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Journal of Experimental & Clinical Cancer Research
Published in: Journal of Experimental & Clinical Cancer Research 1/2024

Open Access 01-12-2024 | Pancreatic Cancer | Research

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

Authors: Dongjie Chen, Pengyi Liu, Xiongxiong Lu, Jingfeng Li, Debin Qi, Longjun Zang, Jiayu Lin, Yihao Liu, Shuyu Zhai, Da Fu, Yuanchi Weng, Hongzhe Li, Baiyong Shen

Published in: Journal of Experimental & Clinical Cancer Research | Issue 1/2024

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Abstract

Background

Immunotherapy has emerged as a potent clinical approach for cancer treatment, but only subsets of cancer patients can benefit from it. Targeting lactate metabolism (LM) in tumor cells as a method to potentiate anti-tumor immune responses represents a promising therapeutic strategy.

Methods

Public single-cell RNA-Seq (scRNA-seq) cohorts collected from patients who received immunotherapy were systematically gathered and scrutinized to delineate the association between LM and the immunotherapy response. A novel LM-related signature (LM.SIG) was formulated through an extensive examination of 40 pan-cancer scRNA-seq cohorts. Then, multiple machine learning (ML) algorithms were employed to validate the capacity of LM.SIG for immunotherapy response prediction and survival prognostication based on 8 immunotherapy transcriptomic cohorts and 30 The Cancer Genome Atlas (TCGA) pan-cancer datasets. Moreover, potential targets for immunotherapy were identified based on 17 CRISPR datasets and validated via in vivo and in vitro experiments.

Results

The assessment of LM was confirmed to possess a substantial relationship with immunotherapy resistance in 2 immunotherapy scRNA-seq cohorts. Based on large-scale pan-cancer data, there exists a notably adverse correlation between LM.SIG and anti-tumor immunity as well as imbalance infiltration of immune cells, whereas a positive association was observed between LM.SIG and pro-tumorigenic signaling. Utilizing this signature, the ML model predicted immunotherapy response and prognosis with an AUC of 0.73/0.80 in validation sets and 0.70/0.87 in testing sets respectively. Notably, LM.SIG exhibited superior predictive performance across various cancers compared to published signatures. Subsequently, CRISPR screening identified LDHA as a pan-cancer biomarker for estimating immunotherapy response and survival probability which was further validated using immunohistochemistry (IHC) and spatial transcriptomics (ST) datasets. Furthermore, experiments demonstrated that LDHA deficiency in pancreatic cancer elevated the CD8+ T cell antitumor immunity and improved macrophage antitumoral polarization, which in turn enhanced the efficacy of immunotherapy.

Conclusions

We unveiled the tight correlation between LM and resistance to immunotherapy and further established the pan-cancer LM.SIG, holds the potential to emerge as a competitive instrument for the selection of patients suitable for immunotherapy.
Appendix
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Metadata
Title
Pan-cancer analysis implicates novel insights of lactate metabolism into immunotherapy response prediction and survival prognostication
Authors
Dongjie Chen
Pengyi Liu
Xiongxiong Lu
Jingfeng Li
Debin Qi
Longjun Zang
Jiayu Lin
Yihao Liu
Shuyu Zhai
Da Fu
Yuanchi Weng
Hongzhe Li
Baiyong Shen
Publication date
01-12-2024
Publisher
BioMed Central
Published in
Journal of Experimental & Clinical Cancer Research / Issue 1/2024
Electronic ISSN: 1756-9966
DOI
https://doi.org/10.1186/s13046-024-03042-7

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