Clinical parameter-based prediction of DNA methylation classification generates a prediction model of prognosis in patients with juvenile myelomonocytic leukemia.

Juvenile myelomonocytic leukemia (JMML) is a rare heterogeneous hematological malignancy of early childhood characterized by causative RAS pathway mutations. Classifying patients with JMML using global DNA methylation profiles is useful for risk stratification. We implemented machine learning algorithms (decision tree, support vector machine, and naïve Bayes) to produce a DNA methylation-based classification according to recent international consensus definitions using a well-characterized pooled cohort of patients with JMML (n = 128). DNA methylation was originally categorized into three subgroups: high methylation (HM), intermediate methylation (IM), and low methylation (LM), which is a trichotomized classification. We also dichotomized the subgroups as HM/IM and LM. The decision tree model showed high concordances with 450k-based methylation [82.3% (106/128) for the dichotomized and 83.6% (107/128) for the trichotomized subgroups, respectively]. With an independent cohort (n = 72), we confirmed that these models using both the dichotomized and trichotomized classifications were highly predictive of survival. Our study demonstrates that machine learning algorithms can generate clinical parameter-based models that predict the survival outcomes of patients with JMML and high accuracy. These models enabled us to rapidly and effectively identify candidates for augmented treatment following diagnosis

International Consensus Definition of DNA Methylation Subgroups in Juvenile Myelomonocytic Leukemia

PurposeKnown clinical and genetic markers have limitations in predicting disease course and outcome in juvenile myelomonocytic leukemia (JMML). DNA methylation patterns in JMML have correlated with outcome across multiple studies, suggesting it as a biomarker to improve patient stratification. However, standardized approaches to classify JMML on the basis of DNA methylation patterns are lacking. We, therefore, sought to define an international consensus for DNA methylation subgroups in JMML and develop classification methods for clinical implementation.Experimental designPublished DNA methylation data from 255 patients with JMML were used to develop and internally validate a classifier model. Accuracy across platforms (EPIC-arrays and MethylSeq) was tested using a technical validation cohort (32 patients). The suitability of both methods for single-patient classification was demonstrated using an independent cohort (47 patients).ResultsAnalysis of pooled, published data established three DNA methylation subgroups as a de facto standard. Unfavorable prognostic parameters (PTPN11 mutation, elevated fetal hemoglobin, and older age) were significantly enriched in the high methylation (HM) subgroup. A classifier was then developed that predicted subgroups with 98% accuracy across different technological platforms. Applying the classifier to an independent validation cohort confirmed an association of HM with secondary mutations, high relapse incidence, and inferior overall survival (OS), while the low methylation subgroup was associated with a favorable disease course. Multivariable analysis established DNA methylation subgroups as the only significant factor predicting OS.ConclusionsThis study provides an international consensus definition for DNA methylation subgroups in JMML. We developed and validated methods which will facilitate the design of risk-stratified clinical trials in JMML

Image1_Single cell phototransfection of mRNAs encoding SARS-CoV2 spike and nucleocapsid into human astrocytes results in RNA dependent translation interference.TIF

Multi-RNA co-transfection is starting to be employed to stimulate immune responses to SARS-CoV-2 viral infection. While there are good reasons to utilize such an approach, there is little background on whether there are synergistic RNA-dependent cellular effects. To address this issue, we use transcriptome-induced phenotype remodeling (TIPeR) via phototransfection to assess whether mRNAs encoding the Spike and Nucleocapsid proteins of SARS-CoV-2 virus into single human astrocytes (an endogenous human cell host for the virus) and mouse 3T3 cells (often used in high-throughput therapeutic screens) synergistically impact host cell biologies. An RNA concentration-dependent expression was observed where an increase of RNA by less than 2-fold results in reduced expression of each individual RNAs. Further, a dominant inhibitory effect of Nucleocapsid RNA upon Spike RNA translation was detected that is distinct from codon-mediated epistasis. Knowledge of the cellular consequences of multi-RNA transfection will aid in selecting RNA concentrations that will maximize antigen presentation on host cell surface with the goal of eliciting a robust immune response. Further, application of this single cell stoichiometrically tunable RNA functional genomics approach to the study of SARS-CoV-2 biology promises to provide details of the cellular sequalae that arise upon infection in anticipation of providing novel targets for inhibition of viral replication and propagation for therapeutic intervention.</p