Smaller p-values in genomics studies using distilled auxiliary information.

Medical research institutions have generated massive amounts of biological data by genetically profiling hundreds of cancer cell lines. In parallel, academic biology labs have conducted genetic screens on small numbers of cancer cell lines under custom experimental conditions. In order to share information between these two approaches to scientific discovery, this article proposes a "frequentist assisted by Bayes" (FAB) procedure for hypothesis testing that allows auxiliary information from massive genomics datasets to increase the power of hypothesis tests in specialized studies. The exchange of information takes place through a novel probability model for multimodal genomics data, which distills auxiliary information pertaining to cancer cell lines and genes across a wide variety of experimental contexts. If the relevance of the auxiliary information to a given study is high, then the resulting FAB tests can be more powerful than the corresponding classical tests. If the relevance is low, then the FAB tests yield as many discoveries as the classical tests. Simulations and practical investigations demonstrate that the FAB testing procedure can increase the number of effects discovered in genomics studies while still maintaining strict control of type I error and false discovery rate.

Small-sample behavior of novel phase I cancer trial designs.

BACKGROUND: Novel dose-finding designs for Phase I cancer clinical trials, using estimation to assign the best estimated Maximum Tolerated Dose (MTD) at each point in the experiment, most prominently via Bayesian techniques, have been widely discussed and promoted since 1990. PURPOSE: To examine the small-sample behavior of these 'Bayesian Phase I' designs, and also of non-Bayesian designs sharing the same main 'Long-Memory' traits of using likelihood estimation and assigning the estimated MTD to the next patient. METHODS: Data from several recently published experiments are presented and discussed, and Long-Memory designs' operating principles are explained. Simulation studies compare the small-sample behavior of Long-Memory designs with short-memory 'Up-and-Down' designs. RESULTS: In simulation, Long-Memory and Up-and-Down designs achieved similar success rates in finding the MTD. However, for all Long-Memory designs examined, the number n (*) of cohorts treated at the true MTD was highly variable between simulated experiments drawn from the same toxicity-threshold distribution. Further investigation using the same set of thresholds in permuted order indicates that this Long-Memory behavior is driven by sensitivity to the order in which participants enter the experiment. This sensitivity is related to Long-Memory designs' 'winner-takes-all' dose-assignment rule, which grants the early cohorts a disproportionately large influence, and causes many experiments to settle early on a specific dose. Additionally for the Bayesian Long-Memory designs, the prior-predictive distribution over the dose levels has a substantial impact upon MTD-finding performance, long into the experiment. LIMITATIONS: While the numerical evidence for Long-Memory designs' order sensitivity is broad, and plausible explanations for it are provided, we do not present a theoretical proof of the phenomenon. CONCLUSIONS: Method developers, analysts, and practitioners should be aware of Long-Memory designs' order sensitivity and related phenomena. In particular, they should be informed that settling on a single dose does not guarantee that this dose is the MTD. Presently, Up-and-Down designs offer a simpler and more robust alternative for the sample sizes of 10-40 patients used in most Phase I trials. Future designs might benefit from combining the two approaches. We also suggest that the field's paradigm change from dose-selection to dose-estimation.

Dose-finding designs: the role of convergence properties.

It is common for novel dose-finding designs to be presented without a study of their convergence properties. In this article we suggest that examination of convergence is a necessary quality check for dose-finding designs. We present a new convergence proof for a nonparametric family of methods called "interval designs," under certain conditions on the toxicity-frequency function F. We compare these conditions with the convergence conditions for the popular CRM one-parameter Phase I cancer design, via an innovative numerical sensitivity study generating a diverse sample of dose-toxicity scenarios. Only a small fraction of scenarios meet the Shen-O'Quigley convergence conditions for CRM. Conditions for "interval design" convergence are met more often, but still less than half the time. In the discussion, we illustrate how convergence properties and limitations help provide insight about small-sample behavior.

Array-based comparative genomic hybridization in ulcerative colitis neoplasia: single non-dysplastic biopsies distinguish progressors from non-progressors.

Approximately 10% of ulcerative colitis patients develop colorectal neoplasia. At present, identification of this subset is markedly limited and necessitates lifelong colonoscopic surveillance for the entire ulcerative colitis population. Better risk markers are needed to focus surveillance onto the patients who are most likely to benefit. Using array-based comparative genomic hybridization, we analyzed single, non-dysplastic biopsies from three patient groups: ulcerative colitis progressors (n=9) with cancer or high-grade dysplasia at a mean distance of 18 cm from the analyzed site; ulcerative colitis non-progressors (n=8) without dysplasia during long-term surveillance; and non-ulcerative colitis normal controls (n=2). Genomic DNA from fresh colonic epithelium purified from stroma was hybridized to 287 (low-density) and 4342 (higher-density) feature bacterial artificial chromosome arrays. Sample-to-reference fluorescence ratios were calculated for individual chromosomal targets and globally across the genome. The low-density arrays yielded pronounced genomic gains and losses in 3 of 9 (33%) ulcerative colitis progressors but in none of the 10 control patients. Identical DNA samples analyzed on the higher-density arrays, using a combination of global and individual high variance assessments, distinguished all nine progressors from all 10 controls. These data confirm that genomic alterations in ulcerative colitis progressors are widespread, even involving single non-dysplastic biopsies that are far distant from neoplasia. They therefore show promise toward eliminating full colonoscopic surveillance with extensive biopsy sampling in the majority of ulcerative colitis patients.

Subset clustering of binary sequences, with an application to genomic abnormality data.

This article develops a model-based approach to clustering multivariate binary data, in which the attributes that distinguish a cluster from the rest of the population may depend on the cluster being considered. The clustering approach is based on a multivariate Dirichlet process mixture model, which allows for the estimation of the number of clusters, the cluster memberships, and the cluster-specific parameters in a unified way. Such a clustering approach has applications in the analysis of genomic abnormality data, in which the development of different types of tumors may depend on the presence of certain abnormalities at subsets of locations along the genome. Additionally, such a mixture model provides a nonparametric estimation scheme for dependent sequences of binary data.

Tumor regionality in the mouse intestine reflects the mechanism of loss of Apc function.

Inherited colorectal cancer syndromes in humans exhibit regional specificity for tumor formation. By using mice with germline mutations in the adenomatous polyposis coli gene (Apc) and/or DNA mismatch repair genes, we have analyzed the genetic control of tumor regionality in the mouse small intestine. In C57BL/6 mice heterozygous for the Apc multiple intestinal neoplasia mutation (Apc(Min)), in which tumors are initiated by loss of heterozygosity by means of somatic recombination, tumors form preferentially in the distal region of the small intestine. By contrast, the formation of tumors initiated by allelic silencing on the AKR Apc(Min) genetic background is strongly skewed toward the ileocecal junction. A third tumor regionality is displayed by tumors that develop in MMR-deficient Apc(Min/+) mice, in which mutation of the Apc gene is responsible for tumor initiation. Thus, tumor regionality in the small intestine of Apc(Min/+) reflects the mechanism by which the wild-type allele of Apc is inactivated. We have reexamined the mechanism of Apc loss in tumors from Apc(1638N/+) mice, in which tumors of the small intestine develop in a regional pattern overlapping that of mismatch repair-deficient mice. In contrast to previous reports, we find that tumors from Apc(1638N/+) mice on a congenic C57BL/6 background maintain the wild-type allele of Apc. Our studies demonstrate a pathway-specific regionality for tumor development in mouse models for inherited intestinal cancer, an observation that is reminiscent of the regional preference for tumor development in the human colon. Perhaps, the power of mouse genetics and biology can be harnessed to identify genetic and other factors that contribute to tumor regionality.