We investigated the data-driven relationship between features in the tumor microenvironment (TME) and the overall and 5-year survival in triple-negative breast cancer (TNBC) and non-TNBC (NTNBC) patients by using Explainable Artificial Intelligence (XAI) models. We used clinical information from patients with invasive breast carcinoma from The Cancer Genome Atlas and from two studies from the cbioPortal, the PanCanAtlas project and the GDAC Firehose study. In this study, we used a normalized RNA sequencing data-driven cohort from 1,015 breast cancer patients, alive or deceased, from the UCSC Xena data set and performed integrated deconvolution with the EPIC method to estimate the percentage of seven different immune and stromal cells from RNA sequencing data. Novel insights derived from our XAI model showed that CD4+ T cells and B cells are more critical than other TME features for enhanced prognosis for both TNBC and NTNBC patients. Our XAI model revealed the critical inflection points (i.e., threshold fractions) of CD4+ T cells and B cells above or below which 5-year survival rates improve. Subsequently, we ascertained the conditional probabilities of $\geq$ 5-year survival in both TNBC and NTNBC patients under specific conditions inferred from the inflection points. In particular, the XAI models revealed that a B-cell fraction exceeding 0.018 in the TME could ensure 100% 5-year survival for NTNBC patients. The findings from this research could lead to more accurate clinical predictions and enhanced immunotherapies and to the design of innovative strategies to reprogram the TME of breast cancer patients.
翻译:我们利用可解释的人工智能模型(XAI),调查肿瘤微环境(TME)特征与三阴乳腺癌(TNBC)患者和非TNBC(NTNBC)患者总体和5年存活率之间的数据驱动关系,我们使用了癌症基因组图集以及生物港、PanCanAtlas项目和GDAC Firehose研究的两份研究中侵入性乳腺癌患者的临床信息,调查了肿瘤微环境(TME)特征与三阴乳腺癌(TNBC)患者(TNBC)和非TNBC患者(NTNBC)总体和5年存活率之间的数据驱动群之间的数据驱动关系。我们使用了来自UCSC Xena数据集的1 015个有生命或已死亡的乳腺癌患者的正常的RNA数据排序,并与EPIC方法进行了综合解剖,以估计RNA测得的7个不同的免疫细胞和运动细胞的百分比。我们XAD4+B细胞和NTNBC癌症患者5年存活率的模型和5年稳定值的精确值,可以提高X的NBBCSBC 和5年或5年生存率。