--- name: cancer-metabolism-agent description: "---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. license: MIT metadata: author: AI Group version: \"1.0.0\" created: \"2026-01-..." --- --- 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). 2. **Warburg Effect Quantification**: Measure aerobic glycolysis and lactate production signatures. 3. **Glutamine Dependency Analysis**: Identify glutamine-addicted tumors vulnerable to GLS inhibitors. 4. **Lipid Metabolism Profiling**: Analyze de novo lipogenesis and fatty acid oxidation. 5. **Metabolic Flux Analysis**: Integrate 13C tracer data for pathway flux quantification. 6. **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. 2. **Normalization**: Process metabolomics data with appropriate normalization. 3. **Pathway Scoring**: Calculate metabolic pathway activity scores. 4. **Phenotype Classification**: Assign metabolic phenotype clusters. 5. **Vulnerability Identification**: Identify metabolic dependencies. 6. **Drug Matching**: Predict sensitivity to metabolism-targeting agents. 7. **Output**: Metabolic phenotype, pathway activities, therapeutic recommendations. ## Example Usage **User**: "Analyze this tumor's metabolic profile and identify targetable metabolic vulnerabilities." **Agent Action**: ```bash 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 --- Source attribution: mdbabumiamssm Original project: https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-/blob/ef3c40b235ba23083e67263a921759784f0dd251/Skills/Oncology/Cancer_Metabolism_Agent/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2025 MD BABU MIA , PHD Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: mdbabumiamssm Original project: https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-/blob/ef3c40b235ba23083e67263a921759784f0dd251/Skills/Oncology/Cancer_Metabolism_Agent/SKILL.md Attribution basis: Recovered original package and original project license at a pinned revision Fields of work: Data & Analytics, Education & Research, Healthcare & Life Sciences Roles: Business Intelligence Analyst, Clinical Research, Data Scientist, Health Informatics, Researcher Specialties: API, Database Tools: Python Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 4f52948748d79f775bc064f18b7c3f0fe0d4894e030c04e555c39a5206e36179 Metadata reviewed: 2026-10-27 This metadata supersedes earlier Dave AI catalog attribution. Original instruction and supporting files are preserved.