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

Author Manuscript Published OnlineFirst on January 17, 2020; DOI: 10.1158/1078-0432.CCR-19-2184 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Title Surfactant expression defines an inflamed subtype of lung adenocarcinoma brain metastases that correlates with prolonged survival Authors

Kolja Pocha1*, Andreas Mock1,2,3,4*, Carmen Rapp1, Steffen Dettling1, Rolf Warta1,4, Christoph Geisenberger1, Christine Jungk1, Leila Martins5, Niels Grabe6, David Reuss7,8, Juergen Debus4,9, Andreas von Deimling4,7,8, Amir Abdollahi4,9, Andreas Unterberg1, Christel Herold-Mende1,4

Affiliations

1 Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University Hospital, Heidelberg, Germany 2 Department of Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg University Hospital, Heidelberg, Germany 3 Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg, German Cancer Research Center (DKFZ), Heidelberg, Germany 4 German Cancer Consortium (DKTK), Heidelberg, Germany 5 Division of Applied Functional Genomics, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany 6 Hamamatsu Tissue Imaging and Analysis Center (TIGA), BIOQUANT, University of Heidelberg, Heidelberg, Germany 7 Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany 8 Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Institute of Pathology, Heidelberg University Hospital, Heidelberg 69120, Germany 9 Department of Radiation Oncology, University of Heidelberg, Heidelberg, Germany * equal contribution

Running Title Surfactant defines inflammation in brain metastases

Correspondence

Prof. Dr. Christel Herold-Mende, Division of Experimental Neurosurgery, Department of Neurosurgery, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany. Phone: +49 (0) 6221-5637927; Fax: +49 (0) 6221-5633979; E-mail: [email protected] heidelberg.de

Conflict of interest: The authors have declared that no conflict of interest exists.

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Downloaded from clincancerres.aacrjournals.org on October 1, 2021. © 2020 American Association for Cancer Research. Author Manuscript Published OnlineFirst on January 17, 2020; DOI: 10.1158/1078-0432.CCR-19-2184 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

ABSTRACT

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

Translational Relevance

Immunotherapies have become a powerful addition to the established therapies in metastatic non- small cell lung cancer. Given the dismal prognosis of brain metastases in these patients it is essential to identify subsets of patients that have a tumor microenvironment that might be more accessible to immunotherapeutic approaches. We provide evidence that a fraction of LUAD brain metastases is indeed characterized by a higher TIL numbers and in close association with an inflamed microenvironment and high expression of surfactant genes.

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INTRODUCTION

Lung cancer is the most common type of cancer worldwide and the most common cause of cancer- related death (1,2). Lung cancer can histologically be divided into two groups: small cell lung cancer

(SCLC) and non-small cell lung cancer (NSCLC). NSCLC can be further subdivided into subtypes, the most frequent being adenocarcinoma (2,3). Lung adenocarcinoma (LUAD) also accounts for the largest histological subset of brain metastases (BM) (4). Of note, BM are even more frequent than primary brain tumors (5). Patients with BMs have a poor median overall survival of 7-13 months (6).

Treatment is usually multimodal and consists of systemic chemotherapy combined with microsurgery, stereotactic radiosurgery, and/or radiotherapy (7).

There is increasing evidence for the role of the host immune system in cancer development, suppression, and recurrence (8). In colorectal cancer for instance, the quantification of TILs has become a valid prognostic marker for patient survival and is believed to be superior to the TNM classification (9). Many studies have since confirmed the prognostic power of immune cell infiltrates in a large number of cancer types. The positive effect of cytotoxic T cells and Th1 T cells on survival has been shown; however, the influence of intratumoral Th2, Th17, and Tregs on survival is less clear

(10). In patients with LUAD with high overall T cell counts and high cytotoxic T cell counts, prolonged survival has been observed (11). Regulatory T cells, on the other hand, seem to impair prognosis (12).

However, in addition to quantity, the spatial distribution of immune cells and their vicinity to tumor cells in terms of localization within tumor islands or retention in the tumor stroma (13,14) and the differences between primary tumors and subsequent metastases must be taken into account. It has been shown that infiltration with cytotoxic T cells decreases from primary lung cancer to metastases

(15,16). Nevertheless, data on the impact of T cell infiltration on survival in BM remain controversial, with some studies suggesting a favorable role for CD3+, CD8+, and CD45R0+ T cells (17) and other studies showing no benefit (18,19).

Understanding the interplay between the immune system and cancer cells is becoming even more critical in patients with BMs as checkpoint inhibitor therapies are on the rise for treating LUAD (20).

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Knowledge about the immune microenvironment in primary lung cancer cannot be extrapolated to include BMs, due to specialized resident immune and stromal cells, including microglia and astrocytes (21-23), and physical restriction by the blood-brain barrier (BBB) (24). Genomic alterations between primary lung cancer and BM as well as their connection to the immune system are the subject of ongoing research (25). Other data suggest that the amount of T cell infiltration in BM might be related to different DNA methylation patterns (26). Thus, a thorough examination of BM biology, including similarities and differences to the primary tumor, is needed to enable us to understand the role of the immune system in BM.

Our study aims to provide a comprehensive analysis of the LUAD BM microenvironment. We assessed the number of TILs able to enter tumor islands and get into contact with tumor cells and showed a positive effect of overall TIL infiltration, as well as helper and cytotoxic T cell infiltration on patient survival. Moreover, based on comparative transcriptomic analyses, we related the extent of infiltration in BMs with the expression of surfactant pathway-related genes and showed that this was also the case in primary LUAD. Multi-omic analysis suggested a surfactant pathway associated change in the immune microenvironment, that might render LUAD susceptible to immune checkpoint inhibition.

MATERIAL AND METHODS

Patients

BM samples were obtained from 81 patients who underwent surgery between November 2002 and

December 2014 at the Department of Neurosurgery at Heidelberg University Hospital, Germany. All patients were diagnosed with LUAD. Histology and a tumor cell content ≥ 60% were confirmed by board-certified neuropathologists (AvD, DR). Clinical follow-up and survival information was obtained from clinical records and the respective citizens' registration offices (between November 2003 and

January 2016). The study was approved by the ethics committee of the Medical Faculty, University of

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Heidelberg, Germany (reference number: 005/2003) and written informed consent was obtained from all patients in accordance with the Declaration of Helsinki and its later amendments.

Immunofluorescence staining

Tumor tissue was snap-frozen in liquid nitrogen-cooled isopentane immediately after resection.

Frozen tissues were cut into 5-7 μm slices, acetone-fixed, and stored at –80° C until staining.

Immunofluorescent staining was performed using anti-CD3 (Dako, A0452), anti-CD8 (Clone

YTC182.20, Abcam, Ab60076), and anti-FOXP3 (Clone 236A/E7, Abcam, Ab20034) as previously described (27). DAPI (Invitrogen, D1306) was used to counterstain the nuclei. The incubation time was one 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 applied for one hour, and this was followed by three washing steps. Tumor cells were labelled using anti-pan cytokeratin (Progen, 61835; Dako, M0630) staining in combination with an antibody labeling kit

(Thermo Fisher, Z25002). Isotype controls were rabbit IgG (Dako, X0936), IgG2b (eBioscience, 14-

4031), IgG1 (Abcam, ab91353). The antibody characteristics are listed in Suppl. Table S1.

Semi-automated quantification of T cell infiltration

Following immunofluorescent staining, whole slide images were generated for all 81 tumor samples using an automated microscope (Olympus IX51 equipped with a F-View II camera, both Olympus) at

20-fold magnification with the cellSens Software (Olympus). TissueQuest Software (version

4.0.1.0137, TissueGnostics GmbH) was then used for a semi-automated, objective, and quantitative analysis of stained cells in the whole tumor section. Regions of stroma and necrosis were manually excluded to enable exclusive analysis of vital tumor islands. The following gating strategy was used.

First, DAPI staining was used to identify nuclei and thus intact cells. CD3-expressing T cells were further characterized into cytotoxic and regulatory T cells through co-expression of CD8 and FOXP3, respectively. CD4 cells were indirectly assessed by the CD3+ / CD8- population. Parameters measured [5]

included staining intensity, range and variance of intensity, and nuclei size. Manual backward gating was performed for quality control. T cell infiltration was quantified (cells / mm2), analyzed, and grouped into tissues with high and low infiltration based on the median. Further detail including definition of different cell types is provided in Suppl. Fig. S1A.

RNA and DNA isolation and further analysis

RNA (n=63) and DNA (n=20) were extracted from the 81 tumor samples using the AllPrep

DNA/RNA/Protein Mini Kit (Qiagen). Analyte quality and concentration were monitored using the

Nanodrop 2000 spectrophotometer (Thermo Scientific) and Bioanalyzer 2100 (Agilent). Microarray and methylation analysis were performed at the DKFZ genomics and proteomics core facility

(Heidelberg) using HumanHT-12 v4 Expression BeadChip and Infinium MethylationEPIC BeadChips

Kits (Illumina). Microarray data obtained were normalized, log2-transformed, and median-centered

(28). Differences in genes expressed between tumor tissues with high and low T cell infiltration were identified by an independent two-sample t-test and the log2-fold change (p < 0.05, FC > |1.5|). The transcriptomic classification of BMs into the surfactanthigh and surfactantlow group was done using k- means clustering with k=2. Methylation data were further processed using the Minfi package

(version 1.28.4) in the R Software environment and split by the mRNA surfactant groups. Data sets are accessible at ArrayExpress (accession numbers E-MTAB-8659 and E-MTAB-8660).

Multi-omic TCGA data analysis

Normalized RNA-Seq data (TCGA RNA-Seq v2 Level 3, n=230) for LUADs was obtained through the

FireBrowse database (www.firebrowse.org, accessed on 2016/01/28). Mutation (n=230), SNP-array

(SNP6 level 3, n=357), and methylation data (450k methylation data level 1 and 3, n=206) and associated clinical data were obtained from the supplementary data of the primary TCGA publication

(29).

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The surfactant class (surfactantlow vs. surfactanthigh) was defined by k-means clustering (k=2) of all

LUAD samples (n=230) according to the RNA expression of surfactant metabolism-related genes

SFTPB, STFPA1, and NAPSA.

Only protein-changing variants were considered for mutation data. Analyses of copy number data were performed using the GenomicRanges package (version 1.34.0) in the R Software environment.

Methylation of the target genes was assessed and compared to their level of RNA expression using an independent two-sample t-test. Differentially methylated genes were identified between the matched gene expression profiles by t-test and log2-fold change CpG-sites were mapped to the genome using the Gviz package (version 1.26.5), and immune cell counts were estimated using TCGA data level I methylation data and a deconvolution algorithm within the Minfi package, as mentioned above.

The immune subtype of TCGA samples (C1-C6) was derived from supplementary data recently published by Thorsson et al. (30).

Correlation of bulk transcriptomics to single cell sequencing analysis in NSCLC and normal lung tissues

To assess the cell type-specific expression of the surfactant-related genes SFTPA1, SFTPB and NAPSA, we downloaded pre-processed single cell RNA sequencing data (log2 counts per million; log2cpm), tSNE map as well as the categorization into the different cell types from ArrayExpress (accession numbers E-MTAB-6149 and E-MTAB-6653). The data set published by Lambrechts et al. (31) comprises a catalog of 52,698 cells from both normal lung tissue as well as non-small cell lung carcinomas. AT2 cell fractions was estimated in the bulk transcriptomic BM microarray data using the standard least-squares methodology (32) in the CellMix R package (33).

Statistical methods

All data and statistical analyses were carried out using the R software (version 3.5.1). Log-rank tests and the Cox proportional hazard model were used for univariate and multivariate survival [7]

comparisons, respectively. Correlation analysis was done using the Pearson product-moment correlation coefficient. A p-value <0.05 was considered statistically significant. Significance levels were labelled as follows p < 0.05 *, p < 0.01 **, and p < 0.001 ***.

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RESULTS

Clinicopathological parameters of study set

Samples from 81 LUAD BM patients were included in the semi-automated whole slide analysis of immune infiltrates (Table 1). Univariate analysis of clinicopathological parameters revealed a significant survival benefit for younger patients (p = 0.003, age ≤ 61 years), patients with a Karnofsky score above 70 (p < 0.001), and patients without extracranial metastases (p = 0.048) (Suppl. Fig. S1B-

D).

Semi-automated whole slide analysis of BMs reveals vast difference in TIL density

Multicolor immunofluorescent staining was performed (Fig. 1A-B) to quantify TILs in general (CD3+), helper T cells (CD3+CD8-FOXP3-), and cytotoxic T cells (CD3+CD8+FOXP3-). FOXP3 staining was used to define regulatory T cells (CD3+CD8-FOXP3+ for classical regulatory T cells and CD3+CD8+FOXP3+ for

CD8+ regulatory T cells; Suppl. Fig S2). The majority of stained T cells, independent of their subtype, was retained in the tumor stroma while few cells were found to infiltrate tumor islands and thus be in direct contact with tumor cells (Fig. 1B). The term TILs is usually imprecisely used for both stromal and epithelial T cells (34). To obtain more accurate data on T cells that can reach and kill tumor cells, we excluded the stromal areas from our analysis and focused only on the actual tumor-infiltrating T cells. Marked differences were observed in overall TIL density (range 10 to 1700 CD3+ cells / mm2)

(Fig. 1C). Median intratumoral T cell Infiltration was 162 cells / mm2. T helper cells were the most common subtype (156 / mm2) followed by cytotoxic T cells (87 / mm2), regulatory T cells (19 / mm2), and CD8+ regulatory T cells (2 / mm2). The overall frequency of FOXP3+ T cells was 8 / mm2 (ranging from 0 to 170 / mm2).

A correlation analysis was performed to assess the extent to which T cell subtypes infiltrate tumor islands simultaneously or independently from one another (Suppl. Fig. S3). A Pearson correlation coefficient greater than 0.6 was found for all except one test, indicating a positive correlation between all T cell subtypes except T helper and CD8+ regulatory T cells. We further assessed the

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impact of synchronous vs. metachronous metastases, as well as preoperative therapeutic regimens on the TIL measurements. No correlation was found (Fig. 1C, Suppl. Fig. S4).

We observed a substantial but highly variable intratumoral T cell infiltration of BM. This mainly consisted of cytotoxic and helper T cells, with a dominating majority of helper T cells, while the frequency of FOXP3+ T cells was more than 19-fold lower. Correlation analysis revealed that infiltration of T cell subsets correlated with the total T cell count and were independent from time between diagnosis of cancer and the occurrence of brain metastases.

Tumor-entering TILs are an independent prognostic parameter for overall survival in patients with

BMs

We used log-rank tests to assess whether the observed infiltration differences had an impact on overall survival. As previously mentioned, we divided our study sample into high (n = 40) and low (n =

41) infiltration for overall TIL density and each of the T cell subtypes.

A significant survival benefit was observed in tumors with high overall TIL density (p = 0.007), and for helper T cell (p = 0.021) and cytotoxic T cell (p = 0.011) subtypes (Fig. 2A-G). There was however no significant difference in survival for FOXP3+ cells (regulatory T cells: p = 0.415; CD8+ regulatory T cells: p = 0.767). In the multivariate model, age at diagnosis and Karnofsky-score maintained significance.

High overall tumor-entering TIL density was an independent prognostic marker (p = 0.016, HR: 2.06).

A trend was observed for helper T cells (p = 0.057, HR: 1.78), cytotoxic T cells (p = 0.110, HR: 1.61).

Classical regulatory T cells as well as CD8+ regulatory T cells did not convey a survival benefit (p =

0.592, HR: 1.17; p = 0.472, HR: 1.24, respectively).

In conclusion, high overall TIL density and high helper and cytotoxic T cell density were found to improve survival in univariate analyses. Overall TIL density remained significant in multivariate analysis. The density of FOXP3+ T cells had no impact on survival. CD3+ T cells were identified as the most reliable prognostic immune parameter in both the univariate and multivariate analysis.

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TIL density was associated with overexpression of surfactant pathway-related genes in LUAD BMs

As a result of the observed survival association, we next aimed to identify molecular determinants related to high and low TIL infiltration and performed comparative transcriptomic analyses in 63 of the 81 BM samples. Ten genes were found to be differentially expressed between the overall TIL and/or T helper/cytotoxic cell high and low groups, including FOLR1, CLIC6, CEACAM5, SFTPA1,

NAPSA, MUC16, C9orf152, TMPRSS2, SFTPB, and CLDN10 (Fig. 3A, Suppl. Table S2). Of these, SFTPB and NAPSA showed the most distinct differences in expression (Suppl. Fig. S5A-B). These genes also showed significantly higher expression in the highly infiltrated groups (Suppl. Fig. S5B). Pathway and gene function analysis revealed these genes, together with SFTPA1, to be part of the surfactant metabolism pathway.

Hierarchical clustering of the BM samples, on the basis of the expression of the three surfactant metabolism pathway genes SFTPA1, SFTPB, and NAPSA (referred to as surfactant genes from now on), revealed two clusters and co-expression the three genes. The clusters are further referred to as the classes surfactanthigh and surfactantlow (Fig. 3B; surfactanthigh, n=22; surfactantlow, n=41). We tested for survival differences between the two groups, and observed a clear survival benefit in the surfactanthigh group (Fig. 3C; p = 0.002). When analyzing the influence of each of the three genes on survival separately, we found that the prognostic performance of the SFTPA1 expression was even more pronounced than the combined model of all three genes (Suppl. Fig. S6A; p < 0.0001). NAPSA also maintained significance when divided by its median (Suppl. Fig. S6B; p = 0.003). The effect on survival for SFTPB was most pronounced when divided into a smaller low expression (n = 22) and a larger high expression group (n = 41), which was more similar to its distribution in the heatmap

(Suppl. Fig. S6C; p = 0.007). We further analyzed possible associations between clinical parameters and surfactant gene expression. Only patient age was identified to differ between the two surfactant groups (older age in surfactanthigh group; Suppl. Table S3). As done for the full study cohort of 81 patients, we again performed univariate survival analysis for the patient subset with mRNA microarray information (n=63; Suppl. Table S4). Here, age and Karnofsky-score but not extracranial

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metastases were prognostic in this subcohort. In a multivariate model, the surfactant grouping remained an independent prognostic parameter (Suppl. Table S5).

Altogether, we found 10 differentially expressed genes, three of which belonged to the surfactant metabolism pathway (surfactant genes). The two classes surfactanthigh and surfactantlow revealed considerable survival differences in favor of patients with high surfactant gene expression, independent of clinical parameters.

Surfactant classes are present in primary lung adenocarcinomas

After identifying two surfactant classes of prognostic relevance, we tried to confirm this using an independent dataset. We used the publicly available TCGA primary LUAD datasets for this purpose, which contain RNA-Seq, mutation, copy number, and methylation data.

When the 230 LUAD samples with RNA sequencing data available were clustered according to expression of SFTPB, SFTPA1, and NPASA, 196 samples were found to belong to the surfactanthigh class and 24 to the surfactantlow class (Fig. 3D).

In contrast to BMs, the fraction of primary LUAD harboring a surfactanthigh class was greater. As before, we queried the prognostic impact regarding overall survival of the surfactant class. Among the prognostic clinical parameters of our BM study sample, only age at diagnosis was available for the TCGA data set. Univariately, the prognostic performance of surfactant class and age at diagnosis just failed to reach significance (p = 0.052 and p = 0.053, respectively). When combined in a multivariate model however, they had a significant effect on patient survival (p < 0.0001; Fig. 3E).

The subset with the most dismal prognosis were older patients with a surfactantlow tumor.

In primary LUAD a subset of tumors shows coordinated downregulation of the surfactant genes

(surfactantlow class) although in a lower proportion of patients than in BM. This, in combination with age at diagnosis, was associated with poor survival in a multivariate model.

A subset of primary LUAD tumor cells resemble surfactant expressing AT2 cells

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In the normal lung, expression of surfactant genes is characteristic for alveolar cells type 2 (AT2 cells).

To understand the expression of surfactant genes in the LUAD brain metastases, we investigated the intratumoral heterogeneity of primary LUADs by mining the single cell RNA-seq atlas published by

Lambrechts and coworkers (31). The atlas is comprised of 52,689 cells of non-small cell lung cancers

(NSCLC) and normal lung samples. A subset of tumor cells showed a transcriptomic profile clustering together with AT2 cells from normal lung samples (Fig. 4A). Visualizing the expression landscape separately for AT2 cells and tumor cells confirms an AT2-like high expression of surfactant genes in a considerable subset of tumor cells (Fig. 4B). Notably, while SFTPA1 and NAPSA were expressed almost exclusively in AT 2 cells, SFTPB was also found in AT1 and epithelial cells and quite prominently in club cells (Suppl. Fig. S6D-F). Furthermore, applying the expression signature of AT 2 cells to our mRNA study sample of BM using a deconvolution method (standard least-squares), we found that the estimated fraction of AT2 cells was significantly higher in the surfactanthigh group

(p=0.024; Fig. 4C). In conclusion, these findings suggest that in LUAD BMs a higher TIL density is associated with a higher fraction of AT2-like tumor cells.

Expression of surfactant genes was not driven by mutations or copy number changes

We performed multi-omic analysis using the TCGA data to shed light on the underlying mechanism leading to differential expression of the surfactant genes. We preprocessed and matched the available mutation data (total number of mutations across the study cohort = 68,270) to our preexisting surfactant classes. We first had a look at the number of mutations found within each of the three surfactant genes (Suppl. Table. S6). We found one patient with a missense mutation (c.230G>T) in SFTPA1; all other patients had no mutation in the gene. Two patients had a mutation in SFTPB (one nonsense mutation c.133C>T, one splice site, c.393_splice), and one patient had a missense mutation

(c.25C>T) in NAPSA. There was no defined difference between the most recurrent genes in the two surfactant groups (Suppl. Table. S7).

We subsequently assessed whether alterations in copy number affected differential surfactant gene expression. The SNP6 data were matched to the RNA-Seq data in the aforementioned way. We [13]

plotted the chromosomes on which our target genes are located (chromosomes 2, 10, and 19) and colored the region of interest according to the respective surfactant group (Suppl. Fig. S6G). None of the genes showed any remarkable difference in copy numbers.

In summary, mutations and alterations in copy number did not play a role in the differential expression of the surfactant genes.

DNA methylation analysis of surfactant genes

DNA methylation was investigated after mutations and alterations in copy number were ruled out as reasons for differential expression in the surfactant genes. Thus, we took the 450k methylation dataset available from the TCGA study sample and calculated differential beta values between the two surfactant groups. While we could not observe a global difference in the methylome (Suppl Fig.

S7A) between surfactanthigh and surfactantlow tumors, significant hypermethylation of CpG sites could be observed for all three surfactant genes in the surfactantlow class (SFTPA1 p = 0.001; SFTPB p =

0.005; NAPSA p < 0.001; Suppl. Fig. S7B-E; Suppl. Table S8).

As this observation was only made in the primary LUADs of the TCGA, we aimed to validate this finding in our LUAD BM cohort and performed methylation analysis of ten surfactanthigh and surfactantlow tumors. Likely due to the small sample size, we could only validate a differential methylation for SFTPB (Suppl. Fig. S7F-G).

In conclusion, we observed a hypermethylation of the surfactant genes in surfactantlow primary

LUAD. However, this finding could only be validated for SFTPB.

Estimation of immune cell fractions identified higher abundance of CD4 T cells and NK cells in surfactanthigh primary LUADs

Immune cell fractions in primary LUAD were estimated from the methylome data using a deconvolution algorithm (35). This enabled comparison with our initial T cell count experiment

(Fig. 5A). CD4+ T cells were the largest group with an estimated median abundance of over 30%. This [14]

was followed by B cells, monocytes, NK cells, and granulocytes with an abundance of around 20% each. CD4+ T cells were significantly more prevalent in the surfactanthigh group, whereas NK cells were significantly more abundant in the surfactantlow group (Fig. 5B). There was no difference in CD8+

T cells between the two groups. However, reliable estimates for CD8+ T cells were difficult to achieve due to a smaller proportion of this cell type.

Surfactanthigh LUADs display a transcriptomic profile resembling an inflammatory microenvironment

To explore differences in the microenvironment of surfactantlow and surfactanthigh tumors, we obtained the transcriptome-based immune subtypes (C1-C6) recently published by the TCGA working group (30). Types C1 (wound healing) and C2 (INF-γ dominant) were most prevalent in the surfactantlow group (38.2% and 41.2%, respectively; Fig. 5C). The other types, including C3

(inflammatory, 2.9%), C4 (lymphocyte depleted, 5.9%), C5 (immunologically quiet, 0.0%), and C6

(TGF-β dominant, 11.8%), were less frequent. In the surfactanthigh group, the largest group was C3

(39.4 %), followed by C2 (35.8%), C1 (12.4%), C6 (7.8%), C5 (4.7%), and C4 (0.0%). Most strikingly, there was a clear difference in the proportions of immune subtype C3 (inflammatory). Likewise, expression for the surfactant genes was highest in the subtype C3 (Fig. 5E).

As the proposed immune subtypes have not been tested for their predictive performance in prospective clinical trials, we related the TCGA classification to the ImmunoScore developed by Galon et al. (36) (Fig. 5D). Galon and colleagues mainly differentiate the tumor immune status (TIS) into cold, altered, and hot. C1 and C2 are most closely related to an altered TIS and respond less well to immunotherapies due to higher proliferation rates (37). In contrast, the inflammatory C3 type corresponds to a hot TIS which is an indicator of good response to immunotherapy (36). The immune types C4 and C5 correspond to cold TIS, for which immune checkpoint inhibition is believed to be unsuccessful. It remains unclear how the TGF-beta dominant group (C6) corresponds to TIS (36). We further evaluated the expression of surfactant genes stratified by immune subtype. Expression of all three genes was found to be highest in the C3 type. [15]

In summary, our data were matched to preexisting nomenclatures to characterize the different immune subtypes of surfactanthigh and surfactantlow tumors. We found the inflammatory C3 type to be the largest group in surfactanthigh tumors, but it was rarely found in surfactantlow tumors.

Interestingly, the C3 type is believed to be most susceptible to immunotherapies (30,36-38).

DISCUSSION

The role of TILs as a prognostic marker has been increasingly recognized in a number of tumor entities (10). In NSCLC, CD3 and CD8 TILs in particular have been associated with a favorable prognosis (34). Few studies on TILs in BM have been published, and despite a robust design their results are controversial (17,39). Data regarding the relevance of TILs that are able to get in close contact with tumor cells in BM and are not retained in the tumor stroma, and the molecular determinants involved in this process, are missing.

In this study, we found lymphocyte density to be highly variable in LUAD BMs. Some tissues were barely infiltrated, as expected in primary brain tumors (27), but as previously described some had abundant infiltrates (17). With regards to the association between infiltration of lymphocytes in direct contact with tumor cells and patient outcome, we showed that high overall TIL density is an independent positive prognostic marker for LUAD BM patients. Helper and cytotoxic T cells appear to play the most important role within the overall TILs, despite not being significant in the multivariate model. These findings are in accordance with recent studies that showed the beneficial effect of high

T cell infiltration (measured in the entire BM tissue, derived from LUAD or otherwise) on patient survival (40,41). In contrast, Castaneda at al. found no significant difference between high and low

CD3 TILs in 64 cases of BM (42). Regulatory T cells, which have been shown to impair the immune response to primary brain malignancies (43) and primary LUAD (44), did not impact patient survival in our study.

Our study is the most comprehensive of this sort to date. First, we used an innovative, semi- automated, objective and quantitative, whole-tumor slide multicolor immunofluorescent approach and separated tumor islands from tumor stroma, as emphasized by Geng et al. (34). Second, our

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sample size of 81 LUAD BM patients was relatively large. Third, we assessed the survival benefit of intraepithelial lymphocyte infiltration and thus of T cells with the potential to kill tumor cells.

To gain insight into the tumor microenvironment as well as differences in gene expression associated with T cell infiltration, we performed mRNA microarray analysis of 63 LUAD BM. Investigating differential expression between high and low T cell infiltration, we found that three surfactant genes,

SFTPA1, SFTPB, and NAPSA, were significantly overexpressed in patients with high intraepithelial T cell infiltration. Multivariate survival analysis revealed that surfactanthigh gene expression was a strong positive indicator of survival independent of the clinical prognostic parameters age and

Karnofsky-score. To the best of our knowledge, this has never previously been shown for LUAD BM.

There are data however, which suggest a favorable role of surfactant in primary lung cancer. Recent studies suggest that surfactant proteins A and B are able to suppress the progression in NSCLC

(45,46), possibly through interaction with immune cells (46,47) or by reducing the activity of EGFR and thereby acting in a similar manner to tyrosine-kinase inhibitors (48). The role of NAPSA in lung cancer is much less established, but recent studies suggest that NAPSA could have a supportive function with regards to the effect of EGFR-tyrosine-kinase inhibitors (49). Nevertheless, a limitation of the prognostic performance of the surfactant groups remains the lack of information regarding the post-operative palliative systemic therapies.

Next, we analyzed published scRNA-seq data to interrogate the cell type-specific expression of the surfactant genes. In normal lung tissues, surfactant is produced by alveolar type 2 cells (AT2 cells).

This could be confirmed in the scRNA-seq landscape. Intriguingly, the surfactant genes were also expressed by a subset of tumor cells that transcriptionally resemble AT2 cells. While it has been proposed that AT2 cells are the cell of origin of LUADs (Lambrechts et al.), our data for the first time shows that AT2-like tumor cells are also present in BMs and seem to be linked to a high TIL density.

We subsequently performed an integrative multi-omic analysis in the primary LUAD TCGA cohort to question potential drivers of surfactant gene expression. This approach seemed most feasible because multi-omics data sets for LUAD BMs are not available.

[17]

Analysis of RNA-Seq data revealed the presence of surfactanthigh and surfactantlow groups in both BM and the primary tumor; however, the proportions of high and low expression differed significantly between the groups. In BM, approximately two-thirds of patients presented with low expression of the surfactant genes. This is in sharp contrast to healthy lung tissue [www.proteinatlas.org] where the surfactant genes are highly expressed (50), and also in contrast to our findings in primary lung cancer (especially LUAD) where only 17.3% percent of cases presented with downregulation of the surfactant genes.

Mutation, methylation, and alteration in copy number were investigated in primary LUAD patients to further understand the cause of differential expression. While mutation analysis did reveal some differences between the surfactanthigh and surfactantlow groups, this could not be linked to the differential expression of surfactant genes. This indicates that mutational burden is not the driver for overexpression. Methylation analysis however, revealed significant differences in the methylation across the CpG-sites of the three surfactant genes. To validate this finding in BMs, we analyzed a small BM subset of 20 cases. Most due to the small study sample we could only validate the differential expression for SFTPB. Larger studies are warranted to investigate this change in DNA methylation.

After having identified no clear driver for the overexpression of the surfactant genes, we next aimed to compare the tumor microenvironment between the surfactanthigh and surfactantlow groups.

Methylation-based estimation of immune cell fractions in primary LUAD could validate the association between surfactant gene expression and a higher T cell count (35). Yet, the deconvolution algorithms are not on par with the quantitative cell-based approach we used for BM.

It has been shown that numerous factors may play a role in the effectiveness of immunotherapies, including mutational burden, T cell infiltration, and the tumor microenvironment with its various components (30,51). Different classification systems are emerging to accommodate the overwhelming and yet-to-be integrated new information. Thorsson and colleagues provide one of the most comprehensive, pan-cancer classifications using TCGA data from more than 10,000 patients, and divide the tumor microenvironment into six distinct immune subtypes (30). Further [18]

efforts by Galon and colleagues have been directed to classify tumors into hot, altered, and cold by the amount of infiltrating lymphocytes (36). By using these findings, we predicted which immune types surfactanthigh and surfactantlow tumors correspond to and tried to understand what impact this could have on the effectiveness of immunotherapy. Interestingly, we found an enrichment of an inflammatory tumor subtype (C3) in surfactanthigh tumors, likely to correspond to the best response due to its inflammatory nature and intermediate proliferation (30,36-38). Nevertheless, this putative biomarker warrants further preclinical and clinical confirmation. The association with the C3 subtype was also true for the expression of surfactant genes NAPSA, SFTPA1, and SFTPB.

Altogether, we identified the expression of the surfactant pathway-related genes SFTPA1, STFPB and

NAPSA to be a promising molecular determinant of the lung adenocarcinoma microenvironment.

Intriguingly, this feature was shared between primary LUADs and LUAD brain metastases. Tumors with a high surfactant gene expression (surfactanthigh class) harbored a high intraepithelial T cell infiltration and were characterized by an inflammatory and less immunosuppressive tumor environment.

[19]

Acknowledgements

We thank Mandy Barthel, Frederik Enders and Anja Metzner for review of patient data. Furthermore, we thank Farzaneh Kashfi, Hildegard Göltzer, Ilka Hearn and Melanie Greibich for their excellent technical assistance.

REFERENCES

1. Parkin DM, Bray F, Ferlay J, Pisani P. Global cancer statistics, 2002. CA Cancer J Clin. 2005;55:74–108.

2. Ginsberg MS, Grewal RK, Heelan RT. Lung cancer. Radiol Clin North Am. 2007;45:21–43.

3. Youlden DR, Cramb SM, Baade PD. The International Epidemiology of Lung Cancer: geographical distribution and secular trends. J Thorac Oncol. 2008;3:819–31.

4. Nayak L, Lee EQ, Wen PY. Epidemiology of brain metastases. Curr Oncol Rep. 2012;14:48–54.

5. Gavrilovic IT, Posner JB. Brain metastases: epidemiology and pathophysiology. J Neurooncol. 2005;75:5–14.

6. Kondziolka D, Martin JJ, Flickinger JC, Friedland DM, Brufsky AM, Baar J, et al. Long-term survivors after gamma knife radiosurgery for brain metastases. Cancer. 2005;104:2784–91.

7. Kim YH, Nagai H, Ozasa H, Sakamori Y, Mishima M. Therapeutic strategy for non-small-cell lung cancer patients with brain metastases (Review). Biomed Rep. 2013;1:691–6.

8. Galon J, Pagès F, Marincola FM, Angell HK, Thurin M, Lugli A, et al. Cancer classification using the Immunoscore: a worldwide task force. J Transl Med. Journal of Translational Medicine; 2012;10:205–10.

9. Galon J, Mlecnik B, Bindea G, Angell HK, Berger A, Lagorce C, et al. Towards the introduction of the “Immunoscore” in the classification of malignant tumours. Jubb A, Koeppen H, Reis- Filho J, editors. J Pathol. 2014;232:199–209.

10. Fridman WH, Pagès F, Sautès-Fridman C, Galon J. The immune contexture in human tumours: impact on clinical outcome. Nature Publishing Group. Nature Publishing Group; 2012;12:298– 306.

11. Bremnes RM, Busund L-T, Kilvær TL, Andersen S, Richardsen E, Paulsen EE, et al. The Role of Tumor-Infiltrating Lymphocytes in Development, Progression, and Prognosis of Non-Small Cell Lung Cancer. J Thorac Oncol. Elsevier Inc; 2016;:1–12.

12. Tao H, Mimura Y, Aoe K, Kobayashi S, Yamamoto H, Matsuda E, et al. Prognostic potential of FOXP3 expression in non-small cell lung cancer cells combined with tumor-infiltrating regulatory T cells. Lung Cancer. Elsevier Ireland Ltd; 2012;75:95–101.

13. Al-Shibli KI, Donnem T, Al-Saad S, Persson M, Bremnes RM, Busund L-T. Prognostic effect of epithelial and stromal lymphocyte infiltration in non-small cell lung cancer. Clinical Cancer Research. American Association for Cancer Research; 2008;14:5220–7.

[20]

14. Zhou J, Gong Z, Jia Q, Wu Y, Yang Z-Z, Zhu B. Programmed death ligand 1 expression and CD8+ tumor-infiltrating lymphocyte density differences between paired primary and brain metastatic lesions in non-small cell lung cancer. Biochemical and Biophysical Research Communications. Elsevier Ltd; 2018;498:751–7.

15. Müller P, Rothschild SI, Arnold W, Hirschmann P, Horvath L, Bubendorf L, et al. Metastatic spread in patients with non-small cell lung cancer is associated with a reduced density of tumor-infiltrating T cells. Cancer Immunology, Immunotherapy. Springer Berlin Heidelberg; 2016;65:1–11.

16. Lee M, Heo S-H, Song IH, Rajayi H, Park HS, Park IA, et al. Presence of tertiary lymphoid structures determines the level of tumor-infiltrating lymphocytes in primary breast cancer and metastasis. Modern Pathology. Springer US; 2018;:1–11.

17. Berghoff AS, Fuchs E, Ricken G, Mlecnik B, Bindea G, Spanberger T, et al. Density of tumor- infiltrating lymphocytes correlates with extent of brain edema and overall survival time in patients with brain metastases. OncoImmunology; 2016;5:e1057388–9.

18. Berghoff AS, Ricken G, Widhalm G, Rajky O, Dieckmann K, Birner P, et al. Tumour-infiltrating lymphocytes and expression of programmed death ligand 1 (PD-L1) in melanoma brain metastases. Histopathology. 2015;66:289–99.

19. Harter PN, Bernatz S, Scholz A, Zeiner PS, Zinke J, Kiyose M, et al. Distribution and prognostic relevance of tumor-infiltrating lymphocytes (TILs) and PD-1/PD-L1 immune checkpoints in human brain metastases. Oncotarget. Impact Journals; 2015;6:40836–49.

20. Jindal V, Gupta S. Expected Paradigm Shift in Brain Metastases Therapy—Immune Checkpoint Inhibitors. Molecular Neurobiology; 2018;:1–7.

21. Galea I, Bechmann I, Perry VH. What is immune privilege (not)? Trends Immunol. 2007;28:12– 8.

22. Michell-Robinson MA, Touil H, Healy LM, Owen DR, Durafourt BA, Bar-Or A, et al. Roles of microglia in brain development, tissue maintenance and repair. Brain. 2015;138:1138–59.

23. Gimsa U, Mitchison NA, Brunner-Weinzierl MC. Immune privilege as an intrinsic CNS property: astrocytes protect the CNS against T-cell-mediated neuroinflammation. Mediators Inflamm. 2013;2013:320519.

24. Farber SH, Tsvankin V, Narloch JL, Kim GJ, Salama AKS, Vlahovic G, et al. Embracing rejection: Immunologic trends in brain metastasis. OncoImmunology. 2016;5:e1172153.

25. Popper HH. Progression and metastasis of lung cancer. Cancer Metastasis Rev. 2016;35:75– 91.

26. Zeiner PS, Zinke J, Kowalewski DJ, Bernatz S, Tichy J, Ronellenfitsch MW, et al. CD74 regulates complexity of tumor cell HLA class II peptidome in brain metastasis and is a positive prognostic marker for patient survival. Acta Neuropathologica Communications; 2018;:1–16.

27. Lohr J, Ratliff T, Huppertz A, Ge Y, Dictus C, Ahmadi R, et al. Effector T-cell infiltration positively impacts survival of glioblastoma patients and is impaired by tumor-derived TGF-β. Clinical Cancer Research. 2011;17:4296–308.

[21]

28. Geisenberger C, Mock A, Warta R, Rapp C, Schwager C, Korshunov A, et al. Molecular profiling of long-term survivors identifies a subgroup of glioblastoma characterized by chromosome 19/20 co-gain. Acta Neuropathol. Springer Berlin Heidelberg; 2015;130:419–34.

29. Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature. 2014;511:543–50.

30. Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang T-H, et al. The Immune Landscape of Cancer. Immunity. 2018;48:812–4.

31. Lambrechts D, Wauters E, Boeckx B, Aibar S, Nittner D, Burton O, et al. Phenotype molding of stromal cells in the lung tumor microenvironment. Nat Med. Nature Publishing Group; 2018;24:1277–89.

32. Abbas AR, Wolslegel K, Seshasayee D, Modrusan Z, Clark HF. Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus. PLoS ONE. 2009;4:e6098.

33. Gaujoux R, Seoighe C. CellMix: a comprehensive toolbox for gene expression deconvolution. Bioinformatics. 2013;29:2211–2.

34. Geng Y, Shao Y, He W, Hu W, Xu Y, Chen J, et al. Prognostic Role of Tumor-Infiltrating Lymphocytes in Lung Cancer: a Meta-Analysis. Cell Physiol Biochem. Karger Publishers; 2015;37:1560–71.

35. Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012;13:12.

36. Galon J, Bruni D. Approaches to treat immune hot, altered and cold tumours with combination immunotherapies. Nat Rev Drug Discov. Nature Publishing Group; 2019;410:1107.

37. Pabla S, Conroy JM, Nesline MK, Glenn ST, Papanicolau-Sengos A, Burgher B, et al. Proliferative potential and resistance to immune checkpoint blockade in lung cancer patients. J Immunother Cancer. BioMed Central; 2019;7:27.

38. Punt S, Langenhoff JM, Putter H, Fleuren GJ, Gorter A, Jordanova ES. The correlations between IL-17 vs. Th17 cells and cancer patient survival: a systematic review. OncoImmunology. 2015;4:e984547.

39. Mustafa DAM, Pedrosa RMSM, Smid M, van der Weiden M, de Weerd V, Nigg AL, et al. T lymphocytes facilitate brain metastasis of breast cancer by inducing Guanylate-Binding Protein 1 expression. Acta Neuropathol. Springer Berlin Heidelberg; 2018;135:581–99.

40. Zakaria R, Platt-Higgins A, Rathi N, Radon M, Das S, Das K, et al. T-Cell Densities in Brain Metastases Are Associated with Patient Survival Times and Diffusion Tensor MRI Changes. Cancer Res. 2018;78:610–6.

41. Téglási V, Reiniger L, Fábián K, Pipek O, Csala I, Bagó AG, et al. Evaluating the significance of density, localization, and PD-1/PD-L1 immunopositivity of mononuclear cells in the clinical course of lung adenocarcinoma patients with brain metastasis. Neuro-oncology. 2017;19:1058–67.

[22]

42. Castaneda CA, Castillo M, Bernabe LA, Sanchez J, Casavilca S, García-Corrochano P, et al. Impact of pathological features of brain metastases in prognosis. Biomark Med. 2018;12:475– 85.

43. Fecci PE, Mitchell DA, Whitesides JF, Xie W, Friedman AH, Archer GE, et al. Increased regulatory T-cell fraction amidst a diminished CD4 compartment explains cellular immune defects in patients with malignant glioma. Cancer Res. 2006;66:3294–302.

44. Shimizu K, Nakata M, Hirami Y, Yukawa T, Maeda A, Tanemoto K. Tumor-infiltrating Foxp3+ regulatory T cells are correlated with cyclooxygenase-2 expression and are associated with recurrence in resected non-small cell lung cancer. J Thorac Oncol. International Association for the Study of Lung Cancer; 2010;5:585–90.

45. Lee S, Kim D, Kang J, Kim E, Kim W, Youn H, et al. Surfactant Protein B Suppresses Lung Cancer Progression by Inhibiting Secretory Phospholipase A2 Activity and Arachidonic Acid Production. Cell Physiol Biochem. 2017;42:1684–700.

46. Mitsuhashi A, Goto H, Kuramoto T, Tabata S, Yukishige S, Abe S, et al. Surfactant protein A suppresses lung cancer progression by regulating the polarization of tumor-associated macrophages. Am J Pathol. Elsevier; 2013;182:1843–53.

47. De Masson A, Giustiniani J, Marie-Cardine A, Bouaziz JD, Dulphy N, Gossot D, et al. Identification of CD245 as myosin 18A, a receptor for surfactant A: A novel pathway for activating human NK lymphocytes. OncoImmunology. 2016;5:e1127493.

48. Hasegawa Y, Takahashi M, Ariki S, Saito A, Uehara Y, Takamiya R, et al. Surfactant protein A downregulates epidermal growth factor receptor by mechanisms different from those of surfactant protein D. J Biol Chem. 2017.

49. Zhou L, Lv X, Yang J, Zhu Y, Wang Z, Xu T. Napsin A is negatively associated with EMT‑mediated EGFR‑TKI resistance in lung cancer cells. Mol Med Rep. Spandidos Publications; 2018;18:1247–52.

50. Peng L, Bian XW, Li DK, Xu C, Wang GM, Xia QY, et al. Large-scale RNA-Seq Transcriptome Analysis of 4043 Cancers and 548 Normal Tissue Controls across 12 TCGA Cancer Types. Sci Rep. 2015;5:13413.

51. Chan TA, Yarchoan M, Jaffee E, Swanton C, Quezada SA, Stenzinger A, et al. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Ann Oncol. 2019;30:44–56.

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Table 1: Descriptive statistics and univariate survival analysis of clinical parameters in the lung adenocarcinoma brain metastases study set (n=81). The presented p-value is based on the log-rank test. variable descriptive statistics survival analysis n patients [%] median (range) p-value HR 95% CI [months] gender male 40 [49.4] 0.606 1.16 6.5 - 30.5 female 41 [50.6]

< median 41 [50.6] 61 (40-80) 0.003 ** 0.43 12.5 - 33.9 age at diagnosis [years] ≥ 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 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] a Karnofsky-score > 70 60 [74.1] 90 (40-100) < 0.001 *** 0.35 3.3 - 17.6 ≤ 70 17 [21.0] unknown 4 [4.9]

b smoker yes 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 yes 19 [23.4] 0.048 * 1.89 2.8 - 17.6 metastasesa no 60 [74.1] unknown 2 [ 2.5] Abbreviations: BM = brain metastases, CI = confidence interval, HR = hazard ratio, NA = not available, n = number, WBRT = whole brain radiotherapy. a At the time of neurosurgical resection b Current and past

[24]

c At the time of first diagnosis. d Macroscopically no tumor tissue left in the brain e Incomplete resection of singular metastasis or other metastases left in the brain

[25]

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= 81) and RNA microarray analysis (n = 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. Additionally, a single cell RNA sequencing data set 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. Helper T cells

(CD3+CD8-FOXP3-, median: 156 / mm2), cytotoxic T cells (CD3+CD8+FOXP3-, median: 87 / mm2), classical regulatory T cells (CD3+CD8-FOXP3+, median: 19 / mm2), and CD8+ regulatory T cells

(CD3+CD8+FOXP3+, median: 2 / mm2). Clinical parameters are indicated below the barplot.

[26]

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 helper T cell density, and high cytotoxic T cell density improved survival. D - F No survival difference was observed for regulatory T cells. G Age at diagnosis, Karnofsky score, and overall TIL density remained independent prognostic factors in multivariate analysis (Cox proportional hazard ratio).

[27]

Figure 3. Differentially expressed genes and further analysis of surfactant genes in our cohort (n=63) and a TCGA data set (n=230). A (Heidelberg BM data set) Scatterplot of differentially expressed genes between the groups of high and low overall TIL density, helper T cell density, and cytotoxic T cell density. Cut-off for differential expression: Log-rank p-value < 0.05 and log2 fold-change > 1.5. B

(Heidelberg BM data set) Heatmap of SFTPA1, SFTPB, and NAPSA expression. Two groups can be identified by hierarchical clustering. C (Heidelberg BM data set) Survival analysis of the two clusters using Log-rank test and Kaplan-Meier plots. Cluster 2 (surfactanthigh class) is positively associated with survival. D (TCGA data set) Heatmap of RNA-seq expression of SFTPA1, SFTPB, and NAPSA. Clustering was done as described above and revealed a surfactanthigh and surfactantlow group. E (TCGA data set)

Multivariate survival analysis using Cox-proportional hazard ratio. Parameters included patient age at diagnosis and surfactant expression clustering. Significant differences were observed, especially in the group of older patients.

[28]

Figure 4. Single cell RNA sequencing 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 LUAD BM cohort (n=63) to estimate the fraction of AT2 cells in the bulk expression data.

[29]

Figure 5. Dissecting the tumor microenvironment of surfactantlow and surfactanthigh primary LUADs. A

Abundance distributions of CD4+ T cells, B cells, Monocytes, NK cells, Granulocytes, and CD8+ 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 CD4+ 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 et al. (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.

[30]

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.

Updated version Access the most recent version of this article at: doi:10.1158/1078-0432.CCR-19-2184

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