The Microbiome in Pancreatic Ductal Adenocarcinoma: History
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Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Nina Pfisterer
,
Catharina Lingens
,
Cathleen Heuer
,
Linh Dang
,
Albrecht Neesse
,
Christoph Ammer-Herrmenau

Pancreatic cancer is a highly lethal cancer and less than 10% of patients survive the 5-year mark. The molecular and biological underpinnings leading to this dismal prognosis are well-described, however, translation of these findings with subsequent improvement of the poor prognosis has been slow. The complex and dynamic accumulation of microbes, also called the microbiome, has recently attracted scientific interest in the pathogenesis of several diseases including pancreatic cancer. Since then, a limited number of significant findings were published pointing towards an important role of the microbiome in cancer, in particular pancreatic cancer.

  • microbiome
  • pancreatic ductal adenocarcinoma
  • biomarker

1. The Role of the Microbiome in Pancreatic Carcinogenesis

In 2012, the International Agency for Research on Cancer (IARC) Working Group on the Evaluation of Carcinogenic Risks to Humans reported that about 13% of all global cancer cases are caused by so-called “oncomicrobes”. Eleven distinctly defined microbes evidently induce cancer, and there is experimental evidence for even more [1]. Contrary to these well-defined oncomicrobes in certain tumor entities, there is emerging evidence that the tumoral microbiome contributes to carcinogenesis in different ways. Figure 1 illustrates the established and putative associations between the microbiota and oncogenesis.
/media/item_content/202212/639bdb1f19607cancers-14-05974-g002.png
Figure 1. Potential involvement of the microbiome in (pancreatic) oncogenesis. There is growing evidence on how different microbiomes contribute to carcinogenesis, e.g., via promoting oncogenic signaling, direct and indirect genetic alterations, chronic inflammation, and interaction with the immune system and secretion of microbe-derived metabolites. However, most of these theories have yet to be validated in PDAC patients. Tumor microenvironment (TME); mutant p53 (mutp53); pancreatic ductal adenocarcinoma (PDAC); oral squamous cell carcinoma (OSCC); desoxyribonucleic acid (DNA); double-strand break (DSB); microbe-associated molecular pattern (MAMP); lipopolysaccharide (LPS); pattern recognition receptor (PRR); myeloid-derived suppressor cell (MDSC); short-chain fatty acid (SCFA); epithelial-to-mesenchymal transition (EMT); and pondus hydrogenii (pH).

2. Diagnostic Aspects of the Microbiome in PDAC

2.1. Difficulties in Establishing Screening Tools for PDAC

Considering the available descriptive and preliminary mechanistic findings on the PDAC tumor microbiome, the question of its potential diagnostic value and possible implication as a biomarker may arise. One of the main problems with PDAC is most often the late-stage diagnosis as the tumor is often locally advanced or metastasized. This is mostly due to a lack of early-stage symptoms. To date, a reliable screening method for pancreatic cancer is not available in the clinical routine [2]. Studies investigating different site-specific microbiomes, such as the oral and fecal microbiome, point towards a possible application of the microbiome as a diagnostic biomarker in PDAC [3][4].

2.2. The Orointestinal Microbiome as PDAC Biomarker

Indeed, there are numerous publications addressing the microbiome in the oral cavity and its diagnostic potential for PDAC, of which the latest are summarized in Table 1. One of the largest studies was published by Fan et al., which was a population-based nested case-control study on the predictive power of the oral microbiome to assess the risk for pancreatic cancer [5]. Over 730 oral wash samples from two prospective cohort studies were evaluated. The authors found oral pathogens such as Porphyromonas gingivalis to be associated with an increased pancreatic cancer risk. The pitfall of the microbial patterns of the oral cavity, however, is their rather pronounced heterogeneity and low specificity, as they may also be present in other cancer entities [6]. Microbiome studies present contradictory results concerning the microbial composition and differential abundances of these microbes (Table 1). This can be mainly ascribed to the different kinds of sampling methods, e.g., sputum, dorsal tongue, buccal, or gingival swabs. Furthermore, due to different sequencing approaches, i.e., depending on the selected variable (V) region of the 16S rRNA gene, the results significantly vary [7].
Table 1. Summary of studies regarding the oral, intestinal, and fecal microbiome of patients as a non-invasive biomarker for pancreatic cancer.
Year Authors Study Design; Country of Conduction Sample Type Detection Method Number of Patients Change in Bacterial Composition Ref.
2012 Farrell et al. Prospective study; USA Saliva Microarray, qRT-PCR 38 PC
27 CP
38 HC		 Neisseria elongata, Streptococcus mitis increased in PC cases [3]
2013 Lin et al. Cross-sectional study; USA Oral wash samples 16S rRNA amplicon sequencing (NGS) 13 PC
3 CP
12 HC		 Bacteroides increased in PC cases as compared with HC;
Corynebacterium, Aggregatibacter decreased in PC cases as compared with HC		 [8]
2013 Michaud et al. Prospective study; European countries Blood Immunoblot array 405 PC
416 HC		 Plasma IgG against Porphyromonas gingivalis ATCC 53978 increased in PC cases [9]
2015 Torres et al. Cross-sectional study; USA Saliva 16S rRNA amplicon sequencing (V4 region) (NGS); qRT-PCR 8 PC
78 other diseases (including pancreatic disease, non-pancreatic digestive disease/cancer, and non-digestive disease/cancer)
22 HC		 Leptotrichia:Porphyromonas ratio increased in PC cases;
Neisseria, Aggregatibacter decreased in PC cases		 [10]
2016 Fan et al. Case-control study; USA Oral wash samples 16S rRNA amplicon sequencing (V3–V4 region) (NGS) 361 PDAC
371 HC		 Porphyromonas gingivalis, Aggregatibacter actinomycetemcomitans increased in PDAC cases;
Leptotrichia decreased in PDAC cases		 [5]
2017 Ren et al. Prospective study; China Feces 16S rRNA amplicon sequencing (V3–V5 region) (NGS) 85 PC
57 HC		 Veillonella, Klebsiella, Selenomonas, LPS-producing bacteria (Prevotella, Hallella, Enterobacter, Cronobacter) increased in PC cases;
Bifidobacterium, butyrate-producing bacteria (Coprococcus, Clostridium IV, Blautia, Flavonifractor, Anaerostipes bifidum, Butyricicoccus, Dorea, Gemmiger) decreased in PC cases		 [11]
2017 Olson et al. Cross-sectional study; USA Saliva 16S rRNA amplicon sequencing (V4–V5 region) (NGS) 40 PDAC
39 IPMN
58 HC		 Firmicutes (e.g., Streptococcus) increased in PDAC cases;
Proteobacteria (e.g., Haemophilus, Neisseria) decreased in PDAC cases as compared with HC		 [12]
2018 Pushalkar et al. Case-control study; USA Rectal swabs 16S rRNA amplicon sequencing (V3–V4 region) (NGS) 32 PDAC
31 HC		 Proteobacteria, Actinobacteria, Fusobacteria, Verrucomicrobia, Synergistetes, Euryarchaeota increased in PDAC cases [13]
2018 Mei et al. Case-control study; China Duodenal mucosa 16S rRNA amplicon sequencing (V3–V4 region) (NGS) 14 PC (pancreatic head cancer)
14 HC		 Acinetobacter, Aquabacterium, Oceanobacillus, Rahnella, Massilia, Delftia, Deinococcus, Sphingobium increased in PC cases;
Porphyromonas, Paenibacillus, Enhydrobacter, Escherichia, Shigella, Pseudomonas decreased in PC cases		 [14]
2019 Lu et al. Case- control study; China Tongue coat samples 16S rRNA amplicon sequencing (V3–V4 region) (NGS) 30 PC (pancreatic head cancer)
25 HC		 Leptotrichia, Fusobacterium, Rothia, Actinomyces, Corynebacterium, Atopobium, Peptostreptococcus, Catonella, Oribacterium, Filifactor, Campylobacter, Moraxella, Tannerella increased in PC cases;
Haemophilus, Porphyromonas, Paraprevotella decreased in PC cases		 [15]
2019 del Castillo et al. Cross-sectional study; USA Tissue samples (pancreatic duct, duodenum, pancreas);
swabs (bile duct, jejunum, stomach);
feces		 16S rRNA amplicon sequencing (V3–V4 region) (NGS) 39 PC
12 periampullary cancer
18 non-cancer pancreatic conditions
8 non-cancer gastrointestinal conditions
34 HC		 Porphyromonas, Prevotella, Selenomonas, Gemella, Fusobacterium spp. increased in cancer cases as compared with non-cancer cases;
Lactobacillus decreased in cancer cases as compared with non-cancer cases		 [16]
2019 Half et al. Case-control study; Israel Feces 16S rRNA amplicon sequencing (V3–V4 region) (NGS) 30 PDAC
6 pre-cancerous lesions
16 NAFLD
13 HC		 Veillonellaceae, Akkermansia, Odoribacter increased in PDAC cases as compared with HC;
Clostridiacea, Erysipelotrichaeceae, Ruminococcaceae, Lachnospiraceae, Anaerostipes decreased in PDAC cases as compared with HC		 [17]
2020 Vogtmann et al. Case-control study; Iran Saliva 16S rRNA amplicon sequencing (V4 region) (NGS) 273 PDAC
285 HC		 Enterobacteriaceae, Lachnospiraceae G7, Bacteroidaceae, Staphylococcaceae increased in PDAC cases;
Haemophilus decreased in PDAC cases		 [18]
2020 Sun et al. Case-control study; China Saliva 16S rRNA amplicon sequencing (V3–V4 region) (NGS) 10 PC
17 BPD
10 HC		 Fusobacteria (e.g., Fusobacterium periodonticum), Bacteroidetes, Firmicutes increased in PC cases;
Proteobacteria (e.g., Neisseria mucosa) decreased in PC cases		 [19]
2020 Kohi et al. Case-control study; USA Duodenal fluid 16S and 18S rRNA amplicon sequencing (16S V3–V4 rRNA region, 18S ITS1 rRNA region) (NGS) 74 PDAC
98 pancreatic cysts
134 HC		 Fusobacterium, Bifidobacterium genera, Enterococcus increased in PDAC cases as compared with HC;
Escherichia/Shigella, Enterococcus, Clostridium sensu stricto 1, Bifidobacterium increased in PDAC cases as compared with pancreatic cysts;
Fusobacterium, Rothia, Neisseria increased in PDAC cases with short-term survival		 [20]
2020 Wei et al. Case- control study; China Saliva 16S rRNA amplicon sequencing (V3–V4 region) (NGS) 41 PDAC
69 HC		 Leptotrichia, Actinomyces, Lachnospiraceae, Micrococcaceae, Solobacterium, Coriobacteriaceae, Moraxellaceae, Streptococcus, Rothia, Peptostreptococcus, Oribacterium increased in PDAC cases;
Porphyromonas gingivalis, Fusobacteriaceae, Campylobacter, Spirochaetaceae,
Veillonella, Neisseria, Selenomona, Tannerella forsythia, Prevotella intermedia decreased in PDAC cases		 [21]
2021 Zhou et al. Case-control study; China Feces Metagenomic shotgun sequencing (NGS) 32 PDAC
32 AIP
32 HC		 Gammaproteobacteria (e.g., Escherichia coli), Veillonella (V. atypica, V. parvula, V. dispar), Clostridium (e.g., Clostridium bolteae, Clostridium symbiosum), Fusobacterium nucleatum, Streptococcus parasanguinis, Prevotella stercorea increased in PDAC cases as compared with HC;
Butyrate-producing bacteria (Eubacterium rectale, Faecalibacterium prausnitzii, Roseburia intestinalis, Coprococcus), Ruminococcus, Dialister succinatiphilus decreased in PDAC cases as compared with HC		 [22]
2021 Matsukawa et al. Case-control study; Japan Feces Whole-genome sequencing (including PCR) (NGS) 24 PC (thereof 22 PDAC)
18 HC		 Klebsiella pneumoniae, Clostridium bolteae, Clostridium symbiosum, Streptococcus mutans, Alistipes shahii, Bacteroides, Parabacteroides, Lactobacillus increased in PC cases [23]
2021 Sugimoto et al. Case-control study; Japan Duodenal fluid 16S rRNA terminal restriction fragment length polymorphism method (5’ FAM-labeled 516F and 1510R primers) 22 benign pancreaticobiliary diseases (thereof 16 BPD)
12 pancreaticobiliary cancer (thereof 9 PC)		 Bifidobacterium, Clostridium cluster XVIII increased in PC cases as compared with BPD [24]
2022 Petrick et al. Prospective study; USA Oral wash samples Metagenomic shotgun sequencing (NGS) 148 PDAC (thereof 122 of African Americans, 26 of Caucasians)
441 HC (thereof 354 of African Americans, 87 of Caucasians)		 No significant changes in PDAC cases among African Americans;
Porphyromonas gingivalis increased in PDAC cases among Caucasians;
Porphyromonas gingivalis, Prevotella intermedia, Tannerella forsythia increased in PDAC cases among never-smokers		 [25]
2022 Kartal et al. Case- control study; Spain, Germany Saliva Metagenomic shotgun sequencing (NGS)
(43 PDAC, 12 CP, 45 HC)

16S rRNA amplicon sequencing (V4 region) (NGS)
(59 PDAC, 28 CP, 55 HC)		 59 PDAC
28 CP
55 HC
(Spanish cohort only)		 No significant changes in PDAC cases [4]
Feces Metagenomic shotgun sequencing (NGS)
(101 PDAC, 29 CP, 82 HC)

16S rRNA amplicon sequencing (V4 region) (NGS)
(51 PDAC, 23 CP, 46 HC)		 101 PDAC
29 CP
82 HC
(thereof 57 PDAC, 29 CP and 50 HC from Spanish cohort; 44 PDAC and 32 HC from German cohort)		 Streptococcus, Akkermansia, Veillonella atypica, Fusobacterium nucleatum/hwasookii, Alloscardovia omnicolens increased in PDAC cases as compared with HC;
Romboutsia timonensis, Faecalibacterium prausnitzii, Bacteroides coprocola, Bifidobacterium bifidum decreased in PDAC cases as compared with HC
2022 Guo et al. Case-control study; China Feces 16S rRNA amplicon sequencing (27F, 1492R primer) (NGS) 36 resectable PDAC
36 unresectable PDAC		 Pseudonocardia, Cloacibacterium, Mucispirillum, Anaerotruncus increased in unresectable PDAC cases;
Alistipes, Anaerostipes, Faecalibacterium, Parvimonas decreased in unresectable PDAC cases		 [26]
2022 Nagata et al. Case-control study; Japan, Spain, Germany Saliva Metagenomic shotgun sequencing (NGS) 90 PDAC
280 HC
(thereof 47 PDAC and
235 HC from Japanese cohort; others from Kartal et al., 2022)		 Firmicutes (unknown Firmicutes, Dialister and Solobacterium spp.), Prevotella spp. (Prevotella pallens, Prevotella sp. C561) increased in PDAC cases among Japanese cohort;
Streptococcus spp. (e.g., Streptococcus salivarius, Streptococcus thermophilus, Streptococcus australis) decreased in PDAC cases among Japanese cohort;
No significant changes in PDAC cases among Spanish cohort;
No correlation for oral species between the Japanese and Spanish datasets		 [27]
Feces Metagenomic shotgun sequencing (NGS) 144 PDAC
65 CP
150 IPMN
317 HC
(thereof 43 PDAC, 65 CP, 150 IPMN and 235 HC from Japanese cohort; others from Kartal et al., 2022)		 Streptococcus oralis, Streptococcus vestibularis, Streptococcus anginosus, Veillonella atypica, Veillonella parvula, Actinomyces spp., Clostridium symbiosum, unknown Mogibacterium, Clostridium clostridioforme increased in PDAC cases as compared with HC among Japanese cohort;
Unknown Lachnospiraceae, Eubacterium ventriosum, unknown Butyricicoccus, Faecalibacterium prausnitzii decreased in PDAC cases as compared with HC among Japanese cohort;
Clostridium symbiosum, Streptococcus oralis, unknown Mogibacterium increased in PDAC cases as compared with IPMN and CP among Japanese cohort;
Significant correlation for gut species between the Japanese and Spanish datasets and between the Japanese and German datasets;
Streptococcus spp. (S. anginosus and S. oralis), Veillonella spp. (V. parvula and V. atypica) increased in PDAC cases among all three cohorts;
Faecalibacterium prausnitzii decreased in PDAC cases among all three cohorts
United States of America (USA), quantitative real-time polymerase chain reaction (qRT-PCR), pancreatic cancer (PC), chronic pancreatitis (CP), healthy control (HC), Svedberg unit (S), ribosomal ribonucleic acid (rRNA), next-generation sequencing (NGS), immunoglobulin G (IgG), American Type Culture Collection (ATCC), variable (V), pancreatic ductal adenocarcinoma (PDAC), lipopolysaccharide (LPS), intraductal papillary mucinous neoplasm (IPMN), non-alcoholic fatty liver disease (NAFLD), benign pancreatic disease (BPD), internal transcribed spacer between 18S and 5.8S rRNA (ITS1), autoimmune pancreatitis (AIP), polymerase chain reaction (PCR), fluorescein amidite (FAM), forward (F), reverse (R).
Many studies have demonstrated a positive correlation between the pancreas and gut microbiome. For example, Ren et al. reported that the gut microbiome analyzed via stool samples was unique in PDAC and may serve as a non-invasive biomarker for the diagnosis of this disease [11]. Recently, Kartal et al. explored the fecal and salivary microbiota in PDAC patient samples from a Spanish and German case-control study as potential biomarkers; they found 27 fecal species that could be employed to identify PDAC throughout early and late stages with high accuracy. Thus, the authors suggested the fecal microbiome as a feasible early-stage PDAC biomarker, particularly in combination with carbohydrate antigen 19-9 [4]. However, these findings require validation in larger patient cohorts. Only a few months later, Nagata et al. reused the data from Kartal et al. and added their Japanese cohort dataset, which also included oral and gut bacteriophages [27]. Their aim was to further identity oral and gut metagenomic microbial signatures to predict PDAC. The authors found 30 gut and 18 oral species to be significantly associated with PDAC in their newly introduced Japanese cohort, and their metagenomic classifiers were also able to predict PDAC accurately. Consistently with Kartal et al., Nagata et al. found the gut microbiomes of European and Asian patients to present a globally robust and powerful biomarker for identifying PDAC.
Taken together, the orointestinal microbiome might be used as a non-invasive screening tool. However, the translational implication to the clinical setting remains unclear at present. Given the high cost of sequencing, a multiplex PCR or microarray for those identified bacteria might be more feasible. Furthermore, it must be discussed who will be screened, whether it be only high-risk patients or a broader screening population. Further studies with high sample numbers, such as one already completed in the U.S. (NCT03302637), will hopefully provide answers to these questions.

2.3. Blood-Derived Microbial Signatures as PDAC Biomarker

One of the most common sampling techniques in the clinical routine is blood drawing. Bacterial extracellular vesicles (bEV) are nano-sized, lipid membrane-delimited particles that contain different molecules, such as DNA, metabolites, proteins, and lipids. Recently, there is growing evidence that bEVs play an important role in bacteria–bacteria and bacteria–host communication [28][29]. These bEVs can be detected in the host’s blood, urine, bile, and stool. The exploitation of these vesicles for therapeutic and diagnostic purposes is still in its infancy [30]. One recent study revealed the diagnostic value of bEVs for differentiating between benign and malignant tumors [31]. Another Korean study performed a retrospective propensity score matching analysis showing a distinguishable composition of bEVs in blood by 16S rRNA sequencing [32]. Here again, environmental bacteria were detected in peripheral blood, emphasizing the need for thorough decontamination protocols for blood samples as well, in cases where bacterial DNA is found in very low concentrations [28]. Poore et al. demonstrated that microbial plasma profiles in over 10,000 patients, which were different from their respective healthy tissue signature, can predict different cancer types [33]. The authors used whole-genome and whole-transcriptome sequencing studies from TCGA. Moreover, pre-diagnosis blood samples from PDAC patients were subject to oral microbiota antibody measurements in a study by Michaud et al. Indeed, high levels of antibodies against Porphyromonas gingivalis, the pathogen responsible for periodontitis, was correlated with a two-fold increased PDAC risk [9].

This entry is adapted from the peer-reviewed paper 10.3390/cancers14235974

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