# Molecularly-based outcome and toxicity prediction after radiotherapy for lung cancer

> **NIH NIH R01** · STANFORD UNIVERSITY · 2021 · $632,011

## Abstract

PROJECT SUMMARY/ABSTRACT
PIs: Maximilian Diehn, M.D./Ph.D. & Ash Alizadeh, M.D./Ph.D.
Non-small cell lung cancer (NSCLC) is the most common cancer in the U.S. and the number one
cause of cancer-related deaths. Radiation therapy (RT) plays a critical role in the treatment of
NSCLC, both in the curative and palliative settings. While advances in tumor imaging and
radiation delivery techniques over the past several decades have significantly improved RT,
advances in genomic and molecular understanding of tumors have largely failed to impact
management of patients treated with RT. Therefore, development of “precision radiation
oncology” approaches, defined as the use of molecular biomarkers to personalize RT, remains a
major unmet need. Additionally, predicting which patients will develop RT-induced toxicity remains
a challenge and prevents early intervention prior to onset of symptoms.
Our long-term goal is to develop novel, molecularly-based precision radiation oncology
approaches for NSCLC patients treated with RT. Our central hypothesis is that novel biomarkers
of recurrence risk, such as analysis of ctDNA and genetic profiling, can be used for early prediction
of treatment outcomes while a patient is still on therapy. We will test our hypothesis via three
specific aims: (1) To establish the ability of mid-treatment ctDNA changes to predict ultimate
outcomes in locally advanced NSCLC patients treated with RT, (2) To develop novel,
personalized risk models that integrate molecular and clinical factors and can accurately predict
the risk of recurrence, and (3) To test the hypothesis that a novel liquid biopsy approach we have
recently developed can predict which patients will develop symptomatic radiation pneumonitis.
If successful, our project will lead to novel ways to personalize therapy for locally advanced
NSCLC patients treated with RT. Our innovative approach, in which we will employ blood-based
methods for tumor genotyping, disease monitoring, and toxicity prediction that were developed
by our group, will lay the foundation for studies aimed at reducing risk of treatment failure and
toxicity in NSCLC patients treated with RT. We envision that our approach will enable future trial
designs that implement molecularly-driven precision radiation oncology and will facilitate
treatment escalation for patients at highest risk of recurrence and de-escalation for those at lowest
risk. Additionally, our work will serve as proof-of-principle for an approach that could also be
applied to other areas of radiation oncology.

## Key facts

- **NIH application ID:** 10224926
- **Project number:** 5R01CA254179-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Ash Arash Alizadeh
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $632,011
- **Award type:** 5
- **Project period:** 2020-08-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10224926

## Citation

> US National Institutes of Health, RePORTER application 10224926, Molecularly-based outcome and toxicity prediction after radiotherapy for lung cancer (5R01CA254179-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10224926. Licensed CC0.

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