Publications by authors named "Jeremy Mason"

62 Publications

Model Development of CDK4/6 Predicted Efficacy in Patients With Hormone Receptor-Positive, Human Epidermal Growth Factor Receptor 2-Negative Advanced or Metastatic Breast Cancer.

JCO Clin Cancer Inform 2021 Jun;5:758-767

Office of Oncologic Diseases, US Food and Drug Administration, Silver Spring, MD.

Purpose: Three cyclin-dependent kinase 4/6 inhibitors (CDKIs) are approved by the US Food and Drug Administration for the treatment of patients with hormone receptor-positive, human epidermal growth factor receptor 2-negative advanced or metastatic breast cancer in combination with hormonal therapy (HT). We hypothesized that on an individual basis, efficacy outcomes and adverse event (AE) development can be predicted using baseline patient and tumor characteristics.

Methods: Individual-level data from seven randomized controlled trials submitted to the US Food and Drug Administration for new or supplemental marketing applications of CDKIs were pooled. Progression-free survival (PFS), overall survival (OS), and AE prediction models were developed for specific treatment regimens (HT HT plus CDKI). An individual's characteristics were used in all models simultaneously to create a group of predicted outcomes that are comparable across treatment settings.

Results: Accuracy of the PFS and OS prediction models for HT were 66% and 64%, respectively, with the strongest predictors being menopausal status and therapy line. The corresponding AE prediction models resulted in an average area under the curve of 0.613. Accuracy of the PFS and OS prediction models for HT plus CDKI were 62% and 63%, respectively, with the strongest predictors being histologic grade for both. The corresponding AE prediction models resulted in an average area under the curve of 0.639.

Conclusion: This exploratory analysis demonstrated that models of efficacy outcomes and AE development can be developed using baseline patient and tumor characteristics. Comparison of paired models can inform treatment selection for individuals on the basis of the patient's personalized goals and concerns. Although use of CDKIs is standard of care in the first- or second-line setting, this model provides prognostic information that may inform individual treatment decisions.
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http://dx.doi.org/10.1200/CCI.21.00025DOI Listing
June 2021

Practical Gram-Scale Synthesis of Iboxamycin, a Potent Antibiotic Candidate.

J Am Chem Soc 2021 Jul 15;143(29):11019-11025. Epub 2021 Jul 15.

Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States.

A gram-scale synthesis of iboxamycin, an antibiotic candidate bearing a fused bicyclic amino acid residue, is presented. A pivotal transformation in the route involves an intramolecular hydrosilylation-oxidation sequence to set the ring-fusion stereocenters of the bicyclic scaffold. Other notable features of the synthesis include a high-yielding, highly diastereoselective alkylation of a pseudoephenamine amide, a convergent sp-sp Negishi coupling, and a one-pot transacetalization-reduction reaction to form the target compound's oxepane ring. Implementation of this synthetic strategy has provided ample quantities of iboxamycin to allow for its profiling in murine models of infection.
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http://dx.doi.org/10.1021/jacs.1c03529DOI Listing
July 2021

OpenStats: A robust and scalable software package for reproducible analysis of high-throughput phenotypic data.

PLoS One 2020 30;15(12):e0242933. Epub 2020 Dec 30.

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom.

Reproducibility in the statistical analyses of data from high-throughput phenotyping screens requires a robust and reliable analysis foundation that allows modelling of different possible statistical scenarios. Regular challenges are scalability and extensibility of the analysis software. In this manuscript, we describe OpenStats, a freely available software package that addresses these challenges. We show the performance of the software in a high-throughput phenomic pipeline in the International Mouse Phenotyping Consortium (IMPC) and compare the agreement of the results with the most similar implementation in the literature. OpenStats has significant improvements in speed and scalability compared to existing software packages including a 13-fold improvement in computational time to the current production analysis pipeline in the IMPC. Reduced complexity also promotes FAIR data analysis by providing transparency and benefiting other groups in reproducing and re-usability of the statistical methods and results. OpenStats is freely available under a Creative Commons license at www.bioconductor.org/packages/OpenStats.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242933PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773254PMC
January 2021

Mouse mutant phenotyping at scale reveals novel genes controlling bone mineral density.

PLoS Genet 2020 12 28;16(12):e1009190. Epub 2020 Dec 28.

Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America.

The genetic landscape of diseases associated with changes in bone mineral density (BMD), such as osteoporosis, is only partially understood. Here, we explored data from 3,823 mutant mouse strains for BMD, a measure that is frequently altered in a range of bone pathologies, including osteoporosis. A total of 200 genes were found to significantly affect BMD. This pool of BMD genes comprised 141 genes with previously unknown functions in bone biology and was complementary to pools derived from recent human studies. Nineteen of the 141 genes also caused skeletal abnormalities. Examination of the BMD genes in osteoclasts and osteoblasts underscored BMD pathways, including vesicle transport, in these cells and together with in silico bone turnover studies resulted in the prioritization of candidate genes for further investigation. Overall, the results add novel pathophysiological and molecular insight into bone health and disease.
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http://dx.doi.org/10.1371/journal.pgen.1009190DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7822523PMC
December 2020

Sexual dimorphism in trait variability and its eco-evolutionary and statistical implications.

Elife 2020 11 17;9. Epub 2020 Nov 17.

Evolution & Ecology Research Center, School of Biological, Earth, and Environmental Sciences, University of New South Wales, Sydney, Australia.

Biomedical and clinical sciences are experiencing a renewed interest in the fact that males and females differ in many anatomic, physiological, and behavioural traits. Sex differences in trait variability, however, are yet to receive similar recognition. In medical science, mammalian females are assumed to have higher trait variability due to estrous cycles (the 'estrus-mediated variability hypothesis'); historically in biomedical research, females have been excluded for this reason. Contrastingly, evolutionary theory and associated data support the 'greater male variability hypothesis'. Here, we test these competing hypotheses in 218 traits measured in >26,900 mice, using meta-analysis methods. Neither hypothesis could universally explain patterns in trait variability. Sex bias in variability was trait-dependent. While greater male variability was found in morphological traits, females were much more variable in immunological traits. Sex-specific variability has eco-evolutionary ramifications, including sex-dependent responses to climate change, as well as statistical implications including power analysis considering sex difference in variance.
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http://dx.doi.org/10.7554/eLife.63170DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7704105PMC
November 2020

A dynamic COVID-19 immune signature includes associations with poor prognosis.

Nat Med 2020 10 17;26(10):1623-1635. Epub 2020 Aug 17.

Molecular Pathology Research Group, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.

Improved understanding and management of COVID-19, a potentially life-threatening disease, could greatly reduce the threat posed by its etiologic agent, SARS-CoV-2. Toward this end, we have identified a core peripheral blood immune signature across 63 hospital-treated patients with COVID-19 who were otherwise highly heterogeneous. The signature includes discrete changes in B and myelomonocytic cell composition, profoundly altered T cell phenotypes, selective cytokine/chemokine upregulation and SARS-CoV-2-specific antibodies. Some signature traits identify links with other settings of immunoprotection and immunopathology; others, including basophil and plasmacytoid dendritic cell depletion, correlate strongly with disease severity; while a third set of traits, including a triad of IP-10, interleukin-10 and interleukin-6, anticipate subsequent clinical progression. Hence, contingent upon independent validation in other COVID-19 cohorts, individual traits within this signature may collectively and individually guide treatment options; offer insights into COVID-19 pathogenesis; and aid early, risk-based patient stratification that is particularly beneficial in phasic diseases such as COVID-19.
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http://dx.doi.org/10.1038/s41591-020-1038-6DOI Listing
October 2020

Distribution of Topological Types in Grain-Growth Microstructures.

Phys Rev Lett 2020 Jul;125(1):015501

Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong SAR China.

An open question in studying normal grain growth concerns the asymptotic state to which microstructures converge. In particular, the distribution of grain topologies is unknown. We introduce a thermodynamiclike theory to explain these distributions in two- and three-dimensional systems. In particular, a bendinglike energy E_{i} is associated to each grain topology t_{i}, and the probability of observing that particular topology is proportional to [1/s(t_{i})]e^{-βE_{i}}, where s(t_{i}) is the order of an associated symmetry group and β is a thermodynamiclike constant. We explain the physical origins of this approach and provide numerical evidence in support.
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http://dx.doi.org/10.1103/PhysRevLett.125.015501DOI Listing
July 2020

Liquid Biopsy in Colorectal Carcinoma: Clinical Applications and Challenges.

Cancers (Basel) 2020 May 27;12(6). Epub 2020 May 27.

Convergent Science Institute in Cancer, Michelson Center for Convergent Bioscience, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA 90089, USA.

Colorectal carcinoma (CRC) is characterized by wide intratumor heterogeneity with general genomic instability and there is a need for improved diagnostic, prognostic, and therapeutic tools. The liquid biopsy provides a noninvasive route of sample collection for analysis of circulating tumor cells (CTCs) and genomic material, including cell-free DNA (cfDNA), as a complementary biopsy to the solid tumor tissue. The solid biopsy is critical for molecular characterization and diagnosis at the time of collection. The liquid biopsy has the advantage of longitudinal molecular characterization of the disease, which is crucial for precision medicine and patient-oriented treatment. In this review, we provide an overview of CRC and the different methodologies for the detection of CTCs and cfDNA, followed by a discussion on the potential clinical utility of the liquid biopsy in CRC patient care, and lastly, current challenges in the field.
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http://dx.doi.org/10.3390/cancers12061376DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7352156PMC
May 2020

Water-Compatible Cycloadditions of Oligonucleotide-Conjugated Strained Allenes for DNA-Encoded Library Synthesis.

J Am Chem Soc 2020 04 16;142(17):7776-7782. Epub 2020 Apr 16.

Chemical Biology and Therapeutics Science Program, Broad Institute, 415 Main Street, Cambridge, Massachusetts 02142, United States.

DNA-encoded libraries of small molecules are being explored extensively for the identification of binders in early drug-discovery efforts. Combinatorial syntheses of such libraries require water- and DNA-compatible reactions, and the paucity of these reactions currently limit the chemical features of resulting barcoded products. The present work introduces strain-promoted cycloadditions of cyclic allenes under mild conditions to DNA-encoded library synthesis. Owing to distinct cycloaddition modes of these reactive intermediates with activated olefins, 1,3-dipoles, and dienes, the process generates diverse molecular architectures from a single precursor. The resulting DNA-barcoded compounds exhibit unprecedented ring and topographic features, related to elements found to be powerful in phenotypic screening.
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http://dx.doi.org/10.1021/jacs.9b13186DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7294439PMC
April 2020

A comprehensive and comparative phenotypic analysis of the collaborative founder strains identifies new and known phenotypes.

Mamm Genome 2020 02 14;31(1-2):30-48. Epub 2020 Feb 14.

Department of Neurology, Friedrich-Baur-Institute, Klinikum Der Ludwig-Maximilians-Universität München, Ziemssenstr. 1a, 80336, Munich, Germany.

The collaborative cross (CC) is a large panel of mouse-inbred lines derived from eight founder strains (NOD/ShiLtJ, NZO/HILtJ, A/J, C57BL/6J, 129S1/SvImJ, CAST/EiJ, PWK/PhJ, and WSB/EiJ). Here, we performed a comprehensive and comparative phenotyping screening to identify phenotypic differences and similarities between the eight founder strains. In total, more than 300 parameters including allergy, behavior, cardiovascular, clinical blood chemistry, dysmorphology, bone and cartilage, energy metabolism, eye and vision, immunology, lung function, neurology, nociception, and pathology were analyzed; in most traits from sixteen females and sixteen males. We identified over 270 parameters that were significantly different between strains. This study highlights the value of the founder and CC strains for phenotype-genotype associations of many genetic traits that are highly relevant to human diseases. All data described here are publicly available from the mouse phenome database for analyses and downloads.
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http://dx.doi.org/10.1007/s00335-020-09827-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7060152PMC
February 2020

High-throughput phenotyping reveals expansive genetic and structural underpinnings of immune variation.

Nat Immunol 2020 01 16;21(1):86-100. Epub 2019 Dec 16.

Wellcome Sanger Institute, Hinxton, UK.

By developing a high-density murine immunophenotyping platform compatible with high-throughput genetic screening, we have established profound contributions of genetics and structure to immune variation (http://www.immunophenotype.org). Specifically, high-throughput phenotyping of 530 unique mouse gene knockouts identified 140 monogenic 'hits', of which most had no previous immunologic association. Furthermore, hits were collectively enriched in genes for which humans show poor tolerance to loss of function. The immunophenotyping platform also exposed dense correlation networks linking immune parameters with each other and with specific physiologic traits. Such linkages limit freedom of movement for individual immune parameters, thereby imposing genetically regulated 'immunologic structures', the integrity of which was associated with immunocompetence. Hence, we provide an expanded genetic resource and structural perspective for understanding and monitoring immune variation in health and disease.
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http://dx.doi.org/10.1038/s41590-019-0549-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338221PMC
January 2020

Soft windowing application to improve analysis of high-throughput phenotyping data.

Bioinformatics 2020 03;36(5):1492-1500

Korea Mouse Phenotyping Center (KMPC), Korea.

Motivation: High-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximizes analytic power while minimizing noise from unspecified environmental factors.

Results: Here we introduce 'soft windowing', a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype-phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant P-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft-windowed and non-windowed approaches, respectively, from a set of 2082 mutant mouse lines. Our method is generalizable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources.

Availability And Implementation: The method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btz744DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7115897PMC
March 2020

A novel approach to describe chemical environments in high-dimensional neural network potentials.

J Chem Phys 2019 Apr;150(15):154102

Department of Mechanical Engineering, Bogazici University, Istanbul, Turkey.

A central concern of molecular dynamics simulations is the potential energy surfaces that govern atomic interactions. These hypersurfaces define the potential energy of the system and have generally been calculated using either predefined analytical formulas (classical) or quantum mechanical simulations (ab initio). The former can accurately reproduce only a selection of material properties, whereas the latter is restricted to short simulation times and small systems. Machine learning potentials have recently emerged as a third approach to model atomic interactions, and are purported to offer the accuracy of ab initio simulations with the speed of classical potentials. However, the performance of machine learning potentials depends crucially on the description of a local atomic environment. A set of invariant, orthogonal, and differentiable descriptors for an atomic environment is proposed, implemented in a neural network potential for solid-state silicon, and tested in molecular dynamics simulations. Neural networks using the proposed descriptors are found to outperform ones using the Behler-Parinello and smooth overlap of atomic position descriptors in the literature.
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http://dx.doi.org/10.1063/1.5086167DOI Listing
April 2019

Prediction of Bone Metastasis in Inflammatory Breast Cancer Using a Markov Chain Model.

Oncologist 2019 10 5;24(10):1322-1330. Epub 2019 Apr 5.

Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA

Background: Inflammatory breast cancer (IBC) is a rare yet aggressive variant of breast cancer with a high recurrence rate. We hypothesized that patterns of metastasis differ between IBC and non-IBC. We focused on the patterns of bone metastasis throughout disease progression to determine statistical differences that can lead to clinically relevant outcomes. Our primary outcome of this study is to quantify and describe this difference with a view to applying the findings to clinically relevant outcomes for patients.

Subjects, Materials, And Methods: We retrospectively collected data of patients with nonmetastatic IBC ( = 299) and non-IBC ( = 3,436). Probabilities of future site-specific metastases were calculated. Spread patterns were visualized to quantify the most probable metastatic pathways of progression and to categorize spread pattern based on their propensity to subsequent dissemination of cancer.

Results: In patients with IBC, the probabilities of developing bone metastasis after chest wall, lung, or liver metastasis as the first site of progression were high: 28%, 21%, and 21%, respectively. For patients with non-IBC, the probability of developing bone metastasis was fairly consistent regardless of initial metastasis site.

Conclusion: Metastatic patterns of spread differ between patients with IBC and non-IBC. Selection of patients with IBC with known liver, chest wall, and/or lung metastasis would create a population in whom to investigate effective methods for preventing future bone metastasis.

Implications For Practice: This study demonstrated that the patterns of metastasis leading to and following bone metastasis differ significantly between patients with inflammatory breast cancer (IBC) and those with non-IBC. Patients with IBC had a progression pattern that tended toward the development of bone metastasis if they had previously developed metastases in the liver, chest wall, and lung, rather than in other sites. Selection of patients with IBC with known liver, chest wall, and/or lung metastasis would create a population in whom to investigate effective methods for preventing future bone metastasis.
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http://dx.doi.org/10.1634/theoncologist.2018-0713DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6795167PMC
October 2019

Machine learning models for predicting post-cystectomy recurrence and survival in bladder cancer patients.

PLoS One 2019 20;14(2):e0210976. Epub 2019 Feb 20.

Department of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, University Park Campus, Los Angeles, CA, United States of America.

Currently in patients with bladder cancer, various clinical evaluations (imaging, operative findings at transurethral resection and radical cystectomy, pathology) are collectively used to determine disease status and prognosis, and recommend neoadjuvant, definitive and adjuvant treatments. We analyze the predictive power of these measurements in forecasting two key long-term outcomes following radical cystectomy, i.e., cancer recurrence and survival. Information theory and machine learning algorithms are employed to create predictive models using a large prospective, continuously collected, temporally resolved, primary bladder cancer dataset comprised of 3503 patients (1971-2016). Patient recurrence and survival one, three, and five years after cystectomy can be predicted with greater than 70% sensitivity and specificity. Such predictions may inform patient monitoring schedules and post-cystectomy treatments. The machine learning models provide a benchmark for predicting oncologic outcomes in patients undergoing radical cystectomy and highlight opportunities for improving care using optimal preoperative and operative data collection.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210976PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6382101PMC
October 2019

The Alstoscholarisine Alkaloids: Isolation, Structure Determination, Biogenesis, Biological Evaluation, and Synthesis.

Alkaloids Chem Biol 2019;81:115-150. Epub 2018 Nov 15.

Department of Chemistry, The Pennsylvania State University, University Park, PA, United States. Electronic address:

The alstoscholarisines are a small family of biologically and structurally interesting polycyclic monoterpenoid indole alkaloids isolated from the leaf extracts of Alstonia scholaris. The alkaloids can be divided into three different subtypes based upon their structures and putative biogenesis: (1) (-)-alstoscholarisines A-E, (2) (+)-alstoscholarisine G, and (3) (+)-alstoscholarisines H-J. This review discusses the isolation, structure determination, biological activity, and proposed biosynthesis of these metabolites. In addition, synthetic studies on the alkaloids are described including total syntheses of racemic alstoscholarisines A-E, a total synthesis of (-)-alstoscholarisine A, and a synthesis of racemic alstoscholarisine H.
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http://dx.doi.org/10.1016/bs.alkal.2018.09.001DOI Listing
April 2019

Identification of genes required for eye development by high-throughput screening of mouse knockouts.

Commun Biol 2018 21;1:236. Epub 2018 Dec 21.

Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.

Despite advances in next generation sequencing technologies, determining the genetic basis of ocular disease remains a major challenge due to the limited access and prohibitive cost of human forward genetics. Thus, less than 4,000 genes currently have available phenotype information for any organ system. Here we report the ophthalmic findings from the International Mouse Phenotyping Consortium, a large-scale functional genetic screen with the goal of generating and phenotyping a null mutant for every mouse gene. Of 4364 genes evaluated, 347 were identified to influence ocular phenotypes, 75% of which are entirely novel in ocular pathology. This discovery greatly increases the current number of genes known to contribute to ophthalmic disease, and it is likely that many of the genes will subsequently prove to be important in human ocular development and disease.
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http://dx.doi.org/10.1038/s42003-018-0226-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6303268PMC
December 2018

PDX Finder: A portal for patient-derived tumor xenograft model discovery.

Nucleic Acids Res 2019 01;47(D1):D1073-D1079

The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA.

Patient-derived tumor xenograft (PDX) mouse models are a versatile oncology research platform for studying tumor biology and for testing chemotherapeutic approaches tailored to genomic characteristics of individual patients' tumors. PDX models are generated and distributed by a diverse group of academic labs, multi-institution consortia and contract research organizations. The distributed nature of PDX repositories and the use of different metadata standards for describing model characteristics presents a significant challenge to identifying PDX models relevant to specific cancer research questions. The Jackson Laboratory and EMBL-EBI are addressing these challenges by co-developing PDX Finder, a comprehensive open global catalog of PDX models and their associated datasets. Within PDX Finder, model attributes are harmonized and integrated using a previously developed community minimal information standard to support consistent searching across the originating resources. Links to repositories are provided from the PDX Finder search results to facilitate model acquisition and/or collaboration. The PDX Finder resource currently contains information for 1985 PDX models of diverse cancers including those from large resources such as the Patient-Derived Models Repository, PDXNet and EurOPDX. Individuals or organizations that generate and distribute PDXs are invited to increase the 'findability' of their models by participating in the PDX Finder initiative at www.pdxfinder.org.
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http://dx.doi.org/10.1093/nar/gky984DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323912PMC
January 2019

Development of metastatic brain disease involves progression through lung metastases in mutated non-small cell lung cancer.

Converg Sci Phys Oncol 2017 Sep 13;3(3). Epub 2017 Jul 13.

Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, 1441 Eastlake Ave, NOR 3444, Los Angeles, CA 90033.

Lung cancer is often classified by the presence of oncogenic drivers, such as epidermal growth factor receptor (), rather than patterns of anatomical distribution. While metastatic spread may seem a random and unpredictable process, we explored the possibility of using its quantifiable nature as a measure of describing and comparing different subsets of disease. We constructed a database of 664 non-small cell lung cancer (NSCLC) patients treated at the University of Southern California Norris Comprehensive Cancer Center and the Los Angeles County Medical Center. Markov mathematical modeling was employed to assess metastatic sites in a spatiotemporal manner through every time point in progression of disease. Our findings identified a preferential pattern of primary lung disease progressing through lung metastases to the brain amongst mutated ( ) NSCLC patients, with exon 19 deletions or exon 21 L858R mutations, as compared to wild type ( ). The brain was classified as an anatomic "sponge", with a higher ratio of incoming to outgoing spread, for NSCLC. Bone metastases were more commonly identified in patients. Our study supports a link between the anatomical and molecular characterization of lung metastatic cancer. Improved understanding of the differential biology that drives discordant patterns of anatomic spread, based on genotype specific profiling, has the potential to improve personalized oncologic care.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6166474PMC
September 2017

Synthesis of Alstoscholarisines A-E, Monoterpene Indole Alkaloids with Modulating Effects on Neural Stem Cells.

J Org Chem 2018 06 10;83(11):5877-5896. Epub 2018 May 10.

Department of Chemistry , The Pennsylvania State University University Park , Pennsylvania 16802 , United States.

A divergent synthetic strategy has been developed for stereoselective total syntheses of alstoscholarisines A-E, monoterpenoid indole alkaloids which are modulators of adult neuronal stem cells. A pivotal step includes an intermolecular Michael addition of an indole-2-acetic acid methyl ester enolate to an α,β-unsaturated N-sulfonyllactam to form the C15, C16 bond of the alkaloids. Other features of the strategy involve a selective partial reduction of an intermediate N-sulfonyllactam followed by cyclization to a bridged aminal system that serves as a key precursor for all five of the alkaloids as well as the use of an allyl group as a masked aldehyde equivalent.
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http://dx.doi.org/10.1021/acs.joc.8b00889DOI Listing
June 2018

Identification of genetic elements in metabolism by high-throughput mouse phenotyping.

Nat Commun 2018 01 18;9(1):288. Epub 2018 Jan 18.

Monterotondo Mouse Clinic, Italian National Research Council (CNR), Institute of Cell Biology and Neurobiology, Adriano Buzzati-Traverso Campus, Via E. Ramarini 32, Monterotondo Scalo, RM, 00015, Italy.

Metabolic diseases are a worldwide problem but the underlying genetic factors and their relevance to metabolic disease remain incompletely understood. Genome-wide research is needed to characterize so-far unannotated mammalian metabolic genes. Here, we generate and analyze metabolic phenotypic data of 2016 knockout mouse strains under the aegis of the International Mouse Phenotyping Consortium (IMPC) and find 974 gene knockouts with strong metabolic phenotypes. 429 of those had no previous link to metabolism and 51 genes remain functionally completely unannotated. We compared human orthologues of these uncharacterized genes in five GWAS consortia and indeed 23 candidate genes are associated with metabolic disease. We further identify common regulatory elements in promoters of candidate genes. As each regulatory element is composed of several transcription factor binding sites, our data reveal an extensive metabolic phenotype-associated network of co-regulated genes. Our systematic mouse phenotype analysis thus paves the way for full functional annotation of the genome.
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http://dx.doi.org/10.1038/s41467-017-01995-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5773596PMC
January 2018

Total Syntheses of the Monoterpenoid Indole Alkaloids (±)-Alstoscholarisine B and C.

Angew Chem Int Ed Engl 2017 12 1;56(52):16674-16676. Epub 2017 Dec 1.

Department of Chemistry, The Pennsylvania State University, University Park, PA, 16802, USA.

Total syntheses of the monoterpenoid indole alkaloids (±)-alstoscholarisine B and C were accomplished starting from a readily available indole-2-acetic ester and an α,β-unsaturated N-sulfonyllactam.
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http://dx.doi.org/10.1002/anie.201710943DOI Listing
December 2017

PDX-MI: Minimal Information for Patient-Derived Tumor Xenograft Models.

Cancer Res 2017 11;77(21):e62-e66

Greehey Children's Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas.

Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models minimal information standard (PDX-MI) for reporting on the generation, quality assurance, and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient's tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use of PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models. .
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http://dx.doi.org/10.1158/0008-5472.CAN-17-0582DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5738926PMC
November 2017

A large scale hearing loss screen reveals an extensive unexplored genetic landscape for auditory dysfunction.

Nat Commun 2017 10 12;8(1):886. Epub 2017 Oct 12.

RIKEN BioResource Center, Tsukuba, Ibaraki, 305-0074, Japan.

The developmental and physiological complexity of the auditory system is likely reflected in the underlying set of genes involved in auditory function. In humans, over 150 non-syndromic loci have been identified, and there are more than 400 human genetic syndromes with a hearing loss component. Over 100 non-syndromic hearing loss genes have been identified in mouse and human, but we remain ignorant of the full extent of the genetic landscape involved in auditory dysfunction. As part of the International Mouse Phenotyping Consortium, we undertook a hearing loss screen in a cohort of 3006 mouse knockout strains. In total, we identify 67 candidate hearing loss genes. We detect known hearing loss genes, but the vast majority, 52, of the candidate genes were novel. Our analysis reveals a large and unexplored genetic landscape involved with auditory function.The full extent of the genetic basis for hearing impairment is unknown. Here, as part of the International Mouse Phenotyping Consortium, the authors perform a hearing loss screen in 3006 mouse knockout strains and identify 52 new candidate genes for genetic hearing loss.
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http://dx.doi.org/10.1038/s41467-017-00595-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5638796PMC
October 2017

Nanolayering around and thermal resistivity of the water-hexagonal boron nitride interface.

J Chem Phys 2017 Jul;147(4):044709

Department of Mechanical Engineering, Boğaziçi University, Istanbul, 43210 Turkey.

The water-hexagonal boron nitride interface was investigated by molecular dynamics simulations. Since the properties of the interface change significantly with the interatomic potential, a new method for calibrating the solid-liquid interatomic potential is proposed based on the experimental energy of the interface. The result is markedly different from that given by Lorentz-Berthelot mixing for the Lennard-Jones parameters commonly used in the literature. Specifically, the extent of nanolayering and interfacial thermal resistivity is measured for several interatomic potentials, and the one calibrated by the proposed method gives the least thermal resistivity.
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http://dx.doi.org/10.1063/1.4985913DOI Listing
July 2017

Prevalence of sexual dimorphism in mammalian phenotypic traits.

Nat Commun 2017 06 26;8:15475. Epub 2017 Jun 26.

Mouse Genetics Project, The Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.

The role of sex in biomedical studies has often been overlooked, despite evidence of sexually dimorphic effects in some biological studies. Here, we used high-throughput phenotype data from 14,250 wildtype and 40,192 mutant mice (representing 2,186 knockout lines), analysed for up to 234 traits, and found a large proportion of mammalian traits both in wildtype and mutants are influenced by sex. This result has implications for interpreting disease phenotypes in animal models and humans.
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http://dx.doi.org/10.1038/ncomms15475DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5490203PMC
June 2017
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