Nivolumab plus ipilimumab versus chemotherapy as first-line treatment in advanced non-small-cell lung cancer with high tumour mutational burden: Patient-reported outcomes results from the randomised, open-label, phase III CheckMate 227 trial

BACKGROUND: In the phase III CheckMate 227 study, first-line nivolumab + ipilimumab significantly prolonged progression-free survival (co-primary end-point) versus chemotherapy in patients with advanced non-small-cell lung cancer (NSCLC) and high tumour mutational burden (TMB; ≥10 mutations/megabase). AIM: To evaluate patient-reported outcomes (PROs) in this population. METHODS: Disease-related symptoms and general health status were assessed using the validated PRO questionnaires Lung Cancer Symptom Scale (LCSS) and EQ-5D, respectively. LCSS average symptom burden index (ASBI) and three-item global index (3-IGI) and EQ-5D visual analogue scale (VAS) and utility index (UI) scores and changes from baseline were analysed descriptively. Longitudinal changes were assessed by mixed-effect model repeated measures (MMRMs) and time to first deterioration/improvement analyses. RESULTS: In the high TMB population, PRO questionnaire completion rates were ∼90% at baseline and \u3e80% for most on-treatment assessments. During treatment, mean changes from baseline with nivolumab + ipilimumab showed early, clinically meaningful improvements in LCSS ASBI/3-IGI and EQ-5D VAS/UI; with chemotherapy, symptoms and health-related quality of life remained stable (LCSS ASBI/3-IGI, EQ-5D UI) or improved following induction (EQ-5D VAS). MMRM-assessed changes in symptom burden were improved with nivolumab + ipilimumab versus chemotherapy. Symptom deterioration by week 12 was lower with nivolumab + ipilimumab versus chemotherapy (22.3% versus 35.0%; absolute risk reduction: 12.7% [95% confidence interval 2.4-22.5]), irrespective of discontinuation. Time to first deterioration was delayed with nivolumab + ipilimumab versus chemotherapy across LCSS and EQ-5D summary measures. CONCLUSION: First-line nivolumab + ipilimumab demonstrated early, sustained improvements in PROs versus chemotherapy in patients with advanced NSCLC and high TMB. CLINICAL TRIAL REGISTRATION: NCT02477826

Nivolumab plus ipilimumab versus chemotherapy as first-line treatment in advanced non-small-cell lung cancer with high tumour mutational burden: Patient-reported outcomes results from the randomised, open-label, phase III CheckMate 227 trial

BACKGROUND: In the phase III CheckMate 227 study, first-line nivolumab + ipilimumab significantly prolonged progression-free survival (co-primary end-point) versus chemotherapy in patients with advanced non-small-cell lung cancer (NSCLC) and high tumour mutational burden (TMB; ≥10 mutations/megabase). AIM: To evaluate patient-reported outcomes (PROs) in this population. METHODS: Disease-related symptoms and general health status were assessed using the validated PRO questionnaires Lung Cancer Symptom Scale (LCSS) and EQ-5D, respectively. LCSS average symptom burden index (ASBI) and three-item global index (3-IGI) and EQ-5D visual analogue scale (VAS) and utility index (UI) scores and changes from baseline were analysed descriptively. Longitudinal changes were assessed by mixed-effect model repeated measures (MMRMs) and time to first deterioration/improvement analyses. RESULTS: In the high TMB population, PRO questionnaire completion rates were ∼90% at baseline and \u3e80% for most on-treatment assessments. During treatment, mean changes from baseline with nivolumab + ipilimumab showed early, clinically meaningful improvements in LCSS ASBI/3-IGI and EQ-5D VAS/UI; with chemotherapy, symptoms and health-related quality of life remained stable (LCSS ASBI/3-IGI, EQ-5D UI) or improved following induction (EQ-5D VAS). MMRM-assessed changes in symptom burden were improved with nivolumab + ipilimumab versus chemotherapy. Symptom deterioration by week 12 was lower with nivolumab + ipilimumab versus chemotherapy (22.3% versus 35.0%; absolute risk reduction: 12.7% [95% confidence interval 2.4-22.5]), irrespective of discontinuation. Time to first deterioration was delayed with nivolumab + ipilimumab versus chemotherapy across LCSS and EQ-5D summary measures. CONCLUSION: First-line nivolumab + ipilimumab demonstrated early, sustained improvements in PROs versus chemotherapy in patients with advanced NSCLC and high TMB. CLINICAL TRIAL REGISTRATION: NCT02477826

The tale of TILs in breast cancer : a report from The International Immuno-Oncology Biomarker Working Group

The advent of immune-checkpoint inhibitors (ICI) in modern oncology has significantly improved survival in several cancer settings. A subgroup of women with breast cancer (BC) has immunogenic infiltration of lymphocytes with expression of programmed death-ligand 1 (PD-L1). These patients may potentially benefit from ICI targeting the programmed death 1 (PD-1)/PD-L1 signaling axis. The use of tumor-infiltrating lymphocytes (TILs) as predictive and prognostic biomarkers has been under intense examination. Emerging data suggest that TILs are associated with response to both cytotoxic treatments and immunotherapy, particularly for patients with triple-negative BC. In this review from The International Immuno-Oncology Biomarker Working Group, we discuss (a) the biological understanding of TILs, (b) their analytical and clinical validity and efforts toward the clinical utility in BC, and (c) the current status of PD-L1 and TIL testing across different continents, including experiences from low-to-middle-income countries, incorporating also the view of a patient advocate. This information will help set the stage for future approaches to optimize the understanding and clinical utilization of TIL analysis in patients with BC

The tale of TILs in breast cancer: A report from The International Immuno-Oncology Biomarker Working Group

The advent of immune-checkpoint inhibitors (ICI) in modern oncology has significantly improved survival in several cancer settings. A subgroup of women with breast cancer (BC) has immunogenic infiltration of lymphocytes with expression of programmed death-ligand 1 (PD-L1). These patients may potentially benefit from ICI targeting the programmed death 1 (PD-1)/PD-L1 signaling axis. The use of tumor-infiltrating lymphocytes (TILs) as predictive and prognostic biomarkers has been under intense examination. Emerging data suggest that TILs are associated with response to both cytotoxic treatments and immunotherapy, particularly for patients with triple-negative BC. In this review from The International Immuno-Oncology Biomarker Working Group, we discuss (a) the biological understanding of TILs, (b) their analytical and clinical validity and efforts toward the clinical utility in BC, and (c) the current status of PD-L1 and TIL testing across different continents, including experiences from low-to-middle-income countries, incorporating also the view of a patient advocate. This information will help set the stage for future approaches to optimize the understanding and clinical utilization of TIL analysis in patients with BC

Prospective Observational Study of Pazopanib in Patients with Advanced Renal Cell Carcinoma (PRINCIPAL Study)

Background: Real-world data are essential to accurately assessing efficacy and toxicity of approved agents in everyday practice. PRINCIPAL, a prospective, observational study, was designed to confirm the real-world safety and efficacy of pazopanib in patients with advanced renal cell carcinoma (RCC). Subjects, Materials, and Methods: Patients with clear cell advanced/metastatic RCC and a clinical decision to initiate pazopanib treatment within 30 days of enrollment were eligible. Primary objectives included progression-free survival (PFS), overall survival (OS), objective response rate (ORR), relative dose intensity (RDI) and its effect on treatment outcomes, change in health-related quality of life (HRQoL), and safety. We also compared characteristics and outcomes of clinical-trial-eligible (CTE) patients, defined using COMPARZ trial eligibility criteria, with those of non-clinical-trial-eligible (NCTE) patients. Secondary study objectives were to evaluate clinical efficacy, safety, and RDI in patient subgroups. Results: Six hundred fifty-seven patients were enrolled and received ≥1 dose of pazopanib. Median PFS and OS were 10.3 months (95% confidence interval [CI], 9.2–12.0) and 29.9 months (95% CI, 24.7 to not reached), respectively, and the ORR was 30.3%. HRQoL showed no or little deterioration over time. Treatment-related serious adverse events (AEs) and AEs of special interest occurred in 64 (9.7%), and 399 (60.7%) patients, respectively. More patients were classified NCTE than CTE (85.2% vs. 14.8%). Efficacy of pazopanib was similar between the two groups. Conclusion: PRINCIPAL confirms the efficacy and safety of pazopanib in patients with advanced/metastatic RCC in a real-world clinical setting. Implications for Practice: PRINCIPAL is the largest (n = 657) prospective, observational study of pazopanib in patients with advanced/metastatic renal cell carcinoma, to the authors’ knowledge. Consistent with clinical trial results that often contain specific patient types, the PRINCIPAL study demonstrated that the effectiveness and safety of pazopanib is similarly safe and effective in patients with advanced kidney cancer in a real-world clinical setting. The PRINCIPAL study showed that patients with advanced kidney cancer who are treated with first-line pazopanib generally do not show disease progression for approximately 10 months and generally survive for nearly 30 months

A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience

The curation of neuroscience entities is crucial to ongoing efforts in neuroinformatics and computational neuroscience, such as those being deployed in the context of continuing large-scale brain modelling projects. However, manually sifting through thousands of articles for new information about modelled entities is a painstaking and low-reward task. Text mining can be used to help a curator extract relevant information from this literature in a systematic way. We propose the application of text mining methods for the neuroscience literature. Specifically, two computational neuroscientists annotated a corpus of entities pertinent to neuroscience using active learning techniques to enable swift, targeted annotation. We then trained machine learning models to recognise the entities that have been identified. The entities covered are Neuron Types, Brain Regions, Experimental Values, Units, Ion Currents, Channels, and Conductances and Model organisms. We tested a traditional rule-based approach, a conditional random field and a model using deep learning named entity recognition, finding that the deep learning model was superior. Our final results show that we can detect a range of named entities of interest to the neuroscientist with a macro average precision, recall and F1 score of 0.866, 0.817 and 0.837 respectively. The contributions of this work are as follows: 1) We provide a set of Named Entity Recognition (NER) tools that are capable of detecting neuroscience entities with performance above or similar to prior work. 2) We propose a methodology for training NER tools for neuroscience that requires very little training data to get strong performance. This can be adapted for any sub-domain within neuroscience. 3) We provide a small corpus with annotations for multiple entity types, as well as annotation guidelines to help others reproduce our experiments