Lack of data integration remains a significant impediment to cancer research, and many analyses still require customized software to transform and prepare cancer data. We describe a software package to harmonize genetic and clinical cancer data into the GA4GH Phenopacket Schema, an ISO standard for representing clinical case data. We integrated demographic, mutation, morphology, diagnosis, intervention, and survival data using case data from the National Cancer Institute (NCI) for 12 cancer types. The Phenopacket standard provides a foundation for downstream use, including sophisticated statistical and AI/ML analyses. We demonstrate fitness for purpose by using the integrated data to recapitulate a known association between mutations in the gene encoding isocitrate dehydrogenase 1 (IDH1) and survival time in brain cancer patients.

High-quality metadata is essential for downstream AI applications, yet metadata curation remains a persistent bottleneck in biomedical research. The core challenge lies in balancing quality and scalability. Manual curation delivers high quality, reliable annotations but is time-intensive and non-scalable; automated approaches, including those based on natural language processing, can scale but often fall short in accuracy and completeness. This tradeoff is particularly severe in public datasets, where metadata is frequently distributed across multiple sources for a single dataset. A notable example includes multi-omics datasets available on GEO and similar public repositories, which are often accompanied by associated publications. These supplementary sources can be leveraged to substantially enhance metadata quality, thereby supporting downstream applications such as classifier model development or the identification of biologically relevant cohorts, etc.

Access to accurate, non-invasive diagnostics remains a critical unmet need in women’s health. Menstrual effluence, containing endometrial tissue, immune cells, and microbial communities, represents a clinically relevant specimen for genomic and molecular pathology applications, yet has historically been underutilized due to concerns about sample integrity and variability. We developed and validated a standardized, at-home tampon-based collection system designed to preserve nucleic acids at ambient temperature for clinical-grade analyses. 1,067 tampon samples from 328 participants underwent, exome sequencing, RNA sequencing, and metatranscriptomic profiling to assess specimen transcript integrity, diagnostic fidelity, and microbial composition over time.

Rhythmicity is a cornerstone of behavioral and biological processes, especially metabolism, yet the mechanisms behind metabolite cycling remain elusive. This study uncovers a robust oscillation in key metabolite pathways downstream of glucose in humans. A purpose-built 13C6-glucose isotope tracing platform was used to sample Drosophila every 4h and probe these pathways, revealing a striking peak in biosynthesis shortly after lights-on in wild-type flies. A hyperactive mutant (fumin) demonstrates increased Krebs cycle labelling and dawn-specific glycolysis labelling. Surprisingly, neither underlying feeding rhythms nor the presence of food availability explain the rhythmicity of glucose processing across genotypes, suggesting a robust internal mechanism for metabolic control of glucose processing. These results align with clinical data highlighting detrimental effects of mistimed energy intake. Our approach offers a unique insight into the dynamic range of daily metabolic processing and provides a mechanistic foundation for exploring circadian metabolic homeostasis in disease contexts.

Rhythmicity is a central feature of behavioral and biological processes including metabolism, however, the mechanisms of metabolite cycling are poorly understood. A robust oscillation in a network of key metabolite pathways downstream of glucose is described in humans, then these pathways mechanistically probed through purpose-built 13C6-glucose isotope tracing in Drosophila every 4h. A temporal peak in biosynthesis was noted by broad labelling of pathways downstream of glucose in wild-type flies shortly following lights on. Krebs cycle labelling was generally increased in a hyperactive mutant (fumin) along with glycolysis labelling primarily observed at dawn. Surprisingly, neither underlying feeding rhythms nor the presence of food explains the rhythmicity of glucose processing across genotypes. These results are consistent with clinical data demonstrating detrimental effects of mis-timed energy intake. This approach provides a window into the dynamic range of metabolic processing ability through the day and mechanistic basis for exploring circadian metabolic homeostasis in disease states.

Altered metabolism helps sustain cancer cell proliferation and survival. Most cancers, including prostate cancers, express the M2 splice isoform of pyruvate kinase (PKM2), which can support anabolic metabolism to support cell proliferation. However, Pkm2 expression is dispensable for the formation and growth of many cancers in vivo. Expression of pyruvate kinase isoform M1 (Pkm1) is restricted to relatively few tissues and has been reported to promote growth of select tumors, but the role of PKM1 in cancer has been less studied than PKM2. To test how differential expression of pyruvate kinase isoforms affects cancer initiation and progression, we generated mice harboring a conditional allele of Pkm1 and crossed these mice, or those with a Pkm2 conditional allele, with a Pten loss-driven prostate cancer model. Pkm1 loss led to increased PKM2 expression and accelerated prostate cancer development, whereas Pkm2 deletion led to increased PKM1 expression and suppressed tumor progression. Metabolic profiling revealed altered nucleotide levels in tumors with high PKM1 expression, and failure of these tumors to progress was associated with DNA replication stress and senescence. Consistent with these data, a small molecule pyruvate kinase activator that mimics a high activity PKM1-like state suppressed progression of established prostate tumors. Analysis of human specimens showed PKM2 expression is retained in most human prostate cancers. Overall, this study uncovers a role for pyruvate kinase isoforms in prostate cancer initiation and progression, and argues that pharmacologic pyruvate kinase activation may be beneficial for treating prostate cancer.

Inhibitory signaling in dysfunctional CD8 T cells through the programmed cell death 1 (PD-1) axis is well established in chronic viral infections and cancers. PD-1 is also transiently induced to high concentrations during priming of acute infections and immunizations, yet its impact on the development of long-lived antigen-independent T cell memory remains unclear. In addition to its expected role in restraining clonal effector expansion, here, we show that PD-1 expression on antigen-specific CD8 T cells is required for the development of a durable CD8 T cell memory pool after antigen clearance. Loss of T cell–specific PD-1 signaling led to increased contraction and a defect in antigen-independent renewal of memory CD8 T cells in response to homeostatic cytokine signals, thus resulting in attrition of the memory pool over time. Whereas exhausted CD8 T cells regain function after PD-1 checkpoint blockade during chronic viral infection, the preexisting pool of resting functional bystander memory CD8 T cells established in response to a previously administered immunogen decreased. Metabolically, PD-1 signals were necessary for regulating the critical balance of mTOR-dependent anabolic glycolysis and fatty acid oxidation programs to meet the bioenergetic needs of quiescent CD8 T cell memory. These results define PD-1 as a key metabolic regulator of protective T cell immunity. Furthermore, these results have potential clinical implications for preexisting CD8 T cell memory during PD-1 checkpoint blockade therapy.

Pheochromocytomas (PCCs) and paragangliomas (PGLs) are neuroendocrine tumors that are mostly benign. Metastatic disease does occur in about 10% of cases of PCC and up to 25% of PGL, and for these patients no effective therapies are available. Patients with mutations in the succinate dehydrogenase subunit B (SDHB) gene tend to have metastatic disease. We hypothesized that a down-regulation in the active succinate dehydrogenase B subunit should result in notable changes in cellular metabolic profile and could present a vulnerability point for successful pharmacological targeting.

Analysis of large metabolomic datasets is becoming commonplace with the increased realization of the role that metabolites play in biology and pathophysiology. While there are many open-source analysis tools to extract peaks from liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and tandem mass spectrometry (LC-MS/MS) data, these tools are not very interactive and are suboptimal when a large number of samples are to be analyzed. El-MAVEN is an open-source analysis platform that extends MAVEN and provides fast, powerful, and interactive analysis capabilities especially for datasets containing over 100 samples. The El-MAVEN workflow is easy to use with just four steps from loading data to exporting of the results. Advanced analysis and software techniques such as multiprocessing, machine learning, and reduction of memory leaks are implemented so as to provide a seamless and interactive user experience. Results from El-MAVEN can be exported in a range of formats allowing continued analysis on other platforms. Additionally, El-MAVEN is also fully integrated with Polly™, a cloud-based analysis platform that provides a range of tools for flux analysis and integrative-omics analysis. El-MAVEN is a powerful tool that enables fast and efficient analysis of large metabolomic datasets to accelerate the process of gaining insight from raw data.

Cultured cells convert glucose to lactate, and glutamine is the major source of tricarboxylic acid (TCA)-cycle carbon, but whether the same metabolic phenotype is found in tumors is less studied. We infused mice with lung cancers with isotope-labeled glucose or glutamine and compared the fate of these nutrients in tumor and normal tissue. As expected, lung tumors exhibit increased lactate production from glucose. However, glutamine utilization by both lung tumors and normal lung was minimal, with lung tumors showing increased glucose contribution to the TCA cycle relative to normal lung tissue. Deletion of enzymes involved in glucose oxidation demonstrates that glucose carbon contribution to the TCA cycle is required for tumor formation. These data suggest that understanding nutrient utilization by tumors can predict metabolic dependencies of cancers in vivo. Furthermore, these data argue that the in vivo environment is an important determinant of the metabolic phenotype of cancer cells.