Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Impact of Frontline Systemic Treatment Choice on Clinical Outcome in Advanced Leiomyosarcoma: A CanSaRCC Study.

Cancer medicine·2026
Same author

Prostate Cancer Working Group 4 Recap.

European urology focus·2026
Same author

Longitudinal validation of ENLIGHT, an AI predictor of immunotherapy response and resistance, in pan-cancer cohorts.

NPJ precision oncology·2026
Same author

Detecting ctDNA Using Personalized Structural Variants to Forecast Recurrence in Localized Soft Tissue Sarcoma.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

Treatment and outcomes of immunotherapy related colitis and hepatitis- a multi-centre cohort study in the United Kingdom by the National Oncology Trainee Collaborative for Healthcare Research (NOTCH).

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer·2026
Same author

A Consensus Approach to the Incorporation of Total Neoadjuvant Therapy in a Treatment Algorithm for Stage I-III Resectable Rectal Cancer.

Current oncology (Toronto, Ont.)·2026

Related Experiment Video

Updated: May 7, 2026

Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

8.0K

Predicting immunotherapy benefit in leiomyosarcoma through active chromatin cfDNA profiling.

Carlos Diego Holanda Lopes1,2, Hsin-Ta Wu3, Katharine Dilger3

  • 1Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.

NPJ Precision Oncology
|May 5, 2026
PubMed
Summary

Circulating cell-free DNA active chromatin (cfDNAac) profiling shows promise for predicting treatment response in leiomyosarcoma (LMS). This non-invasive liquid biopsy method identifies biomarkers associated with clinical benefit from immunotherapy.

More Related Videos

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
10:27

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

Published on: July 25, 2020

6.5K
Methods for Evaluating the Role of c-Fos and Dusp1 in Oncogene Dependence
10:09

Methods for Evaluating the Role of c-Fos and Dusp1 in Oncogene Dependence

Published on: January 7, 2019

7.7K

Related Experiment Videos

Last Updated: May 7, 2026

Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

8.0K
Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
10:27

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

Published on: July 25, 2020

6.5K
Methods for Evaluating the Role of c-Fos and Dusp1 in Oncogene Dependence
10:09

Methods for Evaluating the Role of c-Fos and Dusp1 in Oncogene Dependence

Published on: January 7, 2019

7.7K

Area of Science:

  • Oncology
  • Genomics
  • Immunotherapy

Background:

  • Leiomyosarcoma (LMS) is an aggressive soft-tissue sarcoma with unpredictable responses to cancer immunotherapies.
  • Epigenomic dysregulation is a hallmark of LMS, influencing treatment outcomes.
  • Checkpoint inhibitors (CPIs) offer potential therapeutic benefits, but patient selection remains a challenge.

Purpose of the Study:

  • To evaluate a novel circulating cell-free DNA active chromatin (cfDNAac) liquid biopsy platform.
  • To identify baseline biomarkers predictive of clinical benefit rate (CBR) in LMS patients treated with CPIs.
  • To assess the association of cfDNAac profiles with progression-free survival (PFS).

Main Methods:

  • Analysis of cfDNAac profiles from 30 LMS patients receiving durvalumab plus olaparib or cediranib.
  • Utilized recursive feature elimination to identify discriminative cfDNAac signatures from 1,570 molecular features.
  • Correlated cfDNAac signatures and tumor fraction with clinical benefit and survival outcomes.

Main Results:

  • Patients achieving CBR showed enrichment in cell death, interferon-gamma signaling, and immune activation pathways.
  • cfDNAac signatures related to B-cell and T-cell activation, and extracellular matrix organization correlated with improved PFS.
  • A baseline tumor fraction >5% was negatively associated with CBR and PFS.

Conclusions:

  • cfDNAac profiling is a promising non-invasive strategy for predicting clinical benefit from CPI-based therapy in LMS.
  • Distinct copy number variation profiles identified in cfDNAac can characterize patients with CBR and predict PFS.
  • Prospective validation of cfDNAac profiling in independent cohorts is needed to confirm its clinical utility.