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Tumor Progression

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
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Predicting Disease Progression in Inoperable Localized NSCLC Patients Using ctDNA Machine Learning Model.

Yuqi Wu¹, Canjun Li¹, Yin Yang¹

¹Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Cancer Medicine

|October 24, 2024

Summary

A novel cell-free DNA (cfDNA) neomer profiling assay effectively predicts disease progression in non-small cell lung cancer (NSCLC) patients. This non-invasive method demonstrates superior predictive power compared to ctDNA mutation profiling, aiding treatment decisions.

Keywords:

MRD detection NSCLC machine learning neomer non‐invasive

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Published on: August 14, 2019

Area of Science:

Oncology
Genomics
Biomarkers

Background:

Accurate prediction of disease progression in post-treatment non-small cell lung cancer (NSCLC) patients is critical.
Current circulating tumor DNA (ctDNA) mutation profiling methods have limited sensitivity for risk prediction.
A non-invasive liquid biopsy assay using cfDNA neomer profiling is proposed for predicting progression in inoperable NSCLC.

Purpose of the Study:

To develop and evaluate a cfDNA neomer profiling assay for predicting disease progression in patients with inoperable NSCLC.
To compare the predictive performance of cfDNA neomer profiling against ctDNA mutation-based methods.
To assess the clinical utility of this assay in guiding treatment decisions.

Main Methods:

Collected 97 plasma samples from 44 inoperable NSCLC patients at multiple time points during and post-treatment.
Utilized cfDNA neomer profiling based on target sequencing data.
Developed survival support vector machine models and performed leave-one-out cross-validation for performance evaluation.

Main Results:

The cfDNA neomer profiling assay demonstrated significant predictive power for disease progression, with hazard ratios (HR) ranging from 3.62 to 4.00.
Neomer profiling showed higher HRs compared to ctDNA mutation-based results (HR 2.08 at TP1, HR 1.49 at TP3).
At TP1, the neomer model achieved 40% sensitivity at 92.9% specificity, outperforming mutation-based methods. Longitudinal analysis combining both methods reached 88.9% sensitivity at 80% specificity.

Conclusions:

A cfDNA neomer profiling assay was successfully developed for predicting disease progression in inoperable NSCLC patients.
The assay exhibits enhanced predictive power compared to ctDNA mutation-based methods, particularly during and post-treatment.
This non-invasive approach holds significant clinical potential for optimizing treatment strategies in NSCLC management.