TEMPORAL ORDER OF DISEASE PAIRS AFFECTS SUBSEQUENT DISEASE TRAJECTORIES: THE CASE OF DIABETES AND SLEEP APNEA.

Most studies of disease etiologies focus on one disease only and not the full spectrum of multimorbidities that many patients have. Some disease pairs have shared causal origins, others represent common follow-on diseases, while yet other co-occurring diseases may manifest themselves in random order of appearance. We discuss these different types of disease co-occurrences, and use the two diseases "sleep apnea" and "diabetes" to showcase the approach which otherwise can be applied to any disease pair. We benefit from seven million electronic medical records covering the entire population of Denmark for more than 20 years. Sleep apnea is the most common sleep-related breathing disorder and it has previously been shown to be bidirectionally linked to diabetes, meaning that each disease increases the risk of acquiring the other. We confirm that there is no significant temporal relationship, as approximately half of patients with both diseases are diagnosed with diabetes first. However, we also show that patients diagnosed with diabetes before sleep apnea have a higher disease burden compared to patients diagnosed with sleep apnea before diabetes. The study clearly demonstrates that it is not only the diagnoses in the patient's disease history that are important, but also the specific order in which these diagnosis are given that matters in terms of outcome. We suggest that this should be considered for patient stratification.

Diagnosis trajectories of prior multi-morbidity predict sepsis mortality.

Sepsis affects millions of people every year, many of whom will die. In contrast to current survival prediction models for sepsis patients that primarily are based on data from within-admission clinical measurements (e.g. vital parameters and blood values), we aim for using the full disease history to predict sepsis mortality. We benefit from data in electronic medical records covering all hospital encounters in Denmark from 1996 to 2014. This data set included 6.6 million patients of whom almost 120,000 were diagnosed with the ICD-10 code: A41 'Other sepsis'. Interestingly, patients following recurrent trajectories of time-ordered co-morbidities had significantly increased sepsis mortality compared to those who did not follow a trajectory. We identified trajectories which significantly altered sepsis mortality, and found three major starting points in a combined temporal sepsis network: Alcohol abuse, Diabetes and Cardio-vascular diagnoses. Many cancers also increased sepsis mortality. Using the trajectory based stratification model we explain contradictory reports in relation to diabetes that recently have appeared in the literature. Finally, we compared the predictive power using 18.5 years of disease history to scoring based on within-admission clinical measurements emphasizing the value of long term data in novel patient scores that combine the two types of data.

Establishment and characterization of models of chemotherapy resistance in colorectal cancer: Towards a predictive signature of chemoresistance.

Current standard treatments for metastatic colorectal cancer (CRC) are based on combination regimens with one of the two chemotherapeutic drugs, irinotecan or oxaliplatin. However, drug resistance frequently limits the clinical efficacy of these therapies. In order to gain new insights into mechanisms associated with chemoresistance, and departing from three distinct CRC cell models, we generated a panel of human colorectal cancer cell lines with acquired resistance to either oxaliplatin or irinotecan. We characterized the resistant cell line variants with regards to their drug resistance profile and transcriptome, and matched our results with datasets generated from relevant clinical material to derive putative resistance biomarkers. We found that the chemoresistant cell line variants had distinctive irinotecan- or oxaliplatin-specific resistance profiles, with non-reciprocal cross-resistance. Furthermore, we could identify several new, as well as some previously described, drug resistance-associated genes for each resistant cell line variant. Each chemoresistant cell line variant acquired a unique set of changes that may represent distinct functional subtypes of chemotherapy resistance. In addition, and given the potential implications for selection of subsequent treatment, we also performed an exploratory analysis, in relevant patient cohorts, of the predictive value of each of the specific genes identified in our cellular models.

A generally applicable translational strategy identifies S100A4 as a candidate gene in allergy.

The identification of diagnostic markers and therapeutic candidate genes in common diseases is complicated by the involvement of thousands of genes. We hypothesized that genes co-regulated with a key gene in allergy, IL13, would form a module that could help to identify candidate genes. We identified a T helper 2 (TH2) cell module by small interfering RNA-mediated knockdown of 25 putative IL13-regulating transcription factors followed by expression profiling. The module contained candidate genes whose diagnostic potential was supported by clinical studies. Functional studies of human TH2 cells as well as mouse models of allergy showed that deletion of one of the genes, S100A4, resulted in decreased signs of allergy including TH2 cell activation, humoral immunity, and infiltration of effector cells. Specifically, dendritic cells required S100A4 for activating T cells. Treatment with an anti-S100A4 antibody resulted in decreased signs of allergy in the mouse model as well as in allergen-challenged T cells from allergic patients. This strategy, which may be generally applicable to complex diseases, identified and validated an important diagnostic and therapeutic candidate gene in allergy.

Anti-S100A4 antibody suppresses metastasis formation by blocking stroma cell invasion.

The small Ca-binding protein, S100A4, has a well-established metastasis-promoting activity. Moreover, its expression is tightly correlated with poor prognosis in patients with numerous types of cancer. Mechanistically, the extracellular S100A4 drives metastasis by affecting the tumor microenvironment, making it an attractive target for anti-cancer therapy. In this study, we produced a function-blocking anti-S100A4 monoclonal antibody with metastasis-suppressing activity. Antibody treatment significantly reduced metastatic burden in the lungs of experimental animals by blocking the recruitment of T cells to the site of the primary tumor. In vitro studies demonstrated that this antibody efficiently reduced the invasion of T cells in a fibroblast monolayer. Moreover, it was capable of suppressing the invasive growth of human and mouse fibroblasts. We presume therefore that the antibody exerts its activity by suppressing stroma cell recruitment to the site of the growing tumor. Our epitope mapping studies suggested that the antibody recognition site overlaps with the target binding interface of human S100A4. We conclude here that this antibody could serve as a solid basis for development of an efficient anti-metastatic therapy.