Disease networks identify specific conditions and pleiotropy influencing multimorbidity in the general population.

Multimorbidity is an emerging topic in public health policy because of its increasing prevalence and socio-economic impact. However, the age- and gender-dependent trends of disease associations at fine resolution, and the underlying genetic factors, remain incompletely understood. Here, by analyzing disease networks from electronic medical records of primary health care, we identify key conditions and shared genetic factors influencing multimorbidity. Three types of diseases are outlined: "central", which include chronic and non-chronic conditions, have higher cumulative risks of disease associations; "community roots" have lower cumulative risks, but inform on continuing clustered disease associations with age; and "seeds of bursts", which most are chronic, reveal outbreaks of disease associations leading to multimorbidity. The diseases with a major impact on multimorbidity are caused by genes that occupy central positions in the network of human disease genes. Alteration of lipid metabolism connects breast cancer, diabetic neuropathy and nutritional anemia. Evaluation of key disease associations by a genome-wide association study identifies shared genetic factors and further supports causal commonalities between nervous system diseases and nutritional anemias. This study also reveals many shared genetic signals with other diseases. Collectively, our results depict novel population-based multimorbidity patterns, identify key diseases within them, and highlight pleiotropy influencing multimorbidity.

Prediction of pathological response to neoadjuvant treatment in rectal cancer with a two-protein immunohistochemical score derived from stromal gene-profiling.

BACKGROUND: Preoperative chemoradiotherapy followed by surgical mesorectal resection is the standard of care for locally advanced rectal carcinomas. Yet, predicting that patients will respond to treatment remains an unmet clinical challenge. EXPERIMENTAL DESIGN: Using laser-capture microdissection we isolated RNA from stroma and tumour glands from prospective pre-treatment samples (n = 15). Transcriptomic profiles were obtained hybridising PrimeView Affymetrix arrays. We modelled a carcinoma-associated fibroblast-specific genes filtering data using GSE39396. RESULTS: The analysis of differentially expressed genes of stroma/tumour glands from responder and non-responder patients shows that most changes were associated with the stromal compartment; codifying mainly for extracellular matrix and ribosomal components. We built a carcinoma-associated fibroblast (CAF) specific classifier with genes showing changes in expression according to the tumour regression grade (FN1, COL3A1, COL1A1, MMP2 and IGFBP5). We assessed these five genes at the protein level by means of immunohistochemical staining in a patient's cohort (n = 38). For predictive purposes we used a leave-one-out cross-validated model with a positive predictive value (PPV) of 83.3%. Random Forest identified FN1 and COL3A1 as the best predictors. Rebuilding the leave-one-out cross-validated regression model improved the classification performance with a PPV of 93.3%. An independent cohort was used for classifier validation (n = 36), achieving a PPV of 88.2%. In a multivariate analysis, the two-protein classifier proved to be the only independent predictor of response. CONCLUSION: We developed a two-protein immunohistochemical classifier that performs well at predicting the non-response to neoadjuvant treatment in rectal cancer.

Stem cell-like transcriptional reprogramming mediates metastatic resistance to mTOR inhibition.

Inhibitors of the mechanistic target of rapamycin (mTOR) are currently used to treat advanced metastatic breast cancer. However, whether an aggressive phenotype is sustained through adaptation or resistance to mTOR inhibition remains unknown. Here, complementary studies in human tumors, cancer models and cell lines reveal transcriptional reprogramming that supports metastasis in response to mTOR inhibition. This cancer feature is driven by EVI1 and SOX9. EVI1 functionally cooperates with and positively regulates SOX9, and promotes the transcriptional upregulation of key mTOR pathway components (REHB and RAPTOR) and of lung metastasis mediators (FSCN1 and SPARC). The expression of EVI1 and SOX9 is associated with stem cell-like and metastasis signatures, and their depletion impairs the metastatic potential of breast cancer cells. These results establish the mechanistic link between resistance to mTOR inhibition and cancer metastatic potential, thus enhancing our understanding of mTOR targeting failure.

The transcriptional repressor HDAC7 promotes apoptosis and c-Myc downregulation in particular types of leukemia and lymphoma.

The generation of B cells is a complex process requiring several cellular transitions, including cell commitment and differentiation. Proper transcriptional control to establish the genetic programs characteristic of each cellular stage is essential for the correct development of B lymphocytes. Deregulation of these particular transcriptional programs may result in a block in B-cell maturation, contributing to the development of hematological malignancies such as leukemia and lymphoma. However, very little is currently known about the role of transcriptional repressors in normal and aberrant B lymphopoiesis. Here we report that histone deacetylase 7 (HDAC7) is underexpressed in pro-B acute lymphoblastic leukemia (pro-B-ALL) and Burkitt lymphoma. Ectopic expression of HDAC7 induces apoptosis, leads to the downregulation of c-Myc and inhibits the oncogenic potential of cells in vivo, in a xenograft model. Most significantly, we have observed low levels of HDAC7 expression in B-ALL patient samples, which is correlated with the increased levels of c-Myc. From a mechanistic angle, we show that ectopically expressed HDAC7 localizes to the nucleus and interacts with the transcription factor myocyte enhancer factor C (MEF2C) and the corepressors HDAC3 and SMRT. Accordingly, both the HDAC7-MEF2C interaction domain as well as its catalytic domain are involved in the reduced cell viability induced by HDAC7. We conclude that HDAC7 has a potent anti-oncogenic effect on specific B-cell malignancies, indicating that its deregulation may contribute to the pathogenesis of the disease.

Tools for protein-protein interaction network analysis in cancer research

As cancer is a complex disease, the representation of a malignant cell as a protein-protein interaction network (PPIN) and its subsequent analysis can provide insight into the behaviour of cancer cells and lead to the discovery of new biomarkers. The aim of this review is to help life-science researchers without previous computer programming skills to extract meaningful biological information from such networks, taking advantage of easy-to-use, public bioinformatics tools. It is structured in four parts: the first section describes the pipeline of consecutive steps from network construction to biological hypothesis generation. The second part provides a repository of public, user-friendly tools for network construction, visualisation and analysis. Two different and complementary approaches of network analysis are presented: the topological approach studies the network as a whole by means of structural graph theory, whereas the global approach divides the PPIN into sub-graphs, or modules. In section three, some concepts and tools regarding heterogeneous molecular data integration through a PPIN are described. Finally, the fourth part is an example of how to extract meaningful biological information from a colorectal cancer PPIN using some of the described tools (AU)

Biological reprogramming in acquired resistance to endocrine therapy of breast cancer.

Endocrine therapies targeting the proliferative effect of 17ß-estradiol through estrogen receptor α (ERα) are the most effective systemic treatment of ERα-positive breast cancer. However, most breast tumors initially responsive to these therapies develop resistance through molecular mechanisms that are not yet fully understood. The long-term estrogen-deprived (LTED) MCF7 cell model has been proposed to recapitulate acquired resistance to aromatase inhibitors in postmenopausal women. To elucidate this resistance, genomic, transcriptomic and molecular data were integrated into the time course of MCF7-LTED adaptation. Dynamic and widespread genomic changes were observed, including amplification of the ESR1 locus consequently linked to an increase in ERα. Dynamic transcriptomic profiles were also observed that correlated significantly with genomic changes and were predicted to be influenced by transcription factors known to be involved in acquired resistance or cell proliferation (for example, interferon regulatory transcription factor 1 and E2F1, respectively) but, notably, not by canonical ERα transcriptional function. Consistently, at the molecular level, activation of growth factor signaling pathways by EGFR/ERBB/AKT and a switch from phospho-Ser118 (pS118)- to pS167-ERα were observed during MCF7-LTED adaptation. Evaluation of relevant clinical settings identified significant associations between MCF7-LTED and breast tumor transcriptome profiles that characterize ERα-negative status, early response to letrozole and tamoxifen, and recurrence after tamoxifen treatment. In accordance with these profiles, MCF7-LTED cells showed increased sensitivity to inhibition of FGFR-mediated signaling with PD173074. This study provides mechanistic insight into acquired resistance to endocrine therapies of breast cancer and highlights a potential therapeutic strategy.