P-values in genomics: apparent precision masks high uncertainty.

Scientists often interpret P-values as measures of the relative strength of statistical findings. This is common practice in large-scale genomic studies where P-values are used to choose which of numerous hypothesis test results should be pursued in subsequent research. In this study, we examine P-value variability to assess the degree of certainty P-values provide. We develop prediction intervals for the P-value in a replication study given the P-value observed in an initial study. The intervals depend on the initial value of P and the ratio of sample sizes between the initial and replication studies, but not on the underlying effect size or initial sample size. The intervals are valid for most large-sample statistical tests in any context, and can be used in the presence of single or multiple tests. While P-values are highly variable, future P-value variability can be explicitly predicted based on a P-value from an initial study. The relative size of the replication and initial study is an important predictor of the P-value in a subsequent replication study. We provide a handy calculator implementing these results and apply them to a study of Alzheimer's disease and recent findings of the Cross-Disorder Group of the Psychiatric Genomics Consortium. This study suggests that overinterpretation of very significant, but highly variable, P-values is an important factor contributing to the unexpectedly high incidence of non-replication. Formal prediction intervals can also provide realistic interpretations and comparisons of P-values associated with different estimated effect sizes and sample sizes.

Endogenous knockdown of survivin improves chemotherapeutic response in ALL models.

Although the cure rate of newly diagnosed acute lymphoblastic leukemia (ALL) has improved over the past four decades, the outcome for patients who relapse remains poor. New therapies are needed for these patients. Our previous global gene expression analysis in a series of paired diagnosis-relapse pediatric patient samples revealed that the antiapoptotic gene survivin was consistently upregulated upon disease relapse. In this study, we demonstrate a link between survivin expression and drug resistance and test the efficacy of a novel antisense agent in promoting apoptosis when combined with chemotherapy. Gene-silencing experiments targeting survivin mRNA using either short-hairpin RNA (shRNA) or a locked antisense oligonucleotide (LNA-ON) specifically reduced gene expression and induced apoptosis in leukemia cell lines. When used in combination with chemotherapy, the survivin shRNA and LNA-ON potentiated the chemotherapeutic antileukemia effect. Moreover, in a mouse primary xenograft model of relapse ALL, the survivin LNA-ON decreased survivin expression in a subset of animals, and produced a statistically significant decrease in tumor progression. Taken together, these findings suggest that targeting endogenous levels of survivin mRNA by LNA-ON methods may augment the response to standard chemotherapy by sensitizing otherwise resistant tumor cells to chemotherapy.