A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas

The computational identification of oncogenic lesions is still a key open problem in cancer biology. Although several methods have been proposed, they fail to model how such events are mediated by the network of molecular interactions in the cell. In this paper, we introduce a systems biology approach, based on the analysis of molecular interactions that become dysregulated in specific tumor phenotypes. Such a strategy provides important insights into tumorigenesis, effectively extending and complementing existing methods. Furthermore, we show that the same approach is highly effective in identifying the targets of molecular perturbations in a human cellular context, a task virtually unaddressed by existing computational methods. To identify interactions that are dysregulated in three distinct non-Hodgkin's lymphomas and in samples perturbed with CD40 ligand, we use the B-cell interactome (BCI), a genome-wide compendium of human B-cell molecular interactions, in combination with a large set of microarray expression profiles. The method consistently ranked the known gene in the top 20 (0.3%), outperforming conventional approaches in 3 of 4 cases

Simple nutrients bypass the requirement for HLH-30 in coupling lysosomal nutrient sensing to survival

Lysosomes are ubiquitous acidified organelles that degrade intracellular and extracellular material trafficked via multiple pathways. Lysosomes also sense cellular nutrient levels to regulate target of rapamycin (TOR) kinase, a signaling enzyme that drives growth and suppresses activity of the MiT/TFE family of transcription factors that control biogenesis of lysosomes. In this study, we subjected worms lacking basic helix–loop–helix transcription factor 30 (hlh-30), the Caenorhabditis elegans MiT/TFE ortholog, to starvation followed by refeeding to understand how this pathway regulates survival with variable nutrient supply. Loss of HLH-30 markedly impaired survival in starved larval worms and recovery upon refeeding bacteria. Remarkably, provision of simple nutrients in a completely defined medium (C. elegans maintenance medium [CeMM]), specifically glucose and linoleic acid, restored lysosomal acidification, TOR activation, and survival with refeeding despite the absence of HLH-30. Worms deficient in lysosomal lipase 2 (lipl-2), a lysosomal enzyme that is transcriptionally up-regulated in starvation in an HLH-30–dependent manner, also demonstrated increased mortality with starvation–refeeding that was partially rescued with glucose, suggesting a critical role for LIPL-2 in lipid metabolism under starvation. CeMM induced transcription of vacuolar proton pump subunits in hlh-30 mutant worms, and knockdown of vacuolar H+-ATPase 12 (vha-12) and its upstream regulator, nuclear hormone receptor 31 (nhr-31), abolished the rescue with CeMM. Loss of Ras-related GTP binding protein C homolog 1 RAGC-1, the ortholog for mammalian RagC/D GTPases, conferred starvation–refeeding lethality, and RAGC-1 overexpression was sufficient to rescue starved hlh-30 mutant worms, demonstrating a critical need for TOR activation with refeeding. These results show that HLH-30 activation is critical for sustaining survival during starvation–refeeding stress via regulating TOR. Glucose and linoleic acid bypass the requirement for HLH-30 in coupling lysosome nutrient sensing to survival.</div

Hadroproduction of χc states in 530 GeV/c π− interactions with nuclear targets

We are studying production of χc states in 530 GeV/c π− interactions with several targets. χc mesons are observed in the mode (χ→J/ψ+γ). Only photons that converted to e+e− pairs are used in the reconstruction of the χc mesons. Preliminary analysis shows that the fraction of observed J/ψs coming from χc radiative decays is 0.44±0.09±0.08, and that the relative production rate of χc1 to χc2 is 1.3±0.6.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87707/2/1062_1.pd

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

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

Computer support for home-based health care

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (leaves 75-76).by Kartik M. Mani.M.Eng