基于机器学习的放射组学特征用于评估乳腺癌 TME 表型和预测抗 PD-1/PD-L1 免疫治疗反应的建立。

BACKGROUNDS: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

背景：由于乳腺癌患者对免疫疗法的反应各不相同，因此迫切需要探索新的生物标志物，以准确预测临床反应并提高治疗效果。我们目前的研究目的是通过基于机器学习的放射组学方法构建和独立验证肿瘤微环境（TME）表型的生物标志物，并揭示该生物标志物与 TME 表型和受体临床反应之间的相互关系。

方法：在这项回顾性多队列研究中，我们招募了五组不同的乳腺癌患者，通过放射组学特征来测量乳腺癌的 TME 表型，该特征是通过整合 RNA-seq 数据和 DCE-MRI 图像来构建和验证的，用于预测免疫治疗反应。首先，我们使用 TCGA 数据库中 1089 名乳腺癌患者的 RNA-seq 构建 TME 表型。然后，将来自 TCIA 的 94 名乳腺癌患者的平行 DCE-MRI 图像和 RNA-seq 应用于机器学习中的随机森林，以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。另外两个独立的外部验证集用于重新评估该特征。免疫表型队列（n=158）根据 CD8 细胞浸润分为免疫炎症和免疫荒漠表型；利用这些数据来研究免疫表型与该特征之间的关系。最后，我们利用包含 77 名接受抗 PD-1/PD-L1 治疗的免疫治疗患者的队列来评估该特征在临床结局方面的预测效率。

结果：乳腺癌的 TME 表型分为两个异质簇：簇 A，“免疫炎症”簇，包含大量固有和适应性免疫细胞浸润；簇 B，“免疫荒漠”簇，TME 细胞浸润适度。我们构建了一个用于 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，该特征可识别免疫炎症或免疫荒漠肿瘤（0.814；0.717-0.911；p<0.05）。在免疫治疗组中，有客观反应的患者基线放射组学评分高于病情稳定或进展的患者（p<0.05）；此外，放射组学特征对预测免疫治疗反应的 AUC 为 0.784（0.643-0.926；p<0.05）。

结论：我们的成像生物标志物是一种可行的放射组学特征，有助于预测抗 PD-1/PD-L1 治疗的乳腺癌患者的 TME 表型和临床反应。它特别有助于识别“免疫荒漠”表型，并可能有助于将其转化为“免疫炎症”表型。