Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer.
Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer.
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机器学习将 T 细胞功能和空间定位与胰腺癌的新辅助免疫治疗和临床结果联系起来。
DOI：
10.1101/2023.10.20.563335
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Byrne,KatelynT
中科院分区：
文献类型：
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作者：
Blise,KatieE;Sivagnanam,Shamilene;Betts,CourtneyB;Betre,Konjit;Kirchberger,Nell;Tate,Benjamin;Furth,EmmaE;DiasCosta,Andressa;Nowak,JonathanA;Wolpin,BrianM;Vonderheide,RobertH;Goecks,Jeremy;Coussens,LisaM;Byrne,KatelynT
肿瘤分子数据集正变得越来越复杂，这使得人类几乎不可能单独有效地分析它们。在这里，我们展示了使用机器学习（ML）分析人类胰腺癌单细胞，空间和高度多重蛋白质组数据集的能力，并揭示了可能有助于临床结果的潜在生物学机制。我们设计了一种多重免疫组织化学抗体组，以比较来自未经治疗的局限性胰腺导管腺癌（PDAC）患者的切除肿瘤与来自接受新辅助激动性CD40（抗CD40）单克隆抗体治疗的第二队列患者的切除肿瘤中的T细胞功能和空间定位。总共分析了来自两个队列中29名患者的306个组织区域的近250万个细胞，并量化了1，000多个肿瘤微环境（TME）特征。然后，我们训练ML模型，以基于TME特征准确预测抗CD40治疗状态和抗CD40治疗后的无病生存期（DFS）。通过对ML模型预测的下游解释，我们发现与未经治疗的TME相比，抗CD40治疗减少了TME内T细胞耗竭的典型方面。使用自动聚类方法，我们发现抗CD40治疗后DFS的改善与CD44+CD4+Th1细胞的增加相关，这些细胞特异性地位于细胞邻域内，其特征在于T细胞增殖增加、抗原经历和免疫聚集体中的细胞毒性。总体而言，我们的研究结果证明了ML在分子癌症免疫学应用中的实用性，突出了抗CD40治疗对TME内T细胞的影响，并确定了抗CD40治疗的PDAC患者DFS的潜在候选生物标志物。
Tumor molecular data sets are becoming increasingly complex, making it nearly impossible for humans alone to effectively analyze them. Here, we demonstrate the power of using machine learning (ML) to analyze a single-cell, spatial, and highly multiplexed proteomic data set from human pancreatic cancer and reveal underlying biological mechanisms that may contribute to clinical outcomes. We designed a multiplex immunohistochemistry antibody panel to compare T-cell functionality and spatial localization in resected tumors from treatment-naïve patients with localized pancreatic ductal adenocarcinoma (PDAC) with resected tumors from a second cohort of patients treated with neoadjuvant agonistic CD40 (anti-CD40) monoclonal antibody therapy. In total, nearly 2.5 million cells from 306 tissue regions collected from 29 patients across both cohorts were assayed, and over 1,000 tumor microenvironment (TME) features were quantified. We then trained ML models to accurately predict anti-CD40 treatment status and disease-free survival (DFS) following anti-CD40 therapy based on TME features. Through downstream interpretation of the ML models’ predictions, we found anti-CD40 therapy reduced canonical aspects of T-cell exhaustion within the TME, as compared with treatment-naïve TMEs. Using automated clustering approaches, we found improved DFS following anti-CD40 therapy correlated with an increased presence of CD44+CD4+Th1 cells located specifically within cellular neighborhoods characterized by increased T-cell proliferation, antigen experience, and cytotoxicity in immune aggregates. Overall, our results demonstrate the utility of ML in molecular cancer immunology applications, highlight the impact of anti-CD40 therapy on T cells within the TME, and identify potential candidate biomarkers of DFS for anti-CD40–treated patients with PDAC.