---name: immune-checkpoint-combination-agent description: AI-powered analysis for predicting optimal immune checkpoint inhibitor combinations based on tumor microenvironment, biomarkers, and molecular profiling. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-19" compatibility:

• system: Python 3.10+ allowed-tools:
• run_shell_command
• read_file
• write_file

keywords:

• immune-checkpoint-combination-agent
• automation
• biomedical measurable_outcome: execute task with >95% success rate. ---"

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:

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