Since breast cancer patients respond diversely to immunotherapy, there is an urgent need to explore novel biomarkers to precisely predict clinical responses and enhance therapeutic efficacy. The purpose of our present research was to construct and independently validate a biomarker of tumor microenvironment (TME) phenotypes via a machine learning-based radiomics way. The interrelationship between the biomarker, TME phenotypes and recipients’ clinical response was also revealed. In this retrospective multi-cohort investigation, five separate cohorts of breast cancer patients were recruited to measure breast cancer TME phenotypes via a radiomics signature, which was constructed and validated by integrating RNA-seq data with DCE-MRI images for predicting immunotherapy response. Initially, we constructed TME phenotypes using RNA-seq of 1089 breast cancer patients in the TCGA database. Then, parallel DCE-MRI images and RNA-seq of 94 breast cancer patients obtained from TCIA were applied to develop a radiomics-based TME phenotypes signature using random forest in machine learning. The repeatability of the radiomics signature was then validated in an internal validation set. Two additional independent external validation sets were analyzed to reassess this signature. The Immune phenotype cohort (n = 158) was divided based on CD8 cell infiltration into immune-inflamed and immune-desert phenotypes; these data were utilized to examine the relationship between the immune phenotypes and this signature. Finally, we utilized an Immunotherapy-treated cohort with 77 cases who received anti-PD-1/PD-L1 treatment to evaluate the predictive efficiency of this signature in terms of clinical outcomes. The TME phenotypes of breast cancer were separated into two heterogeneous clusters: Cluster A, an "immune-inflamed" cluster, containing substantial innate and adaptive immune cell infiltration, and Cluster B, an "immune-desert" cluster, with modest TME cell infiltration. We constructed a radiomics signature for the TME phenotypes ([AUC] = 0.855; 95% CI 0.777–0.932; p < 0.05) and verified it in an internal validation set (0.844; 0.606–1; p < 0.05). In the known immune phenotypes cohort, the signature can identify either immune-inflamed or immune-desert tumor (0.814; 0.717–0.911; p < 0.05). In the Immunotherapy-treated cohort, patients with objective response had higher baseline radiomics scores than those with stable or progressing disease (p < 0.05); moreover, the radiomics signature achieved an AUC of 0.784 (0.643–0.926; p < 0.05) for predicting immunotherapy response. Our imaging biomarker, a practicable radiomics signature, is beneficial for predicting the TME phenotypes and clinical response in anti-PD-1/PD-L1-treated breast cancer patients. It is particularly effective in identifying the "immune-desert" phenotype and may aid in its transformation into an "immune-inflamed" phenotype.

中文翻译：

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开发基于机器学习的放射组学特征，用于估计乳腺癌 TME 表型并预测抗 PD-1/PD-L1 免疫治疗反应

由于乳腺癌患者对免疫治疗的反应不同，迫切需要探索新的生物标志物来精确预测临床反应并提高治疗效果。我们目前研究的目的是通过基于机器学习的放射组学方式构建并独立验证肿瘤微环境（TME）表型的生物标志物。生物标志物、TME 表型和受者临床反应之间的相互关系也被揭示。在这项回顾性多队列研究中，招募了五个独立的乳腺癌患者队列，通过放射组学特征测量乳腺癌 TME 表型，该特征是通过将 RNA-seq 数据与 DCE-MRI 图像整合来构建和验证的，用于预测免疫治疗反应。最初，我们使用 TCGA 数据库中 1089 名乳腺癌患者的 RNA-seq 构建了 TME 表型。然后，应用从 TCIA 获得的 94 名乳腺癌患者的并行 DCE-MRI 图像和 RNA-seq，利用机器学习中的随机森林开发基于放射组学的 TME 表型特征。然后在内部验证集中验证放射组学特征的可重复性。分析了两个额外的独立外部验证集以重新评估此签名。免疫表型队列 (n = 158) 根据 CD8 细胞浸润分为免疫炎症表型和免疫沙漠表型；这些数据用于检查免疫表型与该特征之间的关系。最后，我们利用包含 77 例接受抗 PD-1/PD-L1 治疗的免疫治疗队列来评估该特征在临床结果方面的预测效率。乳腺癌的 TME 表型分为两个异质簇：簇 A，“免疫炎症”簇，包含大量先天性和适应性免疫细胞浸润；簇 B，“免疫沙漠”簇，具有适度的 TME 细胞浸润。我们构建了 TME 表型的放射组学特征（[AUC] = 0.855；95% CI 0.777–0.932；p < 0.05），并在内部验证集中对其进行了验证（0.844；0.606–1；p < 0.05）。在已知的免疫表型队列中，该特征可以识别免疫炎症或免疫沙漠肿瘤（0.814；0.717-0.911；p < 0.05）。在接受免疫疗法治疗的队列中，具有客观缓解的患者比疾病稳定或进展的患者具有更高的基线放射组学评分（p < 0.05）；此外，放射组学特征在预测免疫治疗反应方面的 AUC 为 0.784（0.643-0.926；p < 0.05）。我们的成像生物标志物是一种实用的放射组学特征，有助于预测接受抗 PD-1/PD-L1 治疗的乳腺癌患者的 TME 表型和临床反应。它在识别“免疫沙漠”表型方面特别有效，并可能有助于其转化为“免疫炎症”表型。