# Integrating imaging and biopsy-derived molecular markers for the pre-surgical detection of indolent and aggressive early stage lung adenocarcinoma

> **NIH NIH R01** · BOSTON UNIVERSITY MEDICAL CAMPUS · 2024 · $607,822

## Abstract

ABSTRACT
Lung adenocarcinoma (LUAD) is the most common lung cancer subtype diagnosed in the US; characterized by
a broad spectrum of biological behaviors and clinical trajectories. Yet, LUAD is managed uniformly based on
clinical stage, with the potential for under- and over-treatment of aggressive and indolent lesions, respectively.
This contributes both to suboptimal lung cancer outcomes and unnecessary morbidity, mortality and healthcare
costs. While histologic grade of resected tumors correlates with patient outcome, it is only available after surgical
treatment and cannot be used to inform pre-surgery management or surgical planning. We have developed and
validated CANARY, a radiomic biomarker that predicts LUAD aggressiveness. We have further developed two
gene expression biomarkers from resected FFPE Stage I LUAD for predicting indolent or aggressive tumor
histology. These gene expression biomarkers are insensitive to intratumoral heterogeneity, suggesting that they
might retain good performance when measured in limited tissue available from small, presurgical biopsies. This
is potentially transformative as histologic assessment of these small biopsies is frequently insufficient for
predicting tumor aggressiveness. Our goal is to refine and validate these radiomic and gene expression
biomarkers and then integrate them into a single model for detecting indolent and aggressive Stage I LUAD,
which is supported by our preliminary data. To accomplish these goals, we will prospectively enroll a cohort of
patients undergoing transthoracic or transbronchial biopsy for suspected lung cancer and collect additional
specimens for research. In the subset of tumors who are later resected for Stage I LUAD, we will perform a
central pathologic assessment of tumor grade. Predicting tumor histologic grade at resection will be the primary
endpoint for assessing the performance of the integrated presurgical prediction model. Refinement of the
radiomic biomarker will involve testing whether the addition of features extracted from the peri-nodular lung using
deep learning can improve the prediction of the Stage I LUAD histologic grade. Refinement of the gene
expression biomarker will involve determining their performance in biopsy tumor tissue relative to resected tumor
tissue and optimizing the biomarkers for assessment in biopsies. Finally, we will develop and assess an
integrated model combining both radiomics and gene expression. As a secondary endpoint, we will compare
the association between recurrence free survival and predicted tumor grade vs. actual tumor grade at resection.
An improved ability to predict tumor aggressiveness prior to treatment has the potential to transform the
management of Stage I LUAD as it could allow clinicians and patients to confidently choose precisely tailored
treatment. The team from Boston University, Boston Medical Center, Vanderbilt University Medical Center, and
Lahey Hospital & Medical Center has the diverse expertise i...

## Key facts

- **NIH application ID:** 10885188
- **Project number:** 5R01CA275015-02
- **Recipient organization:** BOSTON UNIVERSITY MEDICAL CAMPUS
- **Principal Investigator:** Marc Elliott Lenburg
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $607,822
- **Award type:** 5
- **Project period:** 2023-07-10 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10885188

## Citation

> US National Institutes of Health, RePORTER application 10885188, Integrating imaging and biopsy-derived molecular markers for the pre-surgical detection of indolent and aggressive early stage lung adenocarcinoma (5R01CA275015-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10885188. Licensed CC0.

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