Citations

AbstractBACKGROUNDFor many years, basic and clinical researchers have taken advantage of the analytical sensitivity and specificity afforded by mass spectrometry in the measurement of proteins. Clinical laboratories are now beginning to deploy these work flows as well. For assays that use proteolysis to generate peptides for protein quantification and characterization, synthetic stable isotope–labeled internal standard peptides are of central importance. No general recommendations are currently available surrounding the use of peptides in protein mass spectrometric assays.CONTENTThe Clinical Proteomic Tumor Analysis Consortium of the National Cancer Institute has collaborated with clinical laboratorians, peptide manufacturers, metrologists, representatives of the pharmaceutical industry, and other professionals to develop a consensus set of recommendations for peptide procurement, characterization, storage, and handling, as well as approaches to the interpretation of the data generated by mass spectrometric protein assays. Additionally, the importance of carefully characterized reference materials—in particular, peptide standards for the improved concordance of amino acid analysis methods across the industry—is highlighted. The alignment of practices around the use of peptides and the transparency of sample preparation protocols should allow for the harmonization of peptide and protein quantification in research and clinical care.

Abstract Purpose: Pheochromocytomas and paragangliomas (PPGL) are genetically heterogeneous tumors of neural crest origin, but the molecular basis of most PPGLs is unknown. Experimental Design: We performed exome or transcriptome sequencing of 43 samples from 41 patients. A validation set of 136 PPGLs was used for amplicon-specific resequencing. In addition, a subset of these tumors was subjected to microarray-based transcription, protein expression, and histone methylation analysis by Western blotting or immunohistochemistry. In vitro analysis of mutants was performed in cell lines. Results: We detected mutations in chromatin-remodeling genes, including histone-methyltransferases, histone-demethylases, and histones in 11 samples from 8 patients (20%). In particular, we characterized a new cancer syndrome involving PPGLs and giant cell tumors of bone (GCT) caused by a postzygotic G34W mutation of the histone 3.3 gene, H3F3A. Furthermore, mutations in kinase genes were detected in samples from 15 patients (37%). Among those, a novel germline kinase domain mutation of MERTK detected in a patient with PPGL and medullary thyroid carcinoma was found to activate signaling downstream of this receptor. Recurrent germline and somatic mutations were also detected in MET, including a familial case and sporadic PPGLs. Importantly, in each of these three genes, mutations were also detected in the validation group. In addition, a somatic oncogenic hotspot FGFR1 mutation was found in a sporadic tumor. Conclusions: This study implicates chromatin-remodeling and kinase variants as frequent genetic events in PPGLs, many of which have no other known germline driver mutation. MERTK, MET, and H3F3A emerge as novel PPGL susceptibility genes. Clin Cancer Res; 22(9); 2301–10. ©2015 AACR.

Abstract The myocardium is known to be an anisotropic medium where the muscle fiber orientation changes through the thickness of the wall. Shear wave elastography methods use propagating waves which are measured by ultrasound or magnetic resonance imaging (MRI) techniques to characterize the mechanical properties of various tissues. Ultrasound- or MR-based methods have been used and the excitation frequency ranges for these various methods cover a large range from 24–500 Hz. Some of the ultrasound-based methods have been shown to be able to estimate the fiber direction. We constructed a model with layers of elastic, transversely isotropic materials that were oriented at different angles to simulate the heart wall in systole and diastole. We investigated the effect of frequency on the wave propagation and the estimation of fiber direction and wave speeds in the different layers of the assembled models. We found that waves propagating at low frequencies such as 30 or 50 Hz showed low sensitivity to the fiber direction but also had substantial bias in estimating the wave speeds in the layers. Using waves with higher frequency content (>200 Hz) allowed for more accurate fiber direction and wave speed estimation. These results have particular relevance for MR- and ultrasound-based elastography applications in the heart.

Abstract Pancreatic cancer is projected to become the second leading cause of cancer-related death in the United States by 2020. A familial aggregation of pancreatic cancer has been established, but the cause of this aggregation in most families is unknown. To determine the genetic basis of susceptibility in these families, we sequenced the germline genomes of 638 patients with familial pancreatic cancer and the tumor exomes of 39 familial pancreatic adenocarcinomas. Our analyses support the role of previously identified familial pancreatic cancer susceptibility genes such as BRCA2, CDKN2A, and ATM, and identify novel candidate genes harboring rare, deleterious germline variants for further characterization. We also show how somatic point mutations that occur during hematopoiesis can affect the interpretation of genome-wide studies of hereditary traits. Our observations have important implications for the etiology of pancreatic cancer and for the identification of susceptibility genes in other common cancer types. Significance: The genetic basis of disease susceptibility in the majority of patients with familial pancreatic cancer is unknown. We whole genome sequenced 638 patients with familial pancreatic cancer and demonstrate that the genetic underpinning of inherited pancreatic cancer is highly heterogeneous. This has significant implications for the management of patients with familial pancreatic cancer. Cancer Discov; 6(2); 166–75. ©2015 AACR. This article is highlighted in the In This Issue feature, p. 109

AbstractKernel machine based association tests (KAT) have been increasingly used in testing the association between an outcome and a set of biological measurements due to its power to combine multiple weak signals of complex relationship with the outcome through the specification of a relevant kernel. Human genetic and microbiome association studies are two important applications of KAT. However, the classic KAT framework relies on large sample theory, and conservativeness has been observed for small sample studies, especially for microbiome association studies. The common approach for addressing the small sample problem relies on computationally intensive resampling methods. Here, we derive an exact test for KAT with continuous traits, which resolve the small sample conservatism of KAT without the need for resampling. The exact test has significantly improved power to detect association for microbiome studies. For binary traits, we propose a similar approximate test, and we show that the approximate test is very powerful for a wide range of kernels including common variant‐ and microbiome‐based kernels, and the approximate test controls the type I error well for these kernels. In contrast, the sequence kernel association tests have slightly inflated genomic inflation factors after small sample adjustment. Extensive simulations and application to a real microbiome association study are used to demonstrate the utility of our method.