BACKGROUND Distinguishing ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) regions in clinical biopsies constitutes a diagnostic challenge. Spatial transcriptomics (ST) is an in situ capturing method, which allows quantification and visualization of transcriptomes in individual tissue sections. In the past, studies have shown that breast cancer samples can be used to study their transcriptomes with spatial resolution in individual tissue sections. Previously, supervised machine learning methods were used in clinical studies to predict the clinical outcomes for cancer types. METHODS We used four publicly available ST breast cancer datasets from breast tissue sections annotated by pathologists as non-malignant, DCIS, or IDC. We trained and tested a machine learning method (support vector machine) based on the expert annotation as well as based on automatic selection of cell types by their transcriptome profiles. RESULTS We identified expression signatures for expert annotated regions (non-malignant, DCIS, and IDC) and build machine learning models. Classification results for 798 expression signature transcripts showed high coincidence with the expert pathologist annotation for DCIS (100%) and IDC (96%). Extending our analysis to include all 25,179 expressed transcripts resulted in an accuracy of 99% for DCIS and 98% for IDC. Further, classification based on an automatically identified expression signature covering all ST spots of tissue sections resulted in prediction accuracy of 95% for DCIS and 91% for IDC. CONCLUSIONS This concept study suggest that the ST signatures learned from expert selected breast cancer tissue sections can be used to identify breast cancer regions in whole tissue sections including regions not trained on. Furthermore, the identified expression signatures can classify cancer regions in tissue sections not used for training with high accuracy. Expert-generated but even automatically generated cancer signatures from ST data might be able to classify breast cancer regions and provide clinical decision support for pathologists in the future.

中文翻译：

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鉴定和转移用于癌症诊断的空间转录组签名。

背景技术在临床活检中将导管原位癌（DCIS）与浸润性导管癌（IDC）区区分开构成诊断挑战。空间转录组学（ST）是一种原位捕获方法，可以对单个组织切片中的转录组进行定量和可视化。过去，研究表明，乳腺癌样本可用于在单个组织切片中以空间分辨率研究其转录组。以前，在临床研究中使用有监督的机器学习方法来预测癌症类型的临床结果。方法我们使用了来自乳腺组织切片的四个可公开获得的ST乳腺癌数据集，病理学家将其标记为非恶性，DCIS或IDC。我们根据专家注释以及通过转录组概况自动选择细胞类型，对机器学习方法（支持向量机）进行了培训和测试。结果我们确定了专​​家注释区域（非恶性，DCIS和IDC）的表达签名，并建立了机器学习模型。798个表达签名笔录的分类结果与DCIS（100％）和IDC（96％）的专家病理学家注释高度吻合。将我们的分析扩展到包括所有25,179个表达的成绩单，DCIS的准确性为99％，IDC的准确性为98％。此外，基于覆盖组织切片的所有ST点的自动识别的表达特征进行分类，DCIS的预测准确性为95％，IDC的预测准确性为91％。结论该概念研究表明，从专家选择的乳腺癌组织切片中获悉的ST签名可用于识别整个组织切片中的乳腺癌区域，包括未经训练的区域。此外，所识别的表达特征可以高精度地将未用于训练的组织切片中的癌区域分类。由ST数据生成的专家生成的，甚至自动生成的癌症特征也许能够对乳腺癌区域进行分类，并在将来为病理学家提供临床决策支持。所识别的表达特征可以将未用于训练的组织切片中的癌区域进行高精度分类。由ST数据生成的专家生成的，甚至自动生成的癌症特征也许能够对乳腺癌区域进行分类，并在将来为病理学家提供临床决策支持。所识别的表达特征可以将未用于训练的组织切片中的癌区域进行高精度分类。由ST数据生成的专家生成的，甚至自动生成的癌症特征也许能够对乳腺癌区域进行分类，并在将来为病理学家提供临床决策支持。