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Ewy Mathe - One of the best experts on this subject based on the ideXlab platform.
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computational approaches for predicting the Biological Effect of p53 missense mutations a comparison of three sequence analysis based methods
Nucleic Acids Research, 2006Co-Authors: Ewy Mathe, Magali Olivier, Shunsuke Kato, Chikashi Ishioka, Pierre Hainaut, Sean V TavtigianAbstract:Prediction of the Biological Effect of missense substitutions has become important because they are often observed in known or candidate disease susceptibility genes. In this paper, we carried out a 3-step analysis of 1514 missense substitutions in the DNA-binding domain (DBD) of TP53, the most frequently mutated gene in human cancers. First, we calculated two types of conservation scores based on a TP53 multiple sequence alignment (MSA) for each substitution: (i) Grantham Variation (GV), which measures the degree of biochemical variation among amino acids found at a given position in the MSA; (ii) Grantham Deviation (GD), which reflects the ‘biochemical distance’ of the mutant amino acid from the observed amino acid at a particular position (given by GV). Second, we used a method that combines GV and GD scores, Align-GVGD, to predict the transactivation activity of each missense substitution. We compared our predictions against experimentally measured transactivation activity (yeast assays) to evaluate their accuracy. Finally, the prediction results were compared with those obtained by the program Sorting Intolerant from Tolerant (SIFT) and Dayhoff's classification. Our predictions yielded high prediction accuracy for mutants showing a loss of transactivation (∼88% specificity) with lower prediction accuracy for mutants with transactivation similar to that of the wild-type (67.9 to 71.2% sensitivity). Align-GVGD results were comparable to SIFT (88.3 to 90.6% and 67.4 to 70.3% specificity and sensitivity, respectively) and outperformed Dayhoff's classification (80 and 40.9% specificity and sensitivity, respectively). These results further demonstrate the utility of the Align-GVGD method, which was previously applied to BRCA1. Align-GVGD is available online at http://agvgd.iarc.fr.
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computational approaches for predicting the Biological Effect of p53 missense mutations a comparison of three sequence analysis based methods
Nucleic Acids Research, 2006Co-Authors: Ewy Mathe, Magali Olivier, Shunsuke Kato, Chikashi Ishioka, Pierre Hainaut, Sean V TavtigianAbstract:Prediction of the Biological Effect of missense substitutions has become important because they are often observed in known or candidate disease susceptibility genes. In this paper, we carried out a 3-step analysis of 1514 missense substitutions in the DNA-binding domain (DBD) of TP53, the most frequently mutated gene in human cancers. First, we calculated two types of conservation scores based on a TP53 multiple sequence alignment (MSA) for each substitution: (i) Grantham Variation (GV), which measures the degree of biochemical variation among amino acids found at a given position in the MSA; (ii) Grantham Deviation (GD), which reflects the 'biochemical distance' of the mutant amino acid from the observed amino acid at a particular position (given by GV). Second, we used a method that combines GV and GD scores, Align-GVGD, to predict the transactivation activity of each missense substitution. We compared our predictions against experimentally measured transactivation activity (yeast assays) to evaluate their accuracy. Finally, the prediction results were compared with those obtained by the program Sorting Intolerant from Tolerant (SIFT) and Dayhoff's classification. Our predictions yielded high prediction accuracy for mutants showing a loss of transactivation ( approximately 88% specificity) with lower prediction accuracy for mutants with transactivation similar to that of the wild-type (67.9 to 71.2% sensitivity). Align-GVGD results were comparable to SIFT (88.3 to 90.6% and 67.4 to 70.3% specificity and sensitivity, respectively) and outperformed Dayhoff's classification (80 and 40.9% specificity and sensitivity, respectively). These results further demonstrate the utility of the Align-GVGD method, which was previously applied to BRCA1. Align-GVGD is available online at http://agvgd.iarc.fr.
Sean V Tavtigian - One of the best experts on this subject based on the ideXlab platform.
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computational approaches for predicting the Biological Effect of p53 missense mutations a comparison of three sequence analysis based methods
Nucleic Acids Research, 2006Co-Authors: Ewy Mathe, Magali Olivier, Shunsuke Kato, Chikashi Ishioka, Pierre Hainaut, Sean V TavtigianAbstract:Prediction of the Biological Effect of missense substitutions has become important because they are often observed in known or candidate disease susceptibility genes. In this paper, we carried out a 3-step analysis of 1514 missense substitutions in the DNA-binding domain (DBD) of TP53, the most frequently mutated gene in human cancers. First, we calculated two types of conservation scores based on a TP53 multiple sequence alignment (MSA) for each substitution: (i) Grantham Variation (GV), which measures the degree of biochemical variation among amino acids found at a given position in the MSA; (ii) Grantham Deviation (GD), which reflects the ‘biochemical distance’ of the mutant amino acid from the observed amino acid at a particular position (given by GV). Second, we used a method that combines GV and GD scores, Align-GVGD, to predict the transactivation activity of each missense substitution. We compared our predictions against experimentally measured transactivation activity (yeast assays) to evaluate their accuracy. Finally, the prediction results were compared with those obtained by the program Sorting Intolerant from Tolerant (SIFT) and Dayhoff's classification. Our predictions yielded high prediction accuracy for mutants showing a loss of transactivation (∼88% specificity) with lower prediction accuracy for mutants with transactivation similar to that of the wild-type (67.9 to 71.2% sensitivity). Align-GVGD results were comparable to SIFT (88.3 to 90.6% and 67.4 to 70.3% specificity and sensitivity, respectively) and outperformed Dayhoff's classification (80 and 40.9% specificity and sensitivity, respectively). These results further demonstrate the utility of the Align-GVGD method, which was previously applied to BRCA1. Align-GVGD is available online at http://agvgd.iarc.fr.
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computational approaches for predicting the Biological Effect of p53 missense mutations a comparison of three sequence analysis based methods
Nucleic Acids Research, 2006Co-Authors: Ewy Mathe, Magali Olivier, Shunsuke Kato, Chikashi Ishioka, Pierre Hainaut, Sean V TavtigianAbstract:Prediction of the Biological Effect of missense substitutions has become important because they are often observed in known or candidate disease susceptibility genes. In this paper, we carried out a 3-step analysis of 1514 missense substitutions in the DNA-binding domain (DBD) of TP53, the most frequently mutated gene in human cancers. First, we calculated two types of conservation scores based on a TP53 multiple sequence alignment (MSA) for each substitution: (i) Grantham Variation (GV), which measures the degree of biochemical variation among amino acids found at a given position in the MSA; (ii) Grantham Deviation (GD), which reflects the 'biochemical distance' of the mutant amino acid from the observed amino acid at a particular position (given by GV). Second, we used a method that combines GV and GD scores, Align-GVGD, to predict the transactivation activity of each missense substitution. We compared our predictions against experimentally measured transactivation activity (yeast assays) to evaluate their accuracy. Finally, the prediction results were compared with those obtained by the program Sorting Intolerant from Tolerant (SIFT) and Dayhoff's classification. Our predictions yielded high prediction accuracy for mutants showing a loss of transactivation ( approximately 88% specificity) with lower prediction accuracy for mutants with transactivation similar to that of the wild-type (67.9 to 71.2% sensitivity). Align-GVGD results were comparable to SIFT (88.3 to 90.6% and 67.4 to 70.3% specificity and sensitivity, respectively) and outperformed Dayhoff's classification (80 and 40.9% specificity and sensitivity, respectively). These results further demonstrate the utility of the Align-GVGD method, which was previously applied to BRCA1. Align-GVGD is available online at http://agvgd.iarc.fr.
Mark A Hill - One of the best experts on this subject based on the ideXlab platform.
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physical characteristics at the turnover points of relative Biological Effect rbe with linear energy transfer let
Physics in Medicine and Biology, 2019Co-Authors: Bleddyn Jones, Mark A HillAbstract:This paper considers the kinematic physical characteristics of ionic beams for maximum relative bio-Effectiveness (RBE). RBE studies, based on heterogenous cell survival studies at different laboratories and linear energy transfer (LET) conditions for proton, helium, carbon, neon and argon ions, have been further analysed to determine the LETU values where RBE is maximal and the LET-RBE relationship has a turnover point. The SRIM stopping power software and other classical equations are used to determine the particle velocities, kinetic energies and their Effective ionic charges at LETU. The estimated mean LETU values increase with atomic number (Z). Each LETU has a unique relativistic velocity, β = v/c, the velocity v expressed as a fraction of the speed of light, (c), and which is non-linearly proportional to Z. For ions helium and heavier ions, these velocities indicate that the Effective charge Z * is around 0.99 of the full Z value at each LETU, with remarkably stable velocities of 3-4 nm · fs-1 per nucleon, or around 6-8 nm · fs-1 per unit Z. For Z = 1, (protons and deuterium) some values fall outside these ranges but the result depends on the mix of proton and deuterium used in experiments. An alternative index of βA/Z 2 (A is the atomic mass number), suggests an average velocity of around 15 nm · fs-1 for each particle at LETU. These distances, traversed in the time of the radiochemical process initiation, are all within the dimensions of the nucleosome. Curve fitting of the data set provides a predictive equation for LETU for any ion, as LETU = 30.4 + [Formula: see text] (1 - Exp[-0.61 √ (Z - 1)]) when normalised to protons. These data can be extended to heavier ions such as silicon and iron and give values that are consistent with experimental data. Each ion probably has a unique LETU value. Kinematic studies show maximum bio-Effectiveness occurs at particle velocities where electron stripping remains at around 99% and where the velocity per nucleon is around 3-4 nm · fs-1. This study enhances the limited prior knowledge about the physical conditions of particle beams that provide maximum bio-Effectiveness, with applications in particle radiotherapy, radiation protection and space travel.
Romain Marignier - One of the best experts on this subject based on the ideXlab platform.
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comparison of the response to rituximab between myelin oligodendrocyte glycoprotein and aquaporin 4 antibody diseases
Annals of Neurology, 2020Co-Authors: Pierre Durozard, Audrey Rico, Clemence Boutiere, Adil Maarouf, Romaric Lacroix, Sylvie Cointe, Shirley Fritz, Corinne Brunet, Jean Pelletier, Romain MarignierAbstract:OBJECTIVE To compare response to rituximab (RTX) between adult patients positive for myelin oligodendrocyte glycoprotein (MOG) and aquaporin-4 (AQP4) antibodies. METHODS We prospectively studied adult patients with MOG or AQP4 antibodies who received RTX under an individualized dosing schedule adapted to the Biological Effect of RTX monitored by memory B-cell measurement. Memory B cells were counted monthly and when relapse occurred. The Biological Effect of RTX was considered significant with <0.05% memory B cells in peripheral blood lymphocytes. RESULTS In 16 patients with MOG antibodies and 29 with AQP4 antibodies, mean follow-up was 19 (range = 9-38) and 38 (13-79) months. Under RTX, 10 relapses occurred in 6 of 16 (37.5%) patients with MOG antibodies, and 13 occurred in 7 of 29 (24%) with AQP4 antibodies. The median time of relapse after the most recent infusion was 2.6 (0.6-5.8) and 7 (0.8-13) months, respectively (p < 0.001). Memory B cells had reemerged in 2 of 10 (20%) relapses in patients with MOG antibodies and 12 of 13 (92.5%) with AQP4 antibodies (p < 0.001). INTERPRETATION In AQP4 antibody-associated disorder, relapse mostly occurs when the Biological Effect of RTX decreases, which argues for treatment efficacy. In MOG antibody-associated disorder, the efficacy of RTX is not constant, because one-third of patients showed relapse despite an Effective Biological Effect of RTX. In this subpopulation, memory B-cell depletion was unable to prevent relapse, which was probably caused by different immunological mechanisms. These findings should be used to improve treatment strategies for MOG antibody-associated disorder. ANN NEUROL 2020;87:256-266.
H Paganetti - One of the best experts on this subject based on the ideXlab platform.
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maximizing the Biological Effect of proton dose delivered with scanned beams via inhomogeneous daily dose distributions
Medical Physics, 2013Co-Authors: Chuan Zeng, D Giantsoudi, C Grassberger, Saveli Goldberg, Andrzej Niemierko, H Paganetti, Jason A Efstathiou, A TrofimovAbstract:Purpose: Biological Effect of radiation can be enhanced with hypofractionation, localized dose escalation, and, in particle therapy, with optimized distribution of linear energy transfer (LET). The authors describe a method to construct inhomogeneous fractional dose (IFD) distributions, and evaluate the potential gain in the therapeutic Effect from their delivery in proton therapy delivered by pencil beam scanning. Methods: For 13 cases of prostate cancer, the authors considered hypofractionated courses of 60 Gy delivered in 20 fractions. (All doses denoted in Gy include the proton's mean relative Biological Effectiveness (RBE) of 1.1.) Two types of plans were optimized using two opposed lateral beams to deliver a uniform dose of 3 Gy per fraction to the target by scanning: (1) in conventional full-target plans (FTP), each beam irradiated the entire gland, (2) in split-target plans (STP), beams irradiated only the respective proximal hemispheres (prostate split sagittally). Inverse planning yielded intensity maps, in which discrete position control points of the scanned beam (spots) were assigned optimized intensity values. FTP plans preferentially required a higher intensity of spots in the distal part of the target, while STP, by design, employed proximal spots. To evaluate the utility of IFD delivery, IFD plans were generated bymore » rearranging the spot intensities from FTP or STP intensity maps, separately as well as combined using a variety of mixing weights. IFD courses were designed so that, in alternating fractions, one of the hemispheres of the prostate would receive a dose boost and the other receive a lower dose, while the total physical dose from the IFD course was roughly uniform across the prostate. IFD plans were normalized so that the equivalent uniform dose (EUD) of rectum and bladder did not increase, compared to the baseline FTP plan, which irradiated the prostate uniformly in every fraction. An EUD-based model was then applied to estimate tumor control probability (TCP) and normal tissue complication probability (NTCP). To assess potential local RBE variations, LET distributions were calculated with Monte Carlo, and compared for different plans. The results were assessed in terms of their sensitivity to uncertainties in model parameters and delivery. Results: IFD courses included equal number of fractions boosting either hemisphere, thus, the combined physical dose was close to uniform throughout the prostate. However, for the entire course, the prostate EUD in IFD was higher than in conventional FTP by up to 14%, corresponding to the estimated increase in TCP to 96% from 88%. The extent of gain depended on the mixing factor, i.e., relative weights used to combine FTP and STP spot weights. Increased weighting of STP typically yielded a higher target EUD, but also led to increased sensitivity of dose to variations in the proton's range. Rectal and bladder EUD were same or lower (per normalization), and the NTCP for both remained below 1%. The LET distributions in IFD also depended strongly on the mixing weights: plans using higher weight of STP spots yielded higher LET, indicating a potentially higher local RBE. Conclusions: In proton therapy delivered by pencil beam scanning, improved therapeutic outcome can potentially be expected with delivery of IFD distributions, while administering the prescribed quasi-uniform dose to the target over the entire course. The Biological Effectiveness of IFD may be further enhanced by optimizing the LET distributions. IFD distributions are characterized by a dose gradient located in proximity of the prostate's midplane, thus, the fidelity of delivery would depend crucially on the precision with which the proton range could be controlled.« less
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nuclear interactions in proton therapy dose and relative Biological Effect distributions originating from primary and secondary particles
Physics in Medicine and Biology, 2002Co-Authors: H PaganettiAbstract:The dose distribution delivered in charged particle therapy is due to both primary and secondary particles. The secondaries, originating from non-elastic nuclear interactions, are of interest for three reasons. First, if fast Monte Carlo treatment planning is envisaged, the question arises whether all nuclear interaction products deliver a significant contribution to the total dose and, hence, need to be tracked. Second, there could be an enhanced relative Biological Effectiveness (RBE) due to low energy and/or heavy secondaries. Third, neutrons originating from nuclear interactions may deliver dose outside the target volume. The particle yield from different nuclear interaction channels as a function of proton penetration depth was studied theoretically for different proton beam energies. Three-dimensional dose distributions from primary and secondary particles were simulated for an unmodulated 160 MeV proton beam with and without including a slice of bone material and for a spread-out Bragg peak (SOBP) of 3 × 3 × 3 cm3 in water. Secondary protons deliver up to ≈10% of the total dose proximal to the Bragg peak of an unmodulated proton beam and they affect the flatness of the SOBP. Furthermore, they cause a dose build-up due to forward emission of secondary particles from nuclear interactions. The dose deposited by d, t, 3He and α-particles was found to contribute less than 0.1% of the total dose. The dose distal to the target volume caused by liberated neutrons was studied for four proton beam energies in the range of 160–250 MeV and found to be below 0.05% (2 cm distal to SOBP) of the prescribed target dose for a 3 × 3 × 3 cm3 target. RBE values relative to 60Co were calculated proximal to and within the SOBP. The RBE proximal to the Bragg peak (100% dose) is influenced by secondary particles (mainly protons and α-particles) with a strong dose dependency resulting in RBE values up to 1.2 (2 Gy; inactivation of V79). Depending on the endpoint considered, secondary particles cause a shift in RBE by up to 8% at 2 Gy. In contrast, the RBE in the Bragg peak is almost entirely determined by primary protons due to a decreasing secondary particle fluence with depth. RBE values up to 1.3 (2 Gy; inactivation of V79) at 1 cm distal to the Bragg peak maximum were found. The inactivations of human skin fibroblasts and mouse lymphoma cells were also analysed and reveal a substantial tissue dependency of the total RBE. The outcome of this study shows that elevated RBE values occur not only at the distal edge of the SOBP. Although the variations are modest, and in most cases might have no observable clinical Effect, they might have to be considered in certain treatment situations. The Biological Effect downstream of the target caused by neutrons was analysed using a radiation quality factor of 10. The Biological dose was found to be below 0.5% of the prescribed target dose (for a 3 × 3 × 3 cm3 SOBP) but depends on the size of the SOBP. This dose should not be significant with respect to late Effects, e.g. cancer induction.