Medicine and Health

Imaging and AI based chromatin biomarkers for diagnosis and therapy evaluation from liquid biopsies

K. Challa, D. Paysan, et al.

This groundbreaking study explores chromatin organization in PBMCs as game-changing biomarkers for cancer diagnosis and treatment evaluation. Authored by Kiran Challa, Daniel Paysan, Dominic Leiser, Nadia Sauder, Damien C. Weber, and G. V. Shivashankar, the research showcases a highly accurate machine learning pipeline capable of distinguishing healthy individuals from tumor patients and evaluating treatment effects. Discover the future of cancer diagnostics!

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Index

Abstract

This study investigates the use of chromatin organization in peripheral blood mononuclear cells (PBMCs) from liquid biopsies as biomarkers for cancer diagnosis and treatment evaluation. The researchers hypothesized that chromatin organization, integrating secretome signals with gene expression, provides efficient biomarkers. Using a combined chromatin imaging and machine learning pipeline, they classified healthy and pan-tumor patients, and further distinguished between three tumor groups (meningioma, glioma, head and neck) with up to 89% accuracy. The pipeline also monitored treatment effects of proton therapy, demonstrating its potential for cancer diagnostics and therapy evaluation.

Publisher

npj Precision Oncology

Published On

Dec 14, 2023

Authors

Kiran Challa, Daniel Paysan, Dominic Leiser, Nadia Sauder, Damien C. Weber, G. V. Shivashankar

DOI

https://doi.org/10.1038/s41698-023-00484-8

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