Peripheral Immune Cell Gene Expression Predicts Survival of Patients with Non-Small Cell Lung Cancer

Prediction of cancer recurrence in patients with non-small cell lung cancer (NSCLC) currently relies on the assessment of clinical characteristics including age, tumor stage, and smoking history. A better prediction of early stage cancer patients with poorer survival and late stage patients with better survival is needed to design patient-tailored treatment protocols. We analyzed gene expression in RNA from peripheral blood mononuclear cells (PBMC) of NSCLC patients to identify signatures predictive of overall patient survival. We find that PBMC gene expression patterns from NSCLC patients, like patterns from tumors, have information predictive of patient outcomes. We identify and validate a 26 gene prognostic panel that is independent of clinical stage. Many additional prognostic genes are specific to myeloid cells and are more highly expressed in patients with shorter survival. We also observe that significant numbers of prognostic genes change expression levels in PBMC collected after tumor resection. These post-surgery gene expression profiles may provide a means to re-evaluate prognosis over time. These studies further suggest that patient outcomes are not solely determined by tumor gene expression profiles but can also be influenced by the immune response as reflected in peripheral immune cells

Structural and functional analysis of the human POT1-TPP1 telomeric complex

POT1 and TPP1 are part of the shelterin complex and are essential for telomere length regulation and maintenance. Naturally occurring mutations of the telomeric POT1?TPP1 complex are implicated in familial glioma, melanoma and chronic lymphocytic leukaemia. Here we report the atomic structure of the interacting portion of the human telomeric POT1? TPP1 complex and suggest how several of these mutations contribute to malignant cancer. The POT1 C-terminus (POT1C) forms a bilobal structure consisting of an OB-fold and a holiday junction resolvase domain. TPP1 consists of several loops and helices involved in extensive interactions with POT1C. Biochemical data shows that several of the cancerassociated mutations, partially disrupt the POT1?TPP1 complex, which affects its ability to bind telomeric DNA efficiently. A defective POT1?TPP1 complex leads to longer and fragile telomeres, which in turn promotes genomic instability and cancer

Breast Cancer's Microarray Data: Pattern Discovery Using Nonnegative Matrix Factorizations.

One challenge in microarray analysis is to discover and capture valuable knowledge to understand biological processes and human disease mechanisms. Nonnegative Matrix Factorization (NMF) – a constrained optimization mechanism which decomposes a data matrix in terms of additive combination of non-negative factors– has been demonstrated to be a useful tool to reduce the dimension of gene expression data and to identify potentially interesting genes which explain latent structure hidden in microarray data. In this paper, we detail how to use Nonnegative Matrix Factorization based on generalized Kullback-Leibler divergence to analyze gene expression profile data related to the cell line of mammary cancer MCF-7 and to pharmaceutical compounds connected to the metabolism of arachidonic acid. NMF technique is able to reduce the dimension of the considered genes-compounds matrix from thousands of genes to few metagenes and to extract information about the drugs that more affect these genes. We provide an experimental framework illustrating the technical steps one has to perform to use NMF to discover useful patterns from microarray data. In fact, the results obtained by NMF method could be used to select and characterize therapies that can be effective on biological functions involved in the neoplastic transformation process and to perform further biological investigations

DNA methylation arrays as surrogate measures of cell mixture distribution

<p>Abstract</p> <p>Background</p> <p>There has been a long-standing need in biomedical research for a method that quantifies the normally mixed composition of leukocytes beyond what is possible by simple histological or flow cytometric assessments. The latter is restricted by the labile nature of protein epitopes, requirements for cell processing, and timely cell analysis. In a diverse array of diseases and following numerous immune-toxic exposures, leukocyte composition will critically inform the underlying immuno-biology to most chronic medical conditions. Emerging research demonstrates that DNA methylation is responsible for cellular differentiation, and when measured in whole peripheral blood, serves to distinguish cancer cases from controls.</p> <p>Results</p> <p>Here we present a method, similar to regression calibration, for inferring changes in the distribution of white blood cells between different subpopulations (e.g. cases and controls) using DNA methylation signatures, in combination with a previously obtained external validation set consisting of signatures from purified leukocyte samples. We validate the fundamental idea in a cell mixture reconstruction experiment, then demonstrate our method on DNA methylation data sets from several studies, including data from a Head and Neck Squamous Cell Carcinoma (HNSCC) study and an ovarian cancer study. Our method produces results consistent with prior biological findings, thereby validating the approach.</p> <p>Conclusions</p> <p>Our method, in combination with an appropriate external validation set, promises new opportunities for large-scale immunological studies of both disease states and noxious exposures.</p

Satb1 regulates the self-renewal of hematopoietic stem cells by promoting quiescence and repressing differentiation commitment

How hematopoietic stem cells coordinate the regulation of opposing cellular mechanisms like self-renewal and differentiation commitment remains unclear. Here, we identified the transcription factor and chromatin remodeler Satb1 as a critical regulator of the hematopoietic stem cell (HSC) fate. HSCs lacking Satb1 displayed defective self-renewal, less quiescence and accelerated lineage commitment, resulting in progressive depletion of functional HSCs. Increased commitment was caused by reduced symmetric self-renewal and increased symmetric differentiation divisions of Satb1-deficient HSCs. Satb1 simultaneously repressed gene sets involved in HSC activation and cellular polarity, including Numb and Myc, two key factors for stem cell fate specification. Thus, Satb1 is a regulator that promotes HSC quiescence and represses lineage commitment