Citations

Abstract Purpose: Predicting surgical outcome could improve individualizing treatment strategies for patients with advanced ovarian cancer. It has been suggested earlier that gene expression signatures (GES) might harbor the potential to predict surgical outcome. Experimental Design: Data derived from high-grade serous tumor tissue of FIGO stage IIIC/IV patients of AGO-OVAR11 trial were used to generate a transcriptome profiling. Previously identified molecular signatures were tested. A theoretical model was implemented to evaluate the impact of medically associated factors for residual disease (RD) on the performance of GES that predicts RD status. Results: A total of 266 patients met inclusion criteria, of those, 39.1% underwent complete resection. Previously reported GES did not predict RD in this cohort. Similarly, The Cancer Genome Atlas molecular subtypes, an independent de novo signature and the total gene expression dataset using all 21,000 genes were not able to predict RD status. Medical reasons for RD were identified as potential limiting factors that impact the ability to use GES to predict RD. In a center with high complete resection rates, a GES which would perfectly predict tumor biological RD would have a performance of only AUC 0.83, due to reasons other than tumor biology. Conclusions: Previously identified GES cannot be generalized. Medically associated factors for RD may be the main obstacle to predict surgical outcome in an all-comer population of patients with advanced ovarian cancer. If biomarkers derived from tumor tissue are used to predict outcome of patients with cancer, selection bias should be focused on to prevent overestimation of the power of such a biomarker. See related commentary by Handley and Sood, p. 9

Abstract The term sarcopenia was introduced in 1988. The original definition was a “muscle loss” of the appendicular muscle mass in the older people as measured by dual energy x‐ray absorptiometry (DXA). In 2010, the definition was altered to be low muscle mass together with low muscle function and this was agreed upon as reported in a number of consensus papers. The Society of Sarcopenia, Cachexia and Wasting Disorders supports the recommendations of more recent consensus conferences, i.e. that rapid screening, such as with the SARC‐F questionnaire, should be utilized with a formal diagnosis being made by measuring grip strength or chair stand together with DXA estimation of appendicular muscle mass (indexed for height2). Assessments of the utility of ultrasound and creatine dilution techniques are ongoing. Use of ultrasound may not be easily reproducible. Primary sarcopenia is aging associated (mediated) loss of muscle mass. Secondary sarcopenia (or disease‐related sarcopenia) has predominantly focused on loss of muscle mass without the emphasis on muscle function. Diseases that can cause muscle wasting (i.e. secondary sarcopenia) include malignant cancer, COPD, heart failure, and renal failure and others. Management of sarcopenia should consist of resistance exercise in combination with a protein intake of 1 to 1.5 g/kg/day. There is insufficient evidence that vitamin D and anabolic steroids are beneficial. These recommendations apply to both primary (age‐related) sarcopenia and secondary (disease related) sarcopenia. Secondary sarcopenia also needs appropriate treatment of the underlying disease. It is important that primary care health professionals become aware of and make the diagnosis of age‐related and disease‐related sarcopenia. It is important to address the risk factors for sarcopenia, particularly low physical activity and sedentary behavior in the general population, using a life‐long approach. There is a need for more clinical research into the appropriate measurement for muscle mass and the management of sarcopenia. Accordingly, this position statement provides recommendations on the management of sarcopenia and how to progress the knowledge and recognition of sarcopenia.

Abstract MYCN is a major driver for the childhood cancer, neuroblastoma, however, there are no inhibitors of this target. Enhanced MYCN protein stability is a key component of MYCN oncogenesis and is maintained by multiple feedforward expression loops involving MYCN transactivation target genes. Here, we reveal the oncogenic role of a novel MYCN target and binding protein, proliferation-associated 2AG4 (PA2G4). Chromatin immunoprecipitation studies demonstrated that MYCN occupies the PA2G4 gene promoter, stimulating transcription. Direct binding of PA2G4 to MYCN protein blocked proteolysis of MYCN and enhanced colony formation in a MYCN-dependent manner. Using molecular modeling, surface plasmon resonance, and mutagenesis studies, we mapped the MYCN–PA2G4 interaction site to a 14 amino acid MYCN sequence and a surface crevice of PA2G4. Competitive chemical inhibition of the MYCN–PA2G4 protein–protein interface had potent inhibitory effects on neuroblastoma tumorigenesis in vivo. Treated tumors showed reduced levels of both MYCN and PA2G4. Our findings demonstrate a critical role for PA2G4 as a cofactor in MYCN-driven neuroblastoma and highlight competitive inhibition of the PA2G4-MYCN protein binding as a novel therapeutic strategy in the disease. Significance: Competitive chemical inhibition of the PA2G4–MYCN protein interface provides a basis for drug design of small molecules targeting MYC and MYCN-binding partners in malignancies driven by MYC family oncoproteins.

See article on pages 739–744 in this issue.

AbstractAlthough myopathies and neuromuscular junction disorders are typically distinct, their coexistence has been reported in several inherited and acquired conditions. Affected individuals have variable clinical phenotypes but typically display both a decrement on repetitive nerve stimulation and myopathic findings on muscle biopsy. Inherited causes include myopathies related to mutations in BIN1, DES, DNM2, GMPPB, MTM1, or PLEC and congenital myasthenic syndromes due to mutations in ALG2, ALG14, COL13A1, DOK7, DPAGT1, or GFPT1. Additionally, a decrement due to muscle fiber inexcitability is observed in certain myotonic disorders. The identification of a defect of neuromuscular transmission in an inherited myopathy may assist in establishing a molecular diagnosis and in selecting patients who would benefit from pharmacological correction of this defect. Acquired cases meanwhile stem from the co‐occurrence of myasthenia gravis or Lambert‐Eaton myasthenic syndrome with an immune‐mediated myopathy, which may be due to paraneoplastic disorders or exposure to immune checkpoint inhibitors.

Background: Variations in cardiac troponin concentrations by age, sex, and time between samples in patients with suspected myocardial infarction are not currently accounted for in diagnostic approaches. We aimed to combine these variables through machine learning to improve the assessment of risk for individual patients. Methods: A machine learning algorithm (myocardial-ischemic-injury-index [MI 3 ]) incorporating age, sex, and paired high-sensitivity cardiac troponin I concentrations, was trained on 3013 patients and tested on 7998 patients with suspected myocardial infarction. MI 3 uses gradient boosting to compute a value (0–100) reflecting an individual’s likelihood of a diagnosis of type 1 myocardial infarction and estimates the sensitivity, negative predictive value, specificity and positive predictive value for that individual. Assessment was by calibration and area under the receiver operating characteristic curve. Secondary analysis evaluated example MI 3 thresholds from the training set that identified patients as low risk (99% sensitivity) and high risk (75% positive predictive value), and performance at these thresholds was compared in the test set to the 99th percentile and European Society of Cardiology rule-out pathways. Results: Myocardial infarction occurred in 404 (13.4%) patients in the training set and 849 (10.6%) patients in the test set. MI 3 was well calibrated with a very high area under the receiver operating characteristic curve of 0.963 [0.956–0.971] in the test set and similar performance in early and late presenters. Example MI 3 thresholds identifying low- and high-risk patients in the training set were 1.6 and 49.7, respectively. In the test set, MI 3 values were <1.6 in 69.5% with a negative predictive value of 99.7% (99.5–99.8%) and sensitivity of 97.8% (96.7–98.7%), and were ≥49.7 in 10.6% with a positive predictive value of 71.8% (68.9–75.0%) and specificity of 96.7% (96.3–97.1%). Using these thresholds, MI 3 performed better than the European Society of Cardiology 0/3-hour pathway (sensitivity, 82.5% [74.5–88.8%]; specificity, 92.2% [90.7–93.5%]) and the 99th percentile at any time point (sensitivity, 89.6% [87.4–91.6%]); specificity, 89.3% [88.6–90.0%]). Conclusions: Using machine learning, MI 3 provides an individualized and objective assessment of the likelihood of myocardial infarction, which can be used to identify low- and high-risk patients who may benefit from earlier clinical decisions. Clinical Trial Registration: URL: https://www.anzctr.org.au . Unique identifier: ACTRN12616001441404.