Personalizing treatment selection in Crohn's disease: a meta-analysis of individual participant data from fifteen randomized controlled trials.

BACKGROUND: Meta-analyses have found anti-TNF drugs to be the best treatment, on average, for Crohn's disease. We performed a subgroup analysis to determine if it is possible to achieve more efficacious outcomes by individualizing treatment selection. METHODS: We obtained participant-level data from 15 trials of FDA-approved treatments (N=5703). We used sequential regression and simulation to model week six disease activity as a function of drug class, demographics, and disease-related features. We performed hypothesis testing to define subgroups based on rank-ordered preferences for treatments. We queried health records from University of California Health (UCH) to estimate the impacts these models could have on practice. We computed the sample size needed to prospectively test a prediction of our models. RESULTS: 45% of the participants (N=2561) showed greater efficacy with at least one drug class (anti-TNF, anti-IL-12/23, anti-integrin) over another. They were classifiable into 6 subgroups, two showing greatest efficacy with anti-TNFs (36%, N=2064). Women over 50 showed superior responses with anti-IL-12/23s. Although they represented only 2% of the trial-based cohort, 25% of Crohn's patients at UCH are women over 50 (N=5,647), consistent with potential selection bias in trials. Moreover, 75% of biologic-exposed women over 50 did not receive an anti-IL12/23 first-line, supporting the potential value of these models. A future trial with 250 patients per arm will have 97% power to confirm the superiority of anti-IL-12/23s over anti-TNFs in these patients. A treatment recommendation tool is available at https://crohnsrx.org. CONCLUSIONS: Personalizing treatment can improve outcomes in Crohn's disease. Future work is needed to confirm these findings, and improve representativeness in Crohn's trials.

Sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control group.

BACKGROUND: The advent of clinical trial data sharing platforms has created opportunities for making new discoveries and answering important questions using already collected data. However, existing methods for meta-analyzing these data require the presence of shared control groups across studies, significantly limiting the number of questions that can be confidently addressed. We sought to develop a method for meta-analyzing potentially heterogeneous clinical trials even in the absence of a common control group. METHODS: This work was conducted within the context of a broader effort to study comparative efficacy in Crohn's disease. Following a search of clnicaltrials.gov we obtained access to the individual participant data from nine trials of FDA-approved treatments in Crohn's Disease (N = 3392). We developed a method involving sequences of regression and simulation to separately model the placebo- and drug-attributable effects, and to simulate head-to-head trials against an appropriately normalized background. We validated this method by comparing the outcome of a simulated trial comparing the efficacies of adalimumab and ustekinumab against the recently published results of SEAVUE, an actual head-to-head trial of these drugs. This study was pre-registered on PROSPERO (#157,827) prior to the completion of SEAVUE. RESULTS: Using our method of sequential regression and simulation, we compared the week eight outcomes of two virtual cohorts subject to the same patient selection criteria as SEAVUE and treated with adalimumab or ustekinumab. Our primary analysis replicated the corresponding published results from SEAVUE (p = 0.9). This finding proved stable under multiple sensitivity analyses. CONCLUSIONS: This new method may help reduce the bias of individual participant data meta-analyses, expand the scope of what can be learned from these already-collected data, and reduce the costs of obtaining high-quality evidence to guide patient care.

Single-cell transcriptomic profiling of satellite glial cells in stellate ganglia reveals developmental and functional axial dynamics.

Stellate ganglion neurons, important mediators of cardiopulmonary neurotransmission, are surrounded by satellite glial cells (SGCs), which are essential for the function, maintenance, and development of neurons. However, it remains unknown whether SGCs in adult sympathetic ganglia exhibit any functional diversity, and what role this plays in modulating neurotransmission. We performed single-cell RNA sequencing of mouse stellate ganglia (n = 8 animals), focusing on SGCs (n = 11,595 cells). SGCs were identified by high expression of glial-specific transcripts, S100b and Fabp7. Microglia and Schwann cells were identified by expression of C1qa/C1qb/C1qc and Ncmap/Drp2, respectively, and excluded from further analysis. Dimensionality reduction and clustering of SGCs revealed six distinct transcriptomic subtypes, one of which was characterized the expression of pro-inflammatory markers and excluded from further analyses. The transcriptomic profiles and corresponding biochemical pathways of the remaining subtypes were analyzed and compared with published astrocytic transcriptomes. This revealed gradual shifts of developmental and functional pathways across the subtypes, originating from an immature and pluripotent subpopulation into two mature populations of SGCs, characterized by upregulated functional pathways such as cholesterol metabolism. As SGCs aged, these functional pathways were downregulated while genes and pathways associated with cellular stress responses were upregulated. These findings were confirmed and furthered by an unbiased pseudo-time analysis, which revealed two distinct trajectories involving the five subtypes that were studied. These findings demonstrate that SGCs in mouse stellate ganglia exhibit transcriptomic heterogeneity along maturation or differentiation axes. These subpopulations and their unique biochemical properties suggest dynamic physiological adaptations that modulate neuronal function.

IDOL regulates systemic energy balance through control of neuronal VLDLR expression.

Liver X receptors limit cellular lipid uptake by stimulating the transcription of Inducible Degrader of the LDL Receptor (IDOL), an E3 ubiquitin ligase that targets lipoprotein receptors for degradation. The function of IDOL in systemic metabolism is incompletely understood. Here we show that loss of IDOL in mice protects against the development of diet-induced obesity and metabolic dysfunction by altering food intake and thermogenesis. Unexpectedly, analysis of tissue-specific knockout mice revealed that IDOL affects energy balance, not through its actions in peripheral metabolic tissues (liver, adipose, endothelium, intestine, skeletal muscle), but by controlling lipoprotein receptor abundance in neurons. Single-cell RNA sequencing of the hypothalamus demonstrated that IDOL deletion altered gene expression linked to control of metabolism. Finally, we identify VLDLR rather than LDLR as the primary mediator of IDOL effects on energy balance. These studies identify a role for the neuronal IDOL-VLDLR pathway in metabolic homeostasis and diet-induced obesity.

Mutant NRASQ61 shares signaling similarities across various cancer types--potential implications for future therapies.

Oncogenic mutations in the Neuroblastoma Rat Sarcoma oncogene (NRAS) are frequent in melanoma, but are also found in several other cancer types, such as lung cancer, neuroblastoma and colon cancer. We designed our study to analyze changes in NRAS mutant tumor cells derived from malignancies other than melanoma. A variety of small molecule inhibitors as well as their combinations was tested in order to find beneficial inhibitory modalities in NRASQ61mutant lung cancer and neuroblastoma cell lines. Signaling changes after incubation with inhibitors were studied and compared to those found in NRAS mutant melanoma. All cell lines were most sensitive to inhibition in the MAPK pathway with the MEK inhibitor trametinib. MEK/AKT and MEK/CDK4,6 inhibitor combinations did not show any beneficial effects in vitro. However, we observed strong synergism combining MEK and PI3K/mTOR inhibitors in all cell lines. Our study provides evidence that NRAS mutant cancers share signaling similarities across different malignancies. We demonstrate that dual pathway inhibition of the MAPK and PI3K/AKT/mTOR pathway synergistically reduces cell viability in NRAS mutant cancers regardless of their tissue origin. Our results suggest that such inhibitor combinations may be a potential treatment option for non-melanoma tumors harboring activating NRAS mutations.