RNA1-DA：一种用于正向与反向转化的领域自适应RNA基础模型

Introduction. Preclinical cancer models and patient tumors differ in cellular composition, molecular profiles, and environmental contexts. To enable translation between clinical and preclinical systems, we developed a new foundation model, RNA1-DA, with domain adaptation between tumor, cell line, organoid, and xenograft samples. We demonstrate that RNA1-DA enables key translational research tasks, including molecular subtype transfer, preclinical model selection, and drug response prediction. Methods. We previously described RNA1 - a transformer-based RNA expression foundation model trained on 182,383 bulk RNA-seq cancer samples with self-supervised and multi-task training. Here we develop RNA1-DA, which extends RNA1 to enable the joint integration of clinical and preclinical samples using (a) a layer to deconvolve cancer cell expression from tumor samples, and (b) a domain adaptation layer using an adversarial autoencoder to integrate RNA1 embeddings across sample types. We developed a systematic evaluation framework to assess clinical-preclinical alignment in RNA1-DA embeddings by measuring the preservation and transfer of disease identity, molecular subtypes, cancer driver gene biology, and drug response across systems. Results. Using RNA1-DA, we integrated 30,810 tumor tissue, 94,973 cell line, 714 organoid, 1290 cell line-derived xenograft, and 2526 patient-derived xenograft samples. RNA1-DA aligned key biological structure across systems, including disease identity, molecular subtypes, and driver gene alterations. Preclinical samples achieved 62-88% accurate disease classification based on proximity to clinical tumors, outperforming comparator methods. Thirteen TCGA and 61 RNA1-derived clinical molecular subtypings were systematically transferred from clinical to preclinical samples and showed concordance with canonical markers and genetic dependencies; for example, assigned breast cancer subtypes matched canonical cell line subtype annotations (p=1.8e-8) and known genetic dependencies (e.g., ESR1, ERBB2, CDK4). Translational model selection was further supported by a novel transcriptomic-genomic neighbor overlap metric, which demonstrated significant correspondence (p<0.05) between RNA1-DA embedding neighbors and driver gene alteration neighbors across all 15 cancers evaluated. In addition, RNA1-DA enabled improved cell line drug response prediction through multi-task fine-tuning on CTRP screens, achieving substantially higher performance than baseline methods (median Spearman correlation 0.60 vs 0.35). Together, these results demonstrate the utility of RNA1-DA in supporting key translational research tasks within a unified framework, including molecular subtype transfer, preclinical model selection, and drug response prediction.