Journal Article

Biological heterogeneity in idiopathic pulmonary arterial hypertension identified through unsupervised transcriptomic profiling of whole blood

Corris, Paul A., Wort, Stephen, Wang, Dennis, Morrell, Nicholas W., Wharton, John, Rischard, Franz, Kariotis, Sokratis, Rhodes, Christopher J., Iremonger, James, Yuan, Jason X.-J., Toshner, Mark, Coghlan, Gerry, Romanoski, Casey E., Church, Colin, Howard, Luke S., Mascarenhas, Thomas S., Condliffe, Robin, Lordan, Jim, Dunning, Mark J., Pandya, Divya, Gräf, Stefan, Otero, Pablo, Errington, Niamh, Pepke-Zaba, Joanna, Jammeh, Emmanuel, Swietlik, Emilia M., Desai, Ankit A., Pickworth, Josephine A., Thompson, A. A. Roger, Wilkins, Martin R., Lawrie, Allan, Garcia, Joe G. N., Kiely, David G., An, Tae-Hwi Schwantes

2021

Overview

Idiopathic pulmonary arterial hypertension (IPAH) is a rare but fatal disease diagnosed by right heart catheterisation and the exclusion of other forms of pulmonary arterial hypertension, producing a heterogeneous population with varied treatment response. Here we show unsupervised machine learning identification of three major patient subgroups that account for 92% of the cohort, each with unique whole blood transcriptomic and clinical feature signatures. These subgroups are associated with poor, moderate, and good prognosis. The poor prognosis subgroup is associated with upregulation of the ALAS2 and downregulation of several immunoglobulin genes, while the good prognosis subgroup is defined by upregulation of the bone morphogenetic protein signalling regulator NOG , and the C/C variant of HLA-DPA1/DPB1 (independently associated with survival). These findings independently validated provide evidence for the existence of 3 major subgroups (endophenotypes) within the IPAH classification, could improve risk stratification and provide molecular insights into the pathogenesis of IPAH. Idiopathic pulmonary arterial hypertension is a rare and fatal disease with a heterogeneous treatment response. Here the authors show that unsupervised machine learning of whole blood transcriptomes from 359 patients with idiopathic pulmonary arterial hypertension identifies 3 subgroups (endophenotypes) that improve risk stratification and provide new molecular insights.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 38/91 / 49/39 / 5-Aminolevulinate Synthetase / 631/114/1386 / 631/114/2404 / 631/1647/2217 / 692/4019/592/75 / Blood / Coronary artery disease / Down-Regulation / Familial Primary Pulmonary Hypertension - genetics / Familial Primary Pulmonary Hypertension - metabolism / Gene Expression Profiling / Health services / Heart diseases / Heterogeneity / Histocompatibility antigen HLA / HLA-DP beta-Chains / Humanities and Social Sciences / Humans / Hypertension / Interferon / Learning algorithms / Life Sciences / Machine learning / Medical prognosis / multidisciplinary / Pathogenesis / Patients / Prognosis / Pulmonary Arterial Hypertension / Pulmonary hypertension / Science / Science (multidisciplinary) / Subgroups / Transcriptome / Transcriptomes / Transcriptomics