# Artificial intelligence enhanced cancer cell classification based organelle morphology and topology

> **NIH NIH R21** · ALBANY MEDICAL COLLEGE · 2022 · $230,154

## Abstract

ABSTRACT
Breast cancer is a highly heterogenous disease, both phenotypically and genetically. The quantity and
subcellular location of cancer protein biomarkers are used to classify breast cancer types. Transcriptomics,
multiplexed imaging, or mass cytometry have been used to classify breast tumor cell heterogeneity with varying
success. Although genomics and proteomics have been successful in the identification of tumor cell populations
involved in metastatic progression, the ability to determine whether patient tumors contain metastatic
subpopulations is still lacking. Recently, organelle morphology and function has been used as a direct readout
of the functional phenotypic state of an individual cancer cell. We propose to use the spatial context of organelles,
specifically their subcellular location and inter-organelle relationships (topology), to classify novel and distinct
metastatic cancer cell subpopulations. We developed an Organelle Topology-based Cell Classification Pipeline
(OTCCP) to quantify, for the first time, the topological features of subcellular organelles, defined as the distance
between each organelle object and all its neighbors within a cell. Under RFA-CA-21-013 (Development of
Innovative Informatics Methods and Algorithms for Cancer Research and Management), we will adapt or develop
Machine learning and Deep Learning methodologies to accelerate and automate OTCCP-based organelle-
based topology cancer cell classification to identify subpopulations of metastatic cells within heterogeneous
primary tumors with potential diagnostic and prognostic value. This approach will also have major impact as a
discovery tool to advance our understanding of cancer cell biology on a subcellular level.

## Key facts

- **NIH application ID:** 10528867
- **Project number:** 1R21CA274622-01
- **Recipient organization:** ALBANY MEDICAL COLLEGE
- **Principal Investigator:** Margarida Barroso
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $230,154
- **Award type:** 1
- **Project period:** 2022-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10528867

## Citation

> US National Institutes of Health, RePORTER application 10528867, Artificial intelligence enhanced cancer cell classification based organelle morphology and topology (1R21CA274622-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10528867. Licensed CC0.

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