Nailfold dermoscopy predicts the absence of a capillaroscopy sclerodermic pattern: The multicentre, prospective VASCUL-R trial.

BACKGROUND: Nailfold capillaroscopy is recommended to diagnose primary or secondary Raynaud's phenomenon (RP). Capillaroscopy is normal in primary RP, which is the most frequent. Screening for RP capillary anomalies with nailfold dermoscopy has been promising. OBJECTIVE: To determine whether normal nailfold dermoscopy-based on the absence of five criteria that define a sclerodermic pattern-is able to predict normal capillaroscopy with good positive-predictive value (PPV). METHODS: Prospective, 2-phase (monocentre and multicentre) study on patients at first consultation for RP undergoing nailfold video capillaroscopy (NVC) and nailfold dermoscopy by two different 'blinded' trained observers, respectively, a vascular specialist and a dermatologist, not familiar with capillaroscopy. The five criteria noted were as follows: disorganization, megacapillaries, low capillary density, avascular areas and haemorrhages. RESULTS: Based on 105 patients, the dermoscopy PPV for a normal NVC was 100% (p = 0.015), with 37.9% sensitivity, when no criterion was observed. Excluding haemorrhages, the PPV remained 100% (p < 0.0001), with sensitivity rising to 73.7% and 100% specificity. CONCLUSION: Normal nailfold dermoscopy with the absence of four easy-to-observe criteria predicts normal NVC with an excellent PPV.

A catalog of numerical centrosome defects in epithelial ovarian cancers.

Centrosome amplification, the presence of more than two centrosomes in a cell is a common feature of most human cancer cell lines. However, little is known about centrosome numbers in human cancers and whether amplification or other numerical aberrations are frequently present. To address this question, we have analyzed a large cohort of primary human epithelial ovarian cancers (EOCs) from 100 patients. We found that rigorous quantitation of centrosome number in tumor samples was extremely challenging due to tumor heterogeneity and extensive tissue disorganization. Interestingly, even if centrosome clusters could be identified, the incidence of centrosome amplification was not comparable to what has been described in cultured cancer cells. Surprisingly, centrosome loss events where a few or many nuclei were not associated with centrosomes were clearly noticed and overall more frequent than centrosome amplification. Our findings highlight the difficulty of characterizing centrosome numbers in human tumors, while revealing a novel paradigm of centrosome number defects in EOCs.

Insights for Quantifying the Long-Term Benefit of Immunotherapy Using Quantile Regression.

PURPOSE: Immunotherapy has been approved to treat many tumor types. However, one characteristic of this therapeutic class is that survival benefit is due to late immune response, which leads to a delayed treatment effect. Quantifying the benefit, if any, of such treatment, will thus require other metrics than the usual hazard ratio and different approaches have been proposed to quantify the long-term response of immunotherapy. METHOD: In this paper, we suggest to use quantile regression for survival data to quantify the long-term benefit of immunotherapy. Our motivation is that this approach is not trial-specific and provides clinically understandable results without specifying arbitrary time points or the necessity to reach median survival, as is the case with other methods. We use reconstructed data from published Kaplan-Meier curves to illustrate our method. RESULTS: On average, patients from the immunotherapy group have 60% chance to survive 5.46 months (95% CI, 2.57 to 9.02) more than patients in the chemotherapy group.

On evaluating how well a biomarker can predict treatment response with survival data.

One of the objectives of personalized medicine is to take treatment decisions based on a biomarker measurement. Therefore, it is often interesting to evaluate how well a biomarker can predict the response to a treatment. To do so, a popular methodology consists of using a regression model and testing for an interaction between treatment assignment and biomarker. However, the existence of an interaction is not sufficient for a biomarker to be predictive. It is only necessary. Hence, the use of the marker-by-treatment predictiveness curve has been recommended. In addition to evaluate how well a single continuous biomarker predicts treatment response, it can further help to define an optimal threshold. This curve displays the risk of a binary outcome as a function of the quantiles of the biomarker, for each treatment group. Methods that assume a binary outcome or rely on a proportional hazard model for a time-to-event outcome have been proposed to estimate this curve. In this work, we propose some extensions for censored data. They rely on a time-dependent logistic model, and we propose to estimate this model via inverse probability of censoring weighting. We present simulations results and three applications to prostate cancer, liver cirrhosis, and lung cancer data. They suggest that a large number of events need to be observed to define a threshold with sufficient accuracy for clinical usefulness. They also illustrate that when the treatment effect varies with the time horizon which defines the outcome, then the optimal threshold also depends on this time horizon.