# Development of a multi-omic clinical decision platform to guide personalized therapy

> **NIH NIH R01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2022 · $525,071

## Abstract

PROJECT SUMMARY
The era of “big data” has opened the door for genomic and systems biology approaches to be applied to
current challenges in life sciences and precision medicine. One critical challenge in these areas is how to
prioritize research findings to validate and identify actionable insights that can translate into better outcomes
for patients. In this regard, we have assembled a multidisciplinary group of scientists and physicians from
academia and industry with a focus on creating discovery pipelines that combine high-throughput profiling
technologies with advanced statistical and machine learning approaches to generate predictive tools that
enable us to move rapidly from big data to better diagnoses and treatment. In this regard, we propose to apply
these approaches to develop a computational clinical decision tool that will improve disease forecasting and
treatment plans for Multiple Myeloma (MM), an incurable cancer that originates in bone marrow plasma cells
and affects more than 30,000 patients a year. Though there have been some advances in the number and
diversity of available therapeutic options for these patients, relapse remains inevitable, and MM ultimately
remains a terminal diagnosis. The clinical assay and computational pipeline developed in this project will
combine a targeted sequencing panel specific to myeloma patients and clonality estimates with RNA-
sequencing and drug repurposing to expand therapeutic options for MM patients. We will develop this unique
tool with the following specific aims: (1) Develop an integrated genomic clinical decision tool to guide precision
treatment of MM and validate therapy recommendations using PDX profiling, and (2) Validate MM precision
medicine platform in a prospective clinical trial and generate clone-specific treatment recommendations. To
achieve these objectives, we will integrate a Cancer Genetic, Inc.'s FOCUS::Myeloma panel, a targeted panel
designed to specifically interrogateall the genes and copy number alterations commonly altered in myeloma,
and into a computational drug selection pipeline that utilizes RNA-sequencing data
and drug repurposing algorithms to generate therapeutic recommendations matched to a patient's unique
disease profile. These recommendations will be validated in mouse avatars of myeloma to confirm and refine
drug predictions. We will implement our assay in a prospective clinical trial of 100 patients to determine if the
treatment decisions generated by our pipeline achieves an improvement in standard-of-care. Finally, we will
perform clonal modeling on relapsed patients to retrospectively evaluate clone-specific treatment responses.
Completion of these studies will result in a clinic-ready assay and computational tool that will guide MM
precision treatment decisions and inform new therapeutic strategies based on a patient's unique cancer profile.
genomic
clonal
modeling

## Key facts

- **NIH application ID:** 10337223
- **Project number:** 5R01CA244899-03
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Samir Parekh
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $525,071
- **Award type:** 5
- **Project period:** 2020-02-06 → 2025-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10337223

## Citation

> US National Institutes of Health, RePORTER application 10337223, Development of a multi-omic clinical decision platform to guide personalized therapy (5R01CA244899-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10337223. Licensed CC0.

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