Intra-tumor Genetic Heterogeneity and Mortality in Head and Neck Cancer: Analysis of Data from The Cancer Genome Atlas

Background: Although the involvement of intra-tumor genetic heterogeneity in tumor progression, treatment resistance, and metastasis is established, genetic heterogeneity is seldom examined in clinical trials or practice. Many studies of heterogeneity have had prespecified markers for tumor subpopulations, limiting their generalizability, or have involved massive efforts such as separate analysis of hundreds of individual cells, limiting their clinical use. We recently developed a general measure of intra-tumor genetic heterogeneity based on whole-exome sequencing (WES) of bulk tumor DNA, called mutant-allele tumor heterogeneity (MATH). Here, we examine data collected as part of a large, multi-institutional study to validate this measure and determine whether intra-tumor heterogeneity is itself related to mortality. Methods and Findings: Clinical and WES data were obtained from The Cancer Genome Atlas in October 2013 for 305 patients with head and neck squamous cell carcinoma (HNSCC), from 14 institutions. Initial pathologic diagnoses were between 1992 and 2011 (median, 2008). Median time to death for 131 deceased patients was 14 mo; median follow-up of living patients was 22 mo. Tumor MATH values were calculated from WES results. Despite the multiple head and neck tumor subsites and the variety of treatments, we found in this retrospective analysis a substantial relation of high MATH values to decreased overall survival (Cox proportional hazards analysis: hazard ratio for high/low heterogeneity, 2.2; 95% CI 1.4 to 3.3). This relation of intra-tumor heterogeneity to survival was not due to intra-tumor heterogeneity’s associations with other clinical or molecular characteristics, including age, human papillomavirus status, tumor grade and TP53 mutation, and N classification. MATH improved prognostication over that provided by traditional clinical and molecular characteristics, maintained a significant relation to survival in multivariate analyses, and distinguished outcomes among patients having oral-cavity or laryngeal cancers even when standard disease staging was taken into account. Prospective studies, however, will be required before MATH can be used prognostically in clinical trials or practice. Such studies will need to examine homogeneously treated HNSCC at specific head and neck subsites, and determine the influence of cancer therapy on MATH values. Analysis of MATH and outcome in human-papillomavirus-positive oropharyngeal squamous cell carcinoma is particularly needed. Conclusions: To our knowledge this study is the first to combine data from hundreds of patients, treated at multiple institutions, to document a relation between intra-tumor heterogeneity and overall survival in any type of cancer. We suggest applying the simply calculated MATH metric of heterogeneity to prospective studies of HNSCC and other tumor types

Dominance and parent-of-origin effects of coding and non-coding alleles at the acylCoA-diacylglycerol-acyltransferase (DGAT1) gene on milk production traits in German Holstein cows

<p>Abstract</p> <p>Background</p> <p>Substantial gene substitution effects on milk production traits have formerly been reported for alleles at the K232A and the promoter VNTR loci in the bovine acylCoA-diacylglycerol-acyltransferase 1 (<it>DGAT1</it>) gene by using data sets including sires with accumulated phenotypic observations of daughters (breeding values, daughter yield deviations). However, these data sets prevented analyses with respect to dominance or parent-of-origin effects, although an increasing number of reports in the literature outlined the relevance of non-additive gene effects on quantitative traits.</p> <p>Results</p> <p>Based on a data set comprising German Holstein cows with direct trait measurements, we first confirmed the previously reported association of <it>DGAT1 </it>promoter VNTR alleles with milk production traits. We detected a dominant mode of effects for the <it>DGAT1 </it>K232A and promoter VNTR alleles. Namely, the contrasts between the effects of heterozygous individuals at the <it>DGAT1 </it>loci differed significantly from the midpoint between the effects for the two homozygous genotypes for several milk production traits, thus indicating the presence of dominance. Furthermore, we identified differences in the magnitude of effects between paternally and maternally inherited <it>DGAT1 </it>promoter VNTR – K232A haplotypes indicating parent-of-origin effects on milk production traits.</p> <p>Conclusion</p> <p>Non-additive effects like those identified at the bovine <it>DGAT1 </it>locus have to be accounted for in more specific QTL detection models as well as in marker assisted selection schemes. The <it>DGAT1 </it>alleles in cattle will be a useful model for further investigations on the biological background of non-additive effects in mammals due to the magnitude and consistency of their effects on milk production traits.</p

'Tipping the Balance': Karl Friedrich Meyer, Latent Infections, and the Birth of Modern Ideas of Disease Ecology

The Swiss-born medical researcher Karl Friedrich Meyer (1884–1974) is best known as a ‘microbe hunter’ who pioneered investigations into diseases at the intersection of animal and human health in California in the 1920s and 1930s. In particular, historians have singled out Meyer’s 1931 Ludwig Hektoen Lecture in which he described the animal kingdom as a ‘reservoir of disease’ as a forerunner of ‘one medicine’ approaches to emerging zoonoses. In so doing, however, historians risk overlooking Meyer’s other intellectual contributions. Developed in a series of papers from the mid-1930s onwards, these were ordered around the concept of latent infections and sought to link microbial behavior to broader bio-ecological, environmental, and social factors that impact hostpathogen interactions. In this respect Meyer—like the comparative pathologist Theobald Smith and the immunologist Frank Macfarlane Burnet—can be seen as a pioneer of modern ideas of disease ecology. However, while Burnet’s and Smith’s contributions to this scientific field have been widely acknowledged, Meyer’s have been largely ignored. Drawing on Meyer’s published writings and private correspondence, this paper aims to correct that lacuna while contributing to a reorientation of the historiography of bacteriological epidemiology. In particular I trace Meyer’s intellectual exchanges with Smith, Burnet and the animal ecologist Charles Elton, over brucellosis, psittacosis and plague—exchanges that not only showed how environmental and ecological conditions could ‘tip the balance’ in favor of parasites but which transformed Meyer thinking about resistance to infection and disease

Delegation and coordination with multiple threshold public goods: experimental evidence

When multiple charities, social programs and community projects simultaneously vie for funding, donors risk mis-coordinating their contributions leading to an inefficient distribution of funding across projects. Community chests and other intermediary organizations facilitate coordination among donors and reduce such risks. To study this, we extend a threshold public goods framework to allow donors to contribute through an intermediary rather than directly to the public goods. Through a series of experiments, we show that the presence of an intermediary increases public good success and subjects’ earnings only when the intermediary is formally committed to direct donations to socially beneficial goods. Without such a restriction, the presence of an intermediary has a negative impact, complicating the donation environment, decreasing contributions and public good success.When multiple charities, social programs and community projects simultaneously vie for funding, donors risk mis-coordinating their contributions leading to an inefficient distribution of funding across projects. Community chests and other intermediary organizations facilitate coordination among donors and reduce such risks. To study this, we extend a threshold public goods framework to allow donors to contribute through an intermediary rather than directly to the public goods. Through a series of experiments, we show that the presence of an intermediary increases public good success and subjects’ earnings only when the intermediary is formally committed to direct donations to socially beneficial goods. Without such a restriction, the presence of an intermediary has a negative impact, complicating the donation environment, decreasing contributions and public good success

Detection of Sclerotic Spine Metastases via Random Aggregation of Deep Convolutional Neural Network Classifications

Automated detection of sclerotic metastases (bone lesions) in Computed Tomography (CT) images has potential to be an important tool in clinical practice and research. State-of-the-art methods show performance of 79 % sensitivity or truepositive (TP) rate, at 10 false-positives (FP) per volume. We design a two-tiered coarse-to-fine cascade framework to first operate a highly sensitive candidate generation system at a maximum sensitivity of ∼92% but with high FP level (∼50 per patient). Regions of interest (ROI) for lesion candidates are generated in this step and function as input for the second tier. In the second tier we generate N 2D views, via scale, random translations, and rotations with respect to each ROI centroid coordinates. These random views are used to train a deep Convolutional Neural Network (CNN) classifier. In testing, the CNN is employed to assign individual probabilities for a new set of N random views that are averaged at each ROI to compute a final per-candidate classification probability. This second tier behaves as a highly selective process to reject difficult false positives while preserving high sensitivities. We validate the approach on CT images of 59 patients (49 with sclerotic metastases and 10 normal controls). The proposed method reduces the number of FP/vol.from 4 to 1.2, 7 to 3, and 12 to 9.5 when comparing a sensitivity rates of 60, 70, and 80% respectively in testing. The Area-Under-the-Curve (AUC) is 0.834. The results show marked improvement upon previous work