ABSTRACT
Hereditary leiomyomatosis and renal cell carcinoma (HLRCC) is caused by loss of function mutations in fumarate hydratase (FH) and results in an aggressive subtype of renal cell carcinoma with limited treatment options. Loss of FH leads to accumulation of fumarate, an oncometabolite that disrupts multiple cellular processes and drives tumor progression. High levels of fumarate inhibit alpha ketoglutarate-dependent dioxygenases, including the ten eleven translocation (TET) enzymes and can lead to global DNA hypermethylation. Here, we report patterns of hypermethylation in FH-mutant cell lines and tumor samples are associated with silencing of nicotinate phosphoribosyl transferase (NAPRT), a rate-limiting enzyme in the Preiss-Handler pathway of NAD+ biosynthesis in a subset of HLRCC cases. NAPRT is hypermethylated at a CpG island in the promoter in cell line models and patient samples, resulting in loss of NAPRT expression. We find that FH-deficient RCC models with loss of NAPRT expression, as well as other oncometabolite-producing cancer models that silence NAPRT, are extremely sensitive to nicotinamide phosphoribosyl transferase inhibitors (NAMPTis). NAPRT silencing was also associated with synergistic tumor cell killing with poly(ADP)-ribose polymerase inhibitors (PARPis) and NAMPTis, which was associated with effects on PAR-mediated DNA repair. Overall, our findings indicate that NAPRT-silencing can be targeted in oncometabolite-producing cancers and elucidates how oncometabolite associated hypermethylation can impact diverse cellular processes and leads to therapeutically relevant vulnerabilities in cancer cells. Implications: NAPRT is a novel biomarker for targeting NAD+ metabolism in FH-deficient HLRCCs with NAMPTis alone and targeting DNA repair processes with the combination of NAMPTis and PARPis.
ABSTRACT
Atrial fibrillation (AF) prediction can be valuable at many timescales and in many populations. In this issue of Patterns, Gavidia et al. train a model called WARN for short-term prediction of AF in the timescale of minutes in patients wearing 24-h continuous Holter electrocardiograms. The ability to predict near-term (e.g., 30 min) AF has the potential to enable preventive therapies with rapid mechanisms of action (e.g., oral anticoagulation, anti-arrhythmic drugs). In this way, efficient, continuous, and algorithmic monitoring of AF risk could reduce burden on healthcare workers and represents a valuable clinical pursuit.
ABSTRACT
OBJECTIVE: Can machine learning (ML) enable data-driven discovery of how changes in sentiment correlate with different psychoactive experiences? We investigate by training models directly on text testimonials from a diverse 52-drug pharmacopeia. METHODS: Using large language models (i.e. BERT) and 11,816 publicly-available testimonials, we predicted 28-dimensions of sentiment across each narrative, and then validated these predictions with adjudication by a clinical psychiatrist. BERT was then fine-tuned to predict biochemical and demographic information from these narratives. Lastly, canonical correlation analysis linked the drugs' receptor affinities with word usage, revealing 11 statistically-significant latent receptor-experience factors, each mapped to a 3D cortical Atlas. RESULTS: These methods elucidate a neurobiologically-informed, sequence-sensitive portrait of drug-induced subjective experiences. The models' results converged, revealing a pervasive distinction between the universal psychedelic heights of feeling in contrast to the grim, mundane, and personal experiences of addiction and mental illness. Notably, MDMA was linked to "Love", DMT and 5-MeO-DMT to "Mystical Experiences" and "Entities and Beings", and other tryptamines to "Surprise", "Curiosity" and "Realization". CONCLUSIONS: ML methods can create unified and robust quantifications of subjective experiences with many different psychoactive substances and timescales. The representations learned are evocative and mutually confirmatory, indicating great potential for ML in characterizing psychoactivity.