Mapping multipathology via spatial omic integration.

Atherosclerosis, a chronic inflammatory disease of the vascular system, presents significant challenges to developing effective molecular diagnostics and novel therapies. A systems biology approach integrating data from large-scale measurements (e.g. transcriptomics, proteomics and genomics) is successfully contributing to deciphering regulatory networks underlying the response of many different cellular systems to perturbations. Such a network analysis strategy using pathway information and data from multiple measurement platforms, tissues and species is a promising approach to elucidate the mechanistic underpinnings of complex diseases. Here, we present our views on the contributions that a systems approach can bring to the study of atherosclerosis, propose ways to tackle the complexity of the disease in a systems manner and review recent systems-level studies of the disease.

313 Background: First-line NIVO+IPI demonstrates superior survival and response benefits in intent-to-treat (ITT) patients (pts) with aRCC after long-term follow-up in the phase 3 CheckMate 214 trial. Data are scarce on tumor relapse and patterns of disease progression with immuno-oncology agents in this setting. This exploratory analysis of CheckMate 214 characterizes patterns of progression with NIVO+IPI vs SUN with 4 years minimum follow-up. Methods: Pts with clear cell aRCC were randomized to NIVO+IPI Q3W×4 followed by NIVO monotherapy Q2W, or SUN QD×4 weeks (6-week cycle). Patterns of progression were characterized in ITT pts and analyzed post hoc using descriptive statistics. Progression patterns were defined by ≥ 20% target lesion growth (T), unequivocal progression of nontarget lesions (NT), and new lesion(s) (NL). Response and progression were assessed per independent radiology review committee via RECIST v1.1. Results: Radiographic progression (RP) was documented in 299/550 (54.4%) ITT pts with NIVO+IPI vs 289/546 (52.9%) with SUN. Among ITT pts with a confirmed response (objective response = 215/550 [39.1%, NIVO+IPI] vs 177/546 [32.4%, SUN]), 71/215 (33.0%) vs 84/177 (47.5%) pts experienced post-response RP with NIVO+IPI vs SUN; 8/59 (13.6%) vs 3/14 (21.4%) progressed after complete response, and 63/156 (40.4%) vs 81/163 (49.7%) progressed after partial response, respectively. The pattern of RP differed between arms (Table). With NIVO+IPI, 106/299 (35.5%) RPs resulted from NL only vs 74/289 (25.6%) with SUN, and this differential was more pronounced in pts with an initial confirmed response (36/71 [50.7%] vs 23/84 [27.4%]). Most NL-only RPs in initial responders occurred in a single organ (34/36 [94.4%] for NIVO+IPI; 20/23 [87.0%] for SUN) with the most common being lymph nodes (11/34 [32.4%]), brain (8/34 [23.5%]), and lung (5/34 [14.7%]) with NIVO+IPI, and lymph nodes (7/20 [35.0%]), brain (4/20 [20.0%]) and adrenal gland (3/20 [15.0%]) with SUN. Additional progression details, baseline characteristics, and key efficacy outcomes in progressors will be reported. Conclusions: Differential patterns of tumor relapse and disease progression were observed after long-term follow up of patients treated with NIVO+IPI vs SUN in CheckMate 214. NL-only progression occurred more often with NIVO+IPI vs SUN, in particular in the subset of pts who progressed post-response. These patterns may have therapeutic implications. Clinical trial information: NCT02231749 . [Table: see text]

Taming Data

Chapter Eight - Translational Bioinformatics Methods for Drug Discovery and Development

1. Abstract Neurodegenerative diseases are irredeemable and incapacitating conditions that result in progressive degeneration. It is difficult to define the complexity of neuro-system quantitatively or meaningfully from a system standpoint. Thus, inclined towards the progress in developing new and effective therapeutic intervention, it is important to understand the underlying molecular mechanism and significance of neuro system and their complex molecular interaction. A biomarker discovery is an important need for early disease diagnosis, prognosis and monitoring of new therapy for neurological disorders. The emergence of system biology and network-based computational model approaches provides the underlying molecular mechanism and significance of disease and their complex molecular interaction. Thus, it becomes quite easy to understand the specific nature of neuro system as well as it plays a significant role in integrating the omics data at multiple levels that lead to key success in the development of more accurate and efficient biomarker for neurological disorders. The current review focused on significant contributions of system biology and network-based computational model approaches in biomarker discovery with special reference to neurological disorders. 2. Keywords: Biomarker discovery; Bioinformatics; System biology; Network-based computational model; Neurological disorders

Protein aggregation kinetics, mechanism, and curve-fitting: A review of the literature

A simple model of the reaction between polyadenylic acid and polyuridylic acid

MotivationNeurodegenerative diseases (NDs), including amyotrophic lateral sclerosis, Parkinson's disease, Alzheimer's disease, and Huntington's disease, occur as a result of neurodegenerative processes. Thus, it has been increasingly appreciated that many neurodegenerative conditions overlap at multiple levels. However, traditional clinicopathological correlation approaches to better classify a disease have met with limited success. Discovering this overlap offers hope for therapeutic advances that could ameliorate many ND simultaneously. In parallel, in the last decade, systems biology approaches have become a reliable choice in complex disease analysis for gaining more delicate biological insights and have enabled the comprehension of the higher order functions of the biological systems.ResultsToward this orientation, we developed a systems biology approach for the identification of common links and pathways of ND, based on well-established and novel topological and functional measures. For this purpose, a molecular pathway network was constructed, using molecular interactions and relations of four main neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease). Our analysis captured the overlapped subregions forming molecular subpathways fully enriched in these four NDs. Also, it exported molecules that act as bridges, hubs, and key players for neurodegeneration concerning either their topology or their functional role.ConclusionUnderstanding these common links and central topologies under the perspective of systems biology and network theory and greater insights are provided to uncover the complex neurodegeneration processes.

Disease-associated oligodendrocyte signatures in neurodegenerative disease: the known and unknown.

According to census statistics, Spanish-speaking workers totaled 38.3 million or about 13% of the total population of the United States in the year 2001-2002. Today’s Latino worker makes up a big part of our nation’s workforce. Statistics show that in states like California, Florida, Texas, Illinois, Colorado and Kentucky Hispanic/ Latino workers make up greater than 50% of the workforce. Part of the communication problem shows that some of the primary reasons consist of the cultural gap, poor training and language barriers.

Autoimmune hepatitis in patients with chronic HBV and HCV infections: patterns of clinical characteristics, disease progression and outcome

Network analysis is an essential component of systems biology approaches toward understanding the molecular and cellular interactions underlying biological systems functionalities and their perturbations in disease. Regulatory and signalling pathways involve DNA, RNA, proteins and metabolites as key elements to coordinate most aspects of cellular functioning. Cellular processes depend on the structure and dynamics of gene regulatory networks and can be studied by employing a network representation of molecular interactions. This chapter describes several types of biological networks, how combination of different analytic approaches can be used to study diseases, and provides a list of selected tools for network visualization and analysis. It also introduces protein–protein interaction networks, gene regulatory networks, signalling networks and metabolic networks to illustrate concepts underlying network representation of cellular processes and molecular interactions. It finally discusses how the level of accuracy in inferring functional relationships influences the choice of methods applied for the analysis of a particular biological network type.

Together with computational analysis and modeling, the development of whole-genome measurement technologies holds the potential to fundamentally change research on complex disorders such as coronary artery disease. With these tools, the stage has been set to reveal the full repertoire of biological components (genes, proteins, and metabolites) in complex diseases and their interplay in modules and networks. Here we review how network identification based on reverse engineering, as applied to whole-genome datasets from simpler organisms, is now being adapted to more complex settings such as datasets from human cell lines and organs in relation to physiological and pathological states. Our focus is on the use of a systems biological approach to identify gene networks in coronary atherosclerosis. We also address how gene networks will probably play a key role in the development of early diagnostics and treatments for complex disorders in the coming era of individualized medicine.

For decades, stratification criteria for first-line clinical studies have been highly uniform. However, there is no principle or consensus for restratification after systemic treatment progression based on immune checkpoint inhibitors (ICIs). The aim of this study was to assess the patterns of disease progression in patients with advanced hepatocellular carcinoma (HCC) who are not eligible for surgical intervention, following the use of immune checkpoint inhibitors. This is a retrospective study that involved patients with inoperable China liver stage (CNLC) IIIa and/or IIIb. The patients were treated at eight centers across China between January 2017 and October 2022. All patients received at least two cycles of first-line treatment containing immune checkpoint inhibitors. The patterns of disease progression were assessed using RECIST criteria 1.1. Different progression modes have been identified based on the characteristics of imaging progress. The study's main outcome measures were post-progression survival (PPS) and overall survival (OS). Survival curves were plotted using the Kaplan-Meier method to compare the difference among the four groups. Subgroup analysis was conducted to compare the efficacy of different immunotherapy combinations. Variations in the efficacy of immunotherapy have also been noted across patient groups exhibiting alpha-fetoprotein (AFP) levels equal to or exceeding 400ng/mL, in contrast to those with AFP levels below 400ng/mL. The study has identified four distinct patterns of progress, namely p-IIb, p-IIIa, p-IIIb, and p-IIIc. Diverse patterns of progress demonstrate notable variations in both PPS and OS. The group p-IIb had the longest PPS of 12.7m (95% 9.3-16.1) and OS 19.6m (95% 15.6-23.5), the remaining groups exhibited p-IIIb at PPS 10.5 months (95%CI: 7.9-13.1) and OS 19.2 months (95%CI 15.1-23.3). Similarly, p-IIIc at PPS 5.7 months (95%CI: 4.2-7.2) and OS 11.0 months (95%CI 9.0-12.9), while p-IIIa at PPS 3.4 months (95%CI: 2.7-4.1) and OS 8.2 months (95%CI 6.8-9.5) were also seen. Additional stratified analysis was conducted and showed there were no differences of immunotherapy alone or in combination in OS (HR= 0.92, 95%CI: 0.59-1.43, P=0.68) and PPS (HR= 0.88, 95%CI: 0.57-1.36, P=0.54); there was no significant difference in PPS (HR=0.79, 95% CI: 0.55-1.12, P=0.15) and OS (HR=0.86, 95% CI: 0.61-1.24, P=0.39) for patients with AFP levels at or over 400ng/mL. However, it was observed that patients with AFP levels above 400ng/mL experienced a shorter median progression of PPS (8.0 months vs. 5.0 months) after undergoing immunotherapy. In this investigation of advanced hepatocellular carcinoma among Chinese patients treated with immune checkpoint inhibitors, we identified four distinct progression patterns (p-IIb, p-IIIa, p-IIIb and p-IIIc) that showed significant differences in PPS and OS. These findings demonstrate the heterogeneity of disease progression and prognosis after immunotherapy failure. Further validation in large cohorts is necessary to develop prognostic models that integrate distinct progression patterns to guide subsequent treatment decisions. Additionally, post-immunotherapy progression in patients with AFP levels ≥400ng/mL indicates a shortened median PPS. These findings provide valuable insights for future personalized treatment decisions.

To characterize patterns of disease progression in the designation of progressive pulmonary fibrosis (PPF), including their relative prevalence and subsequent prognostic significance, in patients with fibrotic interstitial lung disease (ILD), including key patient sub-groups. In recent large clinical cohorts, PPF criteria suited to early PPF identification, based on their prevalence and short time to progression, include a relative forced vital capacity (FVC) decline exceeding 10% and various combinations of lower thresholds for FVC decline, symptomatic worsening and serial progression of fibrosis on imaging. Amongst numerous candidate PPF criteria, these progression patterns may have the greatest prognostic significance based on subsequent mortality, although there are conflicting data based on subsequent FVC progression. The prevalence of patterns of progression is similar across major diagnostic sub-groups with the striking exception of patients with underlying inflammatory myopathy. Based on prevalence and the prognostic significance of PPF criteria, and the need for early identification of disease progression, recent published data in large clinical cohorts provide support for the use of the INBUILD PPF criteria. The patterns of disease progression used to designate PPF in a recent multinational guideline are mostly not based on data in previous and subsequent real-world cohorts.