Lander Institute

A 21-year-old male presented with chronic left-sided rhinosinusitis and was found to have a left frontal sinus mass on maxillofacial computed tomography (CT). The mass measured approximately 1.9 × 1.3 × 2.3 cm in size and was contiguous with the intrasinus septum and anterior table of the frontal sinus. Imaging features were consistent with osteoma. An endoscopic endonasal approach resulted in subtotal resection of the mass. Eleven months later, the patient underwent a combined left transorbital and endoscopic surgical resection with oculofacial plastic surgery and otolaryngology. The mass was resected in its entirety, and histopathology confirmed an osteoma. This is one of the few reports of a unilateral transorbital frontal sinus osteoma resection and highlights the utility of the transorbital approach for minimally invasive access to the far-lateral frontal sinus.

Reprogrammed lipid metabolism with massive upregulation of tumor cell-autonomous synthesis of saturated fatty acids is a hallmark of prostate cancer (PCa) and is driven in part by aberrations in androgen receptor (AR) signaling. While lipid alterations are well described in primary PCa, the extent to which the circulating lipidome reflects tumor-associated metabolic changes in metastatic disease, and its role in therapy response, remains to be determined. This study aims to assess whether plasma lipid profiling captures tumor metabolic rewiring, and whether this reflects response to AR-targeting therapy, in metastatic castration-resistant PCa (mCRPC). Quantitative plasma lipidomics was performed on plasma samples collected from patients with mCRPC (n = 50) and cancer-free subjects (C-FS, n = 14). Samples from patients with mCRPC were collected longitudinally at the time of progression on androgen deprivation therapy prior to initiation of first-line enzalutamide (Enza), after the start of treatment with Enza, before progression on Enza. Compared to C-FS, patients with mCRPC showed distinct lipidomic signatures, characterized by increased levels of monounsaturated lipids and altered composition of the phospholipid and sphingolipid pool, mimicking the aberrations known to occur in primary PCa tissue. Enza treatment markedly reduced total lipid levels, decreased major phospholipid classes and ceramides, while increasing sphingomyelins. Notably, quantitative differences in specific sphingolipid species occurring after Enza treatment correlated with survival outcomes. Plasma lipidomics reflects key metabolic features of PCa and is profoundly impacted by AR inhibition, with prognostic relevance in patients with mCRPC. These findings support its potential as a non-invasive tool for monitoring disease activity and treatment response, and lay the groundwork for lipid-based biomarkers in mCRPC, while indicating that the lipidomic alterations observed may help inform ongoing and forthcoming research on metabolic targeting.

Context. Massive stars emit copious amounts of radiation, profoundly affecting their environment in galaxies and contributing to the reionization of the Universe. However, their evolution and thus their ionizing feedback are still not fully understood. One of the largest gaps in current stellar evolution calculations is the lack of a model for the mass ejections that occur when the stars reach the Eddington limit, such as during a Luminous Blue Variable (LBV) phase. Aims. Here, we aim to remedy this situation by providing a physically motivated and empirically calibrated method applicable in any 1D stellar evolution code to approximate the effect of such mass loss on stellar evolution. Methods. We employed the 1D stellar evolution code MESA, in which we implement a new mass-loss prescription that becomes active when stellar models inflate too much when reaching the Eddington limit. We used lines of constant inflation factors in the Hertzsprung-Russell diagram (HRD) for a simple empirical calibration of the threshold value. We calculated synthetic massive-star stellar populations using grids of single-star models with this mass loss prescription compared them with the observed populations in the Large and Small Magellanic Clouds. Further, with already computed grids of binary evolution models, we investigated the impact of binarity on our predictions. Results. Our single-star models reproduce key features of the observed stellar populations, namely, (i) the absence of stars located beyond the Humphreys-Davidson limit; (ii) an upper limit of red supergiant (RSG) luminosities; (iii) the faintest observed single Wolf-Rayet (WR) stars; (iv) the absolute number of O-stars, WRs, and RSGs; (v) WO stars in low metallicity environments; and (vi) the positions of LBV stars in the HRD. We show that binarity still plays an important role in explaining the observed WR stars. However, a large fraction of the binary population can also be explained via self-stripping. At the same time, our binary population explains the 70% binary fraction of O-stars and the 40% binary fraction of WR stars. However, our synthetic population also has caveats, such as an overproduction of bright H-free WN stars. Conclusions. Our results show that the effect of the Eddington-limit induced mass ejections on the structure and evolution of massive stars can remove the tension between predicted and observed massive star populations. A more fundamental treatment of these effects, particularly for hydrogen-poor stars, is needed to fully comprehend massive star evolution.

Prostate cancer resembles differentiated secretory luminal cells and shows cell-autonomous dependence on androgen receptor (AR) signaling, yet normal luminal cells are often considered dependent on paracrine stromal AR signaling. To resolve this, we conditionally deleted Ar in luminal acinar cells in vivo . Ar -deleted luminal cells persisted short-term, in contrast to the rapid regression observed after castration, but were impaired in regeneration and progressively lost. Their depletion was accompanied by replacement through basal-to-luminal differentiation of AR intact basal cells. Transcriptomic and chromatin profiling showed cell-autonomous suppression of the secretory program with induction of stemness, inflammatory, and epithelial-to-mesenchymal transition signatures after AR loss. Mechanistically, the MAP kinase pathway and downstream AP-1 transcription factors were activated and functionally validated, and MAP kinase inhibition selectively depleted AR-deleted luminal cells, indicating a compensatory survival pathway. These findings define intrinsic roles for luminal AR in maintaining differentiation, restraining plasticity, and sustaining regeneration and homeostatic turnover, providing a mechanistic basis for AR dependence in prostate cancer.

ABSTRACT Purpose A subset of metabolic diseases is caused by rare monogenic variants. Next generation sequencing (NGS)-based testing offers a promising approach for identifying such variants. However, its application in clinical diagnostics for metabolic diseases is limited, and the diagnostic yield is unknown. Methods We performed clinical genome sequencing (GS) on 560 adults seen in New York clinical practices between August 2020 and December 2023. Participants presented with hyperlipidemia/hypertriglyceridemia (HLD/HTG), pre-diabetes, Type 2 diabetes mellitus (T2DM), and metabolic dysfunction-associated fatty liver disease/steatohepatitis (MAFLD/MASH). Variants in a curated set of 90 genes associated with monogenic forms of these conditions were classified as Pathogenic (P), Likely Pathogenic (LP), and Variant of Uncertain Significance (VUS) using ACMG and ClinGen Sequence Variant Interpretation Working Group guidelines. P/LP variants in ACMG secondary findings (v3.1) genes were also reported with participant consent. Results The cohort included a female-to-male ratio of 1.7, with 18.6% African American and 22.6% Latino participants. The most common primary enrollment diagnoses were HLD/HTG (25%), T2DM (9%), pre-diabetes (7%), and MAFLD/MASH (4%). Many participants had multiple conditions (42% with two, 12% with three). Approximately one-third had reportable variants with 6% classified as P/LP. The most common P/LP variants were in APOB and LDLR. Conclusions The prevalence of clinically significant (P/LP) variants related to primary metabolic disease in this cohort was 6%. An additional 5.5% of participants had P/LP variants in genes recommended for return as ACMG secondary findings. Future studies should focus on refining participant selection for genome sequencing to optimize its diagnostic and clinical value.

Liquid biopsy (LB) has emerged as a transformative tool in oncology, providing a minimally invasive approach for tumor detection, molecular characterization, and real-time treatment monitoring. By analyzing circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), extracellular vesicles (EVs), and microRNA (miRNA), LB enables comprehensive tumor profiling without the need for traditional tissue biopsies. Over the past decade, research in this field has expanded exponentially, leading to the integration of LB into clinical practice for specific cancer types, including lung and breast cancer. In 2024, the Journal of Liquid Biopsy (JLB) published innovative studies exploring the latest advancements in LB technologies, biomarkers, and their applications for cancer detection, minimal residual disease (MRD) monitoring, and therapy response assessment. This review synthesizes recent findings on the role of LB in cancer treatment and monitoring across different biomarkers, with a particular focus on newly published studies and their context within translational research. Additionally, it highlights emerging techniques such as fragmentomics, artificial intelligence, and multiomics, paving the way for more precise, personalized treatment decisions. Despite these advancements, challenges remain in standardizing methodologies, optimizing clinical validation, and integrating LB into routine oncological workflows. This mini-review highlights the evolving landscape of LB research and its potential to revolutionize cancer diagnosis, treatment monitoring, and therapeutic decision-making, ushering in a new era of precision oncology.

Differentiating sequencing errors from true variants is a central genomics challenge, calling for error suppression strategies that balance costs and sensitivity. For example, circulating cell-free DNA (ccfDNA) sequencing for cancer monitoring is limited by sparsity of circulating tumor DNA, abundance of genomic material in samples and preanalytical error rates. Whole-genome sequencing (WGS) can overcome the low abundance of ccfDNA by integrating signals across the mutation landscape, but higher costs limit its wide adoption. Here, we applied deep (~120×) lower-cost WGS (Ultima Genomics) for tumor-informed circulating tumor DNA detection within the part-per-million range. We further leveraged lower-cost sequencing by developing duplex error-corrected WGS of ccfDNA, achieving 7.7 × 10-7 error rates, allowing us to assess disease burden in individuals with melanoma and urothelial cancer without matched tumor sequencing. This error-corrected WGS approach will have broad applicability across genomics, allowing for accurate calling of low-abundance variants at efficient cost and enabling deeper mapping of somatic mosaicism as an emerging central aspect of aging and disease.