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The alveolar epithelium is characteristically abnormal in fibrotic lung disease, and we recently established a direct link between injury to the type II alveolar epithelial cell (AEC) and the accumulation of interstitial collagen. The mechanisms by which damage to the epithelium induces lung scarring remain poorly understood. It is particularly controversial whether an insult to the type II AEC initiates an inflammatory response that is required for the development of fibrosis. To explore whether local inflammation occurs following a targeted epithelial insult and contributes to lung fibrosis, we administered diphtheria toxin to transgenic mice with type II AEC–restricted expression of the diphtheria toxin receptor. We used immunophenotyping techniques and diphtheria toxin receptor–expressing, chemokine receptor-2–deficient (CCR2−/−) mice to determine the participation of lung leukocyte subsets in pulmonary fibrogenesis. Our results demonstrate that targeted type II AEC injury induces an inflammatory response that is enriched for CD11b+ nonresident exudate macrophages (ExM) and their precursors, Ly-6Chigh monocytes. CCR2 deficiency abrogates the accumulation of both cell populations and protects mice from fibrosis, weight loss, and death. Further analyses revealed that the ExM are alternatively activated and that ExM and Ly-6Chigh monocytes express mRNA for IL-13, TGF-β, and the collagen genes, COL1A1 and COLIIIA1. Furthermore, the accumulated ExM and Ly-6Chigh monocytes contain intracellular collagen, as detected by immunostaining. Together, these results implicate CCR2 and the accumulation of ExM and Ly-6Chigh monocytes as critical determinants of pulmonary fibrosis induced by selective type II AEC injury.

A new algorithm to optimize stope design for the sublevel stoping mining method is described. The model is based on a cylindrical coordinate defined around the initial vertical raise. Geotechnical constraints on hanging wall and footwall slopes are translated as precedence relations between blocks in the cylindrical coordinate system. Two control parameters with clear engineering meaning are defined to further constrain the solution: (a) the maximum distance of a block from the raise and (b) the horizontal width required to bring the farthest block to the raise. The graph obtained is completed by the addition of a source and a sink node allowing to transform the optimization program to a problem of maximum flow over the graph. The (conditional) optimal stope corresponding to the current raise location and height is obtained. The best location and height for the raise are determined by global optimization. The performance of the algorithm is evaluated with three simple synthetic deposits and one real deposit. Comparison is made with the floating stope technique. The results show that the algorithm effectively meets the geotechnical constraints and control parameters, and produce realistic optimal stope for engineering use.

Bovine tuberculosis (BTB) is an important neglected zoonosis that affects livestock, wildlife and human. A study to determine prevalence and geospatial clusters for BTB was conducted from June 2010 to March 2012 at livestock–wildlife interface areas (LWIA). A total of 1,288 cattle located in vicinity of Mikumi-Selous ecosystem Tanzania were tested. Single Intradermal Comparative Tuberculin Test and spatial scan statistic analysis were applied to establish the status of the disease and identify significant spatial BTB clusters. Overall individual prevalence was 3.7 % (n = 1,288) (95 % CI = 2.8–4.9) and 7.8 % (95 % CI = 6.4–9.4) with cut-off of >4 and >2 mm, respectively. Villages with at least one reactor were 55.8 % (n = 43). Reactivity was significantly higher in Mvomero and Kilosa districts compared with Kilombero and Ulanga districts (χ 2 = 15.9; P

Multiparametric magnetic resonance imaging and fusion biopsy detect more high risk prostate cancer and less low risk prostate cancer than systematic biopsy. However, there remains a small subset of patients in whom systematic biopsy captures higher grade disease than fusion biopsy. We sought to identify potential mechanisms of the failure of fusion biopsy in the detection of clinically significant prostate cancer.We reviewed a prospectively maintained database of patients who underwent multiparametric magnetic resonance imaging followed by fusion biopsy and systematic biopsy from 2007 to 2014. In patients in whom disease was upgraded to clinically significant disease (Gleason 7 or greater) by systematic biopsy over fusion biopsy, independent re-review of magnetic resonance imaging, archived biopsy imaging and whole mount pathology as well as needle coordinate mapping were performed. Multivariate logistic regression analysis was done to determine predictors of upgrading by systematic biopsy.Disease was upgraded based on systematic biopsy over fusion biopsy in 135 of 1,003 patients (13.5%), of whom only 62 (6.2%) were upgraded to intermediate (Gleason 7) and high risk (Gleason 8 or greater) prostate cancer (51 or 5.1% and 11 or 1.1%, respectively). On multivariate analysis lower prostate specific antigen (p0.001), higher magnetic resonance imaging prostate volume (p0.001) and a lower number of target cores (p = 0.001) were predictors of upgrading by systematic biopsy. Main mechanisms of under grading by fusion biopsy included multiparametric magnetic resonance imaging reader oversight, presence of magnetic resonance imaging invisible cancer, fusion biopsy technique error and intralesion Gleason heterogeneity.Magnetic resonance imaging and fusion biopsy rarely missed clinically significant prostate cancer as only 62 of 1,003 cases (6.2%) were upgraded to clinically significant disease by systematic biopsy. Imaging and biopsy techniques are continually refined. Further studies will help clarify mechanisms of fusion biopsy failure and the patient populations that benefit from systematic biopsy in addition to fusion biopsy.

This study investigated human exposure to inorganic arsenic (As), a risk factor for cancer and non-cancerous health effects, in Cornwall, UK - a region of elevated environmental As resulting from naturally occurring mineralisation and historical mining. Recent exposures to As from private water supplies (PWS) were detected by measuring As in drinking water samples (n=127) and urine samples (n=207). Exceedances of the WHO 10 As µg L-1 guidance value were measured in drinking waters from 5 % of households. The Spearman correlation calculated for drinking water versus unadjusted total urinary As concentrations was 0.36. Urinary As speciation was used to distinguish between environmental inorganic As exposure and non-toxic dietary sources. Seafood derived urinary arsenobetaine exclusion and osmolality hydration adjustment yielded an improved correlation of 0.62 between drinking water and urinary As concentrations. Urinary hydration adjustment methods were improved and comparatively assessed using data from the US National Health and Nutrition Examination Survey (NHANES). Correlations of urinary concentrations of As, iodine (I), lead (Pb) and cadmium (Cd) against urinary flow rate (UFR) (low correlations desired) and urinary Pb and Cd against respective blood concentrations (high correlations desired) were used as independent performance criteria. Osmolality adjustment and a modified UFR-based adjustment method using empirically derived coefficients (slopes of analyte concentrations as a function of UFR) generally performed better than creatinine, excretion rate and bodyweight-adjusted excretion rate methods. The findings demonstrated the analyte specific nature of adjustment methods, their misuse in the literature and suggested a pathway to a more robust adjustment framework. Prolonged exposure to As from PWS was identified by the stability of 127 drinking water As concentrations measured up to 31 months apart. Drinking water As concentrations were correlated with those measured in toenails (Pearson’s r: 0.53; n=200) and hair (Pearson’s r: 0.38; n=104). The successful elimination of external contamination of toenail samples was indicated by low As concentrations in final-stage rinse solutions (geometric mean contribution: 0.4 %). A positive association between seafood consumption and toenail As and a negative association between home-grown vegetable consumption and hair As was observed when As in drinking water was

Background This paper examines the separate effects of the perceived amount, source, and quality of support during labor and delivery on women's positive and negative evaluations of their birth experiences. Methods Data come from the Listening to Mothers I and II (LTM) surveys (n = 2,765). Women's perception of support was regressed separately onto indices of positive and negative words that women associated with their labor and delivery. Results The total number of support sources, type of support person, and quality of support all impacted women's birth evaluations across different regression models, controlling for demographics, birth interventions, and other birth characteristics. Support overall had a greater effect on increasing women's positive evaluations, but was not as protective against negative evaluations. Support from medical and birth professionals (doctors, nurses, doulas) had the greatest effect on women's positive evaluations. Good partner support was complexly related: it was associated with less positive evaluations but also appeared to have a protective effect against negative birth evaluations. Discussion Support in childbirth is a complex concept with multiple dimensions that matter for women's birth evaluations. Support from nursing staff, doctors, and doulas is important for enabling positive evaluations while support from partners is more complexly related to women's evaluations. Research on support for laboring women should more extensively address the division of labor between different sources of support.

Fibrotic disorders of the lung are associated with perturbations in the plasminogen activation system. Specifically, plasminogen activator inhibitor-1 (PAI-1) expression is increased relative to the plasminogen activators. A direct role for this imbalance in modulating the severity of lung scarring following injury has been substantiated in the bleomycin model of pulmonary fibrosis. However, it remains unclear whether derangements in the plasminogen activation system contribute more generally to the pathogenesis of lung fibrosis beyond bleomycin injury. To answer this question, we employed an alternative model of lung scarring, in which type II alveolar epithelial cells (AECs) are specifically injured by administering diphtheria toxin (DT) to mice genetically engineered to express the human DT receptor (DTR) off the surfactant protein C promoter. This targeted AEC injury results in the diffuse accumulation of interstitial collagen. In the present study, we found that this targeted type II cell insult also increases PAI-1 expression in the alveolar compartment. We identified AECs and lung macrophages to be sources of PAI-1 production. To determine whether this elevated PAI-1 concentration was directly related to the severity of fibrosis, DTR + mice were crossed into a PAI-1-deficient background (DTR + :P AI-1 −/− ). DT administration to DTR + :P AI-1 −/− animals caused significantly less fibrosis than was measured in DTR + mice with intact PAI-1 production. PAI-1 deficiency also abrogated the accumulation of CD11b + exudate macrophages that were found to express PAI-1 and type-1 collagen. These observations substantiate the critical function of PAI-1 in pulmonary fibrosis pathogenesis and provide new insight into a potential mechanism by which this pro-fibrotic molecule influences collagen accumulation. Copyright © 2012 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Most new drug development in oncology is based on targeting specific molecules. Genomic profiles and deregulated drug targets vary from patient to patient making new treatments likely to benefit only a subset of patients traditionally grouped in the same clinical trials. Predictive biomarkers are being developed to identify patients who are most likely to benefit from a particular treatment; however, their biological basis is not always conclusive. The inclusion of marker-negative patients in a trial is therefore sometimes necessary for a more informative evaluation of the therapy. In this paper, we present a two-stage Bayesian design that includes both marker-positive and marker-negative patients in a clinical trial. We formulate a family of prior distributions that represent the degree of a priori confidence in the predictive biomarker. To avoid exposing patients to a treatment to which they may not be expected to benefit, we perform an interim analysis that may stop accrual of marker-negative patients or accrual of all patients. We demonstrate with simulations that the design and priors used control type I errors, give adequate power, and enable the early futility analysis of test-negative patients to be based on prior specification on the strength of evidence in the biomarker.

Collagen deposition by fibroblasts contributes to scarring in fibrotic diseases. Activation of protein kinase A (PKA) by cAMP represents a pivotal brake on fibroblast activation, and the lipid mediator prostaglandin E(2) (PGE(2)) exerts its well known anti-fibrotic actions through cAMP signaling. However, fibrotic fibroblasts from the lungs of patients with idiopathic pulmonary fibrosis, or of mice with bleomycin-induced fibrosis, are resistant to the normal collagen-inhibiting action of PGE(2). In this study, we demonstrate that plasminogen activation to plasmin restores PGE(2) sensitivity in fibrotic lung fibroblasts from human and mouse. This involves amplified PKA signaling resulting from the promotion of new interactions between AKAP9 and PKA regulatory subunit II in the perinuclear region as well as from the inhibition of protein phosphatase 2A. This is the first report to show that an extracellular mediator can dramatically reorganize and amplify the intracellular PKA-A-kinase anchoring protein signaling network and suggests a new strategy to control collagen deposition by fibrotic fibroblasts.

Plasminogen activator inhibitor-1 (PAI-1) is increased in the lungs of patients with pulmonary fibrosis, and animal studies have shown that experimental manipulations of PAI-1 levels directly influence the extent of scarring that follows lung injury. PAI-1 has 2 known properties that could potentiate fibrosis, namely an antiprotease activity that inhibits the generation of plasmin, and a vitronectin-binding function that interferes with cell adhesion to this extracellular matrix protein. To determine the relative importance of each PAI-1 function in lung fibrogenesis, we administered mutant PAI-1 proteins that possessed either intact antiprotease or vitronectin-binding activity to bleomycin-injured mice genetically deficient in PAI-1. We found that the vitronectin-binding capacity of PAI-1 was the primary determinant required for its ability to exacerbate lung scarring induced by intratracheal bleomycin administration. The critical role of the vitronectin-binding function of PAI-1 in fibrosis was confirmed in the bleomycin model using mice genetically modified to express the mutant PAI-1 proteins. We conclude that the vitronectin-binding function of PAI-1 is necessary and sufficient in its ability to exacerbate fibrotic processes in the lung.

Current genomics and biotechnology promise the development of biomarkers to predict individual disease risk, enable early detection of disease, and improve diagnostic classification to better inform individualized treatment. I discuss these objectives, commenting on progress to date and obstacles to future success. The discussion mainly uses examples from oncology where the nature of the disease has expedited genomic approaches for developing biomarkers. Many of the lessons being learned in oncology, however, should be applicable to other chronic diseases.

We demonstrate that clinical trials using response adaptive randomized treatment assignment rules are subject to substantial bias if there are time trends in unknown prognostic factors and standard methods of analysis are used. We develop a general class of randomization tests based on generating the null distribution of a general test statistic by repeating the adaptive randomized treatment assignment rule holding fixed the sequence of outcome values and covariate vectors actually observed in the trial. We develop broad conditions on the adaptive randomization method and the stochastic mechanism by which outcomes and covariate vectors are sampled that ensure that the type I error is controlled at the level of the randomization test. These conditions ensure that the use of the randomization test protects the type I error against time trends that are independent of the treatment assignments. Under some conditions in which the prognosis of future patients is determined by knowledge of the current randomization weights, the type I error is not strictly protected. We show that response-adaptive randomization can result in substantial reduction in statistical power when the type I error is preserved. Our results also ensure that type I error is controlled at the level of the randomization test for adaptive stratification designs used for balancing covariates.

Resampling techniques are often used to provide an initial assessment of accuracy for prognostic prediction models developed using high-dimensional genomic data with binary outcomes. Risk prediction is most important, however, in medical applications and frequently the outcome measure is a right-censored time-to-event variable such as survival. Although several methods have been developed for survival risk prediction with high-dimensional genomic data, there has been little evaluation of the use of resampling techniques for the assessment of such models. Using real and simulated datasets, we compared several resampling techniques for their ability to estimate the accuracy of risk prediction models. Our study showed that accuracy estimates for popular resampling methods, such as sample splitting and leave-one-out cross validation (Loo CV), have a higher mean square error than for other methods. Moreover, the large variability of the split-sample and Loo CV may make the point estimates of accuracy obtained using these methods unreliable and hence should be interpreted carefully. A k-fold cross-validation with k = 5 or 10 was seen to provide a good balance between bias and variability for a wide range of data settings and should be more widely adopted in practice.

SU MMARY F or medical classification problems, it is often desirable to have a probability associated with each class. Probabilistic classifiers have received relatively little attention for small n large p classification problems despite of their importance in medical decision making. In this paper, we introduce 2 criteria for assessment of probabilistic classifiers: well-calibratedness and refinement and develop corresponding evaluation measures. We evaluated several published high-dimensional probabilistic classifiers and developed 2 extensions of the Bayesian compound covariate classifier. Based on simulation studies and analysis of gene expression microarray data, we found that proper probabilistic classification is more difficult than deterministic classification. It is important to ensure that a probabilistic classifier is well calibrated or at least not “anticonservative” using the methods developed here. We provide this evaluation for several probabilistic classifiers and also evaluate their refinement as a function of sample size under weak and strong signal conditions. We also present a cross-validation method for evaluating the calibration and refinement of any probabilistic classifier on any data set.

Attention deficit hyperactivity disorder (ADHD) is one of the most prevalent disorders among school-going children. The aim of this study was to compare academic achievement of learners with and without ADHD in international primary schools of Mombasa (Kenya) and Kampala (Uganda). A comparative study was conducted among 377 respondents using Attention Deficit Hyperactivity Disorder Scale Questionnaire (SNAP-IV) which assessed the three ADHD subtypes in form of a closed-ended questionnaire. Results revealed that learners with ADHD had low overall academic achievement compared to those without ADHD in Mombasa (p

We used gene expression information and mutational signature analysis of cell lines from a diverse range of cancerous tissues to examine the activity of two members of the APOBEC family of cytidine deaminases, APOBEC3A and APOBEC3B, and three additional genes. APOBEC3B is known to increase the mutation load in many cancers, generating kataegis clusters of closely spaced, single strand-specific DNA substitutions with a characteristic hypermutation signature. Some studies also suggested that APOBEC3A, REV1, UNG, and FHIT may also participate in hypermutation processes associated with APOBEC activity. We investigated how the activities of these five genes may affect the abundance of APOBEC-like signatures and whether they may be associated with sensitivity of cancer cells to treatment in different cancer categories. We analyzed several data sources for 1,408 cell lines from 26 cancer types available from the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC) resources. These data included whole exome sequencing (WES) data, microarray gene expression information, and drug sensitivity information. We examined correlations of the abundance of APOBEC-associated motifs and WES-wide mutation loads with APOBEC3A, APOBEC3B, REV1, UNG, and FHIT gene expression, as well as their association with cell line chemosensitivity to 255 antitumor drugs in multiple cancer subtypes. We were able to confirm several previously reported expression and chemosensitivity associations and found additional correlations, which may be clinically important. Sensitivity to JQ1, JNK inhibitors AS601245 and VIII, BMS-509744, bicalutamide, and several other agents was correlated with candidate gene expression levels or with abundance of APOBEC-like motif clusters in specific cancer categories such as pancreatic, breast and non-small cell lung cancer cell lines. For example, we observed a strong negative correlation between APOBEC3A expression in pancreatic cell lines and sensitivity to JQ1, a BET inhibitor, which had been reported to inhibit pancreatic cancer cells in vitro and in vivo. In glioma cell lines, APOBEC3B expression was significantly negatively correlated with sensitivity to a CDK9 inhibitor THZ-2-49 and HSP90 inhibitors AUY922 and 17-AAG (tanespimycin). We observed a strong correlation between the combined length of kataegis clusters and chemoresistance to bicalutamide, a nonsteroidal antiandrogen drug, in breast cancer cell lines. Our findings suggest that associations of sensitivity to drug treatment with activities of APOBEC3A/B, REV1, UNG, and FHIT and with the rates of APOBEC-associated mutagenic processes may vary among different cancer categories. Citation Format: Suleyman Vural, Julia Krushkal, Richard Simon. Activity of APOBEC3A, APOBEC3B, REV1, UNG, and FHIT is associated with drug sensitivity in specific cancer subtypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1282.

Chronic myelogenous leukemia (CML) was the first malignancy for which clinical outcome was drastically improved by kinase inhibitor therapy. Kinase inhibitors targeting other well-known oncogenes have been introduced into clinical practice, but none have shown the same magnitude of clinical benefit as ABL1 inhibition in CML. CML is sometimes described as an atypical malignancy or a one-hit malignancy. However careful examination of accumulation of additional oncogenic alterations in genes such as TP53, CDKN2A/B, IKZF1 and RUNX1 during CML progression from chronic phase to blast phase indicates that this is not the case. Instead CML provides a window into how many types of cancer may look and behave at an early stage, prior to diagnosis and the development of additional genomic alterations. The remarkable clinical benefits of ABL1 inhibition is likely due to early detection of CML at a stage in which the tumor is driven by single oncogenic alteration which can be successfully controlled by the inhibitor. The majority of CML patients in chronic phase of the disease treated with ABL1 inhibitor achieve a major molecular response (MMR) which corresponds to 1000 times decrease in BCR-ABL fusion levels. As a result, there is drastic reduction in number of cells which may accumulate additional alterations in genes like TP53, CDKN2A/B, IKZF1, RUNX1 leading to greatly decreased chance of progression to blast crisis. Thinking of CML as a prototype for effective systemic treatment based on early cancer detection may help to provide strategies for improving treatment for other types of cancer. Citation Format: Dmitriy Sonkin, Richard Simon. CML as a prototypical malignancy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 207.

Plasminogen activation to plasmin protects from lung fibrosis, but the mechanism underlying this antifibrotic effect remains unclear. We found that mice lacking plasminogen activation inhibitor–1 (PAI-1), which are protected from bleomycin-induced pulmonary fibrosis, exhibit lung overproduction of the antifibrotic lipid mediator prostaglandin E2 (PGE2). Plasminogen activation upregulated PGE2 synthesis in alveolar epithelial cells, lung fibroblasts, and lung fibrocytes from saline- and bleomycin-treated mice, as well as in normal fetal and adult primary human lung fibroblasts. This response was exaggerated in cells from Pai1–/– mice. Although enhanced PGE2 formation required the generation of plasmin, it was independent of proteinase-activated receptor 1 (PAR-1) and instead reflected proteolytic activation and release of HGF with subsequent induction of COX-2. That the HGF/COX-2/PGE2 axis mediates in vivo protection from fibrosis in Pai1–/– mice was demonstrated by experiments showing that a selective inhibitor of the HGF receptor c-Met increased lung collagen to WT levels while reducing COX-2 protein and PGE2 levels. Of clinical interest, fibroblasts from patients with idiopathic pulmonary fibrosis were found to be defective in their ability to induce COX-2 and, therefore, unable to upregulate PGE2 synthesis in response to plasmin or HGF. These studies demonstrate crosstalk between plasminogen activation and PGE2 generation in the lung and provide a mechanism for the well-known antifibrotic actions of the fibrinolytic pathway.

Well-developed and validated genomic signatures can lead to personalized treatment decisions resulting in improved patient management. However, the pace of acceptance of these signatures in clinical practice has been slow because many of the signatures have been developed without clear focus on the intended clinical use, and proper independent validation studies establishing their medical utility have rarely been performed. The authors of this Review focus on guidelines that physicians could refer to when evaluating studies on prognostic gene-expression signatures. Most cancer treatments benefit only a minority of patients. This has led to a widespread interest in the identification of gene-expression-based prognostic signatures. Well-developed and validated genomic signatures can lead to personalized treatment decisions resulting in improved patient management. However, the pace of acceptance of these signatures in clinical practice has been slow. This is because many of the signatures have been developed without clear focus on the intended clinical use, and proper independent validation studies establishing their medical utility have rarely been performed. The practicing physician and the patient are thus left in doubt about the reliability and medical utility of the signatures. We aim to provide guidance to physicians in critically evaluating published studies on prognostic gene-expression signatures so that they are better equipped to decide which signatures, if any, have sufficient merit for use, in conjunction with other factors in helping their patients to make good treatment decisions. A discussion of the lessons to be learned from the successful development of the Oncotype DX® genetic test for breast cancer is presented and contrasted with a review of the current status of prognostic gene-expression signatures in non-small-cell lung cancer.

The use of positional scanning peptide libraries in combination with biometrical analysis is one of the few approaches, which allows the identification of stimulatory peptides for T cells of unknown specificity. Despite the successful application of this strategy in different studies, not every T cell is suited for analysis. For as yet unknown reasons some T cells do not recognize these highly complex libraries, and even more importantly the predictive capacity of the current approach shows high variability among individual T cell clones and their TCRs. A number of factors probably contribute to differences in T cell recognition and have to be taken into account in order to overcome these difficulties. Our results suggest that the ability of some T cells to recognize peptides in the context of more than one HLA class II molecule expressed by autologous APCs could diminish the predictive capacity of the approach. In contrast, the use of B cell lines expressing single HLA class II molecules as APCs instead of autologous peripheral blood mononuclear cells markedly improves the capacity to identify target peptides for this type of T cells.

Ineffective repair of a damaged alveolar epithelium has been postulated to cause pulmonary fibrosis. In support of this theory, epithelial cell abnormalities, including hyperplasia, apoptosis, and persistent denudation of the alveolar basement membrane, are found in the lungs of humans with idiopathic pulmonary fibrosis and in animal models of fibrotic lung disease. Furthermore, mutations in genes that affect regenerative capacity or that cause injury/apoptosis of type II alveolar epithelial cells have been identified in familial forms of pulmonary fibrosis. Although these findings are compelling, there are no studies that demonstrate a direct role for the alveolar epithelium or, more specifically, type II cells in the scarring process.To determine if a targeted injury to type II cells would result in pulmonary fibrosis.A transgenic mouse was generated to express the human diphtheria toxin receptor on type II alveolar epithelial cells. Diphtheria toxin was administered to these animals to specifically target the type II epithelium for injury. Lung fibrosis was assessed by histology and hydroxyproline measurement.Transgenic mice treated with diphtheria toxin developed an approximately twofold increase in their lung hydroxyproline content on Days 21 and 28 after diphtheria toxin treatment. The fibrosis developed in conjunction with type II cell injury. Histological evaluation revealed diffuse collagen deposition with patchy areas of more confluent scarring and associated alveolar contraction.The development of lung fibrosis in the setting of type II cell injury in our model provides evidence for a causal link between the epithelial defects seen in idiopathic pulmonary fibrosis and the corresponding areas of scarring.

The plasminogen system plays a crucial role in the repair of a variety of tissues, including skeletal muscle. We hypothesized that urokinase-type plasminogen activator (uPA) promotes muscle regeneration by activating hepatocyte growth factor (HGF), which, in turn, stimulates proliferation of myoblasts required for regeneration. In our studies, levels of active HGF and phosphorylation of the HGF receptor c-met were increased after muscle injury in wild-type mice. Compared with wild-type animals, mice deficient in uPA (uPA−/−) had markedly reduced HGF levels and c-met activation after muscle damage. This reduced HGF activity in uPA−/− animals was associated with decreased cell proliferation, myoblast accumulation, and new muscle fiber formation. On the other hand, HGF activity was enhanced at early time points in PAI-1−/− mice compared with wild-type mice and the PAI-1−/− animals exhibited accelerated muscle fiber regeneration. Furthermore, administration of exogenous uPA rescued HGF levels and muscle regeneration in uPA−/− mice, and an HGF-blocking antibody reduced HGF activity and muscle regeneration in wild-type mice. We also found that uPA promotes myoblast proliferation in vitro through its proteolytic activity, and this process was inhibited by an HGF-blocking antibody. Together, our findings demonstrate that uPA promotes muscle regeneration through HGF activation and subsequent myoblast proliferation.

Cancers of the same primary site are in many cases heterogeneous in molecular pathogenesis, clinical course, and treatment responsiveness. Current approaches for treatment development, evaluation, and use result in treatment of many patients with ineffective drugs and lead to the conduct of large clinical trials to identify small, average treatment benefits for heterogeneous groups of patients. New genomic and proteomic technologies provide powerful tools for the identification of patients who require systemic or aggressive treatment and the selection of those likely or unlikely to benefit from a specific regimen. In spite of the large literature on developing prognostic and predictive biomarkers and on statistical methodology for analysis of high dimensional data, there is considerable uncertainty about proper approaches for the validation of biomarker-based diagnostic tests. This article attempts to clarify these issues and provide a guide to recent publications on the design of clinical trials for evaluating the clinical utility and robustness of prognostic and predictive biomarkers.

The traditional oncology drug development paradigm of single arm phase II studies followed by a randomized phase III study has limitations for modern oncology drug development. Interpretation of single arm phase II study results is difficult when a new drug is used in combination with other agents or when progression-free survival is used as the endpoint rather than tumor shrinkage. Randomized phase II studies are more informative for these objectives but increase both the number of patients and time required to determine the value of a new experimental agent. In this article, we compare different phase II study strategies to determine the most efficient drug development path in terms of number of patients and length of time to conclusion of drug efficacy on overall survival. (Clin Cancer Res 2009;15(19):5950–5)

Using a question and answer format we describe important aspects of using genomic technologies in cancer research. The main challenges are not managing the mass of data, but rather the design, analysis and accurate reporting of studies that result in increased biological knowledge and medical utility. Many analysis issues address the use of expression microarrays but are also applicable to other whole genome assays. Microarray based clinical investigations have generated both unrealistic hyperbole and excessive skepticism. Genomic technologies are tremendously powerful and will play instrumental roles in elucidating the mechanisms of oncogenesis and in devlopingan era of predictive medicine in which treatments are tailored to individual tumors. Achieving these goals involves challenges in re-thinking many paradigms for the conduct of basic and clinical cancer research and for the organization of interdisciplinary collaboration.

Developments in genomics and biotechnology provide unprecedented opportunities for the development of effective therapeutics and companion diagnostics for matching the right drug to the right patient. Effective co-development involves many new challenges with increased opportunity for success as well as delay and failure.Clinical trial designs and adaptive analysis plans for the prospective design of pivotal trials of new therapeutics and companion diagnostics are reviewed.Effective co-development requires careful prospective planning of the design and analysis strategy for pivotal clinical trials. Randomized clinical trials continue to be important for evaluating the effectiveness of new treatments, but the target populations for analysis should be prospectively specified based on the companion diagnostic. Post hoc analyses of traditionally designed randomized clinical trials are often deeply problematic. Clear separation is generally required of the data used for developing the diagnostic test, including their threshold of positivity, from the data used for evaluating treatment effectiveness in subsets determined by the test. Adaptive analysis can be used to provide flexibility to the analysis but the use of such methods requires careful planning and prospective definition in order to assure that the pivotal trial adequately limits the chance of erroneous conclusions.

Many syndromes traditionally viewed as individual diseases are heterogeneous in molecular pathogenesis and treatment responsiveness. This often leads to the conduct of large clinical trials to identify small average treatment benefits for heterogeneous groups of patients. Drugs that demonstrate effectiveness in such trials may subsequently be used broadly, resulting in ineffective treatment of many patients. New genomic and proteomic technologies provide powerful tools for the selection of patients likely to benefit from a therapeutic without unacceptable adverse events. In spite of the large literature on developing predictive biomarkers, there is considerable confusion about the development and validation of biomarker based diagnostic classifiers for treatment selection. In this paper we attempt to clarify some of these issues and to provide guidance on the design of clinical trials for evaluating the clinical utility and robustness of pharmacogenomic classifiers.

Apoptosis of fibroblasts/myofibroblasts is a critical event in the resolution of tissue repair responses; however, mechanisms for the regulation of (myo)fibroblast apoptosis/survival remain unclear. In this study, we demonstrate counter-regulatory interactions between the plasminogen activation system and transforming growth factor-beta1 (TGF-beta1) in the control of fibroblast apoptosis. Plasmin treatment induced fibroblast apoptosis in a time- and dose-dependent manner in association with proteolytic degradation of extracellular matrix proteins, as detected by the release of soluble fibronectin peptides. Plasminogen, which was activated to plasmin by fibroblasts, also induced fibronectin proteolysis and fibroblast apoptosis, both of which were blocked by alpha2-antiplasmin but not by inhibition of matrix metalloproteinase activity. TGF-beta1 protected fibroblasts from apoptosis induced by plasminogen but not from apoptosis induced by exogenous plasmin. The protection from plasminogen-induced apoptosis conferred by TGF-beta1 is associated with the up-regulation of plasminogen activator-1 (PAI-1) expression and inhibition of plasminogen activation. Moreover, lung fibroblasts from mice genetically deficient in PAI-1 lose the protective effect of TGF-beta1 against plasminogen-induced apoptosis. These findings support a novel role for the plasminogen activation system in the regulation of fibroblast apoptosis and a potential role of TGF-beta1/PAI-1 in promoting (myo)fibroblast survival in chronic fibrotic disorders.

To characterize patterns of global transcriptional deregulation in primary colon carcinomas, we did gene expression profiling of 73 tumors [Unio Internationale Contra Cancrum stage II (n = 33) and stage III (n = 40)] using oligonucleotide microarrays. For 30 of the tumors, expression profiles were compared with those from matched normal mucosa samples. We identified a set of 1,950 genes with highly significant deregulation between tumors and mucosa samples (P < 1e−7). A significant proportion of these genes mapped to chromosome 20 (P = 0.01). Seventeen genes had a >5-fold average expression difference between normal colon mucosa and carcinomas, including up-regulation of MYC and of HMGA1, a putative oncogene. Furthermore, we identified 68 genes that were significantly differentially expressed between lymph node–negative and lymph node–positive tumors (P < 0.001), the functional annotation of which revealed a preponderance of genes that play a role in cellular immune response and surveillance. The microarray-derived gene expression levels of 20 deregulated genes were validated using quantitative real-time reverse transcription-PCR in >40 tumor and normal mucosa samples with good concordance between the techniques. Finally, we established a relationship between specific genomic imbalances, which were mapped for 32 of the analyzed colon tumors by comparative genomic hybridization, and alterations of global transcriptional activity. Previously, we had conducted a similar analysis of primary rectal carcinomas. The systematic comparison of colon and rectal carcinomas revealed a significant overlap of genomic imbalances and transcriptional deregulation, including activation of the Wnt/β-catenin signaling cascade, suggesting similar pathogenic pathways. [Cancer Res 2007;67(1):41–56]

Our increased understanding of the mechanistic heterogeneity of diseases has pushed the development of targeted therapeutics. We do not expect all patients with a given disease to benefit from a targeted drug; only those in the target population. That is, those with sufficient dysregulation in the biomolecular pathway targeted by treatment. However, due to complexity of the pathway, and/or technical issues with our characterizing assay, it is often hard to characterize the target population until well into large-scale clinical trials. This has stimulated the development of adaptive enrichment trials; clinical trials in which the target population is adaptively learned; and enrollment criteria are adaptively updated to reflect this growing understanding. This paper proposes a framework for group-sequential adaptive enrichment trials. Building on the work of Simon & Simon (2013). Adaptive enrichment designs for clinical trials. Biostatistics 14(4), 613-625), it includes a frequentist hypothesis test at the end of the trial. However, it uses Bayesian methods to optimize the decisions required during the trial (regarding how to restrict enrollment) and Bayesian methods to estimate effect size, and characterize the target population at the end of the trial. This joint frequentist/Bayesian design combines the power of Bayesian methods for decision making with the use of a formal hypothesis test at the end of the trial to preserve the studywise probability of a type I error.

Background Recent work indicates that the airways of persons with cystic fibrosis (CF) typically harbor complex bacterial communities. However, the day-to-day stability of these communities is unknown. Further, airway community dynamics during the days corresponding to the onset of symptoms of respiratory exacerbation have not been studied. Results Using 16S rRNA amplicon sequencing of 95 daily sputum specimens collected from four adults with CF, we observed varying degrees of day-to-day stability in airway bacterial community structures during periods of clinical stability. Differences were observed between study subjects with respect to the degree of community changes at the onset of exacerbation. Decreases in the relative abundance of dominant taxa were observed in three subjects at exacerbation. We observed no relationship between total bacterial load and clinical status and detected no viruses by multiplex PCR. Conclusion CF airway microbial communities are relatively stable during periods of clinical stability. Changes in microbial community structure are associated with some, but not all, pulmonary exacerbations, supporting previous observations suggesting that distinct types of exacerbations occur in CF. Decreased abundance of species that are dominant at baseline suggests a role for less abundant taxa in some exacerbations. Daily sampling revealed patterns of change in microbial community structures that may prove useful in the prediction and management of CF pulmonary exacerbations. Electronic supplementary material The online version of this article (doi:10.1186/s40168-015-0074-9) contains supplementary material, which is available to authorized users.

INTRODUCTORY OVERVIEW Clinical Trials for Predictive Medicine: New Paradigms and Challenges Richard Simon, Shigeyuki Matsui, and Marc Buyse An Industry Statistician's Perspective on Personalized Health Care Drug Development Jane Fridlyand, Ru-Fang Yeh, Howard Mackey, Thomas Bengtsson, Paul Delmar, Greg Spaniolo, and Grazyna Lieberman Analytical Validation of In Vitro Diagnostic Tests Robert Becker, Jr. EARLY CLINICAL TRIALS USING BIOMARKERS Phase I Dose-Finding Designs and Their Applicability to Targeted Therapies Takashi Daimon, Akihiro Hirakawa, and Shigeyuki Matsui An Overview of Phase II Clinical Trial Designs with Biomarkers Lisa McShane and Sally Hunsberger Bayesian Adaptive Methods for Clinical Trials of Targeted Agents Peter Thall Outcome-Adaptive Randomization in Early Clinical Trials Edward Korn and Boris Freidlin Challenges of Using Predictive Biomarkers in Clinical Trials Sumithra Mandrekar and Daniel Sargent PHASE III RANDOMIZED CLINICAL TRIALS USING BIOMARKERS Comparison of Randomized Clinical Trial Designs for Targeted Agents Antje Hoering, Mike LeBlanc, and John Crowley Phase III All-Comers Clinical Trials with a Predictive Biomarker Shigeyuki Matsui, Yuki Choai, and Takahiro Nonaka Evaluation of Clinical Utility and Validation of Gene Signatures in Clinical Trials Stefan Michiels and Federico Rotolo ANALYSIS OF HIGH-DIMENSIONAL DATA AND GENOMIC SIGNATURE DEVELOPMENTS Statistical Issues in Clinical Development and Validation of Genomic Signatures Shigeyuki Matsui Univariate Analysis for Gene Screening: Beyond Multiple Testing Hisashi Noma and Shigeyuki Matsui Statistical and Machine-Learning Methods for Class Prediction in High Dimension Osamu Komori and Shinto Eguchi Survival Risk Prediction Using High-Dimensional Molecular Data Harald Binder, Thomas Gerds, and Martin Schumacher RANDOMIZED TRIALS WITH BIOMARKER DEVELOPMENT AND VALIDATION Adaptive Clinical Trial Designs with Biomarker Development and Validation Boris Freidlin and Richard Simon Development and Validation of Continuous Genomic Signatures in Randomized Clinical Trials Shigeyuki Matsui EVALUATION OF SURROGATE BIOMARKERS Biomarker-Based Surrogate Endpoints Marc Buyse, Tomasz Burzykowski, Geert Molenberghs, and Ariel Alonso

Physicians need improved tools for selecting treatments for individual patients. Many syndromes traditionally viewed as individual diseases are heterogeneous in molecular pathogenesis and treatment responsiveness. This results in treatment of many patients with ineffective drugs and leads to the conduct of large clinical trials to identify small average treatment benefits for heterogeneous groups of patients. New genomic and proteomic technologies provide powerful tools for the selection of patients likely to benefit from a therapeutic without unacceptable adverse events. In spite of the large literature on developing predictive biomarkers and on statistical methodology for analysis of high dimensional data, there is considerable uncertainty about the validation of biomarker based diagnostic classifiers for treatment selection. In this paper we attempt to clarify these issues and to provide guidance on the design of clinical trials for evaluating the clinical utility and robustness of pharmacogenomic classifiers.

The doxycycline-inducible, gene regulatory system allows tight control of transgene expression for the study of organ development and disease pathogenesis. Multiple recent reports have employed this model to investigate various lung diseases including emphysema. For our study, we used this transgenic system to test whether prolonged, lung-specific, overexpression of the serine protease urokinase plasminogen activator (uPA) would result in alveolar wall destruction. Double transgenic mice were generated that possessed: (1) the rat Clara cell secretory protein promoter controlling the reverse tetracycline transactivator gene (CCSP:rtTA) and (2) the tetracycline operator controlling the murine uPA cDNA (tet[O]:muPA). Mice were treated with doxycycline beginning at age 6 wk to initiate uPA overexpression. Single transgenic and wild-type animals served as controls. A second group of double transgenic and control animals were maintained off of doxycycline. At ages 10, 18, and 30 wk, the mice underwent measurements of alveolar size, lung compliance, and total lung capacity. We found that, although the uPA overexpressing mice demonstrated an emphysema phenotype, similar abnormalities occurred in the CCSP-rtTA control animals. These CCSP-rtTA-related alterations occurred even without doxycycline exposure. Evaluation of a second transgenic line possessing the human surfactant protein C promoter controlling rtTA expression also exhibited lung abnormalities consistent with emphysema. These findings indicate that pulmonary epithelial expression of rtTA alone causes an emphysema phenotype in mice. Therefore, when using this system to study emphysema pathogenesis, the inclusion of proper controls is essential for accurate data interpretation.

The three HLA class II alleles of the DR2 haplotype, DRB1*1501, DRB5*0101, and DQB1*0602, are in strong linkage disequilibrium and confer most of the genetic risk to multiple sclerosis. Functional redundancy in Ag presentation by these class II molecules would allow recognition by a single TCR of identical peptides with the different restriction elements, facilitating T cell activation and providing one explanation how a disease-associated HLA haplotype could be linked to a CD4+ T cell-mediated autoimmune disease. Using combinatorial peptide libraries and B cell lines expressing single HLA-DR/DQ molecules, we show that two of five in vivo-expanded and likely disease-relevant, cross-reactive cerebrospinal fluid-infiltrating T cell clones use multiple disease-associated HLA class II molecules as restriction elements. One of these T cell clones recognizes >30 identical foreign and human peptides using all DR and DQ molecules of the multiple sclerosis-associated DR2 haplotype. A T cell signaling machinery tuned for efficient responses to weak ligands together with structural features of the TCR-HLA/peptide complex result in this promiscuous HLA class II restriction.

Objective To evaluate several alternative measures of forced expiratory volume in 1 second percent predicted (FEV 1 %pred) variability as potential predictors of future FEV 1 %pred decline in patients with cystic fibrosis. Study design We included 13 827 patients age ≥6 years from the Epidemiologic Study of Cystic Fibrosis 1994-2002 with ≥4 FEV 1 %pred measurements spanning ≥366 days in both a 2-year baseline period and a 2-year follow-up period. We predicted change from best baseline FEV 1 %pred to best follow-up FEV 1 %pred and change from baseline to best in the second follow-up year by using multivariable regression stratified by 4 lung-disease stages. We assessed 5 measures of variability (some as deviations from the best and some as deviations from the trend line) both alone and after controlling for demographic and clinical factors and for the slope and level of FEV 1 %pred. Results All 5 measures of FEV 1 %pred variability were predictive, but the strongest predictor was median deviation from the best FEV 1 %pred in the baseline period. The contribution to explanatory power (R 2 ) was substantial and exceeded the total contribution of all other factors excluding the FEV 1 %pred rate of decline. Adding the other variability measures provided minimal additional value. Conclusions Median deviation from the best FEV 1 %pred is a simple metric that markedly improves prediction of FEV 1 %pred decline even after the inclusion of demographic and clinical characteristics and the FEV 1 %pred rate of decline. The routine calculation of this variability measure could allow clinicians to better identify patients at risk and therefore in need of increased intervention.

Dr Walton: Certainly. This was in actuality a P'hase II study, not the seamless Phase lI/Ill that was initially being considered. But as a Phase II study, it is a good example of an approach to addressing a very important question in the process of drug development, that is, to establish a good uinderstanding of the performance of the drug, including the dose-response relationship of the drug. This question is broadly applicable to many different settings and many different drugs. One of my questions was, what is unique about this setting that made this approach feasible or worthwhile? My answer would be, perhaps not much. There are certain aspects of this setting that the design really grappled with directly. Buit conceptually, the idea of wanting to establish the dose response, wanting to explore a broad range of relatively closely spaced doses, occurs time and again in nmany different drug development programs. There is a lot in this example that will be broadly applicable and that is not uniq-ue to this particular setting. The model of the early clinical evaluation, predicting the 90-day clinical status of a patient, was an important feature of this study. It sped the adaptive random allocation of patients to the dose that would be most informative. I want to note that this model had relatively little regulatory impact. From a regulatory point of view, when we look at what we have learned at the end, we rely upon the 90day data for all patients. The importance of the oneweek anid the four-week data was only in selecting which doses to allocate to the next patient. The meaning of those data evaporated as the patients' time in stidy matured. By the end, it has relatively little importance in interpreting the final result of the study and low regulatory impact. You can view that as a nice way of utilizing these approachies without impinging upon our abilitv to use the data. In estimating the dose response during the study, the Bayesian approach allowed you to include multiple kinds of uncertainties that have relatively little final concern for us, for the same reasons. A feature of the study design that was not emphasized in your talk was our concern about the adaptive algorithm allocating patients to doses that would not teach us most about the dose responise curve. We had a concern that, at some point, the utility of adding patients to the placebo group, if this was an effective drug, could become much less than focusing upon doses in the area where the upper bend in the curve lies. This was of concern because in studies like this there is the potential for a shift in the patient population as the different sites enroll and leave. There can be site-related differences related to subjective physician assessments as well. We had concern that there might not be full comparability, and we might be misled by doses only within a narrow range. IThis study did include a lower bound on the probability of assigning a patient to placebo, so we could be assured that there would be a reasonably good distribution of placebo patients throughout the study, providing us with a good comparison beinchmark. We can think of that as purchasing insurance. It may have slightly decreased the efficiency of learning about the dose response curve, but it ensured that we would still be able to reliably interpret the study in the event of siteor time-related effects. The final analysis was not heavily influenced by the prior. The expectation was, and proved to be the case, that the prior would be really swamped out the data obtained during the study. From a regulatory point of view, the validity of prior knowledge did not become a concern, because we relied mainly upon the study data. Coming back to the idea of the seamless approach, although that was the initial plan, it was wisely not the final plan. It is the rare developinent program where the Phase 11 stutdy needs to answer onily the q-uestion of what dose to use in the Phase III. We need the opportunity to

We used multistage models that incorporate the age dependent dynamics of normal breast tissue, clonal expansion of intermediate cells and mutational events to fit data for the age-specific incidence of breast cancers in the surveillance, epidemiology, and end results (SEER) registry. Our results suggest that two or three rate limiting events occurring at rates characteristic of point mutation rates for normal mammalian cells set in motion a sequence of other genomic changes that lead with high probability to breast carcinoma.

Numerous expressions have been developed to represent the locus of yielding, plastic potential, and failure conditions of geomaterials. In this paper, the authors first recall the characteristics of some relatively common tridimensional criteria used to describe the elastic limit and failure strength of porous materials. It is then shown that the main features of a large number of existing expressions can be represented by a set of unified equations. The ensuing locus in the multiaxial stress space is applicable to a wide range of geomaterials and loadings. A particularity of the proposed formulation is that it is explicitly porosity dependent. The validity of the general formulation is demonstrated by experimental results obtained for various types of materials. A sample application is then shown for the elastoplastic analysis of the stress distribution around a cylindrical opening.Key words: failure, plasticity, yield, porous material, porosity, circular opening.

During inflammatory lung injury, the fibrinolytic activity that is normally present within bronchoalveolar lavage (BAL) fluid (BALF) is often suppressed due to increased levels of inhibitors, including plasminogen activator inhibitor (PAI)-1. Despite this suppression, BALF frequently contains fibrin degradation products, indicating persistence of fibrinolytic activity within the lung. To address this discrepancy and determine the sites where plasminogen activation is occurring, we developed an in situ zymographic technique for frozen sections of lung tissue that localizes plasminogen activator activity at the cellular level. After validating the method using enzyme inhibitors and mice with genetic manipulations of their plasminogen system genes, we applied the technique to lungs of normal and bleomycin-exposed mice. In normal mice, plasminogen activator activity was localized to bronchial epithelial cells, cells of the alveolar walls, and alveolar macrophages. After bleomycin exposure, in situ zymography showed that, despite loss of fibrinolytic activity within BALF, abundant enzymatic activity was associated with aggregates of inflammatory cells. PAI-1-deficient mice that are protected from bleomycin-induced fibrosis had preserved plasminogen activator activity in BALF and increased tissue activity, as determined by in situ zymography. We conclude that analysis of BALF does not adequately reflect the fibrinolytic activity that persists within microenvironments of the lung during inflammation.

Researchers conducting gene expression microarray experiments often are interested in identifying genes that are differentially expressed between two groups of specimens. A straightforward approach to the identification of such “differentially expressed” genes is to perform a univariate analysis of group mean differences for each gene, and then identify those genes that are most statistically significant. However, with the large number of genes typically represented on a microarray, using nominal significance levels (unadjusted for the multiple comparisons) will lead to the identification of many genes that truly are not differentially expressed, “false discoveries.” A reasonable strategy in many situations is to allow a small number of false discoveries, or a small proportion of the identified genes to be false discoveries. Although previous work has considered control for the expected proportion of false discoveries (commonly known as the false discovery rate), we show that these methods may be inadequate. We propose two stepwise permutation-based procedures to control with specified confidence the actual number of false discoveries and approximately the actual proportion of false discoveries. Limited simulation studies demonstrate substantial gain in sensitivity to detect truly differentially expressed genes even when allowing as few as one or two false discoveries. We apply these new methods to analyze a microarray data set consisting of measurements on approximately 9000 genes in paired tumor specimens, collected both before and after chemotherapy on 20 breast cancer patients. The methods described are broadly applicable to the problem of identifying which variables of any large set of measured variables differ between pre-specified groups.

Mice deficient in the plasminogen activator inhibitor-1 gene (PAI-1−/− mice) are relatively protected from developing pulmonary fibrosis from bleomycin administration. We hypothesized that one of the protective mechanisms may be the ability of the plasminogen system to enhance hepatocyte growth factor (HGF) effects, which have been reported to be anti-fibrotic in the lung. HGF is known to be sequestered in tissues by binding to extracellular matrix components. Following bleomycin administration, we found that HGF protein levels were higher in bronchoalveolar lavage fluid from PAI-1−/− mice compared to wild-type (PAI-1+/+) mice. This increase could be suppressed by administering tranexamic acid, which inhibits plasmin activity. Conversely, intratracheal instillation of urokinase into bleomycin-injured PAI-1+/+ mice to activate plasminogen caused a significant increase in HGF within bronchoalveolar lavage and caused less collagen accumulation in the lungs. Administration of an anti-HGF neutralizing antibody markedly increased collagen accumulation in the lungs of bleomycin-injured PAI-1−/− mice. These results support the hypothesis that increasing the availability of HGF, possibly by enhancing its release from extracellular matrix by a plasmin-dependent mechanism, is an important means by which activation of the plasminogen system can limit pulmonary fibrosis.

We propose a new method for predicting MHC binding of peptides using biophysical parameters of the constituent amino acids. Unlike conventional matrix-based methods, our method does not assume independent binding of the individual side chains and uses a model that simultaneously represents all the residues. The model discovers the quantified 9-mer "property model" within the longer peptides that are most common among binders. Prediction for a new peptide is based on its statistical "distance" from the extracted peptide property model. MHC-specific peptide property models were constructed from compiled binder/nonbinder data using this method. We report the results of cross-validation of the prediction method and comparison with other methods. The comparison suggests that our method performs substantially better for some MHC class II molecules and equally well for other MHC types. To demonstrate large-scale utility, 30 HIV-1 reference genomes covering diverse subtypes were analyzed. Regions that are likely to bind MHC (A2, DR1, or DR4) and that are conserved across the HIV-1 subtypes were identified. These "epitope profiles" of the diverse HIV-1 strains can also be visually presented to facilitate discovery of conserved patterns naturally occurring in the viral genomes. As an essential step in designing vaccines, the revealed patterns may provide valuable information in identifying the immunologically important regions.

New genomic and proteomic technologies provide measurements of thousands of features for each case. This provides a context for enhanced discovery and false discovery. Most statistical and machine learning procedures were not developed for the p>>n setting and the literature of DNA microarray studies contains many examples of mis-use of analytic and computatinal methods such a cross-validation. This paper highlights some of key aspects of p>>n problems for identifying informative features and developing accurate classifiers.

Motivation: In cDNA microarray experiments all samples are labelled with either Cy3 dye or Cy5 dye. Certain genes exhibit dye bias—a tendency to bind more efficiently to one of the dyes. The common reference design avoids the problem of dye bias by running all arrays ‘forward’, so that the samples being compared are always labelled with the same dye. But comparison of samples labelled with different dyes is sometimes of interest. In these situations, it is necessary to run some arrays‘ reverse’—with the dye labelling reversed—in order to correct for the dye bias. The design of these experiments will impact one’s ability to identify genes that are differentially expressed in different tissues or conditions. We address the design issue of how many specimens are needed, how many forward and reverse labelled arrays to perform, and how to optimally assign Cy3 and Cy5 labels to the specimens. Results: We consider three types of experiments for which some reverse labelling is needed: paired samples, samples from two predefined groups, and reference design data when comparison with the reference is of interest. We present simple probability models for the data, derive optimal estimators for relative gene expression, and compare the efficiency of the estimators for a range of designs. In each case, we present the optimal design and sample size formulas. We show that reverse labelling of individual arrays is generally not required. Contact: dobbinke@mail.nih.gov. Supplementary information: Supplementary material referenced in the text is available at http://linus.nci.nih.gov/~brb/TechReport.htm * To whom correspondence should be addressed.