Aldo Solari

A Mutant-p53/Smad Complex Opposes p63 to Empower TGFβ-Induced Metastasis

TGFbeta ligands act as tumor suppressors in early stage tumors but are paradoxically diverted into potent prometastatic factors in advanced cancers. The molecular nature of this switch remains enigmatic. Here, we show that TGFbeta-dependent cell migration, invasion and metastasis are empowered by mutant-p53 and opposed by p63. Mechanistically, TGFbeta acts in concert with oncogenic Ras and mutant-p53 to induce the assembly of a mutant-p53/p63 protein complex in which Smads serve as essential platforms. Within this ternary complex, p63 functions are antagonized. Downstream of p63, we identified two candidate metastasis suppressor genes associated with metastasis risk in a large cohort of breast cancer patients. Thus, two common oncogenic lesions, mutant-p53 and Ras, selected in early neoplasms to promote growth and survival, also prefigure a cellular set-up with particular metastasis proclivity by TGFbeta-dependent inhibition of p63 function.

Multiple hypothesis testing in genomics

This paper presents an overview of the current state of the art in multiple testing in genomics data from a users perspective. We describe methods for familywise error control, false discovery rate control and false discovery proportion estimation and confidence, both conceptually and practically, and explain when to use which type of error rate. We elaborate on the assumptions underlying the methods and discuss pitfalls in the interpretation of results. In our discussion, we take into account the exploratory nature of genomics experiments, looking at selection of genes before or after testing, and at the role of validation experiments.

The sequential rejection principle of familywise error control

Closed testing and partitioning are recognized as fundamental principles of familywise error control. In this paper, we argue that sequential rejection can be considered equally fundamental as a general principle of multiple testing. We present a general sequentially rejective multiple testing procedure and show that many well-known familywise error controlling methods can be constructed as special cases of this procedure, among which are the procedures of Holm, Shaffer and Hochberg, parallel and serial gatekeeping procedures, modern procedures for multiple testing in graphs, resampling-based multiple testing procedures and even the closed testing and partitioning procedures themselves. We also give a general proof that sequentially rejective multiple testing procedures strongly control the familywise error if they fulfill simple criteria of monotonicity of the critical values and a limited form of weak familywise error control in each single step. The sequential rejection principle gives a novel theoretical perspective on many well-known multiple testing procedures, emphasizing the sequential aspect. Its main practical usefulness is for the development of multiple testing procedures for null hypotheses, possibly logically related, that are structured in a graph. We illustrate this by presenting a uniform improvement of a recently published procedure.

Multiple Testing for Exploratory Research

Motivated by the practice of exploratory research, we formulate an approach to multiple testing that reverses the conventional roles of the user and the multiple testing procedure. Traditionally, the user chooses the error criterion, and the procedure the resulting rejected set. Instead, we propose to let the user choose the rejected set freely, and to let the multiple testing procedure return a confidence statement on the number of false rejections incurred. In our approach, such confidence statements are simultaneous for all choices of the rejected set, so that post hoc selection of the rejected set does not compromise their validity. The proposed reversal of roles requires nothing more than a review of the familiar closed testing procedure, but with a focus on the non-consonant rejections that this procedure makes. We suggest several shortcuts to avoid the computational problems associated with closed testing.

Three‐sided hypothesis testing: Simultaneous testing of superiority, equivalence and inferiority

We propose three-sided testing, a testing framework for simultaneous testing of inferiority, equivalence and superiority in clinical trials, controlling for multiple testing using the partitioning principle. Like the usual two-sided testing approach, this approach is completely symmetric in the two treatments compared. Still, because the hypotheses of inferiority and superiority are tested with one-sided tests, the proposed approach has more power than the two-sided approach to infer non-inferiority or non-superiority. Applied to the classical point null hypothesis of equivalence, the three-sided testing approach shows that it is sometimes possible to make an inference on the sign of the parameter of interest, even when the null hypothesis itself could not be rejected. Relationships with confidence intervals are explored, and the effectiveness of the three-sided testing approach is demonstrated in a number of recent clinical trials.

Testing marginal homogeneity against stochastic order in multivariate ordinal data.

SUMMARY Many assessment instruments used in the evaluation of toxicity, safety, pain, or disease progression consider multiple ordinal endpoints to fully capture the presence and severity of treatment effects. Contingency tables underlying these correlated responses are often sparse and imbalanced, rendering asymptotic results unreliable or model fitting prohibitively complex without overly simplistic assumptions on the marginal and joint distribution. Instead of a modeling approach, we look at stochastic order and marginal inhomogeneity as an expression or manifestation of a treatment effect under much weaker assumptions. Often, endpoints are grouped together into physiological domains or by the body function they describe. We derive tests based on these subgroups, which might supplement or replace the individual endpoint analysis because they are more powerful. The permutation or bootstrap distribution is used throughout to obtain global, subgroup, and individual significance levels as they naturally incorporate the correlation among endpoints. We provide a theorem that establishes a connection between marginal homogeneity and the stronger exchangeability assumption under the permutation approach. Multiplicity adjustments for the individual endpoints are obtained via stepdown procedures, while subgroup significance levels are adjusted via the full closed testing procedure. The proposed methodology is illustrated using a collection of 25 correlated ordinal endpoints, grouped into six domains, to evaluate toxicity of a chemical compound.

Nonparametric iterated combined tests for genetic differentiation

A permutation solution to the problem of genetic differentiation is presented. The focus is on determining whether there are any differences between two populations. The ability to obtain plausible genetic heterogeneities by combining the information of several loci differs depending on the method used, so no statistical test uniformly dominates the others. The procedure proposed here is robust and flexible with regard to the possible configurations of the global alternative hypothesis. Moreover, such a procedure guarantees the rejection of the global hypothesis with probability @a when all null (local) hypotheses are true.

Allosteric modulation of metabotropic glutamate receptor 4 activates IDO1-dependent, immunoregulatory signaling in dendritic cells.

Metabotropic glutamate receptor 4 (mGluR4) possesses immune modulatory properties in vivo, such that a positive allosteric modulator (PAM) of the receptor confers protection on mice with relapsing-remitting experimental autoimmune encephalomyelitis (RR-EAE). ADX88178 is a newly-developed, one such mGluR4 modulator with high selectivity, potency, and optimized pharmacokinetics. Here we found that application of ADX88178 in the RR-EAE model system converted disease into a form of mild—yet chronic—neuroinflammation that remained stable for over two months after discontinuing drug treatment. In vitro, ADX88178 modulated the cytokine secretion profile of dendritic cells (DCs), increasing production of tolerogenic IL-10 and TGF-β. The in vitro effects required activation of a Gi-independent, alternative signaling pathway that involved phosphatidylinositol-3-kinase (PI3K), Src kinase, and the signaling activity of indoleamine 2,3-dioxygenase 1 (IDO1). A PI3K inhibitor as well as small interfering RNA targeting Ido1—but not pertussis toxin, which affects Gi protein-dependent responses—abrogated the tolerogenic effects of ADX88178-conditioned DCs in vivo. Thus our data indicate that, in DCs, highly selective and potent mGluR4 PAMs such as ADX88178 may activate a Gi-independent, long-lived regulatory pathway that could be therapeutically exploited in chronic autoimmune diseases such as multiple sclerosis.

Rotation-based multiple testing in the multivariate linear model

In observational microarray studies, issues of confounding invariably arise. One approach to account for measured confounders is to include them as covariates in a multivariate linear model. For this model, however, the application of permutation-based multiple testing procedures is problematic because exchangeability of responses, in general, does not hold. Nevertheless, it is possible to achieve rotatability of transformed responses at the cost of a distributional assumption. We argue that rotation-based multiple testing, by allowing for adjustments for confounding, represents an important extension of permutation-based multiple testing procedures. The proposed methodology is illustrated with a microarray observational study on breast cancer tumors. Software to perform the procedure described in this article is available in the flip R package.

Testing goodness of fit in regression: a general approach for specified alternatives

When fitting generalized linear models or the Cox proportional hazards model, it is important to have tools to test for lack of fit. Because lack of fit comes in all shapes and sizes, distinguishing among different types of lack of fit is of practical importance. We argue that an adequate diagnosis of lack of fit requires a specified alternative model. Such specification identifies the type of lack of fit the test is directed against so that if we reject the null hypothesis, we know the direction of the departure from the model. The goodness-of-fit approach of this paper allows to treat different types of lack of fit within a unified general framework and to consider many existing tests as special cases. Connections with penalized likelihood and random effects are discussed, and the application of the proposed approach is illustrated with medical examples. Tailored functions for goodness-of-fit testing have been implemented in the R package global test.