immune-checkpoint-combination-agent

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.