Targeting a regulator of protein homeostasis in myeloproliferative neoplasms

LaFave, Lindsay M.; Levine, Ross L.

doi:10.1038/nm.4028

Cited by 8 publications

(14 citation statements)

References 12 publications

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“…In conclusion, using platelet transcriptome profiling, we observed dynamic shifts in MPN immune inflammatory profile and preferential expression of interferon-, proliferation-, and proteostasis-associated genes as a progressive gradient across the three MPN subtypes. Our findings highlight that MPN progression may be influenced by defects in protein homeostasis (impaired protein folding and an accumulation of misfolded proteins within the endoplasmic reticulum) and an abnormal integrated stress response—consistent with recent studies 61 , 69 , 70 , 85 indicating dysregulated proteostasis as a primary effector of myeloid transformation. While this work has been focused on the overarching progressive platelet transcriptome across MPNs, these data open an important avenue for utilizing platelet RNA signatures to better understand specific MPN complications such as the risk of thrombosis and bleeding, or fibrosis, and transformation to AML.…”

Section: Discussionsupporting

confidence: 91%

“…Our analyses confirm and extend many important observations made previously either in vitro, 69 , 70 , 71 in other transcriptome or microarray analyses 15 , 38 , 40 , 4 , 72 , 73 including our own early work, 36 or using animal models. 31 , 74 , 75 , 76 By highlighting intersecting mechanisms in transcription across MPN, and by annotating MPN subtype-specific gene signatures, this dataset facilitates predictive machine-learning algorithms, that aid in MPN classification and potential prognostication.…”

Section: Discussionsupporting

confidence: 89%

“…Most notably in our data on MF, high expression of ER stress and unfolded protein response (UPR) biomarkers (e.g., CREB3L1, CALR) associated with impaired proteostasis signaling and emerged as a key feature of MPN pathobiology. Indeed, recently published works from distinct research groups 69,70,85 highlight protein quality control in ER-associated degradation and proteostasis 69,70 deregulation as a primary effector of myeloid transformation, highlighting the importance of protein homeostasis for normal hematopoiesis. These findings too are in line with reports 86 implicating chronic ER stress, malfunctioning protein quality control, and loss of proteostasis as aggravating factors in age-related disorders.…”

Section: Discussionmentioning

confidence: 99%

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Platelet transcriptome identifies progressive markers and potential therapeutic targets in chronic myeloproliferative neoplasms

Shen¹,

Du²,

Perkins³

et al. 2021

Cell Reports Medicine

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Predicting disease progression remains a particularly challenging endeavor in chronic degenerative disorders and cancer, thus limiting early detection, risk stratification, and preventive interventions. Here, profiling the three chronic subtypes of myeloproliferative neoplasms (MPNs), we identify the blood platelet transcriptome as a proxy strategy for highly sensitive progression biomarkers that also enables prediction of advanced disease via machine-learning algorithms. The MPN platelet transcriptome reveals an incremental molecular reprogramming that is independent of patient driver mutation status or therapy. Subtype-specific markers offer mechanistic and therapeutic insights, and highlight impaired proteostasis and a persistent integrated stress response. Using a LASSO model with validation in two independent cohorts, we identify the advanced subtype MF at high accuracy and offer a robust progression signature toward clinical translation. Our platelet transcriptome snapshot of chronic MPNs demonstrates a proof-of-principle for disease risk stratification and progression beyond genetic data alone, with potential utility in other progressive disorders.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Platelet transcriptome identifies progressive markers and potential therapeutic targets in chronic myeloproliferative neoplasms

Shen¹,

Du²,

Perkins³

et al. 2021

Cell Reports Medicine

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“…Our analyses confirm and extend many important observations made previously either in vitro (LaFave and Levine, 2016;Merlinsky et al, 2019;Osorio et al, 2016), in other transcriptome analyses (Gangaraju et al, 2020;Guo et al, 2019;Skov et al, 2012) including our own early work (Krishnan et al, 2017), or using animal models (Dunbar et al, 2017;Mullally et al, 2013). By highlighting intersecting mechanisms in transcription and splicing across MPN, and by annotating MPN subtype-specific gene signatures, this dataset facilitates predictive machine learning algorithms, that aid in MPN classification and potential prognostication.…”

Section: Discussionsupporting

confidence: 90%

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

Shen

Perkins

et al. 2021

Preprint

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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.

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“…3,4 AIRAPL is an endoplasmic reticulum (ER) protein that contains a CAAX motif, similar to those present in Ras proteins or nuclear lamins. Currently available functional data on AIRAPL had been obtained from its Caenorhabditis elegans ortologue aip-1.…”

mentioning

confidence: 99%