Pan-cancer analysis implicates novel insights of lactate metabolism into immunotherapy response prediction and...

Area of Science:

• Oncology
• Immunology
• Metabolic pathways

Background:

• Immunotherapy is a key cancer treatment, but effectiveness varies among patients.
• Lactate metabolism (LM) in tumor cells is a potential target to enhance anti-tumor immune responses.

Purpose of the Study:

• To investigate the association between lactate metabolism and immunotherapy response.
• To develop a predictive signature for immunotherapy efficacy.
• To identify novel therapeutic targets within lactate metabolism pathways.

Main Methods:

• Analysis of public single-cell RNA-Seq (scRNA-seq) cohorts from immunotherapy patients.
• Development and validation of a lactate metabolism-related signature (LM.SIG) using machine learning (ML) on pan-cancer datasets.
• Identification and validation of potential therapeutic targets using CRISPR screening, immunohistochemistry (IHC), and spatial transcriptomics (ST).

Main Results:

• LM was significantly correlated with immunotherapy resistance.
• The LM.SIG demonstrated strong predictive performance for immunotherapy response (AUC 0.73-0.87) and prognosis across various cancers.
• LDHA was identified as a pan-cancer biomarker for immunotherapy response and survival, with LDHA deficiency enhancing anti-tumor immunity in preclinical models.

Conclusions:

• A strong link exists between lactate metabolism and immunotherapy resistance.
• The pan-cancer LM.SIG is a promising tool for selecting patients who will benefit from immunotherapy.
• Targeting LDHA may enhance anti-tumor immunity and improve immunotherapy efficacy.