--- name: 'immune-checkpoint-combination-agent' description: 'AI-powered analysis for predicting optimal immune checkpoint inhibitor combinations based on tumor microenvironment, biomarkers, and molecular profiling.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- # Immune Checkpoint Combination Agent The **Immune Checkpoint Combination Agent** analyzes tumor molecular profiles to predict optimal immune checkpoint inhibitor (ICI) combinations. It integrates TME characterization, checkpoint expression, resistance mechanisms, and clinical evidence for rational immunotherapy combination design. ## When to Use This Skill * When selecting checkpoint inhibitor combinations for individual patients. * To predict response to ICI combinations (PD-1/PD-L1 + CTLA-4, TIGIT, LAG-3). * For identifying resistance mechanisms suggesting specific combinations. * When analyzing tumor microenvironment to guide combination selection. * To match patients to combination immunotherapy clinical trials. ## Core Capabilities 1. **Checkpoint Expression Profiling**: Quantify expression of PD-1, PD-L1, CTLA-4, TIGIT, LAG-3, TIM-3, and others. 2. **TME Characterization**: Classify tumors as "hot" (inflamed), "excluded", or "cold" (desert) for combination rationale. 3. **Resistance Mechanism Analysis**: Identify primary and acquired resistance patterns. 4. **Combination Prediction**: ML models predicting response to specific checkpoint combinations. 5. **Synergy Scoring**: Evaluate potential synergies based on mechanism of action overlap. 6. **Clinical Evidence Integration**: Match combinations to published efficacy data. ## Checkpoint Inhibitor Landscape | Target | Approved Agents | Mechanism | Combination Rationale | |--------|-----------------|-----------|----------------------| | PD-1 | Pembrolizumab, Nivolumab | Block T-cell inhibition | Backbone therapy | | PD-L1 | Atezolizumab, Durvalumab | Block tumor immune evasion | Alternative backbone | | CTLA-4 | Ipilimumab, Tremelimumab | Enhance T-cell priming | Non-redundant to PD-1 | | LAG-3 | Relatlimab | Block exhausted T-cells | PD-1 refractory | | TIGIT | Tiragolumab | Block NK/T suppression | NK cell engagement | | TIM-3 | Multiple in trials | Terminal exhaustion | Highly exhausted TME | ## Workflow 1. **Input**: Tumor RNA-seq, IHC markers, TMB/MSI status, clinical data. 2. **Checkpoint Profiling**: Quantify checkpoint ligand/receptor expression. 3. **TME Classification**: Determine immune infiltration pattern. 4. **Resistance Analysis**: Identify potential resistance mechanisms. 5. **Combination Scoring**: Rank combinations by predicted efficacy. 6. **Evidence Matching**: Link to clinical trial data. 7. **Output**: Ranked combinations, rationale, supporting evidence, trial matches. ## Example Usage **User**: "Recommend optimal checkpoint inhibitor combination for this melanoma patient based on their tumor profile." **Agent Action**: ```bash python3 Skills/Immunology_Vaccines/Immune_Checkpoint_Combination_Agent/ici_combination.py \ --rnaseq tumor_expression.tsv \ --ihc pd-l1_tps_60.json \ --mutations tumor_mutations.maf \ --tmb 12.5 \ --msi stable \ --tumor_type melanoma \ --prior_treatment pembrolizumab \ --output ici_recommendations.json ``` ## TME-Based Combination Rationale **Inflamed ("Hot") Tumors**: - High TIL infiltration - PD-L1 high - Respond to anti-PD-1 monotherapy - Add CTLA-4 for improved depth **Excluded Tumors**: - TILs at margin, not infiltrating - Physical/chemical barriers - Consider anti-CTLA-4 for priming - Add chemotherapy for barrier disruption **Desert ("Cold") Tumors**: - Low TIL infiltration - Low PD-L1 - Need to induce inflammation first - Consider chemo, radiation, or vaccines + ICI ## Resistance Mechanisms and Solutions | Mechanism | Biomarkers | Combination Strategy | |-----------|------------|---------------------| | Alternative checkpoints | LAG-3+, TIGIT+, TIM-3+ | Add second checkpoint | | WNT/β-catenin | CTNNB1 mutations | Poor ICI candidate | | IFN signaling loss | JAK1/2, B2M mutations | Limited benefit | | MHC loss | HLA-A/B/C loss | NK-engaging therapies | | T-cell exclusion | TGF-β high | TGF-β inhibitor combination | ## AI/ML Models **Response Prediction**: - Multi-modal model (expression + mutations + clinical) - Trained on TCGA + clinical trial data - AUC 0.72-0.80 for response **Synergy Prediction**: - Network-based combination scoring - Mechanistic pathway analysis - Clinical validation integration ## Combination Evidence Database | Combination | Indication | Key Trial | Benefit | |-------------|------------|-----------|---------| | Nivo + Ipi | Melanoma | CheckMate-067 | OS improvement | | Nivo + Rela | Melanoma | RELATIVITY-047 | PFS improvement | | Atezo + Tira | NSCLC | CITYSCAPE | PFS improvement (PD-L1 high) | | Durva + Treme | HCC | HIMALAYA | OS improvement | ## Prerequisites * Python 3.10+ * scikit-learn, XGBoost for ML * Gene signature databases * Clinical evidence database ## Related Skills * TCell_Exhaustion_Analysis_Agent - For exhaustion profiling * Tumor_Microenvironment - For TME characterization * Neoantigen_Vaccine_Agent - For vaccine combinations ## Clinical Considerations 1. **Toxicity**: Combinations increase irAE risk 2. **Sequencing**: Optimal order of agents 3. **Biomarkers**: TMB, PD-L1, MSI as selection criteria 4. **Cost**: Combination therapy costs ## Author AI Group - Biomedical AI Platform --- Source attribution: FreedomIntelligence Original project: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2026 FridrichMethod 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. --- NOTE ON BUNDLED CONTENT This repository aggregates skills from multiple upstream projects (see the README for the source list). Each `skills//` directory may carry its own license inherited from its upstream source. The MIT license above applies to this repository's curation and the scripts/workflows in `/`, `/scripts/`, and `/.github/` — not to the upstream skill content itself. Consult the upstream repository linked in the README and any LICENSE file inside a skill directory before reusing skill content commercially. # Dave AI resource metadata Creator / publisher: FreedomIntelligence Original project: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills Attribution basis: Exact package in biomedical source index and publisher source table Fields of work: Education & Research, Healthcare & Life Sciences, Product Management Roles: Clinical Research, Health Informatics, Medical Writer, Product Manager, Researcher Specialties: API Tools: Python Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 4b00580bdac19cf6c67b6961d7b98910f10e018c553aff8f55178db3a42c0c54 Metadata reviewed: 2026-10-27 This metadata supersedes earlier Dave AI catalog attribution. Original instruction and supporting files are preserved.