LBPO.BCS02 · 生物信息与计算 · Late-Breaking
基于大规模基础模型的PDX模型选择与癌症亚型分配
Large-scale foundation model-based PDX model selection and cancer subtype assignment
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
引言。患者来源异种移植（PDX）模型保留了患者特异性的肿瘤生物学特征和临床异质性；然而，由于各系统和数据集之间标准不一、特征差异，在临床和临床前数据集背景下选择PDX模型具有挑战性。此处，我们将RNA1-DA基础模型（在来自Data4Cure肿瘤样本宇宙的超过180,000份RNA-seq癌症样本上训练）应用于来自Champions Oncology的TumorGraft库的超过1,500个低传代PDX模型的大型队列，从而实现临床与临床前领域间的样本对齐以及转化性PDX模型选择。
方法。将RNA1-DA应用于来自Champions Oncology的TumorGraft库的1,549份低传代PDX RNA-seq样本，以获得领域自适应的样本嵌入。使用UMAP可视化Champions样本与130,313份公共临床和临床前癌症RNA-seq样本的联合嵌入，并通过K近邻（KNN）疾病分类量化样本整合。通过KNN将分子亚型从临床样本迁移至Champions样本，并评估其与生物标志物状态、驱动基因突变以及以肿瘤生长抑制（TGI）衡量的药物反应的一致性。
结果。基于RNA1-DA的Champions PDX样本嵌入与肿瘤样本宇宙中的临床和临床前RNA-seq样本良好整合，实现了75%的准确疾病分类。模型分配的癌症亚型在肿瘤和PDX样本间显示出一致的分子特征，其中乳腺癌亚型再现了预期的ER/PR/HER2标志物状态（p=2e-22），并显示出与临床肿瘤相当的RNA标志物表达。此外，TCGA样本中1964/2270（87%）的亚型-突变和亚型-拷贝数改变关联（q<0.01；跨14种癌症类型）在Champions PDX样本中得到重现，各亚型间变异差异患病率具有强相关性（Pearson r=0.78），支持准确的亚型迁移和模型可转化性。另外，若干PDX迁移的亚型与药物诱导的体内反应表现出显著（p<0.05）关联，这些关联与已知的临床药物反应关联一致。总之，我们的结果支持基础模型整合PDX数据集在临床疗效信号发现、患者分层或生物标志物验证方面的预测和转化价值。
查看英文原文 English abstract
Introduction. Patient-derived xenograft (PDX) models preserve patient-specific tumor biology and clinical heterogeneity; however, selecting PDX models in the context of clinical and preclinical datasets is challenging due to varied criteria and characteristic differences across systems and datasets. Here, we apply the RNA1-DA foundation model, trained on over 180,000 RNA-seq cancer samples from the Data4Cure Oncology Sample Universe, to a large cohort of more than 1,500 low-passage PDX models from Champions Oncology's TumorGraft bank, enabling alignment of samples across clinical and preclinical domains and translational PDX model selection.
Methods. RNA1-DA was applied to 1,549 low-passage PDX RNA-seq samples from Champions Oncology's TumorGraft bank to derive domain-adapted sample embeddings. Joint embedding of Champions samples with 130,313 public clinical and preclinical cancer RNA-seq samples was visualized with UMAP and sample integration was quantified by K-nearest-neighbor (KNN) disease classification. Molecular subtypes were transferred from clinical to Champions samples by KNN and evaluated for concordance with biomarker status, driver gene mutations, and drug response measured by tumor growth inhibition (TGI).
Results. RNA1-DA-based Champions PDX sample embeddings were well-integrated with clinical and preclinical RNA-seq samples in the Oncology Sample Universe, achieving 75% accurate disease classification. Model-assigned cancer subtypes showed concordant molecular profiles across tumor and PDX samples, with breast cancer subtypes reproducing the expected ER/PR/HER2 marker status (p=2e-22) and showing comparable RNA marker expression to clinical tumors. Furthermore, 1964/2270 (87%) of subtype-mutation and subtype-copy number alteration associations in TCGA samples (q<0.01; across 14 cancer types) were recapitulated in Champions PDX samples with strong correlation of variant differential prevalence across subtypes (Pearson r=0.78), supporting accurate subtype transfer and model translatability. Additionally, a number of PDX-transferred subtypes exhibited significant (p<0.05) associations with drug-induced in vivo response that align with known clinical drug response associations. Altogether, our results support the predictive and translational value of foundation model integration of PDX datasets for clinical efficacy signal discovery, patient stratification or biomarker validation.
利益披露 Disclosure
E. O'Brien,
Seres Therapeutics Employment, Stock, Patent.
A. Moreau, None..
G. Henry, None..
G. Silberberg, None..
T. Farid, None..
R. Ronen, None..
J. Dutkowski, None.