Multiple multi-sample testing under arbitrary covariance dependency.

Modern high-throughput biomedical devices routinely produce data on a large scale, and the analysis of high-dimensional datasets has become commonplace in biomedical studies. However, given thousands or tens of thousands of measured variables in these datasets, extracting meaningful features poses a challenge. In this article, we propose a procedure to evaluate the strength of the associations between a nominal (categorical) response variable and multiple features simultaneously. Specifically, we propose a framework of large-scale multiple testing under arbitrary correlation dependency among test statistics. First, marginal multinomial regressions are performed for each feature individually. Second, we use an approach of multiple marginal models for each baseline-category pair to establish asymptotic joint normality of the stacked vector of the marginal multinomial regression coefficients. Third, we estimate the (limiting) covariance matrix between the estimated coefficients from all marginal models. Finally, our approach approximates the realized false discovery proportion of a thresholding procedure for the marginal p-values for each baseline-category logit pair. The proposed approach offers a sensible trade-off between the expected numbers of true and false findings. Furthermore, we demonstrate a practical application of the method on hyperspectral imaging data. This dataset is obtained by a matrix-assisted laser desorption/ionization (MALDI) instrument. MALDI demonstrates tremendous potential for clinical diagnosis, particularly for cancer research. In our application, the nominal response categories represent cancer (sub-)types.

Multiple two-sample testing under arbitrary covariance dependency with an application in imaging mass spectrometry.

Large-scale hypothesis testing has become a ubiquitous problem in high-dimensional statistical inference, with broad applications in various scientific disciplines. One relevant application is constituted by imaging mass spectrometry (IMS) association studies, where a large number of tests are performed simultaneously in order to identify molecular masses that are associated with a particular phenotype, for example, a cancer subtype. Mass spectra obtained from matrix-assisted laser desorption/ionization (MALDI) experiments are dependent, when considered as statistical quantities. False discovery proportion (FDP) estimation and  control under arbitrary dependency structure among test statistics is an active topic in modern multiple testing research. In this context, we are concerned with the evaluation of associations between the binary outcome variable (describing the phenotype) and multiple predictors derived from MALDI measurements. We propose an inference procedure in which the correlation matrix of the test statistics is utilized. The approach is based on multiple marginal models. Specifically, we fit a marginal logistic regression model for each predictor individually. Asymptotic joint normality of the stacked vector of the marginal regression coefficients is established under standard regularity assumptions, and their (limiting) correlation matrix is estimated. The proposed method extracts common factors from the resulting empirical correlation matrix. Finally, we estimate the realized FDP of a thresholding procedure for the marginal p-values. We demonstrate a practical application of the proposed workflow to MALDI IMS data in an oncological context.

The cellular ratio of immune tolerance (immunoCRIT) is a definite marker for aggressiveness of solid tumors and may explain tumor dissemination patterns.

The adaptive immune system is involved in tumor establishment and aggressiveness. Tumors of the ovaries, an immune-privileged organ, spread via transceolomic routes and rarely to distant organs. This is contrary to tumors of non-immune privileged organs, which often disseminate hematogenously to distant organs. Epigenetics-based immune cell quantification allows direct comparison of the immune status in benign and malignant tissues and in blood. Here, we introduce the "cellular ratio of immune tolerance" (immunoCRIT) as defined by the ratio of regulatory T cells to total T lymphocytes. The immunoCRIT was analyzed on 273 benign tissue samples of colorectal, bronchial, renal and ovarian origin as well as in 808 samples from primary colorectal, bronchial, mammary and ovarian cancers. ImmunoCRIT is strongly increased in all cancerous tissues and gradually augmented strictly dependent on tumor aggressiveness. In peripheral blood of ovarian cancer patients, immunoCRIT incrementally increases from primary diagnosis to disease recurrence, at which distant metastases frequently occur. We postulate that non-pathological immunoCRIT values observed in peripheral blood of immune privileged ovarian tumor patients are sufficient to prevent hematogenous spread at primary diagnosis. Contrarily, non-immune privileged tumors establish high immunoCRIT in an immunological environment equivalent to the bloodstream and thus spread hematogenously to distant organs. In summary, our data suggest that the immunoCRIT is a powerful marker for tumor aggressiveness and disease dissemination.

Epigenetic quantification of tumor-infiltrating T-lymphocytes.

The immune system plays a pivotal role in tumor establishment. However, the role of T-lymphocytes within the tumor microenvironment as major cellular component of the adaptive effector immune response and their counterpart, regulatory T-cells (Treg), responsible for suppressive immune modulation, is not completely understood. This is partly due to the lack of reliable technical solutions for specific cell quantification in solid tissues. Previous reports indicated that epigenetic marks of immune cells, such as the Treg specifically demethylated region (TSDR) within the FOXP3 gene, may be exploited as robust analytical tool for Treg-quantification. Here, we expand the concept of epigenetic immunophenotyping to overall T-lymphocytes (oTL). This tool allows cell quantification with at least equivalent precision to FACS and is adoptable for analysis of blood and solid tissues. Based on this method, we analyse the frequency of Treg, oTL and their ratio in independent cohorts of healthy and tumorous ovarian, colorectal and bronchial tissues with 616 partly donor-matched samples. We find a shift of the median ratio of Treg-to-oTL from 3-8% in healthy tissue to 18-25% in all tumor entities. Epigenetically determined oTL frequencies correlate with the outcome of colorectal and ovarian cancers. Together, our data show that the composition of immune cells in tumor microenvironments can be quantitatively assessed by epigenetic measurements. This composition is disturbed in solid tumors, indicating a fundamental mechanism of tumor immune evasion. Epigenetic quantification of T-lymphocytes serves as independent clinical parameter for outcome prognosis.