PRISM：从单个复合 RNA-seq 样本中恢复细胞类型特异性表达谱。

PRISM: recovering cell-type-specific expression profiles from individual composite RNA-seq samples.

机构信息

Research Programs Unit, Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, FI-00014 Helsinki, Finland.

Department of Pathology, University of Helsinki and HUSLAB, Helsinki University Hospital, FI-00014 Helsinki, Finland.

出版信息

Bioinformatics. 2021 Sep 29;37(18):2882-2888. doi: 10.1093/bioinformatics/btab178.

DOI:10.1093/bioinformatics/btab178

PMID:33720334

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8479664/

Abstract

MOTIVATION

A major challenge in analyzing cancer patient transcriptomes is that the tumors are inherently heterogeneous and evolving. We analyzed 214 bulk RNA samples of a longitudinal, prospective ovarian cancer cohort and found that the sample composition changes systematically due to chemotherapy and between the anatomical sites, preventing direct comparison of treatment-naive and treated samples.

RESULTS

To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell-type-specific whole-transcriptome profiles adapted to each individual sample. Our results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. We validated our findings in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell-type-specific expression through whole-genome sequencing and RNA in situ hybridization experiments.

AVAILABILITYAND IMPLEMENTATION

https://bitbucket.org/anthakki/prism.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

分析癌症患者转录组的一个主要挑战是肿瘤具有内在的异质性和进化性。我们分析了 214 个纵向、前瞻性卵巢癌队列的批量 RNA 样本，发现由于化疗和解剖部位之间的样本组成发生了系统变化，阻止了对未经治疗和治疗样本的直接比较。

结果

为了克服这一问题，我们开发了 PRISM，这是一个潜在的统计框架，可以同时提取适用于每个个体样本的样本组成和细胞类型特异性全转录组谱。我们的结果表明，PRISM 衍生的无成分转录组谱及其衍生的特征比复合原始批量数据更好地预测了患者的反应。我们在独立的卵巢癌和黑色素瘤队列中验证了我们的发现，并验证了 PRISM 通过全基因组测序和 RNA 原位杂交实验准确估计了组成和细胞类型特异性表达。