NMR quantification of lactate production and efflux and glutamate fractional enrichment in living human prostate biopsies cultured with [1,6-13 C2 ]glucose.

PURPOSE: Image-guided prostate biopsies are routinely acquired in the diagnosis and treatment monitoring of prostate cancer, yielding useful tissue for identifying metabolic biomarkers and therapeutic targets. We developed an optimized biopsy tissue culture protocol in combination with [1,6-13 C2 ]glucose labeling and quantitative high-resolution NMR to measure glycolysis and tricarboxcylic acid (TCA) cycle activity in freshly acquired living human prostate biopsies. METHODS: We acquired 34 MRI-ultrasound fusion-guided prostate biopsies in vials on ice from 22 previously untreated patients. Within 15 min, biopsies were transferred to rotary tissue culture in 37°C prostate medium containing [1,6-13 C2 ]glucose. Following 24 h of culture, tissue lactate and glutamate pool sizes and fractional enrichments were quantified using quantitative 1 H high resolution magic angle spinning Carr-Purcell-Meiboom-Gill (CPMG) spectroscopy at 1°C with and without 13 C decoupling. Lactate effluxed from the biopsy tissue was quantified in the culture medium using quantitative solution-state high-resolution NMR. RESULTS: Lactate concentration in low-grade cancer (1.15 ± 0.78 nmol/mg) and benign (0.74 ± 0.15 nmol/mg) biopsies agreed with prior published measurements of snap-frozen biopsies. There was substantial fractional enrichment of [3-13 C]lactate (≈70%) and [4-13 C]glutamate (≈24%) in both low-grade cancer and benign biopsies. Although a significant difference in tissue [3-13 C]lactate fractional enrichment was not observed, lactate efflux was significantly higher (P < 0.05) in low-grade cancer biopsies (0.55 ± 0.14 nmol/min/mg) versus benign biopsies (0.31 ± 0.04 nmol/min/mg). CONCLUSION: A protocol was developed for quantification of lactate production-efflux and TCA cycle activity in single living human prostate biopsies, allowing metabolic labeling on a wide spectrum of human tissues (e.g., metastatic, post-non-surgical therapy) from patients not receiving surgery.

Computerized analysis of mammographic parenchymal patterns on a large clinical dataset of full-field digital mammograms: robustness study with two high-risk datasets.

The purpose of this study was to demonstrate the robustness of our prior computerized texture analysis method for breast cancer risk assessment, which was developed initially on a limited dataset of screen-film mammograms. This current study investigated the robustness by (1) evaluating on a large clinical dataset, (2) using full-field digital mammograms (FFDM) as opposed to screen-film mammography, and (3) incorporating analyses over two types of high-risk patient sets, as well as patients at low risk for breast cancer. The evaluation included the analyses on the parenchymal patterns of women at high risk of developing of breast cancer, including both BRCA1/2 gene mutation carriers and unilateral cancer patients, and of women at low risk of developing breast cancer. A total of 456 cases, including 53 women with BRCA1/2 gene mutations, 75 women with unilateral cancer, and 328 low-risk women, were retrospectively collected under an institutional review board approved protocol. Regions-of-interest (ROIs), were manually selected from the central breast region immediately behind the nipple. These ROIs were subsequently used in computerized feature extraction to characterize the mammographic parenchymal patterns in the images. Receiver operating characteristic analysis was used to assess the performance of the computerized texture features in the task of distinguishing between high-risk and low-risk subjects. In a round robin evaluation on the FFDM dataset with Bayesian artificial neural network analysis, AUC values of 0.82 (95% confidence interval [0.75, 0.88]) and 0.73 (95% confidence interval [0.67, 0.78]) were obtained between BRCA1/2 gene mutation carriers and low-risk women, and between unilateral cancer and low-risk women, respectively. These results from computerized texture analysis on digital mammograms demonstrated that high-risk and low-risk women have different mammographic parenchymal patterns. On this large clinical dataset, we validated our methods for quantitative analyses of mammographic patterns on FFDM, statistically demonstrating again that women at high risk tend to have dense breasts with coarse and low-contrast texture patterns.