# Development and Pre-Clinical Validation of Quantitative Imaging of Cell State Kinetics (QuICK) for Functional Precision Oncology

> **NIH NIH R01** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2024 · $372,336

## Abstract

PROJECT ABSTRACT
Functional precision oncology is the practice of assessing the phenotype of biopsied tumor cells upon
perturbation, e.g. treatment with candidate therapies, to yield actionable information fast enough to influence
clinical decision making. For a functional precision approach to provide actionable information on tumors’
response to candidate therapeutics, it must: retain specimen heterogeneity, monitor all biologically important
phenotypes, make longitudinal observations, and - since clonal expansion of individual cells is sufficient to drive
tumor progression or resistance – have single cell resolution. One example where functional precision oncology
could be valuable is the choice of therapy for the approximately 50% of melanoma patients harboring the
BRAFV600E mutation. Such patients have two options – immune checkpoint inhibition (ICI) or targeted therapy
(TT). Either strategy is capable of curing patients in many cases but neither option works for all patients. Poor
response to either is caused by the pre-existence and/or emergence of phenotypes resistant to each therapeutic
option. A clinical test that could predict, on a personalized level, which patients are likely to respond or acquire
resistance to either of these therapies is among the most pressing clinical needs in melanoma care. On a
population level, more patients present with durable response to ICI than to TT, and so ICI is the default standard
of care and a predictive test must achieve high accuracy to influence clinical decision making. We have pioneered
the use of quantitative phase imaging (QPI) for rapid and label-free phenotype assessment of melanoma cells,
including monitoring for therapeutic resistance. Our area under the receiver operator characteristic curve (AUC)
for predicting resistance under 48 h is 0.84-0.90 – which is promising, but insufficient and needs validation. We
propose to construct a new technological and analytical platform with two modifications. First, we will augment
QPI with a second imaging module to measure light scatter via a new method we have developed based on
darkfield microscopy. Light scatter is traditionally measured using flow cytometry and is predictive of relevant
cell phenotypes in a myriad of cancer types, including, our preliminary data show, therapeutic resistance in
melanoma. Second, we will establish an analytical pipeline for assessing cell state dynamics which we anticipate
will yield a classifier that is more accurate across heterogeneous biopsies as compared to current approaches.
In this proposal, we describe a series of engineering and analytical steps, coupled with technical milestones and
target quantitative goals benchmarked against existing approaches for developing an approach we call
Quantitative Imaging of Cell state Kinetics (QuICK), as well as a proof of principle study using clinical biopsies.
If successful, we will have built a prototype platform with high potential to improve the care of melanoma p...

## Key facts

- **NIH application ID:** 10928191
- **Project number:** 5R01CA276653-02
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Robert Laird Judson-Torres
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $372,336
- **Award type:** 5
- **Project period:** 2023-09-12 → 2028-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10928191

## Citation

> US National Institutes of Health, RePORTER application 10928191, Development and Pre-Clinical Validation of Quantitative Imaging of Cell State Kinetics (QuICK) for Functional Precision Oncology (5R01CA276653-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10928191. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*