In one paragraphArticle in Cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from itWhat it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
18 authors.
Marc D Ryser *Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina.ORCID 0000-0002-4990-0849 Joseph GeradtsDepartment of Pathology and Laboratory Medicine, Brody School of Medicine, East Carolina University, Greenville, North Carolina.ORCID 0009-0002-3817-8499 Donald L WeaverLarner College of Medicine, University of Vermont and UVM Cancer Center, Burlington, Vermont.ORCID 0009-0003-9946-9369 Shannon T HollowayDepartment of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina.ORCID 0009-0005-6757-7503 Kevin A MurgasDepartment of Biomedical Informatics, Stony Brook University School of Medicine, Stony Brook, New York.ORCID 0000-0001-8634-2893 Darryl Shibata *Department of Pathology, University of Southern California Keck School of Medicine, Los Angeles, California.ORCID 0000-0002-4567-1639 Funding
PHYLOGENIES of Barrett's Esophagus LineagesP01CA091955 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI GRADY, WILLIAM MALLORY · 2002 to 2019
$35.9MMolecular, Cellular and Tissue Characterization UnitU2CCA233254 · NCI · DUKE UNIVERSITY · PI HWANG, E.SHELLEY, MALEY, CARLO · 2018 to 2023
$11.4MThe Role of the Microbiome in Cancer Suppression and Susceptibility Across SpeciesU54CA217376 · NCI · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI MALEY, CARLO · 2018 to 2022
$8.6MModeling Neoplastic Progression in Barrett's Esophagus - Renewal -2R01CA140657 · NCI · WISTAR INSTITUTE · PI Carlo Maley · 2009 to 2026
$5.0MBreast Cancer Detection ConsortiumU01CA214183 · NCI · DUKE UNIVERSITY · PI MARKS, JEFFREY R., TANG, CHA-MEI · 2016 to 2021
$2.4MDynamic imaging and tissue biomarker models to delineate indolent from aggressive breast calcificationsR01CA271237 · NCI · DUKE UNIVERSITY · PI GRIMM, LARS J, MARKS, JEFFREY R. · 2022 to 2025
$2.1M(PQC3) Genomic Diversity and Microenvironment as Drivers of Metastasis in DCISR01CA185138 · NCI · DUKE UNIVERSITY · PI HWANG, E.SHELLEY, MALEY, CARLO · 2014 to 2017
$2.0MThe Mathematics of Breast Cancer Overtreatment: Improving Treatment Choice through Effective Communication of Personalized Cancer RiskR00CA207872 · NCI · DUKE UNIVERSITY · PI RYSER, MARC · 2018 to 2020
$746kA cell-cycle induced genetic recorder for simultaneous recovery of cell divisions and lineageR21CA257980 · NCI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI MALEY, CARLO, WEISS, RON · 2022 to 2023
$576kThe Mathematics of Breast Cancer Overtreatment: Improving Treatment Choice through Effective Communication of Personalized Cancer RiskK99CA207872 · NCI · DUKE UNIVERSITY · PI RYSER, MARC · 2016 to 2017
$318kArizona Biomedical Research Commission (ABRC) ADHS18-198847Breast Cancer Research Foundation (BCRF) BCRF-19-074Congressionally Directed Medical Research Programs (CDMRP) BC132057National Institutes of Health (NIH) K99-CA207872National Institutes of Health (NIH) P01-CA91955National Institutes of Health (NIH) R00-CA207872National Institutes of Health (NIH) R01-CA140657National Institutes of Health (NIH) R01-CA185138National Institutes of Health (NIH) R01-CA271237National Institutes of Health (NIH) U01-CA214183National Institutes of Health (NIH) U2C-CA233254National Institutes of Health (NIH) U54-CA217376National Science Foundation (NSF) DMS-1614838NCI NIH HHS K99 CA207872NCI NIH HHS P01 CA091955NCI NIH HHS R00 CA207872NCI NIH HHS R01 CA140657NCI NIH HHS R01 CA185138NCI NIH HHS R01 CA271237NCI NIH HHS R21 CA257980NCI NIH HHS U01 CA214183NCI NIH HHS U2C CA233254NCI NIH HHS U54 CA217376U.S. Department of Defense (DOD) BC132057
6 · The paper itselfAbstract
Preinvasive breast cancer, or ductal carcinoma in situ (DCIS), shares many morphologic and genomic features with invasive breast cancer, yet most DCIS tumors remain indolent over decades. In this study, we performed spatial analyses of somatic mutation patterns in 18 DCIS tumors to infer the underlying growth dynamics. Experimental data combined with mathematical models and Bayesian computation revealed a growth model that mimics the cellular dynamics of branching morphogenesis, which governs normal mammary gland development during puberty. Unlike a traditional model of clonal evolution, this noncompetitive growth model allowed for extensive DCIS spread without the selective pressures of subclone competition that promote the emergence of invasive phenotypes. This finding provides a framework to understand the biological basis for the limited progression of DCIS and adds rationale for ongoing clinical efforts to reduce DCIS overtreatment. SIGNIFICANCE: The spatial mutation landscape in ductal carcinoma in situ supports a noncompetitive growth model mirroring normal branching morphogenesis, which has implications for managing low-risk patients and for cancers in other branching organs. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .
Indexed as
Breast NeoplasmsCarcinoma, Intraductal, NoninfiltratingMutationBayes TheoremFemaleHumans
Identifiers
PMID41066536
PMCPMC12950944
What OpenQuestion holds
Textmetadata
LicenceTDM
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