Targeted gene expression classifier identifies pediatric T-cell acute lymphoblastic leukemia (T-ALL) patients at high risk for end induction minimal residual disease positivity.

10002 Background: The heterogeneity of T-ALL has hindered biomarker identification and limited biology-based risk stratification. Historically, minimal residual disease (MRD) has been the strongest predictor of poor outcomes. However, stratification by MRD does not allow for risk-adapted therapy early in treatment, which may induce deeper remissions and decrease risk of relapse. We hypothesized that gene expression profiling at diagnosis may have prognostic value in identifying high risk patients. Methods: We analyzed RNA-seq data from 189 diagnostic samples from the Children’s Oncology Group (COG) AALL0434 trial. Using leave-one-out cross-validation, we identified a set of genes that optimally differentiated MRD+ and MRD- samples. We then derived a risk score (RS) that indicates a probability of being MRD+ for a given gene expression pattern. Finally, we validated this model in an independent cohort of COG AALL1231 samples. Results: The AALL0434 early T-cell precursor (ETP) samples (n = 19), which have high rates of MRD+, had the highest RS, with an average of 81.3 (SD 18.7), versus 24.9 (SD 22.7) for non-ETPs (n = 146). Intriguingly, non-ETPs with RS > 50 had a gene expression pattern that mirrored ETPs and was distinct from the remaining non-ETPs. In this RS > 50 non-ETP cohort, 80% were MRD+, versus 20% of the < 50 cohort (p < 0.0001). When applied to 31 diagnostic non-ETP samples from COG AALL1231, 57% of the RS > 50 cohort were MRD+, versus 17% of the RS < 50 cohort (p = 0.05). Importantly, AALL0434 used prednisone during induction, while AALL1231 used dexamethasone, indicating that the predictive value is independent of the induction steroid. Finally, we converted our model to the customizable Nanostring nCounter platform by analyzing 96 AALL0434 samples on the Nanostring assay. The Nanostring data closely recapitulated the RNA-seq data, with a tight correlation between the resulting RS (concordance correlation coefficient = 0.91). Conclusions: We have developed a gene expression classifier that differentiates a subset of non-ETP T-ALLs with an ETP-like gene expression pattern and a high risk of MRD+, and have adapted the classifier to a clinically tractable targeted platform. Identification of this high-risk subset at diagnosis has the potential to facilitate risk-adapted trials to evaluate the utility of novel or more intensive therapies aimed at improving clinical outcomes.

Integrated Analysis of Methylomic and Transcriptomic Data to Identify Potential Diagnostic Biomarkers for Major Depressive Disorder

Major depressive disorder (MDD) is a mental illness with high incidence and complex etiology, that poses a serious threat to human health and increases the socioeconomic burden. Currently, high-accuracy biomarkers for MDD diagnosis are urgently needed. This paper aims to identify novel blood-based diagnostic biomarkers for MDD. Whole blood DNA methylation data and gene expression data from the Gene Expression Omnibus database are downloaded. Then, differentially expressed/methylated genes (DEGs/DMGs) are identified. In addition, we made a systematic analysis of the DNA methylation on 5′-C-phosphate-G-3′ (CpGs) in all of the gene regions, as well as different gene regions, and then we defined a “dominant” region. Subsequently, integrated analysis is employed to identify the robust MDD-related blood biomarkers. Finally, a gene expression classifier and a methylation classifier are constructed using the random forest algorithm and the leave-one-out cross-validation method. Our results demonstrate that DEGs are mainly involved in the inflammatory response-associated pathways, while DMGs are primarily concentrated in the neurodevelopment- and neuroplasticity-associated pathways. Our integrated analysis identified 46 hypo-methylated and up-regulated (hypo-up) genes and 71 hyper-methylated and down-regulated (hyper-down) genes. One gene expression classifier and two DNA methylation classifiers, based on the CpGs in all of the regions or in the dominant regions are constructed. The gene expression classifier possessed the best predictive ability, followed by the DNA methylation classifiers, based on the CpGs in both the dominant regions and all of the regions. In summary, the integrated analysis of DNA methylation and gene expression has identified 46 hypo-up genes and 71 hyper-down genes, which could be used as diagnostic biomarkers for MDD.