Fluorescence spectroscopy as a potential metabonomic tool for early detection of colorectal cancer

Abstract Fluorescence spectroscopy Excitation Emission Matrix (EEM) measurements were applied on human blood plasma samples from a case control study on colorectal cancer. Samples were collected before large bowel endoscopy and included patients with colorectal cancer or with adenomas, and from individuals with other non malignant findings or no findings (N = 308). The objective of the study was to explore the possibilities for applying fluorescence spectroscopy as a tool for detection of colorectal cancer. Parallel Factor Analysis (PARAFAC) was applied to decompose the fluorescence EEMs into estimates of the underlying fluorophores in the sample. Both the pooled score matrix from PARAFAC, holding the relative concentrations of the derived components, and the raw unfolded spectra were used as basis for discrimination models between cancer and the various controls. Both methods gave test set validated sensitivity and specificity values around 0.75 between cancer and controls, and poor discriminations between the various controls. The PARA-FAC solution gave better options for analyzing the chemical mechanisms behind the discrimination, and revealed a blue shift in tryptophan emission in the cancer patients, a result that supports previous findings. The present findings show how fluorescence spectroscopy and chemometrics can help in cancer diagnostics, and with PARAFAC fluorescence spectroscopy can be a potential metabonomic tool

Human Serum Metabolites Associate With Severity and Patient Outcomes in Traumatic Brain Injury.

Traumatic brain injury (TBI) is a major cause of death and disability worldwide, especially in children and young adults. TBI is an example of a medical condition where there are still major lacks in diagnostics and outcome prediction. Here we apply comprehensive metabolic profiling of serum samples from TBI patients and controls in two independent cohorts. The discovery study included 144 TBI patients, with the samples taken at the time of hospitalization. The patients were diagnosed as severe (sTBI; n=22), moderate (moTBI; n=14) or mild TBI (mTBI; n=108) according to Glasgow Coma Scale. The control group (n=28) comprised of acute orthopedic non-brain injuries. The validation study included sTBI (n=23), moTBI (n=7), mTBI (n=37) patients and controls (n=27). We show that two medium-chain fatty acids (decanoic and octanoic acids) and sugar derivatives including 2,3-bisphosphoglyceric acid are strongly associated with severity of TBI, and most of them are also detected at high concentrations in brain microdialysates of TBI patients. Based on metabolite concentrations from TBI patients at the time of hospitalization, an algorithm was developed that accurately predicted the patient outcomes (AUC=0.84 in validation cohort). Addition of the metabolites to the established clinical model (CRASH), comprising clinical and computed tomography data, significantly improved prediction of patient outcomes. The identified 'TBI metabotype' in serum, that may be indicative of disrupted blood-brain barrier, of protective physiological response and altered metabolism due to head trauma, offers a new avenue for the development of diagnostic and prognostic markers of broad spectrum of TBIs.European Union FP7 project TBIcare (Grant ID: 270259), GE-NFL Head Health Challenge I Award (Grant ID: 7620), EVO (Finland), Maire Taponen Foundation, National Institute for Health Research, National Institute for Health Research Biomedical Research Centre Cambridge (Neuroscience Theme; Brain Injury and Repair Theme)This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.ebiom.2016.07.01

Forecasting individual breast cancer risk using plasma metabolomics and biocontours

Breast cancer is a major cause of death for women. To improve treatment, current oncology research focuses on discovering and validating new biomarkers for early detection of cancer; so far with limited success. Metabolic profiling of plasma samples and auxiliary lifestyle information was combined by chemometric data fusion. It was possible to create a biocontour, which we define as a complex pattern of relevant biological and phenotypic information. While single markers or known risk factors have close to no predictive value, the developed biocontour provides a forecast which, several years before diagnosis, is on par with how well most current biomarkers can diagnose current cancer. Hence, while e.g. mammography can diagnose current cancer with a sensitivity and specificity of around 75 %, the currently developed biocontour can predict that there is an increased risk that breast cancer will develop in a subject 2-5 years after the sample is taken with sensitivity and specificity well above 80 %. The model was built on data obtained in 1993-1996 and tested on persons sampled a year later in 1997. Metabolic forecasting of cancer by biocontours opens new possibilities for early prediction of individual cancer risk and thus for efficient screening. This may provide new avenues for research into disease mechanisms