Prediction of Response to Immunotherapy based on scRNA-seq analysis

Immune checkpoint blockade (ICB) therapy has demonstrated remarkable treatment efficacy in a diverse range of cancers. However, it faces the challenge that only a small proportion of patients benefit from it. Using single-cell RNA-sequencing (scRNA-seq) data to predict patients' responses to ICB is a potential strategy for realizing precision medicine in ICB therapy.We set up an accessible pipeline for scRNA-seq data obtaining and analysis in the context of clinical use, and develop a tool named RedeTIL which can analyze single-cell features, including cell abundance and spatial topology of cells, to predict ICB-response. In order to demonstrate the predictive strength of scRNA-seq data, we obtained scRNA-seq data from 11 colorectal cancer (CRC) patients undergoing anti-PD-1 therapy according to our setted pipeline. Besides we collected publicly available scRNA-seq data of 39 breast cancer patients prior to receiving anti-PD-1 therapy, and data derived from 427 patients/samples across 15 tumor types for which objective response rates of anti-PD-1 or anti-PD-L1 were already reported. We used RedeTIL to extract these single-cell-derived features and then correlated them with clinical outcomes.Correlation analysis between single-cell based features and clinical outcomes shows the strong predictive strength of scRNA-seq data. For example, abundance of specific T cell subsets (e,g. CXCL13+ T and PDCD1+ T) has been found to correlate with shrinkage of CRC, and is more enriched in breast cancer responders compared to non-responders. Notably, the infiltration change metrics evaluated by RedeTIL have emerged as the most robust and accurate predictors. The spatial proximity of CD69+ T cells to cancer cells has been shown to correlate with shrinkage of CRC (Pearson R = 0.67, P = 0.02), and is also significantly enriched in Breast cancer responders than non-responders (P = 0.001), moreover, it has exhibited a high correlation with clinical objective response rate to Anti-PD-1/PD-L1 therapy across various cancer types (Pearson R = 0.8, P &lt; 0.001). Additionally, RedeTIL can be utilized to recommend mono- or combined drugs for individual patients.Our results proven that the application of scRNA-seq in predicting the response to immunotherapy in the clinical field is feasible, which can provide abundant and accurate information for treatment decisions, so as to achieve precision therapy.免疫检查点阻断（Immune checkpoint blockade, ICB）疗法在多种癌症中展现出显著的治疗疗效，但面临仅少数患者能从中获益的核心挑战。利用单细胞RNA测序（single-cell RNA-sequencing, scRNA-seq）数据预测患者对ICB的应答，是实现ICB疗法精准医学的潜在策略。本研究搭建了一套适配临床场景的scRNA-seq数据获取与分析流程，并开发了一款名为RedeTIL的工具，该工具可分析细胞丰度、细胞空间拓扑结构等单细胞特征，以预测ICB治疗应答。为验证scRNA-seq数据的预测效能，我们按照搭建的流程，从11名接受抗PD-1治疗的结直肠癌（colorectal cancer, CRC）患者中获取了scRNA-seq数据；此外，我们还收集了39名接受抗PD-1治疗前的乳腺癌患者的公开scRNA-seq数据，以及涵盖15种肿瘤类型、共427名患者/样本的公开数据集，该数据集已报道了对应样本接受抗PD-1或抗PD-L1治疗的客观缓解率。我们使用RedeTIL提取这些单细胞来源的特征，并将其与临床结局进行关联分析。基于单细胞特征与临床结局的相关性分析结果，证实了scRNA-seq数据具备较强的预测能力。例如，特定T细胞亚群（如CXCL13阳性T细胞、PDCD1阳性T细胞）的丰度与结直肠癌病灶缩小呈正相关，且在乳腺癌应答者中的富集程度显著高于非应答者。值得注意的是，通过RedeTIL评估的浸润特征变化，已成为最稳健且精准的预测指标。CD69阳性T细胞与癌细胞的空间邻近性与结直肠癌病灶缩小相关（皮尔逊相关系数R=0.67，P=0.02），且在乳腺癌应答者中的富集程度显著高于非应答者（P=0.001）；此外，该指标在多种癌症类型中与抗PD-1/PD-L1治疗的临床客观缓解率呈现高度相关性（皮尔逊相关系数R=0.8，P<0.001）。此外，RedeTIL还可用于为个体患者推荐单药或联合用药方案。本研究结果证实，将scRNA-seq应用于临床领域的免疫治疗应答预测具备可行性，其可为治疗决策提供丰富且准确的信息，从而实现精准治疗。