A large-scale LC-MS dataset of murine liver proteome from time course of heavy water metabolic labeling.

Metabolic stable isotope labeling with heavy water followed by liquid chromatography coupled with mass spectrometry (LC-MS) is a powerful tool for in vivo protein turnover studies. Several algorithms and tools have been developed to determine the turnover rates of peptides and proteins from time-course stable isotope labeling experiments. The availability of benchmark mass spectrometry data is crucial to compare and validate the effectiveness of newly developed techniques and algorithms. In this work, we report a heavy water-labeled LC-MS dataset from the murine liver for protein turnover rate analysis. The dataset contains eighteen mass spectral data with their corresponding database search results from nine different labeling durations and quantification outputs from d2ome+ software. The dataset also contains eight mass spectral data from two-dimensional fractionation experiments on unlabeled samples.

Quantifying label enrichment from two mass isotopomers increases proteome coverage for in vivo protein turnover using heavy water metabolic labeling.

Heavy water metabolic labeling followed by liquid chromatography coupled with mass spectrometry is a powerful high throughput technique for measuring the turnover rates of individual proteins in vivo. The turnover rate is obtained from the exponential decay modeling of the depletion of the monoisotopic relative isotope abundance. We provide theoretical formulas for the time course dynamics of six mass isotopomers and use the formulas to introduce a method that utilizes partial isotope profiles, only two mass isotopomers, to compute protein turnover rate. The use of partial isotope profiles alleviates the interferences from co-eluting contaminants in complex proteome mixtures and improves the accuracy of the estimation of label enrichment. In five different datasets, the technique consistently doubles the number of peptides with high goodness-of-fit characteristics of the turnover rate model. We also introduce a software tool, d2ome+, which automates the protein turnover estimation from partial isotope profiles.

Geriatric assessment may help decision-making in elderly patients with inoperable, locally advanced non-small-cell lung cancer.

BACKGROUND: Although concurrent chemoradiotherapy (cCRT) increases survival in patients with inoperable, locally advanced non-small-cell lung cancer (NSCLC), there is no consensus on the treatment of elderly patients. The aim of this study was to determine the prognostic value of the comprehensive geriatric assessment (CGA) and its ability to predict toxicity in this setting. METHODS: We enrolled 85 consecutive elderly (⩾75 years) participants, who underwent CGA and the Vulnerable Elders Survey (VES-13). Those classified as fit and medium-fit by CGA were deemed candidates for cCRT (platinum-based chemotherapy concurrent with thoracic radiation therapy), while unfit patients received best supportive care. RESULTS: Fit (37%) and medium-fit (48%) patients had significantly longer median overall survival (mOS) (23.9 and 16.9 months, respectively) than unfit patients (15%) (9.3 months, log-rank P=0.01). In multivariate analysis, CGA groups and VES-13 were independent prognostic factors. Fit and medium-fit patients receiving cCRT (n=54) had mOS of 21.1 months (95% confidence interval: 16.2, 26.0). In those patients, higher VES-13 (⩾3) was associated with shorter mOS (16.33 vs 24.3 months, P=0.027) and higher risk of G3-4 toxicity (65 vs 32%, P=0.028). CONCLUSIONS: Comprehensive geriatric assessment and VES-13 showed independent prognostic value. Comprehensive geriatric assessment may help to identify elderly patients fit enough to be treated with cCRT.