Predictive integration of gene functional similarity and co-expression defines treatment response of endothelial progenitor cells

<p>Abstract</p> <p>Background</p> <p>Endothelial progenitor cells (EPCs) have been implicated in different processes crucial to vasculature repair, which may offer the basis for new therapeutic strategies in cardiovascular disease. Despite advances facilitated by functional genomics, there is a lack of systems-level understanding of treatment response mechanisms of EPCs. In this research we aimed to characterize the EPCs response to adenosine (Ado), a cardioprotective factor, based on the systems-level integration of gene expression data and prior functional knowledge. Specifically, we set out to identify novel biosignatures of Ado-treatment response in EPCs.</p> <p>Results</p> <p>The predictive integration of gene expression data and standardized functional similarity information enabled us to identify new treatment response biosignatures. Gene expression data originated from Ado-treated and -untreated EPCs samples, and functional similarity was estimated with Gene Ontology (GO)-based similarity information. These information sources enabled us to implement and evaluate an integrated prediction approach based on the concept of <it>k</it>-nearest neighbours learning (<it>k</it>NN). The method can be executed by expert- and data-driven input queries to guide the search for biologically meaningful biosignatures. The resulting <it>integrated kNN </it>system identified new candidate EPC biosignatures that can offer high classification performance (areas under the operating characteristic curve > 0.8). We also showed that the proposed models can outperform those discovered by standard gene expression analysis. Furthermore, we report an initial independent <it>in vitro </it>experimental follow-up, which provides additional evidence of the potential validity of the top biosignature.</p> <p>Conclusion</p> <p>Response to Ado treatment in EPCs can be accurately characterized with a new method based on the combination of gene co-expression data and GO-based similarity information. It also exploits the incorporation of human expert-driven queries as a strategy to guide the automated search for candidate biosignatures. The proposed biosignature improves the systems-level characterization of EPCs. The new integrative predictive modeling approach can also be applied to other phenotype characterization or biomarker discovery problems.</p

Targeting leukemia stem cells in the bone marrow niche

The bone marrow (BM) niche encompasses multiple cells of mesenchymal and hematopoietic origin and represents a unique microenvironment that is poised to maintain hematopoietic stem cells. In addition to its role as a primary lymphoid organ through the support of lymphoid development, the BM hosts various mature lymphoid cell types, including naïve T cells, memory T cells and plasma cells, as well as mature myeloid elements such as monocyte/macrophages and neutrophils, all of which are crucially important to control leukemia initiation and progression. The BM niche provides an attractive milieu for tumor cell colonization given its ability to provide signals which accelerate tumor cell proliferation and facilitate tumor cell survival. Cancer stem cells (CSCs) share phenotypic and functional features with normal counterparts from the tissue of origin of the tumor and can self-renew, differentiate and initiate tumor formation. CSCs possess a distinct immunological profile compared with the bulk population of tumor cells and have evolved complex strategies to suppress immune responses through multiple mechanisms, including the release of soluble factors and the over-expression of molecules implicated in cancer immune evasion. This chapter discusses the latest advancements in understanding of the immunological BM niche and highlights current and future immunotherapeutic strategies to target leukemia CSCs and overcome therapeutic resistance in the clinic

Joint transcriptomic analysis of lung cancer and other lung diseases

Q2Q1Completo1-18Background: Epidemiological and clinical evidence points cancer comorbidity with pulmonary chronic disease. The acquisition of some hallmarks of cancer by cells affected with lung pathologies as a cell adaptive mechanism to a shear stress, suggests that could be associated with the establishment of tumoral processes. Objective: To propose a bioinformatic pipeline for the identification of all deregulated genes and the transcriptional regulators (TFs) that are coexpressed during lung cancer establishment, and therefore could be important for the acquisition of the hallmarks of cancer. Methods: Ten microarray datasets (six of lung cancer, four of lung diseases) comparing normal and diseases-related lung tissue were selected to identify hub differentiated expressed genes (DEGs) in common between lung pathologies and lung cancer, along with transcriptional regulators through the utilization of specialized libraries from R language. DAVID bioinformatics tool for gene enrichment analyses was used to identify genes with experimental evidence associated to tumoral processes and signaling pathways. Coexpression networks of DEGs and TFs in lung cancer establishment were created with Coexnet library, and a survival analysis of the main hub genes was made. Results: Two hundred ten DEGs were identified in common between lung cancer and other lung diseases related to the acquisition of tumoral characteristics, which are coexpressed in a lung cancer network with TFs, suggesting that could be related to the establishment of the tumoral pathology in lung. The comparison of the coexpression networks of lung cancer and other lung diseases allowed the identification of common connectivity patterns (CCPs) with DEGs and TFs correlated to important tumoral processes and signaling pathways, that haven´t been studied to experimentally validate their role in the early stages of lung cancer. Some of the TFs identified showed a correlation between its expression levels and the survival of lung cancer patients. Conclusion: Our findings indicate that lung diseases share genes with lung cancer which are coexpressed in lung cancer, and might be able to explain the epidemiological observations that point to direct and inverse comorbid associations between some chronic lung diseases and lung cancer and represent a complex transcriptomic scenario

Pluripotent Stem Cell Biology

Pluripotent stem cells have the potential to revolutionize treatment options for a range of diseases and conditions. This book presents recent advances in our understanding of the biological mechanisms of stem cell self-renewal, reprograming and regeneration. Also covered are novel methodological advances in the culture, purification and use of stem cells, as well as the ethical and moral dilemmas of embryo donation and adoption. These advances will shape the utilization of stem cells for future basic and applied applications

Deciphering the Immune Evolution Landscape of Multiple Myeloma Long-Term Survivors Using Single Cell Genomics

Multiple myeloma (MM) is a malignant bone marrow (BM) disease characterized by somatic hypermutation and DNA damage in plasma cells; leading to the overproduction of dysfunctional malignant myeloma cells. Accumulation of myeloma cells has direct and indirect effects on the BM and other organs. Despite the development of new therapeutic options; MM remains incurable and only a small fraction of patients experiences long-term survival (LTS). The past has shown that ultimately all patients still relapse; leading to the hypothesis that a state of active immune-surveillance is required to control the residual disease. To understand the long-term survival phenomenon and its link to the immune-phenotypes in MM disease; we collected paired bone marrow samples from 24 patients who survived for about 7 to 17 years after Autologous Stem Cell Transplant (ASCT), with a high plasma cell infiltration in the BM (median 49.5%) at diagnosis time. Response assessment according to the International Myeloma Working Group (IMWG) revealed that 15 patients were in complete remission (CR), whereas 9 patients were in non-complete remission (non-CR) that had tumor cells which remained stable over recent years. We performed single-cell RNA-seq sequencing on more than 290,000 bone marrow cells from 11 patients before treatment (BT) and in LTS, as well as three healthy controls using 10x Genomics technology. I developed a computational approach using the state-of-the-art single cell methods, statistical inference and machine learning models to decipher the bone marrow immune cell types and states across all clinical groups. I performed in-depth analyses of the bone marrow immune microenvironment across all captured cell types, and provided the global landscape of cellular states across all clinical groups. In this work, I defined new cellular states, marker genes, and gene signatures associated with the patients’ clinical and survival states. Additionally, I defined a new myeloid population termed Myeloma-associated Neutrophils (MAN) cells and a T cell exhaustion population termed Aberrant Memory Cytotoxic (AMC) CD8+ T cells in newly diagnosed Multiple Myeloma patients. Moreover, I propose new therapeutic targets CXCR3 and NR4A2 in AMC CD8+ T cells, which could be further investigated to reverse the T cell exhaustion state in newly diagnosed MM patients. Furthermore, I defined new prognostic markers in the CD8+ T cell compartment which could be predictive for the global disease state. Finally, I propose that MM long-term survivors go through a complex and evolving immune landscape and acquire cellular states in a stepwise manner. Furthermore, I propose the Continuum Immune Landscape (CIL) Model which explains the immune landscape of MM patients before and after long-term survival. Additionally, I introduced the Disease-State Trajectories (DST) hypothesis regarding the disease-associated dysregulated cellular states in MM context, which could be generalized into other tumor entities and diseases

Deciphering the Immune Landscape in Renal Cell Carcinoma and in Anti-PD-1 Therapy

Antigen recognition and T-cell mediated cytotoxicity are major tenets of cancer immunology that are not fully understood in clear-cell renal cell carcinoma (ccRCC). We evaluated multiregional treatment naïve nephrectomy samples from 27 patients as well as bloods samples from 21 and normal kidney tissue from 11 patients from the TRACERx Renal (TRAcking Cancer Evolution through therapy [Rx]) study via high dimensional flow cytometry. Results showed that the T cells in the tumour, normal kidney and blood have different phenotypes and differentiation patterns. A predominantly exhausted CD8 cell phenotype with expression of PD-1, TIM-3, Eomes, CD38 and CD39 was seen in the tumour immune microenvironment. ADAPTeR is a phase II study evaluating nivolumab (anti-PD1 antibody) in patients with treatment-naive metastatic ccRCC. Immunophenotyping by using high dimensional flow cytometry and multiplex immunofluorescence in addition to T cell receptor (TCR) sequencing was performed on 93 pre- and post-treatment, multi-region tumour and peripheral blood samples from 15 patients. We showed that an increased Granzyme B production in the CD8 cells and higher B cell infiltration at baseline were associated with response to Nivolumab. TCR sequencing analysis showed that maintenance of expanded TCR clones during the anti-PD1 treatment which were present pre-treatment and increased clustering of TCR clonotypes are associated with response to therapy. Comparing a responder patient with a non-responder by using single cell RNA Sequencing (SC RNA Seq) showed a more dysfunctional phenotype in the responder. In addition, Nivolumab bound CD8 cells in the responder also had higher Granzyme B and TCF7 expression suggesting a more cytotoxic and progenitor-like phenotype is associated with response. This study provides important data that needs to be validated in a bigger cohort to identify biomarkers of response to anti-PD-1 therapy in ccRCC

Ethnic differences in endothelial function and monocyte subsets in heart failure

Introduction and Aims: The progressive nature of heart failure (HF) is reflected by its complex pathophysiology, featured by imbalance of damaging and reparative factors. The overall aim was to assess the implication of endothelial (dys)function, monocyte subsets, different types of endothelial progenitors and plasma microparticles in subjects with HF. A special focus was an investigation of possible ethnic differences in these parameters. Methods: Parameters of vascular function, monocyte subsets, endothelial progenitors, and cellular microparticles were compared between South Asian subjects with systolic HF, and those with heart disease without HF and healthy controls. Ethnic differences in HF were assessed in three ethnic groups: South Asians, Whites, and African-Caribbeans. Additionally, leukocyte counts were compared between subjects with HF with reduced or preserved ejection fraction, whose outcome (mortality) was recorded during follow-up. Results: South Asian subjects with HF had significantly impaired micro- and macrovascular endothelial function, reduced levels of endothelial progenitors, and monocytes with reparative potential, but increased levels of microparticles. In HF patients, a high count of monocyte microparticles was associated with low ejection fraction. There were significant ethnic differences in characteristics of microvascular endothelial function, counts of CD14++CD16+ and CD14+CD16++ monocytes and monocyte-derived endothelial progenitors. On multivariate analysis, a high monocyte count was a significant predictor of death in HF with preserved ejection fraction unlike in those with systolic HF. Conclusions: Significant impairment of microvascular endothelial function is present in South Asian subjects with HF. High monocyte count is an independent predictor of death in HF with preserved ejection fraction. The value of the tested biological markers as therapeutic targets should be explored in future studies