# Molecular imaging meets big data: a systems-level approach for application-driven, unbiased selection of metabolic imaging substrates for visualization of cancer dormancy

> **NIH NIH F30** · UNIVERSITY OF PENNSYLVANIA · 2020 · $32,585

## Abstract

The majority of cancer-related deaths result from recurrent or metastatic disease. The ability to detect tumor
regions in a state of growth dormancy may aid in prediction of cell populations capable of these phenomenona,
and thus help to guide treatment selection to effect complete response to therapy. The current inability to detect
cancer dormancy represents an important deficiency in current conventional cancer imaging techniques, which
traditionally rely on signatures of rapidly dividing and replicating cells, including lesion size and vascularity, to
isolate regions of tumor viability. Hepatocellular Carcinoma (HCC) is the fastest growing cause of cancer death
in the United States and provides a prime example of this deficiency. The most commonly used therapy to treat
patients with HCC is transarterial embolization or transarterial chemoembolization (TA(C)E), a procedure
performed by interventional radiologists that targets tumors by obstructing arterial blood flow into a tumor region
with (TACE) or without the administration of intra-arterial chemotherapy (TAE). While traditional markers of tumor
viability are absent after this intervention, recurrence is common. Prior research has demonstrated that
recurrence in HCC following TA(C)E is made possible by the persistence of dormant cell populations in a nutrient-
deprived environment. The ability to detect dormant cell populations in cancer following treatment is an unmet
need in the management of HCC. The long-term goal is to prevent recurrence following TAE in HCC by
supplemental pharmacologic or surgical treatment when dormant populations persist. The overall objective of
this proposal is to develop a metabolic molecular imaging platform using hyperpolarized magnetic resonance
imaging that can detect dormant populations in HCC on the basis of changes in metabolic pathway flux. The
rationale is based on the fact that cells in a state of dormancy have alterations in metabolic gene expression and
protein activity that result in changes in flux through metabolic pathways that can be detected non-invasively with
appropriately selected imaging probes. The central hypothesis is that dormant cancer cells will have decreased
anabolic metabolism and increased catabolic metabolism, which can be detected with hyperpolarized isotopes
of metabolites in related pathways. This objective and central hypothesis will be pursued by the following specific
aims: 1) identify differences in metabolic pathway flux in vitro for HCC cells in dormant and proliferative states to
guide selection of hyperpolarized imaging probes, 2) validate the capacity of hyperpolarized probes to distinguish
dormant and proliferative phenotypes of HCC in vitro, and 3) validate the capacity of hyperpolarized probes to
detect cancer dormancy in a HCC rat model following TAE. The successful achievement of the proposed aims
will have an immediate impact on clinical care by addressing a clinical deficiency in the care of patients with
HCC....

## Key facts

- **NIH application ID:** 9955216
- **Project number:** 5F30CA232388-03
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Nicholas Rainer Perkons
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $32,585
- **Award type:** 5
- **Project period:** 2018-07-03 → 2021-07-02

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9955216

## Citation

> US National Institutes of Health, RePORTER application 9955216, Molecular imaging meets big data: a systems-level approach for application-driven, unbiased selection of metabolic imaging substrates for visualization of cancer dormancy (5F30CA232388-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/9955216. Licensed CC0.

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