majiayu000 ---name: microbiome-cancer-agent description: AI-powered analysis of microbiome-cancer interactions including tumor microbiome profiling, immunotherapy response prediction, and microbiome-targeted therapeutic opportunities. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-19" compatibility:
- system: Python 3.10+ allowed-tools:
- run_shell_command
- read_file
- write_file
keywords:
- microbiome-cancer-agent
- automation
- biomedical measurable_outcome: execute task with >95% success rate. ---"
Microbiome-Cancer Interaction Agent
The Microbiome-Cancer Interaction Agent analyzes relationships between the microbiome and cancer, including tumor-associated bacteria, gut microbiome effects on immunotherapy, and microbiome-targeted therapeutic strategies.
When to Use This Skill
- When analyzing tumor microbiome composition from sequencing data.
- To predict immunotherapy response based on gut microbiome profiles.
- For identifying microbiome-based biomarkers in cancer.
- When assessing antibiotic impact on cancer treatment efficacy.
- To design microbiome-modulating therapeutic interventions.
Core Capabilities
Tumor Microbiome Analysis: Profile intratumoral bacteria from tumor sequencing data.
Gut-Cancer Axis: Analyze fecal microbiome associations with cancer outcomes.
ICI Response Prediction: Predict checkpoint inhibitor response from microbiome.
Metabolite Profiling: Link microbial metabolites to cancer phenotypes.
Antibiotic Impact: Model antibiotic effects on treatment efficacy.
FMT/Probiotic Design: Support microbiome-modulating interventions.
Microbiome-Cancer Associations
| Cancer Type | Key Bacteria | Association |
|---|---|---|
| Colorectal | Fusobacterium nucleatum | Promotion, poor prognosis |
| Colorectal | Bacteroides fragilis (ETBF) | Carcinogenesis |
| Gastric | Helicobacter pylori | Established carcinogen |
| Pancreatic | Gammaproteobacteria | Drug metabolism |
| Breast | Fusobacterium | Metastasis |
| Oral | Porphyromonas gingivalis | Oral SCC |
Workflow
Input: 16S/shotgun metagenomics, tumor sequencing, clinical data.
Taxonomy Profiling: Identify bacterial composition at genus/species level.
Diversity Analysis: Calculate alpha and beta diversity metrics.
Association Testing: Correlate microbiome with outcomes.
Functional Prediction: Infer metabolic potential (PICRUSt2, HUMAnN).
Prediction Modeling: Build response prediction models.
Output: Microbiome profile, associations, predictions, interventions.
Example Usage
User: "Analyze gut microbiome from melanoma patients and predict anti-PD-1 response."
Agent Action:
python3 Skills/Microbiome/Microbiome_Cancer_Agent/microbiome_cancer.py \
--metagenomics fecal_shotgun.fastq.gz \
--tumor_data melanoma_rnaseq.tsv \
--clinical treatment_outcomes.csv \
--analysis ici_response \
--reference metaphlan_db \
--output microbiome_report/
ICI Response and Microbiome
Favorable Microbiome:
- Akkermansia muciniphila
- Faecalibacterium prausnitzii
- Bifidobacterium spp.
- Ruminococcaceae family
- High diversity
Unfavorable Microbiome:
- Bacteroidales (in some studies)
- Low diversity
- Post-antibiotic dysbiosis
Microbial Metabolites in Cancer
| Metabolite | Source | Effect |
|---|---|---|
| Butyrate | Clostridia | Anti-inflammatory, anti-tumor |
| Inosine | Akkermansia | Enhanced ICI response |
| TMAO | Various | Pro-tumorigenic |
| Secondary bile acids | Various | Variable, context-dependent |
| LPS | Gram-negative | Inflammation, mixed effects |
AI/ML Components
Response Prediction:
- Random forest on microbiome features
- Neural networks for metagenomic profiles
- Integration with host factors
Microbiome-Metabolite Linking:
- Genome-scale metabolic models
- Correlation networks
- Causal inference methods
Intervention Design:
- FMT donor selection
- Probiotic consortium optimization
- Antibiotic avoidance recommendations
Tumor Microbiome Analysis
Challenges:
- Low bacterial biomass in tumors
- Contamination from reagents/environment
- Batch effects
- Need for stringent controls
Best Practices:
- Negative controls (extraction, PCR)
- Decontamination algorithms (decontam, SCRuB)
- Multiple validation methods
- Orthogonal confirmation (FISH, culture)
Clinical Implications
- Biomarker Development: Microbiome-based response prediction
- Intervention Timing: Avoid antibiotics pre-ICI
- FMT Trials: Responder microbiome transfer
- Probiotics: Rationally designed consortia
- Prebiotics: Fiber to support beneficial bacteria
Prerequisites
- Python 3.10+
- QIIME2, Metaphlan, HUMAnN
- R (phyloseq, vegan)
- ML frameworks
Related Skills
- Metagenomics - For general microbiome analysis
- Immune_Checkpoint_Combination_Agent - For ICI optimization
- Metabolomics - For metabolite analysis
Research Frontiers
- Intratumoral bacteria: Direct tumor effects
- Phage therapy: Targeting pathobionts
- Engineered probiotics: Drug-producing bacteria
- Diet interventions: Modulating microbiome for therapy
Author
AI Group - Biomedical AI Platform