# Utilizing Precision Medicine to elucidate the mechanisms underpinning treatment efficacy

> **NIH NIH R00** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2021 · $220,218

## Abstract

Project Summary:
Precision oncology is still in its infancy, and while groups envision personalizing care based on the responses of
individual tumors there are a wide array of potential uses for the data generated by precision medicine pipelines.
Examining data from iterative drug screening performed in collaboration with the Englander Institute of Precision
Medicine this project seeks to examine the molecular mechanisms that underpin the efficacy of two PI3K inhibitor
based combinations. Specifically aim one explore the impact of the PLK phosphorylation of both wildtype and
mutant PI3K to understand the ramifications of this interaction and to identify patient populations who could
benefit from combination treatment with PLK and PI3K inhibitors. The second aim seeks to understand the
impact of the insulin release that occurs with PI3K inhibitor treatments, and to ask if therapeutic responses can
be improved by preventing this feedback. In this manner this project will seek to explain the molecular
mechanisms that underlie observations from our precision medicine pipeline and seek to identify patient
populations that would benefit from these therapeutic approaches.

## Key facts

- **NIH application ID:** 10164733
- **Project number:** 5R00CA230384-04
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Benjamin D. Hopkins
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $220,218
- **Award type:** 5
- **Project period:** 2019-07-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10164733

## Citation

> US National Institutes of Health, RePORTER application 10164733, Utilizing Precision Medicine to elucidate the mechanisms underpinning treatment efficacy (5R00CA230384-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10164733. Licensed CC0.

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