Predicting disease natural history remains a particularly challenging endeavor in chronic degenerative disorders and cancer, thus limiting early detection, risk stratification, and preventive interventions. Here, profiling the spectrum of chronic myeloproliferative neoplasms (MPNs), as a model, we identify the blood platelet transcriptome as a generalizable strategy for highly sensitive progression biomarkers that also enable prediction via machine learning algorithms. Using RNA sequencing (RNA seq), we derive disease relevant gene expression and alternative splicing in purified platelets from 120 peripheral blood samples constituting two independently collected and mutually validating patient cohorts of the three MPN subtypes: essential thrombocythemia, ET (n=24), polycythemia vera, PV (n=33), and primary or post ET/PV secondary myelofibrosis, MF (n=42), as well as healthy donors (n=21). The MPN platelet transcriptome discriminates each clinical phenotype and reveals an incremental molecular reprogramming that is independent of patient driver mutation status or therapy. Leveraging this dataset, in particular the progressive expression gradient noted across MPN, we develop a machine learning model (Lasso-penalized regression) predictive of the advanced subtype MF at high accuracy (AUC-ROC of 0.95-0.96) with validation under two conditions: i) temporal, with training on the first cohort (n=71) and independent testing on the second (n=49) and ii) 10 fold cross validation on the entire dataset. Lasso-derived signatures offer a robust core set of < 10 MPN progression markers. Mechanistic insights from our data highlight impaired protein homeostasis as a prominent driver of MPN evolution, with persistent integrated stress response. We also identify JAK inhibitor-specific signatures and other interferon, proliferation, and proteostasis associated markers as putative targets for MPN-directed therapy. Our platelet transcriptome snapshot of chronic MPNs establishes a methodological foundation for deciphering disease risk stratification and progression beyond genetic data alone, thus presenting a promising avenue toward potential utility in a wide range of age-related disorders.
Platelet transcriptome identifies progressive markers and potential therapeutic targets in chronic myeloproliferative neoplasms
