ArticleiScience2023
Spatial topology of organelle is a new breast cancer cell classifier.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
The 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.
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Who cites it
9 citing papers in PubMed.
- A data fusion deep learning approach for accurate organelle-based classification of cancer cells.Health information science and systems · 2026Article
- Neurons and astrocytes have distinct organelle signatures and responses to stress.Cell reports · 2025Article
- Informatics at the Frontier of Cancer Research.Cancer research · 2025Review
- Collagen hydroxylation couples NAD+/NADH dynamics to tumor dormancy and reactivation.Research square · 2025Article
- Peripheral positioning of lysosomes supports melanoma aggressiveness.Nature communications · 2025Article
- Tumoroids, a valid preclinical screening platform for monitoring cancer angiogenesis.Stem cell research & therapy · 2024Review
- C. elegans Presenilin Mediates Inter-Organelle Contacts and Communication that Is Required for Lysosome Activity.Aging and disease · 2024Article
- Organelle morphology and positioning orchestrate physiological and disease-associated processes.Current opinion in cell biology · 2024Review
- Trafficking in cancer: from gene deregulation to altered organelles and emerging biophysical properties.Frontiers in cell and developmental biology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Genomics and proteomics have been central to identify tumor cell populations, but more accurate approaches to classify cell subtypes are still lacking. We propose a new methodology to accurately classify cancer cells based on their organelle spatial topology. Herein, we developed an organelle topology-based cell classification pipeline (OTCCP), which integrates artificial intelligence (AI) and imaging quantification to analyze organelle spatial distribution and inter-organelle topology. OTCCP was used to classify a panel of human breast cancer cells, grown as 2D monolayer or 3D tumor spheroids using early endosomes, mitochondria, and their inter-organelle contacts. Organelle topology allows for a highly precise differentiation between cell lines of different subtypes and aggressiveness. These findings lay the groundwork for using organelle topological profiling as a fast and efficient method for phenotyping breast cancer function as well as a discovery tool to advance our understanding of cancer cell biology at the subcellular level.
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Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.