cancer-metabolism-agent

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---

name: 'cancer-metabolism-agent'

description: 'AI-powered analysis of cancer metabolic reprogramming including Warburg effect, glutamine addiction, lipid metabolism, and metabolic vulnerabilities for therapeutic targeting.'

measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.

allowed-tools:

• read_file
• run_shell_command

---

Cancer Metabolism Agent

The Cancer Metabolism Agent analyzes tumor metabolic reprogramming to identify vulnerabilities for therapeutic targeting. It integrates metabolomics, transcriptomics, and flux analysis to characterize Warburg effect, glutamine addiction, lipid synthesis, and other cancer-specific metabolic alterations.

When to Use This Skill

• When analyzing tumor metabolomic profiles to identify metabolic phenotypes.
• To identify metabolic vulnerabilities as therapeutic targets.
• For predicting response to metabolism-targeting drugs (metformin, 2-DG, CB-839).
• When integrating metabolomics with transcriptomics for pathway analysis.
• To analyze tumor-microenvironment metabolic competition.

Core Capabilities

1. Metabolic Phenotyping: Classify tumors by dominant metabolic programs (glycolytic, oxidative, lipogenic).

1. Warburg Effect Quantification: Measure aerobic glycolysis and lactate production signatures.

1. Glutamine Dependency Analysis: Identify glutamine-addicted tumors vulnerable to GLS inhibitors.

1. Lipid Metabolism Profiling: Analyze de novo lipogenesis and fatty acid oxidation.

1. Metabolic Flux Analysis: Integrate 13C tracer data for pathway flux quantification.

1. Drug Sensitivity Prediction: Predict response to metabolism-targeting therapeutics.

Key Metabolic Pathways in Cancer

| Pathway | Key Enzymes | Cancer Relevance | Therapeutic Targets |

|---------|-------------|------------------|---------------------|

| Glycolysis | HK2, PKM2, LDHA | Warburg effect | 2-DG, lonidamine |

| Glutaminolysis | GLS1, GDH | Nitrogen/carbon source | CB-839, BPTES |

| Fatty acid synthesis | FASN, ACC, ACLY | Membrane biogenesis | TVB-2640, ND-646 |

| Oxidative phosphorylation | Complex I-V | OXPHOS tumors | Metformin, IACS-010759 |

| One-carbon metabolism | SHMT, MTHFD | Nucleotide synthesis | Methotrexate |

| Serine synthesis | PHGDH, PSAT1 | Amino acid auxotrophy | NCT-503 |

Workflow

1. Input: Metabolomics data (LC-MS, GC-MS), RNA-seq expression, clinical annotations.

1. Normalization: Process metabolomics data with appropriate normalization.

1. Pathway Scoring: Calculate metabolic pathway activity scores.

1. Phenotype Classification: Assign metabolic phenotype clusters.

1. Vulnerability Identification: Identify metabolic dependencies.

1. Drug Matching: Predict sensitivity to metabolism-targeting agents.

1. Output: Metabolic phenotype, pathway activities, therapeutic recommendations.

Example Usage

User: "Analyze this tumor's metabolic profile and identify targetable metabolic vulnerabilities."

Agent Action:

python3 Skills/Oncology/Cancer_Metabolism_Agent/metabolism_analyzer.py \
    --metabolomics tumor_lcms.csv \
    --rnaseq tumor_expression.tsv \
    --tumor_type NSCLC \
    --normalize mtic \
    --pathway_analysis true \
    --drug_prediction true \
    --output metabolism_report/

Metabolic Phenotype Classification

Glycolytic (Warburg):

• High HK2, PKM2, LDHA expression
• Elevated lactate/pyruvate ratio
• Low mitochondrial gene expression
• Sensitive to glycolysis inhibitors

Oxidative (OXPHOS-dependent):

• High ETC complex expression
• Active TCA cycle
• PGC1α driven
• Sensitive to metformin, IACS-010759

Lipogenic:

• High FASN, ACC, SREBP1/2
• Active de novo lipogenesis
• Common in prostate, breast cancer
• Sensitive to FASN inhibitors

Glutamine-addicted:

• High GLS1, MYC-driven
• Glutamine-dependent anaplerosis
• Common in KRAS-mutant cancers
• Sensitive to CB-839

AI/ML Models

Metabolic Phenotype Classifier:

• Random forest on metabolite ratios
• 85% accuracy on validation cohorts
• Integrates with molecular subtypes

Flux Balance Analysis:

• Genome-scale metabolic models (Recon3D)
• Constraint-based optimization
• Predicts essential metabolic genes

Drug Response Prediction:

• GDSC/CCLE metabolic drug data
• Multi-omic feature integration
• AUC 0.75-0.85 for metabolic drugs

Metabolomics Data Processing

| Step | Method | Purpose |

|------|--------|---------|

| Peak detection | XCMS, MZmine | Identify metabolites |

| Annotation | HMDB, KEGG | Assign identities |

| Normalization | MTIC, median | Remove batch effects |

| Imputation | KNN, RF | Handle missing values |

| Enrichment | MSEA, Mummichog | Pathway analysis |

TME Metabolic Competition

The agent analyzes tumor-immune metabolic crosstalk:

• Glucose competition (T-cell activation)
• Lactate immunosuppression
• Arginine depletion by MDSCs
• Tryptophan-IDO axis
• Adenosine immunosuppression

Prerequisites

• Python 3.10+
• COBRApy for flux balance
• MetaboAnalyst interface
• Pathway databases (KEGG, Reactome)

Related Skills

• Metabolomics_Agent - For general metabolomics
• Multi_Omics_Integration - For omic integration
• Drug_Repurposing - For therapeutic matching

Clinical Applications

1. Treatment Selection: Match metabolic phenotype to drugs
2. Combination Therapy: Identify synergistic metabolic targets
3. Resistance Mechanisms: Metabolic adaptation under therapy
4. Diet Interventions: Ketogenic diet in glycolytic tumors

Author

AI Group - Biomedical AI Platform

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