Accounting for treatment during the development or validation of prediction models.

Clinical prediction models are widely used to predict adverse outcomes in patients, and are often employed to guide clinical decision-making. Clinical data typically consist of patients who received different treatments. Many prediction modeling studies fail to account for differences in patient treatment appropriately, which results in the development of prediction models that show poor accuracy and generalizability. In this paper, we list the most common methods used to handle patient treatments and discuss certain caveats associated with each method. We believe that proper handling of differences in patient treatment is crucial for the development of accurate and generalizable models. As different treatment strategies are employed for different diseases, the best approach to properly handle differences in patient treatment is specific to each individual situation. We use the Ma-Spore acute lymphoblastic leukemia data set as a case study to demonstrate the complexities associated with differences in patient treatment, and offer suggestions on incorporating treatment information during evaluation of prediction models. In clinical data, patients are typically treated on a case by case basis, with unique cases occurring more frequently than expected. Hence, there are many subtleties to consider during the analysis and evaluation of clinical prediction models.

Inhibiting Glycine Decarboxylase Suppresses Pyruvate-to-Lactate Metabolism in Lung Cancer Cells.

Glycine decarboxylase (GLDC) gene is frequently upregulated in various types of cancer including lung, prostate and brain. It catabolizes glycine to yield 5,10-methylenetetrahydrofolate, an important substrate in one-carbon metabolism for nucleotide synthesis. In this study, we used exon splicing modulating steric hindrance antisense oligonucleotide (shAON) to suppress GLDC expression and investigated its effect on pyruvate metabolism via hyperpolarized carbon-13 magnetic resonance spectroscopy (MRS). The MRS technique allows us to study in vivo metabolic flux in tumor tissues with/without GLDC-shAON intervention. Here, we show that GLDC-shAON treatment is able to suppress lung cancer cell growth and tumorigenesis, both in vitro and in vivo. The carbon-13 MRS results indicated that the conversion of pyruvate into lactate in GLDC-shAON-treated tumor tissues was significantly reduced, when compared with the control groups. This observation corroborated with the reduced activity of lactate dehydrogenase and pyruvate dehydrogenase in GLDC-shAON-treated lung cancer cells and tumor tissues. Glycolysis stress test showed that extracellular acidification rate was significantly suppressed after GLDC-shAON treatment. Besides lung cancer, the antitumor effect of GLDC-shAON was also observed in brain, liver, cervical, and prostate cancer cell lines. Furthermore, it enhanced the treatment efficacy of cisplatin in lung cancer cells. Taken together, our findings illustrate that pyruvate metabolism decreases upon GLDC inhibition, thereby starving cancer cells from critical metabolic fuels.