# Developing liquid biopsy tests for malignant effusions using artificial intelligence-assisted, morphology-based isolation of tumor cells

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $679,188

## Abstract

PROJECT SUMMARY/ABSTRACT
 Background. Malignant effusions (ME) are frequent complications of metastatic breast cancer (MBC)
associated with severe symptoms and dire prognosis. Treating MEs involves palliation through the serial removal
of excess fluids, which are then typically discarded. Instead, these fluids could be used as substrates for liquid
biopsy (LB) to guide the treatment of MEs and advanced MBCs.
 Problem. Drug targets and predictors of response for MBC are tremendous unmet clinical needs. Procuring
solid metastatic tissue can be challenging due to the inaccessibility of disease sites and the risks associated with
tissue collection. A major impetus for this proposed research is the opportunity for LB to circumvent these
limitations. ME circulating tumor cells (ME-CTCs) can serve as surrogates for metastatic tissue for molecular
characterization. However, the low proportion of METCs relative to immune cells in many MEs complicates
profiling efforts.
 Solution. We have collaborated with Deepcell (DC), a company that developed an artificial intelligence (AI)-
assisted, morphology-based approach to isolate ME-CTCs. DC’s biomarker-agnostic platform provides an
advantage over traditional biomarker-based tumor enrichment methods by creating morphological atlases of ME-
CTCs for mining novel biomarkers of treatment response and resistance. Our pilot studies demonstrate the
feasibility of molecular characterization of ME-CTCs isolated using the DC platform.
 Hypothesis. We hypothesize that isolating ME-CTCs using the DC platform and downstream profiling can
facilitate the development of LB tools for evaluating known actionable breast cancer (BCa) biomarkers (e.g.,
ER/PR/HER2, PIK3CA & ESR1 mutations) and discovering new predictive molecular and morphology-based
biomarkers and drug targets.
 Specific Aims. In Aim 1, we will first validate the DC platform using primary cells from MEs and ME-derived
organoids. Next, we will use the validated platform to isolate ME-CTCs, generate copy number and mutation
profiles of these cells and matched archival tumors, compare the status of genes frequently mutated in BCa
(e.g., PIK3CA and ESR1), and detect ME-CTC-specific aberrations. In Aim 2, we will perform single-cell RNA
sequencing and immunocytochemistry of isolated ME-CTCs and ME-derived organoids to discover expression-
based biomarkers and assess the status of known BCa biomarkers (e.g., ER/PR/HER2). In Aim 3, we will perform
correlative analyses between treatment response vs. ME-CTC morphology and molecular signatures (Aims 1 &
2) and use ME-derived organoids for validation studies.
 Translational impact. Developing a platform for isolating tumor cells from MEs and liquid biopsy tools to
discover novel response biomarkers and drug targets can transform the treatment of MEs from a palliative setting
to a therapeutic opportunity to improve the outcomes of patients who develop these devastating complications.

## Key facts

- **NIH application ID:** 10946304
- **Project number:** 1R01CA292019-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Mark Jesus Magbanua
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $679,188
- **Award type:** 1
- **Project period:** 2024-08-12 → 2029-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10946304

## Citation

> US National Institutes of Health, RePORTER application 10946304, Developing liquid biopsy tests for malignant effusions using artificial intelligence-assisted, morphology-based isolation of tumor cells (1R01CA292019-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10946304. Licensed CC0.

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