What question did this study set out to answer?

To evaluate the potential of cell-free DNA analysis for classifying mature lymphoid neoplasms non-invasively.

December 8, 2025BloodOpen Access

Non-invasive classification & molecular subtyping of mature lymphoid neoplasms by cell-free DNA profiling

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Authors

JMJurik Mutter BSBrian J. Sworder BTBenoît Tessoulin

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Overview

This research demonstrates a liquid biopsy test that accurately classifies mature lymphoid neoplasms using a machine learning model and gene expression profiling.

Key Points

To evaluate the potential of cell-free DNA analysis for classifying mature lymphoid neoplasms non-invasively.
Applied EPIC-Seq technology to analyze plasma cell-free DNA signals from blood samples.
Developed a targeted capture panel based on previous gene expression profiling studies.
Trained a machine learning model to predict tumor burden from cfDNA mutational data.
Achieved a classification accuracy of 94% across diverse lymphoid neoplasm types.
Demonstrated significant differentiation of molecular HL subtypes with an AUC greater than 0.8.
Validated the model's predictions against known mutation-based tumor burden measurements.

Cite This Study

Mutter et al. (2025) studied this question.

synapsesocial.com/papers/69362f5a4fa91c937236daf7 https://doi.org/10.1182/blood-2025-5330

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December 8, 2025BloodOpen Access

Non-invasive classification & molecular subtyping of mature lymphoid neoplasms by cell-free DNA profiling

View Full Paper

Ask AI

Bookmark

Authors

JMJurik Mutter BSBrian J. Sworder BTBenoît Tessoulin

Discussion

Member takes

Overview

This research demonstrates a liquid biopsy test that accurately classifies mature lymphoid neoplasms using a machine learning model and gene expression profiling.

Key Points

To evaluate the potential of cell-free DNA analysis for classifying mature lymphoid neoplasms non-invasively.
Applied EPIC-Seq technology to analyze plasma cell-free DNA signals from blood samples.
Developed a targeted capture panel based on previous gene expression profiling studies.
Trained a machine learning model to predict tumor burden from cfDNA mutational data.
Achieved a classification accuracy of 94% across diverse lymphoid neoplasm types.
Demonstrated significant differentiation of molecular HL subtypes with an AUC greater than 0.8.
Validated the model's predictions against known mutation-based tumor burden measurements.

Cite This Study

Mutter et al. (2025) studied this question.

synapsesocial.com/papers/69362f5a4fa91c937236daf7 https://doi.org/10.1182/blood-2025-5330

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