Tesaro (United States)

Space-based remote sensing facilitates the determination of greenhouse gas concentrations, enhancing the comprehension of carbon fluxes at the Earth’s surface in the context of climate change. High-resolution spectrometers, such as the CNES/UKSA MicroCarb and the upcoming ESA Copernicus Carbon Dioxide Monitoring (CO2M) spectrometers, are crucial tools for this purpose. These instruments require a precise calibration, especially regarding the relative approximation errors of the Instrument Spectral Response Functions (ISRFs). To ease ISRF estimation, parametric models such as Gaussian and Super-Gaussian models have been investigated. However, these models cannot always take into account the diversity of ISRF shapes that are encountered in practical applications. For example, in MicroCarb simulations, the expected performance is not always achieved by these two conventional ISRF estimation methods, even when there is no spectral and radiometric errors. This paper investigates a novel approach based on the sparse representation of ISRFs in a dictionary. This method decomposes the spectral responses of interest as sparse linear combinations of atoms belonging to a dictionary, which are built using representative ISRFs. This new method can be applied both for on-ground ISRF denoising, and in-flight ISRF estimation through the resolution of an appropriate inverse problem. Experiments conducted using realistic simulated datasets associated with the MicroCarb instrument are used to evaluate the performance of the proposed method for on-ground and in-flight ISRF estimation, yielding promising estimation performance compared to the state of the art.

recm-nascido.

The restricted mean survival time (RMST) has been popularly used to assess the treatment effect in survival trials. Greenwood's formula is often used to estimate the variance of RMST, and the resulting Wald confidence interval (CI) tends to be liberal in small and moderate samples. We propose the empirical likelihood ratio, score-type, and loglog transformed CIs for RMST in a single sample. The method of variance estimates recovery technique is used to derive the CIs for the difference and ratio parameters in the two sample inference. A variance estimate, which assumes equal survival curves, but possibly different censoring rates in the two groups, is proposed for comparing two groups. The new variance estimate shows excellent performance in testing for superiority, and also works well for a noninferiority test with a small margin, and for the interval estimation when the two survival curves are close. We use similar techniques to construct CIs for comparing two milestone survival probabilities. Numerical examples are used to assess these interval estimation methods.

Combined PARP and immune checkpoint inhibition has yielded encouraging results in ovarian cancer, but predictive biomarkers are lacking. We performed immunogenomic profiling and highly multiplexed single-cell imaging on tumor samples from patients enrolled in a Phase I/II trial of niraparib and pembrolizumab in ovarian cancer (NCT02657889). We identify two determinants of response; mutational signature 3 reflecting defective homologous recombination DNA repair, and positive immune score as a surrogate of interferon-primed exhausted CD8 + T-cells in the tumor microenvironment. Presence of one or both features associates with an improved outcome while concurrent absence yields no responses. Single-cell spatial analysis reveals prominent interactions of exhausted CD8 + T-cells and PD-L1 + macrophages and PD-L1 + tumor cells as mechanistic determinants of response. Furthermore, spatial analysis of two extreme responders shows differential clustering of exhausted CD8 + T-cells with PD-L1 + macrophages in the first, and exhausted CD8 + T-cells with cancer cells harboring genomic PD-L1 and PD-L2 amplification in the second.

Aim: To assess real-world occurrence of common clinical trial-reported adverse events (AE) among patients with recurrent ovarian cancer initiating niraparib 200 mg/day. Materials & methods: This retrospective observational study used physician-extracted anonymized medical record data of eligible patients initiating niraparib 200 mg/day after platinum-based chemotherapy. Results: Of 153 patients, 57 (37%) experienced ≥1 of the three most common all-grade AEs within 3 months after niraparib initiation: nausea (16%; grade 3/4: 2%), thrombocytopenia (14%; grade 3/4: 3%) and fatigue (24%; grade 3/4: 3%). In the ENGOT-OV16/NOVA trial, these respective AEs occurred in 74, 61 and 59% of patients. Conclusion: Incidence of common clinical trial-reported AEs was lower among patients initiating niraparib 200 mg/day in real-world practice versus patients initiating niraparib 300 mg/day in ENGOT-OV16/NOVA.

PURPOSEThis study estimated time without symptoms or toxicity (TWiST) with niraparib compared with routine surveillance (RS) in the maintenance treatment of patients with recurrent ovarian cancer.PATIENTS AND METHODSMean progression-free survival (PFS) was estimated for niraparib and RS by fitting parametric survival distributions to Kaplan-Meier data for 553 patients with recurrent ovarian cancer who were enrolled in the phase III ENGOT-OV16/NOVA trial. Patients were categorized according to the presence or absence of a germline BRCA mutation—gBRCAmut and non-gBRCAmut cohorts. Mean time with toxicity was estimated based on the area under the Kaplan-Meier curve for symptomatic grade 2 or greater fatigue, nausea, and vomiting adverse events (AEs). Time with toxicity was the number of days a patient experienced an AE post–random assignment and before disease progression. TWiST was estimated as the difference between mean PFS and time with toxicity. Uncertainty was explored using alternative PFS estimates and considering all symptomatic grade 2 or greater AEs.RESULTSIn the gBRCAmut and non-gBRCAmut cohorts, niraparib treatment resulted in a mean PFS benefit of 3.23 years and 1.44 years, respectively, and a mean time with toxicity of 0.28 years and 0.10 years, respectively, compared with RS. Hence, niraparib treatment resulted in a mean TWiST benefit of 2.95 years and 1.34 years, respectively, compared with RS, which is equivalent to more than four-fold and two-fold increases in mean TWiST between niraparib and RS in the gBRCAmut and non-gBRCAmut cohorts, respectively. This TWiST benefit was consistent across all sensitivity analyses, including modeling PFS over 5-, 10-, and 15-year time horizons.CONCLUSIONPatients who were treated with niraparib compared with RS experienced increased mean TWiST. Thus, patients who were treated with niraparib in the ENGOT-OV16/NOVA trial experienced more time without symptoms or symptomatic toxicities compared with control.

The mixed effects model for repeated measures has been widely used for the analysis of longitudinal clinical data collected at a number of fixed time points. We propose a robust extension of the mixed effects model for repeated measures for skewed and heavy-tailed data on basis of the multivariate skew-t distribution, and it includes the multivariate normal, t, and skew-normal distributions as special cases. An efficient Markov chain Monte Carlo algorithm is developed using the monotone data augmentation and parameter expansion techniques. We employ the algorithm to perform controlled pattern imputations for sensitivity analyses of longitudinal clinical trials with nonignorable dropouts. The proposed methods are illustrated by real data analyses. Sample SAS programs for the analyses are provided in the online supplementary material.

Abstract Osimertinib is current recommended treatment for EGFR T790M-positive NSCLC following progression on prior EGFR tyrosine kinase inhibitor therapy. However, acquired resistance to osimertinib therapy inevitably developed after a period of effective treatment. We had a patient of EGFR L858R/T790M-positive NSCLC who initially responded to osimertinib therapy but eventually developed resistance. Plasma cell-free DNA analysis disclosed exon-16-skipping HER2 (HER2Δ16) in one osimertinib treated patient. HER2Δ16 was regarded as an oncogenic driver in breast cancer and has never been reported in lung cancer before. Therefore, it’s intriguing to investigate the role of HER2Δ16 in osimertinib resistance in NSCLC. The purpose of this study is to explore the mechanism of HER2Δ16-mediated resistance to osimertinib and to explore strategies to overcome the resistance. We constructed and established H1975 cells stably expressing either vehicle, WT-HER2 or HER2Δ16. We observed that without osimertinib treatment, there was no difference in cell growth and the response to EGF-stimulation. However, when treated with osimertinib, H1975-HER2Δ16 cells were more resistant under normal and EGF-stimulating conditions compared to vehicle or WT-HER2 expressing cells. Moreover, osimertinib treatment in H1975-HER2Δ16 cells had less inhibitory effect on phosphor-ERK and phosphor-AKT compared to vehicle or H1975-WT-HER2 cells. Interestingly, co-treatment of osimertinib with Src-kinase inhibitor, dasatinib, failed to reverse the resistance, indicating that HER2Δ16-driven osimertinib-resistance was independent of Src-kinase. Finally, we revealed that combination of osimertinib with the HER2 small molecular inhibitor, afatinib, can suppress cell growth in H1975-WT-HER2 and H1975-HER2Δ16 cells. In summary, we have shown that HER2Δ16, may be a resistance mechanism to osimertinib in EGFR mutated NSCLC and that HER2Δ16-driven resistance was Src-kinase independent. Moreover, we found the HER2Δ16-driven resistance could be rescued by combination of osimertinib with afatinib. Citation Format: Chia-Chi Hsu, Bin-Chi Liao, Wei-Yu Liao, Aleksandra Markovets, Daniel Stetson, Kenneth Thress, Chih-Hsin Yang. Exon-16-skipping HER2 contributes to osimertinib-resistance through Src-independent manner in EGFRL858R/T790M-positive non-small-cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4752.

Abstract Poly (ADP-ribose) polymerase (PARP) inhibitors exert their effect intracellularly within tumor, thus sufficient tumor penetration is essential for a pharmacological response. Preclinical mouse xenograft data show a 3.3-fold higher tumor versus plasma exposure of niraparib, while for olaparib tumor exposure was less than plasma. This study aimed to build a physiologically-based pharmacokinetic (PBPK) model extended with a tissue composition-based permeability-limited tumor model to: (a) gain a mechanistic understanding of the differences in tumor exposure of niraparib and olaparib; and (b) to predict clinical tumor exposure in ovarian cancer patients at clinically relevant dosing regimens. A permeability-limited tumor model was developed that integrates data on tumor composition and drug physicochemical properties analogous to the established permeability-limited organ model available for the liver in the Simcyp Simulator [1,2]. The model assumes that unbound unionized drug is in equilibrium between the vascular and interstitial compartments and movement of the drug between the interstitial and intracellular space is via passive permeability. Total tumor concentration is dependent on passive permeability of the drug, drug binding to PARP and nonspecific binding to neutral lipids, neutral phospholipids, and acidic phospholipids in the intracellular space, albumin in the interstitial and pH of the tumor interstitial and intracellular spaces. Clinical and preclinical tumor physiological parameters such as volume, blood flow, and tissue composition are defined using published data. The model was developed using the Simcyp Simulator V17.1 and R [3]. Consistent with preclinical data, the model predicts a 5-7-fold higher tumor exposure relative to plasma, as measured by the AUC tumor to plasma ratio of niraparib compared with olaparib. Significant binding to acidic phospholipids contributes to the increased tumor exposure to niraparib, a basic drug, a mechanism that is not relevant to the neutral drug olaparib. Ongoing work aims to extrapolate the model to predict the clinical tumor concentration of olaparib and niraparib in ovarian cancer patients and to investigate the sensitivity of the model to key tumor attributes, including blood flow and interstitial pH that may contribute to variability in tumor drug exposure. A similar modeling approach may be used to predict the tumor exposure of other small molecule anticancer drugs from their plasma concentration and physicochemical properties in different types of solid tumor. References Jamei M et al, Clin Pharmacokinet. 2014;53:73-87 Poulin P et al, J Pharm Sci. 2015;104:1508-21 R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Citation Format: Rachel H. Rose, Kaiming Sun, Linzhong Li, Keyur Gada, Jing Yu Wang, Yongchang Qiu. Predicting concentration of PARP inhibitors in human tumor tissue using PBPK modeling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2952.