Metabolic Phenotyping Predicts Gemcitabine and Cisplatin Chemosensitivity in Patients With Cholangiocarcinoma

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Identifying CDC7 as a synergistic target of chemotherapy in resistant small-cell lung cancer via CRISPR/Cas9 screening

There is currently a lack of efficacious treatments for patients with chemo-resistant small-cell lung cancer (SCLC), leading to poor prognoses. We examined a chemo-resistant SCLC cell line using genome-wide CRISPR/Cas9 screening and identified serine/threonine kinase cell division cycle 7 (CDC7) as a potential synergistic target. Silencing CDC7 in chemo-resistant SCLC cells decreased the IC₅₀ and improved the efficacy of chemotherapy. Based on the highest single agent model, the CDC7 inhibitor XL413 had a synergistic effect with both cisplatin and etoposide in chemo-resistant SCLC cells, but had no such effect in chemo-sensitive SCLC cells; the combination of XL413 and chemotherapy significantly inhibited cell growth. Western blot and flow cytometry showed that the combined treatments increased apoptosis, whereas XL413 alone had little effect on apoptosis. An analysis of cell cycle and cyclin protein levels indicated that the combination of XL413 and chemotherapy-induced G1/S phase arrest and DNA damage in chemo-resistant SCLC cells. Xenografted tumor and histoculture drug response assays using patient-derived xenografts showed that XL413 improved the efficacy of chemotherapy in vivo and with SCLC tissues. These results suggest that XL413 exerts a synergistic effect with chemotherapy on chemo-resistant SCLC.

1H NMR fecal metabolic phenotyping of periductal fibrosis- and cholangiocarcinoma-specific metabotypes defining perturbation in gut microbial-host co-metabolism

Background

The liver fluke Opisthorchis viverrini (OV), which subsequently inhabits the biliary system and results in periductal fibrosis (PDF), is one of the primarily causes of cholangiocarcinoma (CCA), a bile duct cancer with an exceptionally high incidence in the northeast of Thailand and other Greater Mekong Subregion (GMS) countries. Insights in fecal metabolic changes associated with PDF and CCA are required for further molecular research related to gut health and potential diagnostic biological marker development.

Methods

In this study, nuclear magnetic resonance (NMR) metabolomics was applied for fecal metabolic phenotyping from 55 fecal water samples across different study groups including normal bile duct, PDF and CCA groups.

Results

By using NMR spectroscopy-based metabolomics, fecal metabolic profiles of patients with CCA or PDF and of individuals with normal bile duct have been established with a total of 40 identified metabolites. Further multivariate statistical analysis and hierarchical clustering heat map have demonstrated the PDF- and CCA-specific metabotypes through various altered metabolite groups including amino acids, alcohols, amines, anaerobic glycolytic metabolites, fatty acids, microbial metabolites, sugar, TCA cycle intermediates, tryptophan catabolism substrates, and pyrimidine metabolites. Compared to the normal bile duct group, PDF individuals showed the significantly elevated relative concentrations of fecal ethanol, glycine, tyrosine, and N-acetylglucosamine whereas CCA patients exhibited the remarkable fecal metabolic changes that can be evident through the increased relative concentrations of fecal uracil, succinate, and 5-aminopentanoate. The prominent fecal metabolic alterations between CCA and PDF were displayed by the reduction of relative concentration of methanol observed in CCA. The metabolic alterations associated with PDF and CCA progression have been proposed with the involvement of various metabolic pathways including TCA cycle, ethanol biogenesis, hexamine pathway, methanol biogenesis, pyrimidine metabolism, and lysine metabolism. Among them, ethanol, methanol, and lysine metabolism strongly reflect the association of gut-microbial host metabolic crosstalk in PDF and/or CCA patients.

Conclusion

The PDF- and CCA-associated metabotypes have been investigated displaying their distinct fecal metabolic patterns compared to that of normal bile duct group. Our study also demonstrated that the perturbation in co-metabolism of host and gut bacteria has been involved from the early step since OV infection to CCA tumorigenesis.

Monoisotopic silver nanoparticles-based mass spectrometry imaging of human bladder cancer tissue: Biomarker discovery

PURPOSE

Bladder cancer (BC) is the 10th most common form of cancer worldwide and the 2nd most common cancer of the urinary tract after prostate cancer, taking into account both incidence and prevalence.

MATERIALS/METHODS

Tissues from patients with BC and also tissue extracts were analyzed by laser desorption/ionization mass spectrometry imaging (LDI-MSI) with monoisotopic silver-109 nanoparticles-enhanced target (¹⁰⁹AgNPET).

RESULTS

Univariate and multivariate statistical analyses revealed 10 metabolites that differentiated between tumor and normal tissues from six patients with diagnosed BC. Selected metabolites are discussed in detail in relation to their mass spectrometry (MS) imaging results. The pathway analysis enabled us to link these compounds with 17 metabolic pathways.

CONCLUSIONS

According to receiver operating characteristic (ROC) analysis of biomarkers, 10 known metabolites were identified as the new potential biomarkers with areas under the curve (AUC) higher than >0.99. In both univariate and multivariate analysis, it was predicted that these compounds could serve as useful discriminators of cancerous versus normal tissue in patients diagnosed with BC.

Chemotherapeutic resistant cholangiocarcinoma displayed distinct intratumoral microbial composition and metabolic profiles

Background

Cholangiocarcinoma (CCA) is a malignancy of the cholangiocytes. One of the major issues regarding treatment for CCA patients is the development of chemotherapeutic resistance. Recently, the association of intratumoral bacteria with chemotherapeutic response has been reported in many cancer types.

Method

In the present study, we aimed to investigate the association between the intratumoral microbiome and its function on gemcitabine and cisplatin response in CCA tissues using 16S rRNA sequencing and 1H NMR spectroscopic analysis.

Result

The results of 16S rRNA sequencing demonstrated that Gammaproteobacteria were significantly higher in both gemcitabine- and cisplatin-resistance groups compared to sensitive groups. In addition, intratumoral microbial diversity and abundance were significantly different compared between gemcitabine-resistant and sensitive groups. Furthermore, the metabolic phenotype of the low dose gemcitabine-resistant group significantly differed from that of low dose gemcitabine-sensitive group. Increased levels of acetylcholine, adenine, carnitine and inosine were observed in the low dose gemcitabine-resistant group, while the levels of acetylcholine, alpha-D-glucose and carnitine increased in the low dose cisplatin-resistant group. We further performed the intergrative microbiome-metabolome analysis and revealed a correlation between the intratumoral bacterial and metabolic profiles which reflect the chemotherapeutics resistance pattern in CCA patients.

Conclusion

Our results demonstrated insights into the disruption of the microbiome and metabolome in the progression of chemotherapeutic resistance. The altered microbiome-metabolome fingerprints could be used as predictive markers for drug responses potentially resulting in the development of an appropriate chemotherapeutic drug treatment plan for individual CCA patients.