Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Research Institute of Pancreatic Diseases, Shanghai Key Laboratory of Translational Research for Pancreatic Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
J Exp Clin Cancer Res. 2024 Apr 25;43(1):125. doi: 10.1186/s13046-024-03042-7.
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.
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.
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.
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.
免疫疗法已成为癌症治疗的一种有效临床方法，但只有部分癌症患者从中受益。将肿瘤细胞中的乳酸代谢（LM）作为增强抗肿瘤免疫反应的一种方法，代表了一种很有前途的治疗策略。
系统收集并仔细研究了接受免疫治疗的患者的公共单细胞 RNA-Seq（scRNA-seq）队列，以描绘 LM 与免疫治疗反应之间的关联。通过广泛检查 40 个泛癌 scRNA-seq 队列，制定了一个新的 LM 相关特征（LM.SIG）。然后，使用多种机器学习（ML）算法，基于 8 个免疫治疗转录组队列和 30 个癌症基因组图谱（TCGA）泛癌数据集，验证了 LM.SIG 预测免疫治疗反应和生存预后的能力。此外，基于 17 个 CRISPR 数据集确定了免疫治疗的潜在靶点，并通过体内和体外实验进行了验证。
在 2 个免疫治疗 scRNA-seq 队列中，评估 LM 被证实与免疫治疗耐药性有很大关系。基于大规模泛癌数据，发现 LM.SIG 与抗肿瘤免疫和免疫细胞失衡浸润之间存在显著负相关，而与促肿瘤发生信号之间存在正相关。利用该特征，ML 模型在验证集和测试集的免疫治疗反应和预后预测中 AUC 分别为 0.73/0.80 和 0.70/0.87。值得注意的是，与已发表的特征相比，LM.SIG 在各种癌症中具有优越的预测性能。随后，CRISPR 筛选鉴定出 LDHA 作为一种泛癌生物标志物，用于估计免疫治疗反应和生存概率，并用免疫组织化学（IHC）和空间转录组学（ST）数据集进一步验证。此外，实验表明，胰腺癌细胞中 LDHA 的缺乏提高了 CD8 T 细胞的抗肿瘤免疫，增强了巨噬细胞的抗肿瘤极化，从而提高了免疫治疗的疗效。
我们揭示了 LM 与免疫治疗耐药性之间的紧密关联，并进一步建立了泛癌 LM.SIG，有可能成为选择适合免疫治疗患者的竞争工具。
