Citations

Abstract Background: Prostate cancer is the second leading cause of cancer-related death in men in the United States and is dependent on androgens and androgen receptor (AR) activation. Androgen deprivation therapy (ADT) is the mainstay treatment, but it as well as next-generation treatments eventually fail. Disease then progresses as castration-resistant prostate cancer (CRPC), which is driven by reactivation of the androgen-AR axis. 3β-hydroxysteroid dehydrogenase-1 (3βHSD1) catalyzes the rate-limiting step for potent androgen synthesis from adrenal-derived precursors, thereby stimulating CRPC. HSD3B1, which encodes for 3βHSD1, has a common missense-encoding germline variation that enhances conversion from adrenal DHEA to potent androgens. Clinical studies demonstrate that this “adrenal-permissive” HSD3B1 genotype confers more rapid progression on ADT, and recent studies show that overall survival is shorter in patients with the adrenal-permissive genotype who are treated with ADT + enzalutamide. The Etk/BMX kinase may be involved in the development and progression of CRPC. We sought to determine whether phosphorylation of 3βHSD1 is required for its activation and that the necessary kinase could be a potential target. Methods: In prostate cancer lines, mouse xenograft models, and fresh patient prostatectomy tissues, mass spectrometry and HPLC were used to identify steroid metabolites. Genetic and pharmacological methods were used to identify inhibition of BMX-mediated 3βHSD1 phosphorylation in CRPC. Results: BMX inhibition suppressed castration-resistant prostate cancer through targeting 3βHSD1 phosphorylation. 3βHSD1 transcription level and enzyme activity were induced in enzalutamide-resistant prostate cancer cell lines. 3βHSD1 and SF-1 transcription levels were positively correlated in prostate adenocarcinoma (TCGA database). BMX inhibition by zanubrutinib suppressed enzalutamide resistance induced 3βHSD1 enzyme activity. Conclusions: BMX promotes CRPC by regulating 3βHSD1 phosphorylation. BMX inhibition has potential as a new therapy to optimize CRPC treatment. Citation Format: Xiuxiu Li, Michael Berk, Christopher Goins, Mohammad Alyamani, Yoon-Mi Chung, Chenyao Wang, Monaben Patel, Nityam Rathi, Ziqi Zhu, Belinda Willard, Shaun Stauffer, Eric Klein, Nima Sharifi. BMX inhibition reverses HSD3B1-driven resistance in prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr A026.

ObjectivesThis study examines oncologic outcomes in patients with HPV‐related oropharyngeal squamous cell carcinoma (HPV(+)OPSCC) who had evidence of gross cranial nerve invasion (CNI) identified at the time of surgery.Study DesignRetrospective cohort study comparing demographics, clinical features, and outcomes of HPV(+)OPSCC patients with and without gross CNI.MethodsPatients with biopsy proven HPV(+)OPSCC involving the base of tongue, tonsil, or unknown primary site, who underwent surgery as a part of their treatment between 1/1/2006–12/31/2020 (n = 874), were included in this study. Gross CNI was identified during operative intervention (n = 36). Statistical analyses were performed using SAS version 9.4 and R version 3.6.2. P‐values <0.05 were considered statistically significant.ResultsHPV(+)OPSCC patients with gross CNI were nearly 5 times as likely to suffer death by cancer (HR = 5.41, 95% CI 2.51 to 11.67, p < 0.0001), over 4 times as likely to see disease progression (HR = 4.25, 95% CI 2.31 to 7.84, p < 0.0001), and nearly 5 times as likely to experience metastasis (HR = 4.46, 95% CI 2.20 to 9.06, p < 0.0001) when compared to patients without CNI. Patients with gross CNI had significantly lower overall survival, cancer‐specific survival, progression‐free survival, and distant‐metastasis free survival (p < 0.0001). Patients with gross CNI were significantly more likely to present with higher clinical N stage, higher pathological N stage and extracapsular spread than patients without gross CNI.ConclusionsOur findings indicate that the presence of CNI is associated with significantly poorer oncologic outcomes in HPV(+)OPSCC patients.Level of Evidence3 Laryngoscope, 134:170–177, 2024

Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The detection of SVGs is analogous to the identification of differentially expressed genes and permits us to understand how genes and associated molecular processes are spatially distributed within cellular niches. However, the expression activities of SVGs fail to encode all information inherent in the spatial distribution of cells. Here, we devised a deep learning model, Spatially Informed Artificial Intelligence (SPIN-AI), to identify spatially predictive genes (SPGs), whose expression can predict how cells are organized in space. We used SPIN-AI on spatial transcriptomic data from squamous cell carcinoma (SCC) as a proof of concept. Our results demonstrate that SPGs not only recapitulate the biology of SCC but also identify genes distinct from SVGs. Moreover, we found a substantial number of ribosomal genes that were SPGs but not SVGs. Since SPGs possess the capability to predict spatial cellular organization, we reason that SPGs capture more biologically relevant information for a given cellular niche than SVGs. Thus, SPIN-AI has broad applications for detecting SPGs and uncovering which biological processes play important roles in governing cellular organization.

Accurate prediction of progression in subjects at risk of Alzheimer's disease is crucial for enrolling the right subjects in clinical trials. However, a prospective comparison of state-of-the-art algorithms for predicting disease onset and progression is currently lacking. We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of patient-specific biomarkers. On a limited, cross-sectional subset of the data emulating clinical trials, performance of the best algorithms at predicting clinical diagnosis decreased only slightly (2 percentage points) compared to the full longitudinal dataset. The submission system remains open via the website https://tadpole.grand-challenge.org, while TADPOLE SHARE (https://tadpole-share.github.io/) collates code for submissions. TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease. However, results call into question the usage of cognitive test scores for patient selection and as a primary endpoint in clinical trials.

Anticipating and understanding cancers’ need for specific gene activities is key for novel therapeutic development. Here we utilized DepMap, a cancer gene dependency screen, to demonstrate that machine learning combined with network biology can produce robust algorithms that both predict what genes a cancer is dependent on and what network features coordinate such gene dependencies. Using network topology and biological annotations, we constructed four groups of novel engineered machine learning features that produced high accuracies when predicting binary gene dependencies. We found that in all examined cancer types, F1 scores were greater than 0.90, and model accuracy remained robust under multiple hyperparameter tests. We then deconstructed these models to identify tumor type-specific coordinators of gene dependency and identified that in certain cancers, such as thyroid and kidney, tumors’ dependencies are highly predicted by gene connectivity. In contrast, other histologies relied on pathway-based features such as lung, where gene dependencies were highly predictive by associations with cell death pathway genes. In sum, we show that biologically informed network features can be a valuable and robust addition to predictive pharmacology models while simultaneously providing mechanistic insights.

The objective of this study was to examine the effect of pulling angle on time-zero mechanical properties of intact infraspinatus tendon or infraspinatus tendon repaired with the modified Mason-Allen technique in a canine model in vitro. Thirty-six canine shoulder samples were used. Twenty intact samples were randomly allocated into functional pull (135°) and anatomic pull (70°) groups (n = 10 per group). The remaining sixteen infraspinatus tendons were transected from the insertion and repaired using the modified Mason-Allen technique before being randomly allocated into functional pull or anatomic pull groups (n = 8 per group). Load to failure testing was performed on all specimens. The ultimate failure load and ultimate stress of the functional pulled intact tendons were significantly lower compared with anatomic pulled tendons (1310.2 ± 167.6 N vs. 1687.4 ± 228.2 N, p = 0.0005: 55.6 ± 8.4 MPa vs. 67.1 ± 13.3 MPa, p = 0.0334). For the tendons repaired with the modified Mason-Allen technique, no significant differences were observed in ultimate failure load, ultimate stress or stiffness between functional pull and anatomic pull groups. The variance of pulling angle had a significant influence on the biomechanical properties of the rotator cuff tendon in a canine shoulder model in vitro. Load to failure of the intact infraspinatus tendon was lower at the functional pulling position compared to the anatomic pulling position. This result indicates that uneven load distribution across tendon fibers under functional pull may predispose the tendon to tear. However, this mechanical character is not presented after rotator cuff repair using the modified Mason-Allen technique.

Background: Alzheimer’s disease (AD) is the most common type of age-related dementia, affecting 6.2 million people aged 65 or older according to CDC data. It is commonly agreed that discovering an effective AD diagnosis biomarker could have enormous public health benefits, potentially preventing or delaying up to 40% of dementia cases. Tau neurofibrillary tangles are the primary driver of downstream neurodegeneration and subsequent cognitive impairment in AD, resulting in structural deformations such as hippocampal atrophy that can be observed in magnetic resonance imaging (MRI) scans. Objective: To build a surface-based model to 1) detect differences between APOE subgroups in patterns of tau deposition and hippocampal atrophy, and 2) use the extracted surface-based features to predict cognitive decline. Methods: Using data obtained from different institutions, we develop a surface-based federated Chow test model to study the synergistic effects of APOE, a previously reported significant risk factor of AD, and tau on hippocampal surface morphometry. Results: We illustrate that the APOE-specific morphometry features correlate with AD progression and better predict future AD conversion than other MRI biomarkers. For example, a strong association between atrophy and abnormal tau was identified in hippocampal subregion cornu ammonis 1 (CA1 subfield) and subiculum in e4 homozygote cohort. Conclusion: Our model allows for identifying MRI biomarkers for AD and cognitive decline prediction and may uncover a corner of the neural mechanism of the influence of APOE and tau deposition on hippocampal morphology.

Autologous mesenchymal stem/stromal cells (MSCs) have demonstrated important therapeutic effects in several diseases. Cardiovascular risk factors may impair MSC mitochondrial structure and function, but the underlying mechanisms remain unknown. We hypothesized that metabolic syndrome (MetS) induces epigenetic alterations in mitochondria-related genes in swine MSCs. Pigs were fed a Lean or MetS diet (n = 6 each) for 16 weeks. MSCs were collected from subcutaneous abdominal fat, and DNA hydroxymethylation (5 hmC) profiles of mitochondria-related genes (MitoCarta-2.0) were analyzed by hydroxymethylated DNA immunoprecipitation and next-generation sequencing (hMeDIP-seq) in Lean- and MetS-MSCs untreated or treated with the epigenetic modulator vitamin (Vit)-C (n = 3 each). Functional analysis of genes with differential 5 hmC regions was performed using DAVID6.8. Mitochondrial structure (electron microscopy), oxidative stress, and membrane potential were assessed. hMeDIP-seq identified 172 peaks (associated with 103 mitochondrial genes) with higher and 416 peaks (associated with 165 mitochondrial genes) with lower 5 hmC levels in MetS-MSCs versus Lean-MSCs (≥2-fold, p < 0.05). Genes with higher 5 hmC levels in MetS + MSCs were primarily implicated in fatty acid metabolism, whereas those with lower 5 hmC levels were associated with electron transport chain activity. Vit-C increased 5 hmC levels in mitochondrial antioxidant genes, improved mitochondrial structure and membrane potential, and decreased oxidative stress. MetS alters 5 hmC levels of mitochondria-related genes in swine MSCs. Vit-C modulated 5 hmC levels in these genes and preserved mitochondrial structure and function in MetS-MSCs. These observations may contribute to development of strategies to overcome the deleterious effects of MetS on MSCs.