<!-- # COPYRIGHT NOTICE # This file is part of the "Universal Biomedical Skills" project. # Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu> # All Rights Reserved. # # This code is proprietary and confidential. # Unauthorized copying of this file, via any medium is strictly prohibited. # # Provenance: Authenticated by MD BABU MIA -->

---

name: 'microbiome-cancer-agent' description: 'AI-powered analysis of microbiome-cancer interactions including tumor microbiome profiling, immunotherapy response prediction, and microbiome-targeted therapeutic opportunities.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

---

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

1. Tumor Microbiome Analysis: Profile intratumoral bacteria from tumor sequencing data.

2. Gut-Cancer Axis: Analyze fecal microbiome associations with cancer outcomes.

3. ICI Response Prediction: Predict checkpoint inhibitor response from microbiome.

4. Metabolite Profiling: Link microbial metabolites to cancer phenotypes.

5. Antibiotic Impact: Model antibiotic effects on treatment efficacy.

6. 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

1. Input: 16S/shotgun metagenomics, tumor sequencing, clinical data.

2. Taxonomy Profiling: Identify bacterial composition at genus/species level.

3. Diversity Analysis: Calculate alpha and beta diversity metrics.

4. Association Testing: Correlate microbiome with outcomes.

5. Functional Prediction: Infer metabolic potential (PICRUSt2, HUMAnN).

6. Prediction Modeling: Build response prediction models.

7. 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

1. Biomarker Development: Microbiome-based response prediction
2. Intervention Timing: Avoid antibiotics pre-ICI
3. FMT Trials: Responder microbiome transfer
4. Probiotics: Rationally designed consortia
5. 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

1. Intratumoral bacteria: Direct tumor effects
2. Phage therapy: Targeting pathobionts
3. Engineered probiotics: Drug-producing bacteria
4. Diet interventions: Modulating microbiome for therapy

Author

AI Group - Biomedical AI Platform

<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->