Profiling the Somatic Mutational Landscape of Breast Tumors from Hispanic/Latina Women Reveals Conserved and Unique Characteristics.

Somatic mutational profiling is increasingly being used to identify potential targets for breast cancer. However, limited tumor-sequencing data from Hispanic/Latinas (H/L) are available to guide treatment. To address this gap, we performed whole-exome sequencing (WES) and RNA sequencing on 146 tumors and WES of matched germline DNA from 140 H/L women in California. Tumor intrinsic subtype, somatic mutations, copy-number alterations, and expression profiles of the tumors were characterized and compared with data from tumors of non-Hispanic White (White) women in The Cancer Genome Atlas (TCGA). Eight genes were significantly mutated in the H/L tumors including PIK3CA, TP53, GATA3, MAP3K1, CDH1, CBFB, PTEN, and RUNX1; the prevalence of mutations in these genes was similar to that observed in White women in TCGA. Four previously reported Catalogue of Somatic Mutations in Cancer (COSMIC) mutation signatures (1, 2, 3, 13) were found in the H/L dataset, along with signature 16 that has not been previously reported in other breast cancer datasets. Recurrent amplifications were observed in breast cancer drivers including MYC, FGFR1, CCND1, and ERBB2, as well as a recurrent amplification in 17q11.2 associated with high KIAA0100 gene expression that has been implicated in breast cancer aggressiveness. In conclusion, this study identified a higher prevalence of COSMIC signature 16 and a recurrent copy-number amplification affecting expression of KIAA0100 in breast tumors from H/L compared with White women. These results highlight the necessity of studying underrepresented populations. SIGNIFICANCE: Comprehensive characterization of genomic and transcriptomic alterations in breast tumors from Hispanic/Latina patients reveals distinct genetic alterations and signatures, demonstrating the importance of inclusive studies to ensure equitable care for patients. See related commentary by Schmit et al., p. 2443.

Characterization of pig skin microbiome and appraisal as an in vivo subcutaneous injection model.

Pig skin is commonly used in the medical industry as an injection model due to its compelling physiological affinity to human skin. However, the pig neck skin microflora remains largely uncharacterized, which may have undesirable implications for the translatability of results to humans. This study aimed to characterize pig neck skin microbiome with direct comparison with human skin microflora at emblematic injection sites to appraise its suitability as an injection model. Ten minipigs were sampled with tape strips and swabs and analysed by matrix-assisted laser desorption/ionization-time of flight mass spectrometry and 16S/ITS high throughput sequencing and confocal laser scanning microscopy. Results were directly compared with previous investigations of human injection sites. Pig skin was dominated by phyla 94.8% Firmicutes, 3% Proteobacteria, and 2.2% Actinobacteria. Staphylococcus spp. prevailed (44.4%) at the genus level with S. capitis and S. chromogenes present in all samples. Pig skin revealed populations in the 104 colony-forming units (CFU)/cm2 range with 3% identified as Gram-negative and increased alpha diversity (compared with 102 CFU/cm2 and 10% in humans). While notable taxonomical differences on species levels were seen, pig skin encompassed 97.1% of genera found in human samples. The increased population and variation found support the pig neck as an imperfect but fidelitous subcutaneous injection model that can adequately challenge devices from a microbial standpoint.

The Diabetes teleMonitoring of patients in insulin Therapy (DiaMonT) trial: study protocol for a randomized controlled trial.

BACKGROUND: The effect of telemedicine solutions in diabetes remains inconclusive. However, telemedicine studies have shown a positive trend in regards to glycemic control. The telemedicine interventions that facilitate adjustment of medication seems to improve glycemic control more effectively. Hence, it is recommended that future telemedicine studies for patients with diabetes include patient-specific suggestions for changes in medicine. Hence, the aim of the trial is to explore the effect of telemonitoring in patients with type 2 diabetes (T2D) on insulin therapy. METHODS: The trial is an open-label randomized controlled trial with a trial period of 3 months conducted in two sites in Denmark. Patients with T2D on insulin therapy will be randomized (1:1) to a telemonitoring group (intervention) or a usual care group (control). The telemonitoring group will use a continuous glucose monitor (CGM), an insulin pen, an activity tracker, and smartphone applications throughout the trial. Hospital staff will monitor the telemonitoring group and contact the subjects by telephone repeatedly throughout the trial period. The usual care group will use a blinded CGM the first and last 20 days of the trial and will use a blinded insulin pen for the entire period. The primary endpoint will be changed from baseline in CGM time in range (3.9-10.0 mmol/L) 3 months after randomization. Secondary endpoints include change from baseline in glycated hemoglobin (HbA1c), total daily dose, time above range, and time below range 3 months after randomization. Exploratory endpoints include health-related quality of life, diabetes-related quality of life, etc. DISCUSSION: The DiaMonT trial will test a telemonitoring setup including various devices. Such a setup may be criticized, because it is impossible to determine which element(s) add to the potential effect. However, it is not possible and counterproductive to test the elements individually, since it is the full telemedicine setup that is being evaluated. The DiaMonT trial is the first Danish trial to explore the effect of telemonitoring on patients on insulin therapy. Thus, the DiaMonT trial has the potential to form the basis for the implementation of telemedicine for patients with T2D in Denmark. TRIAL REGISTRATION: ClinicalTrials.gov NCT04981808. Registered on 8 June 2021.

Correlation in Dose-Response to Rapid- and Long-Acting Insulin for People with Type 1 Diabetes.

In diabetes, it can become necessary to switch between pump- and pen-based insulin treatment. This switch involves a translation between rapid- and long-acting insulin analogues. In standard-of-care translation algorithms, a unit-to-unit conversion is applied. However, this simplification may not fit all individuals. In this paper, we investigate the correlation between dose-response to rapid- and long-acting insulin in the same individual, and compare the correlation across individuals. As a measure of dose-response, we estimate the insulin sensitivity in clinical data from 25 subjects with type 1 diabetes. For parameter estimation, we use maximum likelihood with a continuous-discrete extended Kalman filter and Bergman's minimal model. The results show a weak correlation between insulin sensitivity to rapid- and long-acting insulin across individuals. On this sparse data set, the analysis suggests that the standardized unit-to-unit translation between insulin analogues may not benefit all subjects.

Injection site microflora in persons with diabetes: why needle reuse is not associated with increased infections?

Needle reuse is a common practice and primary cause of customer compliance issues such as pain, bruising, clogging, injection site reactions (ISR), and associated lipodystrophy. This study aimed to characterize skin microflora at injection sites and establish microbial contamination of used pen injectors and needles. The second objective was to evaluate the risk of infections during typical and repeated subcutaneous injections. 50 participants with diabetes and 50 controls (n = 100) were sampled through tape strips and skin swabs on the abdomen and thigh for skin microflora. Used pen injectors and needles were collected after in-home use and from the hospital after drug administration by health care professionals (HCPs). Samples were analyzed by conventional culture, matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF), mass spectrometry (MS), confocal laser scanning microscopy (CLSM), and 16S/ITS high throughput sequencing (HTS). A mathematical model simulated the risk of needle contamination during injections. Injection site populations were in 102 cells/cm2 order, with increased viable bacteria and anaerobic bacteria on the skin in persons with diabetes (p = 0.05). Interpersonal variation dominated other factors such as sex or location. A higher prevalence of Staphylococcus aureus on abdominal skin was found in persons with diabetes than control skin (p ≤ 0.05). Most needles and cartridges (95% and 86%) contained no biological signal. The location of the device collection (hospital vs home-use) and use regimen did not affect contamination. CLSM revealed scarcely populated skin microflora scattered in aggregates, diplo, or single cells. Our mathematical model demonstrated that penetrating bacteria colonies during subcutaneous injection is unlikely. These findings clarify the lack of documented skin infections from subcutaneous insulin injections in research. Furthermore, these results can motivate the innovation and development of durable, reusable injection systems with pharmacoeconomic value and a simplified and enhanced user experience for patients.

Early Glycemic Control Assessment Based on Consensus CGM Metrics.

Continuous glucose monitoring (CGM) has revolutionized the world of diabetes and transformed the approach to diabetes care. In this context, an expert panel has reached consensus on clinical targets for CGM data interpretation based on eight CGM metrics. At least 70% of 14 consecutive CGM days (referred to as a period) are recommended to assess glycemic control based on the metrics. In clinical practice less CGM data may be available. Therefore, the primary aim of this study is to explore the ability to recover the consensus metrics utilizing less than 14 days of CGM data (intra-period). As a secondary aim, we investigate the recovery considering two consecutive periods (inter-period). The analyses are based on real-world CGM data from 484 diabetes users (4726 periods) acquired from the Cornerstones4Care® Powered by Glooko app. Using up to 14 accumulated days, the consensus metrics are calculated for each user and period, and compared to the fully 14 accumulated intra- and inter-period days. Relatively low deviations were observed for time in range (TIR) and average based metrics when using less than 14 days, however, we observed large deviations in metrics characterizing infrequent events such as time below range (TBR). Furthermore, the consensus metrics obtained in two consecutive 14 day periods have clear discrepancies (inter-period). Recovering consensus metrics using less than 14 days might still be valuable in terms of interpreting CGM data in certain clinical contexts. However, caution should be taken if treatment decisions would be made with less than 14 days of data on critical metrics such as TBR, since the metrics characterizing infrequent events deviate substantially when less data are available. Substantial deviation is also seen when comparing across two consecutive periods, which means that care should be taken not to over-generalize consensus metric based glycemic control conclusions from one period to subsequent periods.

Feasibility of a New Approach to Initiate Insulin in Type 2 Diabetes.

BACKGROUND: Treatment inertia and prescription complexity are among reasons that people with type 2 diabetes (T2D) do not reach glycemic targets. This study investigated feasibility of a new approach to basal insulin initiation, where the dose needed to reach a glycemic target is estimated from two weeks of insulin and continuous glucose monitoring (CGM) data. METHODS: This was an exploratory single arm study with a maximum length of 84 days. Eight insulin naïve people with T2D, planning to initiate basal insulin, wore a CGM throughout the study period. A predetermined regime was followed for the first two weeks after which the end dose was estimated. The clinician decided whether to follow this advice and continued the titration until target was reached using a twice weekly stepwise titration algorithm. The primary outcome was the comparison between the estimated and the actual end doses. RESULTS: Median age of participants was 57 years (range: 50-77 years), duration of diabetes was 16 years (range: 5-29 years), and Bodi Mass Index (BMI) was 30.2 kg/m2 (range: 22.0-36.0 kg/m2). The median study end dose was 37 U (range: 20-123 U). The estimated end dose was smaller than or equal to the study end dose in all cases, with median error of 26.7% (range: 0.0%-75.8% underestimation). No self-monitoring of blood glucose values were below 70 mg/dL and no severe hypoglycemia occurred. CONCLUSION: While accuracy may be improved, it was found safe to predict the study end dose of insulin degludec from two weeks of data.

Machine Learning-Based Adherence Detection of Type 2 Diabetes Patients on Once-Daily Basal Insulin Injections.

BACKGROUND: Lack of treatment adherence can lead to life-threatening health complications for people with type 2 diabetes (T2D). Recent improvements and availability in continuous glucose monitoring (CGM) technology have enabled various possibilities to monitor diabetes treatment. Detection of missed once-daily basal insulin injections can be used to provide feedback to patients, thus improving their diabetes management. In this study, we explore how machine learning (ML) based on CGM data can be used for detecting adherence to once-daily basal insulin injections. METHODS: In-silico CGM data were generated to simulate a cohort of T2D patients on once-daily insulin injection (Tresiba®). Deep learning methods within ML based on automatic feature extraction including convolutional neural networks were explored and compared with simple feature-engineered ML classification models for adherence detection. It was further investigated whether fused expert-dependent and automatically learned features could improve performance, resulting in a comparison of six different detection models. Adherence was detected throughout each day with an increasing amount of CGM data available. RESULTS: The adherence detection accuracy improved as more CGM data became available on the day of classification. The three classification models based on expert-engineered features obtained mean accuracies of 78.6%, 78.2%, and 78.3%. The classification model based purely on learned features obtained a mean accuracy of 79.7%. The two classification models fusing expert-engineered and learned features obtained mean accuracies of 79.7% and 79.8%. All the mentioned results were obtained 16 hours after time of injection. CONCLUSION: The results suggest that adherence detection based on CGM data is feasible. Even though our study based on in-silico data indicates only slightly improved performance of more complex models, the question remains whether advanced models would outperform the simple in a real-world setting. Thus, future studies on adherence monitoring using real CGM data are relevant.

Multiple Tissue Biomarkers Independently and Additively Predict Prostate Cancer Pathology Outcomes.

BACKGROUND: Distinguishing indolent from aggressive prostate cancer remains a key challenge for decision making regarding prostate cancer management. A growing number of biomarkers are now available to help address this need, but these have rarely been examined together in the same patients to determine their potentially additive value. OBJECTIVE: To determine whether two previously validated plasma markers (transforming growth factor ß1 [TGFß1] and interleukin-6 soluble receptor [IL6-SR]) and two validated tissue scores (the Genomic Evaluators of Metastatic Prostate Cancer [GEMCaP] and cell cycle progression [CCP] scores) can improve on clinical parameters in predicting adverse pathology after prostatectomy, and how much they vary within tumors with heterogeneous Gleason grade. DESIGN, SETTING, AND PARTICIPANTS: A case-control study was conducted among men with low-risk cancers defined by biopsy grade group (GG) 1, prostate-specific antigen (PSA) ≤10 ng/mL, and clinical stage ≤ T2 who underwent immediate prostatectomy. We collected paraffin-fixed prostatectomy tissue and presurgical plasma samples from 381 cases from the University of California, San Francisco, and 260 cases from the University of Washington. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Pathologic outcomes were minor upgrading/upstaging (GG 2 or pT3a) or major upgrading/upstaging (GG ≥ 3 or ≥ pT3b), and multinomial regression was performed to determine putative markers' ability to predict these outcomes, controlling for PSA, percent of positive biopsy cores, age, and clinical site. For upgraded tumors, a secondary analysis of the GEMCaP and CCP scores from the higher-grade tumor was also performed to evaluate for heterogeneity. RESULTS AND LIMITATIONS: Overall, 357 men had no upgrading/upstaging event at prostatectomy, 236 had a minor event, and 67 had a major event. Neither TGFß1 nor IL6-SR was statistically significantly associated with any upgrading/upstaging. On the contrary, both the CCP and the GEMCaP score obtained from Gleason pattern 3 tissue were directly associated with minor and major upgrading/upstaging on univariate analysis. The two scores correlated with each other, but weakly. On multinomial analysis including both scores in the model, the CCP score predicted minor upgrading/upstaging (odds ratio [OR] 1.62, 95% confidence interval [CI] 1.05-2.49) and major upgrading/upstaging (OR 2.26, 95% CI 1.05-4.90), p =  0.04), and the GEMCaP score also predicted minor upgrading/upstaging (OR 1.05, 95% CI 1.03-1.08) and major upgrading/upstaging (OR 1.07, 95% CI 1.04-1.11), p <  0.01). The other clinical parameters were not significant in this model. Among upgraded tumors including both Gleason patterns 3 and 4, both the GEMCaP and the CCP score tended to be higher from the higher-grade tumor. The main limitation was the use of virtual biopsies from prostatectomy tissue as surrogates for prostate biopsies. CONCLUSIONS: Biomarker signatures based on analyses of both DNA and RNA significantly and independently predict adverse pathology among men with clinically low-risk prostate cancer undergoing prostatectomy. PATIENT SUMMARY: Validated biomarker scores derived from both prostate cancer DNA and prostate cancer RNA can add independent information to help predict outcomes after prostatectomy.

Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data.

Continuous Glucose Monitoring (CGM) has enabled important opportunities for diabetes management. This study explores the use of CGM data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for Short Term Blood Glucose (STBG) prediction and compare the RNNs to conventional time-series forecasting using Autoregressive Integrated Moving Average (ARIMA). A prediction horizon up to 90 min into the future is considered. In this context, we evaluate both population-based and patient-specific RNNs and contrast them to patient-specific ARIMA models and a simple baseline predicting future observations as the last observed. We find that the population-based RNN model is the best performing model across the considered prediction horizons without the need of patient-specific data. This demonstrates the potential of RNNs for STBG prediction in diabetes patients towards detecting/mitigating severe events in the STBG, in particular hypoglycemic events. However, further studies are needed in regards to the robustness and practical use of the investigated STBG prediction models.

Human pediatric B-cell acute lymphoblastic leukemias can be classified as B-1 or B-2-like based on a minimal transcriptional signature.

The finding that transformed mouse B-1 and B-2 progenitors give rise to B-cell acute lymphoblastic leukemias (B-ALLs) with varied aggressiveness suggests that B-cell lineage might also be a factor in the initiation and progression of pediatric B-ALLs in humans. If this is the case, we hypothesized that human pediatric B-ALLs would share gene expression patterns with mouse B-1 or B-2 progenitors. We tested this premise by deriving a distinct 30-gene B-1 and B-2 progenitor signature that was applied to a microarray data set of human pediatric ALLs. Cluster analysis revealed that CRLF2, E2A-PBX1, ERG, and ETV6-RUNX1 leukemias were B-1-like, whereas BCR-ABL1, hyperdiploid, and MLL leukemias were B-2-like. Examination of the 30-gene signature in two independent data sets of pediatric ALLs supported this result. Our data suggest that common genetic subtypes of human ALL have their origin in the B-1 or B-2 lineage.

Fast Assessment of Glycemic Control based on Continuous Glucose Monitoring Data.

Diabetes has become a major public health problem in the world. In this context, early assessment of glycemic control is essential in order to avoid life-threatening health complications. A panel of diabetes experts have recently proposed a list of recommendations when using Continuous Glucose Monitoring (CGM) for glycemic control assessment including a minimum of two weeks of CGM data. A recent study has further introduced a metric called Glucose Profile Indicator (GPI) for CGM based diabetes management including a subset of the recommended CGM metrics. In this pilot study, it was investigated if less than two weeks of CGM data would impact the performance of GPI compared to the proposed two weeks of CGM data. Furthermore, logistic regression (LR) was used to examine if an improvement could be achieved taking as input the CGM metrics used to quantify GPI. The population mean accuracy for accumulated day 1 to 13 varied between 72.8 ± 2.0% - 98.3 ± 0.4% with no clear sign of improvement using LR. Hence, this indicates a trade-off between the amount of available CGM data and the precision in which the GPI outcome using all 14 days can be achieved when considering features of the GPI alone. Future work is needed to investigate if this trade-off can be improved by the use of additional features of the CGM.

Modelling of fasting glucose-insulin dynamics from sparse data.

With the fast growth of diabetes prevalence, the disease is now considered an epidemic. Diabetes is characterized by elevated glucose levels, that may be treated with insulin. Tight control of glucose is essential for prevention of complications and patients' well-being. In this paper we model the fasting glucose-insulin dynamics in type 2 diabetes, aiming at controlling the glucose level. Relevant clinical data are typically sparse and have a sampling period much greater than the fast dynamics in the glucose-insulin dynamics in humans. We adapt a physiological model such that important slow non-linear dynamics are identifiable and test the resulting model on deterministic simulated data and sparse, slow sampled clinical data.

Improved accuracy assessment for 3D genome reconstructions.

BACKGROUND: Three dimensional (3D) genome spatial organization is critical for numerous cellular functions, including transcription, while certain conformation-driven structural alterations are frequently oncogenic. Genome conformation had been difficult to elucidate but the advent chromatin conformation capture assays, notably Hi-C, has transformed understanding of chromatin architecture and yielded numerous biological insights. Although most of these findings have flowed from analysis of proximity data produced by these assays, added value in generating 3D reconstructions has been demonstrated, deriving, in part, from superposing genomic features on the reconstruction. However, advantages of 3D structure-based analyses are clearly conditional on the accuracy of the attendant reconstructions, which is difficult to assess. Proponents of competing reconstruction algorithms have evaluated their accuracy by recourse to simulation of toy structures and/or limited fluorescence in situ hybridization (FISH) imaging that features a handful of low resolution probes. Accordingly, new methods of reconstruction accuracy assessment are needed. RESULTS: Here we utilize two recently devised assays to develop methodology for assessing 3D reconstruction accuracy. Multiplex FISH increases the number of probes by an order of magnitude and hence the number of inter-probe distances by two orders, providing sufficient information for structure-level evaluation via mean-squared deviations (MSD). Crucially, underscoring multiplex FISH applications are large numbers of coordinate-system aligned replicates that provide the basis for a referent distribution for MSD statistics. Using this system we show that reconstructions based on Hi-C data for IMR90 cells are accurate for some chromosomes but not others. The second new assay, genome architecture mapping, utilizes large numbers of thin cryosections to obtain a measure of proximity. We exploit the planarity of the cryosections - not used in inferring proximity - to obtain measures of reconstruction accuracy, with referents provided via resampling. Application to mouse embryonic stem cells shows reconstruction accuracies that vary by chromosome. CONCLUSIONS: We have developed methods for assessing the accuracy of 3D genome reconstructions that exploit features of recently advanced multiplex FISH and genome architecture mapping assays. These approaches can help overcome the absence of gold standards for making such assessments which are important in view of the considerable uncertainties surrounding 3D genome reconstruction.

Replication confers ß cell immaturity.

Pancreatic ß cells are highly specialized to regulate systemic glucose levels by secreting insulin. In adults, increase in ß-cell mass is limited due to brakes on cell replication. In contrast, proliferation is robust in neonatal ß cells that are functionally immature as defined by a lower set point for glucose-stimulated insulin secretion. Here we show that ß-cell proliferation and immaturity are linked by tuning expression of physiologically relevant, non-oncogenic levels of c-Myc. Adult ß cells induced to replicate adopt gene expression and metabolic profiles resembling those of immature neonatal ß that proliferate readily. We directly demonstrate that priming insulin-producing cells to enter the cell cycle promotes a functionally immature phenotype. We suggest that there exists a balance between mature functionality and the ability to expand, as the phenotypic state of the ß cell reverts to a less functional one in response to proliferative cues.

Validation of GEMCaP as a DNA Based Biomarker to Predict Prostate Cancer Recurrence after Radical Prostatectomy.

PURPOSE: We aimed to validate GEMCaP (Genomic Evaluators of Metastatic Cancer of the Prostate) as a novel copy number signature predictive of prostate cancer recurrence. MATERIALS AND METHODS: We randomly selected patients who underwent radical prostatectomy at Cleveland Clinic or University of Rochester from 2000 to 2005. DNA isolated from the cancer region was extracted and subjected to high resolution array comparative genomic hybridization. A high GEMCaP score was defined as 20% or greater of genomic loci showing copy number gain or loss in a given tumor. Cox regression was used to evaluate associations between the GEMCaP score and the risk of biochemical recurrence. RESULTS: We report results in 140 patients. Overall 38% of patients experienced recurrence with a median time to recurrence of 45 months. Based on the CAPRA-S (Cancer of the Prostate Risk Assessment Post-Surgical) score 39% of the patients were at low risk, 42% were at intermediate risk and 19% were at high risk. The GEMCaP score was high (20% or greater) in 31% of the cohort. A high GEMCaP score was associated with a higher risk of biochemical recurrence (HR 2.69, 95% CI 1.51-4.77) and it remained associated after adjusting for CAPRA-S score and age (HR 1.94, 95% CI 1.06-3.56). The C-index of GEMCaP alone was 0.64, which improved when combined with the CAPRA-S score and patient age (C-index = 0.75). CONCLUSIONS: A high GEMCaP score was associated with biochemical recurrence in 2 external cohorts. This remained true after adjusting for clinical and pathological factors. The GEMCaP biomarker could be an efficient and effective clinical risk assessment tool to identify patients with prostate cancer for early adjuvant therapy.

Genomic analysis of the origins and evolution of multicentric diffuse lower-grade gliomas.

Background: Rare multicentric lower-grade gliomas (LGGs) represent a unique opportunity to study the heterogeneity among distinct tumor foci in a single patient and to infer their origins and parallel patterns of evolution. Methods: In this study, we integrate clinical features, histology, and immunohistochemistry for 4 patients with multicentric LGG, arising both synchronously and metachronously. For 3 patients we analyze the phylogeny of the lesions using exome sequencing, including one case with a total of 8 samples from the 2 lesions. Results: One patient was diagnosed with multicentric isocitrate dehydrogenase 1 (IDH1) mutated diffuse astrocytomas harboring distinct IDH1 mutations, R132H and R132C; the latter mutation has been associated with Li-Fraumeni syndrome, which was subsequently confirmed in the patient's germline DNA and shown in additional cases with The Cancer Genome Atlas data. In another patient, phylogenetic analysis of synchronously arising grade II and grade III diffuse astrocytomas demonstrated a single shared mutation, IDH1 R132H, and revealed convergent evolution via non-overlapping mutations in ATRX and TP53. In 2 cases, there was divergent evolution of IDH1-mutated and 1p/19q-codeleted oligodendroglioma and IDH1-mutated and 1p/19q-intact diffuse astrocytoma, occurring synchronously in one case and metachronously in a second. Conclusions: Each tumor in multicentric LGG cases may arise independently or may diverge very early in their development, presenting as genetically and histologically distinct tumors. Comprehensive sampling of these lesions can therefore significantly alter diagnosis and management. Additionally, somatic IDH1 R132C mutation in either multicentric or solitary LGG identifies unsuspected germline TP53 mutation, validating the limited number of published cases.

A deep learning approach to adherence detection for type 2 diabetics.

Diabetes has become one of the biggest health problems in the world. In this context, adherence to insulin treatment is essential in order to avoid life-threatening complications. In this pilot study, a novel adherence detection algorithm using Deep Learning (DL) approaches was developed for type 2 diabetes (T2D) patients, based on simulated Continuous Glucose Monitoring (CGM) signals. A large and diverse amount of CGM signals were simulated for T2D patients using a T2D adapted version of the Medtronic Virtual Patient (MVP) model for T1D. By using these signals, different classification algorithms were compared using a comprehensive grid search. We contrast a standard logistic regression baseline to Multi- Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). The best classification performance with an average accuracy of 77:5% was achieved with CNN. Hence, this indicates the potential of DL, when considering adherence detection systems for T2D patients.

Clonal expansion and epigenetic reprogramming following deletion or amplification of mutant IDH1.

IDH1 mutation is the earliest genetic alteration in low-grade gliomas (LGGs), but its role in tumor recurrence is unclear. Mutant IDH1 drives overproduction of the oncometabolite d-2-hydroxyglutarate (2HG) and a CpG island (CGI) hypermethylation phenotype (G-CIMP). To investigate the role of mutant IDH1 at recurrence, we performed a longitudinal analysis of 50 IDH1 mutant LGGs. We discovered six cases with copy number alterations (CNAs) at the IDH1 locus at recurrence. Deletion or amplification of IDH1 was followed by clonal expansion and recurrence at a higher grade. Successful cultures derived from IDH1 mutant, but not IDH1 wild type, gliomas systematically deleted IDH1 in vitro and in vivo, further suggestive of selection against the heterozygous mutant state as tumors progress. Tumors and cultures with IDH1 CNA had decreased 2HG, maintenance of G-CIMP, and DNA methylation reprogramming outside CGI. Thus, while IDH1 mutation initiates gliomagenesis, in some patients mutant IDH1 and 2HG are not required for later clonal expansions.

Discovering hotspots in functional genomic data superposed on 3D chromatin configuration reconstructions.

The spatial organization of the genome influences cellular function, notably gene regulation. Recent studies have assessed the three-dimensional (3D) co-localization of functional annotations (e.g. centromeres, long terminal repeats) using 3D genome reconstructions from Hi-C (genome-wide chromosome conformation capture) data; however, corresponding assessments for continuous functional genomic data (e.g. chromatin immunoprecipitation-sequencing (ChIP-seq) peak height) are lacking. Here, we demonstrate that applying bump hunting via the patient rule induction method (PRIM) to ChIP-seq data superposed on a Saccharomyces cerevisiae 3D genome reconstruction can discover 'functional 3D hotspots', regions in 3-space for which the mean ChIP-seq peak height is significantly elevated. For the transcription factor Swi6, the top hotspot by P-value contains MSB2 and ERG11 - known Swi6 target genes on different chromosomes. We verify this finding in a number of ways. First, this top hotspot is relatively stable under PRIM across parameter settings. Second, this hotspot is among the top hotspots by mean outcome identified by an alternative algorithm, k-Nearest Neighbor (k-NN) regression. Third, the distance between MSB2 and ERG11 is smaller than expected (by resampling) in two other 3D reconstructions generated via different normalization and reconstruction algorithms. This analytic approach can discover functional 3D hotspots and potentially reveal novel regulatory interactions.