Platelet transcriptome yields progressive markers in chronic myeloproliferative neoplasms and identifies putative targets of therapy

Abstract

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.

Enkurin: A novel marker for myeloproliferative neoplasms from platelet, megakaryocyte, and whole blood specimens

“…Four candidate markers from our prior platelet RNA-seq analyses 20 were identified based on specific trends in progressive expression across the 3 MPN subtypes vis-à-vis healthy donors (FDR<0.01, n=120) independent of patient driver mutational status. ENKUR and CALR, in particular, were identified based on opposing trends in progressive expression (Figure 1A and Figure S1A), CDC20 on its specific high expression in MF patients alone in contrast with healthy donors and ET or PV patients (Figure 1A, top right), and CREB3L1 based on its substantially increased (> 50-fold) expression in all MPNs vs healthy (Figure S1A, top right).…”

“…However, this study was primarily in mice and remains to be confirmed in independent additional MPN models and patient-derived specimens. Our recent investigation, profiling the blood platelet transcriptome in all three subtypes of MPN patients 20 identified high expression of genes and pathways associated with impaired proteostasis, ER stress and unfolded protein response (UPR) well-recognized in other age-related disorders [21][22][23][24][25] . Here, we extend our prior MPN patient platelet transcriptomic data with a focus on genes associated with proteostasis, cell proliferation, ER stress or calcium signaling at high statistical significance (FDR <0.01), and representing four types of trends in platelet RNA expression across MPN subtypes: progressively downregulated, progressively upregulated, consistently upregulated, and uniquely upregulated in MF alone.…”

Message in a platelet: decoding platelet transcriptomes in myeloproliferative neoplasms

“…The ability to predict progression to advanced disease and likelihood of complications including thrombosis is crucial for optimal management. In this issue of Cell Reports Medicine , Shen and colleagues 1 demonstrate that the platelet transcriptome can differentiate between clinically distinct sub-types of myeloproliferative neoplasms (MPNs), a group of chronic blood cancers. They identify key features of the platelet transcriptome that, when combined with basic clinical predictors, can predict advanced stage disease.…”

“…Shen et al. 1 analyzed 120 purified platelet samples from healthy controls and two cohorts of patients with MPN (24 ET, 33 PV, and 40 primary or secondary MF), generating the largest dataset of platelet transcriptomes in MPNs reported to date. Each clinical subtype showed differential gene expression signatures as compared to healthy controls, with the most pronounced changes observed in MF (> 6600 differentially expressed genes).…”

“… Platelets are anucleate but contain a rich repertoire of mRNAs. In this issue of Cell Reports Medicine , Shen and colleagues 1 present platelet transcriptomes from patients with myeloproliferative neoplasms to study disease mechanisms and generate a predictive algorithm for fibrotic progression. …”