A deep-transfer-learning framework to transfer clinical information to single cells and spatial locations in cancer tissues

总结 在过去的10年里，出现了新的高分辨率分子数据的爆炸，这彻底改变了 了解和治疗癌症的方式。它们包括单细胞转录组学、空间转录组学和 计算机图像分析然而，研究这些数据与临床结果的相关性， 因为存活、复发、转移和药物反应被抛在后面。与此同时，深度学习领域 随着包括生物数据在内的许多不同应用的迅速成熟。它经常使用多层 神经网络模型来学习和提取数据的高度非线性表示。迁移学习是 机器学习的一个子领域，专注于将从一组源示例中学习到的知识转移到 其他类型的样品。结合这两种方法构成了深度迁移学习，是一种很有前途的方法。 调查和了解这些新癌症数据的高分辨率成分之间的关联的解决方案 以及相应的临床结果。在这里，我们建议使用深度迁移学习来转移患者 从大的患者转录组学群组学习到细胞、细胞类型、空间区域 和图像特征，然后可以根据其分配的风险进一步确定优先级，并将其评估为潜在的 攻击性癌症的靶点。具体来说，我们将开发深度迁移学习框架DEGAS， 细胞类型优先化和对胶质母细胞瘤和多发性骨髓瘤单细胞数据的测试以验证该方法。 然后它将应用于更具侵袭性的癌症类型的单细胞数据，如三阴性乳腺癌， 胰腺导管腺癌、非小细胞肺癌和胃癌，以优先考虑高风险细胞， 细胞类型。然后，它将进一步修改，用于空间转录组（ST）数据，以优先考虑高风险 乳腺癌和胰腺导管腺癌肿瘤的空间区域。由于ST数据可以作为 单细胞到患者水平转录组学和组织学图像之间的桥梁。我们将进一步利用 通过ST数据将组织学图像特征与患者风险联系起来，识别高风险图像特征。 最后，我们的框架将内置到R和Python包中，可通过GitHub和Bioconductor获得， 更广泛的癌症研究社区使用。

SUMMARY In the past 10 years, there has been an explosion of new high-resolution molecular data which revolutionize the way that cancer is understood and treated. They include, single cell transcriptomics, spatial transcriptomics, and computational image analysis. However, the study of the association of those data with clinical outcomes such as survival, relapse, metastasis and drug response were left behind. In the meantime, Deep learning field is maturing very fast with many diverse applications including on biological data. It frequently utilizes multi-layer neural network models to learn and extract highly non-linear representations of data. Transfer learning is the subfield of machine learning, which focuses on transferring knowledge learned from a set of source examples to another types of samples. Combining these two approaches constitutes deep transfer learning and is a promising solution to investigate and understand the association of high-resolution components of these new cancer data with the corresponding clinical outcomes. Here we propose the use of deep transfer learning to transfer patient outcome information learned from large patient transcriptomics cohorts to the cells, cell types, spatial regions, and image features, which can then be further prioritized by their assigned risks and be evaluated as potential targets in the aggressive cancers. Specifically, we will develop deep transfer learning frameworks DEGAS for cell type prioritization and test on glioblastoma and multiple myeloma single cell data to validate this approach. Then it will be applied on single cell data of more aggressive cancer types such as triple negative breast cancer, pancreatic ductal adenocarcinoma, non-small-cell lung cancer, and gastric cancer to prioritize high risk cells and cell types. Then, it will be further modified for use with spatial transcriptomic (ST) data to prioritize high risk spatial regions of breast cancer and pancreatic ductal adenocarcinoma tumors. Since ST data can act as a bridge between single cell to patient-level transcriptomics, and histology images. We will further leverage our framework to identify high risk image features by linking histology image features to patient risk via ST data. Finally, our framework will be built into R and Python packages available through GitHub and Bioconductor for use by the broader cancer research community.