Discovery of Metabolic Biomarkers Predicting Radiation Therapy Late Effects in Prostate Cancer Patients

Metabolomics for Clinical Biomarker Discovery and Therapeutic Target Identification

As links between genotype and phenotype, small-molecule metabolites are attractive biomarkers for disease diagnosis, prognosis, classification, drug screening and treatment, insight into understanding disease pathology and identifying potential targets. Metabolomics technology is crucial for discovering targets of small-molecule metabolites involved in disease phenotype. Mass spectrometry-based metabolomics has implemented in applications in various fields including target discovery, explanation of disease mechanisms and compound screening. It is used to analyze the physiological or pathological states of the organism by investigating the changes in endogenous small-molecule metabolites and associated metabolism from complex metabolic pathways in biological samples. The present review provides a critical update of high-throughput functional metabolomics techniques and diverse applications, and recommends the use of mass spectrometry-based metabolomics for discovering small-molecule metabolite signatures that provide valuable insights into metabolic targets. We also recommend using mass spectrometry-based metabolomics as a powerful tool for identifying and understanding metabolic patterns, metabolic targets and for efficacy evaluation of herbal medicine.

Microbiome and metabolome dynamics during radiotherapy for prostate cancer

BACKGROUND

Prostate cancer patients treated with radiotherapy are susceptible to acute gastrointestinal (GI) toxicity due to substantial overlap of the intestines with the radiation volume. Due to their intimate relationship with GI toxicity, faecal microbiome and metabolome dynamics during radiotherapy were investigated.

MATERIAL & METHODS

This prospective study included 50 prostate cancer patients treated with prostate (bed) only radiotherapy (PBRT) (n = 28) or whole pelvis radiotherapy (WPRT) (n = 22) (NCT04638049). Longitudinal sampling was performed prior to radiotherapy, after 10 fractions, near the end of radiotherapy and at follow-up. Patient symptoms were dichotomized into a single toxicity score. Microbiome and metabolome fingerprints were analyzed by 16S rRNA gene sequencing and ultra-high-performance liquid chromatography hybrid high-resolution mass spectrometry, respectively.

RESULTS

The individual α-diversity did not significantly change over time. Microbiota composition (β-diversity) changed significantly over treatment (PERMANOVA p-value = 0.03), but there was no significant difference in stability when comparing PBRT versus WPRT. Levels of various metabolites were significantly altered during radiotherapy. Baseline α-diversity was not associated with any toxicity outcome. Based on the metabolic fingerprint, no natural clustering according to toxicity profile could be achieved.

CONCLUSIONS

Radiation dose and treatment volume demonstrated limited effects on microbiome and metabolome fingerprints. In addition, no distinctive signature for toxicity status could be established. There is an ongoing need for toxicity risk stratification tools for diagnostic and therapeutic purposes, but the current evidence implies that the translation of metabolic and microbial biomarkers into routine clinical practice remains challenging.

Carbon Ion Radiotherapy Evokes a Metabolic Reprogramming and Individualized Response in Prostate Cancer

Introduction: Carbon ion radiotherapy (CIRT) is a novel treatment for prostate cancer (PCa). However, the underlying mechanism for the individualized response to CIRT is still not clear. Metabolic reprogramming is essential for tumor growth and proliferation. Although changes in metabolite profiles have been detected in patients with cancer treated with photon radiotherapy, there is limited data regarding CIRT-induced metabolic changes in PCa. Therefore, the study aimed to investigate the impact of metabolic reprogramming on individualized response to CIRT in patients with PCa.

Materials and Methods: Urine samples were collected from pathologically confirmed patients with PCa before and after CIRT. A UPLC-MS/MS system was used for metabolite detection. XCMS online, MetDNA, and MS-DIAL were used for peak detection and identification of metabolites. Statistical analysis and metabolic pathway analysis were performed on MetaboAnalyst.

Results: A total of 1,701 metabolites were monitored in this research. Principal component analysis (PCA) revealed a change in the patient's urine metabolite profiles following CIRT. Thirty-five metabolites were significantly altered, with the majority of them being amino acids. The arginine biosynthesis and histidine metabolism pathways were the most significantly altered pathways. Hierarchical cluster analysis (HCA) showed that after CIRT, the patients could be clustered into two groups according to their metabolite profiles. The arginine biosynthesis and phenylalanine, tyrosine, and tryptophan biosynthesis pathways are the most significantly discriminated pathways.

Conclusion: Our preliminary findings indicate that metabolic reprogramming and inhibition are important mechanisms involved in response to CIRT in patients with PCa. Therefore, changes in urine metabolites could be used to timely assess the individualized response to CIRT.

Plasma metabolomic profiles in liver cancer patients following stereotactic body radiotherapy

Background

Stereotactic body radiotherapy (SBRT) is an effective treatment for hepatocellular carcinoma (HCC). This study sought to identify differentially expressed plasma metabolites in HCC patients at baseline and early during SBRT, and to explore if changes in these metabolites early during SBRT may serve as biomarkers for radiation-induced liver injury and/or tumour response.

Methods

Forty-seven HCC patients were treated with SBRT on previously published prospective trials. Plasma samples were collected at baseline and after one to two fractions of SBRT, and analysed by GC/MS and LC/MS for untargeted and targeted metabolomics profiling, respectively.

Findings

Sixty-nine metabolites at baseline and 62 metabolites after one to two fractions of SBRT were differentially expressed, and strongly separated the Child Pugh (CP) B from the CP A HCC patients. These metabolites are associated with oxidative stress and alterations in hepatic cellular metabolism. Differential upregulation of serine, alanine, taurine, and lipid metabolites early during SBRT from baseline was noted in the HCC patients who demonstrated the greatest increase in CP scores at three months post SBRT, suggesting that high protein and lipid turnover early during SBRT may portend increased clinical liver toxicity. Twenty annotated metabolites including fatty acids, glycerophospholipids, and acylcarnitines were differentially upregulated early during SBRT from baseline and separated patients with complete/partial response from those with stable disease at three months post SBRT.

Interpretation

Dysregulation of amino acid and lipid metabolism detected early during SBRT are associated with subsequent clinical liver injury and tumour response in HCC.