Fecal Metabolic Profiling of Breast Cancer Patients during Neoadjuvant Chemotherapy Reveals Potential Biomarkers

Abstract

Breast cancer (BC) is the most common form of cancer among women worldwide. Despite the huge advancements in its treatment, the exact etiology of breast cancer still remains unresolved. There is an increasing interest in the role of the gut microbiome in modulating the anti-cancer therapeutic response. It seems that alteration of the microbiome-derived metabolome potentially promotes carcinogenesis. Taken together, metabolomics has arisen as a fascinating new omics field to screen promising metabolic biomarkers. In this study, fecal metabolite profiling was performed using NMR spectroscopy, to identify potential biomarker candidates that can predict response to neoadjuvant chemotherapy (NAC) for breast cancer. Metabolic profiles of feces from patients (n = 8) following chemotherapy treatment cycles were studied. Interestingly, amino acids were found to be upregulated, while lactate and fumaric acid were downregulated in patients under the second and third cycles compared with patients before treatment. Furthermore, short-chain fatty acids (SCFAs) were significantly differentiated between the studied groups. These results strongly suggest that chemotherapy treatment plays a key role in modulating the fecal metabolomic profile of BC patients. In conclusion, we demonstrate the feasibility of identifying specific fecal metabolic profiles reflecting biochemical changes that occur during the chemotherapy treatment. These data give an interesting insight that may complement and improve clinical tools for BC monitoring.

Pharmacometabolomics by NMR in Oncology: A Systematic Review

“…However, in the PMx studies included in this review the most frequently used biofluids were blood (i.e., serum, plasma, and platelets), followed by urine samples. Other biosamples, such as feces [ 23 , 24 ] or cells [ 25 , 26 ] were used in two studies, whereas saliva [ 27 ], culture medium [ 28 ], and tissue [ 29 ] were only collected in one of the studies. It should be noted that even though tissue and patient-derived cells can provide in situ information of the specific metabolic alterations due to a health condition or an external intervention [ 30 ], the access to these samples is highly dependent on the clinical practice.…”

“…Operating frequencies ranging from 400 to 800 MHz were used in the different PMx studies, although 500 and 600 MHz spectrometers were the most frequently chosen. Furthermore, spectrometers equipped with a cryoprobe [ 24 , 26 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ] were used in over 25% of the studies, and an automatic sample charger was only used in seven of them [ 24 , 26 , 48 , 50 , 51 , 52 , 53 ]. Spectrometer selection in the selected PMx studies does not appear to be associated with the type of sample or any other characteristic of the study.…”

“…Principal Component Analysis (PCA) [ 96 ] was the method of choice in most of the studies, although hierarchical clustering [ 97 ] was also used in other studies [ 26 ]. Then, supervised analysis methods, such as partial least square discriminant analysis (PLS-DA) [ 25 , 41 , 42 , 46 , 48 , 49 , 60 , 67 , 68 , 71 , 73 , 74 , 75 , 76 , 78 , 79 , 98 , 99 ] or orthogonal PLS-DA (OPLS-DA) [ 24 , 26 , 29 , 40 , 43 , 50 , 68 , 70 , 72 , 74 , 75 , 76 , 77 , 98 , 100 , 101 , 102 ] were pursued in the majority of the studies to evaluate the discriminatory potential of the metabolic profile between the groups of study. Herein, alternative supervised approaches were also followed, including multilevel Partial Least Square (mPLS) [ 27 ], Random Forest (RF) [ 45 , 47 , 53 ], K-nearest neighbors (kNN) [ …”

“…To that end, the Student T test or the Mann-Whitney U test were chosen for the mean comparison while Pearson or Spearman correlation analyses were followed for the evaluation of the potential correlations with continuous variables. Additionally, ROC curves were generated in 13 studies to internally validate the discriminatory power of their findings for predicting the response to treatment [ 24 , 26 , 41 , 44 , 45 , 46 , 47 , 52 , 70 , 74 , 77 , 78 , 99 ]. Nevertheless, none of the reviewed studies conducted an external validation to evaluate the relevance of their results in an independent set of samples.…”

“…The 46 PMx studies included in this review can be classified into a total of 11 health categories, based on the Health Research Classification System (HRCS) [ 106 ] ( Figure 2 ). More than one third of the reviewed articles focused on different oncological conditions [ 23 , 24 , 25 , 26 , 29 , 41 , 43 , 46 , 48 , 51 , 52 , 76 , 77 , 94 , 101 , 102 ]. Cardiovascular diseases, including coronary artery disease [ 49 , 70 , 74 ], hypertension [ 44 ], atrial fibrillation [ 78 ], myocardial infarction [ 107 ], and cardiotoxicity [ 28 ] were the second most explored health conditions.…”