Goal‐Striving Stress and the Mental Health of College‐Educated Black American Men: The Protective Effects of System‐Blame

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/112263/1/j.1939-0025.2011.01116.x.pd

T145 Comprehensive flow cytometry tracking of regulatory T cells and other lymphocyte subsets during HD IL-2 therapy for melanoma

High dose IL-2 (HD IL-2) has been extensively used as an immunotherapy against metastatic melanoma However, why HD IL-2 is effective only in a subset of patients and whether predictive biomarkers, before or early during the course of therapy, can be used to improve response rates remain unresolved. In addition, it has been found that IL-2 therapy potently expands CD4+CD25+Foxp3+ T-regulatory cells (Tregs) but how Treg cell levels, phenotype, and function change and whether specific subsets of Tregs are activated and expanded during HD IL-2 therapy is remain unclear. In this study, we performed comprehensive multi-parameter FACS analysis of patient blood before and two days after the last bolus of IL-2 infusion during cycle 1 of HD IL-2 therapy. Two lymphocyte subsets were found to expand the most during the first cycle of IL-2 therapy: CD4+CD25+Foxp3+ Tregs expressing an activation marker, inducible costimulator (ICOS), and CD3-CD56hiCD16loPerforin+ NK cells. ICOS+ Tregs expressed significantly higher levels of CD25, Foxp3 and had a more activated phenotype than ICOS− Tregs as indicated by lower levels of CD45RA and CD127 expression. Further phenotypic characterization revealed a more suppressive phenotype on ICOS+ Treg with higher expression levels of CD39, CD73, and TGF-β/LAP than ICOS− Treg. ICOS+ Tregs were also the predominant Treg cells that secreted IL-10 and have potent T-cell suppressor function. Majority of ICOS+ Tregs from HD IL-2-treated patients were Ki67+ and exhibited an enhanced proliferative response to IL-2 ex vivo relative to ICOS− Tregs. Functional analysis revealed that ICOS+ Tregs secreted little IFN- and IL-2 in comparison to CD4+Foxp3 – cells. Furthermore, analysis on 38 IL-2-treated patients at MD Anderson, we found that non-responders had a significantly higher degree of ICOS+ Treg expansion than responders during the first cycle of IL-2 therapy, while no significant changes in the ICOS− or bulk Treg population. In conclusion, our data suggests that tracking changes in ICOS+ Tregs early during the course of HD IL-2 therapy may be a new predictive biomarker of clinical outcome

Moving towards a cure in genetics : what is needed to bring somatic gene therapy to the clinic?

Clinical trials using somatic gene editing (e.g., CRISPR-Cas9) have started in Europe and the United States and may provide safe and effective treatment and cure, not only for cancers but also for some monogenic conditions. In a workshop at the 2018 European Human Genetics Conference, the challenges of bringing somatic gene editing therapies to the clinic were discussed. The regulatory process needs to be considered early in the clinical development pathway to produce the data necessary to support the approval by the European Medicines Agency. The roles and responsibilities for geneticists may include counselling to explain the treatment possibilities and safety interpretation.Peer reviewe

A Novel Nanoproteomic Approach for the Identification of Molecular Targets Associated with Thyroid Tumors

A thyroid nodule is the most common presentation of thyroid cancer; thus, it is extremely important to differentiate benign from malignant nodules. Within malignant lesions, classification of a thyroid tumor is the primary step in the assessment of the prognosis and selection of treatment. Currently, fine-needle aspiration biopsy (FNAB) is the preoperative test most commonly used for the initial thyroid nodule diagnosis. However, due to some limitations of FNAB, different high-throughput "omics" approaches have emerged that could further support diagnosis based on histopathological patterns. In the present work, formalin-fixed paraffin-embedded (FFPE) tissue specimens from normal (non-neoplastic) thyroid (normal controls (NCs)), benign tumors (follicular thyroid adenomas (FTAs)), and some common types of well-differentiated thyroid carcinoma (follicular thyroid carcinomas (FTCs), conventional or classical papillary thyroid carcinomas (CV-PTCs), and the follicular variant of papillary thyroid carcinomas (FV-PTCs)) were analyzed. For the first time, FFPE thyroid samples were deparaffinized using an easy, fast, and non-toxic method. Protein extracts from thyroid tissue samples were analyzed using a nanoparticle-assisted proteomics approach combined with shotgun LC-MS/MS. The differentially regulated proteins found to be specific for the FTA, FTC, CV-PTC, and FV-PTC subtypes were analyzed with the bioinformatic tools STRING and PANTHER showing a profile of proteins implicated in the thyroid cancer metabolic reprogramming, cancer progression, and metastasis. These proteins represent a new source of potential molecular targets related to thyroid tumors

Identification of a Profile of Neutrophil-Derived Granule Proteins in the Surface of Gold Nanoparticles after Their Interaction with Human Breast Cancer Sera

It is well known that the interaction of a nanomaterial with a biological fluid leads to the formation of a protein corona (PC) surrounding the nanomaterial. Using standard blood analyses, alterations in protein patterns are difficult to detect. PC acts as a "nano-concentrator" of serum proteins with affinity for nanoparticles' surface. Consequently, characterization of PC could allow detection of otherwise undetectable changes in protein concentration at an early stage of a disease, such as breast cancer (BC). Here, we employed gold nanoparticles (AuNPsdiameter: 10.02 +/- 0.91 nm) as an enrichment platform to analyze the human serum proteome of BC patients (n = 42) and healthy controls (n = 42). Importantly, the analysis of the PC formed around AuNPs after their interaction with serum samples of BC patients showed a profile of proteins that could differentiate breast cancer patients from healthy controls. These proteins developed a significant role in the immune and/or innate immune system, some of them being neutrophil-derived granule proteins. The analysis of the PC also revealed serum proteome alterations at the subtype level

Novel Mutation Hotspots within Non-Coding Regulatory Regions of the Chronic Lymphocytic Leukemia Genome

Mutations in non-coding DNA regions are increasingly recognized as cancer drivers. These mutations can modify gene expression in cis or by inducing high-order chormatin structure modifications with long-range effects. Previous analysis reported the detection of recurrent and functional non-coding DNA mutations in the chronic lymphocytic leukemia (CLL) genome, such as those in the 3' untranslated region of NOTCH1 and in the PAX5 super-enhancer. In this report, we used whole genome sequencing data produced by the International Cancer Genome Consortium in order to analyze regions with previously reported regulatory activity. This approach enabled the identification of numerous recurrently mutated regions that were frequently positioned in the proximity of genes involved in immune and oncogenic pathways. By correlating these mutations with expression of their nearest genes, we detected significant transcriptional changes in genes such as PHF2 and S1PR2. More research is needed to clarify the function of these mutations in CLL, particularly those found in intergenic regions

Multifaceted role of BTLA in the control of CD8+ T cell fate after antigen encounter

Purpose: Adoptive T-cell therapy using autologous tumor-infiltrating lymphocytes (TIL) has shown an overall clinical response rate 40%–50% in metastatic melanoma patients. BTLA (B-and-T lymphocyte associated) expression on transferred CD8+ TILs was associated with better clinical outcome. The suppressive function of the ITIM and ITSM motifs of BTLA is well described. Here, we sought to determine the functional characteristics of the CD8+BTLA+TIL subset and define the contribution of the Grb2 motif of BTLA in T-cell costimulation. Experimental Design: We determined the functional role and downstream signal of BTLA in both human CD8+ TILs and mouse CD8+ T cells. Functional assays were used including single-cell analysis, reverse-phase protein array (RPPA), antigen-specific vaccination models with adoptively transferred TCR-transgenic T cells as well as patient-derived xenograft (PDX) model using immunodeficient NOD-scid IL2Rgammanull (NSG) tumor-bearing mice treated with autologous TILs. Results: CD8+BTLA? TILs could not control tumor growth in vivo as well as their BTLA+ counterpart and antigen-specific CD8+BTLA? T cells had impaired recall response to a vaccine. However, CD8+BTLA+ TILs displayed improved survival following the killing of a tumor target and heightened “serial killing” capacity. Using mutants of BTLA signaling motifs, we uncovered a costimulatory function mediated by Grb2 through enhancing the secretion of IL-2 and the activation of Src after TCR stimulation. Conclusions: Our data portrays BTLA as a molecule with the singular ability to provide both costimulatory and coinhibitory signals to activated CD8+ T cells, resulting in extended survival, improved tumor control, and the development of a functional recall response. Clin Cancer Res; 23(20); 6151–64. ©2017 AACR

Improved personalized survival prediction of patients with diffuse large B-cell Lymphoma using gene expression profiling

BACKGROUND: Thirty to forty percent of patients with Diffuse Large B-cell Lymphoma (DLBCL) have an adverse clinical evolution. The increased understanding of DLBCL biology has shed light on the clinical evolution of this pathology, leading to the discovery of prognostic factors based on gene expression data, genomic rearrangements and mutational subgroups. Nevertheless, additional efforts are needed in order to enable survival predictions at the patient level. In this study we investigated new machine learning-based models of survival using transcriptomic and clinical data. METHODS: Gene expression profiling (GEP) of in 2 different publicly available retrospective DLBCL cohorts were analyzed. Cox regression and unsupervised clustering were performed in order to identify probes associated with overall survival on the largest cohort. Random forests were created to model survival using combinations of GEP data, COO classification and clinical information. Cross-validation was used to compare model results in the training set, and Harrel's concordance index (c-index) was used to assess model's predictability. Results were validated in an independent test set. RESULTS: Two hundred thirty-three and sixty-four patients were included in the training and test set, respectively. Initially we derived and validated a 4-gene expression clusterization that was independently associated with lower survival in 20% of patients. This pattern included the following genes: TNFRSF9, BIRC3, BCL2L1 and G3BP2. Thereafter, we applied machine-learning models to predict survival. A set of 102 genes was highly predictive of disease outcome, outperforming available clinical information and COO classification. The final best model integrated clinical information, COO classification, 4-gene-based clusterization and the expression levels of 50 individual genes (training set c-index, 0.8404, test set c-index, 0.7942). CONCLUSION: Our results indicate that DLBCL survival models based on the application of machine learning algorithms to gene expression and clinical data can largely outperform other important prognostic variables such as disease stage and COO. Head-to-head comparisons with other risk stratification models are needed to compare its usefulness