AKT1 (E17K) mutation profiling in breast cancer: prevalence, concurrent oncogenic alterations, and blood-based detection.

BACKGROUND: The single hotspot mutation AKT1 [G49A:E17K] has been described in several cancers, with the highest incidence observed in breast cancer. However, its precise role in disease etiology remains unknown. METHODS: We analyzed more than 600 breast cancer tumor samples and circulating tumor DNA for AKT1 (E17K) and alterations in other cancer-associated genes using Beads, Emulsions, Amplification, and Magnetics digital polymerase chain reaction technology and targeted exome sequencing. RESULTS: Overall AKT1 (E17K) mutation prevalence was 6.3 % and not correlated with age or menopausal stage. AKT1 (E17K) mutation frequency tended to be lower in patients with grade 3 disease (1.9 %) compared with those with grade 1 (11.1 %) or grade 2 (6 %) disease. In two cohorts of patients with advanced metastatic disease, 98.0 % (n = 50) and 97.1 % (n = 35) concordance was obtained between tissue and blood samples for the AKT1 (E17K) mutation, and mutation capture rates of 66.7 % (2/3) and 85.7 % (6/7) in blood versus tissue samples were observed. Although AKT1-mutant tumor specimens were often found to harbor concurrent alterations in other driver genes, a subset of specimens harboring AKT1 (E17K) as the only known driver alteration was also identified. Initial follow-up survival data suggest that AKT1 (E17K) could be associated with increased mortality. These findings warrant additional long-term follow-up. CONCLUSIONS: The data suggest that AKT1 (E17K) is the most likely disease driver in certain breast cancer patients. Blood-based mutation detection is achievable in advanced-stage disease. These findings underpin the need for a further enhanced-precision medicine paradigm in the treatment of breast cancer

Pulmonary vasculitis due to infection with Mycobacterium goodii : A case report

A 57-year-old Caucasian woman suffered from dyspnea on exertion. One year following a supposed pulmonary embolism event, a chronic thromboembolic vasculopathy was diagnosed and a pulmonary thromboendarterectomy was performed. However, a granulomatous pulmonary arterial vasculitis was identified upon examination. DNA of Mycobacterium goodii was detected as the most likely causative agent. Anti-inflammatory and anti-mycobacterial therapy was initiated for more than 12 months. Regular PET-CT scans revealed improvement under therapy. The last PET-CT did not show any tracer uptake following 10 months of therapy

Collaboration for success: the value of strategic col-laborations for precision medicine and biomarker discovery

Precision medicine aims to provide the precise treatment for the patient with the right dose at the right point of time. Biomarkers (BM) are vital for the identification of patients who would benefit the most from individualized treatment. In addition, they help to enable the prediction of prognosis, the detection of early therapeutic and adverse effects, and may serve as surrogate endpoints in clinical trials. BM are becoming essential tools to increase productivity in drug discovery and impressively enhance the way medicine is practiced. However, the identification, sufficient validation and implementation of such BM are challenging. This process requires expertise from different areas and high resource investments. Collaborations of different partners may be helpful to overcome these challenges. In the past decade, collaborations between diagnostics and pharmaceutical companies as well as industrial–academic collaborations have been increasingly pursued. Moreover, public funding may offer support and open new opportunities to form such consortia. Herein we give an overview of the different types of collaborations, their opportunities and challenges, and describe experiences in forming strategic partnerships with other companies

Towards a survival risk prediction model for metastatic NSCLC patients on durvalumab using whole-lung CT radiomics

BackgroundExisting criteria for predicting patient survival from immunotherapy are primarily centered on the PD-L1 status of patients. We tested the hypothesis that noninvasively captured baseline whole-lung radiomics features from CT images, baseline clinical parameters, combined with advanced machine learning approaches, can help to build models of patient survival that compare favorably with PD-L1 status for predicting ‘less-than-median-survival risk’ in the metastatic NSCLC setting for patients on durvalumab. With a total of 1062 patients, inclusive of model training and validation, this is the largest such study yet.MethodsTo ensure a sufficient sample size, we combined data from treatment arms of three metastatic NSCLC studies. About 80% of this data was used for model training, and the remainder was held-out for validation. We first trained two independent models; Model-C trained to predict survival using clinical data; and Model-R trained to predict survival using whole-lung radiomics features. Finally, we created Model-C+R which leveraged both clinical and radiomics features.ResultsThe classification accuracy (for median survival) of Model-C, Model-R, and Model-C+R was 63%, 55%, and 68% respectively. Sensitivity analysis of survival prediction across different training and validation cohorts showed concordance indices ([95 percentile]) of 0.64 ([0.63, 0.65]), 0.60 ([0.59, 0.60]), and 0.66 ([0.65,0.67]), respectively. We additionally evaluated generalization of these models on a comparable cohort of 144 patients from an independent study, demonstrating classification accuracies of 65%, 62%, and 72% respectively.ConclusionMachine Learning models combining baseline whole-lung CT radiomic and clinical features may be a useful tool for patient selection in immunotherapy. Further validation through prospective studies is needed

Novel loci for childhood body mass index and shared heritability with adult cardiometabolic traits

The genetic background of childhood body mass index (BMI), and the extent to which the well-known associations of childhood BMI with adult diseases are explained by shared genetic factors, are largely unknown. We performed a genome-wide association study meta-analysis of BMI in 61,111 children aged between 2 and 10 years. Twenty-five independent loci reached genome-wide significance in the combined discovery and replication analyses. Two of these, located nearNEDD4LandSLC45A3, have not previously been reported in relation to either childhood or adult BMI. Positive genetic correlations of childhood BMI with birth weight and adult BMI, waist-to-hip ratio, diastolic blood pressure and type 2 diabetes were detected (R(g)ranging from 0.11 to 0.76, P-values Author summary Although twin studies have shown that body mass index (BMI) is highly heritable, many common genetic variants involved in the development of BMI have not yet been identified, especially in children. We studied associations of more than 40 million genetic variants with childhood BMI in 61,111 children aged between 2 and 10 years. We identified 25 genetic variants that were associated with childhood BMI. Two of these have not been implicated for BMI previously, located close to the genesNEDD4LandSLC45A3. We also show that the genetic background of childhood BMI overlaps with that of birth weight, adult BMI, waist-to-hip-ratio, diastolic blood pressure, type 2 diabetes, and age at menarche. Our results suggest that the biological processes underlying childhood BMI largely overlap with those underlying adult BMI. However, the overlap is not complete. Additionally, the genetic backgrounds of childhood BMI and other cardio-metabolic phenotypes are overlapping. This may mean that the associations of childhood BMI and later cardio-metabolic outcomes are partially explained by shared genetics, but it could also be explained by the strong association of childhood BMI with adult BMI.Peer reviewe

Septicemia Due to Acinetobacter junii

Acinetobacter spp. are considered to be emerging nosocomial pathogens. Acinetobacter junii is a rare cause of disease in humans and was associated mainly with bacteremia in preterm infants and pediatric oncologic patients. In this report we describe a case of catheter-related infection by A. junii in an adult oncologic patient. Application of molecular methods for precise species identification of Acinetobacter spp. will help to further clarify their role as human pathogens

[18F]fluoro-ethylcholine-PET Plus 4D-CT (FEC-PET-CT): A Break-Through Tool to Localize the “Negative” Parathyroid Adenoma. One Year Follow Up Results Involving 170 Patients

Background: The diagnostic performance of [18F]fluoro-ethylcholine-PET-CT&4D-CT (FEC-PET&4D-CT) to identify parathyroid adenomas (PA) was analyzed when ultrasound (US) or MIBI-Scan (MS) failed to localize. Postsurgical one year follow-up data are presented. Methods: Patients in whom US and MS delivered either incongruent or entirely negative findings were subjected to FEC-PET&4D-CT and cases from July 2017 to June 2020 were analyzed, retrospectively. Cervical exploration with intraoperative PTH-monitoring (IO-PTH) was performed. Imaging results were correlated to intraoperative findings, and short term and one year postoperative follow-up data. Results: From July 2017 to June 2020 in 171 FEC-PET&4D-CTs 159 (92.9%) PAs were suggested. 147 patients already had surgery, FEC-PET&4D-CT accurately localized in 141; false neg. 4, false pos. 2, global sensitivity 0.97; accuracy 0.96, PPV 0.99. All of the 117 patients that already have completed their 12-month postoperative follow up had normal biochemical parameter, i.e., no signs of persisting disease. However, two cases may have a potential for recurrent disease, for a cure rate of at least 98.3%. Conclusion: FEC-PET&4D-CT shows unprecedented results regarding the accuracy localizing PAs. The one-year-follow-up data demonstrate a high cure rate. We, therefore, suggest FEC-PET-CT as the relevant diagnostic tool for the localization of PAs when US fails to localize PA, especially after previous surgery to the neck

Usability of deep learning and H&E images predict disease outcome-emerging tool to optimize clinical trials

Abstract Understanding factors that impact prognosis for cancer patients have high clinical relevance for treatment decisions and monitoring of the disease outcome. Advances in artificial intelligence (AI) and digital pathology offer an exciting opportunity to capitalize on the use of whole slide images (WSIs) of hematoxylin and eosin (H&E) stained tumor tissue for objective prognosis and prediction of response to targeted therapies. AI models often require hand-delineated annotations for effective training which may not be readily available for larger data sets. In this study, we investigated whether AI models can be trained without region-level annotations and solely on patient-level survival data. We present a weakly supervised survival convolutional neural network (WSS-CNN) approach equipped with a visual attention mechanism for predicting overall survival. The inclusion of visual attention provides insights into regions of the tumor microenvironment with the pathological interpretation which may improve our understanding of the disease pathomechanism. We performed this analysis on two independent, multi-center patient data sets of lung (which is publicly available data) and bladder urothelial carcinoma. We perform univariable and multivariable analysis and show that WSS-CNN features are prognostic of overall survival in both tumor indications. The presented results highlight the significance of computational pathology algorithms for predicting prognosis using H&E stained images alone and underpin the use of computational methods to improve the efficiency of clinical trial studies

Pulmonary vasculitis due to infection with Mycobacterium goodii: A case report

A 57-year-old Caucasian woman suffered from dyspnea on exertion. One year following a supposed pulmonary embolism event, a chronic thromboembolic vasculopathy was diagnosed and a pulmonary thromboendarterectomy was performed. However, a granulomatous pulmonary arterial vasculitis was identified upon examination. DNA of Mycobacterium goodii was detected as the most likely causative agent. Anti-inflammatory and anti-mycobacterial therapy was initiated for more than 12 months. Regular PET-CT scans revealed improvement under therapy. The last PET-CT did not show any tracer uptake following 10 months of therapy. (c) 2020 The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-ncnd/4.0/)