Shear measurement bias

We present a study of the dependencies of shear bias on simulation (input) and measured (output) parameters, noise, point-spread function anisotropy, pixel size, and the model bias coming from two different and independent galaxy shape estimators. We used simulated images from GALSIM based on the GREAT3 control-space-constant branch, and we measured shear bias from a model-fitting method (GFIT) and a moment-based method (Kaiser-Squires-Broadhurst). We show the bias dependencies found on input and output parameters for both methods, and we identify the main dependencies and causes. Most of the results are consistent between the two estimators, an interesting result given the differences of the methods. We also find important dependences on orientation and morphology properties such as flux, size, and ellipticity. We show that noise and pixelization play an important role in the bias dependencies on the output properties and galaxy orientation. We show some examples of model bias that produce a bias dependence on the Sérsic index n as well as a different shear bias between galaxies consisting of a single Sérsic profile and galaxies with a disc and a bulge. We also see an important coupling between several properties on the bias dependences. Because of this, we need to study several measured properties simultaneously in order to properly understand the nature of shear bias. This paper serves as a first step towards a companion paper that describes a machine learning approach to modelling shear bias as a complex function of many observed properties.</jats:p

Shear measurement bias: I. Dependencies on methods, simulation parameters, and measured parameters

We present a study of the dependencies of shear bias on simulation (input) and measured (output) parameters, noise, point-spread function anisotropy, pixel size, and the model bias coming from two different and independent galaxy shape estimators. We used simulated images from GALSIM based on the GREAT3 control-space-constant branch, and we measured shear bias from a model-fitting method (GFIT) and a moment-based method (Kaiser-Squires-Broadhurst). We show the bias dependencies found on input and output parameters for both methods, and we identify the main dependencies and causes. Most of the results are consistent between the two estimators, an interesting result given the differences of the methods. We also find important dependences on orientation and morphology properties such as flux, size, and ellipticity. We show that noise and pixelization play an important role in the bias dependencies on the output properties and galaxy orientation. We show some examples of model bias that produce a bias dependence on the Sersic index n as well as a different shear bias between galaxies consisting of a single Sersic profile and galaxies with a disc and a bulge. We also see an important coupling between several properties on the bias dependences. Because of this, we need to study several measured properties simultaneously in order to properly understand the nature of shear bias. This paper serves as a first step towards a companion paper that describes a machine learning approach to modelling shear bias as a complex function of many observed properties.LASTR

Shear measurement bias: I. Dependencies on methods, simulation parameters, and measured parameters

International audienceWe present a study of the dependencies of shear bias on simulation (input) and measured (output) parameters, noise, point-spread function anisotropy, pixel size, and the model bias coming from two different and independent galaxy shape estimators. We used simulated images from GALSIM based on the GREAT3 control-space-constant branch, and we measured shear bias from a model-fitting method (GFIT) and a moment-based method (Kaiser-Squires-Broadhurst). We show the bias dependencies found on input and output parameters for both methods, and we identify the main dependencies and causes. Most of the results are consistent between the two estimators, an interesting result given the differences of the methods. We also find important dependences on orientation and morphology properties such as flux, size, and ellipticity. We show that noise and pixelization play an important role in the bias dependencies on the output properties and galaxy orientation. We show some examples of model bias that produce a bias dependence on the Sérsic index n as well as a different shear bias between galaxies consisting of a single Sérsic profile and galaxies with a disc and a bulge. We also see an important coupling between several properties on the bias dependences. Because of this, we need to study several measured properties simultaneously in order to properly understand the nature of shear bias. This paper serves as a first step towards a companion paper that describes a machine learning approach to modelling shear bias as a complex function of many observed properties

A French prospective pilot study for identifying dihydropyrimidine dehydrogenase (DPD) deficiency in breast cancer patients (pts) receiving capecitabine (cap).

e13519 Background: For fluoropyrimidines, and especially cap, Health Authorities point out that DPD deficiency confers a significant risk of major toxicity (tox). Identification of at-risk pts is thus relevant. This multicentric prospective study of the French GPCO group (Groupe de Pharmacologie Clinique Oncologique, Unicancer) evaluated the sensitivity, specificity and predictive values of DPD phenotyping and genotyping for predicting severe cap-related tox in metastatic breast cancer pts. Methods: 303 pts were included (15 institutions), 88% received cap as monotherapy, 28% were treated as first line (mean dose at 1st cycle 1957 mg/m2/d). Pre-treatment dihydrouracil (UH2) and uracil (U) plasma concentrations were measured in 286 pts (HPLC assay). DPD genotyping (IVS14+1G&gt;A, 2846A&gt;T, 1679T&gt;G, 464T&gt;A) was done on 281 pts. Severe tox (G3-4 CTCAE v3 criteria) was measured over cycles 1-2. Results: Grade 3-4 tox (diarrhea, vomiting, hematoxicity, hand-foot syndrome) was observed in 19.6% of pts (one toxic death). A marked trend for higher U (median 12.7 vs 10.2 ng/ml, p=0.014) and UH2 (median 110 vs 93 ng/ml, p=0.011) concentrations was observed in pts developing severe tox vs those who didn’t. However, ROC curves showed that these differences were too small for use as reliable tox predictors. The distribution of UH2/U ratio was similar between pts with or without tox (median 9.1 vs 9.6, respectively, p=0.80). The patient with toxic death had a UH2/U ratio of 6.5 and U concentration of 17 ng/ml. Among the 7 pts with a DPD mutation (3 pts IVS14+1, 3 pts 2846A&gt;T, one 1679T&gt;G, all heterozygous), 5 developed severe tox (including toxic death, 2846A&gt;T), one did not, and the last one was not documented. Relative risk for developing severe tox was 4.60 in mut pts vs wt pts (95%CI 2.95-7.16, p=0.001); positive and negative predictive values were 83.3% and 81.9%, respectively; specificity was 99.5% and sensitivity was 9.8%. Conclusions: These data point out that breast cancer pts harbouring a DPD variant allele are candidate to develop severe, up to lethal, cap-related tox. In contrast, pre-treatment UH2/U ratio and U measurements are not reliable predictors of cap tox. Clinical trial information: Eudract 2008-004136-20. </jats:p

Sequential Analysis of cfDNA Reveals Clonal Evolution in Patients with Neuroblastoma Receiving <i>ALK</i>-Targeted Therapy

International audiencePurpose:The study of cell-free DNA (cfDNA) enables sequential analysis of tumor cell-specific genetic alterations in patients with neuroblastoma.Experimental Design: Eighteen patients with relapsing neuroblastoma having received lorlatinib, a third-generation ALK inhibitor, were identified (SACHA national registry and/or in the institution). cfDNA was analyzed at relapse for nine patients and sequentially for five patients (blood/bone marrow plasma) by performing whole-genome sequencing library construction followed by ALK-targeted ddPCR of the hotspot mutations [F1174L, R1275Q, and I1170N; variant allele fraction (VAF) detection limit 0.1%] and whole-exome sequencing (WES) to evaluate disease burden and clonal evolution, following comparison with tumor/germline WES.Results: Overall response rate to lorlatinib was 33% (CI, 13%-59%), with response observed in 6/10 cases without versus 0/8 cases with MYCN amplification (MNA). ALK VAFs correlated with the overall clinical disease status, with a VAF &lt; 0.1% in clinical remission, versus higher VAFs (&gt;30%) at progression. Importantly, sequential ALK ddPCR detected relapse earlier than clinical imaging. cfDNA WES revealed new SNVs, not seen in the primary tumor, in all instances of disease progression after lorlatinib treatment, indicating clonal evolution, including alterations in genes linked to tumor aggressivity (TP53) or novel targets (EGFR). Gene pathway analysis revealed an enrichment for genes targeting cell differentiation in emerging clones, and cell adhesion in persistent clones. Evidence of clonal hematopoiesis could be observed in follow-up samples.Conclusions: We demonstrate the clinical utility of combining ALK cfDNA ddPCR for disease monitoring and cfDNA WES for the study of clonal evolution and resistance mechanisms in patients with neuroblastoma receiving ALK-targeted therapy.</div