Microbial dysbiosis and polyamine metabolism as predictive markers for early detection of pancreatic cancer

Abstract

The lack of tools for early detection of pancreatic ductal adenocarcinoma (PDAC) is directly correlated with the abysmal survival rates in patients. In addition to several potential detection tools under active investigation, we tested the gut microbiome and its metabolic complement as one of the earliest detection tools that could be useful in patients at high risk for PDAC. We used a combination of 16s rRNA pyrosequencing and whole-genome sequencing of gut fecal microbiota in a genetically engineered PDAC murine model (KRASG12DTP53R172HPdxCre or KPC). Metabolic reconstruction of microbiome was done using the HUMAnN2 pipeline. Serum polyamine levels were measured from murine and patient samples using chromogenic assay. Our results showed a Proteobacterial and Firmicutes dominance in gut microbiota in early stages of PDAC development. Upon in silico reconstruction of active metabolic pathways within the altered microbial flora, polyamine and nucleotide biosynthetic pathways were significantly elevated. These metabolic products are known to be actively assimilated by the host and eventually utilized by rapidly dividing cells for proliferation validating their importance in the context of tumorigenesis. In KPC mice, as well as PDAC patients, we show significantly elevated serum polyamine concentrations. Therefore, at the early stages of tumorigenesis, there is a strong correlation between microbial changes and release of metabolites that foster host tumorigenesis, thereby fulfilling the 'vicious cycle hypothesis' of the role of microbiome in health and disease states. Our results provide a potential, precise, noninvasive tool for early detection of PDAC, which may result in improved outcomes.

Figure 1.

KPC microbiome exhibits clustering between 1 and 6 months, compared with control microbiome with 16s rRNA pyrosequencing. Various alpha-diversity indices were measured for the two genotypes and two time points. (A) Shannon’s H index was similar between the age groups and genotypes, whereas (B) Chao1 index, (C) Faith’s phylogenetic diversity and (D) observed OTU indices were significantly down in 6-month-old KPC animals, compared with age-matched control animals. Bray–Curtis principal co-ordinate analysis showed that (E) control animals did not form distinct clusters between 1 and 6 months of age, whereas (F) KPC animals for a distinct cluster at 6-month age, compared with 1-month-old animals. This difference, however, was not statistically significant (P = 0.056) with two-tailed test of significance with Bonferroni correction (n = 6–8 per group).

Figure 2.

KPC animals show significant differences at the class and genera levels. In a head-to-head comparison between the OTUs representing the five major phyla, (A) Bacteroidetes did not show any difference between 1- and 6-month-old KPC mice. The other four phyla showed reduced relative abundance, which were statistically insignificant. At the class level (B) however, Alphaproteobacteria exhibited significantly high relative abundance in 6-month-old KPC. With Bacteroidea unchanged between the two age groups, all other Classes exhibited diminished relative abundance in 6-month-old KPC mice (test of significance—non-parametric Mann–Whitney U test. *P < 0.05, **P < 0.01). At the Genus level (C), six genera showed significant increase in relative abundance from 1 to 6 months of age, compensated by 19 genera with severely diminished relative abundance at the same time (all genera P < 0.05). Couple of potential environmental contaminants are marked with ‘#’ despite not appearing in our blank controls.

Figure 3.

Whole-genome sequencing of control and KPC mice at ages 2, 3 and 4 months. As seen above, the microbial composition of KPC animals, which is similar to control (circled) animals at 2 months age (A), starts changing by 3-month age (B). By 4 months, there are significant differences in the control and KPC microbiome (C). In this experiment, only four KPC animals survived for 4 months (arrows point to the individual animals which survived from third to fourth month). This is accompanied by significantly increased Shannon’s H alpha-diversity index for 3- and 4-month-old KPC animals, compared with their control counterparts (D). Although control animals do not exhibit change in microbial composition in pCoA plot with age (E), four surviving members of 4-month-old KPC animals were seen to cluster separately from the 2 or 3 months of age collections (F). The Shannon index was not different within control (G) or KPC (H) animals over time. In KPC animals, between 2 and 4 months age, 82 species were found to be significantly different (see Supplementary Figure 4, available at Carcinogenesis online). Analysis of those 82 species with pCoA plot shows tight clustering of 4-month-old animals, compared with when they were 2 months old (I). Neither the Shannon (J) index nor Chao1 (K) index was different for the two age groups, when analyzed for the significantly changing species only.

Figure 4.

Metabolic reconstruction of the microbiome. The HUMAnN2 pipeline generated the pathway abundance list from whole-genome sequencing input, and we manually curated it for predominantly active pathways found in (A) 2-month-old KPC only and (B) 4-month-old KPC only. While 2 months only was dominated by energy metabolism pathways among others, 4 months only was dominated by polyamine biosynthesis pathway. Overall, between the ages of 2 months and 4 months in KPC animals (C), the most significant metabolic pathways were dominated by biosynthetic pathways, where the majority of metabolites are exchanged between the host and the microbiota. Figure shows activated pathways in 4-month KPC animals compared with when they were 2 months old. Adjoining table shows the coverage and non-parametric 2-tailed P-value for each changing pathway.

Figure 5.

Serum polyamine levels are significantly higher in spontaneous murine model of PDAC and in PDAC patients. When we measured the actual total polyamine levels in KPC mice serum (A), significant elevation was seen with progressing age and cancer. Similarly, serum polyamines were found to be significantly elevated in PDAC patients, compared with healthy controls (B) (n = 5–8 for mice; n = 8 for human serum samples). Test of significance was two-tailed, non-parametric Mann–Whitney U test. P-values are exact and mentioned in the figure.

Figure 6.

Progression to tumor and microbial/metabolic changes. With progressive cellular disorganization and tumor development in KPC mice (A; representative H&E staining), top bacterial species between 2 and 4 months are joined by Lactobacillus reuteri. Lactobacilli is known to actively participate in polyamine metabolism. (B) Blinded histological scoring of PDAC progression in KPC mice (n = 6/group with four fields per section]. Test of significance for (B) was non-parametric Mann–Whitney U test.