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Local tumor microbial signatures and response to checkpoint blockade in non-small cell lung cancer

ABSTRACT In cancer patients, the clinical response to checkpoint-based immunotherapy is associated with the composition and functional quality of the host microbiome. While the relevance of the gut microbiome for checkpoint immunotherapy outcome has been addressed intensively, data on the role of the local tumor microbiome are missing. Here, we set out to molecularly characterize the local non-small cell lung cancer microbiome using 16S rRNA gene amplicon sequencing of bronchoscopic tumor biopsies from patients treated with PD-1/PD-L1-targeted checkpoint inhibitors. Our analyses showed significant diversity of the tumor microbiome with high proportions of Firmicutes, Bacteroidetes and Proteobacteria. Correlations with clinical data revealed that high microbial diversity was associated with improved patient survival irrespective of radiology-based treatment response. Moreover, we found that the presence of Gammaproteobacteria correlated with low PD-L1 expression and poor response to checkpoint-based immunotherapy, translating into poor survival. Our study suggests novel microbiome-specific/derived biomarkers for checkpoint immunotherapy response prediction and prognosis in lung cancer. In a broader sense, our data draw attention to the local tumor microbial habitat as an important addition to the spatially separated microbiome of the gut compartment.


Background
Immune checkpoint inhibitor (ICI) therapy, either alone or in combination with classical chemotherapeutic drugs, constitutes the mainstay of treatment for advanced non-small cell lung cancer (NSCLC), in the absence of targetable molecular driver alterations. 1,2 ICIs are powerful therapeutic agents that harness the natural specificity and adaptability of the immune system to reinvigorate anticancer immunity and tackle tumor heterogeneity 3,4 and cancer-associated immune suppression. 5 Across tumor entities, biomarkers for ICI response prediction include target antigen expression (e.g., PD-1/PD-L1), 6 tumor mutational burden (TMB), 7,8 DNA mismatch repair deficiency/ microsatellite instability, 9 and tumor T cell infiltration at baseline; 10 however, ICI responsiveness also relies on the composition and functional quality of the host microbiome. [11][12][13] Microbes are particularly abundant in the gastrointestinal (GI) tract, 14 and certain species of this compartment (mostly bacterial commensals) have been shown to be associated with ICI treatment efficacy through various immunological and metabolic mechanisms. 15,16 In the case of lung cancer, the GI microbiome is anatomically distant despite a postulated functional interaction via the gut-lung axis, 17,18 which theoretically compromises the relevance of gut microbes in relation to more proximal habitats. 19,20 Therefore, profiling of the local NSCLC tumor microbiome, as a reflection of the overarching pulmonary microbial landscape, 18 is important to (i) get a better understanding of local tumor-microbiome interactions and (ii) potentially establish novel biomarkers with superior predictive value in ICI-treated NSCLC.
In this proof-of-concept study, we aimed at analyzing bacterial species directly in NSCLC tissue, using mostly bronchoscopic tumor biopsies. Our study reports novel microbiomerelated NSCLC biomarkers in the tumor environment and emphasizes the importance of the local tumor microbial habitat for ICI responsiveness and patient outcome.

NSCLC biopsies and patient characteristics
Bronchoscopic (n = 35) and surgical (n = 3) biopsies from 38 patients with advanced-stage NSCLC treated with ICIs were obtained from the St. Gallen Lung Biopsy Biobank, a dedicated biobank for pulmonary samples maintained and operated by the Lung Center of the Cantonal Hospital St. Gallen. Healthy adjacent lung tissue macroscopically free of tumor was obtained from NSCLC patients undergoing curative-intent surgical treatment and was used for control purpose (n = 10). Tumor biopsies were from stage III-IV NSCLC patients who received a PD-1/PD-L1-targeting ICI (pembrolizumab, nivolumab, atezolizumab, durvalumab, or spartalizumab) in various therapy lines (first, second, or third) with or without a chemotherapy doublet (carboplatin-pemetrexed, cisplatinpemetrexed, or carboplatin-paclitaxel) or an investigational treatment (MK-1308/anti-CTLA-4, NIS793/anti-TGF-β, NIR178/adenosine A2a receptor antagonist). Importantly, biopsies were sampled before the start of treatment. The patient population comprised 80% males and the main NSCLC histological subtypes were well-balanced (43% adenocarcinoma (ADC) and 53% squamous cell carcinoma (SCC)). PD-L1 expression status was available for 77% of the cases (12 patients with 0-20% of the cells positive, 11 patients with >20% of the cells positive) and was determined using the VENTANA PD-L1 (SP263) Assay from Roche Diagnostics (Rotkreuz, Switzerland). 30% of the patients had a known mutation in one of the following proto-oncogenes: KRAS, EGFR, ERBB2, PIK3CA, MET. Of note, the MET alteration in one patient concerned a missense mutation in exon 14 (T992I) that may have influenced immunotherapy outcome. 21 In addition, the EGFR alteration found in another patient (a single nucleotide variant in exon 18) may also have impacted immunotherapy outcome. 22 Median progression-free survival (PFS) and median overall survival (OS) were 3.1 and 15.3 months, respectively, and the overall response rate to ICI therapy was 27%. Patient characteristics are specified in Table 1.

16S rRNA gene amplicon sequencing
Bronchoscopic (n = 35) and surgical (n = 3) NSCLC biopsies as well as healthy lung control tissues (n = 10) were subjected to 16S rRNA sequencing using Illumina MiSeq technology, a platform for targeted resequencing, expression profiling and metagenomics. To this end, PCR amplification protocols were optimized, and two-step NextEra PCR libraries were generated using 341 F (5′-CCT ACG GGN GGC WGC AG −3′) and 802 R (5′-GAC TAC HVG GGT ATC TAA TCC −3′) primers specific for the V3 and V4 regions of the bacterial 16S rRNA gene. The amount of total DNA (including host and other nonbacterial DNA) was determined using PicoGreen (Thermo Fisher Scientific, Waltham, MA) and 140.2 ± 33.7 ng of DNA (mean ± SEM) were used as input for PCR amplification emanating from similarly sized biopsies. Libraries were sequenced on the Illumina MiSeq platform employing a 500 cycles MiSeq Reagent Kit v2. Paired-end reads passing Illumina quality filtering were demultiplexed and trimmed of adaptor residuals using MiSeq reporter built-in data analysis software v2.6. The read quality was further analyzed using FastQC v0.11.8 and sequencing reads were trimmed of primers using cutadapt v2.8. Forward and reverse reads were merged to reproduce the sequenced molecule using USEARCH v11.0.667 and a minimum overlap of 15 bases. Further quality filtering allowed one expected error per merged read and discarded reads with ambiguous bases or an outlier amplicon size. Samples with a minimum of 5000 merged reads were denoised using the UNOISE module of the USEARCH package, and operational taxonomic units (OTUs) were defined. OTU abundances were filtered for possible bleed-in contaminations making use of the UNCROSS algorithm. Sequences from the RDP 16S rRNA database served as reference and taxonomies were predicted using the SINTAX module included in the USEARCH package, setting the minimum confidence threshold to .5. Ultimately, 5 NSCLC and 5 healthy lung samples did not pass the quality controls and were excluded from subsequent analysis; thus, a total of 30 NSCLC biopsies (28 bronchoscopic, 2 surgical) and 5 healthy lung samples were finally analyzed. Three non-template controls (NTCs) were run. Signals detected in the NTCs were subtracted from the whole analysis to correct for potential reagent contamination by trace amounts of bacterial DNA as reported. [23][24][25] 16S rRNA gene amplicon sequencing as described in this section was performed by Microsynth AG (Balgach, Switzerland). All samples were processed in parallel, starting from DNA isolation, PCR amplification, library preparation and sequencing, thus eliminating potential batch effects.

Data analysis and statistical considerations
Phylum, class and OTU abundances were analyzed in a descriptive manner. Bacterial α-diversity was estimated using the Shannon diversity index (SDI) 26 as well as the number of unique OTUs observed. Correlation of the SDI with the number of unique OTUs was analyzed using Spearman's rank correlation coefficient. Two-group data were tested for statistical significance using the Mann-Whitney U test, and threegroup data were statistically analyzed using the Kruskal-Wallis test. A Cox proportional hazard model was used to analyze phylum and class abundance with respect to PFS and OS, and Wald test p-values are reported. Time to event data (patient survival) were analyzed using Kaplan-Meier estimates and logrank tests (Χ 2 statistics and associated p-values are reported). Where applicable, optimized cutoffs have been used for data analysis (e.g., to harmonize group sizes). Unless otherwise stated, quantitative data are shown as boxplots with whiskers and outliers. The nominal significance level was .05. Statistical analyses were performed using IBM SPSS Statistics 20 and the R statistical software (www.r-project.org).

Ethics statement
Patients donating their samples to the St. Gallen Lung Biopsy Biobank provided written informed consent for use of their biological material for research purpose (EKSG 11/044). The study was approved by the local Ethical Review Board (Ethikkommission Ostschweiz) under BASEC number 2019-02059.

16S rRNA gene amplicon sequencing deciphers tumor microbial diversity
To characterize the tumor microbial habitat of NSCLC, bronchoscopic (n = 28) and surgical (n = 2) tumor biopsies as well as healthy lung control tissues (n = 5) were subjected to 16S rRNA gene amplicon sequencing. 27,28 Bacterial species from various phyla were detectable in the samples, which could be allocated to 224 individual OTUs. OTU abundances ranged from <5000 reads to >30000 reads and showed high proportions of Firmicutes, Bacteroidetes and Proteobacteria in both NSCLC biopsies and healthy lung samples (Figure 1a). In NSCLC biopsies, the number of unique OTUs observed (ranging from 3 to 109) was highly correlated with the SDI, an established measure for α-diversity taking into account both the number of species and their abundance (r = .926, p < .001, Figure 1b). In summary, our results show that 16S rRNA gene amplicon sequencing represents a suitable method to analyze bacteria in bronchoscopic NSCLC biopsies and suggest significant diversity of the tumor microbial habitat along with marked interpatient variation.

Gammaproteobacteria constitute a significant part of the NSCLC tumor microbiome
We next sought to investigate which types of bacteria would play a role in shaping or predicting the response to PD-1/PD-L1targeted ICIs. As shown in Figure 3a, the phyla of Firmicutes, Bacteroidetes and Proteobacteria were largely dominating the scene, jointly accounting for almost 90% of the detected tumor microbiome. Other phyla still showing appreciable abundance included Fusobacteria and Actinobacteria (Figure 3a). A Cox proportional hazard model revealed that only the phylum of Proteobacteria was significantly associated with PFS (p < .001) and OS (p < .001) in our cohort and that the class of Gammaproteobacteria (accounting for almost 60% of Proteobacteria, Figure 3a), was most significantly correlated with survival (p < .001 for PFS and p = .011 for OS) (Figure 3b). In summary, Gammaproteobacteria account for roughly 7% of the total detected microbiome and are associated with survival in NSCLC patients receiving immunotherapy.

Gammaproteobacteria correlate with low PD-L1 expression and predict poor checkpoint immunotherapy responsiveness
Based on the predictions from the Cox proportional hazard model (Figure 3b), we analyzed the abundance of Gammaproteobacteria in stratified patient subgroups and also comparatively investigated their levels in healthy lung vs. NSCLC tissues. Although not reaching statistical significance, Gammaproteobacteria levels appeared to be higher in lung cancer tissue as compared to healthy lung control tissue (p = .421, Figure 4a). Statistical significance was also not reached when stratifying according to sex (female vs. male, p = .900), the number of packyears (0-40 vs. >40, p = .153), tumor stage (III vs. IVA vs. IVB, p = .755), or the NSCLC histological subtype (ADC vs. SCC, p = .449), even though some trends were observed (Figure 4b-e). In contrast, high abundance of Gammaproteobacteria was significantly associated with low PD-L1 expression (p = .006, figure 4f), a finding that was also partly reflected in the response to ICI therapy (PR vs. SD vs. PD) (p = .275, Figure 4g). In line with these data, patients with a high abundance of Gammaproteobacteria in their tumors showed a significantly worse PFS (χ 2 = 8.594, p = .003, Figure 4h), with OS revealing a similar trend without reaching statistical significance (χ 2 = 1.739, p = .187, Figure 4i). Taken together, Gammaproteobacteria appear to be enriched in the cancerous lung and their abundance in the tumor surroundings correlates with low PD-L1 expression and poor PFS and a trend toward worse OS under ICI therapy.

Discussion
The host microbiome serves important functions in health and disease and is particularly abundant in the GI tract, 14 the respiratory tract, 18 and on the skin. 29 Commensal microbes are essential gatekeepers for epithelial barrier integrity 30 and transient (e.g., antibiotics and infection) or chronic (e.g., long-term medication and underlying conditions) dysbiosis can prime to a variety of immune-related and metabolic disorders including inflammatory bowel disease, obesity, and type 2 diabetes. [31][32][33][34][35] In contrast, 'gainof-function' dysbiosis can lead to the overgrowth of particular microbial strains in specific anatomical compartments, thus causing local opportunistic infections. 36,37 A high diversity of the host microbiome with a well-balanced and time-stable composition of bacteria, viruses and fungi is therefore essential to prevent disease and maintain health until old age. [38][39][40] Interactions of the microbiome with host cells are mediated mostly by immunological and metabolic means, and can be local, distant, or systemic. 18 While, with the notable exception of colorectal cancer, 41 the significance of the microbiome for cancer development remains elusive, accumulating evidence suggests a pivotal role of host microbes in shaping the response to cancer therapy. Specifically, the use of antibiotics curtails ICI treatment efficacy [42][43][44] and also has a negative impact on the performance of classical cytotoxic drugs such as platinum and cyclophosphamide. 45 Importantly,  unresponsiveness of germ-free or antibiotic-treated mice to checkpoint inhibition can be rescued through oral supplementation with certain bacteria. 11,12 In ICI-treated melanoma, the fecal microbiome is significantly different between responding and non-responding patients, 13 and fecal microbiome transplantation (FMT) from responding patients is clinically evaluated as an ICI re-sensitizing intervention in the subgroup of treatmentrefractory patients. 46 While these data clearly bear witness to a key role of host commensal microbes in cancer immunotherapy outcome, most studies have prioritized the GI microbiome irrespective of the tumor site of origin. However, the host microbiome in its totality is much wider, and more proximal tumor ecosystems may be equally important, if not dominant, in governing disease progression and treatment responsiveness. 19,20,[47][48][49][50][51] Here, we have pictured the local tumor microbiome of NSCLC using 16S rRNA gene amplicon sequencing of tumor biopsies from patients treated with PD-1/ PD-L1-targeted ICIs.
Results from this study converge on three key messages as follows: (i) 16S rRNA gene amplicon sequencing is technically feasible in bronchoscopic tumor biopsies that typically provide limited sample input and are further enriched for host cells rather than metagenomic signals (very low microbial biomass). (ii) A higher diversity of the tumor microbiome represents a prognostic factor, as it correlates with improved OS of ICItreated NSCLC patients irrespective of the clinical response to ICI treatment or PFS. (iii) A high abundance of Gammaproteobacteria, a diverse class of gram-negative bacteria, 52 predicts low PD-L1 expression and poor response to PD-1/PD-L1-targeted checkpoint blockade, resulting in unfavorable patient survival. Importantly, the associations of SDI and Gammaproteobacteria with survival were cross-validated using random permutation of both strata based on the log-rank test Χ 2 statistics (data not shown).
While shotgun sequencing represents the gold standard for metagenomics in stool samples and additionally covers viruses, fungi and protozoa, 28 16S rRNA sequencing is a valuable approach especially when microbial density is low such that sample composition is biased toward host cells as is the case for lung tissue 53 and fetal organs. 54 Here, we have characterized the local NSCLC microbiome as a potential mirror of the overarching pulmonary metagenomic landscape 18 using the latter approach. Our data suggest high tumoral abundances especially of Firmicutes, Bacteroidetes, and Proteobacteria. Our observation that a higher α-diversity predicts improved OS independently from the clinical response to ICI treatment is interesting and may indicate that a more diverse microbiome is selected for patients with a better health status or other factors associated with favorable outcome (e.g., less disease burden or more indolent disease). In addition, a higher diversity of the tumor microbiome may increase the chances for protective immunological cross-reactivity between microbial-derived peptide products and current or future arising tumor neoantigens, 15 indicating a more causal role of the microbiome as a mediator of a better tumor response to ICI therapy. It is challenging to uncouple prognostic vs. predictive features of αdiversity in the current study, and it cannot be inferred that a higher microbial diversity would also correlate with longer OS in untreated patients or patients not treated with ICIs but with other systemic agents. In comparison, a higher gut microbial diversity clearly represents a predictive biomarker in anti-PD -1-treated melanoma. 13 Our finding that Gammaproteobacteria correlate with low PD-L1 expression and poor ICI-related survival suggests a new potential biomarker for ICI response prediction in NSCLC while leaving unanswered questions about the underlying mechanisms. Certainly, Gammaproteobacteria represent a highly diverse class of bacteria with numerous metabolic pathways involved, and member species can act as both facultative (e.g., E. coli) and bona fide pathogens (P. aeruginosa, S. typhimurium, H. influenzae, L. pneumophila). 52 Whether the totality of Gammaproteobacteria downregulates PD-L1 expression and/or curtails anticancer immunity through metabolic or immunological rewiring 55 remains to be shown in future mechanistic studies.
The following limitations are applicable to the study, which lower the generalizability of the results: (i) The study is purely correlative and case numbers are limited. (ii) The study population was retrospectively selected and heterogeneous in terms of clinical parameters. (iii) Bronchoscopic biopsies provide a locoregional snapshot of the tumor and may not be representative of the tumor as a whole. (iv) The sampling and handling of bronchoscopic and surgical biopsies in non-sterile environments may have led to some degree of cross-contamination, a potential pitfall that might be more relevant to the smaller biopsy specimens. (v) No systematic data on the use of antibiotics immediately before or during checkpoint immunotherapy were available for the study population, thus prohibiting corresponding investigations. (vi) The study did not investigate non-bacterial microbes such as viruses and fungi. (vii) The bacterial signal in bronchoscopic tumor biopsies was low such that PCR amplification may have introduced some bias. In addition, results need to be interpreted in awareness of the fact that microbial signals detected in NTCs were subtracted from the test samples to account for reagent contamination as reported. [23][24][25] (viii) Many bacterial species were detectable in a few samples only such that species-level analyses could not be performed for statistical reasons.
We here show the technical feasibility of 16S rRNA gene amplicon sequencing-based bacterial identification in bronchoscopic tumor biopsies and report the identification of novel microbiome-specific biomarkers for prognosis and checkpoint immunotherapy response prediction in NSCLC. Our proof-ofconcept study sets the stage for larger validation trials and gives impetus for endeavors to mechanistically dissect the underlying mechanisms and define therapeutic leads. In the long run, it is hoped that basic knowledge about the role of the local tumor microbiome can inspire rational combination therapies to resensitize to -or boost -checkpoint blockade, 56 e.g., through oral or inhaled supplementation with commensal microbes (reinstallation of diversity) or the informed use of narrow-spectrum antibiotic/antiviral/antifungal agents (elimination of detrimental microbes).