# Integrating transcriptomic, proteomic and pharmacogenomic data to inform individualized therapy in cancers

> **NIH NIH K01** · MICHIGAN STATE UNIVERSITY · 2020 · $169,087

## Abstract

PROJECT SUMMARY
As a computational biologist, my long-term goal is to develop methods and tools to discover new or better
therapeutics for cancers. In the past few years, I have identified drug-repositioning candidates for a number of
primary cancers using Big Data approaches. These candidates have been validated successfully in preclinical
mouse models. To maximize the utility of Big Data, I plan to translate the findings into therapeutics; therefore, I
propose to develop methods to utilize transcriptomic, proteomic and pharmacogenomic data to inform
individualized therapy in cancers. Current preclinical and clinical approaches including the NCI MATCH trial
select therapies primarily based on actionable mutations, yet patients may have no actionable mutations or
multiple actionable mutations that are hard to prioritize, suggesting the need for other different types of
molecular biomarkers. The recent efforts have enabled the large-scale identification of various types of
molecular biomarkers through correlating drug sensitivity with molecular profiles of pre-treatment cancer cell
lines. Computational methods to match these biomarkers to individual patients to inform therapy in the clinic
are thus in high demand. The objective of this award is therefore to develop computational approaches to
identify therapeutics for individual patients by leveraging large-scale biomarkers identified from cancer cell
lines. Through conducing this research, I expect to expand my knowledge in cancer clinical trials, cancer
genomics, cancer biology, and statistics. To achieve the goal, I have gathered seven renowned experts from
different fields related to Big Data Science as mentors/advisors/collaborators: Primary Mentor Dr. Atul Butte in
translational bioinformatics from UCSF, Co-mentor Dr. Samuel So in cancer biology from Stanford University,
Co-mentor Dr. Mark Segal in statistics from UCSF, Advisor Dr. Andrei Goga in cancer biology from UCSF,
Advisor Dr. Laura Esserman in breast cancer trials from UCSF, Collaborator Dr. John Gordan in liver cancer
trials from UCSF and Collaborator Dr. Xin Chen in cancer biology from UCSF. With the support from my world-
class mentors, advisors and collaborators, this award will prepare me to be a leader in developing big data
methods that are broadly impactful.

## Key facts

- **NIH application ID:** 9925076
- **Project number:** 5K01ES028047-04
- **Recipient organization:** MICHIGAN STATE UNIVERSITY
- **Principal Investigator:** Bin Chen
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $169,087
- **Award type:** 5
- **Project period:** 2018-05-01 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9925076

## Citation

> US National Institutes of Health, RePORTER application 9925076, Integrating transcriptomic, proteomic and pharmacogenomic data to inform individualized therapy in cancers (5K01ES028047-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9925076. Licensed CC0.

---

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