# Interpretable deep learning models for translational medicine Renewal

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $353,140

## Abstract

Abstract
Conventional precision oncology concentrates on applying molecularly targeted drugs based on
genomic markers from a tumor. Such a genome-informed approach has limitations, mainly
because it covers around 15% of all cancer patients. While chemotherapies remain the
backbone of oncology, their application is not personalized. In this study, we proposed to
develop an advanced artificial intelligence (AI) framework for predicting cancer cell sensitivity to
chemotherapies. We will apply the framework to solve a real-world clinical problem: guiding the
precision application of oxaliplatin in treating colon cancer adjuvant therapy.
Our framework consists of two phases: A representation-learning phase, which involves
developing and applying advanced AI models that can infer and represent the state of cellular
signaling systems of cancer cells by mining diverse omics data of cancers. A cell-state-
oriented drug sensitivity prediction phase, which involves using advanced classification
models to utilize the inferred cancer cell states to predict the drug response of different cancer
cells. Importantly, we will address an unmet clinical need in treating colon cancer patients as a
real-world use case.
With estimated 1.9 million incidences, colorectal cancer (CRC) is the 3rd most common cancer
and the 2nd leading cause of cancer death. Patients with resected high-risk stage II/III colon
cancer (CC) receive adjuvant chemotherapy to prevent cancer recurrence. A regimen consisting
of 5-fluorouracil, leucovorin, and oxaliplatin (FOLFOX) is the standard of care for CC adjuvant
therapy. It is estimated that including oxaliplatin in the regimen benefits 5% - 6% of patients but
subjects ~90% of patients to life-impairing neurotoxicity. A significant effort in oncology is to
identify the patients who would not benefit from oxaliplatin and save them from unnecessary
side effects (de-escalation).
In this study, we will collect large-scale omics data from colon cancer patients. We propose
advanced causal discovery and deep learning to infer the state of signaling systems of cancer
cells and further develop advanced individualized treatment effect prediction models to guide
oxaliplatin treatment.

## Key facts

- **NIH application ID:** 10902947
- **Project number:** 2R01LM012011-09
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Tanner J. Freeman
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $353,140
- **Award type:** 2
- **Project period:** 2015-04-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10902947

## Citation

> US National Institutes of Health, RePORTER application 10902947, Interpretable deep learning models for translational medicine Renewal (2R01LM012011-09). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10902947. Licensed CC0.

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