Forecasting individual breast cancer risk using plasma metabolomics and biocontours.
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ABSTRACT:
SUBMITTER: Bro R
PROVIDER: S-EPMC4559100 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveTo illustrate the benefits of deploying individual headache forecasting models using continuous updating with Bayesian methods.BackgroundThe ability to reliably forecast headache attacks within an individual over time would enhance the study of attacks and allow preemptive treatment. However, deploying a suitable forecasting model in a clinical setting will likely involve several unique challenges related to heterogeneity in the predictor weights, limited or sparse data, and the need for a quick "warm-up." The use of Bayesian methods offers solutions to each of these specific challenges.MethodsThis was a post hoc analysis of a cohort study of individuals with episodic migraine attacks. Individuals completed daily diaries that allowed the estimation of several forecasting models, each using different types of ancillary information incorporated into formal prior probability distributions. An in silico analysis was conducted that mimicked the deployment of these models in a clinical-like setting where the parameters of the models were continuously updated and evaluated each day using root mean square error (RMSE).ResultsIndividuals (N = 95) were followed for 50 days and contributed 3359 days of nonmissing diary data. During the observation period, there were 1293/3359 (38.5%) days with a headache attack. Self-reported baseline headache frequency was associated with the corresponding predicted probability of future attacks, r = 0.15-0.39. At Day 25, the correlation between baseline information and predicted attack likelihood was r = 0.29 (95% CI: 0.09-0.47). Additionally, the use of prior probability distributions for model parameters improved the model fit, especially early in the deployment of the models (e.g., Day 5 RMSE 0.45 vs. 0.43). Models using informative prior probability distributions outperformed the models estimated without this information during the first 42 days of observation, although performance became more similar as more data were collected.ConclusionsThis analysis demonstrates the value of Bayesian methods in using additional available information to improve forecasting model performance, especially early in the deployment of a forecasting model. To obtain the full value of such models or to apply any model in clinical settings, a model with adequate discrimination and calibration will be needed.
| S-EPMC8672809 | biostudies-literature
Project description:Gastric carcinogenesis is a multifactorial process described as a stepwise progression from non-active gastritis (NAG), chronic active gastritis (CAG), precursor lesions of gastric cancer (PLGC) and gastric adenocarcinoma. Gastric cancer (GC) 5-year survival rate is highly dependent upon stage of disease at diagnosis, which is based on endoscopy, biopsy and pathological examinations. Non-invasive GC biomarkers would facilitate its diagnosis at early stages leading to improved GC prognosis. We analyzed plasma samples collected from 80 patients diagnosed with NAG without H. pylori infection (NAG-), CAG with H. pylori infection (CAG+), PLGC and GC. A panel of 208 metabolites including acylcarnitines, amino acids and biogenic amines, sphingolipids, glycerophospholipids, hexoses, and tryptophan and phenylalanine metabolites were quantified using two complementary quantitative approaches: Biocrates AbsoluteIDQ®p180 kit and a LC-MS method designed for the analysis of 29 tryptophan pathway and phenylalanine metabolites. Significantly altered metabolic profiles were found in GC patients that allowing discrimination from NAG-, CAG+ and PLGC patients. Pathway analysis showed significantly altered tryptophan and nitrogen metabolic pathways (FDR P?<?0.01). Three metabolites (histidine, tryprophan and phenylacetylglutamine) discriminated between non-GC and GC groups. These metabolic signatures open new possibilities to improve surveillance of PLGC patients using a minimally invasive blood analysis.
| S-EPMC5736578 | biostudies-literature
Project description:Background: The epidemiologic evidence for associations between dietary factors and breast cancer is weak and etiologic mechanisms are often unclear. Exploring the role of dietary biomarkers with metabolomics can potentially facilitate objective dietary characterization, mitigate errors related to self-reported diet, agnostically test metabolic pathways, and identify mechanistic mediators.Objective: The aim of this study was to evaluate associations of diet-related metabolites with the risk of breast cancer in the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial.Design: We examined prediagnostic serum concentrations of diet-related metabolites in a nested case-control study in 621 postmenopausal invasive breast cancer cases and 621 matched controls in the multicenter PLCO cohort. We calculated partial Pearson correlations between 617 metabolites and 55 foods, food groups, and vitamin supplements on the basis of the 2015 Dietary Guidelines for Americans and derived from a 137-item self-administered food-frequency questionnaire. Diet-related metabolites (P-correlation < 1.47 × 10-6) were evaluated in breast cancer analyses. ORs for the 90th compared with the 10th percentile were calculated by using conditional logistic regression, with body mass index, physical inactivity, other breast cancer risk factors, and caloric intake controlled for (false discovery rate <0.2).Results: Of 113 diet-related metabolites, 3 were associated with overall breast cancer risk (621 cases): caprate (10:0), a saturated fatty acid (OR: 1.77; 95% CI = 1.28, 2.43); ?-carboxyethyl hydrochroman (?-CEHC), a vitamin E (?-tocopherol) derivative (OR: 1.64; 95% CI: 1.18, 2.28); and 4-androsten-3?,17?-diol-monosulfate (1), an androgen (OR: 1.61; 95% CI: 1.20, 2.16). Nineteen metabolites were significantly associated with estrogen receptor (ER)-positive (ER+) breast cancer (418 cases): 12 alcohol-associated metabolites, including 7 androgens and ?-hydroxyisovalerate (OR: 2.23; 95% CI: 1.50, 3.32); 3 vitamin E (tocopherol) derivatives (e.g., ?-CEHC; OR: 1.80; 95% CI: 1.20, 2.70); butter-associated caprate (10:0) (OR: 1.81; 95% CI: 1.23, 2.67); and fried food-associated 2-hydroxyoctanoate (OR: 1.46; 95% CI: 1.03, 2.07). No metabolites were significantly associated with ER-negative breast cancer (144 cases).Conclusions: Prediagnostic serum concentrations of metabolites related to alcohol, vitamin E, and animal fats were moderately strongly associated with ER+ breast cancer risk. Our findings show how nutritional metabolomics might identify diet-related exposures that modulate cancer risk. This trial was registered at clinicaltrials.gov as NCT00339495.
| S-EPMC5525118 | biostudies-literature
Project description:BackgroundHigher circulating carotenoids are associated with lower breast cancer risk. The underlying biology remains under-explored.MethodsWe profiled 293 prediagnostic plasma metabolites in a nested case-control study (n = 887 cases) within the Nurses' Health Studies. Associations between circulating carotenoids and metabolites were identified using linear-mixed models (FDR ≤ 0.05), and we further selected metabolites most predictive of carotenoids with LASSO. Metabolic signatures for carotenoids were calculated as weighted sums of LASSO selected metabolites. We further evaluated the metabolic signatures in relation to breast cancer risk using conditional logistic-regression.ResultsWe identified 48 to 110 metabolites associated with plasma levels of α-carotene, β-carotene, β-cryptoxanthin, estimated-vitamin-A-potential, lutein/zeaxanthin, and lycopene, which included primarily positively associated metabolites implicated in immune regulation (tryptophan), redox balance (plasmalogens, glutamine), epigenetic regulations (acetylated-/methylated-metabolites), and primarily inversely associated metabolites involved in β-oxidation (carnitines; FDR ≤ 0.05). The metabolomic signatures derived for β-carotene (Q4 vs. Q1 relative risk RR = 0.74, P trend = 0.02), and estimated-vitamin-A-potential (Q4 vs. Q1 RR = 0.74, P trend = 0.02)-measured ≥10 years before diagnosis-were associated with lower breast cancer risk. Modest attenuations of RR for measured levels of β-carotene and estimated-vitamin-A-potential were seen when we adjusted for their corresponding metabolic signatures.ConclusionsMetabolites involved in immune regulation, redox balance, membrane signaling, and β-oxidation were associated with plasma carotenoids. Although some metabolites may reflect shared common food sources or compartmental colocalization with carotenoids, others may signal the underlying pathways of carotenoids-associated lowered breast cancer risk.ImpactConsumption of carotenoid-rich diet is associated with a wide-range of metabolic changes which may help to reduce breast cancer risk.
| S-EPMC9015159 | biostudies-literature
Project description:The aim of this study is to identify differential metabolomic signatures in plasma samples of distinct subtypes of breast cancer patients that could be used in clinical practice as diagnostic biomarkers for these molecular phenotypes and to provide a more individualized and accurate therapeutic procedure. Untargeted LC-HRMS metabolomics approach in positive and negative electrospray ionization mode was used to analyze plasma samples from LA, LB, HER2+ and TN breast cancer patients and healthy controls in order to determine specific metabolomic profiles through univariate and multivariate statistical data analysis. We tentatively identified altered metabolites displaying concentration variations among the four breast cancer molecular subtypes. We found a biomarker panel of 5 candidates in LA, 7 in LB, 5 in HER2 and 3 in TN that were able to discriminate each breast cancer subtype with a false discovery range corrected p-value < 0.05 and a fold-change cutoff value > 1.3. The model clinical value was evaluated with the AUROC, providing diagnostic capacities above 0.85. Our study identifies metabolic profiling differences in molecular phenotypes of breast cancer. This may represent a key step towards therapy improvement in personalized medicine and prioritization of tailored therapeutic intervention strategies.
| S-EPMC7795819 | biostudies-literature
Project description:PurposeThis work aims to identify differential metabolites and predicting molecular subtypes of breast cancer (BC).MethodsPlasma samples were collected from 96 BC patients and 79 normal participants. Metabolic profiles were determined by liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry based on multivariate statistical data analysis.ResultsWe observed 64 differential metabolites between BC and normal group. Compared to human epidermal growth factor receptor 2 (HER2)-negative patients, HER2-positive group showed elevated aerobic glycolysis, gluconeogenesis, and increased fatty acid biosynthesis with reduced Krebs cycle. Compared with estrogen receptor (ER)-negative group, ER-positive patients showed elevated alanine, aspartate and glutamate metabolism, decreased glycerolipid catabolism, and enhanced purine metabolism. A panel of 8 differential metabolites, including carnitine, lysophosphatidylcholine (20:4), proline, alanine, lysophosphatidylcholine (16:1), glycochenodeoxycholic acid, valine, and 2-octenedioic acid, was identified for the classification of BC subtypes. These markers showed potential diagnostic value with average area under the curve at 0.925 (95% CI 0.867-0.983) for the training set (n=51) and 0.893 (95% CI 0.847-0.939) for the test set (n=45).ConclusionHuman plasma metabolomics is useful in identifying differential metabolites and predicting breast cancer subtypes.
| S-EPMC4891093 | biostudies-literature
Project description:Breast cancer is the most common cancer among women, and its incidence is on a constant rise. Previous studies suggest that higher levels of plasma prolactin are associated with escalated risk of breast cancer, however, these results are contradictory and inconclusive. PubMed and Medline were used to search and identify published observational studies that assessed the relationship between plasma prolactin levels and the risk of breast cancer. The pooled relative risks (RRs) with 95% confidence intervals (CIs) were calculated using a fixed-effects or random-effects model. A total of 7 studies were included in our analysis. For the highest versus lowest levels of plasma prolactin, the pooled RR (95% CI) of breast cancer were 1.16 (1.04, 1.29). In subgroup analyses, we found a positive association between plasma prolactin levels and the risk of breast cancer among the patients who were postmenopausal, ER(+)/PR(+) or in situ and invasive carcinoma. However, this positive association was not detected in the premenopausal and ER(-)/PR(-) patients. In conclusion, the present study provides evidence supporting a significantly positive association between plasma prolactin levels and the risk of breast cancer.
| S-EPMC4869065 | biostudies-other
Project description:Breast cancer is the most common cancer in women, but its incidence can only be partially explained through established risk factors. Our aim was to use metabolomics to identify novel risk factors for breast cancer and to validate recently reported metabolite-breast cancer findings. We measured levels of 1275 metabolites in prediagnostic serum in a nested case-control study of 782 postmenopausal breast cancer cases and 782 matched controls. Metabolomics analysis was performed by Metabolon Inc using ultra-performance liquid chromatography and a Q-Exactive high resolution/accurate mass spectrometer. Controls were matched by birth date, date of blood draw, and race/ethnicity. Odds ratios (ORs) and 95% confidence intervals (CIs) of breast cancer at the 90th versus 10th percentile (modeled on a continuous basis) of metabolite levels were estimated using conditional logistic regression, with adjustment for age. Twenty-four metabolites were significantly associated with breast cancer risk at a false discovery rate <0.20. For the nine metabolites positively associated with risk, the ORs ranged from 1.75 (95% CI: 1.29-2.36) to 1.45 (95% CI: 1.13-1.85), and for the 15 metabolites inversely associated with risk, ORs ranged from 0.59 (95% CI: 0.43-0.79) to 0.69 (95% CI: 0.55-0.87). These metabolites largely comprised carnitines, glycerolipids, and sex steroid metabolites. Associations for three sex steroid metabolites validated findings from recent studies and the remainder were novel. These findings contribute to growing data on metabolite-breast cancer associations by confirming prior findings and identifying novel leads for future validation efforts.
| S-EPMC7916573 | biostudies-literature
Project description:Background:Elevated body mass index (BMI) is associated with increased risk of postmenopausal breast cancer. The underlying mechanisms, however, remain elusive. Methods:In a nested case-control study of 621 postmenopausal breast cancer case participants and 621 matched control participants, we measured 617 metabolites in prediagnostic serum. We calculated partial Pearson correlations between metabolites and BMI, and then evaluated BMI-associated metabolites (Bonferroni-corrected ? level for 617 statistical tests = P < 8.10?×?10-5) in relation to invasive breast cancer. Odds ratios (ORs) of breast cancer comparing the 90th vs 10th percentile (modeled on a continuous basis) were estimated using conditional logistic regression while controlling for breast cancer risk factors, including BMI. Metabolites with the lowest P values (false discovery rate < 0.2) were mutually adjusted for one another to determine those independently associated with breast cancer risk. Results:Of 67 BMI-associated metabolites, two were independently associated with invasive breast cancer risk: 16a-hydroxy-DHEA-3-sulfate (OR?=?1.65, 95% confidence interval [CI] = 1.22 to 2.22) and 3-methylglutarylcarnitine (OR?=?1.67, 95% CI?=?1.21 to 2.30). Four metabolites were independently associated with estrogen receptor-positive (ER+) breast cancer risk: 16a-hydroxy-DHEA-3-sulfate (OR?=?1.84, 95% CI?=?1.27 to 2.67), 3-methylglutarylcarnitine (OR?=?1.91, 95% CI?=?1.23 to 2.96), allo-isoleucine (OR?=?1.76, 95% CI?=?1.23 to 2.51), and 2-methylbutyrylcarnitine (OR?=?1.89, 95% CI?=?1.22 to 2.91). In a model without metabolites, each 5 kg/m2 increase in BMI was associated with a 14% higher risk of breast cancer (OR?=?1.14, 95% CI?=?1.01 to 1.28), but adding 16a-hydroxy-DHEA-3-sulfate and 3-methylglutarylcarnitine weakened this association (OR?=?1.06, 95% CI?=?0.93 to 1.20), with the logOR attenuating by 57.6% (95% CI?=?21.8% to 100.0+%). Conclusion:These four metabolites may signal metabolic pathways that contribute to breast carcinogenesis and that underlie the association of BMI with increased postmenopausal breast cancer risk. These findings warrant further replication efforts.
| S-EPMC6279273 | biostudies-literature
Project description:BACKGROUND: Breast cancer follow-up is not tailored to the risk of locoregional recurrences (LRRs) in individual patients or as a function of time. The objective of this study was to identify prognostic factors and to estimate individual and time-dependent LRR risk rates. METHODS: Prognostic factors for LRR were identified by a scoping literature review, expert consultation, and stepwise multivariate regression analysis based on 5 years of data from women diagnosed with breast cancer in the Netherlands in 2005 or 2006 (n=17,762). Inter-patient variability was elucidated by examples of 5-year risk profiles of average-, medium-, and high-risk patients, whereby 6-month interval risks were derived from regression estimates. RESULTS: Eight prognostic factors were identified: age, tumour size, multifocality, gradation, adjuvant chemo-, adjuvant radiation-, hormonal therapy, and triple-negative receptor status. Risk profiles of the low-, average-, and high-risk example patients showed non-uniform distribution of recurrence risks (2.9, 7.6, and 9.2%, respectively, over a 5-year period). CONCLUSION: Individual risk profiles differ substantially in subgroups of patients defined by prognostic factors for recurrence and over time as defined in 6-month time intervals. To tailor follow-up schedules and to optimise allocation of scarce resources, risk factors, frequency, and duration of follow-up should be taken into account.
| S-EPMC3749573 | biostudies-other