Synthetic viability induces resistance to immune checkpoint inhibitors in cancer cells

Area of Science:

• Oncology
• Immunology
• Genetics

Background:

• Immune checkpoint inhibitors (ICI) have transformed cancer therapy but face significant patient resistance.
• Synthetic viability (SV) between genes is implicated as a mechanism driving this resistance.
• Predicting ICI efficacy remains a critical challenge in treating various cancers, particularly melanoma.

Purpose of the Study:

• To establish a synthetic viability (SV) signature for predicting the efficacy of immune checkpoint inhibitor (ICI) treatment in melanoma.
• To identify robust SV gene pairs associated with cancer prognosis and ICI resistance.

Main Methods:

• Utilized a random forest classifier to predict SV gene pairs based on 14 features.
• Employed CRISPR/Cas9 screens to prioritize identified SV gene pairs.
• Constructed a six-SV pair signature to predict melanoma patient response to ICI therapy.

Main Results:

• Identified 1,861 SV gene pairs, with a subset of six forming a predictive signature for ICI resistance in melanoma.
• The six-SV pair signature effectively stratified melanoma patients into high-risk and low-risk groups.
• High-risk patients exhibited poorer response to ICI treatment and reduced infiltration of natural killer cells and CD8+ T cells.

Conclusions:

• A 14-feature classifier accurately predicts robust SV gene pairs relevant to cancer.
• The developed six-SV pair signature demonstrates potential for predicting resistance to immune checkpoint inhibitors (ICI).