Bhattacharya Namrata, Rockstroh Anja, Deshpande Sanket Suhas, Thomas Sam Koshy, Yadav Anunay, Goswami Chitrita, Chawla Smriti, Solomon Pierre, Fourgeux Cynthia, Ahuja Gaurav, Hollier Brett, Kumar Himanshu, Roquilly Antoine, Poschmann Jeremie, Lehman Melanie, Nelson Colleen C, Sengupta Debarka
Australian Prostate Cancer Research Centre-Queensland, Faculty of Health, School of Biomedical Sciences, Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, Australia.
Department of Computer Science and Engineering, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi, India.
Elife. 2025 Jun 13;13:RP98469. doi: 10.7554/eLife.98469.
Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and non-malignant cell subtypes from single-cell profiling of tumors. However, given the extent of intra-tumoral heterogeneity, it is challenging to assess the risk associated with individual cell subpopulations, primarily due to the complexity of the cancer phenotype space and the lack of clinical annotations associated with tumor scRNA-seq studies. To this end, we introduce SCellBOW, a scRNA-seq analysis framework inspired by document embedding techniques from the domain of Natural Language Processing (NLP). SCellBOW is a novel computational approach that facilitates effective identification and high-quality visualization of single-cell subpopulations. We compared SCellBOW with existing best practice methods for its ability to precisely represent phenotypically divergent cell types across multiple scRNA-seq datasets, including our in-house generated human splenocyte and matched peripheral blood mononuclear cell (PBMC) dataset. For tumor cells, SCellBOW estimates the relative risk associated with each cluster and stratifies them based on their aggressiveness. This is achieved by simulating how the presence or absence of a specific cell subpopulation influences disease prognosis. Using SCellBOW, we identified a hitherto unknown and pervasive AR-/NE (androgen-receptor-negative, neuroendocrine-low) malignant subpopulation in metastatic prostate cancer with conspicuously high aggressiveness. Overall, the risk-stratification capabilities of SCellBOW hold promise for formulating tailored therapeutic interventions by identifying clinically relevant tumor subpopulations and their impact on prognosis.
单细胞RNA测序（scRNA-seq）与强大的计算分析相结合，有助于表征肿瘤内的表型异质性。当前的scRNA-seq分析流程能够从肿瘤的单细胞分析中识别出无数的恶性和非恶性细胞亚型。然而，鉴于肿瘤内异质性的程度，评估与单个细胞亚群相关的风险具有挑战性，这主要是由于癌症表型空间的复杂性以及缺乏与肿瘤scRNA-seq研究相关的临床注释。为此，我们引入了SCellBOW，这是一种受自然语言处理（NLP）领域的文档嵌入技术启发的scRNA-seq分析框架。SCellBOW是一种新颖的计算方法，有助于有效识别单细胞亚群并进行高质量可视化。我们将SCellBOW与现有的最佳实践方法进行了比较，以评估其在多个scRNA-seq数据集中精确表征表型不同细胞类型的能力，包括我们内部生成的人类脾细胞和匹配的外周血单核细胞（PBMC）数据集。对于肿瘤细胞，SCellBOW估计与每个簇相关的相对风险，并根据其侵袭性对它们进行分层。这是通过模拟特定细胞亚群的存在或缺失如何影响疾病预后来实现的。使用SCellBOW，我们在转移性前列腺癌中鉴定出一种迄今未知且普遍存在的AR-/NE（雄激素受体阴性、神经内分泌低）恶性亚群，其侵袭性明显较高。总体而言，SCellBOW的风险分层能力有望通过识别临床相关的肿瘤亚群及其对预后的影响来制定量身定制的治疗干预措施。
