Latent Variable Approach to Elicit Continuous Toxicity Scores and Severity Weights for Multiple Toxicities in Dose-Finding Oncology Trials

Most dose-finding clinical trials in oncology aim to find the highest dose yielding an acceptable toxicity profile for patients. The conventional dose-finding framework utilizes a binary toxicity endpoint that treats low to moderate toxicities as irrelevant, ignoring potentially harmful combinations of such toxicities. A handful of novel dose- finding methods have been introduced that combine multiple toxicities across varying grades into a composite toxicity severity score. Toxicity scores provide the advantage of accounting for all toxicity information in a patient profile, but calculation of such scores require prior specification of toxicity severity weights to represent the relative toxicity burden each toxicity type of each grade adds to a toxicity profile if observed. Elicitation of severity weights generally rely on subjective specification, and resulting continuous scores may be confusing in clinical settings. In a statistical framework, we propose a novel method of estimating toxicity weights via a cumulative logit model, assuming there to be a latent continuous toxicity score characterized by the set of observed toxicity types and grades a patient exhibits. Toxicity scores are directly associated with an ordinal outcome assigned to toxicity profiles by clinicians, which correspond to simple dose escalation decisions. The toxicity score elicitation method (TSEM) produces an accurate toxicity scoring system through evaluation of a balanced subset of toxicity profiles in terms of severity, and we present an adaptive weight finding algorithm to facilitate this. This approach bridges the gap between relating continuous toxicity scores to clinically logical ordinal outcomes akin to traditional toxicity grades, and provides an objective method for determining toxicity weights and scores

MergeMaid: R Tools for Merging and Cross-Study Validation of Gene Expression Data

Cross-study validation of gene expression investigations is critical in genomic analysis. We developed an R package and associated object definitions to merge and visualize multiple gene expression datasets. Our merging functions use arbitrary character IDs and generate objects that can efficiently support a variety of joint analyses. Visualization tools support exploration and cross-study validation of the data, without requiring normalization across platforms. Tools include “integrative correlation” plots that is, scatterplots of all pairwise correlations in one study against the corresponding pairwise correlations of another, both for individual genes and all genes combined. Gene-specific plots can be used to identify genes whose changes are reliably measured across studies. Visualizations also include scatterplots of gene-specific statistics quantifying relationships between expression and phenotypes of interest, using linear, logistic and Cox regression. Availability: Free open source from url http://www.bioconductor.org. Contact: Xiaogang Zhong [email protected] Supplementary information: Documentation available with the package

OPTIMIZED CROSS-STUDY ANALYSIS OF MICROARRAY-BASED PREDICTORS

Background: Microarray-based gene expression analysis is widely used in cancer research to discover molecular signatures for cancer classification and prediction. In addition to numerous independent profiling projects, a number of investigators have analyzed multiple published data sets for purposes of cross-study validation. However, the diverse microarray platforms and technical approaches make direct comparisons across studies difficult, and without means to identify aberrant data patterns, less than optimal. To address this issue, we previously developed an integrative correlation approach to systematically address agreement of gene expression measurements across studies, providing a basis for cross-study validation analysis. Here we generalize this methodology to provide a metric for evaluating the overall efficacy of preprocessing and cross-referencing, and explore optimal combinations of filtering and cross-referencing strategies. We operate in the context of validating prognostic breast cancer gene expression signatures on data reported by three different groups, each using a different platform. Results: To evaluate overall cross-platform reproducibility in the context of a specific prediction problem, we suggest integrative association, that is the cross-study correlation of gene-specific measure of association with the phenotype predicted. Specifically, in this paper we use the correlation among the Cox proportional hazard coefficients for association of gene expression to relapse free survival (RFS). Gene filtering by integrative correlation to select reproducible genes emerged as the key factor to increase the integrative association, while alternative methods of gene cross-referencing and gene filtering proved only to modestly improve the overall reproducibility. Patient selection was another major factor affecting the validation process. In particular, in one of the studies considered, gene expression association with RFS varied across subsets of patients that differ by their ascertainment criteria. One of the subsets proved to be highly consistent with other studies, while others showed significantly lower consistency. Third, as expected, use of cluster-specific mean expression profiles in the Cox model yielded more generalizable results than expression data from individual genes. Finally, by using our approach we were able to validate the association between the breast cancer molecular classes proposed by Sorlie et al. and RFS. Conclusions: This paper provides a simple, practical and comprehensive technique for measuring consistency of molecular classification results across microarray platforms, without requiring subjective judgments about membership of samples in putative clusters. This methodology will be of value in consistently typing breast and other cancers across different studies and platforms in the future. Although the tumor subtypes considered here have been previously validated by their proponents, this is the first independent validation, and the first to include the Affymetrix platform

ON THE MERITS OF VOXEL-BASED MORPHOMETRIC PATH-ANALYSIS FOR INVESTIGATING VOLUMETRIC MEDIATION OF A TOXICANT\u27S INFLUENCE ON COGNITIVE FUNCTION

We previously showed that lifetime cumulative lead dose, measured as lead concentration in the tibia bone by X-ray fluorescence, was associated with persistent and progressive declines in cognitive function and with decreases in MRI-based brain volumes in former lead workers. Moreover, larger region-specific brain volumes were associated with better cognitive function. These findings motivated us to explore a novel application of path analysis to evaluate effect mediation. Voxel-wise path analysis, at face value, represents the natural evolution of voxel-based morphometry methods to answer questions of mediation. Application of these methods to the former lead worker data demonstrated potential limitations in this approach where there was a tendency for results to be strongly biased towards the null hypothesis (lack of mediation). Moreover, a complimentary analysis using anatomically-derived regions of interest volumes yielded opposing results, suggesting evidence of mediation. Specifically, in the ROI-based approach, there was evidence that the association of tibia lead with function in three cognitive domains was mediated through the volumes of total brain, frontal gray matter, and/or possibly cingulate. A simulation study was conducted to investigate whether the voxel-wise results arose from an absence of localized mediation, or more subtle defects in the methodology. The simulation results showed the same null bias evidenced as seen in the lead workers data. Both the lead worker data results and the simulation study suggest that a null-bias in voxel-wise path analysis limits its inferential utility for producing confirmatory results

Updated standardized definitions for efficacy endpoints in adjuvant breast cancer clinical trials: STEEP Version 2.0

Purpose The Standardized Definitions for Efficacy End Points (STEEP) criteria, established in 2007, provide standardized definitions of adjuvant breast cancer clinical trial end points. Given the evolution of breast cancer clinical trials and improvements in outcomes, a panel of experts reviewed the STEEP criteria to determine whether modifications are needed.Methods We conducted systematic searches of ClinicalTrials.gov for adjuvant systemic and local-regional therapy trials for breast cancer to investigate if the primary end points reported met STEEP criteria. On the basis of common STEEP deviations, we performed a series of simulations to evaluate the effect of excluding non-breast cancer deaths and new nonbreast primary cancers from the invasive disease-free survival end point.Results Among 11 phase III breast cancer trials with primary efficacy end points, three had primary end points that followed STEEP criteria, four used STEEP definitions but not the corresponding end point names, and four used end points that were not included in the original STEEP manuscript. Simulation modeling demonstrated that inclusion of second nonbreast primary cancer can increase the probability of incorrect inferences, can decrease power to detect clinically relevant efficacy effects, and may mask differences in recurrence rates, especially when recurrence rates are low.Conclusion We recommend an additional end point, invasive breast cancer-free survival, which includes all invasive disease-free survival events except second nonbreast primary cancers. This end point should be considered for trials in which the toxicities of agents are well-known and where the risk of second primary cancer is small. Additionally, we provide end point recommendations for local therapy trials, low-risk populations, noninferiority trials, and trials incorporating patient-reported outcomes

Monitoring of Serum DNA Methylation as an Early Independent Marker of Response and Survival in Metastatic Breast Cancer: TBCRC 005 Prospective Biomarker Study

Epigenetic alterations measured in blood may help guide breast cancer treatment. The multisite prospective study TBCRC 005 was conducted to examine the ability of a novel panel of cell-free DNA methylation markers to predict survival outcomes in metastatic breast cancer (MBC) using a new quantitative multiplex assay (cMethDNA)

Enhancing Quality and Impact of Early Phase Dose-Finding Clinical Trial Protocols:The SPIRIT DoseFinding Extension (SPIRIT-DEFINE) Guidance The SPIRIT-DEFINE Statement

International audienc