# Identification and characterization of cancer cell states by novel computational and experimental technologies - Resubmission - 1

> **NIH NIH U01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2022 · $592,969

## Abstract

SUMMARY
Tumors are complex systems composed of genetically and transcriptionally heterogeneous cells, and this
heterogeneity has been implicated as a cause of drug resistance and overall mortality. Understanding intra-
tumor heterogeneity is therefore likely to have widespread impact, both fundamental and clinical. The advent of
single-cell RNA-Seq has led to the detection of transcriptionally distinct states among cancer cells across a
wide range of tumor types and stages. However, the field lacks robust computational and experimental
technologies to functionally identify and characterize these cancer cell states. In this project, we take a gene
module-centric view to define cell states in a rigorous and widely applicable manner. We will validate the
importance of each cell state using human samples and the zebrafish melanoma model, which has exceptional
capabilities for imaging and perturbation of cell states throughout tumor progression and metastasis. To
systematically characterize these cell states experimentally, we propose methods to assay their
interdependencies within the tumor and with elements of the microenvironment. Our genetically engineered
zebrafish will mark each cancer cell state with a fluorescent reporter and an ablation cassette, providing a
flexible experimental platform to study and perturb each cell state. The reporter will enable us to sort cancer
cell states and study them individually, with a particular emphasis on their plasticity. By systematically
disrupting cancer cell states, we will further elucidate their individual contributions to tumor initiation,
progression and metastasis. Using a spatial transcriptomics approach and integrating with single-cell RNA-seq
data, we will map cancer cell states in relation to their microenvironment to screen for putative interactions.
Finally, we will directly test predicted interactions between specific cancer cell states and the immune
compartment using T cell-deficient fish. Throughout our three Aims, we adopt a systems biology workflow that
iteratively cycles through modes of observations, perturbations, and refinement of our model of the functional
role of cancer cell states during tumor progression. Our proposal collectively integrates the complementary
expertise of the White and Yanai labs and sets out to significantly improve our understanding of intratumoral
heterogeneity through the lens of cancer cell states.

## Key facts

- **NIH application ID:** 10448890
- **Project number:** 1U01CA260432-01A1
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Richard Mark White
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $592,969
- **Award type:** 1
- **Project period:** 2022-06-21 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10448890

## Citation

> US National Institutes of Health, RePORTER application 10448890, Identification and characterization of cancer cell states by novel computational and experimental technologies - Resubmission - 1 (1U01CA260432-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10448890. Licensed CC0.

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