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Updated: Dec 26, 2025
Detection and Monitoring of Tumor Associated Circulating DNA in Patient Biofluids
Published on: June 8, 2019
A Comparative Analysis of Tumors and Plasma Circulating Tumor DNA in 145 Advanced Cancer Patients Annotated by 3 Core
Kristian Larson1, Radhamani Kannaiyan2, Ritu Pandey3
1University of Arizona College of Medicine, 1501 N Campbell Ave, Tucson, AZ 85724, USA.
Abstract:
Matched-targeted and immune checkpoint therapies have improved survival in cancer patients, but tumor heterogeneity contributes to drug resistance. Our study categorized gene mutations from next generation sequencing (NGS) into three core processes. This annotation helps decipher complex biologic interactions to guide therapy. We collected NGS data on 145 patients who have failed standard therapy (2016 to 2018). One hundred and forty two patients had data for tissue (Caris MI/X) and plasma cell-free circulating tumor DNA (Guardant360) platforms. The mutated genes were categorized into cell fate (CF), cell survival (CS), and genome maintenance (GM). Comparative analysis was performed for concordance and discordance, unclassified mutations, trends in TP53 alterations, and PD-L1 expression. Two gene mutation maps were generated to compare each NGS platform. Mutated genes predominantly matched to CS with concordance between Guardant360 (64.4%) and Caris (51.5%). TP53 alterations comprised a significant proportion of the mutation pool in Caris and Guardant360, 14.7% and 13.1%, respectively. Twenty-six potentially actionable gene alterations were detected from matching ctDNA to Caris unclassified alterations. The CS core cellular process was the most prevalent in our study population. Clinical trials are warranted to investigate biomarkers for the three core cellular processes in advanced cancer patients to define the next best therapies.
Insights
This study categorizes gene mutations from next-generation sequencing (NGS) into cell fate, cell survival, and genome maintenance processes. Cell survival mutations were most common, offering insights for advanced cancer treatment strategies.
Area of Science:
- Genomics and Bioinformatics
- Cancer Biology
- Precision Medicine
Background:
- Targeted and immune checkpoint therapies have advanced cancer treatment, yet tumor heterogeneity causes drug resistance.
- Understanding complex biological interactions through gene mutation analysis is crucial for guiding effective cancer therapies.
Purpose of the Study:
- To categorize gene mutations identified via next-generation sequencing (NGS) into three core biological processes: cell fate (CF), cell survival (CS), and genome maintenance (GM).
- To compare mutation concordance and discordance between tissue (Caris MI/X) and plasma-based (Guardant360) NGS platforms.
- To identify actionable gene alterations and analyze trends in TP53 mutations and PD-L1 expression in advanced cancer patients.
Main Methods:
- Collected NGS data from 145 advanced cancer patients who had previously failed standard therapy.
- Utilized both Caris MI/X (tissue) and Guardant360 (plasma cell-free circulating tumor DNA) platforms for 142 patients.
- Categorized mutated genes into CF, CS, and GM; performed comparative analysis for concordance, discordance, unclassified mutations, TP53 alterations, and PD-L1 expression.
Main Results:
- Mutated genes were predominantly categorized under the Cell Survival (CS) process, with higher concordance observed for Guardant360 (64.4%) compared to Caris (51.5%).
- TP53 alterations represented a significant fraction of mutations across both platforms (Caris: 14.7%, Guardant360: 13.1%).
- Twenty-six potentially actionable gene alterations were identified by matching circulating tumor DNA (ctDNA) data with unclassified Caris alterations.
Conclusions:
- The Cell Survival (CS) core cellular process was the most prevalent category of gene mutations in the study population.
- The findings highlight the potential of ctDNA analysis and categorized mutation data in identifying actionable targets.
- Further clinical trials are recommended to validate these three core cellular processes as biomarkers for optimizing treatment strategies in advanced cancer.
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