Background: Immune cells have essential auxiliary functions and influence clinical outcomes in cancer, with high immune infiltration being associated with improved clinical outcomes and better response to treatment in breast cancer (BC). However, studies to date have not fully considered the tumor-infiltrating immune cell (TIIC) landscape in tumors. This study investigated potential biomarkers based on TIICs to improve prognosis and treatment effect in BC./nResults: We enrolled 5112 patients for analysis and used cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT), a new computational algorithm, to quantify 22 TIICs in primary BC. From the results of univariate Cox regression, 12 immune cells were determined to be significantly related to the overall survival (OS) of BC patients. Furthermore, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were applied to construct an immune prognostic model based on six potential biomarkers. By dividing patients into low- and high-risk groups, a significant distinction in OS was found in the training cohort, with 20-year survival rates of 42.6% and 26.3%, respectively. Applying a similar protocol to validation and test cohorts, we found that OS was significantly shorter in the high-risk group than in the low-risk group, regardless of the molecular subtype of BC. Using the immune score model to predict the effect of BC patients to chemotherapy, the survival advantage for the low-risk group was evident among those who received chemotherapy, regardless of the chemotherapy regimen. In evaluating the predictive value of the nomogram, a decision curve showed better predictive accuracy than the standard tumor-node-metastasis (TNM) staging system./nConclusion: The immune cell infiltration-based immune score model can be effectively and efficiently used to predict the prognosis of BC patients as well as the effect of chemotherapy.

中文翻译：

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基于免疫细胞浸润的免疫评分模型可预测乳腺癌的预后和化疗效果

背景：免疫细胞具有基本的辅助功能并影响癌症的临床结局，而高度的免疫浸润与改善的临床结局和对乳腺癌（BC）的治疗反应更好有关。但是，迄今为止的研究尚未完全考虑肿瘤中的肿瘤浸润免疫细胞（TIIC）格局。本研究探讨基于TIICs改善预后和治疗效果BC./n潜在生物标志物结果：我们招募了5112名患者进行分析，并通过估计新的计算算法RNA转录本的相对子集（CIBERSORT）（用于量化原发性BC中的22个TIIC）来进行细胞类型鉴定。根据单变量Cox回归的结果，确定有12个免疫细胞与BC患者的总体生存（OS）显着相关。此外，应用最小绝对收缩和选择算子（LASSO）和多元Cox回归分析，基于六个潜在的生物标志物构建免疫预后模型。通过将患者分为低风险和高风险组，在训练队列中发现了OS的显着差异，其20年生存率分别为42.6％和26.3％。将类似的协议应用于验证和测试队列，我们发现，无论BC的分子亚型如何，高风险组的OS明显短于低风险组。使用免疫评分模型预测BC患者接受化疗的效果，无论接受何种化疗方案，接受化疗的患者中低风险组的生存优势均显而易见。在评估列线图的预测值时，决策曲线显示出比标准肿瘤-淋巴结转移（TNM）分期系统更好的预测准确性。结论：基于免疫细胞浸润的免疫评分模型可有效，有效地预测BC患者的预后以及化疗效果。