Kevin K. Dobbin

Rearrangement of CRLF2 is associated with mutation of JAK kinases, alteration of IKZF1, Hispanic/Latino ethnicity, and a poor outcome in pediatric B-progenitor acute lymphoblastic leukemia

Gene expression profiling of 207 uniformly treated children with high-risk B-progenitor acute lymphoblastic leukemia revealed 29 of 207 cases (14%) with markedly elevated expression of CRLF2 (cytokine receptor-like factor 2). Each of the 29 cases harbored a genomic rearrangement of CRLF2: 18 of 29 (62%) had a translocation of the immunoglobulin heavy chain gene IGH@ on 14q32 to CRLF2 in the pseudoautosomal region 1 of Xp22.3/Yp11.3, whereas 10 (34%) cases had a 320-kb interstitial deletion centromeric of CRLF2, resulting in a P2RY8-CRLF2 fusion. One case had both IGH@-CRLF2 and P2RY8-CRLF2, and another had a novel CRLF2 rearrangement. Only 2 of 29 cases were Down syndrome. CRLF2 rearrangements were significantly associated with activating mutations of JAK1 or JAK2, deletion or mutation of IKZF1, and Hispanic/Latino ethnicity (Fisher exact test, P < .001 for each). Within this cohort, patients with CRLF2 rearrangements had extremely poor treatment outcomes compared with those without CRLF2 rearrangements (35.3% vs 71.3% relapse-free survival at 4 years; P < .001). Together, these observations suggest that activation of CRLF2 expression, mutation of JAK kinases, and alterations of IKZF1 cooperate to promote B-cell leukemogenesis and identify these pathways as important therapeutic targets in this disease.

Identification of novel cluster groups in pediatric high-risk B-precursor acute lymphoblastic leukemia with gene expression profiling: correlation with genome-wide DNA copy number alterations, clinical characteristics, and outcome

To resolve the genetic heterogeneity within pediatric high-risk B-precursor acute lymphoblastic leukemia (ALL), a clinically defined poor-risk group with few known recurring cytogenetic abnormalities, we performed gene expression profiling in a cohort of 207 uniformly treated children with high-risk ALL. Expression profiles were correlated with genome-wide DNA copy number abnormalities and clinical and outcome features. Unsupervised clustering of gene expression profiling data revealed 8 unique cluster groups within these high-risk ALL patients, 2 of which were associated with known chromosomal translocations (t(1;19)(TCF3-PBX1) or MLL), and 6 of which lacked any previously known cytogenetic lesion. One unique cluster was characterized by high expression of distinct outlier genes AGAP1, CCNJ, CHST2/7, CLEC12A/B, and PTPRM; ERG DNA deletions; and 4-year relapse-free survival of 94.7% ± 5.1%, compared with 63.5% ± 3.7% for the cohort (P = .01). A second cluster, characterized by high expression of BMPR1B, CRLF2, GPR110, and MUC4; frequent deletion of EBF1, IKZF1, RAG1-2, and IL3RA-CSF2RA; JAK mutations and CRLF2 rearrangements (P < .0001); and Hispanic ethnicity (P < .001) had a very poor 4-year relapse-free survival (21.0% ± 9.5%; P < .001). These studies reveal striking clinical and genetic heterogeneity in high-risk ALL and point to novel genes that may serve as new targets for diagnosis, risk classification, and therapy.

Optimally splitting cases for training and testing high dimensional classifiers.

BackgroundWe consider the problem of designing a study to develop a predictive classifier from high dimensional data. A common study design is to split the sample into a training set and an independent test set, where the former is used to develop the classifier and the latter to evaluate its performance. In this paper we address the question of what proportion of the samples should be devoted to the training set. How does this proportion impact the mean squared error (MSE) of the prediction accuracy estimate?ResultsWe develop a non-parametric algorithm for determining an optimal splitting proportion that can be applied with a specific dataset and classifier algorithm. We also perform a broad simulation study for the purpose of better understanding the factors that determine the best split proportions and to evaluate commonly used splitting strategies (1/2 training or 2/3 training) under a wide variety of conditions. These methods are based on a decomposition of the MSE into three intuitive component parts.ConclusionsBy applying these approaches to a number of synthetic and real microarray datasets we show that for linear classifiers the optimal proportion depends on the overall number of samples available and the degree of differential expression between the classes. The optimal proportion was found to depend on the full dataset size (n) and classification accuracy - with higher accuracy and smaller n resulting in more assigned to the training set. The commonly used strategy of allocating 2/3rd of cases for training was close to optimal for reasonable sized datasets (n ≥ 100) with strong signals (i.e. 85% or greater full dataset accuracy). In general, we recommend use of our nonparametric resampling approach for determing the optimal split. This approach can be applied to any dataset, using any predictor development method, to determine the best split.

Validation of biomarkers to predict response to immunotherapy in cancer: Volume I — pre-analytical and analytical validation

Immunotherapies have emerged as one of the most promising approaches to treat patients with cancer. Recently, there have been many clinical successes using checkpoint receptor blockade, including T cell inhibitory receptors such as cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programmed cell death-1 (PD-1). Despite demonstrated successes in a variety of malignancies, responses only typically occur in a minority of patients in any given histology. Additionally, treatment is associated with inflammatory toxicity and high cost. Therefore, determining which patients would derive clinical benefit from immunotherapy is a compelling clinical question.Although numerous candidate biomarkers have been described, there are currentlyxa0three FDA-approved assays based on PD-1 ligand expression (PD-L1) that have been clinically validated toxa0identify patients whoxa0are more likely to benefit from axa0single-agent anti-PD-1/PD-L1 therapy. Because of the complexity of the immune response and tumor biology, it is unlikely that a single biomarker will be sufficient to predict clinical outcomes in response to immune-targeted therapy. Rather, the integration of multiple tumor and immune response parameters, such as protein expression, genomics, and transcriptomics, may be necessary for accuratexa0prediction of clinicalxa0benefit. Before a candidate biomarker and/or new technology can be used in a clinical setting, several steps are necessary to demonstrate its clinical validity. Although regulatory guidelines provide general roadmaps for the validation process, their applicability to biomarkers in the cancer immunotherapy field is somewhat limited. Thus, Working Group 1 (WG1) of the Society for Immunotherapy of Cancer (SITC) Immune Biomarkers Task Force convened to address this need. In this two volume series, we discuss pre-analytical and analytical (Volume I) as well as clinical and regulatory (Volume II) aspects of the validation process as applied to predictive biomarkers for cancer immunotherapy. To illustrate the requirements for validation, wexa0discuss examples of biomarker assays that have shown preliminary evidence of an association with clinical benefit from immunotherapeutic interventions. The scope includes only those assays and technologies that have established a certain level of validation for clinical use (fit-for-purpose). Recommendations to meet challenges and strategies to guide the choice of analytical and clinical validation design for specific assays are also provided.

Validation of biomarkers to predict response to immunotherapy in cancer: Volume II — clinical validation and regulatory considerations

There is growing recognition that immunotherapy is likely to significantly improve health outcomes for cancer patients in the coming years. Currently, while a subset of patients experience substantial clinical benefit in response to different immunotherapeutic approaches, the majority of patients do not but are still exposed to the significant drug toxicities. Therefore, a growing need for the development and clinical use of predictive biomarkers exists in the field of cancer immunotherapy. Predictive cancer biomarkers can be used to identify the patients who are or who are not likely to derive benefit from specific therapeutic approaches. In order to be applicable in a clinical setting, predictive biomarkers must be carefully shepherded through a step-wise, highly regulated developmental process. Volume I of this two-volume document focused on the pre-analytical and analytical phases of the biomarker development process, by providing background, examples and “good practice” recommendations. In the current Volume II, the focus is on the clinical validation, validation of clinical utility and regulatory considerations for biomarker development. Together, this two volume series is meant to provide guidance on the entire biomarker development process, with a particular focus on the unique aspects of developing immune-based biomarkers. Specifically, knowledge about the challenges to clinical validation of predictive biomarkers, which has been gained from numerous successes and failures in other contexts, will be reviewed together with statistical methodological issues related to bias and overfitting. The different trial designs used for the clinical validation of biomarkers will also be discussed, as the selection of clinical metrics and endpoints becomes critical to establish the clinical utility of the biomarker during the clinical validation phase of the biomarker development. Finally, the regulatory aspects of submission of biomarker assays to the U.S. Food and Drug Administration as well as regulatory considerations in the European Union will be covered.

Comparison of confidence interval methods for an intra-class correlation coefficient (ICC)

BackgroundThe intraclass correlation coefficient (ICC) is widely used in biomedical research to assess the reproducibility of measurements between raters, labs, technicians, or devices. For example, in an inter-rater reliability study, a high ICC value means that noise variability (between-raters and within-raters) is small relative to variability from patient to patient. A confidence interval or Bayesian credible interval for the ICC is a commonly reported summary. Such intervals can be constructed employing either frequentist or Bayesian methodologies.MethodsThis study examines the performance of three different methods for constructing an interval in a two-way, crossed, random effects model without interaction: the Generalized Confidence Interval method (GCI), the Modified Large Sample method (MLS), and a Bayesian method based on a noninformative prior distribution (NIB). Guidance is provided on interval construction method selection based on study design, sample size, and normality of the data. We compare the coverage probabilities and widths of the different interval methods.ResultsWe show that, for the two-way, crossed, random effects model without interaction, care is needed in interval method selection because the interval estimates do not always have properties that the user expects. While different methods generally perform well when there are a large number of levels of each factor, large differences between the methods emerge when the number of one or more factors is limited. In addition, all methods are shown to lack robustness to certain hard-to-detect violations of normality when the sample size is limited.ConclusionsDecision rules and software programs for interval construction are provided for practical implementation in the two-way, crossed, random effects model without interaction. All interval methods perform similarly when the data are normal and there are sufficient numbers of levels of each factor. The MLS and GCI methods outperform the NIB when one of the factors has a limited number of levels and the data are normally distributed or nearly normally distributed. None of the methods work well if the number of levels of a factor are limited and data are markedly non-normal. The software programs are implemented in the popular R language.

Health and personal resources in older patients with cancer undergoing chemotherapy

OBJECTIVESnThe purpose of this study was to gather preliminary data on both direct and moderating effects of health status, the social environment, and perceived personal control on the symptom distress and quality of life (QOL) for older patients with cancer during a treatment regimen of chemotherapy.nnnMATERIALS AND METHODSnParticipants were patients with cancer aged≥65years being treated with a variety of chemotherapy regimens specific to their particular diagnosis. Using a longitudinal study design, we measured patients at baseline prior to beginning chemotherapy, midpoint in the regimen, and upon discharge (approximately 2weeks after chemotherapy completion). Outcomes of interest were symptom distress and QOL. Multivariate linear regression was used to determine the association between the predictors and outcomes, controlling for demographic and clinical characteristics.nnnRESULTSnOur final sample consisted of 94 patients with cancer (35 males; 59 females; mean age 73.5years). In the health status domain, lower body strength was inversely associated with symptom distress (p=0.025) and positively associated with QOL (p=0.015). In the social environment domain, social support was inversely associated with fatigue (p=0.001) and depression (p<0.001), and positively associated with QOL (p=0.016 and p=0.029 at midpoint and endpoint, respectively). Personal control variables, mastery and self-efficacy, were significantly associated with multiple outcomes of interest.nnnDISCUSSIONnMastery was the best predictor of symptom distress and QOL. Self-efficacy, social support, and lower body functioning are important predictors of these outcomes among older patients with cancer undergoing chemotherapy.

Cost-Effectiveness Analysis of Community Active Case Finding and Household Contact Investigation for Tuberculosis Case Detection in Urban Africa

Introduction Case detection by passive case finding (PCF) strategy alone is inadequate for detecting all tuberculosis (TB) cases in high burden settings especially Sub-Saharan Africa. Alternative case detection strategies such as community Active Case Finding (ACF) and Household Contact Investigations (HCI) are effective but empirical evidence of their cost-effectiveness is sparse. The objective of this study was to determine whether adding ACF or HCI compared with standard PCF alone represent cost-effective alternative TB case detection strategies in urban Africa. Methods A static decision modeling framework was used to examine the costs and effectiveness of three TB case detection strategies: PCF alone, PCF+ACF, and PCF+HCI. Probability and cost estimates were obtained from National TB program data, primary studies conducted in Uganda, published literature and expert opinions. The analysis was performed from the societal and provider perspectives over a 1.5 year time-frame. The main effectiveness measure was the number of true TB cases detected and the outcome was incremental cost-effectiveness ratios (ICERs) expressed as cost in 2013 US

Canine spontaneous head and neck squamous cell carcinomas represent their human counterparts at the molecular level.

per additional true TB case detected. Results Compared to PCF alone, the PCF+HCI strategy was cost-effective at US

Covariance Adjustment for Batch Effect in Gene Expression Data

443.62 per additional TB case detected. However, PCF+ACF was not cost-effective at US