Everolimus after failure of one prior VEGF-targeted therapy in metastatic renal cell carcinoma : Final results of the MARC-2 trial

MARC-2, a prospective, multicenter phase IV trial, aimed to investigate clinical outcomes in patients with metastatic renal cell carcinoma (mRCC) treated with everolimus after failure of one initial vascular endothelial growth factor receptor tyrosine kinase inhibitor (VEGFR-TKI) therapy and to identify subgroups benefiting most, based on clinical characteristics and biomarkers. Patients with clear cell mRCC failing one initial VEGFR-TKI received everolimus until progression or unacceptable toxicity. Primary endpoint was 6-month progression-free survival rate (6moPFS). Secondary endpoints were overall response rate (ORR), PFS, overall survival (OS), and safety. Between 2011 and 2015, 63 patients were enrolled. Median age was 65.4 years (range 43.3-81.1). 6moPFS was 39.3% (95% confidence interval [CI], 27.0-51.3) overall, 54.4% (95% CI, 35.2-70.1) vs 23.7% (95% CI, 10.5-39.9) for patients aged ≥65 vs 25 vs ≤25 kg/m2. A Cox proportional hazards model confirmed a longer PFS for patients aged ≥65 years (hazard ratio [HR] 0.46; 95% CI, 0.26-0.80) and a longer OS for patients with BMI >25 kg/m2 (HR 0.36; 95% CI, 0.18-0.71). Median PFS and median OS were 3.8 months (95% CI, 3.2-6.2) and 16.8 months (95% CI, 14.3-24.3). ORR was 7.9% and disease control rate was 60.3%. No new safety signals emerged. Most common adverse events were stomatitis (31.7%), fatigue (31.7%), and anemia (30.2%). One patient died from treatment-related upper gastrointestinal hemorrhage. Everolimus remains a safe and effective treatment option for mRCC patients after one prior VEGFR-TKI therapy. Patients aged ≥65 years and patients with BMI >25 kg/m2 benefited most

Information extraction from free text for aiding transdiagnostic psychiatry: constructing NLP pipelines tailored to clinicians’ needs

Background: Developing predictive models for precision psychiatry is challenging because of unavailability of the necessary data: extracting useful information from existing electronic health record (EHR) data is not straightforward, and available clinical trial datasets are often not representative for heterogeneous patient groups. The aim of this study was constructing a natural language processing (NLP) pipeline that extracts variables for building predictive models from EHRs. We specifically tailor the pipeline for extracting information on outcomes of psychiatry treatment trajectories, applicable throughout the entire spectrum of mental health disorders (“transdiagnostic”). Methods: A qualitative study into beliefs of clinical staff on measuring treatment outcomes was conducted to construct a candidate list of variables to extract from the EHR. To investigate if the proposed variables are suitable for measuring treatment effects, resulting themes were compared to transdiagnostic outcome measures currently used in psychiatry research and compared to the HDRS (as a gold standard) through systematic review, resulting in an ideal set of variables. To extract these from EHR data, a semi-rule based NLP pipeline was constructed and tailored to the candidate variables using Prodigy. Classification accuracy and F1-scores were calculated and pipeline output was compared to HDRS scores using clinical notes from patients admitted in 2019 and 2020. Results: Analysis of 34 questionnaires answered by clinical staff resulted in four themes defining treatment outcomes: symptom reduction, general well-being, social functioning and personalization. Systematic review revealed 242 different transdiagnostic outcome measures, with the 36-item Short-Form Survey for quality of life (SF36) being used most consistently, showing substantial overlap with the themes from the qualitative study. Comparing SF36 to HDRS scores in 26 studies revealed moderate to good correlations (0.62—0.79) and good positive predictive values (0.75—0.88). The NLP pipeline developed with notes from 22,170 patients reached an accuracy of 95 to 99 percent (F1 scores: 0.38 – 0.86) on detecting these themes, evaluated on data from 361 patients. Conclusions: The NLP pipeline developed in this study extracts outcome measures from the EHR that cater specifically to the needs of clinical staff and align with outcome measures used to detect treatment effects in clinical trials

Outcome prediction of electroconvulsive therapy for depression

Introduction: We developed and tested a Bayesian network(BN) model to predict ECT remission for depression, with non-response as a secondary outcome. Methods: We performed a systematic literature search on clinically available predictors. We combined these predictors with variables from a dataset of clinical ECT trajectories (performed in the University Medical Center Utrecht) to create priors and train the BN. Temporal validation was performed in an independent sample. Results: The systematic literature search yielded three meta-analyses, which provided prior knowledge on outcome predictors. The clinical dataset consisted of 248 treatment trajectories in the training set and 44 trajectories in the test set at the same medical center. The AUC for the primary outcome remission estimated on an independent validation set was 0.686 (95%CI 0.513–0.859) (AUC values of 0.505 – 0.763 observed in 5-fold cross validation of the model within the train set). Accuracy 0.73 (balanced accuracy 0.67), sensitivity 0.55, specificity 0.79, after temporal validation in the independent sample. Prior literature information marginally reduced CI width. Discussion: A BN model comprised of prior knowledge and clinical data can predict remission of depression after ECT with reasonable performance. This approach can be used to make outcome predictions in psychiatry, and offers a methodological framework to weigh additional information, such as patient characteristics, symptoms and biomarkers. In time, it may be used to improve shared decision-making in clinical practice

Information extraction from free text for aiding transdiagnostic psychiatry: constructing NLP pipelines tailored to clinicians’ needs

Background: Developing predictive models for precision psychiatry is challenging because of unavailability of the necessary data: extracting useful information from existing electronic health record (EHR) data is not straightforward, and available clinical trial datasets are often not representative for heterogeneous patient groups. The aim of this study was constructing a natural language processing (NLP) pipeline that extracts variables for building predictive models from EHRs. We specifically tailor the pipeline for extracting information on outcomes of psychiatry treatment trajectories, applicable throughout the entire spectrum of mental health disorders (“transdiagnostic”). Methods: A qualitative study into beliefs of clinical staff on measuring treatment outcomes was conducted to construct a candidate list of variables to extract from the EHR. To investigate if the proposed variables are suitable for measuring treatment effects, resulting themes were compared to transdiagnostic outcome measures currently used in psychiatry research and compared to the HDRS (as a gold standard) through systematic review, resulting in an ideal set of variables. To extract these from EHR data, a semi-rule based NLP pipeline was constructed and tailored to the candidate variables using Prodigy. Classification accuracy and F1-scores were calculated and pipeline output was compared to HDRS scores using clinical notes from patients admitted in 2019 and 2020. Results: Analysis of 34 questionnaires answered by clinical staff resulted in four themes defining treatment outcomes: symptom reduction, general well-being, social functioning and personalization. Systematic review revealed 242 different transdiagnostic outcome measures, with the 36-item Short-Form Survey for quality of life (SF36) being used most consistently, showing substantial overlap with the themes from the qualitative study. Comparing SF36 to HDRS scores in 26 studies revealed moderate to good correlations (0.62—0.79) and good positive predictive values (0.75—0.88). The NLP pipeline developed with notes from 22,170 patients reached an accuracy of 95 to 99 percent (F1 scores: 0.38 – 0.86) on detecting these themes, evaluated on data from 361 patients. Conclusions: The NLP pipeline developed in this study extracts outcome measures from the EHR that cater specifically to the needs of clinical staff and align with outcome measures used to detect treatment effects in clinical trials

First‐line pazopanib in intermediate‐ and poor‐risk patients with metastatic renal cell carcinoma: Final results of the FLIPPER

Temsirolimus has long been the only approved first-line standard of care (SOC) with overall survival (OS) benefit in poor-risk patients with advanced or metastatic renal cell cancer (mRCC). However, tyrosine kinase inhibitors are also commonly used in clinical practice. Pazopanib is an SOC for first-line mRCC treatment, but for poor-risk patients data are scarce. The FLIPPER (First-Line Pazopanib in Poor-Risk Patients with Metastatic Renal Cell Carcinoma) study aimed to assess efficacy and safety of first-line pazopanib in poor-risk mRCC patients. FLIPPER was a single-arm, multicenter, Phase IV trial. Key inclusion criteria were treatment-naive clear cell, inoperable advanced or mRCC, poor-risk according to MSKCC with slight modification, Karnofsky performance status (KPS) >= 60% and adequate organ function. Oral pazopanib 800 mg was given daily. Primary endpoint was the 6-month progression-free survival rate (PFS6). Secondary endpoints included PFS, OS, overall response rate (ORR), duration of response (DOR) and safety. For analysis, descriptive statistics were used. Between 2012 and 2016, 60 patients had been included. Forty-three patients qualified for safety analyses, 34 for efficacy. Median age was 66 years, 64.7% of patients were poor-risk, 82.4% had a KPS <= 70%. PFS6 was 35.3% (95% CI, 19.7-53.5). Median PFS and OS were 4.5 months (95% CI, 3.6-7.8) and 9.3 months (95% CI, 6.6-22.2), respectively. ORR was 32.4% (95% CI, 17.4-50.5), median DOR 9.7 months (95% CI, 1.8-12.4). The most common treatment-related grade 3/4 adverse event reported in 4.7% of patients was hypertension. No treatment-related death occurred. Since pazopanib is active and well tolerated in poor-risk patients with clear cell mRCC, our results support its use as first-line treatment in this setting

Spatial specificity of auxin responses coordinates wood formation

Spatial organization of signalling events of the phytohormone auxin is fundamental for maintaining a dynamic transition from plant stem cells to differentiated descendants. The cambium, the stem cell niche mediating wood formation, fundamentally depends on auxin signalling but its exact role and spatial organization is obscure. Here we show that, while auxin signalling levels increase in differentiating cambium descendants, a moderate level of signalling in cambial stem cells is essential for cambium activity. We identify the auxin-dependent transcription factor ARF5/MONOPTEROS to cell-autonomously restrict the number of stem cells by directly attenuating the activity of the stem cell-promoting WOX4 gene. In contrast, ARF3 and ARF4 function as cambium activators in a redundant fashion from outside of WOX4-expressing cells. Our results reveal an influence of auxin signalling on distinct cambium features by specific signalling components and allow the conceptual integration of plant stem cell systems with distinct anatomies