Predictive Immune-Checkpoint Blockade Classifiers Identify Tumors Responding to Inhibition of PD-1 and/or CTLA-4

Area of Science:

• Oncology
• Immunology
• Bioinformatics

Background:

• Combination immune-checkpoint blockade (ICB) with anti-PD-1 and anti-CTLA-4 improves melanoma patient outcomes.
• However, combination ICB is associated with severe immune-related adverse events and high costs.
• Predictive biomarkers are needed to guide treatment selection between monotherapy and combination ICB.

Purpose of the Study:

• To develop and validate predictive signatures for response to anti-PD-1 and anti-CTLA-4 therapies in melanoma.
• To differentiate patients who benefit from monotherapy versus combination ICB.

Main Methods:

• Utilized patient-derived melanoma xenografts (PDX) to isolate tumor cell-intrinsic ('InTumor') and tumor cell-extrinsic ('ExTumor') RNA signatures.
• Employed the XenofilteR deconvolution algorithm to separate human tumor cell RNA from the tumor microenvironment (TME).
• Correlated 'InTumor' and 'ExTumor' signatures with response to anti-PD-1 and anti-CTLA-4, respectively.

Main Results:

• The 'InTumor' signature predicts response to anti-PD-1 monotherapy.
• The 'ExTumor' signature predicts response to anti-CTLA-4 therapy.
• Combined signatures accurately identify patients likely to benefit from combination ICB and those unlikely to gain additional benefit from adding anti-CTLA-4 to anti-PD-1.

Conclusions:

• Novel 'InTumor' and 'ExTumor' signatures can distinguish melanoma patients who require combination ICB from those who benefit from monotherapy.
• These biomarkers offer a potential strategy to personalize ICB treatment selection, improving efficacy and reducing toxicity and cost.