PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 5, 2026Cancer Research0 citations

Abstract 5270: Cell-free RNA changes precede symptomatic radiation pneumonitis in cancer patients receiving thoracic radiotherapy

View Full Paper
IJIsabel JabaraNKNoah KastelowitzMNMonica Nesselbush

Key Points

  • The aim is to identify early biomarkers for radiation pneumonitis using plasma cell-free RNA in lung cancer patients.
  • Analyzed plasma samples from 56 lung cancer patients using RARE-Seq.
  • Compared cfRNA profiles before and after radiation therapy in patients with and without radiation pneumonitis.
  • Evaluated predictive accuracy of cfRNA signatures against traditional dosimetric predictors.
  • Identified enrichment of lung-specific cfRNA transcripts in patients with symptomatic RP.
  • Detected cfRNA signatures indicating early molecular changes before RP symptoms appeared.
  • Achieved AUC of 0.85 for distinguishing symptomatic RP and 0.77 for pre-symptomatic RP, outperforming V20 and Dmean.

Abstract

Abstract Cancer therapies are limited by normal tissue toxicity. Radiation pneumonitis (RP) is a dose-limiting toxicity that commonly occurs in cancer patients who receive thoracic radiation therapy. RP is caused by inflammation and increased vascular permeability in the lungs and can lead to severe pulmonary symptoms and sometimes death. Currently, the strongest predictors of RP are radiation dose metrics, such as the volume of normal lung receiving ≥20 Gy (V20) or mean lung dose (Dmean); however, these have only modest predictive accuracy. Therefore, it is currently not possible to accurately identify patients who might benefit from RP-directed therapy prior to developing symptoms and there remains an unmet need for sensitive, non-invasive biomarkers to identify patients at risk for RP before symptoms develop. Plasma cell-free RNA (cfRNA) is a promising analyte that enables non-invasive profiling of gene expression in diverse tissues. Our group recently developed an ultrasensitive method called RARE-Seq to detect low abundance transcriptional signatures in cfRNA (Nesselbush et al. Nature 2025). In this study, we applied RARE-Seq to analyze cfRNA from 160 plasma samples from 56 lung cancer patients, 34 of whom developed RP, collected before, during, and after radiation therapy. We observed enrichment of lung- and airway-specific gene transcripts in cfRNA from patients with symptomatic RP compared to those who received radiation but did not develop RP. Additionally, pre-symptomatic samples from patients who later developed RP were enriched for cfRNA signatures of lung pneumocytes, suggesting early molecular changes preceding clinical symptoms. Lastly, we identified cfRNA signatures that distinguished symptomatic RP from non-RP samples with an AUC of 0.85 and pre-symptomatic RP from non-RP samples with an AUC of 0.77, significantly outperforming the classic dosimetric predictors lung V20 and Dmean (V20 P = 4.9e-5, Dmean P = 7.4e-5, paired DeLong test). Collectively, these findings demonstrate proof of concept that cfRNA profiling can predict radiation-induced toxicities before onset of symptoms, establishing it as a potentially transformative biomarker for improving monitoring and personalized management of patients treated with radiation therapy. Citation Format: Isabel Jabara, Noah Kastelowitz, Monica Nesselbush, Nick Phillips, Michael S. Binkley, Rene F. Bonilla, Kevin Liu, Alice Jiang, Nataliya Kovalchuk, Ash A. Alizadeh, Maximilian Diehn. Cell-free RNA changes precede symptomatic radiation pneumonitis in cancer patients receiving thoracic radiotherapy abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5270.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Jabara et al. (2026) studied this question.

synapsesocial.com/papers/69d1fd62a79560c99a0a36fbhttps://doi.org/10.1158/1538-7445.am2026-5270
Ask AI
Helpful
Bookmark
Share
View Full Paper