# High-Throughput Functional Genomics to Guide Precision Oncology in Gastrointestinal Tumors

> **NIH NIH K22** · UNIVERSITY OF TX MD ANDERSON CAN CTR · 2020 · $192,240

## Abstract

Abstract:
 This is an application for a K22 award for Dr. John Paul Shen, a medical oncologist currently at the
University of California, San Diego. Dr. Shen is establishing himself as a young translational investigator in the
field of cancer genomics. This K22 award will provide Dr. Shen with the resources and training to accomplish
the following objectives; (1) implement advanced computational methods on genome scale datasets (2) become
an expert in functional genomics, (3) achieve proficiency experimenting in mouse models of cancer, (4)
successfully manage an independent laboratory. To achieve these objectives, after accepting a faculty position
Dr. Shen will assemble a diverse advisory committee including experts in bioinformatics, experimental cancer
biology, and clinical oncology.
 It was proposed by many that the ability to sequence a tumor genome, now made possible by next-
generation sequencing, would bring about a new era of precision oncology in which chemotherapy choices would
be individualized to match a single tumor and patient. However, the use genomic information in clinical practice
remains limited by the fact that currently very few mutations are associated with response to a specific drug. This
is particularly true in Gastrointestinal (GI) malignancies, where there are few targeted therapy options and few
effective biomarkers help guide chemotherapy selection. Dr. Shen seeks to address this pressing need by
employing high-throughput functional genomic methods to identify tumor specific vulnerabilities that could be
exploited therapeutically.
 Recognizing that there will be great heterogeneity from one tumor to the next, even within the same
cancer type, the functional genomic data created here will be combined with systems biology methods to identify
how the vulnerabilities of each unique tumor can be predicted with information readily available to a clinical
oncologist. Using network-based machine learning methods applied to chemo-genomic viability data in
molecularly characterize cell lines it is expected that predictive biomarkers will be identified for both novel
targeted agents and currently used chemotherapy drugs. This will allow oncologists to design individualized
chemotherapy regimens for each patient.

## Key facts

- **NIH application ID:** 9842653
- **Project number:** 5K22CA234406-02
- **Recipient organization:** UNIVERSITY OF TX MD ANDERSON CAN CTR
- **Principal Investigator:** John Paul Ying Ching Shen
- **Activity code:** K22 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $192,240
- **Award type:** 5
- **Project period:** 2019-01-01 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9842653

## Citation

> US National Institutes of Health, RePORTER application 9842653, High-Throughput Functional Genomics to Guide Precision Oncology in Gastrointestinal Tumors (5K22CA234406-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9842653. Licensed CC0.

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