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liquid-biopsy-analytics-agent

Maintainer FreedomIntelligence · Last updated April 1, 2026

Comprehensive liquid biopsy analytics: ctDNA detection, MRD monitoring, treatment response.

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Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/liquid-biopsy-analytics-agent

Skill Snapshot

2 min

The Liquid Biopsy Analytics Agent provides comprehensive AI-driven analysis of blood-based cancer biomarkers. It integrates circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), exosomes, and cell-free RNA for multi-cancer early detection (MCED), minimal residual disease (MRD) monitoring, and treatment response assessment.
For multi-cancer early detection screening from blood samples.
To monitor minimal residual disease (MRD) after curative treatment.
When tracking tumor evolution and resistance during therapy.
For real-time treatment response assessment.

Source Doc

ctDNA Mutation Analysis: Variant calling, VAF tracking, and clonal evolution from cell-free DNA.
Methylation-Based Detection: cfDNA methylation patterns for cancer detection and tissue-of-origin identification.
CTC Enumeration & Analysis: AI-powered CTC detection, enumeration, and molecular characterization.
Multi-Modal Integration: Combines ctDNA, CTCs, and protein biomarkers with clinical/imaging data.
MRD Monitoring: Ultra-sensitive detection of residual disease post-treatment.
Response Prediction: AI models predicting treatment response from longitudinal liquid biopsy data.

Analyte	Detection Method	Clinical Use
ctDNA mutations	NGS, ddPCR	Therapy selection, resistance
ctDNA methylation	WGBS, targeted	MCED, tissue of origin
ctDNA fragmentation	WGS	Cancer detection
CTCs	CellSearch, microfluidics	Prognosis, monitoring
Exosomes	Immunocapture	Biomarker cargo
cfRNA	RT-qPCR, NGS	Gene expression

Input: Liquid biopsy data (ctDNA variants, methylation, CTC counts, protein markers).
Quality Control: Assess sample quality, input DNA amount, background noise.
Variant Analysis: Call mutations, calculate VAF, filter artifacts (CHIP).
Multi-analyte Integration: Combine biomarker signals using ML fusion.
Clinical Interpretation: Generate actionable insights for treatment decisions.
Longitudinal Tracking: Model dynamics for response assessment and recurrence detection.
Output: Cancer detection probability, MRD status, treatment recommendations, clonal evolution.