The p53 protein is activated by stress signals and exhibits both protective and death-promoting functions that are considered important for its tumor suppressor function. Emerging evidence points toward an additional role for p53 in metabolism. Here, we identify Lpin1 as a p53-responsive gene that is induced in response to DNA damage and glucose deprivation. Lpin1 is essential for adipocyte development and fat metabolism, and mutation in this gene is responsible for the lypodystrophy phenotype in fld mice. We show that p53 and Lpin1 regulate fatty acid oxidation in mouse C2C12 myoblasts. p53 phosphorylation on Ser18 in response to low glucose is ROS and ATM dependent. Lpin1 expression in response to nutritional stress is controlled through the ROS-ATM-p53 pathway and is conserved in human cells. Lpin1 provides a critical link between p53 and metabolism that may be an important component in mediating the tumor suppressor function of p53.

Purpose To date there has not been an extensive analysis of the outcomes of biomarker use in oncology. Methods Data were pooled across four indications in oncology drawing upon trial outcomes from http://www.clinicaltrials.gov: breast cancer, non‐small cell lung cancer (NSCLC), melanoma and colorectal cancer from 1998 to 2017. We compared the likelihood drugs would progress through the stages of clinical trial testing to approval based on biomarker status. This was done with multi‐state Markov models, tools that describe the stochastic process in which subjects move among a finite number of states. Results Over 10000 trials were screened, which yielded 745 drugs. The inclusion of biomarker status as a covariate significantly improved the fit of the Markov model in describing the drug trajectories through clinical trial testing stages. Hazard ratios based on the Markov models revealed the likelihood of drug approval with biomarkers having nearly a fivefold increase for all indications combined. A 12, 8 and 7‐fold hazard ratio was observed for breast cancer, melanoma and NSCLC, respectively. Markov models with exploratory biomarkers outperformed Markov models with no biomarkers. Conclusion This is the first systematic statistical evidence that biomarkers clearly increase clinical trial success rates in three different indications in oncology. Also, exploratory biomarkers, long before they are properly validated, appear to improve success rates in oncology. This supports early and aggressive adoption of biomarkers in oncology clinical trials.

Given the high morbidity and mortality associated with metastatic melanoma, considerable attention has been paid to identifying potential therapies. Until recently, few therapies have been specifically approved for treating metastatic melanoma. In an attempt to increase clinical trial successes, many therapies are implementing biomarkers for patient stratification. This strategy narrows down the population in an effort to identify appropriate subpopulations that have increased efficacy or fewer safety concerns. However, the addition of a biomarker constitutes an additional risk to clinical development and may therefore increase the overall clinical trial risk. Here, we examine the clinical trial success rate for therapies targeting metastatic melanoma. In addition, we identify the impact that biomarkers have had on the clinical development of this disease.

Background Although there are thousands of published recommendations in anesthesiology clinical practice guidelines, the extent to which these are supported by high levels of evidence is not known. This study hypothesized that most recommendations in clinical practice guidelines are supported by a low level of evidence. Methods A registered (Prospero CRD42020202932) systematic review was conducted of anesthesia evidence-based recommendations from the major North American and European anesthesiology societies between January 2010 and September 2020 in PubMed and EMBASE. The level of evidence A, B, or C and the strength of recommendation (strong or weak) for each recommendation was mapped using the American College of Cardiology/American Heart Association classification system or the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system. The outcome of interest was the proportion of recommendations supported by levels of evidence A, B, and C. Changes in the level of evidence over time were examined. Risk of bias was assessed using Appraisal of Guidelines for Research and Evaluation (AGREE) II. Results In total, 60 guidelines comprising 2,280 recommendations were reviewed. Level of evidence A supported 16% (363 of 2,280) of total recommendations and 19% (288 of 1,506) of strong recommendations. Level of evidence C supported 51% (1,160 of 2,280) of all recommendations and 50% (756 of 1,506) of strong recommendations. Of all the guidelines, 73% (44 of 60) had a low risk of bias. The proportion of recommendations supported by level of evidence A versus level of evidence C (relative risk ratio, 0.93; 95% CI, 0.18 to 4.74; P = 0.933) or level of evidence B versus level of evidence C (relative risk ratio, 1.63; 95% CI, 0.72 to 3.72; P = 0.243) did not increase in guidelines that were revised. Year of publication was also not associated with increases in the proportion of recommendations supported by level of evidence A (relative risk ratio, 1.07; 95% CI, 0.93 to 1.23; P = 0.340) or level of evidence B (relative risk ratio, 1.05; 95% CI, 0.96 to 1.15; P = 0.283) compared to level of evidence C. Conclusions Half of the recommendations in anesthesiology clinical practice guidelines are based on a low level of evidence, and this did not change over time. These findings highlight the need for additional efforts to increase the quality of evidence used to guide decision-making in anesthesiology. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New