# Systems approaches to understanding the relationships between genotype, signaling, and therapeutic efficacy

> **NIH NIH U01** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2020 · $770,101

## Abstract

Project Summary/Abstract
The promise of precision medicine is that a physician can tailor a therapeutic regimen to suit
each individual patient. In the case of cancer, this means a personalized therapeutic strategy
based on the molecular features of an individual's cancer. But while successes in precision
medicine have garnered significant attention in recent years, precision medicine has not made
an impact for the vast majority of cancer patients. Our overarching goal is to use proteomics and
systems biology to understand the relationships between cancer genotype and therapeutic
response, with the long-term goal of expanding the prospects of precision medicine. Our study
focuses primary on cancers expressing mutant forms of K-Ras, the most commonly mutated
oncoprotein in cancer and one of the best biomarkers for the failure of a cancer to respond to
therapy. Using a variety of experimental and computational approaches, this project will address
three key questions related to K-Ras and the promise of precision medicine. First, we will exploit
a relatively rare circumstance in which colorectal cancers expressing a specific mutant form of
K-Ras are uniquely sensitive to inhibition of the MEK kinase. We will use mass spectrometry
and computational modeling to determine why cancers expressing K-RasG12D and K-RasA146T
are differentially sensitive to inhibition of MEK. Next, we will address the limitation of univariate
genetic prediction of therapeutic efficacy by determining how genetic and epigenetic factors
interact to establish network signaling state. We will use mass cytometry and computational
modeling to explore how signaling downstream of mutant K-Ras is affected by cellular lineage
and by secondary mutations in oncogenes and tumor suppressor genes. Finally, we will move
beyond genotype as a predictor of therapeutic efficacy by developing an algorithm to predict
sensitivity to kinase inhibition based on phospho-proteomic measurements. We will validate the
computational approach via preclinical therapeutics studies in patient-derived xenografts.
Altogether these studies will utilize state-of-the-art experimental and computational approaches
to make personalized medicine a realistic goal for patients suffering from K-Ras mutant cancer.

## Key facts

- **NIH application ID:** 9904544
- **Project number:** 5U01CA215798-04
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Wilhelm Haas
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $770,101
- **Award type:** 5
- **Project period:** 2017-04-05 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9904544

## Citation

> US National Institutes of Health, RePORTER application 9904544, Systems approaches to understanding the relationships between genotype, signaling, and therapeutic efficacy (5U01CA215798-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9904544. Licensed CC0.

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