We propose a new lack-of-fit test for quantile regression models that is suitable even with high-dimensional covariates. The test is based on the cumulative sum of residuals with respect to unidimensional linear projections of the covariates. The test adapts concepts proposed by Escanciano (Econometric Theory, 22, 2006) to cope with many covariates to the test proposed by He and Zhu (Journal of the American Statistical Association, 98, 2003). To approximate the critical values of the test, a wild bootstrap mechanism is used, similar to that proposed by Feng et al. (Biometrika, 98, 2011). An extensive simulation study was undertaken that shows the good performance of the new test, particularly when the dimension of the covariate is high. The test can also be applied and performs well under heteroscedastic regression models. The test is illustrated with real data about the economic growth of 161 countries.

Objective: Medication-related osteonecrosis of the jaw (MRONJ) is a paradoxical effect associated with bone-modifying agents (BMAs) and other drugs. Currently, no valuable diagnostic or prognosis biomarkers exist. The goal of this research was to study MRONJ-related salivary proteome. Materials and Methods:This case-control aimed to study salivary proteome in MRONJ versus control groups (i) formed from BMAs consumers and (ii) healthy individuals to unravel biomarkers. Thirty-eight samples of unstimulated whole saliva (18 MRONJ patients, 10 BMA consumers, and 10 healthy controls) were collected.Proteomic analysis by SWATH-MS coupled with bioinformatics analysis was executed.Results: A total of 586 proteins were identified, 175 proteins showed significant differences among MRONJ versus controls. SWATH-MS revealed differentially expressed proteins among three groups, which have never been isolated. These proteins had distinct roles including cell envelope organization, positive regulation of vesicle fusion, positive regulation of receptor binding, or regulation of low-density lipoprotein particle clearance. Integrative analysis prioritized 3 proteins (MMP9, AACT, and HBD).Under receiver-operating characteristic analysis, this panel discriminated MRONJ with a sensitivity of 90% and a specificity of 78.9%. Conclusion:These findings may inform a novel biomarker panel for MRONJ prediction or diagnosis. Nonetheless, further research is needed to validate this panel.

Despite the increasing use of neoadjuvant chemotherapy (NAC) in HER2-positive breast cancer (BC) patients, the clinical problem of predicting individual treatment response remains unanswered. Furthermore, the use of ineffective chemotherapeutic regimens should be avoided. Serum biomarker levels are being studied more and more for their ability to predict therapy response and aid in the development of personalized treatment regimens. This study aims to identify effective protein networks and biomarkers to predict response to NAC in HER2-positive BC patients through an exhaustive large-scale LC-MS/MS-based qualitative and quantitative proteomic profiling of serum samples from responders and non-responders. Serum samples from HER2-positive BC patients were collected before NAC and were processed by three methods (with and without nanoparticles). The qualitative analysis revealed differences in the proteomic profiles between responders and non-responders, mainly in proteins implicated in the complement and coagulation cascades and apolipoproteins. Qualitative analysis confirmed that three proteins (AFM, SERPINA1, APOD) were correlated with NAC resistance. In this study, we show that serum biomarker profiles can predict treatment response and outcome in the neoadjuvant setting. If these findings are further developed, they will be of significant clinical utility in the design of treatment regimens for individual BC patients.

A new lack-of-fit test for quantile regression models will be presented for the case where the response variable is right-censored. The test is based on the cumulative sum of residuals, and it extends the ideas of He and Zhu (2003) to censored quantile regression. It will be shown that the empirical process associated with the test statistic converges to a Gaussian process under the null hypothesis and is consistent. To approximate the critical values of the test, a bootstrap mechanism will be used.A simulation study will be carried out to study the performance of the new test in comparison with other tests available in the literature. Finally, a real data application will be presented to show the good properties of the new lack-of-fit test in practice. K E Y W O R D Sbootstrap calibration, censored data, lack-of-fit tests, quantile regression INTRODUCTIONAlthough mean regression is still a traditional benchmark in regression studies, the quantile approach is receiving increasing attention, because it allows a more complete description of the conditional distribution of the response given the covariate, and it is more robust to deviations from error normality. That is, while classical regression gives only information on the conditional