Blood-based identification of non-responders to anti-TNF therapy in rheumatoid arthritis.

BACKGROUND: Faced with an increasing number of choices for biologic therapies, rheumatologists have a critical need for better tools to inform rheumatoid arthritis (RA) disease management. The ability to identify patients who are unlikely to respond to first-line biologic anti-TNF therapies prior to their treatment would allow these patients to seek alternative therapies, providing faster relief and avoiding complications of disease. METHODS: We identified a gene expression classifier to predict, pre-treatment, which RA patients are unlikely to respond to the anti-TNF infliximab. The classifier was trained and independently evaluated using four published whole blood gene expression data sets, in which RA patients (n = 116 = 44 + 15 + 30 + 27) were treated with infliximab, and their response assessed 14-16 months post treatment according to the European League Against Rheumatism (EULAR) response criteria. For each patient, prior knowledge was used to group gene expression measurements into disease-relevant biological signaling mechanisms that were used as the input features for regularized logistic regression. RESULTS: The classifier produced a substantial enrichment of non-responders (59 %, given by the cross validated test precision) compared to the full population (27 % non-responders), while identifying nearly a third of non-responders. Given this classifier performance, treatment of predicted non-responders with alternative biologics would decrease their chance of non-response by between a third and a half, substantially improving their odds of effective treatment and stemming further disease progression. The classifier consisted of 18 signaling mechanisms, which together indicated that higher inflammatory signaling mediated by TNF and other cytokines was present pre-treatment in the blood of patients who responded to infliximab treatment. In contrast, non-responders were classified by relatively higher levels of specific metabolic activities in the blood prior to treatment. CONCLUSIONS: We were able to successfully produce a classifier to identify a population of RA patients significantly enriched in anti-TNF non-responders across four different patient cohorts. Additional prospective studies are needed to validate and refine the classifier for clinical use.

Early patient stratification and predictive biomarkers in drug discovery and development: a case study of ulcerative colitis anti-TNF therapy.

The current drug discovery paradigm is long, costly, and prone to failure. For projects in early development, lack of efficacy in Phase II is a major contributor to the overall failure rate. Efficacy failures often occur from one of two major reasons: either the investigational agent did not achieve the required pharmacology or the mechanism targeted by the investigational agent did not significantly contribute to the disease in the tested patient population. The latter scenario can arise due to insufficient study power stemming from patient heterogeneity. If the subset of disease patients driven by the mechanism that is likely to respond to the drug can be identified and selected before enrollment begins, efficacy and response rates should improve. This will not only augment drug approval percentages, but will also minimize the number of patients at risk of side effects in the face of a suboptimal response to treatment. Here we describe a systems biology approach using molecular profiling data from patients at baseline for the development of predictive biomarker content to identify potential responders to a molecular targeted therapy before the drug is tested in humans. A case study is presented where a classifier to predict response to a TNF targeted therapy for ulcerative colitis is developed a priori and verified against a test set of patients where clinical outcomes are known. This approach will promote the tandem development of drugs with predictive response, patient selection biomarkers.

A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue.

BACKGROUND: Humans and other organisms are equipped with a set of responses that can prevent damage from exposure to a multitude of endogenous and environmental stressors. If these stress responses are overwhelmed, this can result in pathogenesis of diseases, which is reflected by an increased development of, e.g., pulmonary and cardiac diseases in humans exposed to chronic levels of environmental stress, including inhaled cigarette smoke (CS). Systems biology data sets (e.g., transcriptomics, phosphoproteomics, metabolomics) could enable comprehensive investigation of the biological impact of these stressors. However, detailed mechanistic networks are needed to determine which specific pathways are activated in response to different stressors and to drive the qualitative and eventually quantitative assessment of these data. A current limiting step in this process is the availability of detailed mechanistic networks that can be used as an analytical substrate. RESULTS: We have built a detailed network model that captures the biology underlying the physiological cellular response to endogenous and exogenous stressors in non-diseased mammalian pulmonary and cardiovascular cells. The contents of the network model reflect several diverse areas of signaling, including oxidative stress, hypoxia, shear stress, endoplasmic reticulum stress, and xenobiotic stress, that are elicited in response to common pulmonary and cardiovascular stressors. We then tested the ability of the network model to identify the mechanisms that are activated in response to CS, a broad inducer of cellular stress. Using transcriptomic data from the lungs of mice exposed to CS, the network model identified a robust increase in the oxidative stress response, largely mediated by the anti-oxidant NRF2 pathways, consistent with previous reports on the impact of CS exposure in the mammalian lung. CONCLUSIONS: The results presented here describe the construction of a cellular stress network model and its application towards the analysis of environmental stress using transcriptomic data. The proof-of-principle analysis described here, coupled with the future development of additional network models covering distinct areas of biology, will help to further clarify the integrated biological responses elicited by complex environmental stressors such as CS, in pulmonary and cardiovascular cells.

Construction of a computable cell proliferation network focused on non-diseased lung cells.

BACKGROUND: Critical to advancing the systems-level evaluation of complex biological processes is the development of comprehensive networks and computational methods to apply to the analysis of systems biology data (transcriptomics, proteomics/phosphoproteomics, metabolomics, etc.). Ideally, these networks will be specifically designed to capture the normal, non-diseased biology of the tissue or cell types under investigation, and can be used with experimentally generated systems biology data to assess the biological impact of perturbations like xenobiotics and other cellular stresses. Lung cell proliferation is a key biological process to capture in such a network model, given the pivotal role that proliferation plays in lung diseases including cancer, chronic obstructive pulmonary disease (COPD), and fibrosis. Unfortunately, no such network has been available prior to this work. RESULTS: To further a systems-level assessment of the biological impact of perturbations on non-diseased mammalian lung cells, we constructed a lung-focused network for cell proliferation. The network encompasses diverse biological areas that lead to the regulation of normal lung cell proliferation (Cell Cycle, Growth Factors, Cell Interaction, Intra- and Extracellular Signaling, and Epigenetics), and contains a total of 848 nodes (biological entities) and 1597 edges (relationships between biological entities). The network was verified using four published gene expression profiling data sets associated with measured cell proliferation endpoints in lung and lung-related cell types. Predicted changes in the activity of core machinery involved in cell cycle regulation (RB1, CDKN1A, and MYC/MYCN) are statistically supported across multiple data sets, underscoring the general applicability of this approach for a network-wide biological impact assessment using systems biology data. CONCLUSIONS: To the best of our knowledge, this lung-focused Cell Proliferation Network provides the most comprehensive connectivity map in existence of the molecular mechanisms regulating cell proliferation in the lung. The network is based on fully referenced causal relationships obtained from extensive evaluation of the literature. The computable structure of the network enables its application to the qualitative and quantitative evaluation of cell proliferation using systems biology data sets. The network is available for public use.

The perichromosomal layer.

In addition to genetic information, mitotic chromosomes transmit essential components for nuclear assembly and function in a new cell cycle. A specialized chromosome domain, called the perichromosomal layer, perichromosomal sheath, chromosomal coat, or chromosome surface domain, contains proteins required for a variety of cellular processes, including the synthesis of messenger RNA, assembly of ribosomes, repair of DNA double-strand breaks, telomere maintenance, and apoptosis regulation. The layer also contains many proteins of unknown function and is a major target in autoimmune disease. Perichromosomal proteins are found along the entire length of chromosomes, excluding centromeres, where sister chromatids are paired and spindle microtubules attach. Targeting of proteins to the perichromosomal layer occurs primarily during prophase, and they generally remain associated until telophase. During interphase, perichromosomal proteins localize to nucleoli, the nuclear envelope, nucleoplasm, heterochromatin, centromeres, telomeres, and/or the cytoplasm. It has been suggested that the perichromosomal layer may contribute to chromosome structure, as several of the associated proteins have functions in chromatin remodeling during interphase. We review the identified proteins associated with this chromosome domain and briefly discuss their known functions during interphase and mitosis.

Relevance of histone acetylation and replication timing for deposition of centromeric histone CENP-A.

A centromere-specific variant of histone H3, centromere protein A (CENP-A), is a critical determinant of centromeric chromatin, and its location on the chromosome may determine centromere identity. To search for factors that direct CENP-A deposition at a specific chromosomal locus, we took advantage of the observation that CENP-A, when expressed at elevated levels, can get incorporated at ectopic sites on the chromosome, in addition to the centromere. As core histone hypoacetylation and DNA replication timing have been implicated as epigenetic factors that may be important for centromere identity, we hypothesized that the sites of preferential CENP-A deposition will be distinguished by these parameters. We found that, on human dicentric chromosomes, ectopically expressed CENP-A preferentially incorporates at the active centromere only, despite the fact that the levels of histone acetylation and replication timing were indistinguishable at the two centromeres. In CHO cells, ectopically expressed CENP-A is preferentially targeted to some, but not all telomeric regions. Again, these regions could not be distinguished from other telomeres by their acetylation levels or replication timing. Thus histone acetylation and replication timing are not sufficient for specifying the sites of CENP-A deposition and likely for centromere identity.

Localization of human SMC1 protein at kinetochores.

Proper cohesion of sister chromatids is prerequisite for correct segregation of chromosomes during cell division. The cohesin multiprotein complex, conserved in eukaryotes, is required for sister chromatid cohesion. Human cohesion is composed of a stable heterodimer of the structural maintenance of chromosomes (SMC) family proteins, hSMC1 and hSMC3, and non-SMC components, hRAD21 and SA1 (or SA2). In yeast, cohesion associates with chromosomes from late G1 to metaphase and is required for the establishment and maintenance of both chromosome arm and centromeric cohesion. However, in human cells, the majority of cohesion dissociates from chromosomes before mitosis. Although it was recently shown that a small amount of hRAD21 localizes to the centromeres during metaphase, the presence of other cohesion components at the centromere has not been demonstrated in human cells. Here we report the mitosis-specific localization of hSMC1 to the kinetochores. hSMC1 is targeted to the kinetochore region during prophase concomitant with kinetochore assembly and remains through anaphase. Importantly, hSMC1 is targeted only to the active centromere on dicentric chromosomes. These results suggest that hSMC1 is an integral component of the functional kinetochore structure during mitosis.

Protein phosphatase 4 is involved in tumor necrosis factor-alpha-induced activation of c-Jun N-terminal kinase.

Protein phosphatase 4 (PP4, previously named protein phosphatase X (PPX)), a PP2A-related serine/threonine phosphatase, has been shown to be involved in essential cellular processes, such as microtubule growth and nuclear factor kappa B activation. We provide evidence that PP4 is involved in tumor necrosis factor (TNF)-alpha signaling in human embryonic kidney 293T (HEK293T) cells. Treatment of HEK293T cells with TNF-alpha resulted in time-dependent activation of endogenous PP4, peaking at 10 min, as well as increased serine and threonine phosphorylation of PP4. We also found that PP4 is involved in relaying the TNF-alpha signal to c-Jun N-terminal kinase (JNK) as indicated by the ability of PP4-RL, a dominant-negative PP4 mutant, to block TNF-alpha-induced JNK activation. Moreover, the response of JNK to TNF-alpha was inhibited in HEK293 cells stably expressing PP4-RL in comparison to parental HEK293 cells. The involvement of PP4 in JNK signaling was further demonstrated by the specific activation of JNK, but not p38 and ERK2, by PP4 in transient transfection assays. However, no direct PP4-JNK interaction was detected, suggesting that PP4 exerts its positive regulatory effect on JNK in an indirect manner. Taken together, these data indicate that PP4 is a signaling component of the JNK cascade and involved in relaying the TNF-alpha signal to the JNK pathway.