Surfactant Expression Defines an Inflamed Subtype of Lung Adenocarcinoma Brain Metastases That Correlates with Prolonged Survival

Published OnlineFirst January 17, 2020; DOI: 10.1158/1078-0432.CCR-19-2184

CLINICAL CANCER RESEARCH | TRANSLATIONAL CANCER MECHANISMS AND THERAPY

Surfactant Expression Deﬁnes an Inﬂamed Subtype of Lung Adenocarcinoma Brain Metastases that Correlates with Prolonged Survival Kolja Pocha1, Andreas Mock1,2,3,4, Carmen Rapp1, Steffen Dettling1, Rolf Warta1,4, Christoph Geisenberger1, Christine Jungk1, Leila R. Martins5, Niels Grabe6, David Reuss7,8, Juergen Debus4,9, Andreas von Deimling4,7,8, Amir Abdollahi4,9, Andreas Unterberg1, and Christel C. Herold-Mende1,4

ABSTRACT ◥ Purpose: To provide a better understanding of the interplay of three surfactant metabolism-related genes (SFTPA1, SFTPB, between the immune system and brain metastases to advance and NAPSA) was closely associated with TIL numbers. Their therapeutic options for this life-threatening disease. expression was not only prognostic in brain metastasis but also Experimental Design: Tumor-infiltrating lymphocytes (TIL) in primary lung adenocarcinoma. Correlation with scRNA-seq were quantified by semiautomated whole-slide analysis in data revealed that brain metastases with high expression of brain metastases from 81 lung adenocarcinomas. Multi-color surfactant genes might originate from tumor cells resembling staining enabled phenotyping of TILs (CD3, CD8, and FOXP3) alveolar type 2 cells. Methylome-based estimation of immune cell on a single-cell resolution. Molecular determinants of the fractions in primary lung adenocarcinoma confirmed a positive extent of TILs in brain metastases were analyzed by transcrip- association between lymphocyte infiltration and surfactant tomics in a subset of 63 patients. Findings in lung adenocarci- expression. Tumors with a high surfactant expression displayed noma brain metastases were related to published multi-omic atranscriptomicprofile of an inflammatory microenvironment. primary lung adenocarcinoma The Cancer Genome Atlas data Conclusion: The expression of surfactant metabolism-related (n ¼ 230) and single-cell RNA-sequencing (scRNA-seq) data genes (SFTPA1, SFTPB,andNAPSA)defines an inflamed subtype (n ¼ 52,698). of lung adenocarcinoma brain metastases characterized by high Results: TIL numbers within tumor islands was an independent abundance of TILs in close vicinity to tumor cells, a prolonged prognostic marker in patients with lung adenocarcinoma brain survival, and a tumor microenvironment which might be more metastases. Comparative transcriptomics revealed that expression accessible to immunotherapeutic approaches.

Introduction histologically be divided into two groups: small-cell lung cancer and non–small cell lung cancer (NSCLC). NSCLC can be further sub- Lung cancer is the most common type of cancer worldwide and the divided into subtypes, the most frequent being adenocarcinoma (2, 3). most common cause of cancer-related death (1, 2). Lung cancer can Lung adenocarcinoma also accounts for the largest histologic subset of brain metastases (4). Of note, brain metastases are even more frequent than primary brain tumors (5). Patients with brain metastases have a 1Division of Experimental Neurosurgery, Department of Neurosurgery, poor median overall survival of 7–13 months (6). Treatment is usually Heidelberg University Hospital, Heidelberg, Germany. 2Department of Medical multimodal and consists of systemic chemotherapy combined with Oncology, National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg 3 microsurgery, stereotactic radiosurgery, and/or radiotherapy (7). University Hospital, Heidelberg, Germany. Department of Translational Medical There is increasing evidence for the role of the host immune system Oncology, National Center for Tumor Diseases (NCT) Heidelberg, German Cancer Research Center (DKFZ), Heidelberg, Germany. 4German Cancer in cancer development, suppression, and recurrence (8). In colorectal Consortium (DKTK), Heidelberg, Germany. 5Division of Applied Functional cancer for instance, the quantification of tumor-infiltrating lympho- Genomics, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, cytes (TIL) has become a valid prognostic marker for patient survival Germany. 6Hamamatsu Tissue Imaging and Analysis Center (TIGA), BIOQUANT, and is believed to be superior to the tumor–node–metastasis classi- 7 University of Heidelberg, Heidelberg, Germany. Department of Neuropathol- fication (9). Many studies have since confirmed the prognostic power ogy, Institute of Pathology, Heidelberg University Hospital, Heidelberg, of immune cell infiltrates in a large number of cancer types. The Germany. 8Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Institute of Pathology, Heidelberg University Hospital, Heidel- positive effect of cytotoxic T cells and Th1 T cells on survival has been berg, Germany. 9Department of Radiation Oncology, University of Heidelberg, shown; however, the influence of intratumoral Th2, Th17, and reg- Heidelberg, Germany. ulatory T cells (Tregs) on survival is less clear (10). In patients with Note: Supplementary data for this article are available at Clinical Cancer lung adenocarcinoma with high overall T-cell counts and high cyto- Research Online (http://clincancerres.aacrjournals.org/). toxic T-cell counts, prolonged survival has been observed (11). Tregs, K. Pocha and A. Mock contributed equally to this article. on the other hand, seem to impair prognosis (12). However, in addition to quantity, the spatial distribution of immune Corresponding Author: Christel C. Herold-Mende, Heidelberg University cells and their vicinity to tumor cells in terms of localization within Hospital, INF 400, Heidelberg 69120, Germany. Phone: 4962-2156-6405; Fax: 4962-2156-33979; E-mail: [email protected] tumor islands or retention in the tumor stroma (13, 14) and the differences between primary tumors and subsequent metastases must Clin Cancer Res 2020;XX:XX–XX be taken into account. It has been shown that infiltration with cytotoxic doi: 10.1158/1078-0432.CCR-19-2184 T cells decreases from primary lung cancer to metastases (15, 16). 2020 American Association for Cancer Research. Nevertheless, data on the impact of T-cell infiltration on survival in

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informed consent was obtained from all patients in accordance with Translational Relevance the Declaration of Helsinki and its later amendments. Immunotherapies have become a powerful addition to the established therapies in metastatic non–small cell lung cancer. Immunofluorescence staining Given the dismal prognosis of brain metastases in these patients Tumor tissue was snap-frozen in liquid nitrogen–cooled isopentane it is essential to identify subsets of patients that have a tumor immediately after resection. Frozen tissues were cut into 5–7 mm slices, microenvironment that might be more accessible to immunother- acetone-fixed, and stored at 80C until staining. Immunofluorescent apeutic approaches. We provide evidence that a fraction of lung staining was performed using anti-CD3 (Dako, A0452), anti-CD8 adenocarcinoma brain metastases is indeed characterized by higher (Clone YTC182.20, Abcam, Ab60076), and anti-FOXP3 (Clone 236A/ tumor-infiltrating lymphocyte numbers and is in close association E7, Abcam, Ab20034) as described previously (27). DAPI (Invitrogen, with an inflamed microenvironment and high expression of sur- D1306) was used to counterstain the nuclei. The incubation time was factant genes. 1 hour, and this was followed by three washing steps. Secondary antibodies, including anti-rabbit Alexa Fluor 647 (Life Technologies, A21245), anti-rat Alexa Fluor 488 (Life Technologies, A11006), and anti-mouse Alexa Fluor 555 (Life Technologies, A31570) were then brain metastasis remain controversial, with some studies suggesting a applied for 1 hour, and this was followed by three washing steps. þ þ þ favorable role for CD3 , CD8 , and CD45R0 T cells (17) and other Tumor cells were labeled using anti-pan cytokeratin (Progen, 61835; studies showing no benefit (18, 19). Dako, M0630) staining in combination with an antibody labeling kit Understanding the interplay between the immune system and (Thermo Fisher Scientific, Z25002). Isotype controls were rabbit IgG cancer cells is becoming even more critical in patients with brain (Dako, X0936), IgG2b (eBioscience, 14-4031), and IgG1 (Abcam, metastases as checkpoint inhibitor therapies are on the rise for ab91353). The antibody characteristics are listed in Supplementary treating lung adenocarcinoma (20). Knowledge about the immune Table S1. microenvironment in primary lung cancer cannot be extrapolated to include brain metastases, because of specialized resident immune Semi-automated quantification of T-cell infiltration and stromal cells, including microglia and astrocytes (21–23), and Following immunofluorescent staining, whole-slide images were physical restriction by the blood–brain barrier (24). Genomic generated for all 81 tumor samples using an automated microscope alterations between primary lung cancer and brain metastasis, as (Olympus IX51 equipped with a F-View II camera, both Olympus) at well as their connection to the immune system are the subject of 20-fold magnification with the cellSens Software (Olympus). Tissue- ongoing research (25). Other data suggest that the amount of T-cell Quest Software (version 4.0.1.0137, TissueGnostics GmbH) was then infiltration in brain metastasis might be related to different DNA used for a semiautomated, objective, and quantitative analysis of methylation patterns (26). Thus, a thorough examination of brain stained cells in the whole-tumor section. Regions of stroma and metastasis biology, including similarities and differences to the necrosis were manually excluded to enable exclusive analysis of vital primary tumor, is needed to enable us to understand the role of tumor islands. The following gating strategy was used. First, DAPI the immune system in brain metastasis. staining was used to identify nuclei and thus intact cells. CD3- Our study aims to provide a comprehensive analysis of the lung expressing T cells were further characterized into cytotoxic and Tregs adenocarcinoma brain metastasis microenvironment. We assessed the through coexpression of CD8 and FOXP3, respectively. CD4 cells were þ number of TILs able to enter tumor islands and get into contact with indirectly assessed by the CD3 /CD8 population. Parameters mea- tumor cells and showed a positive effect of overall TIL infiltration, as sured included staining intensity, range and variance of intensity, and well as helper and cytotoxic T-cell infiltration on patient survival. nuclei size. Manual backward gating was performed for quality Moreover, based on comparative transcriptomic analyses, we related control. T-cell infiltration was quantified (cells/mm2), analyzed, and the extent of infiltration in brain metastases with the expression of grouped into tissues with high and low infiltration based on the surfactant pathway–related genes and showed that this was also the median. Further detail including definition of different cell types is case in primary lung adenocarcinoma. Multi-omic analysis suggested a provided in Supplementary Fig. S1A. surfactant pathway–associated change in the immune microenvironment that might render lung adenocarcinoma susceptible to immune RNA and DNA isolation and further analysis checkpoint inhibition. RNA (n ¼ 63) and DNA (n ¼ 20) were extracted from the 81 tumor samples using the AllPrep DNA/RNA/Protein Mini Kit (Qiagen). Analyte quality and concentration were monitored using Material and Methods the NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific) Patients and Bioanalyzer 2100 (Agilent). Microarray and methylation anal- Brain metastasis samples were obtained from 81 patients who ysis were performed at the DKFZ Genomics and Proteomics Core underwent surgery between November 2002 and December 2014 at Facility (DKFZ) using HumanHT-12 v4 Expression BeadChip and the Department of Neurosurgery at Heidelberg University Hospital Infinium MethylationEPIC BeadChips Kits (Illumina). Microarray (Heidelberg, Germany). All patients were diagnosed with lung ade- data obtained were normalized, log2-transformed, and median- nocarcinoma. Histology and a tumor cell content ≥ 60% were con- centered (28). Differences in genes expressed between tumor tissues firmed by board-certified neuropathologists (A. von Deimling and D. with high- and low–T-cell infiltration were identified by an inde- t P Reuss). Clinical follow-up and survival information was obtained from pendent two-sample test and the log2-fold change ( < 0.05, fold clinical records and the respective citizens' registration offices change > |1.5|). The transcriptomic classification of brain metas- (between November 2003 and January 2016). The study was approved tases into the surfactanthigh and surfactantlow group was done using by the ethics committee of the Medical Faculty, University of Heidel- k-means clustering with k ¼ 2. Methylation data were further berg (Heidelberg, Germany; reference number: 005/2003) and written processed using the Minfi package (version 1.28.4) in the R Software

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environment and split by the mRNA surfactant groups. Datasets are (P ¼ 0.003, age ≤ 61 years), patients with a Karnofsky score above 70 accessible at ArrayExpress (accession numbers E-MTAB-8659 and (P < 0.001), and patients without extracranial metastases (P ¼ 0.048; E-MTAB-8660). Supplementary Fig. S1B–S1D).

Multi-omic The Cancer Genome Atlas data analysis Semiautomated whole-slide analysis of brain metastases Normalized RNA sequencing (RNA-seq) data [The Cancer Genome reveals vast difference in TIL density Atlas (TCGA) RNA-Seq v2 Level 3, n ¼ 230] for lung adenocarcinomas Multicolor immunofluorescent staining was performed (Fig. 1A þ were obtained through the FireBrowse database (www.firebrowse.org, and B) to quantify TILs in general (CD3 ), helper T cells þ þ þ accessed on January 28, 2016). Mutation (n ¼ 230), SNP-array (SNP6 (CD3 CD8 FOXP3 ), and cytotoxic T cells (CD3 CD8 FOXP3 ). þ þ level 3, n ¼ 357), and methylation data (450k methylation data level 1 FOXP3 staining was used to define Tregs (CD3 CD8 FOXP3 for þ þ þ þ and 3, n ¼ 206) and associated clinical data were obtained from the classical Tregs and CD3 CD8 FOXP3 for CD8 Tregs; Supplemen- supplementary data of the primary TCGA publication (29). tary Fig. S2). The majority of stained T cells, independent of their The surfactant class (surfactantlow vs. surfactanthigh) was defined by subtype, were retained in the tumor stroma while few cells were found k-means clustering (k ¼ 2) of all lung adenocarcinoma samples (n ¼ to infiltrate tumor islands and thus be in direct contact with tumor cells 230) according to the RNA expression of surfactant metabolism– (Fig. 1B). The term TILs is usually imprecisely used for both stromal related genes SFTPB, STFPA1, and NAPSA. and epithelial T cells (34). To obtain more accurate data on T cells that Only protein-changing variants were considered for mutation can reach and kill tumor cells, we excluded the stromal areas from our data. Analyses of copy number data were performed using the analysis and focused only on the actual tumor-infiltrating T cells. GenomicRanges package (version 1.34.0) in the R Software environ- Marked differences were observed in overall TIL density (range, 10– þ ment. Methylation of the target genes was assessed and compared with 1,700 CD3 cells/mm2; Fig. 1C). Median intratumoral T-cell infil- their level of RNA expression using an independent two-sample t test. tration was 162 cells/mm2. Th cells were the most common subtype Differentially methylated genes were identified between the matched (156/mm2) followed by cytotoxic T cells (87/mm2), Tregs (19/mm2), fi t þ 2 þ gene expression pro les by test, log2-fold change CpG sites were and CD8 Tregs (2/mm ). The overall frequency of FOXP3 T cells mapped to the genome using the Gviz package (version 1.26.5), and was 8/mm2 (ranging from 0 to 170/mm2). immune cell counts were estimated using TCGA data level I meth- A correlation analysis was performed to assess the extent to which ylation data and a deconvolution algorithm within the Minfi package, T-cell subtypes infiltrate tumor islands simultaneously or indepen- as mentioned above. dently from one another (Supplementary Fig. S3). A Pearson corre- The immune subtype of TCGA samples (C1–C6) was derived lation coefficient greater than 0.6 was found for all except one test, from supplementary data recently published by Thorsson and indicating a positive correlation between all T-cell subtypes except Th þ colleagues (30). and CD8 Tregs. We further assessed the impact of synchronous versus metachronous metastases, as well as preoperative therapeutic Correlation of bulk transcriptomics to single-cell sequencing regimens on the TIL measurements. No correlation was found analysis in NSCLC and normal lung tissues (Fig. 1C; Supplementary Fig. S4). To assess the cell type–specific expression of the surfactant-related We observed a substantial but highly variable intratumoral T-cell genes SFTPA1, SFTPB, and NAPSA, we downloaded preprocessed infiltration of brain metastasis. This mainly consisted of cytotoxic and single-cell RNA-sequencing data (scRNA-seq; log2 counts per million, Th cells, with a dominating majority of Th cells, while the frequency of þ log2cpm), tSNE map, as well as the categorization into the different cell FOXP3 T cells was more than 19-fold lower. Correlation analysis types from ArrayExpress (accession numbers E-MTAB-6149 and revealed that infiltration of T-cell subsets correlated with the total E-MTAB-6653). The dataset published by Lambrechts and collea- T-cell count and were independent from time between diagnosis of gues (31) comprises a catalog of 52,698 cells from both normal lung cancer and the occurrence of brain metastases. tissue as well as non–small cell lung carcinomas. Alveolar cells type 2 (AT2) cell fractions were estimated in the bulk transcriptomic brain Tumor-entering TILs are an independent prognostic parameter metastasis microarray data using the standard least-squares method- for overall survival in patients with brain metastases ology (32) in the CellMix R package (33). We used log-rank tests to assess whether the observed infiltration differences had an impact on overall survival. As previously men- Statistical analysis tioned, we divided our study sample into high (n ¼ 40) and low (n ¼ All data and statistical analyses were carried out using the R software 41) infiltration for overall TIL density and each of the T-cell subtypes. (version 3.5.1). Log-rank tests and the Cox proportional hazard model A significant survival benefit was observed in tumors with high were used for univariate and multivariate survival comparisons, overall TIL density (P ¼ 0.007), and for Th cell (P ¼ 0.021) and respectively. Correlation analysis was done using the Pearson prod- cytotoxic T-cell (P ¼ 0.011) subtypes (Fig. 2A–G). There was, þ uct-moment correlation coefficient. A P < 0.05 was considered sta- however, no significant difference in survival for FOXP3 cells (Tregs, þ tistically significant. Significance levels were labelled as follows , P < P ¼ 0.415; CD8 Tregs, P ¼ 0.767). In the multivariate model, age 0.05; , P < 0.01; and , P < 0.001. at diagnosis and Karnofsky score maintained significance. High overall tumor-entering TIL density was an independent prognostic marker (P ¼ 0.016; HR, 2.06). A trend was observed for Th cells (P ¼ 0.057; Results HR, 1.78), cytotoxic T cells (P ¼ 0.110; HR, 1.61). Classical Tregs as þ Clinicopathologic parameters of study set well as CD8 Tregs did not convey a survival benefit(P ¼ 0.592, HR: Samples from 81 patients with lung adenocarcinoma brain metas- 1.17; P ¼ 0.472, HR: 1.24, respectively). tasis were included in the semiautomated whole-slide analysis of In conclusion, high overall TIL density and high helper and immune infiltrates (Table 1). Univariate analysis of clinicopathologic cytotoxic T-cell density were found to improve survival in univariate parameters revealed a significant survival benefit for younger patients analyses. Overall TIL density remained significant in multivariate

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Table 1. Descriptive statistics and univariate survival analysis of clinical parameters in the lung adenocarcinoma brain metastases study set (n ¼ 81). The presented P value is based on the log-rank test.

Descriptive statistics Survival analysis Variable n patients (%) median (range) P HR [95% CI (months)]

Gender Male 40 (49.4) 0.606 1.16 (6.5–30.5) Female 41 (50.6) Age at diagnosis [years] < Median 41 (50.6) 61 (40–80) 0.003 0.43 (12.5–33.9) ≥ Median 40 (49.4) Number of BMa Single 49 (60.5) 1 (1–19) 0.667 1.13 (10.6–28.3) Multiple 32 (39.5) BM occurrence Synchronous 37 (45.7) 0.936 1.02 (9.1–30.9) Metachronous 42 (51.9) Unknown 2 (2.5) Preoperative Yes 28 (34.6) 0.492 0.60 (10.6–NA) Chemotherapy No 40 (49.4) Unknown 13 (16) Preoperative WBRT Yes 2 (2.5) NA NA No 63 (77.8) Unknown 16 (19.8) Postoperative WBRT Yes 60 (74.1) 0.230 0.64 (15.4–28.3) No 15 (18.5) Karnofsky scorea > 70 60 (74.1) 90 (40–100) < 0.001 0.35 (3.3–17.6) ≤ 70 17 (21.0) Unknown 4 (4.9) Smoker Yesb 69 (85.2) 0.646 0.84 (10.6–25.9) Never 11 (13.6) Unknown 1 (1.2) Tumor stagec Stage I–III 33 (40.7) 0.756 0.91 (10.1–30.5) Stage IV 46 (56.8) Unknown 2 (2.5) Size of resected BM ≥ 3 cm 37 (45.7) 3.0 (0.9–6.0) 0.786 1.08 (8.9–25.9) < 3 cm 37 (45.7) Unknown 7 (8.6) Extent of resection Totald 45 (55.5) 0.868 0.95 (11.2–28.3) Partiale 34 (42.0) Unknown 2 (2.5) Extracranial metastasesa Yes 19 (23.4) 0.048 1.89 (2.8–17.6) No 60 (74.1) Unknown 2 (2.5)

Note: Signiﬁcant values were displayed in bold. P values: , < 0.05; , < 0.01; , < 0.001. Abbreviations: BM, brain metastases; HR, hazard ratio; NA, not available; n, number; WBRT, whole-brain radiotherapy. aAt the time of neurosurgical resection. bCurrent and past. cAt the time of ﬁrst diagnosis. dMacroscopically no tumor tissue left in the brain. eIncomplete resection of singular metastasis or other metastases left in the brain.

þ analysis. The density of FOXP3 T cells had no impact on survival. most distinct differences in expression (Supplementary Fig. S5A and þ CD3 T cells were identified as the most reliable prognostic immune S5B). These genes also showed significantly higher expression in the parameter in both the univariate and multivariate analysis. highly infiltrated groups (Supplementary Fig. S5B). Pathway and gene- function analysis revealed these genes, together with SFTPA1,tobe TIL density was associated with overexpression of surfactant part of the surfactant metabolism pathway. pathway–related genes in lung adenocarcinoma brain Hierarchical clustering of the brain metastasis samples, on the basis metastases of the expression of the three surfactant metabolism pathway genes As a result of the observed survival association, we next aimed to SFTPA1, SFTPB, and NAPSA (referred to as surfactant genes from identify molecular determinants related to high- and low-TIL infil- now on), revealed two clusters and coexpression of the three genes. tration and performed comparative transcriptomic analyses in 63 of The clusters are further referred to as the classes surfactanthigh and the 81 brain metastasis samples. Ten genes were found to be differ- surfactantlow (Fig. 3B; surfactanthigh, n ¼ 22; surfactantlow, n ¼ 41). We entially expressed between the overall TIL and/or Th/cytotoxic cell tested for survival differences between the two groups, and observed a high and low groups, including FOLR1, CLIC6, CEACAM5, SFTPA1, clear survival benefit in the surfactanthigh group (Fig. 3C; P ¼ 0.002). NAPSA, MUC16, C9orf152, TMPRSS2, SFTPB, and CLDN10 (Fig. 3A; When analyzing the influence of each of the three genes on survival Supplementary Table S2). Of these, SFTPB and NAPSA showed the separately, we found that the prognostic performance of the SFTPA1

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Figure 1. Study design and T-cell infiltration in lung adenocarcinoma brain metastases. A, Graphical abstract of the conducted experiments. In the discovery cohort of brain metastases, immunofluorescent stainings (IF, n ¼ 81) and RNA microarray analysis (n ¼ 63) were performed. Validation was done using RNA-seq, methylation, mutation, and alteration of copy number data from the TCGA primary lung adenocarcinoma (LUAD) dataset. In addition, a scRNA-seq dataset of NSCLC was used. Finally, an integrative data analysis was conducted. B, Immunofluorescent stainings of DAPI, CD3, and cytokeratins. Pictures show a representative sample in multicolor view and single-color channels. TILs, defined as infiltrating into the tumor, are marked with a red arrow. C, Barplots and boxplots of the different T-cell subtypes in the cohort. Th cells (CD3þCD8FOXP3, median: 156/mm2), cytotoxic T cells (CD3þCD8þFOXP3, median: 87/mm2), classical Tregs (CD3þCD8FOXP3þ, median: 19/mm2), and CD8þ Tregs (CD3þCD8þFOXP3þ, median: 2/mm2). Clinical parameters are indicated below the barplot.

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Figure 2. Survival association with density of different tumor-entering T-cell subsets and multivariate analysis. Infiltration was grouped by median separation into high (solid) and low (dashed) infiltration density. Log-rank test was used to calculate survival differences, and Kaplan–Meier plots were drawn for display. A–C, High overall TIL density, high TH-cell density, and high-cytotoxic T-cell density improved survival. D–F, No survival difference was observed for Tregs. G, Age at diagnosis, Karnofsky score, and overall TIL density remained independent prognostic factors in multivariate analysis (Cox proportional hazard ratio). Significant values were displayed in bold; P values: , < 0.05; , < 0.01; , < 0.001.

expression was even more pronounced than the combined model of all pronounced when divided into a smaller low-expression (n ¼ 22) and a three genes (P < 0.0001; Supplementary Fig. S6A). NAPSA also larger high-expression group (n ¼ 41), which was more similar to its maintained signiﬁcance when divided by its median (P ¼ 0.003; distribution in the heatmap (P ¼ 0.007; Supplementary Fig. S6C). We Supplementary Fig. S6B). The effect on survival for SFTPB was most further analyzed possible associations between clinical parameters and

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Figure 3. Differentially expressed genes and further analysis of surfactant genes in our cohort (n ¼ 63) and a TCGA dataset (n ¼ 230). A, Scatterplot of differentially expressed genes between the groups of high- and low-overall TIL density, Th-cell density, and cytotoxic T-cell density (Heidelberg brain metastasis dataset). Cutoff for differential expression: log-rank P < 0.05 and log2 fold-change > 1.5. B, Heatmap of SFTPA1, SFTPB,andNAPSA expression (Heidelberg brain metastasis dataset). Two groups can be identiﬁed by hierarchical clustering. C, Survival analysis of the two clusters using log-rank test and Kaplan–Meier plots (Heidelberg brain metastasis dataset). Cluster 2 (surfactanthigh class) is positively associated with survival. D, Heatmap of RNA-seq expression of SFTPA1, SFTPB,andNAPSA (TCGA dataset). Clustering was done as described above and revealed a surfactanthigh and surfactantlow group. E, Multivariate survival analysis using Cox-proportional hazard ratio (TCGA dataset). Parameters included patient age at diagnosis and surfactant expression clustering. Signiﬁcant differences were observed, especially in the group of older patients. LUAD, lung adenocarcinoma.

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surfactant gene expression. Only patient age was identified to differ in the surfactanthigh group (P ¼ 0.024; Fig. 4C). In conclusion, these between the two surfactant groups (older age in surfactanthigh group; findings suggest that in lung adenocarcinoma brain metastases, a Supplementary Table S3). As done for the full study cohort of 81 higher TIL density is associated with a higher fraction of AT2-like patients, we again performed univariate survival analysis for the tumor cells. patient subset with mRNA microarray information (n ¼ 63; Supple- mentary Table S4). Here, age and Karnofsky score but not extracranial Expression of surfactant genes was not driven by mutations or metastases were prognostic in this subcohort. In a multivariate model, copy number changes the surfactant grouping remained an independent prognostic param- We performed multi-omic analysis using the TCGA data to shed eter (Supplementary Table S5). light on the underlying mechanism leading to differential expression of Altogether, we found 10 differentially expressed genes, 3 of which the surfactant genes. We preprocessed and matched the available belonged to the surfactant metabolism pathway (surfactant genes). mutation data (total number of mutations across the study cohort The two classes, surfactanthigh and surfactantlow revealed considerable ¼ 68,270) to our preexisting surfactant classes. We first had a look at survival differences in favor of patients with high-surfactant gene the number of mutations found within each of the three surfactant expression, independent of clinical parameters. genes (Supplementary Table. S6). We found 1 patient with a missense mutation (c.230G>T) in SFTPA1; all other patients had no mutation in Surfactant classes are present in primary lung adenocarcinomas the gene. Two patients had a mutation in SFTPB (one nonsense After identifying two surfactant classes of prognostic relevance, we mutation c.133C>T; one splice site, c.393_splice), and 1 patient had tried to confirm this using an independent dataset. We used the a missense mutation (c.25C>T) in NAPSA. There was no defined publicly available TCGA primary lung adenocarcinoma datasets for difference between the most recurrent genes in the two surfactant this purpose, which contain RNA-seq, mutation, copy number, and groups (Supplementary Table S7). methylation data. We subsequently assessed whether alterations in copy number When the 230 lung adenocarcinoma samples with RNA-seq data affected differential surfactant gene expression. The SNP6 data were available were clustered according to expression of SFTPB, SFTPA1, matched to the RNA-seq data in the aforementioned way. We plotted and NPASA, 196 samples were found to belong to the surfactanthigh the chromosomes on which our target genes are located (chromo- class and 24 to the surfactantlow class (Fig. 3D). somes 2, 10, and 19) and colored the region of interest according to the In contrast to brain metastases, the fraction of primary lung respective surfactant group (Supplementary Fig. S6G). None of the adenocarcinoma harboring a surfactanthigh class was greater. As genes showed any remarkable difference in copy numbers. before, we queried the prognostic impact regarding overall survival In summary, mutations and alterations in copy number did not play of the surfactant class. Among the prognostic clinical parameters of a role in the differential expression of the surfactant genes. our brain metastasis study sample, only age at diagnosis was available for the TCGA dataset. Univariately, the prognostic performance DNA methylation analysis of surfactant genes of surfactant class and age at diagnosis just failed to reach significance DNA methylation was investigated after mutations and altera- (P ¼ 0.052 and P ¼ 0.053, respectively). When combined in a tions in copy number were ruled out as reasons for differential multivariate model, however, they had a significant effect on patient expression in the surfactant genes. Thus, we took the 450k meth- survival (P < 0.0001; Fig. 3E). The subset with the most dismal ylation dataset available from the TCGA study sample and calcu- prognosis was older patients with a surfactantlow tumor. lated differential beta values between the two surfactant groups. In primary lung adenocarcinoma, a subset of tumors shows coor- Whilewecouldnotobserveaglobaldifferenceinthemethylome dinated downregulation of the surfactant genes (surfactantlow class) (Supplementary Fig. S7A) between surfactanthigh and surfactantlow although in a lower proportion of patients than in brain metastasis. tumors, significant hypermethylation of CpG sites could be This, in combination with age at diagnosis, was associated with poor observed for all 3 surfactant genes in the surfactantlow class survival in a multivariate model. (SFTPA1, P ¼ 0.001; SFTPB, P ¼ 0.005; NAPSA, P < 0.001; Supplementary Fig. S7B–S7E; Supplementary Table S8). A subset of primary lung adenocarcinoma tumor cells resemble As this observation was only made in the primary lung adenocar- surfactant-expressing AT2 cells cinomas of the TCGA, we aimed to validate this finding in our lung In the normal lung, expression of surfactant genes is characteristic adenocarcinoma brain metastasis cohort and performed methylation for AT2 cells. To understand the expression of surfactant genes in analysis of 10 surfactanthigh and surfactantlow tumors. Likely due to the lung adenocarcinoma brain metastases, we investigated the intratu- small sample size, we could only validate a differential methylation for moral heterogeneity of primary lung adenocarcinomas by mining the SFTPB (Supplementary Fig. S7F and S7G). scRNA-seq atlas published by Lambrechts and colleagues (31). The In conclusion, we observed a hypermethylation of the surfactant atlas is comprised of 52,689 cells of NSCLC and normal lung samples. genes in surfactantlow primary lung adenocarcinoma. However, this A subset of tumor cells showed a transcriptomic profile clustering finding could only be validated for SFTPB. together with AT2 cells from normal lung samples (Fig. 4A). Visu- alizing the expression landscape separately for AT2 cells and tumor Estimation of immune cell fractions identified higher abundance þ cells confirms an AT2-like high expression of surfactant genes in a of CD4 T cells and natural killer cells in surfactanthigh primary considerable subset of tumor cells (Fig. 4B). Notably, while SFTPA1 lung adenocarcinomas and NAPSA were expressed almost exclusively in AT2 cells, SFTPB Immune cell fractions in primary lung adenocarcinoma were was also found in AT1 and epithelial cells and quite prominently in estimated from the methylome data using a deconvolution algo- club cells (Supplementary Fig. S6D–S6F). Furthermore, applying the rithm (35). This enabled comparison with our initial T-cell count þ expression signature of AT2 cells to our mRNA study sample of brain experiment (Fig. 5A). CD4 T cells were the largest group with an metastasis using a deconvolution method (standard least-squares), we estimated median abundance of over 30%. This was followed by B cells, found that the estimated fraction of AT2 cells was significantly higher monocytes, natural killer (NK) cells, and granulocytes with an

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Figure 4. ScRNA-seq analysis in NSCLC and normal lung atlas (52,698 cells). A, tSNE plot of all tumor cells and AT2 cells only. B, tSNE plot substratiﬁed for AT2 cells and tumor cells. Cells are colored according to the expression of the surfactant genes (log2cpm). C, Deconvolution analysis in lung adenocarcinoma brain metastasis cohort (n ¼ 63) to estimate the fraction of AT2 cells in the bulk expression data.

þ abundance of around 20% each. CD4 T cells were significantly more Surfactanthigh lung adenocarcinomas display a transcriptomic prevalent in the surfactanthigh group, whereas NK cells were signifi- profile resembling an inflammatory microenvironment cantly more abundant in the surfactantlow group (Fig. 5B). There was To explore differences in the microenvironment of surfactantlow þ no difference in CD8 T cells between the two groups. However, and surfactanthigh tumors, we obtained the transcriptome-based þ reliable estimates for CD8 T cells were difficult to achieve due to a immune subtypes (C1–C6) recently published by the TCGA work- smaller proportion of this cell type. ing group (30). Types C1 (wound healing) and C2 (INFg dominant)

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Figure 5. Dissecting the tumor microenvironment of surfactantlow and surfactanthigh primary lung adenocarcinomas. A, Abundance distributions of CD4þ T cells, B cells, monocytes, NK cells, granulocytes, and CD8þ T cells quantified by means of a methylation-based deconvolution algorithm in the full study set. B, Cell estimation differences between the surfactanthigh and surfactantlow classes (CD4, CD8, and NK cells). t Tests revealed significantly higher CD4þ T cells and significantly lower NK cells in the surfactanthigh class. C, Categorization of surfactant groups according to the immune subtypes (C1–C6) proposed by Thorsson and colleagues (30). The inflammatory C3 subtype is overrepresented in the surfactanthigh group. D, Prediction of responsiveness to immunotherapies (ITs) based on immune types in light of recent literature (38). Grouping into hot, altered, and cold tumor immune status (TIS) was based on the recently reviewed ImmunoScore (36). E, Expression levels of surfactant metabolism genes according to immune type.

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were most prevalent in the surfactantlow group (38.2% and 41.2%, Our study is the most comprehensive of this sort to date. First, we respectively; Fig. 5C). The other types, including C3 (inflammatory, used an innovative, semiautomated, objective, and quantitative, 2.9%), C4 (lymphocyte depleted, 5.9%), C5 (immunologically quiet, whole-tumor slide multicolor immunofluorescent approach and sep- 0.0%), and C6 (TGF-b dominant, 11.8%), were less frequent. In the arated tumor islands from tumor stroma, as emphasized by Geng and surfactanthigh group, the largest group was C3 (39.4 %), followed by colleagues (34). Second, our sample size of 81 patients with lung C2 (35.8%), C1 (12.4%), C6 (7.8%), C5 (4.7%), and C4 (0.0%). Most adenocarcinoma brain metastasis was relatively large. Third, we strikingly, there was a clear difference in the proportions of immune assessed the survival benefit of intraepithelial lymphocyte infiltration subtype C3 (inflammatory). Likewise, expression for the surfactant and thus of T cells with the potential to kill tumor cells. genes was highest in the subtype C3 (Fig. 5E). To gain insight into the tumor microenvironment as well as As the proposed immune subtypes have not been tested for their differences in gene expression associated with T-cell infiltration, we predictive performance in prospective clinical trials, we related the performed mRNA microarray analysis of 63 lung adenocarcinoma TCGA classification to the ImmunoScore developed by Galon and brain metastasis. Investigating differential expression between high- colleagues (ref. 36; Fig. 5D). Galon and colleagues mainly differ- and low–T-cell infiltration, we found that 3 surfactant genes, entiate the tumor immune status (TIS)intocold,altered,andhot. SFTPA1, SFTPB,andNAPSA,weresignificantly overexpressed in C1 and C2 are most closely related to an altered TIS and respond patients with high intraepithelial T-cell infiltration. Multivariate less well to immunotherapies due to higher proliferation rates (37). survival analysis revealed that surfactanthigh gene expression was a In contrast, the inflammatory C3 type corresponds to a hot TIS, strong positive indicator of survival independent of the clinical which is an indicator of good response to immunotherapy (36). The prognostic parameters age and Karnofsky score. To the best of our immune types C4 and C5 correspond to cold TIS, for which knowledge, this has never previously been shown for lung adeno- immune checkpoint inhibition is believed to be unsuccessful. It carcinoma brain metastasis. There are data, however, which suggest remains unclear how the TGF-b dominant group (C6) corresponds a favorable role of surfactant in primary lung cancer. Recent studies to TIS (36). We further evaluated the expression of surfactant genes suggest that surfactant proteins A and B are able to suppress the stratified by immune subtype. Expression of all 3 genes was found to progression in NSCLC (45, 46), possibly through interaction with be highest in the C3 type. immune cells (46, 47) or by reducing the activity of EGFR and In summary, our data were matched to preexisting nomenclatures thereby acting in a similar manner to tyrosine-kinase inhibitors (48). to characterize the different immune subtypes of surfactanthigh and The role of NAPSA in lung cancer is much less established, but surfactantlow tumors. We found the inflammatory C3 type to be the recent studies suggest that NAPSA could have a supportive function largest group in surfactanthigh tumors, but it was rarely found in with regards to the effect of EGFR tyrosine kinase inhibitors (49). surfactantlow tumors. Interestingly, the C3 type is believed to be most Nevertheless, a limitation of the prognostic performance of the susceptible to immunotherapies (30, 36–38). surfactant groups remains the lack of information regarding the postoperative palliative systemic therapies. Next, we analyzed published scRNA-seq data to interrogate the cell Discussion type–specific expression of the surfactant genes. In normal lung The role of TILs as a prognostic marker has been increasingly tissues, surfactant is produced by AT2 cells. This could be confirmed recognized in a number of tumor entities (10). In NSCLC, CD3 and in the scRNA-seq landscape. Intriguingly, the surfactant genes were CD8 TILs in particular have been associated with a favorable prog- also expressed by a subset of tumor cells that transcriptionally resemble nosis (34). Few studies on TILs in brain metastasis have been pub- AT2 cells. While it has been proposed that AT2 cells are the cell of lished and despite a robust design, their results are controver- origin of lung adenocarcinomas (Lambrechts and colleagues), our sial (17, 39). Data regarding the relevance of TILs that are able to get data, for the first time, shows that AT2-like tumor cells are also present in close contact with tumor cells in brain metastasis and are not in brain metastases and seem to be linked to a high-TIL density (31). retained in the tumor stroma, and the molecular determinants We subsequently performed an integrative multi-omic analysis in involved in this process, are missing. the primary lung adenocarcinoma TCGA cohort to question potential In this study, we found lymphocyte density to be highly variable drivers of surfactant gene expression. This approach seemed most in lung adenocarcinoma brain metastases. Some tissues were barely feasible because multi-omics datasets for lung adenocarcinoma brain infiltrated, as expected in primary brain tumors (27), but as metastases are not available. previously described some had abundant infiltrates (17). With Analysis of RNA-seq data revealed the presence of surfactanthigh regards to the association between infiltration of lymphocytes in and surfactantlow groups in both brain metastasis and the primary direct contact with tumor cells and patient outcome, we showed that tumor; however, the proportions of high and low expression differed high overall TIL density is an independent positive prognostic significantly between the groups. In brain metastasis, approximately marker for patients with lung adenocarcinoma brain metastasis. two-thirds of patients presented with low expression of the surfactant Helper and cytotoxic T cells appear to play the most important role genes. This is in sharp contrast to healthy lung tissue (www.protei within the overall TILs, despite not being significant in the mul- natlas.org), where the surfactant genes are highly expressed (50), and tivariate model. These findings are in accordance with recent studies also in contrast to our findings in primary lung cancer (especially lung that showed the beneficial effect of high–T-cell infiltration (mea- adenocarcinoma) where only 17.3% of cases presented with down- sured in the entire brain metastasis tissue, derived from lung regulation of the surfactant genes. adenocarcinoma or otherwise) on patient survival (40, 41). In Mutation, methylation, and alteration in copy number were inves- contrast, Castaneda and colleagues found no significant difference tigated in patients with primary lung adenocarcinoma to further between high and low CD3 TILs in 64 cases of brain metastasis (42). understand the cause of differential expression. While mutation Tregs, which have been shown to impair the immune response to analysis did reveal some differences between the surfactanthigh and primary brain malignancies (43) and primary lung adenocarcino- surfactantlow groups, this could not be linked to the differential ma (44), did not impact patient survival in our study. expression of surfactant genes. This indicates that mutational burden

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is not the driver for overexpression. Methylation analysis, however, molecular determinant of the lung adenocarcinoma microenviron- revealed significant differences in the methylation across the CpG sites ment. Intriguingly, this feature was shared between primary lung of the 3 surfactant genes. To validate this finding in brain metastases, adenocarcinomas and lung adenocarcinoma brain metastases. we analyzed a small brain metastasis subset of 20 cases. Mostly due to Tumors with a high-surfactant gene expression (surfactanthigh the small study sample we could only validate the differential expres- class) harbored a high-intraepithelial T-cell infiltration and were sion for SFTPB. Larger studies are warranted to investigate this change characterized by an inflammatory and less immunosuppressive in DNA methylation. tumor environment. After having identified no clear driver for the overexpression of the surfactant genes, we next aimed to compare the tumor microenvi- Disclosure of Potential Conflicts of Interest ronment between the surfactanthigh and surfactantlow groups. Meth- J. Debus reports receiving commercial research grants from Merck Serono, ylation-based estimation of immune cell fractions in primary lung Accuray, and Raysearch, and is an advisory board member/unpaid consultant for Accuray, Merck Serono, and ViewRay. No potential conflicts of interest were adenocarcinoma could validate the association between surfactant disclosed by the other authors. gene expression and a higher T-cell count (35). Yet, the deconvolution algorithms are not on par with the quantitative cell-based approach we Authors’ Contributions used for brain metastasis. Conception and design: K. Pocha, A. Mock, C. Rapp, S. Dettling, J. Debus, It has been shown that numerous factors may play a role in the C.C. Herold-Mende effectiveness of immunotherapies, including mutational burden, T- Development of methodology: K. Pocha, A. Mock, N. Grabe cell infiltration, and the tumor microenvironment with its various Acquisition of data (provided animals, acquired and managed patients, provided components (30, 51). Different classification systems are emerging facilities, etc.): K. Pocha, C. Jungk, D. Reuss, J. Debus, A. von Deimling, A. Abdollahi, to accommodate the overwhelming and yet-to-be integrated new C.C. Herold-Mende Analysis and interpretation of data (e.g., statistical analysis, biostatistics, information. Thorsson and colleagues provide one of the most computational analysis): K. Pocha, A. Mock, S. Dettling, R. Warta, comprehensive, pan-cancer classifications using TCGA data from C. Geisenberger, L.R. Martins, A. von Deimling, C.C. Herold-Mende more than 10,000 patients, and divide the tumor microenvironment Writing, review, and/or revision of the manuscript: K. Pocha, A. Mock, C. Rapp, into six distinct immune subtypes (30). Further efforts by Galon and S. Dettling, C. Geisenberger, C. Jungk, J. Debus, A. Abdollahi, A. Unterberg, colleagues have been directed to classify tumors into hot, altered, C.C. Herold-Mende andcoldbytheamountofinfiltrating lymphocytes (36). By using Administrative, technical, or material support (i.e., reporting or organizing data, fi high constructing databases): R. Warta, J. Debus, A. von Deimling these ndings, we predicted which immune types surfactant and Study supervision: S. Dettling, R. Warta, J. Debus, C.C. Herold-Mende surfactantlow tumors correspond to and tried to understand what impact this could have on the effectiveness of immunotherapy. Acknowledgments Interestingly,wefoundanenrichmentofaninflammatory tumor We thank Mandy Barthel, Frederik Enders, and Anja Metzner for review of patient subtype (C3) in surfactanthigh tumors, likely to correspond to the data. Furthermore, we thank Farzaneh Kashfi, Hildegard Goltzer,€ Ilka Hearn, and best response due to its inflammatory nature and intermediate Melanie Greibich for their excellent technical assistance. proliferation (30, 36–38). Nevertheless, this putative biomarker fi The costs of publication of this article were defrayed in part by the payment of page warrants further preclinical and clinical con rmation. The associ- charges. This article must therefore be hereby marked advertisement in accordance ation with the C3 subtype was also true for the expression of with 18 U.S.C. Section 1734 solely to indicate this fact. surfactant genes NAPSA, SFTPA1,andSFTPB. Altogether, we identified the expression of the surfactant path- Received July 3, 2019; revised December 9, 2019; accepted January 14, 2020; way–related genes SFTPA1, STFPB,andNAPSA to be a promising published first January 17, 2020.

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Surfactant Expression Defines an Inflamed Subtype of Lung Adenocarcinoma Brain Metastases that Correlates with Prolonged Survival

Kolja Pocha, Andreas Mock, Carmen Rapp, et al.

Clin Cancer Res Published OnlineFirst January 17, 2020.

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