Denosumab-associated jaw bone necrosis in cancer patients: retrospective descriptive case series study.

BACKGROUND: Denosumab (DMB) is a bone antiresorptive agent used to treat osteoporosis or metastatic cancer of the bones. However, denosumab-associated osteonecrosis of the jaw (DRONJ) has become a common complication in cancer patients. The prevalence of osteonecrosis of the jaw (ONJ) in cancer patients is estimated to be similar for both bisphosphonate-related cases (1.1 to 1.4%) and denosumab-related cases (0.8 to 2%), with the addition of adjunctive therapy with anti-angiogenic agents reportedly increasing its prevalence to 3%. (Spec Care Dentist 36(4):231-236, 2016). The aim of this study is to report on DRONJ in cancer patients treated with DMB (Xgeva®, 120mg). CASE PRESENTATION: In this study, we identified four cases of ONJ among 74 patients receiving DMB therapy for metastatic cancer. Of the four patients, three had prostate cancer and one had breast cancer. Preceding tooth extraction within 2 months of the last DMB injection was found to be a risk factor for DRONJ. Pathological examination revealed that three patients had acute and chronic inflammation, including actinomycosis colonies. Among the four patients with DRONJ referred to us, three were successfully treated without complications and had no recurrence following surgical treatment, while one did not follow up. After healing, one patient experienced a recurrence at a different site. Sequestrectomy in conjunction with antibiotic therapy and cessation of DMB use proved to be effective in managing the condition, and the ONJ site healed after an average 5-month follow-up period. CONCLUSION: Conservative surgery, along with antibiotic therapy and discontinuation of DMB, was found to be effective in managing the condition. Additional studies are needed to investigate the contribution of steroids and anticancer drugs to jaw bone necrosis, the prevalence of multicenter cases, and whether there is any drug interaction with DMB.

Prediction Models for Suicide Attempts among Adolescents Using Machine Learning Techniques.

Objective: Suicide attempts (SAs) in adolescents are difficult to predict although it is a leading cause of death among adolescents. This study aimed to develop and evaluate SA prediction models based on six different machine learning (ML) algorithms for Korean adolescents using data from online surveys. Methods: Data were extracted from the 2011-2018 Korea Youth Risk Behavior Survey (KYRBS), an ongoing annual national survey. The participants comprised 468,482 nationally representative adolescents from 400 middle and 400 high schools, aged 12 to 18. The models were trained using several classic ML methods and then tested on internal and external independent datasets; performance metrics were calculated. Data analysis was performed from March 2020 to June 2020. Results: Among the 468,482 adolescents included in the analysis, 15,012 cases (3.2%) were identified as having made an SA. Three features (suicidal ideation, suicide planning, and grade) were identified as the most important predictors. The performance of the six ML models on the internal testing dataset was good, with both the area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) ranging from 0.92 to 0.94. Although the AUROC of all models on the external testing dataset (2018 KYRBS) ranged from 0.93 to 0.95, the AUPRC of the models was approximately 0.5. Conclusion: The developed and validated SA prediction models can be applied to detect high risks of SA. This approach could facilitate early intervention in the suicide crisis and may ultimately contribute to suicide prevention for adolescents.

Prediction models for early diagnosis of actinomycotic osteomyelitis of the jaw using machine learning techniques: a preliminary study.

BACKGROUND: This study aimed to develop and validate five machine learning models designed to predict actinomycotic osteomyelitis of the jaw. Furthermore, this study determined the relative importance of the predictive variables for actinomycotic osteomyelitis of the jaw, which are crucial for clinical decision-making. METHODS: A total of 222 patients with osteomyelitis of the jaw were analyzed, and Actinomyces were identified in 70 cases (31.5%). Logistic regression, random forest, support vector machine, artificial neural network, and extreme gradient boosting machine learning methods were used to train the models. The models were subsequently validated using testing datasets. These models were compared with each other and also with single predictors, such as age, using area under the receiver operating characteristic (ROC) curve (AUC). RESULTS: The AUC of the machine learning models ranged from 0.81 to 0.88. The performance of the machine learning models, such as random forest, support vector machine and extreme gradient boosting was significantly superior to that of single predictors. Presumed causes, antiresorptive agents, age, malignancy, hypertension, and rheumatoid arthritis were the six features that were identified as relevant predictors. CONCLUSIONS: This prediction model would improve the overall patient care by enhancing prognosis counseling and informing treatment decisions for high-risk groups of actinomycotic osteomyelitis of the jaw.

Classification of Adolescent Psychiatric Patients at High Risk of Suicide Using the Personality Assessment Inventory by Machine Learning.

OBJECTIVE: There are growing interests on suicide risk screening in clinical settings and classifying high-risk groups of suicide with suicidal ideation is crucial for a more effective suicide preventive intervention. Previous statistical techniques were limited because they tried to predict suicide risk using a simple algorithm. Machine learning differs from the traditional statistical techniques in that it generates the most optimal algorithm from various predictors. METHODS: We aim to analyze the Personality Assessment Inventory (PAI) profiles of child and adolescent patients who received outpatient psychiatric care using machine learning techniques, such as logistic regression (LR), random forest (RF), artificial neural network (ANN), support vector machine (SVM), and extreme gradient boosting (XGB), to develop and validate a classification model for individuals with high suicide risk. RESULTS: We developed prediction models using seven relevant features calculated by Boruta algorithm and subsequently tested all models using the testing dataset. The area under the ROC curve of these models were above 0.9 and the RF model exhibited the best performance. CONCLUSION: Suicide must be assessed based on multiple aspects, and although Personality Assessment Inventory for Adolescent assess an array of domains, further research is needed for predicting high suicide risk groups.

Machine learning to predict distal caries in mandibular second molars associated with impacted third molars.

Impacted mandibular third molars (M3M) are associated with the occurrence of distal caries on the adjacent mandibular second molars (DCM2M). In this study, we aimed to develop and validate five machine learning (ML) models designed to predict the occurrence of DCM2Ms due to the proximity with M3Ms and determine the relative importance of predictive variables for DCM2Ms that are important for clinical decision making. A total of 2642 mandibular second molars adjacent to M3Ms were analyzed and DCM2Ms were identified in 322 cases (12.2%). The models were trained using logistic regression, random forest, support vector machine, artificial neural network, and extreme gradient boosting ML methods and were subsequently validated using testing datasets. The performance of the ML models was significantly superior to that of single predictors. The area under the receiver operating characteristic curve of the machine learning models ranged from 0.88 to 0.89. Six features (sex, age, contact point at the cementoenamel junction, angulation of M3Ms, Winter's classification, and Pell and Gregory classification) were identified as relevant predictors. These prediction models could be used to detect patients at a high risk of developing DCM2M and ultimately contribute to caries prevention and treatment decision-making for impacted M3Ms.

Treatment of intramuscular lipoma of tongue with enveloped mucosal flap design: a case report and review of the literature.

BACKGROUND: Lipomas are benign soft tissue neoplasms of mature adipose tissue commonly occurring in the trunk or extremities. But, intraoral lipomas are rare entities which may be only noticed during routine dental examinations. Especially intramuscular lipomas on the tongue have been reported very rarely. In this study, we report a case of intramuscular lipoma on tongue, with a review of the literature from 1978 to 2019, providing data on age, gender, location, presenting symptoms, size, surgical methods, and recurrence. CASE PRESENTATION: A case of intramuscular lipoma occurring in tongue region in a 65-year-old male is reported. Surgical excision is the mainstay of treatment for the lesion. In order to decrease the deformity and discomfort after the excision, we tried to modify surgical technique using enveloped mucosal flap. This technique provided more comfortable healing procedure on the operative site without recurrence. CONCLUSION: This is a rare case of large intramuscular lipoma on tongue. Surgical excision with enveloped mucosal flap design was performed to diminish postoperative raw surface and discomfort and a 24-month follow-up showed excellent healing without any recurrence. A case of intramuscular lipoma on tongue and relevant literature reviews are presented in this study.

The use of technical replication for detection of low-level somatic mutations in next-generation sequencing.

Accurate genome-wide detection of somatic mutations with low variant allele frequency (VAF, <1%) has proven difficult, for which generalized, scalable methods are lacking. Herein, we describe a new computational method, called RePlow, that we developed to detect low-VAF somatic mutations based on simple, library-level replicates for next-generation sequencing on any platform. Through joint analysis of replicates, RePlow is able to remove prevailing background errors in next-generation sequencing analysis, facilitating remarkable improvement in the detection accuracy for low-VAF somatic mutations (up to ~99% reduction in false positives). The method is validated in independent cancer panel and brain tissue sequencing data. Our study suggests a new paradigm with which to exploit an overwhelming abundance of sequencing data for accurate variant detection.

Brain somatic mutations in SLC35A2 cause intractable epilepsy with aberrant N-glycosylation.

OBJECTIVE: To identify whether somatic mutations in SLC35A2 alter N-glycan structures in human brain tissues and cause nonlesional focal epilepsy (NLFE) or mild malformation of cortical development (mMCD). METHODS: Deep whole exome and targeted sequencing analyses were conducted for matched brain and blood tissues from patients with intractable NLFE and patients with mMCD who are negative for mutations in mTOR pathway genes. Furthermore, tissue glyco-capture and nanoLC/mass spectrometry analysis were performed to examine N-glycosylation in affected brain tissue. RESULTS: Six of the 31 (19.3%) study patients exhibited brain-only mutations in SLC35A2 (mostly nonsense and splicing site mutations) encoding a uridine diphosphate (UDP)-galactose transporter. Glycome analysis revealed the presence of an aberrant N-glycan series, including high degrees of N-acetylglucosamine, in brain tissues with SLC35A2 mutations. CONCLUSION: Our study suggests that brain somatic mutations in SLC35A2 cause intractable focal epilepsy with NLFE or mMCD via aberrant N-glycosylation in the affected brain.

BRAF somatic mutation contributes to intrinsic epileptogenicity in pediatric brain tumors.

Pediatric brain tumors are highly associated with epileptic seizures1. However, their epileptogenic mechanisms remain unclear. Here, we show that the oncogenic BRAF somatic mutation p.Val600Glu (V600E) in developing neurons underlies intrinsic epileptogenicity in ganglioglioma, one of the leading causes of intractable epilepsy2. To do so, we developed a mouse model harboring the BRAFV600E somatic mutation during early brain development to reflect the most frequent mutation, as well as the origin and timing thereof. Therein, the BRAFV600E mutation arising in progenitor cells during brain development led to the acquisition of intrinsic epileptogenic properties in neuronal lineage cells, whereas tumorigenic properties were attributed to high proliferation of glial lineage cells. RNA sequencing analysis of patient brain tissues with the mutation revealed that BRAFV600E-induced epileptogenesis is mediated by RE1-silencing transcription factor (REST), which is a regulator of ion channels and neurotransmitter receptors associated with epilepsy. Moreover, we found that seizures in mice were significantly alleviated by an FDA-approved BRAFV600E inhibitor, vemurafenib, as well as various genetic inhibitions of Rest. Accordingly, this study provides direct evidence of a BRAF somatic mutation contributing to the intrinsic epileptogenicity in pediatric brain tumors and suggests that BRAF and REST could be treatment targets for intractable epilepsy.

Brain Somatic Mutations in MTOR Disrupt Neuronal Ciliogenesis, Leading to Focal Cortical Dyslamination.

Focal malformations of cortical development (FMCDs), including focal cortical dysplasia (FCD) and hemimegalencephaly (HME), are major etiologies of pediatric intractable epilepsies exhibiting cortical dyslamination. Brain somatic mutations in MTOR have recently been identified as a major genetic cause of FMCDs. However, the molecular mechanism by which these mutations lead to cortical dyslamination remains poorly understood. Here, using patient tissue, genome-edited cells, and mouse models with brain somatic mutations in MTOR, we discovered that disruption of neuronal ciliogenesis by the mutations underlies cortical dyslamination in FMCDs. We found that abnormal accumulation of OFD1 at centriolar satellites due to perturbed autophagy was responsible for the defective neuronal ciliogenesis. Additionally, we found that disrupted neuronal ciliogenesis accounted for cortical dyslamination in FMCDs by compromising Wnt signals essential for neuronal polarization. Altogether, this study describes a molecular mechanism by which brain somatic mutations in MTOR contribute to the pathogenesis of cortical dyslamination in FMCDs.

Somatic Mutations in TSC1 and TSC2 Cause Focal Cortical Dysplasia.

Focal cortical dysplasia (FCD) is a major cause of the sporadic form of intractable focal epilepsies that require surgical treatment. It has recently been reported that brain somatic mutations in MTOR account for 15%-25% of FCD type II (FCDII), characterized by cortical dyslamination and dysmorphic neurons. However, the genetic etiologies of FCDII-affected individuals who lack the MTOR mutation remain unclear. Here, we performed deep hybrid capture and amplicon sequencing (read depth of 100×-20,012×) of five important mTOR pathway genes-PIK3CA, PIK3R2, AKT3, TSC1, and TSC2-by using paired brain and saliva samples from 40 FCDII individuals negative for MTOR mutations. We found that 5 of 40 individuals (12.5%) had brain somatic mutations in TSC1 (c.64C>T [p.Arg22Trp] and c.610C>T [p.Arg204Cys]) and TSC2 (c.4639G>A [p.Val1547Ile]), and these results were reproducible on two different sequencing platforms. All identified mutations induced hyperactivation of the mTOR pathway by disrupting the formation or function of the TSC1-TSC2 complex. Furthermore, in utero CRISPR-Cas9-mediated genome editing of Tsc1 or Tsc2 induced the development of spontaneous behavioral seizures, as well as cytomegalic neurons and cortical dyslamination. These results show that brain somatic mutations in TSC1 and TSC2 cause FCD and that in utero application of the CRISPR-Cas9 system is useful for generating neurodevelopmental disease models of somatic mutations in the brain.

Vecuum: identification and filtration of false somatic variants caused by recombinant vector contamination.

MOTIVATION: Advances in sequencing technologies have remarkably lowered the detection limit of somatic variants to a low frequency. However, calling mutations at this range is still confounded by many factors including environmental contamination. Vector contamination is a continuously occurring issue and is especially problematic since vector inserts are hardly distinguishable from the sample sequences. Such inserts, which may harbor polymorphisms and engineered functional mutations, can result in calling false variants at corresponding sites. Numerous vector-screening methods have been developed, but none could handle contamination from inserts because they are focusing on vector backbone sequences alone. RESULTS: We developed a novel method-Vecuum-that identifies vector-originated reads and resultant false variants. Since vector inserts are generally constructed from intron-less cDNAs, Vecuum identifies vector-originated reads by inspecting the clipping patterns at exon junctions. False variant calls are further detected based on the biased distribution of mutant alleles to vector-originated reads. Tests on simulated and spike-in experimental data validated that Vecuum could detect 93% of vector contaminants and could remove up to 87% of variant-like false calls with 100% precision. Application to public sequence datasets demonstrated the utility of Vecuum in detecting false variants resulting from various types of external contamination. AVAILABILITY AND IMPLEMENTATION: Java-based implementation of the method is available at http://vecuum.sourceforge.net/ CONTACT: swkim@yuhs.acSupplementary information: Supplementary data are available at Bioinformatics online.

Brain somatic mutations in MTOR leading to focal cortical dysplasia.

Focal cortical dysplasia type II (FCDII) is a focal malformation of the developing cerebral cortex and the major cause of intractable epilepsy. However, since the molecular genetic etiology of FCD has remained enigmatic, the effective therapeutic target for this condition has remained poorly understood. Our recent study on FCD utilizing various deep sequencing platforms identified somatic mutations in MTOR (existing as low as 1% allelic frequency) only in the affected brain tissues. We observed that these mutations induced hyperactivation of the mTOR kinase. In addition, focal cortical expression of mutant MTOR using in utero electroporation in mice, recapitulated the neuropathological features of FCDII, such as migration defect, cytomegalic neuron and spontaneous seizures. Furthermore, seizures and dysmorphic neurons were rescued by the administration of mTOR inhibitor, rapamycin. This study provides the first evidence that brain somatic activating mutations in MTOR cause FCD, and suggests the potential drug target for intractable epilepsy in FCD patients. [BMB Reports 2016; 49(2): 71-72].

Brain somatic mutations in MTOR cause focal cortical dysplasia type II leading to intractable epilepsy.

Focal cortical dysplasia type II (FCDII) is a sporadic developmental malformation of the cerebral cortex characterized by dysmorphic neurons, dyslamination and medically refractory epilepsy. It has been hypothesized that FCD is caused by somatic mutations in affected regions. Here, we used deep whole-exome sequencing (read depth, 412-668×) validated by site-specific amplicon sequencing (100-347,499×) in paired brain-blood DNA from four subjects with FCDII and uncovered a de novo brain somatic mutation, mechanistic target of rapamycin (MTOR) c.7280T>C (p.Leu2427Pro) in two subjects. Deep sequencing of the MTOR gene in an additional 73 subjects with FCDII using hybrid capture and PCR amplicon sequencing identified eight different somatic missense mutations found in multiple brain tissue samples of ten subjects. The identified mutations accounted for 15.6% of all subjects with FCDII studied (12 of 77). The identified mutations induced the hyperactivation of mTOR kinase. Focal cortical expression of mutant MTOR by in utero electroporation in mice was sufficient to disrupt neuronal migration and cause spontaneous seizures and cytomegalic neurons. Inhibition of mTOR with rapamycin suppressed cytomegalic neurons and epileptic seizures. This study provides, to our knowledge, the first evidence that brain somatic activating mutations in MTOR cause FCD and identifies mTOR as a treatment target for intractable epilepsy in FCD.