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 multisample 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 Paweł P Labaj and Aleksandra Gruca (both nominated by David P Kreil).

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.

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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.