Discordance between peritumoral and subareolar injections for mapping sentinel lymph nodes in the breast.

PURPOSE: Sentinel node biopsy (SNB) is a common staging tool for breast cancer. Initially, peritumoral (PT) injections were used, however subareolar (SA) injections were later introduced to simplify the technique. Controversy remains regarding whether PT and SA injections map the same sentinel lymph nodes (SLNs). This study aimed to determine whether the regional location of breast SLNs differs when using PT versus SA injections using a large dataset from a single institution. METHODS: A total of 1035 patients who underwent breast SNB (PT injections: n = 858 and SA injections: n = 177) with lymphoscintigraphy and SPECT/CT were included. The identified SLN locations using SA injections were compared with those using PT injections. Differences in drainage proportions and odds ratios (ORs) for each clockface breast region and the whole breast were calculated using a two-proportion z-test and Fisher's Exact Test. RESULTS: A higher proportion of internal mammary SLNs were identified using PT injections for the whole breast (0.30 versus 0.09) and for all breast regions, with all regions showing statistical significance except the upper outer quadrant. Similarly, ORs showed identification of internal mammary SLNs was significantly higher when using PT injections (4.35, 95% CI 2.53 to 7.95). There were no significant differences in identifying axillary SLNs between injection sites. CONCLUSION: This is the largest cohort study to compare the regional location of breast SLNs identified using PT injections versus SA injections. Discordance was shown in the SLNs identified between injection techniques, with PT injections more frequently identifying internal mammary SLNs.

Collateralization of the upper extremity lymphatic system after axillary lymph node dissection.

BACKGROUND: Lymphatic drainage from the arm may be altered after axillary lymph node dissection (ALND). Understanding these alterations is important as they may change standard surgical and radiation treatment in recurrent breast cancer or upper extremity skin cancers, including melanoma. METHODS: Utilizing a single-institution planar and single photon emission computed tomography/computed tomography lymphoscintigraphy database, we identified patients with a diagnosis of upper extremity cutaneous melanoma from 2008 to 2023 who previously underwent ALND for cancer treatment and did not develop upper extremity cancer-related lymphedema. ALND patients were matched to control patients presenting with cutaneous melanomas at the same anatomic sites. Sentinel lymph nodes (SLNs) were compared between both groups. RESULTS: Of 3628 upper extremity melanoma cutaneous patients, 934 met inclusion criteria, including 22 ALND and 912 control patients. Level I axillary SLN drainage was observed in 98% of controls and 27% of ALND patients (p < 0.001). Level II axillary SLN drainage was observed in 3% of controls and 27% of ALND patients (p < 0.001). Level III axillary SLN drainage was observed in 1% of controls and 32% of ALND patients (p < 0.001). Epitrochlear SLN drainage was observed in 9% of controls and 32% of ALND patients, respectively (p < 0.046). Brachial SLN drainage was observed in 4% of controls and 23% of ALND patients (p < 0.001). CONCLUSIONS: Distinct changes in functional lymphatic drainage were seen between the arms of patients who previously underwent ALND versus control patients. Levels II and III axillary, epitrochlear, and brachial nodes are possible sites of metastatic disease that should be considered in patients with a prior ALND.

Patterns of forearm lymphatic drainage to the epitrochlear lymph nodes in 1400 cutaneous melanoma patients.

BACKGROUND: Variations of hand and forearm lymphatic drainage to upper-arm lymphatic pathways may impact the route of melanoma metastasis. This study compared rates of lymphatic drainage to epitrochlear nodes between anatomic divisions of the hand and forearm to determine whether the anatomic distribution of hand and forearm melanomas affects the likelihood of drainage to epitrochlear lymph nodes. METHODS: Using a single-institution lymphoscintigraphy database, we identified all patients with cutaneous melanoma on the hand and forearm. A body-map two-dimensional coordinate system was used to classify cutaneous melanoma sites between radial-ulnar and dorsal-volar divisions. Sentinel lymph nodes (SLNs) visualized on lymphoscintigraphy were recorded. Proportions of patients with epitrochlear SLNs were compared between anatomic divisions using χ2 analysis. RESULTS: Of 3628 upper extremity cutaneous melanoma patients who underwent lymphatic mapping with lymphoscintigraphy, 1400 met inclusion criteria. Twenty-one percent of patients demonstrated epitrochlear SLNs. Epitrochlear SLNs were observed in 27% of dorsal forearm melanomas and 15% of volar forearm melanomas (p < 0.001). Epitrochlear SLNs were observed in 31% of ulnar forearm melanomas and 17% of radial forearm melanomas (p < 0.001). CONCLUSIONS: Higher proportions of dorsal and ulnar forearm melanomas have epitrochlear SLNs. Metastasis to epitrochlear SLNs may be more likely from melanomas in these respective forearm regions.

An interactive 3D atlas of sentinel lymph nodes in breast cancer developed using SPECT/CT.

BACKGROUND: The identification and assessment of sentinel lymph nodes (SLNs) in breast cancer is important for optimised patient management. The aim of this study was to develop an interactive 3D breast SLN atlas and to perform statistical analyses of lymphatic drainage patterns and tumour prevalence. METHODS: A total of 861 early-stage breast cancer patients who underwent preoperative lymphoscintigraphy and SPECT/CT were included. Lymphatic drainage and tumour prevalence statistics were computed using Bayesian inference, non-parametric bootstrapping, and regression techniques. Image registration of SPECT/CT to a reference patient CT was carried out on 350 patients, and SLN positions transformed relative to the reference CT. The reference CT was segmented to visualise bones and muscles, and SLN distributions compared with the European Society for Therapeutic Radiology and Oncology (ESTRO) clinical target volumes (CTVs). The SLN atlas and statistical analyses were integrated into a graphical user interface (GUI). RESULTS: Direct lymphatic drainage to the axilla level I (anterior) node field was most common (77.2%), followed by the internal mammary node field (30.4%). Tumour prevalence was highest in the upper outer breast quadrant (22.9%) followed by the retroareolar region (12.8%). The 3D atlas had 765 SLNs from 335 patients, with 33.3-66.7% of axillary SLNs and 25.4% of internal mammary SLNs covered by ESTRO CTVs. CONCLUSION: The interactive 3D atlas effectively displays breast SLN distribution and statistics for a large patient cohort. The atlas is freely available to download and is a valuable educational resource that could be used in future to guide treatment.

Towards optimal heterogeneous prostate radiotherapy dose prescriptions based on patient-specific or population-based biological features.

BACKGROUND: Escalation of prescribed dose in prostate cancer (PCa) radiotherapy enables improvement in tumor control at the expense of increased toxicity. Opportunities for reduction of treatment toxicity may emerge if more efficient dose escalation can be achieved by redistributing the prescribed dose distribution according to the known heterogeneous, spatially-varying characteristics of the disease. PURPOSE: To examine the potential benefits, limitations and characteristics of heterogeneous boost dose redistribution in PCa radiotherapy based on patient-specific and population-based spatial maps of tumor biological features. METHOD: High-resolution prostate histology images, from a cohort of 63 patients, annotated with tumor location and grade, provided patient-specific "maps" and a population-based "atlas" of cell density and tumor probability. Dose prescriptions were derived for each patient based on a heterogeneous redistribution of the boost dose to the intraprostatic lesions, with the prescription maximizing patient tumor control probability (TCP). The impact on TCP was assessed under scenarios where the distribution of population-based biological data was ignored, partially included, or fully included in prescription generation. Heterogeneous dose prescriptions were generated for three combinations of maps and atlas, and for conventional fractionation (CF), extreme hypo-fractionation (EH), moderate hypo-fractionation (MH), and whole Pelvic RT + SBRT Boost (WPRT + SBRT). The predicted efficacy of the heterogeneous prescriptions was compared with equivalent homogeneous dose prescriptions. RESULTS: TCPs for heterogeneous dose prescriptions were generally higher than those for homogeneous dose prescriptions. TCP escalation by heterogeneous dose prescription was the largest for CF. When only using population-based atlas data, the generated heterogeneous dose prescriptions of 55 to 58 patients (out of 63) had a higher TCP than for the corresponding homogeneous dose prescriptions. The TCPs of the heterogeneous dose prescriptions generated with the population-based atlas and tumor probability maps did not differ significantly from those using patient-specific biological information. The generated heterogeneous dose prescriptions achieved significantly higher TCP than homogeneous dose prescriptions in the posterior section of the prostate. CONCLUSION: Heterogeneous dose prescriptions generated via biologically-optimized dose redistribution can produce higher TCP than the homogeneous dose prescriptions for the majority of the patients in the studied cohort. For scenarios where patient-specific biological information was unavailable or partially available, the generated heterogeneous dose prescriptions can still achieve TCP improvement relative to homogeneous dose prescriptions.

Robustness of magnetic resonance imaging and positron emission tomography radiomic features in prostate cancer: Impact on recurrence prediction after radiation therapy.

Background and purpose: Radiomic features from MRI and PET are an emerging tool with potential to improve prostate cancer outcomes. However, feature robustness due to image segmentation variations is currently unknown. Therefore, this study aimed to evaluate the robustness of radiomic features with segmentation variations and their impact on predicting biochemical recurrence (BCR). Materials and methods: Multi-scanner, pre-radiation therapy imaging from 142 patients with localised prostate cancer was used. Imaging included T2-weighted (T2), apparent diffusion coefficient (ADC) MRI, and prostate-specific membrane antigen (PSMA)-PET. The prostate gland and intraprostatic tumours were manually and automatically segmented, and differences were quantified using Dice Coefficient (DC). Radiomic features including shape, first-order, and texture features were extracted for each segmentation from original and filtered images. Intraclass Correlation Coefficient (ICC) and Mean Absolute Percentage Difference (MAPD) were used to assess feature robustness. Random forest (RF) models were developed for each segmentation using robust features to predict BCR. Results: Prostate gland segmentations were more consistent (mean DC = 0.78) than tumour segmentations (mean DC = 0.46). 112 (3.6 %) radiomic features demonstrated 'excellent' robustness (ICC > 0.9 and MAPD < 1 %), and 480 features (15.4 %) demonstrated 'good' robustness (ICC > 0.75 and MAPD < 5 %). PET imaging provided more features with excellent robustness than T2 and ADC. RF models showed strong predictive power for BCR with a mean area under the receiver-operator-characteristics curve (AUC) of 0.89 (range 0.85-0.93). Conclusion: When using radiomic features for predictive modelling, segmentation variability should be considered. To develop BCR predictive models, radiomic features from the entire prostate gland are preferable over tumour segmentation-based features.

Computational fluid dynamic modeling of the lymphatic system: a review of existing models and future directions.

Historically, research into the lymphatic system has been overlooked due to both a lack of knowledge and limited recognition of its importance. In the last decade however, lymphatic research has gained substantial momentum and has included the development of a variety of computational models to aid understanding of this complex system. This article reviews existing computational fluid dynamic models of the lymphatics covering each structural component including the initial lymphatics, pre-collecting and collecting vessels, and lymph nodes. This is followed by a summary of limitations and gaps in existing computational models and reasons that development in this field has been hindered to date. Over the next decade, efforts to further characterize lymphatic anatomy and physiology are anticipated to provide key data to further inform and validate lymphatic fluid dynamic models. Development of more comprehensive multiscale- and multi-physics computational models has the potential to significantly enhance the understanding of lymphatic function in both health and disease.

Toward Development of a Portable System for 3D Fluorescence Lymphography.

Lymphoedema is a debilitating disease that results in chronic swelling of a body region due to a dysfunctional lymphatic system. Since a cure is yet to be identified for this disease, management is currently the best option for preventing disease progression and improving patient outcomes. Fluorescence lymphography is a popular approach for mapping the lymphatic vessels to provide information about the underlying lymphatic dysfunction. However, current clinical fluorescence lymphography tools do not enable the creation of comprehensive 3D maps of lymphatics throughout affected limbs. This work presents the development toward multi-camera 3D reconstruction with fluorescence imaging to overcome the current limitations in clinical tools. Pilot studies have been performed that identify suitable instrumentation for this multi-camera approach and techniques for creating a 3D fluorescence lymphography device are discussed.Clinical Relevance- This paper presents development toward new low-cost and portable clinical tools for lymphoedema diagnosis and to facilitate personalised treatment and self-management of this disease.

Early-life microbiota-immune homeostasis.

As the prevalence of allergy and autoimmune disease in industrialized societies continues to rise, improving our understanding of the mechanistic roles behind microbiota-immune homeostasis has become critical for informing therapeutic interventions in cases of dysbiosis. Of particular importance, are alterations to intestinal microbiota occurring within the critical neonatal window, during which the immune system is highly vulnerable to environmental exposures. This review will highlight recent literature concerning mechanisms of early-life microbiota-immune homeostasis as well as discuss the potential for therapeutics in restoring dysbiosis in early life.

Detecting localised prostate cancer using radiomic features in PSMA PET and multiparametric MRI for biologically targeted radiation therapy.

BACKGROUND: Prostate-Specific Membrane Antigen (PSMA) PET/CT and multiparametric MRI (mpMRI) are well-established modalities for identifying intra-prostatic lesions (IPLs) in localised prostate cancer. This study aimed to investigate the use of PSMA PET/CT and mpMRI for biologically targeted radiation therapy treatment planning by: (1) analysing the relationship between imaging parameters at a voxel-wise level and (2) assessing the performance of radiomic-based machine learning models to predict tumour location and grade. METHODS: PSMA PET/CT and mpMRI data from 19 prostate cancer patients were co-registered with whole-mount histopathology using an established registration framework. Apparent Diffusion Coefficient (ADC) maps were computed from DWI and semi-quantitative and quantitative parameters from DCE MRI. Voxel-wise correlation analysis was conducted between mpMRI parameters and PET Standardised Uptake Value (SUV) for all tumour voxels. Classification models were built using radiomic and clinical features to predict IPLs at a voxel level and then classified further into high-grade or low-grade voxels. RESULTS: Perfusion parameters from DCE MRI were more highly correlated with PET SUV than ADC or T2w. IPLs were best detected with a Random Forest Classifier using radiomic features from PET and mpMRI rather than either modality alone (sensitivity, specificity and area under the curve of 0.842, 0.804 and 0.890, respectively). The tumour grading model had an overall accuracy ranging from 0.671 to 0.992. CONCLUSIONS: Machine learning classifiers using radiomic features from PSMA PET and mpMRI show promise for predicting IPLs and differentiating between high-grade and low-grade disease, which could be used to inform biologically targeted radiation therapy planning.

Patient-specific voxel-level dose prescription for prostate cancer radiotherapy considering tumor cell density and grade distribution.

BACKGROUND: In prostate radiation therapy, recent studies have indicated a benefit in increasing the dose to intraprostatic lesions (IPL) compared with standard whole gland radiation therapy. Such approaches typically aim to deliver a target dose to the IPL(s) with no deliberate effort to modulate the dose within the IPL. Prostate cancers demonstrate intra-tumor heterogeneity and hence it is hypothesized that further gains in the optimal delivery of radiation therapy can be achieved through modulation of the dose distribution within the tumor. To account for tumor heterogeneity, biologically targeted radiation therapy (BiRT) aims to utilize a voxel-wise approach to IPL dose prescription by incorporating knowledge of the spatial distribution of tumor characteristics. PURPOSE: The aim of this study was to develop a workflow for generating voxel-wise optimal dose prescriptions that maximize patient tumor control probability (TCP), and evaluate the feasibility and benefits of applying this workflow on a cohort of 62 prostate cancer patients. METHOD: The source data for this proof-of-concept study included high resolution histology images annotated with tumor location and grade. Image processing techniques were used to compute voxel-level cell density distribution maps. An absolute tumor cell distribution was calculated via linearly scaling according to published estimated tumor cell numbers. For the IPLs of each patient, optimal dose prescriptions were obtained via three alternative methods for redistribution of IPL boost doses according to maximization of TCP. The radiosensitivity uncertainties were considered using a truncated log-normally distributed linear radiosensitivity parameter ( α k ${\alpha }_k$ ) and compared with Gleason pattern (GP) dependent radiosensitivity parameters that were derived based on previously published methods. An ensemble machine learning method was implemented to identify patient-specific features that predict the TCP improvement resulting from dose redistribution relative to a uniform dose distribution. RESULTS: The Gleason pattern-dependent radiosensitivity parameters were calculated for 20 published prostate cancer α / ß ${{\alpha}}/{{\beta}}$ ratios. Optimal voxel-level dose prescriptions were generated for all 62 PCa patients. For all dose redistribution scenarios, the optimal dose distribution always shows a higher (or equivalent) TCP level than the uniform dose distribution. The applied random forest regressor could predict patient-specific TCP improvement with low root mean square error (≤1.5%) by using total tumor number, volume of IPLs and the standard deviation of tumor cell number among all voxels. CONCLUSION: Biologically-optimized redistribution of a boost dose can yield TCP improvement relative to a uniform-boost dose distribution. Patient-specific tumor characteristics can be used to predict the likelihood of benefit from a redistribution approach for the individual patient.

Semi-quantitative and quantitative dynamic contrast-enhanced (DCE) MRI parameters as prostate cancer imaging biomarkers for biologically targeted radiation therapy.

BACKGROUND: Biologically targeted radiation therapy treatment planning requires voxel-wise characterisation of tumours. Dynamic contrast enhanced (DCE) DCE MRI has shown promise in defining voxel-level biological characteristics. In this study we consider the relative value of qualitative, semi-quantitative and quantitative assessment of DCE MRI compared with diffusion weighted imaging (DWI) and T2-weighted (T2w) imaging to detect prostate cancer at the voxel level. METHODS: Seventy prostate cancer patients had multiparametric MRI prior to radical prostatectomy, including T2w, DWI and DCE MRI. Apparent Diffusion Coefficient (ADC) maps were computed from DWI, and semi-quantitative and quantitative parameters computed from DCE MRI. Tumour location and grade were validated with co-registered whole mount histology. Kolmogorov-Smirnov tests were applied to determine whether MRI parameters in tumour and benign voxels were significantly different. Cohen's d was computed to quantify the most promising biomarkers. The Parker and Weinmann Arterial Input Functions (AIF) were compared for their ability to best discriminate between tumour and benign tissue. Classifier models were used to determine whether DCE MRI parameters improved tumour detection versus ADC and T2w alone. RESULTS: All MRI parameters had significantly different data distributions in tumour and benign voxels. For low grade tumours, semi-quantitative DCE MRI parameter time-to-peak (TTP) was the most discriminating and outperformed ADC. For high grade tumours, ADC was the most discriminating followed by DCE MRI parameters Ktrans, the initial rate of enhancement (IRE), then TTP. Quantitative parameters utilising the Parker AIF better distinguished tumour and benign voxel values than the Weinmann AIF. Classifier models including DCE parameters versus T2w and ADC alone, gave detection accuracies of 78% versus 58% for low grade tumours and 85% versus 72% for high grade tumours. CONCLUSIONS: Incorporating DCE MRI parameters with DWI and T2w gives improved accuracy for tumour detection at a voxel level. DCE MRI parameters should be used to spatially characterise tumour biology for biologically targeted radiation therapy treatment planning.

Rapid genome sequencing identifies a novel de novo SNAP25 variant for neonatal congenital myasthenic syndrome.

Congenital myasthenic syndrome (CMS) is a group of 32 disorders involving genetic dysfunction at the neuromuscular junction resulting in skeletal muscle weakness that worsens with physical activity. Precise diagnosis and molecular subtype identification are critical for treatment as medication for one subtype may exacerbate disease in another (Engel et al., Lancet Neurol 14: 420 [2015]; Finsterer, Orphanet J Rare Dis 14: 57 [2019]; Prior and Ghosh, J Child Neurol 36: 610 [2021]). The SNAP25-related CMS subtype (congenital myasthenic syndrome 18, CMS18; MIM #616330) is a rare disorder characterized by muscle fatigability, delayed psychomotor development, and ataxia. Herein, we performed rapid whole-genome sequencing (rWGS) on a critically ill newborn leading to the discovery of an unreported pathogenic de novo SNAP25 c.529C > T; p.Gln177Ter variant. In this report, we present a novel case of CMS18 with complex neonatal consequence. This discovery offers unique insight into the extent of phenotypic severity in CMS18, expands the reported SNAP25 variant phenotype, and paves a foundation for personalized management for CMS18.

A statistical, voxelised model of prostate cancer for biologically optimised radiotherapy.

Background and purpose: Radiation therapy (RT) is commonly indicated for treatment of prostate cancer (PC). Biologicallyoptimised RT for PC may improve disease-free survival. This requires accurate spatial localisation and characterisation of tumour lesions. We aimed to generate a statistical, voxelised biological model to complement in vivomultiparametric MRI data to facilitate biologically-optimised RT. Material and methods: Ex vivo prostate MRI and histopathological imaging were acquired for 63 PC patients. These data were co-registered to derive three-dimensional distributions of graded tumour lesions and cell density. Novel registration processes were used to map these data to a common reference geometry. Voxelised statistical models of tumour probability and cell density were generated to create the PC biological atlas. Cell density models were analysed using the Kullback-Leibler divergence to compare normal vs. lognormal approximations to empirical data. Results: A reference geometry was constructed using ex vivo MRI space, patient data were deformably registered using a novel anatomy-guided process. Substructure correspondence was maintained using peripheral zone definitions to address spatial variability in prostate anatomy between patients. Three distinct approaches to interpolation were designed to map contours, tumour annotations and cell density maps from histology into ex vivo MRI space. Analysis suggests a log-normal model provides a more consistent representation of cell density when compared to a linear-normal model. Conclusion: A biological model has been created that combines spatial distributions of tumour characteristics from a population into three-dimensional, voxelised, statistical models. This tool will be used to aid the development of biologically-optimised RT for PC patients.

Furin cleavage of the SARS-CoV-2 spike is modulated by O-glycosylation.

The SARS-CoV-2 coronavirus responsible for the global pandemic contains a novel furin cleavage site in the spike protein (S) that increases viral infectivity and syncytia formation in cells. Here, we show that O-glycosylation near the furin cleavage site is mediated by members of the GALNT enzyme family, resulting in decreased furin cleavage and decreased syncytia formation. Moreover, we show that O-glycosylation is dependent on the novel proline at position 681 (P681). Mutations of P681 seen in the highly transmissible alpha and delta variants abrogate O-glycosylation, increase furin cleavage, and increase syncytia formation. Finally, we show that GALNT family members capable of glycosylating S are expressed in human respiratory cells that are targets for SARS-CoV-2 infection. Our results suggest that host O-glycosylation may influence viral infectivity/tropism by modulating furin cleavage of S and provide mechanistic insight into the role of the P681 mutations found in the highly transmissible alpha and delta variants.

Biologically Targeted Radiation Therapy: Incorporating Patient-Specific Hypoxia Data Derived from Quantitative Magnetic Resonance Imaging.

PURPOSE: Hypoxia has been linked to radioresistance. Strategies to safely dose escalate dominant intraprostatic lesions have shown promising results, but further dose escalation to overcome the effects of hypoxia require a novel approach to constrain the dose in normal tissue.to safe levels. In this study, we demonstrate a biologically targeted radiotherapy (BiRT) approach that can utilise multiparametric magnetic resonance imaging (mpMRI) to target hypoxia for favourable treatment outcomes. METHODS: mpMRI-derived tumour biology maps, developed via a radiogenomics study, were used to generate individualised, hypoxia-targeting prostate IMRT plans using an ultra- hypofractionation schedule. The spatial distribution of mpMRI textural features associated with hypoxia-related genetic profiles was used as a surrogate of tumour hypoxia. The effectiveness of the proposed approach was assessed by quantifying the potential benefit of a general focal boost approach on tumour control probability, and also by comparing the dose to organs at risk (OARs) with hypoxia-guided focal dose escalation (DE) plans generated for five patients. RESULTS: Applying an appropriately guided focal boost can greatly mitigate the impact of hypoxia. Statistically significant reductions in rectal and bladder dose were observed for hypoxia-targeting, biologically optimised plans compared to isoeffective focal DE plans. CONCLUSION: Results of this study suggest the use of mpMRI for voxel-level targeting of hypoxia, along with biological optimisation, can provide a mechanism for guiding focal DE that is considerably more efficient than application of a general, dose-based optimisation, focal boost.

Furin cleavage of the SARS-CoV-2 spike is modulated by O-glycosylation.

The SARS-CoV-2 coronavirus responsible for the global pandemic contains a unique furin cleavage site in the spike protein (S) that increases viral infectivity and syncytia formation. Here, we show that O-glycosylation near the furin cleavage site is mediated by specific members of the GALNT enzyme family and is dependent on the novel proline at position 681 (P681). We further demonstrate that O-glycosylation of S decreases furin cleavage. Finally, we show that GALNT family members capable of glycosylating S are expressed in human respiratory cells that are targets for SARS-CoV-2 infection. Our results suggest that O-glycosylation may influence viral infectivity/tropism by modulating furin cleavage of S and provide mechanistic insight into the potential role of P681 mutations in the recently identified, highly transmissible B.1.1.7 variant.

Tango1 coordinates the formation of endoplasmic reticulum/Golgi docking sites to mediate secretory granule formation.

Regulated secretion is a conserved process occurring across diverse cells and tissues. Current models suggest that the conserved cargo receptor Tango1 mediates the packaging of collagen into large coat protein complex II (COPII) vesicles that move from the endoplasmic reticulum (ER) to the Golgi apparatus. However, how Tango1 regulates the formation of COPII carriers and influences the secretion of other cargo remains unknown. Here, through high-resolution imaging of Tango1, COPII, Golgi, and secretory cargo (mucins) in Drosophila larval salivary glands, we found that Tango1 forms ring-like structures that mediate the formation of COPII rings rather than vesicles. These COPII rings act as docking sites for the cis-Golgi. Moreover, we observed nascent secretory mucins emerging from the Golgi side of these Tango1-COPII-Golgi complexes, suggesting that these structures represent functional docking sites/fusion points between the ER exit sites and the Golgi. Loss of Tango1 disrupted the formation of COPII rings, the association of COPII with the cis-Golgi, mucin O-glycosylation, and secretory granule biosynthesis. Additionally, we identified a Tango1 self-association domain that is essential for formation of this structure. Our results provide evidence that Tango1 organizes an interaction site where secretory cargo is efficiently transferred from the ER to Golgi and then to secretory vesicles. These findings may explain how the loss of Tango1 can influence Golgi/ER morphology and affect the secretion of diverse proteins across many tissues.

Association analysis between quantitative MRI features and hypoxia-related genetic profiles in prostate cancer: a pilot study.

OBJECTIVE: To investigate the association between multiparametric MRI (mpMRI) imaging features and hypoxia-related genetic profiles in prostate cancer. METHODS: In vivo mpMRI was acquired from six patients prior to radical prostatectomy. Sequences included T2 weighted (T2W) imaging, diffusion-weighted imaging, dynamic contrast enhanced MRI and blood oxygen-level dependent imaging. Imaging data were co-registered with histology using three-dimensional deformable registration methods. Texture features were extracted from T2W images and parametric maps from functional MRI. Full transcriptome genetic profiles were obtained using next generation sequencing from the prostate specimens. Pearson correlation coefficients were calculated between mpMRI data and hypoxia-related gene expression levels. Results were validated using glucose transporter one immunohistochemistry (IHC). RESULTS: Correlation analysis identified 34 candidate imaging features (six from the mpMRI data and 28 from T2W texture features). The IHC validation showed that 16 out of the 28 T2W texture features achieved weak but significant correlations (p < 0.05). CONCLUSIONS: Weak associations between mpMRI features and hypoxia gene expressions were found. This indicates the potential use of MRI in assessing hypoxia status in prostate cancer. Further validation is required due to the low correlation levels. ADVANCES IN KNOWLEDGE: This is a pilot study using radiogenomics approaches to address hypoxia within the prostate, which provides an opportunity for hypoxia-guided selective treatment techniques.

Automatic stratification of prostate tumour aggressiveness using multiparametric MRI: a horizontal comparison of texture features.

Background: Previous studies have identified apparent diffusion coefficient (ADC) from diffusion-weighted imaging (DWI) can stratify prostate cancer into high- and low-grade disease (HG and LG, respectively). In this study, we consider the improvement of incorporating texture features (TFs) from T2-weighted (T2w) multiparametric magnetic resonance imaging (mpMRI) relative to mpMRI alone to predict HG and LG disease. Material and methods: In vivo mpMRI was acquired from 30 patients prior to radical prostatectomy. Sequences included T2w imaging, DWI and dynamic contrast enhanced (DCE) MRI. In vivo mpMRI data were co-registered with 'ground truth' histology. Tumours were delineated on the histology with Gleason scores (GSs) and classed as HG if GS ≥ 4 + 3, or LG if GS ≤ 3 + 4. Texture features based on three statistical families, namely the grey-level co-occurrence matrix (GLCM), grey-level run length matrix (GLRLM) and the grey-level size zone matrix (GLSZM), were computed from T2w images. Logistic regression models were trained using different feature subsets to classify each lesion as either HG or LG. To avoid overfitting, fivefold cross validation was applied on feature selection, model training and performance evaluation. Performance of all models generated was evaluated using the area under the curve (AUC) method. Results: Consistent with previous studies, ADC was found to discriminate between HG and LG with an AUC of 0.76. Of the three statistical TF families, GLCM (plus select mpMRI features including ADC) scored the highest AUC (0.84) with GLRLM plus mpMRI similarly performing well (AUC = 0.82). When all TFs were considered in combination, an AUC of 0.91 (95% confidence interval 0.87-0.95) was achieved. Conclusions: Incorporating T2w TFs significantly improved model performance for classifying prostate tumour aggressiveness. This result, however, requires further validation in a larger patient cohort.