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
Checkpoint Expression Profiling: Quantify expression of PD-1, PD-L1, CTLA-4, TIGIT, LAG-3, TIM-3, and others.
TME Characterization: Classify tumors as "hot" (inflamed), "excluded", or "cold" (desert) for combination rationale.
Resistance Mechanism Analysis: Identify primary and acquired resistance patterns.
Combination Prediction: ML models predicting response to specific checkpoint combinations.
Synergy Scoring: Evaluate potential synergies based on mechanism of action overlap.
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
Input: Tumor RNA-seq, IHC markers, TMB/MSI status, clinical data.
Checkpoint Profiling: Quantify checkpoint ligand/receptor expression.
TME Classification: Determine immune infiltration pattern.
Resistance Analysis: Identify potential resistance mechanisms.
Combination Scoring: Rank combinations by predicted efficacy.
Evidence Matching: Link to clinical trial data.
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:
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
- Toxicity: Combinations increase irAE risk
- Sequencing: Optimal order of agents
- Biomarkers: TMB, PD-L1, MSI as selection criteria
- Cost: Combination therapy costs
Author
AI Group - Biomedical AI Platform
1---2name: immune-checkpoint-combination-agent3description: <!--4---5<!--6# COPYRIGHT NOTICE7# This file is part of the "Universal Biomedical Skills" project.8# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>9# All Rights Reserved.10#11# This code is proprietary and confidential.12# Unauthorized copying of this file, via any medium is strictly prohibited.13#14# Provenance: Authenticated by MD BABU MIA1516-->1718---19name: 'immune-checkpoint-combination-agent'20description: 'AI-powered analysis for predicting optimal immune checkpoint inhibitor combinations based on tumor microenvironment, biomarkers, and molecular profiling.'21measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.22allowed-tools:23 - read_file24 - run_shell_command25---262728# Immune Checkpoint Combination Agent2930The **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.3132## When to Use This Skill3334* When selecting checkpoint inhibitor combinations for individual patients.35* To predict response to ICI combinations (PD-1/PD-L1 + CTLA-4, TIGIT, LAG-3).36* For identifying resistance mechanisms suggesting specific combinations.37* When analyzing tumor microenvironment to guide combination selection.38* To match patients to combination immunotherapy clinical trials.3940## Core Capabilities41421. **Checkpoint Expression Profiling**: Quantify expression of PD-1, PD-L1, CTLA-4, TIGIT, LAG-3, TIM-3, and others.43442. **TME Characterization**: Classify tumors as "hot" (inflamed), "excluded", or "cold" (desert) for combination rationale.45463. **Resistance Mechanism Analysis**: Identify primary and acquired resistance patterns.47484. **Combination Prediction**: ML models predicting response to specific checkpoint combinations.49505. **Synergy Scoring**: Evaluate potential synergies based on mechanism of action overlap.51526. **Clinical Evidence Integration**: Match combinations to published efficacy data.5354## Checkpoint Inhibitor Landscape5556| Target | Approved Agents | Mechanism | Combination Rationale |57|--------|-----------------|-----------|----------------------|58| PD-1 | Pembrolizumab, Nivolumab | Block T-cell inhibition | Backbone therapy |59| PD-L1 | Atezolizumab, Durvalumab | Block tumor immune evasion | Alternative backbone |60| CTLA-4 | Ipilimumab, Tremelimumab | Enhance T-cell priming | Non-redundant to PD-1 |61| LAG-3 | Relatlimab | Block exhausted T-cells | PD-1 refractory |62| TIGIT | Tiragolumab | Block NK/T suppression | NK cell engagement |63| TIM-3 | Multiple in trials | Terminal exhaustion | Highly exhausted TME |6465## Workflow66671. **Input**: Tumor RNA-seq, IHC markers, TMB/MSI status, clinical data.68692. **Checkpoint Profiling**: Quantify checkpoint ligand/receptor expression.70713. **TME Classification**: Determine immune infiltration pattern.72734. **Resistance Analysis**: Identify potential resistance mechanisms.74755. **Combination Scoring**: Rank combinations by predicted efficacy.76776. **Evidence Matching**: Link to clinical trial data.78797. **Output**: Ranked combinations, rationale, supporting evidence, trial matches.8081## Example Usage8283**User**: "Recommend optimal checkpoint inhibitor combination for this melanoma patient based on their tumor profile."8485**Agent Action**:86```bash87python3 Skills/Immunology_Vaccines/Immune_Checkpoint_Combination_Agent/ici_combination.py \88 --rnaseq tumor_expression.tsv \89 --ihc pd-l1_tps_60.json \90 --mutations tumor_mutations.maf \91 --tmb 12.5 \92 --msi stable \93 --tumor_type melanoma \94 --prior_treatment pembrolizumab \95 --output ici_recommendations.json96```9798## TME-Based Combination Rationale99100**Inflamed ("Hot") Tumors**:101- High TIL infiltration102- PD-L1 high103- Respond to anti-PD-1 monotherapy104- Add CTLA-4 for improved depth105106**Excluded Tumors**:107- TILs at margin, not infiltrating108- Physical/chemical barriers109- Consider anti-CTLA-4 for priming110- Add chemotherapy for barrier disruption111112**Desert ("Cold") Tumors**:113- Low TIL infiltration114- Low PD-L1115- Need to induce inflammation first116- Consider chemo, radiation, or vaccines + ICI117118## Resistance Mechanisms and Solutions119120| Mechanism | Biomarkers | Combination Strategy |121|-----------|------------|---------------------|122| Alternative checkpoints | LAG-3+, TIGIT+, TIM-3+ | Add second checkpoint |123| WNT/β-catenin | CTNNB1 mutations | Poor ICI candidate |124| IFN signaling loss | JAK1/2, B2M mutations | Limited benefit |125| MHC loss | HLA-A/B/C loss | NK-engaging therapies |126| T-cell exclusion | TGF-β high | TGF-β inhibitor combination |127128## AI/ML Models129130**Response Prediction**:131- Multi-modal model (expression + mutations + clinical)132- Trained on TCGA + clinical trial data133- AUC 0.72-0.80 for response134135**Synergy Prediction**:136- Network-based combination scoring137- Mechanistic pathway analysis138- Clinical validation integration139140## Combination Evidence Database141142| Combination | Indication | Key Trial | Benefit |143|-------------|------------|-----------|---------|144| Nivo + Ipi | Melanoma | CheckMate-067 | OS improvement |145| Nivo + Rela | Melanoma | RELATIVITY-047 | PFS improvement |146| Atezo + Tira | NSCLC | CITYSCAPE | PFS improvement (PD-L1 high) |147| Durva + Treme | HCC | HIMALAYA | OS improvement |148149## Prerequisites150151* Python 3.10+152* scikit-learn, XGBoost for ML153* Gene signature databases154* Clinical evidence database155156## Related Skills157158* TCell_Exhaustion_Analysis_Agent - For exhaustion profiling159* Tumor_Microenvironment - For TME characterization160* Neoantigen_Vaccine_Agent - For vaccine combinations161162## Clinical Considerations1631641. **Toxicity**: Combinations increase irAE risk1652. **Sequencing**: Optimal order of agents1663. **Biomarkers**: TMB, PD-L1, MSI as selection criteria1674. **Cost**: Combination therapy costs168169## Author170171AI Group - Biomedical AI Platform172173174<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
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