A higher burden of multiple sclerosis genetic risk confers an earlier onset.

BACKGROUND: Age at onset of multiple sclerosis (MS) is an objective, influential predictor of the evolution of MS independent of disease duration. OBJECTIVES: Determine the influence of MS genetic predisposition on age of onset. METHODS: We conducted a comprehensive investigation of MS risk variants and age at onset in 3495 non-Latinx white individuals, including for combinations of HLA-DRB1*15:01 alleles and quintiles of an unweighted genetic risk score (GRS) for 198 of 200 autosomal MS risk variants that reside outside the major histocompatibility complex. RESULTS: The mean age at onset was 32 years, 29% were male, and 46% were HLA-DRB1*15:01 carriers. For those with the greatest genetic risk burden (the highest GRS quintile with two HLA-DRB1*15:01 alleles) were on average 5 years younger at onset (p = 0.002) than those with the lowest genetic risk burden (the lowest GRS quintile with no HLA-DRB1*15:01 alleles). There was a strong inverse relationship between the MS genetic risk burden and age at onset of MS (p < 5 × 10-8). CONCLUSION: We demonstrate a significant gradient between elevated MS genetic risk burden and an earlier onset of MS, suggesting that a higher MS genetic risk burden accelerates onset of the disease.

Endogenous and combination retinoids are active in myelomonocytic leukemias.

Retinoid therapy transformed response and survival outcomes in acute promyelocytic leukemia (APL), but has demonstrated only modest activity in non-APL forms of acute myeloid leukemia (AML). The presence of natural retinoids in vivo could influence the efficacy of pharmacologic agonists and antagonists. We found that natural RXRA ligands, but not RARA ligands, were present in murine MLL-AF9-derived myelomonocytic leukemias in vivo and that the concurrent presence of receptors and ligands acted as tumor suppressors. Pharmacologic retinoid responses could be optimized by concurrent targeting RXR ligands (e.g. bexarotene) and RARA ligands (e.g. all-trans retinoic acid, ATRA), which induced either leukemic maturation or apoptosis depending on cell culture conditions. Co-repressor release from the RARA:RXRA heterodimer occurred with RARA activation, but not RXRA activation, providing an explanation for the combination synergy. Combination synergy could be replicated in additional, but not all, AML cell lines and primary samples, and was associated with improved survival in vivo, although tolerability of bexarotene administration in mice remained an issue. These data provide insight into the basal presence of natural retinoids in leukemias in vivo and a potential strategy for clinical retinoid combination regimens in leukemias beyond acute promyelocytic leukemia.

Recurrent transcriptional responses in AML and MDS patients treated with decitabine.

The molecular events responsible for decitabine responses in myelodysplastic syndrome and acute myeloid leukemia patients are poorly understood. Decitabine has a short serum half-life and limited stability in tissue culture. Therefore, theoretical pharmacologic differences may exist between patient molecular changes in vitro and the consequences of in vivo treatment. To systematically identify the global genomic and transcriptomic alterations induced by decitabine in vivo, we evaluated primary bone marrow samples that were collected during patient treatment and applied whole-genome bisulfite sequencing, RNA-sequencing, and single-cell RNA sequencing. Decitabine induced global, reversible hypomethylation after 10 days of therapy in all patients, which was associated with induction of interferon-induced pathways, the expression of endogenous retroviral elements, and inhibition of erythroid-related transcripts, recapitulating many effects seen previously in in vitro studies. However, at relapse after decitabine treatment, interferon-induced transcripts remained elevated relative to day 0, but erythroid-related transcripts now were more highly expressed than at day 0. Clinical responses were not correlated with epigenetic or transcriptional signatures, although sample size and interpatient variance restricted the statistical power required for capturing smaller effects. Collectively, these data define global hypomethylation by decitabine and find that erythroid-related pathways may be relevant because they are inhibited by therapy and reverse at relapse.

The Impact of Multiple Sclerosis Disease Status and Subtype on Hematological Profile.

Multiple sclerosis (MS) is an immune-mediated, demyelinating disease of the central nervous system. In this study, an MS cohort and healthy controls were stratified into Caucasian and African American groups. Patient hematological profiles-composed of complete blood count (CBC) and complete metabolic panel (CMP) test values-were analyzed to identify differences between MS cases and controls and between patients with different MS subtypes. Additionally, random forest models were used to determine the aggregate utility of common hematological tests in determining MS disease status and subtype. The most significant and relevant results were increased bilirubin and creatinine in MS cases. The random forest models achieved some success in differentiating between MS cases and controls (AUC values: 0.725 and 0.710, respectively) but were not successful in differentiating between subtypes. However, larger samples that adjust for possible confounding variables, such as treatment status, may reveal the value of these tests in differentiating between MS subtypes.

Depression in multiple sclerosis patients associated with risk variant near NEGR1.

BACKGROUND: A substantial number of patients diagnosed with multiple sclerosis (MS) suffer from depression in addition to physical symptoms and disability. Recent evidence suggests a stronger relationship may exist between MS and depression than previously thought, in which a diagnosis of depression may be prodromic to the development of MS. METHODS: A genome-wide association study (GWAS) was performed to identify genetic variants associated with the development of depression in a cohort of MS patients. The control group (n = 1180) was composed of MS patients with no diagnoses of depression as determined by ICD-9 and ICD-10 billing codes present in the electronic health record (EHR). Separate analyses were performed for three different case groups: 1) MS patients having a depression diagnosis at any time (n = 182), 2) MS patients having a depression diagnosis one year pre-MS diagnosis (n = 27), and 3) MS patients having a depression diagnosis one year post-MS diagnosis (n = 130). Logistic regression analyses were also performed to test for associations between the development of depression and an APOE tagging variant, as APOE was previously linked to depressive affect in MS. An additional logistic regression analysis tested for associations between depression in MS patients and SNPs associated with depression in the general population. Pathway enrichment analyses were also conducted to identify pathways that link the two diseases. RESULTS: GWAS identified no novel associations between variants and a diagnosis of depression relative to a diagnosis of MS. One variant, rs1432639, associated with depression in the general population, was significantly associated with the development of depression post-MS diagnosis. The APOE-related SNPs were not associated with depression in this study population. An IGF1 pathway approached statistical significance in patients diagnosed with depression prior to a diagnosis of MS. CONCLUSION: rs1432639 and the IGF1 pathway provide evidence for a genetic link between MS and depression that warrants further research.

Diverse approaches to predicting drug-induced liver injury using gene-expression profiles.

BACKGROUND: Drug-induced liver injury (DILI) is a serious concern during drug development and the treatment of human disease. The ability to accurately predict DILI risk could yield significant improvements in drug attrition rates during drug development, in drug withdrawal rates, and in treatment outcomes. In this paper, we outline our approach to predicting DILI risk using gene-expression data from Build 02 of the Connectivity Map (CMap) as part of the 2018 Critical Assessment of Massive Data Analysis CMap Drug Safety Challenge. RESULTS: First, we used seven classification algorithms independently to predict DILI based on gene-expression values for two cell lines. Similar to what other challenge participants observed, none of these algorithms predicted liver injury on a consistent basis with high accuracy. In an attempt to improve accuracy, we aggregated predictions for six of the algorithms (excluding one that had performed exceptionally poorly) using a soft-voting method. This approach also failed to generalize well to the test set. We investigated alternative approaches-including a multi-sample normalization method, dimensionality-reduction techniques, a class-weighting scheme, and expanding the number of hyperparameter combinations used as inputs to the soft-voting method. We met limited success with each of these solutions. CONCLUSIONS: We conclude that alternative methods and/or datasets will be necessary to effectively predict DILI in patients based on RNA expression levels in cell lines. REVIEWERS: This article was reviewed by Pawel P Labaj and Aleksandra Gruca (both nominated by David P Kreil).