Integration of Serum Metabolomics into Clinical Assessment to Improve Outcome Prediction of Metastatic Soft Tissue Sarcoma Patients Treated with Trabectedin

Microbiome as a biomarker and therapeutic target in pancreatic cancer

Studying the effects of the microbiome on the development of different types of cancer has recently received increasing research attention. In this context, the microbial content of organs of the gastrointestinal tract has been proposed to play a potential role in the development of pancreatic cancer (PC). Proposed mechanisms for the pathogenesis of PC include persistent inflammation caused by microbiota leading to an impairment of antitumor immune surveillance and altered cellular processes in the tumor microenvironment. The limited available diagnostic markers that can currently be used for screening suggest the importance of microbial composition as a non-invasive biomarker that can be used in clinical settings. Samples including saliva, stool, and blood can be analyzed by 16 s rRNA sequencing to determine the relative abundance of specific bacteria. Studies have shown the potentially beneficial effects of prebiotics, probiotics, antibiotics, fecal microbial transplantation, and bacteriophage therapy in altering microbial diversity, and subsequently improving treatment outcomes. In this review, we summarize the potential impact of the microbiome in the pathogenesis of PC, and the role these microorganisms might play as biomarkers in the diagnosis and determining the prognosis of patients. We also discuss novel treatment methods being used to minimize or prevent the progression of dysbiosis by modulating the microbial composition. Emerging evidence is supportive of applying these findings to improve current therapeutic strategies employed in the treatment of PC.

Physiological Indirect Response Model to Omics-Powered Quantitative Systems Pharmacology Model

Over the past several decades, mathematical modeling has been applied to increasingly wider scopes of questions in drug development. Accordingly, the range of modeling tools has also been evolving, as showcased by contributions of Jusko and colleagues: from basic pharmacokinetics/pharmacodynamics (PK/PD) modeling to today's platform-based approach of quantitative systems pharmacology (QSP) modeling. Aimed at understanding the mechanism of action of investigational drugs, QSP models characterize systemic effects by incorporating information about cellular signaling networks, which is often represented by omics data. In this perspective, we share a few examples illustrating approaches for the integration of omics into mechanistic QSP modeling. We briefly overview how the evolution of PK/PD modeling into QSP has been accompanied by an increase in available data and the complexity of mathematical methods that integrate it. We discuss current gaps and challenges of integrating omics data into QSP models and propose several potential areas where integrated QSP and omics modeling may benefit drug development.

Novel metabolomics-biohumoral biomarkers model for predicting survival of metastatic soft-tissue sarcomas

Soft tissue sarcomas (STSs) are rare malignant tumors that are difficult to prognosticate using currently available instruments. Omics sciences could provide more accurate and individualized survival predictions for patients with metastatic STS. In this pilot, hypothesis-generating study, we integrated clinicopathological variables with proton nuclear magnetic resonance (1H NMR) plasma metabolomic and lipoproteomic profiles, capturing both tumor and host characteristics, to identify novel prognostic biomarkers of 2-year survival. Forty-five metastatic STS (mSTS) patients with prevalent leiomyosarcoma and liposarcoma histotypes receiving trabectedin treatment were enrolled. A score combining acetate, triglycerides low-density lipoprotein (LDL)-2, and red blood cell count was developed, and it predicts 2-year survival with optimal results in the present cohort (84.4% sensitivity, 84.6% specificity). This score is statistically significant and independent of other prognostic factors such as age, sex, tumor grading, tumor histotype, frailty status, and therapy administered. A nomogram based on these 3 biomarkers has been developed to inform the clinical use of the present findings.

Metabolic Clues to Bile Acid Patterns and Prolonged Survival in Patients with Metastatic Soft-Tissue Sarcoma Treated with Trabectedin

Metastatic soft-tissue sarcomas (mSTS) encompass a highly heterogeneous group of rare tumours characterized by different clinical behaviours and outcomes. Currently, prognostic factors for mSTS are very limited, posing significant challenges in predicting patient survival. Within a cohort of 39 mSTS patients undergoing trabectedin treatment, it was remarkable to find one patient who underwent 73 cycles of trabectedin achieving an unforeseen clinical outcome. To identify contributing factors to her exceptional long-term survival, we have explored circulation metabolomics and biohumoral biomarkers to uncover a potential distinct host biochemical phenotype. The long-term survival patient compared with the other mSTS patients exhibited a distinctive metabolic profile characterized by remarkably higher levels of ursodeoxycholic acid (UDCA) derivatives and vitamin D and lower levels of lithocholic acid (LCA) derivatives, as well as reduced levels of inflammatory C-Reactive Protein 4 (C-RP4) biomarker. Despite its exploratory nature, this study reveals a potential association between specific bile acid metabolic profiles and mSTS patients' prognosis. Enhanced clinical understanding of the interplay between bile acid metabolism and disease progression could pave the way for new targeted therapeutic interventions which may improve the overall survival of mSTS patients.

Pharmacometabolomics of trabectedin in metastatic soft tissue sarcoma patients

Objective: Trabectedin is an anti-cancer drug commonly used for the treatment of patients with metastatic soft tissue sarcoma (mSTS). Despite its recognized efficacy, significant variability in pharmacological response has been observed among mSTS patients. To address this issue, this pharmacometabolomics study aimed to identify pre-dose plasma metabolomics signatures that can explain individual variations in trabectedin pharmacokinetics and overall clinical response to treatment. Methods: In this study, 40 mSTS patients treated with trabectedin administered by 24 h-intravenous infusion at a dose of 1.5 mg/m2 were enrolled. The patients' baseline plasma metabolomics profiles, which included derivatives of amino acids and bile acids, were analyzed using multiple reaction monitoring LC-MS/MS together with their pharmacokinetics profile of trabectedin. Multivariate Partial least squares regression and univariate statistical analyses were utilized to identify correlations between baseline metabolite concentrations and trabectedin pharmacokinetics, while Partial Least Squares-Discriminant Analysis was employed to evaluate associations with clinical response. Results: The multiple regression model, derived from the correlation between the AUC of trabectedin and pre-dose metabolomics, exhibited the best performance by incorporating cystathionine, hemoglobin, taurocholic acid, citrulline, and the phenylalanine/tyrosine ratio. This model demonstrated a bias of 4.6% and a precision of 17.4% in predicting drug AUC, effectively accounting for up to 70% of the inter-individual pharmacokinetic variability. Through the use of Partial least squares-Discriminant Analysis, cystathionine and hemoglobin were identified as specific metabolic signatures that effectively distinguish patients with stable disease from those with progressive disease. Conclusions: The findings from this study provide compelling evidence to support the utilization of pre-dose metabolomics in uncovering the underlying causes of pharmacokinetic variability of trabectedin, as well as facilitating the identification of patients who are most likely to benefit from this treatment.

Amino acid metabolism in primary bone sarcomas

Primary bone sarcomas, including osteosarcoma (OS) and Ewing sarcoma (ES), are aggressive tumors with peak incidence in childhood and adolescence. The intense standard treatment for these patients consists of combined surgery and/or radiation and maximal doses of chemotherapy; a regimen that has not seen improvement in decades. Like other tumor types, ES and OS are characterized by dysregulated cellular metabolism and a rewiring of metabolic pathways to support the biosynthetic demands of malignant growth. Not only are cancer cells characterized by Warburg metabolism, or aerobic glycolysis, but emerging work has revealed a dependence on amino acid metabolism. Aside from incorporation into proteins, amino acids serve critical functions in redox balance, energy homeostasis, and epigenetic maintenance. In this review, we summarize current studies describing the amino acid metabolic requirements of primary bone sarcomas, focusing on OS and ES, and compare these dependencies in the normal bone and malignant tumor contexts. We also examine insights that can be gleaned from other cancers to better understand differential metabolic susceptibilities between primary and metastatic tumor microenvironments. Lastly, we discuss potential metabolic vulnerabilities that may be exploited therapeutically and provide better-targeted treatments to improve the current standard of care.

Effects of Dangshen Yuanzhi Powder on learning ability and gut microflora in rats with memory disorder

ETHNOPHARMACOLOGICAL RELEVANCE

Yuanzhi Powder is a commonly used traditional Chinese medical formulae for its potency in enhancing memory and learning. In clinical practice, Yuanzhi Powder is a classic formula in TCM to treat amnesia of the type "deficiency of Qi, turbid phlegm harasses the head and eyes, and stagnation of phlegm converting into the fire". Our previous study showed that Yuanzhi Power, used together with Codonopsis Radix (Dangshen Yuanzhi Power, DYP), could improve learning and memory ability in animals with memory disorder (MD) and its efficacy is superior or equivalent to that of the Yuanzhi Power.

AIM OF STUDY

This study aimed to explore the regulatory mechanism of DYP through the "bacteria-gut-brain axis".

MATERIALS AND METHODS

The SD rats were divided randomly into control, model, positive, DYP-L, and DYP-H groups. Except for the control group, the rats were intraperitoneally injected with D-Gal (400 mg/kg) and gavaged with aluminum chloride (200 mg/kg) every day for 50 days. The rats in the DYP group were gavaged with DYP (6.67 and 13.34 g/kg, respectively) from the 15th day, once a day. The rats in the positive group were similarly administrated with piracetam (0.5 g/kg). The rats' bodyweight was recorded from the 16th day. The learning and memory ability of animals was tested by Morris water maze. The levels of MCP-1, NF-L, NSE, and TNF-α in serum were determined by Elisa kit, while the histopathology of duodenum and colon tissues was examined by H & E staining. The diversity of intestinal flora was sequenced and analyzed. In order to reveal the role of intestinal flora in DYP treatment of MD, the intestinal flora composition and the correlation analysis of intestinal flora and the above biochemical indexes were investigated. The intestinal flora function and biological metabolic pathways were predicted and analyzed by the KEGG database.

RESULTS

The MD animals' learning and spatial memory ability decreased significantly, compared with the normal group, accompanied by weight increase and intestinal flora disorder. DYP can improve the learning and memory ability of MD animals, and its efficacy may exert through the following ways: (i) callback the abnormal biochemical indexes of MCP-1, NF-L, NSE, and TNF-α; (ii) decreasing the relative ratio of Firmicutes/Bacteroidetes and repairing the pathology of MD animal intestinal mucosa; and (iii) the regulation of DYP on biochemical blood indexes of MD animals was significantly correlated with the regulation of intestinal flora; (iv) DYP rats showed a strong correlation between cognitive ability improvement and bodyweight loss; (v) besides, DYP could also regulate the metabolic pathways of carbohydrate, amino acid, nucleotide, and energy by affecting related biological functions.

CONCLUSIONS

The results supported that DYP can improve MD animals' learning and memory ability by restoring the intestinal flora disorder and callback the abnormal biochemical indexes in serum, closely related to the "bacteria-gut-brain axis".

Characterization of lipomatous tumors with high-resolution 1H MRS at 17.6T: Do benign lipomas, atypical lipomatous tumors and liposarcomas have a distinct metabolic signature?

Background

Distinguishing between some benign lipomas (BLs), atypical lipomatous tumors (ALTs), and dedifferentiated liposarcomas (DDLs) can be challenging due to overlapping magnetic resonance imaging characteristics, and poorly understood molecular mechanisms underlying the malignant transformation of liposarcomas.

Purpose

To identify metabolic biomarkers of the lipomatous tumor spectrum by examining human tissue specimens using high-resolution ¹H magnetic resonance spectroscopy (MRS).

Materials and methods

In this prospective study, human tissue specimens were obtained from participants who underwent surgical resection for radiologically-indeterminate lipomatous tumors between November 2016 and May 2019. Tissue specimens were obtained from normal subcutaneous fat (n=9), BLs (n=10), ALTs (n=7) and DDLs (n=8). Extracts from specimens were examined with high-resolution MRS at 17.6T. Computational modeling of pattern recognition-based cluster analysis was utilized to identify significant differences in metabolic signatures between the lipomatous tumor types.

Results

Significant differences between BLs and ALTs were observed for multiple metabolites, including leucine, valine, branched chain amino acids, alanine, acetate, glutamine, and formate. DDLs were distinguished from ALTs by increased glucose and lactate, and increased phosphatidylcholine. Multivariate principal component analysis showed clear clustering identifying distinct metabolic signatures of the tissue types.

Conclusion

Metabolic signatures identified in ¹H MR spectra of lipomatous tumors provide new insights into malignant progression and metabolic targeting. The metabolic patterns identified provide the foundation of developing noninvasive MRS or PET imaging biomarkers to distinguish between BLs, ALTs, and DDLs.

Stemness Subtypes and Scoring System Predict Prognosis and Efficacy of Immunotherapy in Soft Tissue Sarcoma

Tumor stemness has been reported to play important roles in cancers. However, a comprehensive analysis of tumor stemness remains to be performed to investigate the specific mechanisms and practical values of stemness in soft tissue sarcomas (STS). Here, we applied machine learning to muti-omic data of patients from TCGA-SARC and GSE21050 cohorts to reveal important roles of stemness in STS. We demonstrated limited roles of existing mRNAsi in clinical application. Therefore, based on stemness-related signatures (SRSs), we identified three stemness subtypes with distinct stemness, immune, and metabolic characteristics using consensus clustering. The low-stemness subtype had better prognosis, activated innate and adaptive immunity (e.g., infiltrating B, DC, Th1, CD8+ T, activated NK, gamma delta T cells, and M1 macrophages), more enrichment of metabolic pathways, more sites with higher methylation level, higher gene mutations, CNA burdens, and immunogenicity indicators. Furthermore, the 16 SRS-based stemness prognostic index (SPi) was developed, and we found that low-SPi patients with low stemness had better prognosis and other characteristics similar to those in the low-stemness subtype. Besides, low-stemness subtype and low-SPi patients could benefit from immunotherapy. The predictive value of SPi in immunotherapy was more accurate after the addition of MSI into SPi. MSI^lowSPi^low patients might be more sensitive to immunotherapy. In conclusion, we highlighted mechanisms and practical values of the stemness in STS. We also recommended the combination of MSI and SPi which is a promising tool to predict prognosis and achieve precise treatments of immunotherapy in STS.

m6A Modification Patterns With Distinct Immunity, Metabolism, and Stemness Characteristics in Soft Tissue Sarcoma

N6-methyladenosine (m6A) RNA methylation has been shown to have prognostic value in cancer. Nonetheless, its potential role regarding immunity, metabolism, and stemness in soft tissue sarcoma (STS) remains unknown. We comprehensively estimated the m6A modification patterns and corresponding immunity, metabolism, and stemness characteristics based on 568 STS samples and 21 m6A regulators. The m6Ascore was constructed to quantify m6A modification patterns in individuals using machine learning algorithms. Two distinct m6A modification patterns among the STS patients were identified, which exhibited differences in prognosis, immune cell infiltration, metabolic pathways, stemness, somatic mutation, and copy number variation. Thereafter, immunity-, metabolism-, and stemness phenotype-related genes associated with m6A modification were identified. Furthermore, patients with lower m6Ascores had increased antitumor immune responses, survival benefit under immunotherapy, tumor mutation burden, immunogenicity, and response to anti-PD-1/L1 immunotherapy. Immunotherapy sensitivity was validated using the IMvigor210 dataset. STS patients with lower m6Ascore might be more sensitive to docetaxel and gemcitabine. Finally, pan-cancer analysis illustrated the significant correlations of m6Ascore with clinical outcomes, immune cell infiltration, metabolism, and stemness. This study revealed that m6A modification plays an important role in immunity, metabolism, and stemness in STS. Evaluating the m6A modification pattern and development of m6Ascore may help to guide more effective immunotherapy and chemotherapy strategies.

Soft Tissue Sarcoma: An Insight on Biomarkers at Molecular, Metabolic and Cellular Level

Simple Summary

Soft tissue sarcoma is a rare mesenchymal malignancy. Despite the advancements in the fields of radiology, pathology and surgery, these tumors often recur locally and/or with metastatic disease. STS is considered to be a diagnostic challenge due to the large variety of histological subtypes with clinical and histopathological characteristics which are not always distinct. One of the important clinical problems is a lack of useful biomarkers. Therefore, the discovery of biomarkers that can be used to detect tumors or predict tumor response to chemotherapy or radiotherapy could help clinicians provide more effective clinical management.

Abstract

Soft tissue sarcomas (STSs) are a heterogeneous group of rare tumors. Although constituting only 1% of all human malignancies, STSs represent the second most common type of solid tumors in children and adolescents and comprise an important group of secondary malignancies. Over 100 histologic subtypes have been characterized to date (occurring predominantly in the trunk, extremity, and retroperitoneum), and many more are being discovered due to molecular profiling. STS mortality remains high, despite adjuvant chemotherapy. New prognostic stratification markers are needed to help identify patients at risk of recurrence and possibly apply more intensive or novel treatments. Recent scientific advancements have enabled a more precise molecular characterization of sarcoma subtypes and revealed novel therapeutic targets and prognostic/predictive biomarkers. This review aims at providing a comprehensive overview of the most relevant cellular, molecular and metabolic biomarkers for STS, and highlight advances in STS-related biomarker research.

Sarcoma Metabolomics: Current Horizons and Future Perspectives

The vast array of metabolic adaptations that cancer cells are capable of assuming, not only support their biosynthetic activity, but also fulfill their bioenergetic demands and keep their intracellular reduction-oxidation (redox) balance. Spotlight has recently been placed on the energy metabolism reprogramming strategies employed by cancer cells to proliferate. Knowledge regarding soft tissue and bone sarcomas metabolome is relatively sparse. Further characterization of sarcoma metabolic landscape may pave the way for diagnostic refinement and new therapeutic target identification, with benefit to sarcoma patients. This review covers the state-of-the-art knowledge on cancer metabolomics and explores in detail the most recent evidence on soft tissue and bone sarcoma metabolomics.

Radical Hemithoracic Radiotherapy Induces Systemic Metabolomics Changes That Are Associated with the Clinical Outcome of Malignant Pleural Mesothelioma Patients

Simple Summary

Radical hemithoracic radiotherapy represents a promising new advance in the field of radiation oncology and encouraging results have been achieved in the treatment of malignant pleural mesothelioma patients. This study showed that this radiotherapy modality produces significant changes in serum metabolomics profile mainly affecting arginine and polyamine biosynthesis pathways. Interestingly, individual metabolomics alterations were found associated with the clinical overall survival outcome of the radiotherapy treatment. These results highlight metabolomics profile analysis as a powerful prognostic tool useful to better understand the mechanisms underlying the interpatients variability and to identify patients who may receive the best benefit from this specific radiotherapy treatment.

Abstract

Radical hemithoracic radiotherapy (RHRT) represents an advanced therapeutic option able to improve overall survival of malignant pleural mesothelioma patients. This study aims to investigate the systemic effects of this radiotherapy modality on the serum metabolome and their potential implications in determining the individual clinical outcome. Nineteen patients undergoing RHRT at the dose of 50 Gy in 25 fractions were enrolled. Serum targeted metabolomics profiles were investigated at baseline and the end of radiotherapy by liquid chromatography and tandem mass spectrometry. Univariate and multivariate OPLS-DA analyses were applied to study the serum metabolomics changes induced by RHRT while PLS regression analysis to evaluate the association between such changes and overall survival. RHRT was found to affect almost all investigated metabolites classes, in particular, the amino acids citrulline and taurine, the C14, C18:1 and C18:2 acyl-carnitines as well as the unsaturated long chain phosphatidylcholines PC ae 42:5, PC ae 44:5 and PC ae 44:6 were significantly decreased. The enrichment analysis showed arginine metabolism and the polyamine biosynthesis as the most perturbed pathways. Moreover, specific metabolic changes encompassing the amino acids and acyl-carnitines resulted in association with the clinical outcome accounting for about 60% of the interpatients overall survival variability. This study highlighted that RHRT can induce profound systemic metabolic effects some of which may have a significant prognostic value. The integration of metabolomics in the clinical assessment of the malignant pleural mesothelioma could be useful to better identify the patients who can achieve the best benefit from the RHRT treatment.