# Integrated blood and radiomic subtyping to guide immunotherapy treatment selection and early response assessment in metastatic non-small cell lung cancer

> **NIH NIH R01** · UNIVERSITY OF TX MD ANDERSON CAN CTR · 2024 · $636,160

## Abstract

ABSTRACT
Immune checkpoint inhibitors (ICIs) have improved outcomes in metastatic non-small cell lung cancer (NSCLC),
and providers may now choose between multiple first-line ICI-based regimens including ICI monotherapy and
ICI with chemotherapy. However, this increase in options has complicated clinical management, with few
biomarkers to guide upfront ICI treatment selection, and incomplete metrics for early on-treatment assessment
of response to ICI therapy. Hence, there is an urgent need for novel analytics tools to optimize and personalize
immunotherapy treatment strategies. While prior biomarker efforts have focused largely on tissue-based
molecular profiling, these have demonstrated limited predictive power and are difficult to implement due to
practical limitations in acquiring pre- and on-treatment tissue. In contrast, imaging and blood-based assays offer
a unique and non-invasive mechanism by which the biology of the tumor and the changes on treatment can be
studied and modeled. Thus, we propose an integrated radiomic-blood analysis to develop predictors of pre- and
on-treatment response to guide the clinical management of NSCLC. Our primary goal is to develop radiomic-
blood signatures for precision immunotherapy in advanced NSCLC by leveraging our expertise in data science,
thoracic oncology, cancer genomics, computational oncology, clinical assay development, and established
research collaborations. Our preliminary data demonstrates our success in utilizing multi-parametric profiling of
circulating tumor DNA to identify molecular phenotypes associated with ICI outcome and disease recurrence,
and in developing novel radiomic subtyping techniques with superior outcome prediction and demonstrated
association with underlying lung cancer biology. Hence, we hypothesize that coupling radiomic and blood-based
metrics can non-invasively inform therapeutic decision-making in NSCLC management while advancing our
understanding of NSCLC biology. To advance this hypothesis, we have assembled a unique set of cohorts of
metastatic NSCLC patients treated with ICI regimens with high-quality radiographic scans, blood samples, and
molecular and clinical data: our in-house lung cancer database (GEMINI, n=5000); a validation dataset from our
collaboration with the Massachusetts General Hospital (MGH) Cancer Center (MGH, n=600); the multicenter
collaborative Stand Up 2 Cancer/Mark Foundation cohort (SU2C, n=400), and a prospective phase III ICI trial
(LONESTAR, n=300). Our proposal builds on these unique cohorts and our promising preliminary data to
construct predictive models to guide up-front ICI therapy selection and improve on-treatment response
assessment, while complementary investigations will uncover the biology underlying these clinical predictors. A
major strength of our proposal is our interdisciplinary team’s expertise in developing, validating, and translating
these innovative predictive models toward highly relevant clinical questions. The dev...

## Key facts

- **NIH application ID:** 10862751
- **Project number:** 5R01CA276178-02
- **Recipient organization:** UNIVERSITY OF TX MD ANDERSON CAN CTR
- **Principal Investigator:** Natalie Vokes
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $636,160
- **Award type:** 5
- **Project period:** 2023-07-01 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10862751

## Citation

> US National Institutes of Health, RePORTER application 10862751, Integrated blood and radiomic subtyping to guide immunotherapy treatment selection and early response assessment in metastatic non-small cell lung cancer (5R01CA276178-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10862751. Licensed CC0.

---

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