Inhibiting the oncogenic translation program is an effective therapeutic strategy in multiple myeloma

Published in final edited form as: Sci Transl Med. 2017 May 10; 9(389). https://doi.org/10.1126/scitranslmed.aal2668.Multiple myeloma (MM) is a frequently incurable hematological cancer in which overactivity of MYC plays a central role, notably through up-regulation of ribosome biogenesis and translation. To better understand the oncogenic program driven by MYC and investigate its potential as a therapeutic target, we screened a chemically diverse small-molecule library for anti-MM activity. The most potent hits identified were rocaglate scaffold inhibitors of translation initiation. Expression profiling of MM cells revealed reversion of the oncogenic MYC-driven transcriptional program by CMLD010509, the most promising rocaglate. Proteome-wide reversion correlated with selective depletion of short-lived proteins that are key to MM growth and survival, most notably MYC, MDM2, CCND1, MAF, and MCL-1. The efficacy of CMLD010509 in mouse models of MM confirmed the therapeutic relevance of these findings in vivo and supports the feasibility of targeting the oncogenic MYC-driven translation program in MM with rocaglates

HIGH CROSSOVER RATE1 encodes PROTEIN PHOSPHATASE X1 and restricts meiotic crossovers in Arabidopsis.

Meiotic crossovers are tightly restricted in most eukaryotes, despite an excess of initiating DNA double-strand breaks. The majority of plant crossovers are dependent on class I interfering repair, with a minority formed via the class II pathway. Class II repair is limited by anti-recombination pathways; however, similar pathways repressing class I crossovers have not been identified. Here, we performed a forward genetic screen in Arabidopsis using fluorescent crossover reporters to identify mutants with increased or decreased recombination frequency. We identified HIGH CROSSOVER RATE1 (HCR1) as repressing crossovers and encoding PROTEIN PHOSPHATASE X1. Genome-wide analysis showed that hcr1 crossovers are increased in the distal chromosome arms. MLH1 foci significantly increase in hcr1 and crossover interference decreases, demonstrating an effect on class I repair. Consistently, yeast two-hybrid and in planta assays show interaction between HCR1 and class I proteins, including HEI10, PTD, MSH5 and MLH1. We propose that HCR1 plays a major role in opposition to pro-recombination kinases to restrict crossovers in Arabidopsis.Marie Curie International Training Network COMREC European Research Council (ERC) National Research Foundation of Korea Suh Kyungbae Foundatio

Horizontal Gene Acquisitions, Mobile Element Proliferation, and Genome Decay in the Host-Restricted Plant Pathogen \u3ci\u3eErwinia Tracheiphila\u3c/i\u3e

Modern industrial agriculture depends on high-density cultivation of genetically similar crop plants, creating favorable conditions for the emergence of novel pathogens with increased fitness in managed compared with ecologically intact settings. Here, we present the genome sequence of six strains of the cucurbit bacterial wilt pathogen Erwinia tracheiphila (Enterobacteriaceae) isolated from infected squash plants in New York, Pennsylvania, Kentucky, and Michigan. These genomes exhibit a high proportion of recent horizontal gene acquisitions, invasion and remarkable amplification of mobile genetic elements, and pseudogenization of approximately 20% of the coding sequences. These genome attributes indicate that E. tracheiphila recently emerged as a host-restricted pathogen. Furthermore, chromosomal rearrangements associated with phage and transposable element proliferation contribute to substantial differences in gene content and genetic architecture between the six E. tracheiphila strains and other Erwinia species. Together, these data lead us to hypothesize that E. tracheiphila has undergone recent evolution through both genome decay (pseudogenization) and genome expansion (horizontal gene transfer and mobile element amplification). Despite evidence of dramatic genomic changes, the six strains are genetically monomorphic, suggesting a recent population bottleneck and emergence into E. tracheiphila’s current ecological niche

Multiethnic Genome-Wide Association Study of Diabetic Retinopathy Using Liability Threshold Modeling of Duration of Diabetes and Glycemic Control

Correction: Volume69, Issue6 Page1306-1306 DOI10.2337/db20-er06a Published JUN 2020To identify genetic variants associated with diabetic retinopathy (DR), we performed a large multiethnic genome-wide association study. Discovery included eight European cohorts (n = 3,246) and seven African American cohorts (n = 2,611). We meta-analyzed across cohorts using inverse-variance weighting, with and without liability threshold modeling of glycemic control and duration of diabetes. Variants with a P valuePeer reviewe

The JCMT BISTRO Survey: A Spiral Magnetic Field in a Hub-filament Structure, Monoceros R2

We present and analyze observations of polarized dust emission at 850 μm toward the central 1 7 1 pc hub-filament structure of Monoceros R2 (Mon R2). The data are obtained with SCUBA-2/POL-2 on the James Clerk Maxwell Telescope (JCMT) as part of the B-fields in Star-forming Region Observations survey. The orientations of the magnetic field follow the spiral structure of Mon R2, which are well described by an axisymmetric magnetic field model. We estimate the turbulent component of the magnetic field using the angle difference between our observations and the best-fit model of the underlying large-scale mean magnetic field. This estimate is used to calculate the magnetic field strength using the Davis–Chandrasekhar–Fermi method, for which we also obtain the distribution of volume density and velocity dispersion using a column density map derived from Herschel data and the C18O (J = 3 - 2) data taken with HARP on the JCMT, respectively. We make maps of magnetic field strengths and mass-to-flux ratios, finding that magnetic field strengths vary from 0.02 to 3.64 mG with a mean value of 1.0 \ub1 0.06 mG, and the mean critical mass-to-flux ratio is 0.47 \ub1 0.02. Additionally, the mean Alfv\ue9n Mach number is 0.35 \ub1 0.01. This suggests that, in Mon R2, the magnetic fields provide resistance against large-scale gravitational collapse, and the magnetic pressure exceeds the turbulent pressure. We also investigate the properties of each filament in Mon R2. Most of the filaments are aligned along the magnetic field direction and are magnetically subcritical

Pharmacogenomic analysis of patient-derived tumor cells in gynecologic cancers

Background Gynecologic malignancy is one of the leading causes of mortality in female adults worldwide. Comprehensive genomic analysis has revealed a list of molecular aberrations that are essential to tumorigenesis, progression, and metastasis of gynecologic tumors. However, targeting such alterations has frequently led to treatment failures due to underlying genomic complexity and simultaneous activation of various tumor cell survival pathway molecules. A compilation of molecular characterization of tumors with pharmacological drug response is the next step toward clinical application of patient-tailored treatment regimens. Results Toward this goal, we establish a library of 139 gynecologic tumors including epithelial ovarian cancers (EOCs), cervical, endometrial tumors, and uterine sarcomas that are genomically and/or pharmacologically annotated and explore dynamic pharmacogenomic associations against 37 molecularly targeted drugs. We discover lineage-specific drug sensitivities based on subcategorization of gynecologic tumors and identify TP53 mutation as a molecular determinant that elicits therapeutic response to poly (ADP-Ribose) polymerase (PARP) inhibitor. We further identify transcriptome expression of inhibitor of DNA biding 2 (ID2) as a potential predictive biomarker for treatment response to olaparib. Conclusions Together, our results demonstrate the potential utility of rapid drug screening combined with genomic profiling for precision treatment of gynecologic cancers

The Mutational Landscape of Circulating Tumor Cells in Multiple Myeloma

The development of sensitive and non-invasive ‘‘liquid biopsies’’ presents new opportunities for longitudinal monitoring of tumor dissemination and clonal evolution. The number of circulating tumor cells (CTCs) is prognostic in multiple myeloma (MM), but there is little information on their genetic features. Here, we have analyzed the genomic landscape of CTCs from 29 MM patients, including eight cases with matched/paired bone marrow (BM) tumor cells. Our results show that 100% of clonal mutations in patient BM were detected in CTCs and that 99% of clonal mutations in CTCs were present in BM MM. These include typical driver mutations in MM such as in KRAS, NRAS, or BRAF. These data suggest that BM and CTC samples have similar clonal structures, as discordances between the two were restricted to subclonal mutations. Accordingly, our results pave the way for potentially less invasive mutation screening of MM patients through characterization of CTCs

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14