Chris Harbron

Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

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.

Evaluating Robustness and Sensitivity of the NanoString Technologies nCounter Platform to Enable Multiplexed Gene Expression Analysis of Clinical Samples.

Analysis of clinical trial specimens such as formalin-fixed paraffin-embedded (FFPE) tissue for molecular mechanisms of disease progression or drug response is often challenging and limited to a few markers at a time. This has led to the increasing importance of highly multiplexed assays that enable profiling of many biomarkers within a single assay. Methods for gene expression analysis have undergone major advances in biomedical research, but obtaining a robust dataset from low-quality RNA samples, such as those isolated from FFPE tissue, remains a challenge. Here, we provide a detailed evaluation of the NanoString Technologies nCounter platform, which provides a direct digital readout of up to 800 mRNA targets simultaneously. We tested this system by examining a broad set of human clinical tissues for a range of technical variables, including sensitivity and limit of detection to varying RNA quantity and quality, reagent performance over time, variability between instruments, the impact of the number of fields of view sampled, and differences between probe sequence locations and overlapping genes across CodeSets. This study demonstrates that Nanostring offers several key advantages, including sensitivity, reproducibility, technical robustness, and utility for clinical application.

Synergistic Effects of p38 Mitogen-Activated Protein Kinase Inhibition with a Corticosteroid in Alveolar Macrophages from Patients with Chronic Obstructive Pulmonary Disease

Corticosteroids partially suppress cytokine production by chronic obstructive pulmonary disease (COPD) alveolar macrophages. p38 mitogen-activated protein kinase (MAPK) inhibitors are a novel class of anti-inflammatory drug. We have studied the effects of combined treatment with a corticosteroid and a p38 MAPK inhibitor on cytokine production by COPD alveolar macrophages, with the aim of investigating dose-sparing and efficacy-enhancing effects. Alveolar macrophages from 10 patients with COPD, six smokers, and six nonsmokers were stimulated with lipopolysaccharide (LPS) after preincubation with five concentrations of dexamethasone alone, five concentrations of the p38 MAPK inhibitor 1-(5-tert-butyl-2-p-tolyl-2H-pyrazol-3-yl)-3(4-(2-morpholin-4-yl-ethoxy)naphthalen-1-yl)urea (BIRB-796) alone, and all combinations of these concentrations. After 24 h, the supernatants were analyzed for interleukin (IL)-8, IL-6, tumor necrosis factor α (TNFα), granulocyte macrophage–colony-stimulating factor (GM-CSF), IL-1α, IL-1β, IL-1ra, IL-10, monocyte chemoattractant protein 3, macrophage-derived chemokine (MDC), and regulated on activation normal T cell expressed and secreted (RANTES). The effect of dexamethasone on p38 MAPK activation was analyzed by Western blotting. Dexamethasone and BIRB-796 both reduced LPS-induced cytokine production in a dose-dependent manner in all subject groups, with no differences between groups. Increasing the concentration of BIRB-796 in combination with dexamethasone produced progressively greater inhibition of cytokine production than dexamethasone alone. There were significant efficacy-enhancing benefits and synergistic dose-sparing effects (p < 0.05) for the combination treatment for IL-8, IL-6, TNFα, GM-CSF, IL-1ra, IL-10, MDC, and RANTES in one or more subject groups. Dexamethasone had no effect on LPS-induced p38 MAPK activation. We conclude that p38 MAPK activation in alveolar macrophages is corticosteroid-insensitive. Combining a p38 MAPK inhibitor with a corticosteroid synergistically enhances the anti-inflammatory effects on LPS-mediated cytokine production by alveolar macrophages from patients with COPD and controls.

Concordance of ATM (Ataxia Telangiectasia Mutated) Immunohistochemistry between Biopsy or Metastatic Tumor Samples and Primary Tumors in Gastric Cancer Patients

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.

Reproducible, Quantitative, and Flexible Molecular Subtyping of Clinical DLBCL Samples Using the NanoString nCounter System

Purpose: Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease with distinct molecular subtypes. The most established subtyping approach, the “Cell of Origin” (COO) algorithm, categorizes DLBCL into activated B-cell (ABC) and germinal center B-cell (GCB)-like subgroups through gene expression profiling. Recently developed immunohistochemical (IHC) techniques and other established methodologies can deliver discordant results and have various technical limitations. We evaluated the NanoString nCounter gene expression system to address issues with current platforms. Experimental Design: We devised a scoring system using 145 genes from published datasets to categorize DLBCL samples. After cell line validation, clinical tissue segmentation was tested using commercially available diagnostic DLBCL samples. Finally, we profiled biopsies from patients with relapsed/refractory DLBCL enrolled in the fostamatinib phase IIb clinical trial using three independent RNA expression platforms: NanoString, Affymetrix, and qNPA. Results: Diagnostic samples showed a typical spread of subtypes with consistent gene expression profiles across matched fresh, frozen, and formalin-fixed paraffin-embedded tissues. Results from biopsy samples across platforms were remarkably consistent, in contrast to published IHC data. Interestingly, COO segmentation of longitudinal fostamatinib biopsies taken at initial diagnosis and then again at primary relapse showed 88% concordance (15/17), suggesting that COO designation remains stable over the course of disease progression. Conclusions: DLBCL segmentation of patient tumor samples is possible using a number of expression platforms. However, we found that NanoString offers the most flexibility and fewest limitations in regards to robust clinical tissue subtype characterization. These subtype distinctions should help guide disease prognosis and treatment options within DLBCL clinical practice. Clin Cancer Res; 21(10); 2367–78. ©2014 AACR. See related commentary by Rimsza, p. 2204

In search of preclinical robustness

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.

RefPlus: an R package extending the RMA Algorithm

UNLABELLED RMA has become a widely used methodology to pre-process Affymetrix gene expression microarrays. A limitation of RMA is that the calculated probeset intensities change when a set of microarrays is re-pre-processed after the inclusion of additional microarrays into the analysis set. Here we report the availability of the RefPlus package containing functions to perform the Extrapolation Strategy and Extrapolation Averaging algorithms which address these issues. AVAILABILITY The software is implemented in the R language and can be downloaded from the Bioconductor project website (http://www.bioconductor.org). SUPPLEMENTARY INFORMATION Further details of the workings and evaluation of these functions are given in the documentation available on the Bioconductor website.

A statistician's perspective on biomarkers in drug development

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 statisticians perspective with reference to examples.

Proteomic Biomarkers for Acute Interstitial Lung Disease in Gefitinib-Treated Japanese Lung Cancer Patients

Interstitial lung disease (ILD) events have been reported in Japanese non-small-cell lung cancer (NSCLC) patients receiving EGFR tyrosine kinase inhibitors. We investigated proteomic biomarkers for mechanistic insights and improved prediction of ILD. Blood plasma was collected from 43 gefitinib-treated NSCLC patients developing acute ILD (confirmed by blinded diagnostic review) and 123 randomly selected controls in a nested case-control study within a pharmacoepidemiological cohort study in Japan. We generated ∼7 million tandem mass spectrometry (MS/MS) measurements with extensive quality control and validation, producing one of the largest proteomic lung cancer datasets to date, incorporating rigorous study design, phenotype definition, and evaluation of sample processing. After alignment, scaling, and measurement batch adjustment, we identified 41 peptide peaks representing 29 proteins best predicting ILD. Multivariate peptide, protein, and pathway modeling achieved ILD prediction comparable to previously identified clinical variables; combining the two provided some improvement. The acute phase response pathway was strongly represented (17 of 29 proteins, p = 1.0×10−25), suggesting a key role with potential utility as a marker for increased risk of acute ILD events. Validation by Western blotting showed correlation for identified proteins, confirming that robust results can be generated from an MS/MS platform implementing strict quality control.

A flexible unified approach to the analysis of pre‐clinical combination studies

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.