The NightLife study — the clinical and cost-effectiveness of thrice-weekly, extended, in-centre nocturnal haemodialysis versus daytime haemodialysis using a mixed methods approach: study protocol for a randomised controlled trial

Background: In-centre nocturnal haemodialysis (INHD) offers extended-hours haemodialysis, 6 to 8 h thrice-weekly overnight, with the support of dialysis specialist nurses. There is increasing observational data demonstrating potential benefits of INHD on health-related quality of life (HRQoL). There is a lack of randomised controlled trial (RCT) data to confirm these benefits and assess safety. Methods: The NightLife study is a pragmatic, two-arm, multicentre RCT comparing the impact of 6 months INHD to conventional haemodialysis (thrice-weekly daytime in-centre haemodialysis, 3.5–5 h per session). The primary outcome is the total score from the Kidney Disease Quality of Life tool at 6 months. Secondary outcomes include sleep and cognitive function, measures of safety, adherence to dialysis and impact on clinical parameters. There is an embedded Process Evaluation to assess implementation, health economic modelling and a QuinteT Recruitment Intervention to understand factors that influence recruitment and retention. Adults (≥ 18 years old) who have been established on haemodialysis for > 3 months are eligible to participate. Discussion: There are 68,000 adults in the UK that need kidney replacement therapy (KRT), with in-centre haemodialysis the treatment modality for over a third of cases. HRQoL is an independent predictor of hospitalisation and mortality in individuals on maintenance dialysis. Haemodialysis is associated with poor HRQoL in comparison to the general population. INHD has the potential to improve HRQoL. Vigorous RCT evidence of effectiveness is lacking. The NightLife study is an essential step in the understanding of dialysis therapies and will guide patient-centred decisions regarding KRT in the future. Trial registration: Trial registration number: ISRCTN87042063. Registered: 14/07/2020

Pathway-based subnetworks enable cross-disease biomarker discovery.

Biomarkers lie at the heart of precision medicine. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers usually involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. Here we present SIMMS (Subnetwork Integration for Multi-Modal Signatures): an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We apply SIMMS to multiple data types across five diseases, and in each it reproducibly identifies known and novel subtypes, and makes superior predictions to the best bespoke approaches. To demonstrate its ability on a new dataset, we profile 33 genes/nodes of the PI3K pathway in 1734 FFPE breast tumors and create a four-subnetwork prediction model. This model out-performs a clinically-validated molecular test in an independent cohort of 1742 patients. SIMMS is generic and enables systematic data integration for robust biomarker discovery

Prognostic factor analysis of the survival of elderly patients with AML in the MRC AML11 and LRF AML14 trials

This analysis, of 2483 patients with acute myeloid leukaemia (AML) aged 60+ years entered into two UK trials, was performed to determine the baseline parameters related to survival and to develop a risk index. The Medical Research Council (MRC) AML11 trial (n = 1071) was used to develop the index; this was validated using data from the Leukaemia Research fund (LRF) AML14 trial on 1137 intensively (AML14I) and 275 nonintensively (AML14NI) treated patients. In AML11, cytogenetic group, age, white blood count, performance status and type of AML (de novo, secondary) were all highly significantly related to prognosis in multivariate analysis. The regression coefficients were used to define good, standard and poor risk groups, with 1-year survival of 53%, 43% and 16% respectively (P < 0Æ0001). The risk index showed very good discrimination in both AML14I and AML14NI (both P < 0Æ0001), thereby providing validation, although survival in all groups was very poor in AML14NI. The risk factors for survival in older AML patients were similar to those in younger ones and discrimination of patient groups with relatively good to very poor prognosis was possible. These risk groups apply to both intensively and non-intensively treated patients. Randomized trials of intensive versus non-intensive therapy are needed to determine which types of patient should be given which type of treatment

Mutational Analysis of PI3K/AKT Signaling Pathway in Tamoxifen Exemestane Adjuvant Multinational Pathology Study

Purpose Deregulation of key PI3K/AKT pathway genes may contribute to endocrine resistance in breast cancer (BC). PIK3CA is the most frequently mutated gene in luminal BC (similar to 35%); however, the effect of mutations in helical versus kinase domains remains controversial. We hypothesize that improved outcomes occur in patients with estrogen receptor-positive (ER positive) BC receiving endocrine therapy and possessing PIK3CA mutations. Materials and Methods DNA was extracted from 4,540 formalin-fixed paraffin-embedded BC samples from the Exemestane Versus Tamoxifen-Exemestane pathology study. Mutational analyses were performed for 25 mutations (PIK3CAx10, AKT1x1, KRASx5, HRASx3, NRASx2 and BRAFx4). Results PIK3CA mutations were frequent (39.8%), whereas RAS/RAF mutations were rare (&lt;1%). In univariable analyses PIK3CA mutations were associated with significantly improved 5-year distant relapse-free survival (DRFS; HR, 0.76; 95% CI, 0.63 to 0.91; P = .003). However, a multivariable analysis correcting for known clinical and biologic prognostic factors failed to demonstrate that PIK3CA mutation status is an independent prognostic marker for DRFS (HR, 0.92; 95% CI, 0.75 to 1.12; P = .4012). PIK3CA mutations were more frequent in low-risk luminal BCs (eg, grade 1 node v 3, node-negative v-positive), confounding the relationship between mutations and outcome. Conclusion PIK3CA mutations are present in approximately 40% of luminal BCs but are not an independent predictor of outcome in the context of endocrine therapy, whereas RAS/RAF mutations are rare in luminal BC. A complex relationship between low-risk cancers and PIK3CA mutations was identified. Although the PI3K/AKT pathway remains a viable therapeutic target as the result of a high mutation frequency, PIK3CA mutations do not seem to affect residual risk following treatment with endocrine therapy. (C) 2014 by American Society of Clinical Oncolog

Molecular stratification of early breast cancer identifies drug targets to drive stratified medicine.

Many women with hormone receptor-positive early breast cancer can be managed effectively with endocrine therapies alone. However, additional systemic chemotherapy treatment is necessary for others. The clinical challenges in managing high-risk women are to identify existing and novel druggable targets, and to identify those who would benefit from these therapies. Therefore, we performed mRNA abundance analysis using the Tamoxifen and Exemestane Adjuvant Multinational (TEAM) trial pathology cohort to identify a signature of residual risk following endocrine therapy and pathways that are potentially druggable. A panel of genes compiled from academic and commercial multiparametric tests as well as genes of importance to breast cancer pathogenesis was used to profile 3825 patients. A signature of 95 genes, including nodal status, was validated to stratify endocrine-treated patients into high-risk and low-risk groups based on distant relapse-free survival (DRFS; Hazard Ratio = 5.05, 95% CI 3.53-7.22, p = 7.51 × 10-19). This risk signature was also found to perform better than current multiparametric tests. When the 95-gene prognostic signature was applied to all patients in the validation cohort, including patients who received adjuvant chemotherapy, the signature remained prognostic (HR = 4.76, 95% CI 3.61-6.28, p = 2.53× 10-28). Functional gene interaction analyses identified six significant modules representing pathways involved in cell cycle control, mitosis and receptor tyrosine signaling; containing a number of genes with existing targeted therapies for use in breast or other malignancies. Thus the identification of high-risk patients using this prognostic signature has the potential to also prioritize patients for treatment with these targeted therapies

Molecular stratification of early breast cancer identifies drug targets to drive stratified medicine

Many women with hormone receptor-positive early breast cancer can be managed effectively with endocrine therapies alone. However, additional systemic chemotherapy treatment is necessary for others. The clinical challenges in managing high-risk women are to identify existing and novel druggable targets, and to identify those who would benefit from these therapies. Therefore, we performed mRNA abundance analysis using the Tamoxifen and Exemestane Adjuvant Multinational (TEAM) trial pathology cohort to identify a signature of residual risk following endocrine therapy and pathways that are potentially druggable. A panel of genes compiled from academic and commercial multiparametric tests as well as genes of importance to breast cancer pathogenesis was used to profile 3825 patients. A signature of 95 genes, including nodal status, was validated to stratify endocrine-treated patients into high-risk and low-risk groups based on distant relapse-free survival (DRFS; Hazard Ratio = 5.05, 95% CI 3.53-7.22, p = 7.51 × 10-19). This risk signature was also found to perform better than current multiparametric tests. When the 95-gene prognostic signature was applied to all patients in the validation cohort, including patients who received adjuvant chemotherapy, the signature remained prognostic (HR = 4.76, 95% CI 3.61-6.28, p = 2.53× 10-28). Functional gene interaction analyses identified six significant modules representing pathways involved in cell cycle control, mitosis and receptor tyrosine signaling; containing a number of genes with existing targeted therapies for use in breast or other malignancies. Thus the identification of high-risk patients using this prognostic signature has the potential to also prioritize patients for treatment with these targeted therapies