Implications of TP53 allelic state for genome stability, clinical presentation and outcomes in myelodysplastic syndromes

Tumor protein p53 (TP53) is the most frequently mutated gene in cancer1,2. In patients with myelodysplastic syndromes (MDS), TP53 mutations are associated with high-risk disease3,4, rapid transformation to acute myeloid leukemia (AML)5, resistance to conventional therapies6–8 and dismal outcomes9. Consistent with the tumor-suppressive role of TP53, patients harbor both mono- and biallelic mutations10. However, the biological and clinical implications of TP53 allelic state have not been fully investigated in MDS or any other cancer type. We analyzed 3,324 patients with MDS for TP53 mutations and allelic imbalances and delineated two subsets of patients with distinct phenotypes and outcomes. One-third of TP53-mutated patients had monoallelic mutations whereas two-thirds had multiple hits (multi-hit) consistent with biallelic targeting. Established associations with complex karyotype, few co-occurring mutations, high-risk presentation and poor outcomes were specific to multi-hit patients only. TP53 multi-hit state predicted risk of death and leukemic transformation independently of the Revised International Prognostic Scoring System (IPSS-R)11. Surprisingly, monoallelic patients did not differ from TP53 wild-type patients in outcomes and response to therapy. This study shows that consideration of TP53 allelic state is critical for diagnostic and prognostic precision in MDS as well as in future correlative studies of treatment response

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Estimating patient peak skin dose with fluoroscopic procedures

During image guided interventional radiology (IR) procedures, acute X-ray induced skin injuries may occur due to high absorbed patient skin dose. These procedures are highly dependent on X-ray imaging both for guiding fluoroscopy and high quality diagnostic image acquisitions. A dose metric that quantifies the peak absorbed skin dose (PSD) is therefore of great importance, both in terms of patient specific follow-up and for imaging protocol optimization. Presently, the cumulative interventional reference point (IRP) air Kerma is the most common skin dose estimation metric in use. This metric lacks several important dose contributions, such as pre-patient attenuation, air-to-skin medium correction, scattering from the patient and the support table, and fluence correction for actual source-to-skin distance. In this manuscript, we present a novel methodology for estimating the maximum absorbed skin dose by using dose related X-ray equipment parameters, such as peak tube voltage, support table position and IRP air Kerma obtained from radiation dose structured reports (RDSR) generated by modern IR equipment. In particular, Siemens Artis Zee (Siemens Healthineers, Erlangen, Germany) and Philips Allura Clarity (Philips, Amsterdam, Netherlands). The calculation process was automated by the development of a series of programming scripts in the PythonTM programming language v3.6 (Python Software Foundation), together with a database storing correction factors and machine specific parameters such as half-value layer (HVL). The proposed calculation model enables the implementation of a dose metric which corresponds better to absorbed skin dose than IRP air Kerma in clinical settings. However, extensive future work is required for a complete PSD implementation, in particular, the development of a skin dose map in which the spatial location of each irradiation event is tracked

Estimating patient peak skin dose with fluoroscopic procedures

During image guided interventional radiology (IR) procedures, acute X-ray induced skin injuries may occur due to high absorbed patient skin dose. These procedures are highly dependent on X-ray imaging both for guiding fluoroscopy and high quality diagnostic image acquisitions. A dose metric that quantifies the peak absorbed skin dose (PSD) is therefore of great importance, both in terms of patient specific follow-up and for imaging protocol optimization. Presently, the cumulative interventional reference point (IRP) air Kerma is the most common skin dose estimation metric in use. This metric lacks several important dose contributions, such as pre-patient attenuation, air-to-skin medium correction, scattering from the patient and the support table, and fluence correction for actual source-to-skin distance. In this manuscript, we present a novel methodology for estimating the maximum absorbed skin dose by using dose related X-ray equipment parameters, such as peak tube voltage, support table position and IRP air Kerma obtained from radiation dose structured reports (RDSR) generated by modern IR equipment. In particular, Siemens Artis Zee (Siemens Healthineers, Erlangen, Germany) and Philips Allura Clarity (Philips, Amsterdam, Netherlands). The calculation process was automated by the development of a series of programming scripts in the PythonTM programming language v3.6 (Python Software Foundation), together with a database storing correction factors and machine specific parameters such as half-value layer (HVL). The proposed calculation model enables the implementation of a dose metric which corresponds better to absorbed skin dose than IRP air Kerma in clinical settings. However, extensive future work is required for a complete PSD implementation, in particular, the development of a skin dose map in which the spatial location of each irradiation event is tracked

MEASUREMENT AND COMPARISON OF DUOLINGO'S AND RENSHUU'S EFFICIENCY FOR LEARNING JAPANESE : A quantitative study that through self-assessment compares two different language learning apps' methods of teaching japanese

Det finns flera olika appar ute på marknaden för att lära sig japanska. Flera av dessa använder sig av olika typer av gamification för att behålla användarnas intresse så de lär sig mer. Duolingo är den största av alla dessa appar, men det finns även nyare appar med andra inlärningsmetoder. I denna studie samlades data in från flera användare av apparna Duolingo och Renshuu för att ta reda på hur mycket bättre de blivit på japanska enligt CEFR-skalan. De fick även förklara vad de gillar med apparna. Resultatet blev att båda apparna är bra på att lära ut japanska, men beroende på vilken typ av användare som använde appen kunde den ena vara bättre än den andra.

MEASUREMENT AND COMPARISON OF DUOLINGO'S AND RENSHUU'S EFFICIENCY FOR LEARNING JAPANESE : A quantitative study that through self-assessment compares two different language learning apps' methods of teaching japanese

Det finns flera olika appar ute på marknaden för att lära sig japanska. Flera av dessa använder sig av olika typer av gamification för att behålla användarnas intresse så de lär sig mer. Duolingo är den största av alla dessa appar, men det finns även nyare appar med andra inlärningsmetoder. I denna studie samlades data in från flera användare av apparna Duolingo och Renshuu för att ta reda på hur mycket bättre de blivit på japanska enligt CEFR-skalan. De fick även förklara vad de gillar med apparna. Resultatet blev att båda apparna är bra på att lära ut japanska, men beroende på vilken typ av användare som använde appen kunde den ena vara bättre än den andra.

Denoising and uncertainty estimation in parameter mapping with approximate Bayesian deep image priors

Purpose: To mitigate the problem of noisy parameter maps with high uncertainties by casting parameter mapping as a denoising task based on Deep Image Priors. Methods: We extend the concept of denoising with Deep Image Prior (DIP) into parameter mapping by treating the output of an image-generating network as a parametrization of tissue parameter maps. The method implicitly denoises the parameter mapping process by filtering low-level image features with an untrained convolutional neural network (CNN). Our implementation includes uncertainty estimation from Bernoulli approximate variational inference, implemented with MC dropout, which provides model uncertainty in each voxel of the denoised parameter maps. The method is modular, so the specifics of different applications (e.g., T1 mapping) separate into application-specific signal equation blocks. We evaluate the method on variable flip angle T1 mapping, multi-echo T2 mapping, and apparent diffusion coefficient mapping. Results: We found that deep image prior adapts successfully to several applications in parameter mapping. In all evaluations, the method produces noise-reduced parameter maps with decreased uncertainty compared to conventional methods. The downsides of the proposed method are the long computational time and the introduction of some bias from the denoising prior. Conclusion: DIP successfully denoise the parameter mapping process and applies to several applications with limited hyperparameter tuning. Further, it is easy to implement since DIP methods do not use network training data. Although time-consuming, uncertainty information from MC dropout makes the method more robust and provides useful information when properly calibrated

Bayesian non-linear regression with spatial priors for noise reduction and error estimation in quantitative MRI with an application in T1 estimation

Purpose. To develop a method that can reduce and estimate uncertainty in quantitative MR parameter maps without the need for hand-tuning of any hyperparameters. Methods. We present an estimation method where uncertainties are reduced by incorporating information on spatial correlations between neighbouring voxels. The method is based on a Bayesian hierarchical non-linear regression model, where the parameters of interest are sampled, using Markov chain Monte Carlo (MCMC), from a high-dimensional posterior distribution with a spatial prior. The degree to which the prior affects the model is determined by an automatic hyperparameter search using an information criterion and is, therefore, free from manual user-dependent tuning. The samples obtained further provide a convenient means to obtain uncertainties in both voxels and regions. The developed method was evaluated on T1 estimations based on the variable flip angle method. Results. The proposed method delivers noise-reduced T1 parameter maps with associated error estimates by combining MCMC sampling, the widely applicable information criterion, and total variation-based denoising. The proposed method results in an overall decrease in estimation error when compared to conventional voxel-wise maximum likelihood estimation. However, this comes with an increased bias in some regions, predominately at tissue interfaces, as well as an increase in computational time. Conclusions. This study provides a method that generates more precise estimates compared to the conventional method, without incorporating user subjectivity, and with the added benefit of uncertainty estimation

Antibody responses after a single dose of ChAdOx1 nCoV-19 vaccine in healthcare workers previously infected with SARS-CoV-2

Background: Recent reports demonstrate robust serological responses to a single dose of messenger RNA (mRNA) vaccines in individuals previously infected with SARS-CoV-2. Data on immune responses following a single-dose adenovirus-vectored vaccine expressing the SARS-CoV-2 spike protein (ChAdOx1 nCoV-19) in individuals with previous SARS-CoV-2 infection are however limited, and current guidelines recommend a two-dose regimen regardless of preexisting immunity. Methods: We compared RBD-specific IgG and RBD-ACE2 blocking antibodies against SARS-CoV-2 wild type and variants of concern following two doses of the mRNA vaccine BNT162b2 in SARS-CoV-2 naive healthcare workers (n=65) and a single dose of the adenovector vaccine ChAdOx1 nCoV-19 in 82 healthcare workers more than (n=45) and less than (n=37) 11 months post mild SARS-CoV-2 infection at time of vaccination. Findings: The post-vaccine levels of RBD-specific IgG and neutralizing antibodies against the SARS-CoV-2 wild type and variants of concern including Delta lineage 1.617.2 were similar or higher in participants receiving a single dose of ChAdOx1 nCoV-19 vaccine post SARS-CoV-2 infection (both more than and less than 11 months post infection) compared to SARS-CoV-2 naive participants who received two doses of BNT162b2 vaccine. Interpretation: Our data support that a single dose ChAdOx1 nCoV-19 vaccine that is administered up to at least 11 months post SARS-CoV-2 infection serves as an effective immune booster. This provides a possible rationale for a single-dose vaccine regimen

Estimation of patient skin dose in fluoroscopy : summary of a joint report by AAPM TG357 and EFOMP

Background: Physicians use fixed C-arm fluoroscopy equipment with many interventional radiological and cardiological procedures. The associated effective dose to a patient is generally considered low risk, as the benefit-risk ratio is almost certainly highly favorable. However, X-ray-induced skin injuries may occur due to high absorbed patient skin doses from complex fluoroscopically guided interventions (FGI). Suitable action levels for patient-specific follow-up could improve the clinical practice. There is a need for a refined metric regarding follow-up of X-ray-induced patient injuries and the knowledge gap regarding skin dose-related patient information from fluoroscopy devices must be filled. The most useful metric to indicate a risk of erythema, epilation or greater skin injury that also includes actionable information is the peak skin dose, that is, the largest dose to a region of skin. Materials and Methods: The report is based on a comprehensive review of best practices and methods to estimate peak skin dose found in the scientific literature and situates the importance of the Digital Imaging and Communication in Medicine (DICOM) standard detailing pertinent information contained in the Radiation Dose Structured Report (RDSR) and DICOM image headers for FGI devices. Furthermore, the expertise of the task group members and consultants have been used to bridge and discuss different methods and associated available DICOM information for peak skin dose estimation. Results: The report contributes an extensive summary and discussion of the current state of the art in estimating peak skin dose with FGI procedures with regard to methodology and DICOM information. Improvements in skin dose estimation efforts with more refined DICOM information are suggested and discussed. Conclusions: The endeavor of skin dose estimation is greatly aided by the continuing efforts of the scientific medical physics community, the numerous technology enhancements, the dose-controlling features provided by the FGI device manufacturers, and the emergence and greater availability of the DICOM RDSR. Refined and new dosimetry systems continue to evolve and form the infrastructure for further improvements in accuracy. Dose-related content and information systems capable of handling big data are emerging for patient dose monitoring and quality assurance tools for large-scale multihospital enterprises