1 INTRODUCTION

Gastrointestinal stromal tumor is a rare soft tissue sarcoma of the GI tract arising from the interstitial cells of Cajal, with an incidence of 1–1.5 per 100,000. Preclinical forms of GIST, small (<1 cm) asymptomatic lesions, also called microGISTs, are much more common, occurring in 20%–30% of the general population.^1-3 The majority of GIST patients harbor specific driver mutations in the KIT or PDGFRA genes (85%–90% of cases), or, in a smaller percentage of cases, in other genes, including NF1, BRAF/RAS, and SDH.⁴ Interestingly, in microGISTs, driver mutations can already be detected.^{1, 5} Therefore, considering that <0.1% of microGISTs progress to cancer, it is clear that these mutations are necessary, but insufficient events to foster tumor progression.¹

Despite significant breakthroughs in target therapies with the development of “smart drugs,” such as imatinib and other tyrosine kinase inhibitors, drug resistance is common and remains a relevant clinical issue. The hypothesis is that the microenvironment of solid tumors continues to confound therapeutics and, in this context, growing importance is being given to the surrounding environment in which the cancer itself grows. In particular, in recent years, studies have suggested a role of the microbiota residing in the GI tract (i.e., the gut microbiota) in the development and prognosis of GIST.^6-8 Most of the available information concerns the luminal or fecal microbiota (usually referred to as the gut microbiota), which is well known to consistently communicate with the host, establishing a mutualistic relationship, whose breakdown can have serious repercussions on the host health.⁷ More recent evidence also underlines the relevance of the tissue microbiota in promoting tumor growth and influencing therapy.^{9, 10} However, knowledge of complex host–microbiota interactions in cancer tissue is still limited, especially for GIST, and is completely absent for microGISTs.^{7, 11, 12}

In light of these considerations, we profiled the microbiota of GI tissue specimens in three different patient cohorts, microGIST, LR GIST, and HR or MET GIST. Specifically, we explored compositional structure, predicted function, and microbial networks, with the aim of providing a complete overview of microbial ecology in GIST and its preclinical form.

2 MATERIALS AND METHODS

3 RESULTS

Approximately 4.2 million paired-end reads (mean ± SD, 70,600 ± 16,800) were generated across all samples; following the trimming, merging, and denoising procedures approximately 59% of the initial sequences were retained. Overall, 5574 ASVs were obtained and according to the taxonomic classification, 232 chloroplast and mitochondrial sequences were identified and removed from further analysis. Thirty additional ASVs were identified as contaminants by using decontam and removed from analysis.

After stratifying for microGIST, LR GIST, and HR/MET GIST, alpha diversity was evaluated through the Shannon and Simpson indices, highlighting a significant difference across the three groups (KW p = 0.011 and KW p = 0.004, respectively; Figure 1A,B). For both indices, post-hoc tests showed statistically relevant differences among microGISTs and both LR and HR/MET GISTs (Shannon index: W p = 0.015, both; Simpson index: W p = 0.009 and 0.005, respectively, Figure 1A,B). In contrast, no significant differences were observed between LR and HR/MET patients for alpha diversity. We also evaluated Shannon and Simpson indices in GISTs versus microGISTs, observing significant difference (W p = 0.0027 and p = 0.0009, respectively; Figure 1C,D). Regardless of the used alpha diversity indices, no relevant differences were observed in female patients. In male patients, the Shannon and Simpson indices were significantly different between LR and microGISTs (p = 0.02) and between microGISTs and both LR (p = 0.02) and HR/MET GISTs (p = 0.03), respectively. Similarly, in GISTs (LR + HR/MET) versus microGISTs, the Simpson index was significantly different in male subjects (p = 0.034) (Figure S1).

Differences in microbiota composition (beta diversity) were evaluated using weighted and unweighted UniFrac and Bray Curtis dissimilarity metrics (Figure S2). The PERMANOVA analyses showed that tumor groups significantly explained the observed variability in all three applied metrics (p < 0.05; Table S2).

3.1 Microbiota composition of microGISTs differs from GISTs

We compared the microbiota composition of microGISTs with GISTs (LR + HR/MET). Figure S3 summarizes the relative abundance at the phylum level for the two groups (Table 1). Significant differences were found in the relative proportion of five phyla; specifically, Armatimonadota (p < 0.0001), Mixococcota (p = 0.01), Chloroflexi (p = 0.01), and Planctomycetota (p = 0.02) were over-represented, whereas Proteobacteria (p = 0.001) was under-represented in microGISTs compared to GISTs. With regard to the family rank, 24 taxa were statistically different. Among these, Nitrosomonadaceae, Sandaracinaceae, Acidobacteriae, Mycoplasmataceae, and Solirubrobacteraceae were more abundant in microGIST (all p = 0), whereas Ethanoligenenaceae, Acidithiobacillaceae, and Rikenellaceae were over-represented in GISTs (all p = 0). At the genus level, these findings were reflected in 93 genera differentially represented in microGISTs compared to GISTs. Considering those most abundant, Ralstonia (p = 0.02), Cloacibacterium (p = 0.04), and Halomonas (p = 0.03) were mostly observed in microGISTs, but Prevotella (p < 0.05) in GISTs. Data are reported in Table S3 together with the information about nondiscussed taxonomic ranks (i.e., class and order).

TABLE 1. Phylum-level relative mean abundance in microbial communities in GIST and microGIST samples^a

	MicroGISTs (%)	GISTs (%)
Proteobacteria	55.06	60.30
Actinobacteriota	13.26	13.36
Firmicutes	12.22	8.47
Bacteroidota	8.46	4.93
Deinococcota	7.92	11.52
Chloroflexi	0.48	0.15
Cyanobacteria	0.39	0.03
Fusobacteriota	0.29	0.46
Verrucomicrobiota	0.19	0.11
Patescibacteria	0.23	0.27
Planctomycetota	0.16	0.02
Bdellovibrionota	0.15	0.07
Gemmatimonadota	0.14	0.02
Elusimicrobiota	0.13	0.00
Acidobacteriota	0.37	0.13
Fibrobacterota	0.16	0.00

• ^a Phyla with a relative abundance ≥1% in at least one sample are listed.

Based on the ASV distribution, the linear discriminant analysis coupled with effect size (LEfSe) tool allowed us to identify possible microbial biomarkers associated with a specific tumor subtype. The cladogram (Figure 2) summarizes the LEfSe association by representing the taxonomic relationship between significant ASVs associated with each group. In particular, an enrichment of Prevotella was observed in GISTs, whereas Halomonas, Shewanella, Escherichia, Enhydrobacter, and Cloacibacterium were enriched in microGISTs.

3.1.1 Prediction of metabolic functional profile and pathways

The metagenomic functional profile, predicted with PICRUSt2, highlighted 13 pathways that were significantly differentially represented among microGISTs and GISTs. Differentially abundant pathways identified by DESeq2 are summarized in Table S4. To identify potential functional markers of GISTs and microGISTs, LEfSe analysis was carried out (Figure 3A shows items with LDA score ≥3).

Additionally, to achieve insights into the relevant super-classes to which the pathways belonged, a Circos plot was generated (Figure 3B, Table S5). In particular, in the GISTs group, the pathways mainly belonged to biosynthesis (n = 12), even if a relevant portion was involved in degradation/utilization/assimilation (n = 9) and generation of precursor metabolites and energy (n = 5). In microGISTs, 10 pathways were involved in biosynthesis, and nine in degradation/utilization/assimilation; only one belonged to the precursor metabolites and energy pathway.

3.3 Microbial networks estimation

We inferred microbial networks for microGIST and GIST (LR + HR/MET) samples (Figure 4). The microGISTs network revealed the presence of two hub nodes (i.e., nodes with the highest centrality), 69fe85 and f50b99, classified as belonging to the genera Deinococcus and Halomonas, respectively. Four hubs were identified in the GISTs network according to the applied setup: 1def51 (Bradyrhizobium), 4c7b8a (Enhydrobacter), 69fe85 (Deinococcus), and f6c929 (Lawsonella). In both inferred networks, the node 69fe85 was therefore identified as a hub. Finally, according to the ARI, a measure of clustering agreement among networks,³⁰ a similar network clustering was observed (ARI = 0.664, p ≤ 0.0001). Details are reported in Tables S8 and S9.

4 DISCUSSION

So far, the studies on microGISTs have been limited due to their rarity and consequently the difficulties in collecting a reasonable number of these asymptomatic and very small lesions. Moreover, the analyses are usually aimed at characterizing a sole genetic landscape. A key factor contributing to therapeutic failure, drug resistance, and ultimately lethal outcome of cancer is intratumor heterogeneity. In this context, genetic and epigenetic changes, in combination with tumor environment, are the driving factors behind tumor heterogeneity.

Recently, growing attention has been paid to the environment where the cancer grows.^{7, 31-33} Based on that, and in view of the involvement of the GI microbiota in tumor progression (e.g., gastric and colorectal cancer³⁴), here we analyzed the tissue microbial communities in a cohort of 60 microGIST and GIST samples.

The microbiota analysis showed the lowest alpha diversity (i.e., intrasample diversity, a measure of the microbial communities’ complexity relying on both the observed ASVs and their relative abundance) in GIST patients compared to microGISTs. This aspect suggests that microGISTs retain most of the healthy tissue’s characteristics. Indeed, several reports show that alpha diversity is usually higher in normal tissues and benign lesions when compared with tumor.^35-38 Moreover, higher alpha diversity is often associated with prolonged overall survival in cancer patients^39-42; however, we did not observe significant differences between LR and HR/MET GISTs, which generally show shorter overall survival.⁴³

In our cohort of patients, Proteobacteria, Actinobacteriota, Firmicutes, and Bacteroidota were the most abundant phyla, regardless of lesion type (i.e., benign or malignant). This finding is in line with reports showing that Bacteroidota and Firmicutes, followed by Actinobacteriota and Proteobacteria, are the most abundant taxa of the intestinal microbiota of a healthy adult, although composition and prevalence could vary in cancer patients.^44-47 Interestingly, when we compared microGISTs and GISTs, both weighted and unweighted UniFrac and Bray–Curtis dissimilarity showed significant community-level separation between them. With regard to metabolic prediction, LEfSe highlighted a high number of pathways involved in the biosynthesis and production of precursor metabolites and energy in GISTs (17 vs. 11 in GISTs and microGISTs, respectively). Interestingly, in GISTs, a number of pathways involved in sugar metabolism (glycolysis II, sucrose degradation III, and superpathway of glycolysis) were also highlighted; this was expected as cancer usually displays high aerobic glycolysis despite oxidative phosphorylation and cancer cells raise glucose flux to promote anabolic request.⁴⁸

Potentially relevant differences were observed when comparing LR and HR GISTs at both microbial community composition and metabolic levels. The phyla Fusobacteriota and Desulfobacterota were the most abundant in LR and HR GISTs, respectively. At the genus level, these differences resulted in an overrepresentation of Fusobacterium in LR GISTs, which was already observed in early stages of colorectal cancer.^{49, 50}

Finally, we estimated the microbiome networks. Key taxa represent those playing a crucial role in the ecological structure and function of the community, irrespective of their overall abundance or prevalence. Looking at microGISTs and GISTs, the general structure of the networks was similar, however, in addition to one shared hub, the GISTs group had additional hubs, revealing that the changes driving the microbiome differences among benign lesions and cancer are probably due to variation of relative abundances of the community members. In general terms, it can be speculated that the microbial community is restructured during the transition from microGIST to GIST. This might also suggest that, even if the general microbiome communities in microGISTs and GISTs share many features, the connections between the bacteria appear to be different.

To the best of our knowledge, this is the first study to analyze microbial composition in microGISTs and GISTs. The study aimed to undertake an exploratory analysis of the tissue microbiome and gain new insights into this under-characterized topic. Given the difficulty in collecting microGISTs, the number of this type of lesion in our cohort represents one of the largest collections to date. Moreover, when considering our study group size, rarity of GIST (1–1.5 cases per 100,000) should be taken into consideration. From a molecular point of view, the cohort was homogeneous and this allowed us to limit potential biases.

Regardless of the interesting results, we are aware that the study is preliminary, and suffers from some limitations, including its retrospective nature. In addition, in the HR/MET GISTs group, a higher heterogeneity was present at the tumor site level compared with the LR GISTs group. Indeed, in 13 of 15 LR GIST cases, the primary site was the stomach, whereas in HR/MET GISTs only 6 of 15 originated from the stomach. However, this is in line with the epidemiology of GISTs; indeed, primary tumors arising from the stomach have a more favorable prognosis with a metastatic rate up to 15%, whereas those from the small intestine present a metastatic rate higher than 50%.^{51, 52} The tumor site, together with the clinical nature of the two patient subsets, might have contributed to the difference observed in relative abundance. With regard to the use of FFPE samples, a recent study showed that FFPE tissues provide a reliable source of germline and malignant human DNA⁵³; therefore, it is expected that FFPE tissues also provide reliable bacterial DNA,⁵⁴ and an increasing number of researchers are undertaking metagenomics analyses using FFPE samples.^{55, 56}

In conclusion, our exploratory analysis highlights that specific differences do exist in the tissue microbiome community between GISTs and benign lesions and that microbiome restructuring could drive the carcinogenesis process. This also underlines that, in addition to the well-characterized molecular alterations contributing to GIST evolution from benign to malignant lesions, a key player could be the tumor microenvironment (i.e., the microbiome). Further studies are needed in order to deepen and better clarify its role.

DISCLOSURE

CS has received research funding (institutional) from Karyopharm, Pfizer, Inc, Deciphera Pharmaceuticals, and Bayer AG, consulting fees (advisory role) from CogentBio, Immunicum AB, Deciphera Pharmaceuticals, and Blueprint Medicines, payment for lectures from Bayer AG and Blueprint Medicines, and travel grants from PharmaMar, Pfizer, Bayer AG, Novartis, and Lilly. The other authors have no conflict of interest.

ETHICS STATEMENT

The research protocol was approved by the Institutional Review Board (registration no. UCSC9421/18-15338/18-ID1969). All of the patients signed informed consent forms.