Cancer is a complex disease driven by genomic alterations, and tumor
sequencing is becoming a mainstay of clinical care for cancer patients. The
emergence of multi-institution sequencing data presents a powerful resource for
learning real-world evidence to enhance precision oncology. GENIE BPC, led by
the American Association for Cancer Research, establishes a unique database
linking genomic data with clinical information for patients treated at multiple
cancer centers. However, leveraging such multi-institutional sequencing data
presents significant challenges. Variations in gene panels result in loss of
information when the analysis is conducted on common gene sets. Additionally,
differences in sequencing techniques and patient heterogeneity across
institutions add complexity. High data dimensionality, sparse gene mutation
patterns, and weak signals at the individual gene level further complicate
matters. Motivated by these real-world challenges, we introduce the Bridge
model. It uses a quantile-matched latent variable approach to derive integrated
features to preserve information beyond common genes and maximize the
utilization of all available data while leveraging information sharing to
enhance both learning efficiency and the model's capacity to generalize. By
extracting harmonized and noise-reduced lower-dimensional latent variables, the
true mutation pattern unique to each individual is captured. We assess the
model's performance and parameter estimation through extensive simulation
studies. The extracted latent features from the Bridge model consistently excel
in predicting patient survival across six cancer types in GENIE BPC data.

癌症是由基因组变异驱动的复杂疾病，肿瘤测序成为癌症患者临床护理的核心，多机构测序数据的出现为增强精准肿瘤学提供了有力资源。然而，利用这种多机构测序数据面临着重大挑战，包括基因面板的差异导致常见基因集上信息的丢失，测序技术和机构间患者异质性的差异增加了复杂性，高数据维度、稀疏基因突变模式和个体基因水平的弱信号进一步复杂化了情况。为了克服这些实际挑战，我们介绍了 Bridge 模型，它采用分位匹配的潜变量方法来获得整合特征，以保留超出常见基因之外的信息，并最大限度地利用所有可用的数据，同时利用信息共享来增强学习效率和模型的泛化能力。通过提取协调和去噪的低维潜变量，捕捉到了每个个体独特的真实突变模式。我们通过广泛的模拟研究评估了模型的性能和参数估计，从 Bridge 模型中提取的潜变量特征在 GENIE BPC 数据中始终表现出色，可以预测六种癌症类型中的患者生存情况。

发掘多机构数据的力量：整合和协调跨机构的基因组数据

Unlocking the Power of Multi-institutional Data: Integrating and  Harmonizing Genomic Data Across Institutions

Specialised transformers-based models (such as BioBERT and BioMegatron) are
adapted for the biomedical domain based on publicly available biomedical
corpora. As such, they have the potential to encode large-scale biological
knowledge. We investigate the encoding and representation of biological
knowledge in these models, and its potential utility to support inference in
cancer precision medicine - namely, the interpretation of the clinical
significance of genomic alterations. We compare the performance of different
transformer baselines; we use probing to determine the consistency of encodings
for distinct entities; and we use clustering methods to compare and contrast
the internal properties of the embeddings for genes, variants, drugs and
diseases. We show that these models do indeed encode biological knowledge,
although some of this is lost in fine-tuning for specific tasks. Finally, we
analyse how the models behave with regard to biases and imbalances in the
dataset.

我们研究了基于 transformer 模型（如 BioBERT 和 BioMegatron）在公开的生物医学语料库的基础上如何适应生物医学领域，以及它们编码和表征生物知识的潜力和在癌症精准医学中的应用 - 即，解释基因组变异的临床意义。通过探测、分析和比较这些模型中的基因、变异、药物和疾病的嵌入内部属性，我们证明这些模型确实对生物知识进行了编码，但在针对特定任务进行微调时会失去部分知识。最后，我们分析了模型在数据集中偏见和不平衡方面的行为。