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

Large Language Models (LLMs) have demonstrated superior results across a wide
range of tasks, while retrieval has long been established as an effective means
of obtaining task-relevant information for humans. Retrieval-augmented
Generation (RAG) are known for their effectiveness in knowledge-intensive tasks
by locating relevant information and placing it within the context window of
the LLM. However, the relationship between retrievers and LLMs is still
under-investigated. Most existing work treats the retriever and the LLM as
independent components and leaves a gap between retrieving human-friendly
information and assembling a LLM-friendly context. In this work, we examine a
novel bridge model, validate the ranking and selection assumptions in
retrievers in the context of RAG, and propose a training framework that chains
together supervised and reinforcement learning to learn a bridge model.
Empirical results demonstrate the effectiveness of our method in both
question-answering and personalized generation tasks.

我们提出了一个新的桥接模型，并验证了 RAG 中检索器的排序和选择假设，并提出了一个将监督学习和强化学习连接起来的培训框架，在问答和个性化生成任务中证明了我们方法的有效性。