-fold in SCLC, and TA CD45+ cells were also visible by IHC staining (Fig. 6C). Unlike Kras- and Egfr-mutant animals (Fig. 1A, 1B), SCLC tumors presented as large discrete foci, and little hyperplasia was observed. CD45+ cells were consequently clustered at the periphery of the SCLC lesions (Fig. 6D), without the inflammatory field effect frequently observed in the ADCA models. Few TI leukocytes were detected (Fig. 6E).

FIGURE 6.

TA inflammation in SCLC. (A) Rbp53 mice develop SCLC tumors within 1 y of AdCre exposure. H&E stain. Scale bar, 1 mm. (B) Flow cytometric analysis of lungs from tumor-bearing Rbp53 mice (n = 4) and non–Cre exposed control mice (n = 3) ∼10 mo after tumor induction. IHC staining reveals that CD45+ immune cells are generally located in the SCLC tumor periphery (arrowheads, C), with some clustering of cells into organized lymphoid structures (arrows, D). (D) Some large CD45+ cells, likely macrophages, were observed in alveolar spaces (arrowheads) proximal to the tumor. (E) Little to no leukocyte tumor infiltration was observed. Scale bars, 1 mm (C), 100 μm (D and E). (F) The ratio of CD3+ T cells/total myeloid population in SCLC mice was increased significantly compared with three ADCA models, as assessed by one-way ANOVA with the Tukey posttest. (G) The percentage of CD45+ live cells present in two resected specimens of human SCLC was greatly reduced compared with five human ADCA specimens. (H) Leukocyte population summary of the flow cytometric analyses, shown as the percentage of live cells, for a representative normal mouse lung, 10-wk Egfr, Kras, and Kp53 ADCAs, and mouse (m) and human (h) SCLC. *p < 0.05. NL, nonadjacent normal lung; Tu, tumor.

FIGURE 6.

TA inflammation in SCLC. (A) Rbp53 mice develop SCLC tumors within 1 y of AdCre exposure. H&E stain. Scale bar, 1 mm. (B) Flow cytometric analysis of lungs from tumor-bearing Rbp53 mice (n = 4) and non–Cre exposed control mice (n = 3) ∼10 mo after tumor induction. IHC staining reveals that CD45+ immune cells are generally located in the SCLC tumor periphery (arrowheads, C), with some clustering of cells into organized lymphoid structures (arrows, D). (D) Some large CD45+ cells, likely macrophages, were observed in alveolar spaces (arrowheads) proximal to the tumor. (E) Little to no leukocyte tumor infiltration was observed. Scale bars, 1 mm (C), 100 μm (D and E). (F) The ratio of CD3+ T cells/total myeloid population in SCLC mice was increased significantly compared with three ADCA models, as assessed by one-way ANOVA with the Tukey posttest. (G) The percentage of CD45+ live cells present in two resected specimens of human SCLC was greatly reduced compared with five human ADCA specimens. (H) Leukocyte population summary of the flow cytometric analyses, shown as the percentage of live cells, for a representative normal mouse lung, 10-wk Egfr, Kras, and Kp53 ADCAs, and mouse (m) and human (h) SCLC. *p < 0.05. NL, nonadjacent normal lung; Tu, tumor.

Close modal

The major immune component of SCLC was found to be CD3+ T lymphocytes (Fig. 6B). This population included a 7-fold increase in the number of γδ T cells and a strong trend toward increased CD4+ Th cells (p = 0.0698). In marked contrast to the ADCA models, expansion of innate immune cells in SCLC tumor–bearing lungs was minimal, with only a 2-fold increase observed in macrophages and a nonsignificant increase in neutrophils (p = 0.0886). To further investigate this phenomenon, we compared the ratio of CD3+ T cells/myeloid cells (macrophages, neutrophils, monocytes, and eosinophils) and found a pronounced lymphocyte-dominant signature in SCLC versus all ADCA models (Fig. 6F). Egfr mice presented the smallest CD3/myeloid ratio and were also significantly different from Kras mice.

As part of an ongoing study of the immune composition of human lung cancer, we obtained two surgical specimens with confirmed small cell pathology. Because resecting SCLC is rarely clinically indicated, these two specimens represented a unique opportunity to measure the immune cell composition present within the SCLC TME. Therefore, we performed flow cytometry analyses on single-cell suspensions generated from these two cases. Similar to the findings in the GEM models, TA inflammation was discernibly lower in SCLC compared with five ADCA specimens; CD45+ cells accounted for a mere 16.2% of live cells in the SCLC resections compared with 81.9% in ADCA (Fig. 6G).

Lung cancer is a heterogeneous disease that can be divided into distinct subtypes based on molecular and cellular characteristics (45). In this study, we tested the hypothesis that these subtypes dictate the inflammatory response to cancer by immune profiling the lung TME in a mouse model of SCLC and in three molecularly distinct models of NSCLC (Fig. 6H). We found that Egfr and Kras mutations give rise to distinct immune responses characterized by differential expansion of B cells, CD8+ T cells, Tregs, and IL-17A–producing T cell populations. Although loss of Trp53 promoted malignancy, it had minimal effect on immune cell composition within the Kras TME. We further demonstrate that SCLC possesses an overall reduced inflammatory presence compared with NSCLC, and one in which lymphocytes predominate over myeloid lineage cells. Therefore, mutational profile and histological origin actively shape the immune contexture of lung cancer, a finding that may have important clinical ramifications.

The strong macrophage field responses that occur in mutant Kras- and Egfr-driven mouse ADCAs are seldom observed in human lung cancer and represent a potential limitation of these GEM models. In Kras mice, this phenomenon of alveolar macrophages flooding the airspaces was likened to desquamative interstitial pneumonitis, a rare interstitial lung disease with similar pathology (46). Moreover, the conditional mouse models in this study use varied induction methodologies (i.e., AdCre- or doxycycline-regulated transgene expression). Although we cannot exclude potential confounding effects of viral infection or doxycycline consumption on the tumor immune response, we attempted to correct for these variables by using adenovirus- or doxycycline-exposed wt animals for the relevant control cohorts.

Few gene-specific investigations of the mouse lung TME have been conducted, and no comprehensive effort has been made to compare and contrast different molecular and histological models of lung cancer. However, our results validate findings from several earlier studies of lung TA inflammation. We were unsurprised to identify macrophages as the dominant immune cell presence in mouse ADCAs given that strong TA macrophage responses were identified in mutant Egfr (21) and Kras (19, 41, 47) mouse lung tumor models. Likewise, as we described in this article, neutrophils were shown to be a modest, but important, component of Kras-driven, but not Egfr-driven, mouse lung ADCA (19, 21, 41). Because the majority of prior data in this regard relied on BALF cell counts, we used flow cytometry to better define the quality of the immune response. Using this methodology, we found that recruitment of lymphoid lineage cells varies greatly among ADCA models, because EgfrL858R mice exhibited a paucity of B cells, CD8+ T cells, Tregs, and IL-17A–producing T cells compared with the Kras and Kp53 lung TME.

The most clinically relevant finding in this study is the lack of a CD8+ lymphocyte response in Egfr-mutant mice and EGFR-mutant human lung ADCA specimens compared with their KRAS-mutant counterparts. Markers of effector/memory status failed to reveal any evidence of CD8+ T cell activation or differentiation in Egfr mice. This suggests that EGFR mutation may not elicit an Ag-driven immune response. Although the same could be said for mutant KRAS, we were able to demonstrate an increase in activated and effector memory CD8+ cells in the Kras mouse model. Furthermore, TI lymphocyte populations that specifically target mutant KRASG12D were identified recently in colon cancer (48). Despite the presence of activated CD8+ cells, tumor growth continued in Kras mice, even with the addition of an anti-CTLA4 therapeutic Ab. Our interpretation of this data is that increases in activated CD8+ cells within the TME in Kras-mutant mice do not impact tumor growth unless they are tumor reactive. Specifically in this case, tumor-derived chemokines, such as Cxcl-10, are likely to increase the number of TA lymphocytes. Although anti-CTLA4 Ab therapy drove an increase in effector T cells, these cells would not be expected to reduce tumor burden if they did not recognize a TA Ag. It is also possible that anti-CTLA4 monotherapy will prove ineffective but that combined immunotherapeutic regimens (e.g., anti-CTLA4 + anti–PD-L1) will prove effective. With respect to human lung ADCA, the association of EGFR-mutant cancers with never-smoker status suggests that these tumors are genetically simplified and, unlike smoking-associated KRAS-mutant cancers, may not possess sufficient mutational burden to harbor neoantigens (49, 50). The mouse models described in this article were not exposed to cigarette smoke or other carcinogens, eliminating this proposed explanation for the differential CD8+ responses that we observed. However, at this time we cannot exclude the possibility that KRAS-mutant human and mouse lung ADCAs achieved the same phenotype of high CD8+ T cell infiltration through different mechanisms of action.

Tregs and IL17A+ T cells have emerged as important cell populations in multiple mouse models of cancer (41, 51, 52), and both cell types exhibited notable patterns of expression or localization in the murine ADCA models. Although TGF-β and IL-6 generate gradients leading independently to Treg or Th17 differentiation, we observed concurrent increases in both populations in Kras- and Kp53-mutant tumor–bearing lungs. Notably, we found that the major source of IL-17A in Kras-mutant ADCA was γδ T cells and not Th17 cells. Because IL-17A deficiency was shown to reduce lung tumor growth (41), and IL-17A–producing γδ T cells are known to promote breast (53) and pancreatic neoplasia (52), these findings suggested to us that expansion of a pulmonary IL-17A–producing γδ T cell subset might overshadow the tumor-surveillance role traditionally ascribed to γδ T cells (54). However, Kras-mutant γδ T cell–deficient mice displayed equivalent lung tumor burden and strikingly similar immune profiles to their γδ T cell–competent counterparts. Therefore, although IL-17A is an important signaling component in the immune landscape of lung ADCA, IL-17A+ γδ T cells appear to contribute little to the process of lung tumorigenesis.

Tregs, in contrast, appear to play a particularly important role in the Kras-mutant lung TME, because they were the only nonmyeloid lineage population to expand over the course of tumor development. Moreover, Tregs display a unique anatomic location in lung ADCA. They are rarely associated with the tumor itself; instead, they are frequently found within LA structures that are believed to function as a local site of Ag presentation and are correlated with good clinical outcomes in NSCLC (55). The presence of Tregs in these structures was recently shown to be detrimental to the generation of an effective immune response in murine lung ADCA (56), highlighting the importance of Treg-targeting strategies for the clinical management of lung cancer patients.

The immune cell composition of SCLC has not been well studied. Our findings in Rbp53 mice point to a less robust, but more lymphocyte-predominant, host immune response to murine SCLC than to ADCA. Moreover, when we analyzed the immune cell content of two human SCLC cases, we identified a strikingly similar immune profile of sparse CD45+ cell content. Although we acknowledge the inherent limitations of n = 2 studies, patients diagnosed with SCLC seldom undergo lung resection (57), making access to such specimens exceedingly rare. Solid tumor malignancies demonstrating the best responses to current ICI therapies are those with high mutational burdens and/or a history of cigarette smoke exposure (e.g., melanoma, head and neck squamous cell carcinoma, and urinary bladder cancer), both traits common to SCLC (2). In light of these correlations, it is tempting to speculate that SCLC patients would exhibit good responses to ICI therapy. However, initial reports suggest that success rates for anti-PD1 therapy in SCLC are, at best, only comparable to NSCLC (58, 59). Our preliminary findings with respect to the immune cell composition in SCLC suggest that the presence of redundant immune-suppressive factors would not be a likely source of treatment failure, which is almost certainly an important concept in NSCLC. These findings point to potentially unique features of the SCLC TME (e.g., matrix protein composition) that require additional study.

A robust CD45+ immune response was observed in the ADCA mouse models and human lung ADCA patients. Leukocytes account for nearly 75% of total cellular content in human ADCA, which is an even greater proportion than we identified in mice (∼55%). With the exception of the aforementioned exaggerated macrophage responses, the robust and diverse immune landscape observed in GEM models of ADCA approximates that seen in human lung cancers (60). Driving mutations, such as in Egfr and Kras, substantially impact the TME through the release of bioactive molecules, which is very well reflected in these GEM models. One potential shortcoming of these models is the genetic simplicity of the tumors, which rely on a single driving mutation. In contrast, human NSCLC harbors an average of ∼150 distinct mutations per case (61). Efforts are underway to construct mouse models of cancer that harbor a greater abundance of single nucleotide variations and, thus, potential neoantigens. However, EGFR-mutant cancers in nonsmokers are typically genetically simplified (5), such that the Egfr-mutant mice described in this article likely constitute an excellent representation of the genetic component of the cancer cell and the immune composition of the TME.

The emergence of ICIs has been a tremendous advance; unfortunately, the majority of lung cancer patients in clinical trials failed to respond to ICI therapy (811). In addition to the PD-1/PD-L1–based drugs currently in use, novel ICI agents are likely to emerge in the near future. Our findings argue that the cellular and molecular characteristics of lung cancer may provide an important framework for patient-targeted immunotherapy. Furthermore, preclinical testing of future immunotherapy agents should be performed in genetically and histologically diverse model systems to enable the assessment of tumor subtype–specific efficacy.

We thank the Fred Hutchinson Cancer Research Center Experimental Histopathology and Flow Cytometry shared resource facilities, the University of Washington Histology and Imaging Core, and the Viral Vector Core Facility at the University of Iowa Carver College of Medicine. We also thank MedImmune for supplying the anti-CTLA4 therapeutic Ab.

This work was supported by National Institutes of Health/National Heart, Lung, and Blood Institute Grant R01 HL108979 (to A.M.H.), European Commission Grant FP7-PEOPLE-2012-IOF 331255 (to J.K.), and the Fred Hutchinson Cancer Research Center.

The online version of this article contains supplemental material.

Abbreviations used in this article:

     
  • ADCA

    adenocarcinoma

  •  
  • AdCre

    adenoviral Cre recombinase

  •  
  • BALF

    bronchoalveolar lavage fluid

  •  
  • FVD

    Fixable Viability Dye

  •  
  • GEM

    genetically engineered mouse

  •  
  • ICI

    immune checkpoint inhibitor

  •  
  • IHC

    immunohistochemical

  •  
  • LA

    lymphoid aggregate

  •  
  • MDSC

    myeloid-derived suppressor cell

  •  
  • Med.F.I.

    median fluorescence intensity

  •  
  • NKG2D

    NK group 2, member D receptor

  •  
  • NSCLC

    non–small cell lung cancer

  •  
  • SCLC

    small cell lung cancer

  •  
  • TA

    tumor-associated

  •  
  • TI

    tumor-infiltrating

  •  
  • TMA

    tissue microarray

  •  
  • TME

    tumor microenvironment

  •  
  • Treg

    regulatory T cell

  •  
  • wt

    wild-type.

1
Howlader N., A. M. Noone, M. Krapcho, J. Garshell, D. Miller, S. F. Altekruse, C. L. Kosary, M. Yu, J. Ruhl, Z. Tatalovich, et al, eds. SEER Cancer Statistics Review, 1975–2011. National Cancer Institute, Bethesda, MD. Available at: http://seer.cancer.gov/csr/1975_2011
.
2
Peifer
M.
,
Fernández-Cuesta
L.
,
Sos
M. L.
,
George
J.
,
Seidel
D.
,
Kasper
L. H.
,
Plenker
D.
,
Leenders
F.
,
Sun
R.
,
Zander
T.
, et al
.
2012
.
Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer.
Nat. Genet.
44
:
1104
1110
.
3
Govindan
R.
,
Page
N.
,
Morgensztern
D.
,
Read
W.
,
Tierney
R.
,
Vlahiotis
A.
,
Spitznagel
E. L.
,
Piccirillo
J.
.
2006
.
Changing epidemiology of small-cell lung cancer in the United States over the last 30 years: analysis of the surveillance, epidemiologic, and end results database.
J. Clin. Oncol.
24
:
4539
4544
.
4
Ding
L.
,
Getz
G.
,
Wheeler
D. A.
,
Mardis
E. R.
,
McLellan
M. D.
,
Cibulskis
K.
,
Sougnez
C.
,
Greulich
H.
,
Muzny
D. M.
,
Morgan
M. B.
, et al
.
2008
.
Somatic mutations affect key pathways in lung adenocarcinoma.
Nature
455
:
1069
1075
.
5
Cancer Genome Atlas Research Network
.
2014
.
Comprehensive molecular profiling of lung adenocarcinoma. [Published erratum appears in 2014 Nature 514: 262.]
Nature
511
:
543
550
.
6
Pao
W.
,
Chmielecki
J.
.
2010
.
Rational, biologically based treatment of EGFR-mutant non-small-cell lung cancer.
Nat. Rev. Cancer
10
:
760
774
.
7
Morgensztern
D.
,
Campo
M. J.
,
Dahlberg
S. E.
,
Doebele
R. C.
,
Garon
E.
,
Gerber
D. E.
,
Goldberg
S. B.
,
Hammerman
P. S.
,
Heist
R. S.
,
Hensing
T.
, et al
.
2015
.
Molecularly targeted therapies in non-small-cell lung cancer annual update 2014.
J. Thorac. Oncol.
10
(
Suppl. 1
):
S1
S63
.
8
Lynch
T. J.
,
Bondarenko
I.
,
Luft
A.
,
Serwatowski
P.
,
Barlesi
F.
,
Chacko
R.
,
Sebastian
M.
,
Neal
J.
,
Lu
H.
,
Cuillerot
J. M.
,
Reck
M.
.
2012
.
Ipilimumab in combination with paclitaxel and carboplatin as first-line treatment in stage IIIB/IV non-small-cell lung cancer: results from a randomized, double-blind, multicenter phase II study.
J. Clin. Oncol.
30
:
2046
2054
.
9
Topalian
S. L.
,
Hodi
F. S.
,
Brahmer
J. R.
,
Gettinger
S. N.
,
Smith
D. C.
,
McDermott
D. F.
,
Powderly
J. D.
,
Carvajal
R. D.
,
Sosman
J. A.
,
Atkins
M. B.
, et al
.
2012
.
Safety, activity, and immune correlates of anti-PD-1 antibody in cancer.
N. Engl. J. Med.
366
:
2443
2454
.
10
Borghaei
H.
,
Paz-Ares
L.
,
Horn
L.
,
Spigel
D. R.
,
Steins
M.
,
Ready
N. E.
,
Chow
L. Q.
,
Vokes
E. E.
,
Felip
E.
,
Holgado
E.
, et al
.
2015
.
Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer.
N. Engl. J. Med.
373
:
1627
1639
.
11
Reck
M.
,
Bondarenko
I.
,
Luft
A.
,
Serwatowski
P.
,
Barlesi
F.
,
Chacko
R.
,
Sebastian
M.
,
Lu
H.
,
Cuillerot
J. M.
,
Lynch
T. J.
.
2013
.
Ipilimumab in combination with paclitaxel and carboplatin as first-line therapy in extensive-disease-small-cell lung cancer: results from a randomized, double-blind, multicenter phase 2 trial.
Ann. Oncol.
24
:
75
83
.
12
Coussens
L. M.
,
Werb
Z.
.
2002
.
Inflammation and cancer.
Nature
420
:
860
867
.
13
Hanahan
D.
,
Coussens
L. M.
.
2012
.
Accessories to the crime: functions of cells recruited to the tumor microenvironment.
Cancer Cell
21
:
309
322
.
14
Pardoll
D. M.
2012
.
The blockade of immune checkpoints in cancer immunotherapy.
Nat. Rev. Cancer
12
:
252
264
.
15
Gabrilovich
D. I.
,
Nagaraj
S.
.
2009
.
Myeloid-derived suppressor cells as regulators of the immune system.
Nat. Rev. Immunol.
9
:
162
174
.
16
Helissey
C.
,
Champiat
S.
,
Soria
J. C.
.
2015
.
Immune checkpoint inhibitors in advanced nonsmall cell lung cancer.
Curr. Opin. Oncol.
27
:
108
117
.
17
Coussens
L. M.
,
Zitvogel
L.
,
Palucka
A. K.
.
2013
.
Neutralizing tumor-promoting chronic inflammation: a magic bullet?
Science
339
:
286
291
.
18
Sparmann
A.
,
Bar-Sagi
D.
.
2004
.
Ras-induced interleukin-8 expression plays a critical role in tumor growth and angiogenesis.
Cancer Cell
6
:
447
458
.
19
Ji
H.
,
Houghton
A. M.
,
Mariani
T. J.
,
Perera
S.
,
Kim
C. B.
,
Padera
R.
,
Tonon
G.
,
McNamara
K.
,
Marconcini
L. A.
,
Hezel
A.
, et al
.
2006
.
K-ras activation generates an inflammatory response in lung tumors.
Oncogene
25
:
2105
2112
.
20
Wislez
M.
,
Fujimoto
N.
,
Izzo
J. G.
,
Hanna
A. E.
,
Cody
D. D.
,
Langley
R. R.
,
Tang
H.
,
Burdick
M. D.
,
Sato
M.
,
Minna
J. D.
, et al
.
2006
.
High expression of ligands for chemokine receptor CXCR2 in alveolar epithelial neoplasia induced by oncogenic kras.
Cancer Res.
66
:
4198
4207
.
21
Akbay
E. A.
,
Koyama
S.
,
Carretero
J.
,
Altabef
A.
,
Tchaicha
J. H.
,
Christensen
C. L.
,
Mikse
O. R.
,
Cherniack
A. D.
,
Beauchamp
E. M.
,
Pugh
T. J.
, et al
.
2013
.
Activation of the PD-1 pathway contributes to immune escape in EGFR-driven lung tumors.
Cancer Discov.
3
:
1355
1363
.
22
Lujambio
A.
,
Akkari
L.
,
Simon
J.
,
Grace
D.
,
Tschaharganeh
D. F.
,
Bolden
J. E.
,
Zhao
Z.
,
Thapar
V.
,
Joyce
J. A.
,
Krizhanovsky
V.
,
Lowe
S. W.
.
2013
.
Non-cell-autonomous tumor suppression by p53.
Cell
153
:
449
460
.
23
Menendez
D.
,
Shatz
M.
,
Resnick
M. A.
.
2013
.
Interactions between the tumor suppressor p53 and immune responses.
Curr. Opin. Oncol.
25
:
85
92
.
24
Politi
K.
,
Zakowski
M. F.
,
Fan
P. D.
,
Schonfeld
E. A.
,
Pao
W.
,
Varmus
H. E.
.
2006
.
Lung adenocarcinomas induced in mice by mutant EGF receptors found in human lung cancers respond to a tyrosine kinase inhibitor or to down-regulation of the receptors.
Genes Dev.
20
:
1496
1510
.
25
Tichelaar
J. W.
,
Lu
W.
,
Whitsett
J. A.
.
2000
.
Conditional expression of fibroblast growth factor-7 in the developing and mature lung.
J. Biol. Chem.
275
:
11858
11864
.
26
Jackson
E. L.
,
Willis
N.
,
Mercer
K.
,
Bronson
R. T.
,
Crowley
D.
,
Montoya
R.
,
Jacks
T.
,
Tuveson
D. A.
.
2001
.
Analysis of lung tumor initiation and progression using conditional expression of oncogenic K-ras.
Genes Dev.
15
:
3243
3248
.
27
Jonkers
J.
,
Meuwissen
R.
,
van der Gulden
H.
,
Peterse
H.
,
van der Valk
M.
,
Berns
A.
.
2001
.
Synergistic tumor suppressor activity of BRCA2 and p53 in a conditional mouse model for breast cancer.
Nat. Genet.
29
:
418
425
.
28
Eberl
G.
,
Marmon
S.
,
Sunshine
M. J.
,
Rennert
P. D.
,
Choi
Y.
,
Littman
D. R.
.
2004
.
An essential function for the nuclear receptor RORgamma(t) in the generation of fetal lymphoid tissue inducer cells.
Nat. Immunol.
5
:
64
73
.
29
Itohara
S.
,
Mombaerts
P.
,
Lafaille
J.
,
Iacomini
J.
,
Nelson
A.
,
Clarke
A. R.
,
Hooper
M. L.
,
Farr
A.
,
Tonegawa
S.
.
1993
.
T cell receptor delta gene mutant mice: independent generation of alpha beta T cells and programmed rearrangements of gamma delta TCR genes.
Cell
72
:
337
348
.
30
Marino
S.
,
Vooijs
M.
,
van Der Gulden
H.
,
Jonkers
J.
,
Berns
A.
.
2000
.
Induction of medulloblastomas in p53-null mutant mice by somatic inactivation of Rb in the external granular layer cells of the cerebellum.
Genes Dev.
14
:
994
1004
.
31
DuPage
M.
,
Dooley
A. L.
,
Jacks
T.
.
2009
.
Conditional mouse lung cancer models using adenoviral or lentiviral delivery of Cre recombinase.
Nat. Protoc.
4
:
1064
1072
.
32
Houghton
A. M.
,
Quintero
P. A.
,
Perkins
D. L.
,
Kobayashi
D. K.
,
Kelley
D. G.
,
Marconcini
L. A.
,
Mecham
R. P.
,
Senior
R. M.
,
Shapiro
S. D.
.
2006
.
Elastin fragments drive disease progression in a murine model of emphysema.
J. Clin. Invest.
116
:
753
759
.
33
Nikitin
A. Y.
,
Alcaraz
A.
,
Anver
M. R.
,
Bronson
R. T.
,
Cardiff
R. D.
,
Dixon
D.
,
Fraire
A. E.
,
Gabrielson
E. W.
,
Gunning
W. T.
,
Haines
D. C.
, et al
.
2004
.
Classification of proliferative pulmonary lesions of the mouse: recommendations of the mouse models of human cancers consortium.
Cancer Res.
64
:
2307
2316
.
34
Jackson
E. L.
,
Olive
K. P.
,
Tuveson
D. A.
,
Bronson
R.
,
Crowley
D.
,
Brown
M.
,
Jacks
T.
.
2005
.
The differential effects of mutant p53 alleles on advanced murine lung cancer.
Cancer Res.
65
:
10280
10288
.
35
Culley
F. J.
2009
.
Natural killer cells in infection and inflammation of the lung.
Immunology
128
:
151
163
.
36
Waldhauer
I.
,
Steinle
A.
.
2008
.
NK cells and cancer immunosurveillance.
Oncogene
27
:
5932
5943
.
37
Groh
V.
,
Wu
J.
,
Yee
C.
,
Spies
T.
.
2002
.
Tumour-derived soluble MIC ligands impair expression of NKG2D and T-cell activation.
Nature
419
:
734
738
.
38
Wiemann
K.
,
Mittrücker
H. W.
,
Feger
U.
,
Welte
S. A.
,
Yokoyama
W. M.
,
Spies
T.
,
Rammensee
H. G.
,
Steinle
A.
.
2005
.
Systemic NKG2D down-regulation impairs NK and CD8 T cell responses in vivo.
J. Immunol.
175
:
720
729
.
39
Esendagli
G.
,
Bruderek
K.
,
Goldmann
T.
,
Busche
A.
,
Branscheid
D.
,
Vollmer
E.
,
Brandau
S.
.
2008
.
Malignant and non-malignant lung tissue areas are differentially populated by natural killer cells and regulatory T cells in non-small cell lung cancer.
Lung Cancer
59
:
32
40
.
40
Mahmoud
S. M.
,
Paish
E. C.
,
Powe
D. G.
,
Macmillan
R. D.
,
Grainge
M. J.
,
Lee
A. H.
,
Ellis
I. O.
,
Green
A. R.
.
2011
.
Tumor-infiltrating CD8+ lymphocytes predict clinical outcome in breast cancer.
J. Clin. Oncol.
29
:
1949
1955
.
41
Chang
S. H.
,
Mirabolfathinejad
S. G.
,
Katta
H.
,
Cumpian
A. M.
,
Gong
L.
,
Caetano
M. S.
,
Moghaddam
S. J.
,
Dong
C.
.
2014
.
T helper 17 cells play a critical pathogenic role in lung cancer.
Proc. Natl. Acad. Sci. USA
111
:
5664
5669
.
42
Ivanov
I. I.
,
McKenzie
B. S.
,
Zhou
L.
,
Tadokoro
C. E.
,
Lepelley
A.
,
Lafaille
J. J.
,
Cua
D. J.
,
Littman
D. R.
.
2006
.
The orphan nuclear receptor RORgammat directs the differentiation program of proinflammatory IL-17+ T helper cells.
Cell
126
:
1121
1133
.
43
Ueda
E.
,
Kurebayashi
S.
,
Sakaue
M.
,
Backlund
M.
,
Koller
B.
,
Jetten
A. M.
.
2002
.
High incidence of T-cell lymphomas in mice deficient in the retinoid-related orphan receptor RORgamma.
Cancer Res.
62
:
901
909
.
44
Meuwissen
R.
,
Linn
S. C.
,
Linnoila
R. I.
,
Zevenhoven
J.
,
Mooi
W. J.
,
Berns
A.
.
2003
.
Induction of small cell lung cancer by somatic inactivation of both Trp53 and Rb1 in a conditional mouse model.
Cancer Cell
4
:
181
189
.
45
Chen
Z.
,
Fillmore
C. M.
,
Hammerman
P. S.
,
Kim
C. F.
,
Wong
K. K.
.
2014
.
Non-small-cell lung cancers: a heterogeneous set of diseases.
Nat. Rev. Cancer
14
:
535
546
.
46
Guo
J. Y.
,
Karsli-Uzunbas
G.
,
Mathew
R.
,
Aisner
S. C.
,
Kamphorst
J. J.
,
Strohecker
A. M.
,
Chen
G.
,
Price
S.
,
Lu
W.
,
Teng
X.
, et al
.
2013
.
Autophagy suppresses progression of K-ras-induced lung tumors to oncocytomas and maintains lipid homeostasis.
Genes Dev.
27
:
1447
1461
.
47
Xu
C.
,
Fillmore
C. M.
,
Koyama
S.
,
Wu
H.
,
Zhao
Y.
,
Chen
Z.
,
Herter-Sprie
G. S.
,
Akbay
E. A.
,
Tchaicha
J. H.
,
Altabef
A.
, et al
.
2014
.
Loss of Lkb1 and Pten leads to lung squamous cell carcinoma with elevated PD-L1 expression.
Cancer Cell
25
:
590
604
.
48
Tran
E.
,
Ahmadzadeh
M.
,
Lu
Y. C.
,
Gros
A.
,
Turcotte
S.
,
Robbins
P. F.
,
Gartner
J. J.
,
Zheng
Z.
,
Li
Y. F.
,
Ray
S.
, et al
.
2015
.
Immunogenicity of somatic mutations in human gastrointestinal cancers.
Science
350
:
1387
1390
.
49
Subramanian
J.
,
Govindan
R.
.
2007
.
Lung cancer in never smokers: a review.
J. Clin. Oncol.
25
:
561
570
.
50
Govindan
R.
,
Ding
L.
,
Griffith
M.
,
Subramanian
J.
,
Dees
N. D.
,
Kanchi
K. L.
,
Maher
C. A.
,
Fulton
R.
,
Fulton
L.
,
Wallis
J.
, et al
.
2012
.
Genomic landscape of non-small cell lung cancer in smokers and never-smokers.
Cell
150
:
1121
1134
.
51
Ganesan
A. P.
,
Johansson
M.
,
Ruffell
B.
,
Yagui-Beltrán
A.
,
Lau
J.
,
Jablons
D. M.
,
Coussens
L. M.
.
2013
.
Tumor-infiltrating regulatory T cells inhibit endogenous cytotoxic T cell responses to lung adenocarcinoma. [Published erratum appears in 2013 J. Immunol. 191: 5319.]
J. Immunol.
191
:
2009
2017
.
52
McAllister
F.
,
Bailey
J. M.
,
Alsina
J.
,
Nirschl
C. J.
,
Sharma
R.
,
Fan
H.
,
Rattigan
Y.
,
Roeser
J. C.
,
Lankapalli
R. H.
,
Zhang
H.
, et al
.
2014
.
Oncogenic Kras activates a hematopoietic-to-epithelial IL-17 signaling axis in preinvasive pancreatic neoplasia.
Cancer Cell
25
:
621
637
.
53
Coffelt
S. B.
,
Kersten
K.
,
Doornebal
C. W.
,
Weiden
J.
,
Vrijland
K.
,
Hau
C. S.
,
Verstegen
N. J.
,
Ciampricotti
M.
,
Hawinkels
L. J.
,
Jonkers
J.
,
de Visser
K. E.
.
2015
.
IL-17-producing γδ T cells and neutrophils conspire to promote breast cancer metastasis.
Nature
522
:
345
348
.
54
Hannani
D.
,
Ma
Y.
,
Yamazaki
T.
,
Déchanet-Merville
J.
,
Kroemer
G.
,
Zitvogel
L.
.
2012
.
Harnessing γδ T cells in anticancer immunotherapy.
Trends Immunol.
33
:
199
206
.
55
Dieu-Nosjean
M. C.
,
Antoine
M.
,
Danel
C.
,
Heudes
D.
,
Wislez
M.
,
Poulot
V.
,
Rabbe
N.
,
Laurans
L.
,
Tartour
E.
,
de Chaisemartin
L.
, et al
.
2008
.
Long-term survival for patients with non-small-cell lung cancer with intratumoral lymphoid structures.
J. Clin. Oncol.
26
:
4410
4417
.
56
Joshi
N. S.
,
Akama-Garren
E. H.
,
Lu
Y.
,
Lee
D. Y.
,
Chang
G. P.
,
Li
A.
,
DuPage
M.
,
Tammela
T.
,
Kerper
N. R.
,
Farago
A. F.
, et al
.
2015
.
Regulatory T cells in tumor-associated tertiary lymphoid structures suppress anti-tumor T cell responses.
Immunity
43
:
579
590
.
57
van Meerbeeck
J. P.
,
Fennell
D. A.
,
De Ruysscher
D. K.
.
2011
.
Small-cell lung cancer.
Lancet
378
:
1741
1755
.
58
Ott
P. A.
,
Fernandez
M. E.
,
Hiret
S.
,
Kim
D.-W.
,
Moss
R. A.
,
Winser
T.
,
Yuan
S.
,
Cheng
J. D.
,
Piperdi
B.
,
Mehnert
J. M.
.
2015
.
Pembrolizumab (MK-3475) in patients with extensive-stage small cell lung cancer: Preliminary safety and efficacy results from KEYNOTE-028 [abstract].
J. Clin. Oncol.
33
:
S7502
.
59
Garon
E. B.
,
Rizvi
N. A.
,
Hui
R.
,
Leighl
N.
,
Balmanoukian
A. S.
,
Eder
J. P.
,
Patnaik
A.
,
Aggarwal
C.
,
Gubens
M.
,
Horn
L.
, et al
KEYNOTE-001 Investigators
.
2015
.
Pembrolizumab for the treatment of non-small-cell lung cancer.
N. Engl. J. Med.
372
:
2018
2028
.
60
Suzuki
K.
,
Kachala
S. S.
,
Kadota
K.
,
Shen
R.
,
Mo
Q.
,
Beer
D. G.
,
Rusch
V. W.
,
Travis
W. D.
,
Adusumilli
P. S.
.
2011
.
Prognostic immune markers in non-small cell lung cancer.
Clin. Cancer Res.
17
:
5247
5256
.
61
Vogelstein
B.
,
Papadopoulos
N.
,
Velculescu
V. E.
,
Zhou
S.
,
Diaz
L. A.
 Jr.
,
Kinzler
K. W.
.
2013
.
Cancer genome landscapes.
Science
339
:
1546
1558
.

The authors have no financial conflicts of interest.

Supplementary data