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Deep neural networks (DNNs) are powerful tools for approximating the distribution of complex data. It is known that data passing through a trained DNN classifier undergoes a series of geometric and topological simplifications. While some progress has been made toward understanding these transformations in neural networks with smooth activation functions, an understanding in the more general setting of non-smooth activation functions, such as the rectified linear unit (ReLU), which tend to perform better, is required. Here we propose that the geometric transformations performed by DNNs during classification tasks have parallels to those expected under Hamilton's Ricci flow-a tool from differential geometry that evolves a manifold by smoothing its curvature, in order to identify its topology. To illustrate this idea, we present a computational framework to quantify the geometric changes that occur as data passes through successive layers of a DNN, and use this framework to motivate a notion of 'global Ricci network flow' that can be used to assess a DNN's ability to disentangle complex data geometries to solve classification problems. By training more than 1500 DNN classifiers of different widths and depths on synthetic and real-world data, we show that the strength of global Ricci network flow-like behaviour correlates with accuracy for well-trained DNNs, independently of depth, width and data set. Our findings motivate the use of tools from differential and discrete geometry to the problem of explainability in deep learning., (© 2024. The Author(s).)

There is growing interest in platform trials that allow for adding of new treatment arms as the trial progresses as well as being able to stop treatments part way through the trial for either lack of benefit/futility or for superiority. In some situations, platform trials need to guarantee that error rates are controlled. This paper presents a multi-stage design, that allows additional arms to be added in a platform trial in a preplanned fashion, while still controlling the family-wise error rate, under the assumption of known number and timing of treatments to be added, and no time trends. A method is given to compute the sample size required to achieve a desired level of power and we show how the distribution of the sample size and the expected sample size can be found. We focus on power under the least favorable configuration which is the power of finding the treatment with a clinically relevant effect out of a set of treatments while the rest have an uninteresting treatment effect. A motivating trial is presented which focuses on two settings, with the first being a set number of stages per active treatment arm and the second being a set total number of stages, with treatments that are added later getting fewer stages. Compared to Bonferroni, the savings in the total maximum sample size are modest in a trial with three arms, <1% of the total sample size. However, the savings are more substantial in trials with more arms., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Purpose: We report an analysis of minimal residual/detectable disease (MRD) as a predictor of outcome in previously untreated patients with follicular lymphoma (FL) from the randomized, multicenter GALLIUM (ClinicalTrials.gov identifier: NCT01332968) trial., Patients and Methods: Patients received induction with obinutuzumab (G) or rituximab (R) plus bendamustine, or cyclophosphamide, doxorubicin, vincristine, prednisone (CHOP) or cyclophosphamide, vincristine, prednisone (CVP) chemotherapy, followed by maintenance with the same antibody in responders. MRD status was assessed at predefined time points (mid-induction [MI], end of induction [EOI], and at 4-6 monthly intervals during maintenance and follow-up). Patients with evaluable biomarker data at diagnosis were included in the survival analysis., Results: MRD positivity was associated with inferior progression-free survival (PFS) at MI (hazard ratio [HR], 3.03 [95% CI, 2.07 to 4.45]; P < .0001) and EOI (HR, 2.25 [95% CI, 1.53 to 3.32]; P < .0001). MRD response was higher after G- versus R-chemotherapy at MI (94.2% v 88.9%; P = .013) and at EOI (93.1% v 86.7%; P = .0077). Late responders (MI-positive/EOI-negative) had a significantly poorer PFS than early responders (MI-negative/EOI-negative; HR, 3.11 [95% CI, 1.75 to 5.52]; P = .00011). The smallest proportion of MRD positivity was observed in patients receiving bendamustine at MI (4.8% v 16.0% in those receiving CHOP; P < .0001). G appeared to compensate for less effective chemotherapy regimens, with similar MRD response rates observed across the G-chemo groups. During the maintenance period, more patients treated with R than with G were MRD-positive (R-CHOP, 20.7% v G-CHOP, 7.0%; R-CVP, 21.7% v G-CVP, 9.4%). Throughout maintenance, MRD positivity was associated with clinical relapse., Conclusion: MRD status can determine outcome after induction and during maintenance, and MRD negativity is a prerequisite for long-term disease control in FL. The higher MRD responses after G- versus R-based treatment confirm more effective tumor cell clearance.

Combination therapies are becoming increasingly used in a range of therapeutic areas such as oncology and infectious diseases, providing potential benefits such as minimising drug resistance and toxicity. Sets of combination studies may be related, for example, if they have at least one treatment in common and are used in the same indication. In this setting, value can be gained by sharing information between related combination studies. We present a framework that allows the study success probabilities of a set of related combination therapies to be updated based on the outcome of a single combination study. This allows us to incorporate both direct and indirect data on a combination therapy in the decision-making process for future studies. We also provide a robustification that accounts for the fact that the prior assumptions on the correlation structure of the set of combination therapies may be incorrect. We show how this framework can be used in practice and highlight the use of the study success probabilities in the planning of clinical studies.

While randomized controlled trials (RCTs) are the gold standard for estimating treatment effects in medical research, there is increasing use of and interest in using real-world data for drug development. One such use case is the construction of external control arms for evaluation of efficacy in single-arm trials, particularly in cases where randomization is either infeasible or unethical. However, it is well known that treated patients in non-randomized studies may not be comparable to control patients-on either measured or unmeasured variables-and that the underlying population differences between the two groups may result in biased treatment effect estimates as well as increased variability in estimation. To address these challenges for analyses of time-to-event outcomes, we developed a meta-analytic framework that uses historical reference studies to adjust a log hazard ratio estimate in a new external control study for its additional bias and variability. The set of historical studies is formed by constructing external control arms for historical RCTs, and a meta-analysis compares the trial controls to the external control arms. Importantly, a prospective external control study can be performed independently of the meta-analysis using standard causal inference techniques for observational data. We illustrate our approach with a simulation study and an empirical example based on reference studies for advanced non-small cell lung cancer. In our empirical analysis, external control patients had lower survival than trial controls (hazard ratio: 0.907), but our methodology is able to correct for this bias. An implementation of our approach is available in the R package ecmeta., (© 2022 John Wiley & Sons Ltd.)

Background and Aim: The objective of this retrospective, observational, noninterventional cohort study was to investigate prognostic factors of overall survival (OS) in patients with advanced non-small cell lung cancer (aNSCLC) and to develop a novel prognostic model., Methods: A total of 4049 patients with aNSCLC diagnosed between January 2011 and February 2020 who received atezolizumab, nivolumab, or pembrolizumab as second-line monotherapy were selected from a real-world deidentified database to build the cohort. Patients could not have received first-line treatment with clinical study drug(s) nor immune checkpoint inhibitors including anti-programmed cell death 1 (PD-1)/programmed death-ligand 1 (PD-L1), and anti-cytotoxic T-lymphocyte-associated protein 4 therapies., Results: Patients had a median age of 69 years; 45% were female, 75% White, 70% had stage IV at initial diagnosis, and 70% had nonsquamous histology. A Cox proportional hazards model with lasso regularization was used to build a prognostic model for OS using 18 baseline demographic and clinical factors based on the real-world data cohort. The risk-increasing prognostic factors were abnormally low albumin and chloride levels, Eastern Cooperative Oncology Group performance status score ≥ 2, and abnormally high levels of alkaline phosphatase and white blood cells. The risk-decreasing prognostic factors were PD-L1 positivity, longer time from advanced diagnosis to start of first-line therapy, and higher systolic blood pressure. The performance of the model was validated using data from the OAK trial, and the c-index for the OAK trial validation cohort was 0.65 and 0.67 for the real-world data cohort., Conclusions: Based on baseline demographic and clinical factors from a real-world setting, this prognostic model was developed to discriminate the risk of death in patients with aNSCLC treated with checkpoint inhibitors as second-line monotherapy, and it performed well in the real-world data and clinical trial cohorts., (© 2022 F.Hoffmann-La Roche Ltd/Genentech, Inc. Cancer Reports published by Wiley Periodicals LLC.)

Quantifying the effectiveness of different cancer therapies in patients with specific tumor mutations is critical for improving patient outcomes and advancing precision medicine. Here we perform a large-scale computational analysis of 40,903 US patients with cancer who have detailed mutation profiles, treatment sequences and outcomes derived from electronic health records. We systematically identify 458 mutations that predict the survival of patients on specific immunotherapies, chemotherapy agents or targeted therapies across eight common cancer types. We further characterize mutation-mutation interactions that impact the outcomes of targeted therapies. This work demonstrates how computational analysis of large real-world data generates insights, hypotheses and resources to enable precision oncology., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Clinical trials are the gatekeepers and bottlenecks of progress in medicine. In recent years, they have become increasingly complex and expensive, driven by a growing number of stakeholders requiring more endpoints, more diverse patient populations, and a stringent regulatory environment. Trial designers have historically relied on investigator expertise and legacy norms established within sponsor companies to improve operational efficiency while achieving study goals. As such, data-driven forecasts of operational metrics can be a useful resource for trial design and planning. We develop a machine learning model to predict clinical trial operational efficiency using a novel dataset from Roche containing over 2,000 clinical trials across 20 years and multiple disease areas. The data includes important operational metrics related to patient recruitment and trial duration, as well as a variety of trial features such as the number of procedures, eligibility criteria, and endpoints. Our results demonstrate that operational efficiency can be predicted robustly using trial features, which can provide useful insights to trial designers on the potential impact of their decisions on patient recruitment success and trial duration., (© 2022. The Author(s), under exclusive licence to American Association of Pharmaceutical Scientists.)

With more and better clinical data being captured outside of clinical studies and greater data sharing of clinical studies, external controls may become a more attractive alternative to randomized clinical trials (RCTs). Both industry and regulators recognize that in situations where a randomized study cannot be performed, external controls can provide the needed contextualization to allow a better interpretation of studies without a randomized control. It is also agreed that external controls will not fully replace RCTs as the gold standard for formal proof of efficacy in drug development and the yardstick of clinical research. However, it remains unclear in which situations conclusions about efficacy and a positive benefit/risk can reliably be based on the use of an external control. This paper will provide an overview on types of external control, their applications and the different sources of bias their use may incur, and discuss potential mitigation steps. It will also give recommendations on how the use of external controls can be justified., (© 2021 John Wiley & Sons Ltd.)

Several methods have been presented in the literature for the management of a pharmaceutical portfolio, i.e. selecting which clinical studies should be conducted. We compare two existing approaches that use stochastic programming techniques and formulate the problem as a mixed integer linear programme (MILP). The first approach will be referred to as the ROV (real option valuation) approach since values are assigned to drug development programmes using methods for real option valuation. The second approach will be referred to as the PS (project scheduling) approach as this approach focusses on the scheduling of clinical studies and is formulated similarly to the resource constrained project scheduling problem. The ROV approach treats the value of a drug development programme as stochastic whereas the PS approach treats the trial outcomes as the stochastic component of the programme. As a consequence, the two approaches may select different portfolios. An advantage of the PS approach is that a schedule for when trials are to be conducted is provided as part of the optimal solution. This advantage comes at a much increased computational burden, however.

p38 MAPK inhibition may have additive and synergistic anti-inflammatory effects when used with corticosteroids. We investigated crosstalk between p38 MAPK inhibitors and corticosteroids in bronchial epithelial cells to investigate synergistic effects on cytokine production and the molecular mechanisms involved. Effects of the p38 MAPK inhibitor BIRB-796 and dexamethasone alone and in combination on LPS, polyI:C or TNFα -induced IL-6, CXCL8 and RANTES were assessed in 16HBEs (human epithelial cell line) and on TNFα-induced IL-6 and CXCL8 in primary human epithelial cells from asthma patients and healthy controls. 16HBEs were used to assess effects of BIRB-796 alone and in combination with dexamethasone on glucocorticoid receptor (GR) activity by reporter gene assay, expression of GR target genes and nuclear localisation using Western blot. The effects of BIRB-796 on TNFα stimulated phosphorylation of p38 MAPK and GR at serine (S) 226 by Western blot. Epithelial levels of phosphorylated p38 MAPK and GR S226 were determined by immunohistochemistry in bronchial biopsies from asthma patients and healthy controls. BIRB-796 in combination with dexamethasone increased inhibition of cytokine production in a synergistic manner. Combination treatment significantly increased GR nuclear localisation compared to dexamethasone alone. BIRB-796 inhibited TNFα-induced p38 MAPK and GR S226 phosphorylation. Phosphorylated GR S226 and p38 MAPK levels were increased in bronchial epithelium of more severe asthma patients. Molecular crosstalk exists between p38 MAPK activation and GR function in human bronchial epithelial cells, which alters GR activity. Combining a p38 MAPK inhibitor and a corticosteroid may demonstrate therapeutic potential in severe asthma. KEY MESSAGES: • Combination of corticosteroid and p38 inhibitor in human bronchial epithelial cells • Combination increased cytokine inhibition synergistically and nuclear GR • p38 MAPK inhibition reduced TNFα-induced phosphorylation of GR at S226 but not S211 • Phosphorylated GRS226 and p38 is increased in bronchial epithelium in severe asthma • Combining a p38 inhibitor and a corticosteroid may be effective in asthma treatment.

We report assessment of minimal residual disease (MRD) status and its association with outcome in rituximab-refractory follicular lymphoma (FL) in the randomized GADOLIN trial (NCT01059630). Patients received obinutuzumab (G) plus bendamustine (Benda) induction followed by G maintenance, or Benda induction alone. Patients with a clonal marker (t[14;18] translocation and/or immunoglobulin heavy or light chain rearrangement) detected at study screening were assessed for MRD at mid-induction (MI), end of induction (EOI), and every 6-24 months post-EOI/discontinuation by real-time quantitative PCR. At MI, 41/52 (79%) patients receiving G-Benda were MRD-negative vs. 17/36 (47%) patients receiving Benda alone (p = 0.0029). At EOI, 54/63 (86%) patients receiving G-Benda were MRD-negative vs. 30/55 (55%) receiving Benda alone (p = 0.0002). MRD-negative patients at EOI had improved progression-free survival (HR, 0.33, 95% CI, 0.19-0.56, p < 0.0001) and overall survival (HR, 0.39, 95% CI, 0.19-0.78, p = 0.008) vs. MRD-positive patients, and maintained their MRD-negative status for longer if they received G maintenance than if they did not. These results suggest that the addition of G to Benda-based treatment during induction can significantly contribute to the speed and depth of response, and G maintenance in MRD-negative patients potentially delays lymphoma regrowth.

Background: Dose-finding trials are essential to drug development as they establish recommended doses for later-phase testing. We aim to motivate wider use of model-based designs for dose finding, such as the continual reassessment method (CRM)., Methods: We carried out a literature review of dose-finding designs and conducted a survey to identify perceived barriers to their implementation., Results: We describe the benefits of model-based designs (flexibility, superior operating characteristics, extended scope), their current uptake, and existing resources. The most prominent barriers to implementation of a model-based design were lack of suitable training, chief investigators' preference for algorithm-based designs (e.g., 3+3), and limited resources for study design before funding. We use a real-world example to illustrate how these barriers can be overcome., Conclusions: There is overwhelming evidence for the benefits of CRM. Many leading pharmaceutical companies routinely implement model-based designs. Our analysis identified barriers for academic statisticians and clinical academics in mirroring the progress industry has made in trial design. Unified support from funders, regulators, and journal editors could result in more accurate doses for later-phase testing, and increase the efficiency and success of clinical drug development. We give recommendations for increasing the uptake of model-based designs for dose-finding trials in academia.

Potential predictive biomarkers are often measured on a continuous scale, but in practice, a threshold value to divide the patient population into biomarker 'positive' and 'negative' is desirable. Early phase clinical trials are increasingly using biomarkers for patient selection, but at this stage, it is likely that little will be known about the relationship between the biomarker and the treatment outcome. We describe a single-arm trial design with adaptive enrichment, which can increase power to demonstrate efficacy within a patient subpopulation, the parameters of which are also estimated. Our design enables us to learn about the biomarker and optimally adjust the threshold during the study, using a combination of generalised linear modelling and Bayesian prediction. At the final analysis, a binomial exact test is carried out, allowing the hypothesis that 'no population subset exists in which the novel treatment has a desirable response rate' to be tested. Through extensive simulations, we are able to show increased power over fixed threshold methods in many situations without increasing the type-I error rate. We also show that estimates of the threshold, which defines the population subset, are unbiased and often more precise than those from fixed threshold studies. We provide an example of the method applied (retrospectively) to publically available data from a study of the use of tamoxifen after mastectomy by the German Breast Study Group, where progesterone receptor is the biomarker of interest. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd., (© 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Background: Poly(ADP)-ribose polymerase (PARP) inhibitors such as olaparib can induce cell death in cancer cells with homologous recombination (HR) DNA repair deficiencies, such as BRCA1/2 mutations., Aim: To identify prognostic biomarkers of long-term outcomes in cancer patients., Methods: Immunohistochemistry was used to analyse expression of key HR pathway proteins (ATM, ATR, BRCA1, MDC1, MRE11) and PARP-1 in 100 serous ovarian cancer (SOC) and 100 triple-negative breast cancer (TNBC) tumour samples from Japanese patients. RECIST assessment was used., Results: Patient demographic data and BRCA1/2 mutation status were unavailable. Most proteins listed previously were detected in > 80% of tissue samples, with BRCA1 expression detected in 60-65%. A potential link between BRCA1 expression and overall survival (M stage adjusted) in SOC patients was observed, but was not statistically significant after multiple testing adjustment. Correlations between other biomarker expression and survival were not observed. In TNBC patients, MDC1 staining was associated with progressive disease, but this was not statistically significant; the analysis did not identify significant correlations between biomarker expression and disease control. Limited event numbers prevented assessment of the prognostic value of BRCA1 in TNBC., Conclusion: BRCA1 expression may be a candidate for a prognostic biomarker in SOC. Further studies are warranted.

Aims: Some asthma patients remain symptomatic despite using high doses of inhaled corticosteroids (ICS). We used alveolar macrophages to identify individual patients with insensitivity to corticosteroids and to evaluate the anti-inflammatory effects of a p38 mitogen-activated protein kinase (MAPK) inhibitor combined with a corticosteroid on these cells., Methods: Alveolar macrophages from 27 asthma patients (classified according to the Global Initiative for Asthma (GINA) treatment stage. Six GINA1, 10 GINA2 and 11 GINA3/4) were stimulated with lipoploysaccharide (LPS) (1 μg ml(-1)). The effects of dexamethasone (dex 1-1000 nm), the p38 MAPK inhibitor 1-(5-tert-butyl-2-p-tolyl-2Hpyrazol-3-yl)-3(4-(2-morpholin-4-yl-ethoxy)naphthalen-1-yl)urea (BIRB-796 1-1000 nm) and both drugs combined at all concentrations on supernatant TNFα, IL-6 and CXCL-8 concentrations were analyzed by ELISA. Dose-sparing and efficacy enhancing effects of combination treatment were determined., Results: Dexamethasone reduced LPS-induced TNFα, IL-6 and CXCL-8 in all groups, but maximum inhibition was significantly reduced for GINA3/4 compared with GINA2 and GINA1 (P < 0.01). A subgroup of corticosteroid insensitive patients with a reduced effect of dexamethasone on cytokine secretion were identified. BIRB-796 in combination with dexamethasone significantly increased cytokine inhibition compared with either drug alone (P < 0.001) in all groups. This effect was greater in corticosteroid insensitive compared with sensitive patients. There were significant synergistic dose-sparing effects (P < 0.05) for the combination treatment on inhibition of TNFα, IL-6 and CXCL-8 in all groups. There was also significant efficacy enhancing benefits (P < 0.05) on TNFα and IL-6., Conclusions: p38 MAPK inhibitors synergistically enhance efficacy of corticosteroids in macrophages from asthma patients. This effect is greater in corticosteroid insensitive asthma patients, suggesting that this class of drug should be targeted to this patient phenotype., (© 2014 The British Pharmacological Society.)

The identification of synergistic interactions between combinations of drugs is an important area within drug discovery and development. Pre-clinically, large numbers of screening studies to identify synergistic pairs of compounds can often be ran, necessitating efficient and robust experimental designs. We consider experimental designs for detecting interaction between two drugs in a pre-clinical in vitro assay in the presence of uncertainty of the monotherapy response. The monotherapies are assumed to follow the Hill equation with common lower and upper asymptotes, and a common variance. The optimality criterion used is the variance of the interaction parameter. We focus on ray designs and investigate two algorithms for selecting the optimum set of dose combinations. The first is a forward algorithm in which design points are added sequentially. This is found to give useful solutions in simple cases but can lack robustness when knowledge about the monotherapy parameters is insufficient. The second algorithm is a more pragmatic approach where the design points are constrained to be distributed log-normally along the rays and monotherapy doses. We find that the pragmatic algorithm is more stable than the forward algorithm, and even when the forward algorithm has converged, the pragmatic algorithm can still out-perform it. Practically, we find that good designs for detecting an interaction have equal numbers of points on monotherapies and combination therapies, with those points typically placed in positions where a 50% response is expected. More uncertainty in monotherapy parameters leads to an optimal design with design points that are more spread out., (Copyright © 2015 John Wiley & Sons, Ltd.)

Pre-clinical studies may be used to screen for synergistic combinations of drugs. The types of in vitro assays used for this purpose will depend upon the disease area of interest. In oncology, one frequently used study measures cell line viability: cells placed into wells on a plate are treated with doses of two compounds, and cell viability is assessed from an optical density measurement corrected for blank well values. These measurements are often transformed and analysed as cell survival relative to untreated wells. The monotherapies are assumed to follow the Hill equation with lower and upper asymptotes at 0 and 1, respectively. Additionally, a common variance about the dose-response curve may be assumed. In this paper, we consider two models for incorporating synergy parameters. We investigate the effect of different models of biological variation on the assessment of synergy from both of these models. We show that estimates of the synergy parameters appear to be robust, even when estimates of the other model parameters are biased. Using untransformed measurements provides better coverage of the 95% confidence intervals for the synergy parameters than using transformed measurements, and the requirement to fit the upper asymptote does not cause difficulties. Assuming homoscedastic variances appears to be robust. The added complexity of determining and fitting an appropriate heteroscedastic model does not seem to be justified., (Copyright © 2013 John Wiley & Sons, Ltd.)

BRCA1 protein measurement has previously been evaluated as a potential diagnostic marker without reaching a conclusive recommendation. In this study, we applied current best practice in antibody validation to further characterize MS110, a widely used antibody targeting BRCA1. Antibody specificity was investigated using different biochemical validation techniques. We found that BRCA1 could not be reliably detected using immunoprecipitation and Western blot in endogenously expressing cells. We used immunohistochemistry on formalin-fixed paraffin-embedded cell pellets to establish compatibility with formalin-fixed paraffin-embedded samples. We demonstrated that in transfected cells and cell lines with known genetic BRCA1 status, MS110 successfully detected BRCA1 giving the expected level of staining in immunohistochemistry. Following this, we investigated the use of BRCA1 protein measurement by immunohistochemistry in a cohort of triple negative breast and serous ovarian tumour samples to explore the use of BRCA1 protein measurement by immunohistochemistry for patient stratification. Using MS110 in repeated standardized experiments, on serial sections from a panel of patient samples, results demonstrated considerable run-to-run variability. We concluded that in formalin-fixed tissue samples, MS110 does detect BRCA1; however, using standard methodologies, BRCA1 expression levels in tissue samples is incompatible with the use of this protein as a statistically robust patient selection marker in immunohistochemistry. These results demonstrate the need for further development to deliver BRCA1 protein quantification by immunohistochemistry as a patient stratification marker.

ATM (ataxia telangiectasia mutated) is one of several DNA repair proteins that are suggested to sensitize tumor cells to the poly(ADP-ribose) polymerase inhibitor olaparib when deficient. The aim of this study was to assess the spatiotemporal concordance of ATM immunohistochemistry (IHC) in gastric cancer in order to determine if measurements made at the level of various sample types and times could be inferred as having the potential to be relevant to treatment decisions made at the patient level. Two independent cohorts composed of 591 gastric cancer patients divided into a gastrectomy cohort (n = 450) and a metastasis cohort (n = 141) were used in this study. A total of 2,705 ATM IHC samples were examined, including 450 whole tissue, 3 sets of 450 tissue microarray (TMA), 301 biopsy, 222 metastatic tumor and 2 additional whole tissue samples of 50 cases from the gastrectomy cohort, and 141 pairs of primary and metastatic tumors from the metastasis cohort. The prevalence of ATM negativity was 13.1% in biopsies, 13.9, 15.1, and 16.0% in TMAs and 15.9% in whole tissue samples of the gastrectomy cohort, and 21.4% in primary tumor and 21.5% in metastatic tumor samples of the metastasis cohort. coefficients were 0.341 for biopsy, 0.572 as the average of 3 TMAs and 0.415 for the largely synchronous metastatic tumors of the gastrectomy cohort, and 0.153 for the largely asynchronous metastatic tumors of the metastasis cohort. Using whole tissue sections from tumor resections or primary tumor, respectively, as the reference standards, specificity and sensitivity were 91.6 and 41.0% for biopsy, 93.9 and 61.9% as the average of 3 TMAs, and 86.6 and 58.8% for metastatic tumors of the gastrectomy cohort and 81.7 and 33.3% for metastatic tumors of the metastasis cohort, respectively. Although we have demonstrated that the IHC assay for ATM was robust and reproducible in gastric tumor samples, we have also found that measurements were subject to significant discordance across multiple sample types from the same patient. Further work will be necessary to determine if classification may be made more consistent by multiple sampling. However, the lack of agreement between primary and asynchronous metastatic samples suggests that such sampling would need to be performed at the time of any treatment decision., (Copyright © 2013 S. Karger AG, Basel.)

Systematic engagement of statisticians in preclinical research could help address the weaknesses that are undermining the likelihood of subsequent success in drug discovery and development.

There is an increasing prevalence of drug-diagnostic combinations in oncology. This has placed diagnostic stakeholders directly into the complex benefit-risk, cost, value and uncertainty-driven development paradigm traditionally the preserve of the drug development community. In this review we focus on the delivery of the clinical data required to advance such drug-diagnostic combination development programmes and ultimately satisfy regulators and payors of the value of contemporaneous changes in diagnostic and treatment practice. Ideally all stakeholders would like to initially estimate, and ultimately specify, the comparative benefit-risk for a new treatment option with and without changing diagnostic practice. Hence, in an ideal world clinical trial design is focused on acquiring biomarker treatment interaction data. In this review we describe the key scientific and feasibility inputs required to design and deliver such trials and the drivers, advantages and disadvantages associated with departing from this model. We do not discuss the discovery of new biomarkers nor the analytical validation and marketing of diagnostic products. Following on from trial design we describe how subsequent success then depends upon the concepts that guide trial design being driven into the complex world of large, multinational clinical trial delivery. For every aspect of a traditional clinical drug trial such as supply, recruitment and adherence, there is a corresponding concept for the diagnostic element. In practice, this means that each patient's contribution to the decision making data-set is subject to double jeopardy (attrition on clinical outcome and biomarker status). Historically, this has led to significantly reduced power for detecting biomarker-treatment interactions, reduced decision making confidence and a waste of valuable human and financial resources. We describe recent practice changes and experience that have led to the successful delivery of such trials focusing on both pre- and on trial aspects. The former includes the pivotal role of tissue banks in accurate estimation of evaluability and prevalence for biomarker assays and the latter several practices designed to engage and incentivize key stakeholders particularly CRAs and pathologists. The result is that in the new world of developing personalized treatments for cancer patients the real-time acquisition and monitoring of biomarker data receives similar support to that traditionally reserved for clinical outcome data and far more patients contribute to the testing of personalized medicine hypotheses., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Biomarkers play an increasingly important role in many aspects of pharmaceutical discovery and development, including personalized medicine and the assessment of safety data, with heavy reliance being placed on their delivery. Statisticians have a fundamental role to play in ensuring that biomarkers and the data they generate are used appropriately and to address relevant objectives such as the estimation of biological effects or the forecast of outcomes so that claims of predictivity or surrogacy are only made based upon sound scientific arguments. This includes ensuring that studies are designed to answer specific and pertinent questions, that the analyses performed account for all levels and sources of variability and that the conclusions drawn are robust in the presence of multiplicity and confounding factors, especially as many biomarkers are multidimensional or may be an indirect measure of the clinical outcome. In all of these areas, as in any area of drug development, statistical best practice incorporating both scientific rigor and a practical understanding of the situation should be followed. This article is intended as an introduction for statisticians embarking upon biomarker-based work and discusses these issues from a practising statistician's perspective with reference to examples., (Copyright © 2011 John Wiley & Sons, Ltd.)

Combinations of drugs are increasingly being used in a variety of diseases. Pre-clinical experiments allow the responses of many drug compounds to be studied in combination with the goal of identifying compounds acting synergistically. This paper presents a unified approach to analysing data from combination studies, calculating a hierarchy of interaction indices to powerfully and flexibly describe the synergistic profile of the combination space studied, utilizing standard statistical software to generate estimates of confidence and provide statistical tests. The approach can work with a wide variety of experimental designs and response patterns and will deal with partial responses and inactive compounds. As well as identifying synergy or antagonism, the same approach can also be used to identify a benefit or detriment to monotherapy. The approach is illustrated with data from an in vitro study., (Copyright (c) 2010 John Wiley & Sons, Ltd)

Genome-wide association (GWA) studies have proved extremely successful in identifying novel genetic loci contributing effects to complex human diseases. In doing so, they have highlighted the fact that many potential loci of modest effect remain undetected, partly due to the need for samples consisting of many thousands of individuals. Large-scale international initiatives, such as the Wellcome Trust Case Control Consortium, the Genetic Association Information Network, and the database of genetic and phenotypic information, aim to facilitate discovery of modest-effect genes by making genome-wide data publicly available, allowing information to be combined for the purpose of pooled analysis. In principle, disease or control samples from these studies could be used to increase the power of any GWA study via judicious use as "genetically matched controls" for other traits. Here, we present the biological motivation for the problem and the theoretical potential for expanding the control group with publicly available disease or reference samples. We demonstrate that a naïve application of this strategy can greatly inflate the false-positive error rate in the presence of population structure. As a remedy, we make use of genome-wide data and model selection techniques to identify "axes" of genetic variation which are associated with disease. These axes are then included as covariates in association analysis to correct for population structure, which can result in increases in power over standard analysis of genetic information from the samples in the original GWA study., ((c) 2010 Wiley-Liss, Inc.)

Selumetinib (AZD6244, ARRY-142886) is a selective, non-ATP-competitive inhibitor of mitogen-activated protein/extracellular signal-regulated kinase kinase (MEK)-1/2. The range of antitumor activity seen preclinically and in patients highlights the importance of identifying determinants of response to this drug. In large tumor cell panels of diverse lineage, we show that MEK inhibitor response does not have an absolute correlation with mutational or phospho-protein markers of BRAF/MEK, RAS, or phosphoinositide 3-kinase (PI3K) activity. We aimed to enhance predictivity by measuring pathway output through coregulated gene networks displaying differential mRNA expression exclusive to resistant cell subsets and correlated to mutational or dynamic pathway activity. We discovered an 18-gene signature enabling measurement of MEK functional output independent of tumor genotype. Where the MEK pathway is activated but the cells remain resistant to selumetinib, we identified a 13-gene signature that implicates the existence of compensatory signaling from RAS effectors other than PI3K. The ability of these signatures to stratify samples according to functional activation of MEK and/or selumetinib sensitivity was shown in multiple independent melanoma, colon, breast, and lung tumor cell lines and in xenograft models. Furthermore, we were able to measure these signatures in fixed archival melanoma tumor samples using a single RT-qPCR-based test and found intergene correlations and associations with genetic markers of pathway activity to be preserved. These signatures offer useful tools for the study of MEK biology and clinical application of MEK inhibitors, and the novel approaches taken may benefit other targeted therapies.

Objective: To compare the effect of isoenergetically-dense, high-protein (HP), high-fat (HF) or high-carbohydrate (HC) breakfasts (at 08.30) on subjective hunger, fullness and appetite (measured hourly on a 100 mm visual analogue scale), macronutrient balance and ad libitum energy intake (EI), at a test meal (13.30) and throughout the rest of the day (until 23.00)., Design: Six men each spent 24 h in a whole-body indirect calorimeter on three separate occasions during which they received breakfasts designed to match 75% of BMR and that comprised, on average 3.1 MJ of protein (HP), carbohydrate (HC) or fat (HF), respectively, the remainder being split between the other two macronutrients. Every item of the ad libitum diet comprised 13% protein, 40% fat and 47% carbohydrate by energy, with an energy density of 550 kJ/100 g., Results: Subjectively-rated pleasantness did not differ between the breakfasts, or any of the subsequent ad libitum meals. Subjective hunger was significantly greater during the hours between breakfast and lunch after the HF (26) treatment relative to the HP (18) or HC (18 mm) meals (P < 0.001), although the HP treatment suppressed hunger to a greater extent than the other two treatments over 24 h. However, mean ad libitum lunch intakes were similar at 5.38, 5.30 and 5.18 MJ (NS) on the HP, HC and HF treatments, respectively. After-lunch intakes were also very similar at 6.14, 6.18 and 5.83 MJ (NS). Mean 24-h energy expenditure amounted to 11.12, 11.14 and 10.93 MJ, respectively, producing energy balances of 5.71, 5.83 and 5.04 MJ (NS), respectively. The HP, HF and HC breakfasts led to enhanced P, F and C oxidation, respectively (P < 0.003)., Conclusions: Large HP, HC or HF breakfasts led to detectable changes in hunger that were not of sufficient magnitude to influence lunch-time intake 5 h later, or EI for the rest of the day. A single positive balance of each macronutrient can be buffered by oxidation and storage capacity, without leading to changes in meal-to-meal EI, when subjects feed ad libitum on unfamiliar diets of fixed composition.

Objective: To test whether overfeeding isoenergetic doses protein, carbohydrate and fat would differentially influence appetite on the same day, and the subsequent day's food intake., Design: Six men were each studied three times on a 5-day protocol. On days 1 and 2 they were fed a medium fat (MF) maintenance diet (comprising 40:47:13% fat, CHO and protein by energy) calculated at 1.6 x RMR. Subjects entered the calorimeter at 08.00 on day 3 for 48 h. On day 3 (manipulation day), they ate a MF diet at 1.5 x RMR with an additional 0.6 x RMR as protein (HP), carbohydrate (HC) or fat (HF). On days 4 and 5, (outcome days), subjects had ad libitum access to isoenergetically dense MF (40:47:13) foods (550kJ/100 g). Subjective hunger and satiety were tracked hourly during waking hours throughout days 1-5., Results: Throughout day 3 subjects felt significantly more full and less hungry on the high protein diet relative to the other two diets (P = 0.002). Also by the end of day 3 each overfed nutrient led to a significant increase in its own balance of the other two diets (P < 0.01). These effects did not influence the subsequent day's energy intake. The alterations in nutrient balance by the end of day 3 were partially buffered by increases in the oxidative disposal of each overfed macronutrient throughout day 4 (which was proportionately greater for protein (P < 0.001) than carbohydrate (P = 0.07) or fat (P = 0.1))., Conclusions: HP diets were more satiating that isoenergetically-dense HC or HF diets on the day they are eaten. The HC diet was transiently more satiating than the HF diet after each meal. This study supports previous work which suggests that relatively large changes in nutrient balance produced on one day appear to be poorly compensated by changes in energy intake on a subsequent day in men.

Objective: High-fat, high energy-density (HF, HED) diets promote an increase in energy intakes relative to low-fat lower-energy density diets (LF, LED). This study examined whether HF diets promote higher levels of energy intake when isoenergetically dense (IE) relative to LF (high carbohydrate) diets, as predicted by glucostatic and glycogenostatic models for energy intake regulation., Subjects: Six normal-weight healthy men [mean age (SD) = 37.33 (13.32 y) mean weight = 73.03 (5.14 kg), mean height = 1.80 (0.05 m)]., Design: Six men were each studied three times (factorial design) during 14-d throughout which they had ad libitum access to one of three covertly-manipulated diets. The fat, carbohydrate (CHO) and protein in each diet (as % energy) were 20:68:12, [low-fat (LF)]; 40:48:12, [medium-fat (MF)]; 60:28:12 [high-fat (HF)], with 2-d maintenance (1.4 x BMR, MF) beforehand. Within each diet every item was of the same composition and offered as a 3-d rotating menu., Measurements: Energy and nutrient intakes, body weight, subjective pleasantness and satisfaction of the food., Results: Energy intakes were 10.69, 11.02 and 10.90 MJ/d on the LF, MF and HF diets respectively. The increase in energy intake that occurred in previous studies when the energy density of the diet was increased by addition of fat was not apparent when LF, MF and HF diets were of the same energy density., Conclusion: Neither carbohydrate nor fat intake were tightly regulated. These data do not support an entirely glucostatic or glucogenostatic model of food intake regulation.

Objective: This study examined whether isoenergetic substitution of MCT for LCT in HF, HE diets (639 kJ/100 g) limits the excess energy intakes frequently observed on high-fat diets of high energy density (HF, HE)., Subjects: Six healthy male volunteers [mean age (SD) = 27.17 (4.17 y) mean weight = 63.33 (7.33 kg), mean height = 1.72 (0.05 m)]., Design: The subjects were each studied three times during 14 d throughout which they had ad libitum access to one of three covertly-manipulated diets, which were randomly assigned in a counter-balanced design. The fat, carbohydrate (CHO) and protein in each diet (as percent energy) were identical at 62:28:10, with 2 day maintenance (1.5 x BMR, MF) beforehand. The ratio of MCT to LCT was 1:2, 1:1 and 2:1 on the low-, (LMCT) medium- (MMCT) and high-MCT (HMCT) diets, respectively. Within each diet every item was of the same composition and offered as a 3 day rotating menu., Results: Energy intakes were significantly lower on the HMCT diet [F (2,240) = 7.52; p < 0.001] giving mean values of 13.50, 13.67, and 12.43 MJ/d on the LMCT, MMCT and HMCT diets, respectively. Food intake followed a parallel trend. By day 14 body weight changes amounted to +0.45, +0.41 and -0.03 kg, respectively., Conclusion: These data suggest that substitution of a readily metabolised fat for a less readily metabolised fat, in very high fat diets can limit the excess energy intakes and weight gain that is usually produced by HF, energy-dense diets.

This study assessed whether human food intake is regulated by negative feedback, directly or indirectly, from carbohydrate stores (glycogenostatic model). Six men were studied on three occasions during 7 d of whole-body indirect calorimetry, throughout which they had ad libitum access to one of three covertly manipulated diets: low fat (20% of energy as fat, 67% of energy as carbohydrate, and 13% of energy as protein; 4.80 kJ/g; LF), medium fat (40% of energy as fat, 47% of energy as carbohydrate, and 13% of energy as protein; 5.59 kJ/g; MF), or high fat (60% of energy as fat, 27% of energy as carbohydrate, and 13% of energy as protein; 7.04 kJ/g; HF). Energy intakes increased with percent fat (F[92,60] = 36.7; P < 0.001), producing average daily balances of -0.27, 0.77, and 2.58 MJ/d during the LF, MF, and HF diets, respectively. Changes in carbohydrate stores were attenuated by autoregulatory changes in carbohydrate oxidation. Carbohydrate balance showed a negative relation to the subsequent day's energy balance (t = 2.696; P = 0.0082) but explained only 5.5% of the variance. The relation for fat was positive (t = 5.245; P < 0.0001), accounting for 19.9% of the variance (stepwise regression). LF, lower-energy diets are more satiating than are HF-higher-energy diets, but carbohydrate stores per se did not entirely account for the change that diet composition had on energy intake. This study suggests that protein and carbohydrate have potential to reduce subsequent energy intake whereas there was no apparent reductive effect due to fat.

The computational cost, in terms of both storage requirements and calculation required, of performing an elimination ordering on a graph is considered as a function of the order in which the vertices of the moral graph are eliminated. Useful properties of the moral graph of a pedigree with respect to vertex elimination are observed and these properties extended to define a k-pedigree as a graph permitting allocation of one of k sexes to each vertex of the graph, such that the subgraph induced by vertices of a single six contains no cycles. Properties of k-pedigrees include an upper bound of 2k on clique size. A novel algorithm, SEXY, based upon these properties is proposed and its performance compared with other algorithms used to generate elimination sequences. It is found to give a widely dispersed range of of sequences, including some sequences requiring under a quarter of the storage and under a half of the computational time than had previously been found using standard methods.