Profiling of serum metabolome of breast cancer: multi-cancer features discriminate between healthy women and patients with breast cancer.

Introduction: The progression of solid cancers is manifested at the systemic level as molecular changes in the metabolome of body fluids, an emerging source of cancer biomarkers. Methods: We analyzed quantitatively the serum metabolite profile using high-resolution mass spectrometry. Metabolic profiles were compared between breast cancer patients (n=112) and two groups of healthy women (from Poland and Norway; n=95 and n=112, respectively) with similar age distributions. Results: Despite differences between both cohorts of controls, a set of 43 metabolites and lipids uniformly discriminated against breast cancer patients and healthy women. Moreover, smaller groups of female patients with other types of solid cancers (colorectal, head and neck, and lung cancers) were analyzed, which revealed a set of 42 metabolites and lipids that uniformly differentiated all three cancer types from both cohorts of healthy women. A common part of both sets, which could be called a multi-cancer signature, contained 23 compounds, which included reduced levels of a few amino acids (alanine, aspartate, glutamine, histidine, phenylalanine, and leucine/isoleucine), lysophosphatidylcholines (exemplified by LPC(18:0)), and diglycerides. Interestingly, a reduced concentration of the most abundant cholesteryl ester (CE(18:2)) typical for other cancers was the least significant in the serum of breast cancer patients. Components present in a multi-cancer signature enabled the establishment of a well-performing breast cancer classifier, which predicted cancer with a very high precision in independent groups of women (AUC>0.95). Discussion: In conclusion, metabolites critical for discriminating breast cancer patients from controls included components of hypothetical multi-cancer signature, which indicated wider potential applicability of a general serum metabolome cancer biomarker.

Transcriptional Inflammatory Signature in Healthy Donors and Different Radiotherapy Cancer Patients.

Cancer and ionizing radiation exposure are associated with inflammation. To identify a set of radiation-specific signatures of inflammation-associated genes in the blood of partially exposed radiotherapy patients, differential expression of 249 inflammatory genes was analyzed in blood samples from cancer patients and healthy individuals. The gene expression analysis on a cohort of 63 cancer patients (endometrial, head and neck, and prostate cancer) before and during radiotherapy (24 h, 48 h, ~1 week, ~4-8 weeks, and 1 month after the last fraction) identified 31 genes and 15 up- and 16 down-regulated genes. Transcription variability under normal conditions was determined using blood drawn on three separate occasions from four healthy donors. No difference in inflammatory expression between healthy donors and cancer patients could be detected prior to radiotherapy. Remarkably, repeated sampling of healthy donors revealed an individual endogenous inflammatory signature. Next, the potential confounding effect of concomitant inflammation was studied in the blood of seven healthy donors taken before and 24 h after a flu vaccine or ex vivo LPS (lipopolysaccharide) treatment; flu vaccination was not detected at the transcriptional level and LPS did not have any effect on the radiation-induced signature identified. Finally, we identified a radiation-specific signature of 31 genes in the blood of radiotherapy patients that were common for all cancers, regardless of the immune status of patients. Confirmation via MQRT-PCR was obtained for BCL6, MYD88, MYC, IL7, CCR4 and CCR7. This study offers the foundation for future research on biomarkers of radiation exposure, radiation sensitivity, and radiation toxicity for personalized radiotherapy treatment.

Influence of Confounding Factors on Radiation Dose Estimation Using In Vivo Validated Transcriptional Biomarkers.

For triage purposes following a nuclear accident, blood-based gene expression biomarkers can provide rapid dose estimates for a large number of individuals. Ionizing-radiation-responsive genes are regulated through the DNA damage-response pathway, which includes activation of multiple transcription factors. Modulators of this pathway could potentially affect the response of these biomarkers and consequently compromise accurate dose estimation calculations. In the present study, four potential confounding factors were selected: cancer condition, sex, simulated bacterial infection (lipopolysaccharide), and curcumin, an anti-inflammatory/antioxidant agent. Their potential influence on the transcriptional response to radiation of the genes CCNG1 and PHPT1, two biomarkers of radiation exposure ex vivo, was assessed. First, both CCNG1 and PHPT1 were detected in vivo in blood samples from radiotherapy patients and as such were validated as biomarkers of exposure. Importantly, their basal expression level was slightly but significantly affected in vivo by patients' cancer condition. Moreover, lipopolysaccharide stimulation of blood irradiated ex vivo led to a significant modification of CCNG1 and PHPT1 transcriptional response in a dose- and time-dependent manner with opposite regulatory effects. Curcumin also affected CCNG1 and PHPT1 transcriptional response counteracting some of the radiation induction. No differences were observed based on sex. Dose estimations calculated using linear regression were affected by lipopolysaccharide and curcumin. In conclusion, several confounding factors tested in this study can indeed modulate the transcriptional response of CCNG1 and PHPT1 and consequently can affect radiation exposure dose estimations but not to a level which should prevent the biomarkers' use for triage purposes.

FDXR is a biomarker of radiation exposure in vivo.

Previous investigations in gene expression changes in blood after radiation exposure have highlighted its potential to provide biomarkers of exposure. Here, FDXR transcriptional changes in blood were investigated in humans undergoing a range of external radiation exposure procedures covering several orders of magnitude (cardiac fluoroscopy, diagnostic computed tomography (CT)) and treatments (total body and local radiotherapy). Moreover, a method was developed to assess the dose to the blood using physical exposure parameters. FDXR expression was significantly up-regulated 24 hr after radiotherapy in most patients and continuously during the fractionated treatment. Significance was reached even after diagnostic CT 2 hours post-exposure. We further showed that no significant differences in expression were found between ex vivo and in vivo samples from the same patients. Moreover, potential confounding factors such as gender, infection status and anti-oxidants only affect moderately FDXR transcription. Finally, we provided a first in vivo dose-response showing dose-dependency even for very low doses or partial body exposure showing good correlation between physically and biologically assessed doses. In conclusion, we report the remarkable responsiveness of FDXR to ionising radiation at the transcriptional level which, when measured in the right time window, provides accurate in vivo dose estimates.

Comparison of peptide cancer signatures identified by mass spectrometry in serum of patients with head and neck, lung and colorectal cancers: association with tumor progression.

Mass spectrometry-based analyses of the low-molecular-weight fraction of serum proteome allow identifying proteome profiles (signatures) that are potentially useful in detection and diagnostics of cancer. Here we compared serum proteome profiles of healthy donors and patients with three different types of cancer aiming to identify peptide signatures that were either common for all cancer samples or specific for cancer type. Blood samples were collected before start of the therapy from patients with head and neck squamous cell cancer, colorectal adenocarcinoma and non-small cell lung cancer, and from a corresponding group of healthy volunteers. Mass profiles of the serum proteome were recorded in the range between 2 and 13 kDa using MALDI-ToF spectrometry and 131 identified peptide ions were used for statistical analyses. Similar degrees of overall similarities were observed in all intra-group and inter-group analyses when general features of serum proteome profiles were compared between individual samples. However, classifiers built of selected spectral components allowed differentiation between healthy donors and three groups of cancer patients with 69-74% sensitivity and 82-84% specificity. There were two common peptide species (3766 and 5867 Da) with increased levels in all cancer samples. Several spectral components permitted differentiation between lung cancer samples and either head and neck cancer or colorectal cancer samples, but two latter types of samples could not be properly discriminated. Abundance of spectral components that putatively corresponded to fragments of serum amyloid A (11511 and 11667 Da) was highest in lung cancer samples, yet increased levels of these peptides appeared to generally associate with more advanced cancer cases. We concluded that certain components of serum peptide signatures are common for different cancer signatures and putatively reflect general response of organism to the disease, yet other components of such signatures are more specific and most likely correspond to clinical stage of the malignancy.