scholarly journals Transcription Factor Profiling Identifies Spatially Heterogenous Mediators of Follicular Thyroid Cancer Invasion

2020 ◽  
Vol 31 (4) ◽  
pp. 367-376
Author(s):  
Norman G. Nicolson ◽  
Johan O. Paulsson ◽  
C. Christofer Juhlin ◽  
Tobias Carling ◽  
Reju Korah
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Thyroid Cancer ◽  
Expression Patterns ◽  
Thyroid Tumor ◽  
Follicular Thyroid Cancer ◽  
Tumor Patients ◽  
Tissue Samples ◽  
Widely Invasive ◽  
Worse Prognosis

AbstractWhile minimally invasive follicular thyroid cancer (miFTC) generally has low risk of recurrence or death, encapsulated angioinvasive (eaFTC) or widely invasive (wiFTC) histological subtypes display significantly worse prognosis. Drivers of invasion are incompletely understood. Therefore, tissue samples including miFTC, eaFTC, and wiFTC tumors, as well as histologically normal thyroid adjacent to benign follicular adenomas, were selected from a cohort (n = 21) of thyroid tumor patients, and the gene expression of selected transcription factors was characterized with quantitative PCR. Invasion-relevant spatial expression patterns of selected transcription factors were subsequently characterized with immunohistochemistry. E2F1 was over-expressed in all 3 subtypes (p<0.01). SP1 was differentially expressed in eaFTC and wiFTC compared with normal (p=0.01 and 0.04, respectively). TCF7L2 was significantly upregulated in wiFTC specifically (p<0.05). While these findings were mRNA specific, immunohistochemistry of additional cancer-associated transcription factors revealed differential expression along the tumor invasive front relative to the central tumor, and histone acetylation modulators emerged as putative invasion markers. These findings may have significant implications for the interpretation of bulk gene expression analysis of thyroid tumor samples or for the development of targeted therapeutics for this rare but aggressive thyroid cancer variant.

scholarly journals Bayesian Framework for Detecting Gene Expression Outliers in Individual Samples

2020 ◽  
pp. 160-170
Author(s):  
John Vivian ◽  
Jordan M. Eizenga ◽  
Holly C. Beale ◽  
Olena M. Vaske ◽  
Benedict Paten
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Rna Seq ◽  
Single Patient ◽  
Tissue Samples ◽  
Composite Tissue ◽  
Patient Sample ◽  
Statistical Framework ◽  
Therapeutic Leads ◽  
Upregulated Genes

PURPOSE Many antineoplastics are designed to target upregulated genes, but quantifying upregulation in a single patient sample requires an appropriate set of samples for comparison. In cancer, the most natural comparison set is unaffected samples from the matching tissue, but there are often too few available unaffected samples to overcome high intersample variance. Moreover, some cancer samples have misidentified tissues of origin or even composite-tissue phenotypes. Even if an appropriate comparison set can be identified, most differential expression tools are not designed to accommodate comparisons to a single patient sample. METHODS We propose a Bayesian statistical framework for gene expression outlier detection in single samples. Our method uses all available data to produce a consensus background distribution for each gene of interest without requiring the researcher to manually select a comparison set. The consensus distribution can then be used to quantify over- and underexpression. RESULTS We demonstrate this method on both simulated and real gene expression data. We show that it can robustly quantify overexpression, even when the set of comparison samples lacks ideally matched tissue samples. Furthermore, our results show that the method can identify appropriate comparison sets from samples of mixed lineage and rediscover numerous known gene-cancer expression patterns. CONCLUSION This exploratory method is suitable for identifying expression outliers from comparative RNA sequencing (RNA-seq) analysis for individual samples, and Treehouse, a pediatric precision medicine group that leverages RNA-seq to identify potential therapeutic leads for patients, plans to explore this method for processing its pediatric cohort.

scholarly journals 1450-1545 Young Investigator Awards & Presentations Basic/Translational Exploring cellular subpopulations in glioblastoma and matched organoids using single-cell RNA-seq 52

2018 ◽  
Vol 45 (S3) ◽  
pp. S13-S14
Author(s):  
VG LeBlanc ◽  
D Trinh ◽  
M Hughes ◽  
I Luthra ◽  
D Livingstone ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Tissue Sample ◽  
Expression Patterns ◽  
Model Systems ◽  
Sequencing Data ◽  
Tissue Samples ◽  
Original Tumour ◽  
Current Therapies ◽  
Intratumoural Heterogeneity

Glioblastomas (GBMs) account for nearly half of all primary malignant brain tumours, and current therapies are often only marginally effective. Our understanding of the underlying biology of these tumours and the development of new therapies have been complicated in part by widespread inter- and intratumoural heterogeneity. To characterize this heterogeneity, we performed regional subsampling of primary glioblastomas and derived organoids from these tissue samples. We then performed single-cell RNA-sequencing (scRNA-seq) on these primary regional subsamples and 1-3 matched organoids per sample. We have profiled samples from six tumour sets to date and have obtained sequencing data for 21,234 primary tissue cells and 14,742 organoid cells. While the most apparent differences in gene expression appear to be between individual tumours, we were also able to identify similar cellular subpopulations across tissue samples and across organoids. Importantly, organoids derived from the same tissue sample appeared to be composed of similar cellular subpopulations and were highly comparable to each other, indicating that replicate organoids faithfully represent the original tumour tissue. Overall, our scRNA-seq approach will help evaluate the utility of tumour-derived organoids as model systems for GBM and will aid in identifying cellular subpopulations defined by gene expression patterns, both in primary GBM regional subsamples and their associated organoids. These analyses will allow for the characterization of clonal or subclonal populations that are likely to respond to different therapeutic approaches and may also uncover novel therapeutic targets previously unrevealed through bulk analyses.

scholarly journals What functional genomics has taught us about transcriptional regulation in malaria parasites

2019 ◽  
Vol 18 (5) ◽  
pp. 290-301 ◽  
Author(s):  
Christa G Toenhake ◽  
Richárd Bártfai
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Life Cycle ◽  
Functional Genomics ◽  
Expression Patterns ◽  
Regulatory Elements ◽  
Complex Life Cycle ◽  
Malaria Parasites ◽  
Gene Regulatory

Abstract Malaria parasites are characterized by a complex life cycle that is accompanied by dynamic gene expression patterns. The factors and mechanisms that regulate gene expression in these parasites have been searched for even before the advent of next generation sequencing technologies. Functional genomics approaches have substantially boosted this area of research and have yielded significant insights into the interplay between epigenetic, transcriptional and post-transcriptional mechanisms. Recently, considerable progress has been made in identifying sequence-specific transcription factors and DNA-encoded regulatory elements. Here, we review the insights obtained from these efforts including the characterization of core promoters, the involvement of sequence-specific transcription factors in life cycle progression and the mapping of gene regulatory elements. Furthermore, we discuss recent developments in the field of functional genomics and how they might contribute to further characterization of this complex gene regulatory network.

scholarly journals Effects of Cutting, Pruning, and Grafting on the Expression of Age-Related Genes in Larix kaempferi

Forests ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 218
Author(s):  
Yao Zhang ◽  
Qiao-Lu Zang ◽  
Li-Wang Qi ◽  
Su-Ying Han ◽  
Wan-Feng Li
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Regular Expression ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Reproductive Development ◽  
Larix Kaempferi ◽  
Clonal Forestry ◽  
Cdna Sequences ◽  
Age Related

Grafting, cutting, and pruning are important horticultural techniques widely used in the establishment of clonal forestry. After the application of these techniques, some properties of the plants change, however, the underlying molecular mechanisms are still unclear. In our previous study, 27 age-related transcripts were found to be expressed differentially between the juvenile vegetative (1- and 2-year-old) and adult reproductive (25- and 50-year-old) phases of Larix kaempferi. Here, we re-analyzed the 27 age-related transcripts, cloned their full-length cDNA sequences, and measured their responses to grafting, cutting, and pruning. After sequence analysis and cloning, 20 transcription factors were obtained and annotated, most of which were associated with reproductive development, and six (LaAGL2-1, LaAGL2-2, LaAGL2-3, LaSOC1-1, LaAGL11, and LaAP2-2) showed regular expression patterns with L. kaempferi aging. Based on the expression patterns of these transcription factors in L. kaempferi trees subjected to grafting, cutting, and pruning, we concluded that (1) cutting and pruning rejuvenate the plants and change their expression, and the effects of cutting on gene expression are detectable within 14 years, although the cutting seedlings are still maturing during these years; (2) within three months after grafting, the rootstock is more sensitive to grafting than the scion and readily becomes mature with the effect of the scion, while the scion is not readily rejuvenated by the effect of the rootstock; and (3) LaAGL2-2 and LaAGL2-3 are more sensitive to grafting, while LaAP2-2 is impervious to it. These findings not only provide potential molecular markers to assess the state of plants but also aid in studies of the molecular mechanisms of rejuvenation.

The HaemAtlas: Characterising Gene Expression in Differentiated Human Blood Cells

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 2453-2453
Author(s):  
Nicholas A. Watkins ◽  
Marloes R. Tijssen ◽  
Arief Gusnanto ◽  
Bernard de Bono ◽  
Subhajyoti De ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Transcription Factors ◽  
Cell Function ◽  
Expression Profiles ◽  
Association Studies ◽  
Cytotoxic T Cells ◽  
Expression Patterns ◽  
Immunoglobulin Superfamily ◽  
Genome Wide Association Studies

Abstract Haematopoiesis is a carefully controlled process that is regulated by complex networks of transcription factors that are, in part, controlled by signals resulting from ligand binding to cell surface receptors. In order to further understand haematopoiesis, we have compared gene expression profiles of human erythroblasts, megakaryocytes, B-cells, cytotoxic and helper T-cells, Natural Killer cells, granulocytes and monocytes using whole genome microarrays. A bioinformatics analysis of this data was performed focusing on transcription factors, immunoglobulin superfamily members and lineage specific transcripts. We observed that the numbers of lineage specific genes varies by two orders of magnitude, ranging from five for cytotoxic T cells to 878 for granulocytes. In addition, we have identified novel co-expression patterns for key transcription factors involved in haematopoiesis (eg. GATA3–GFI1 and GATA2–KLF1). This study represents the most comprehensive analysis of gene expression in haematopoietic cells to date and has identified genes that play key roles in lineage commitment and cell function. The data, which is freely accessible, will be invaluable for future studies on haematopoiesis and the role of specific genes and will also aid the understanding of the recent genome-wide association studies.

Validation and Multicenter Clinical Experience of the Afirma® Gene Expression Classifier

2014 ◽  
Vol 10 (02) ◽  
pp. 117 ◽  
Author(s):  
Trevor E Angell ◽  
Erik K Alexander ◽  
◽  
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Cancer Risk ◽  
Clinical Experience ◽  
Messenger Rna ◽  
Expression Patterns ◽  
Current Data ◽  
Molecular Testing ◽  
Gene Expression Classifier ◽  
Indeterminate Cytology

The current assessment of clinically relevant thyroid nodules is imprecise and nonspecific, primarily due to the presence of indeterminate cytology. This cytologic category implies a low but clinically important risk for thyroid cancer, historically prompting consideration for diagnostic thyroidectomy in most patients. Over the last 5 years, novel molecular testing options have become available and appear able to modify preoperative thyroid cancer risk and better inform clinical decisions. The Afirma® gene expression classifier (GEC) measures messenger RNA (mRNA) expression patterns in fine-needle aspiration (FNA) tissue and seeks to identify a benign signature despite indeterminate cytology. When detected, the cancer risk is low and allows for prevention of unnecessary surgeries. The results of a large, multicenter, blinded validation trial of the Afirma GEC demonstrated the test’s high negative predictive value. Subsequent independent studies of the Afirma GEC in clinical use have documented the influence these results have on preoperative management recommendations. This review summarizes the current data regarding the Afirma GEC, including the original validation trial and recent multicenter clinical experience.

RNA-sequencing and bioinformatic analysis to pre-assess sensitivity to targeted therapeutics in recurrent glioblastoma.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e13533-e13533
Author(s):  
Ella L Kim ◽  
Anton Buzdin ◽  
Maxim Sorokin ◽  
Elena Poddubskaya ◽  
Artem Poddubskiy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Cell Cultures ◽  
Radiation Treatment ◽  
Clinical Stage ◽  
Expression Patterns ◽  
Recurrent Glioblastoma ◽  
Targeted Therapeutics ◽  
Biopsy Material ◽  
Tissue Samples

e13533 Background: This study developed molecular guided tools for individualized selection of chemotherapeutics for recurrent glioblastoma (rGB). A consortium involving clinical neurooncologists, molecular biologists and bioinformaticians identified gene expression patterns in rGB and quantitatively analyzed pathways involved in response to FDA approved oncodrugs. Methods: From2016 to 2018 biopsies from GB were collected using a multisampling approach. Biopsy material was used to isolate glioma stem-like cells and examined by RNA-sequencing. RNA-seq results were subjected to differential expression (DE) analysis and Oncobox analysis – a bioinformatic tool for quantitative pathway activation analysis. Results for newly diagnosed (nGB) and rGB (tissue samples and cell cultures) were compared. Oncobox analysis was further used to examine differential activation of pathways involved in response to existing chemotherapeutics. Results: 128 tissue samples and 28 cell cultures from a total of 44 GBs including 23 nGB, 19 rGB and 2 second-recurrent GBs were analyzed. 14 patient-matched pairs of nGB and rGB were obtained. DE analysis of nGB and rGB, showed a distinct “signature” associated with rGB. Oncobox analysis found down regulation of pathways related to cell cycle and DNA repair and upregulation of immune response pathways in rGB vs corresponding nGB. Specifically, pathways targeted by temozolomide, which is the first line chemotherapy for GB, were found down regulated in rGB. Among the top pathways upregulated in rGB were the pathways targeted by durvalumab and pomalidomide currently under investigation in phase II or III trials for GB. Conclusions: Specific pathway analysis revealed regional and clinical stage-associated differences in the transcriptional landscapes of nGB and rGB. Our results support a concept of treatment-induced resistance to cytotoxic therapeutics and indicate that temozolomide and radiation treatment have important impacts on gene expression changes associated with GB recurrence. Systematic molecular profiling of rGB is a promising avenue towards predicting sensitivity to targeted therapeutics in rGBs on an individual basis.

scholarly journals Alternative mutations of BRAF, RET and NTRK1 are associated with similar but distinct gene expression patterns in papillary thyroid cancer

Oncogene ◽  
2004 ◽  
Vol 23 (44) ◽  
pp. 7436-7440 ◽  
Author(s):  
Milo Frattini ◽  
Cristina Ferrario ◽  
Paola Bressan ◽  
Debora Balestra ◽  
Loris De Cecco ◽  
...  
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Papillary Thyroid Cancer ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Papillary Thyroid ◽  
Distinct Gene

scholarly journals Histologically resolved spatial multi-omics of human oral squamous cell carcinoma

2017 ◽  
Author(s):  
Tao Chen ◽  
Chen Cao ◽  
Jianyun Zhang ◽  
Aaron Streets ◽  
Yanyi Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Spatial Resolution ◽  
Squamous Cell ◽  
Single Cells ◽  
Expression Patterns ◽  
Critical Role ◽  
Molecular Profile ◽  
Tissue Samples

AbstractBoth the composition of cell types and their spatial distribution in a tissue play a critical role in cellular function, organ development, and disease progression. For example, intratumor heterogeneity and the distribution of transcriptional and genetic events in single cells drive the genesis and development of cancer. However, it can be challenging to fully characterize the molecular profile of cells in a tissue with high spatial resolution because microscopy has limited ability to extract comprehensive genomic information, and the spatial resolution of genomic techniques tends to be limited by dissection. There is a growing need for tools that can be used to explore the relationship between histological features, gene expression patterns, and spatially correlated genomic alterations in healthy and diseased tissue samples. Here, we present a technique that combines label-free histology with spatially resolved multi-omics in un-fixed and unstained tissue sections. This approach leverages stimulated Raman scattering microscopy to provide chemical contrast that reveals histological tissue architecture, allowing for high-resolution in situ laser micro-dissection of regions of interests. These micro-tissue samples are then processed for DNA and RNA sequencing to identify unique genetic profiles that correspond to distinct anatomical regions. We demonstrate the capabilities of this technique by mapping gene expression and copy number alterations to histologically defined regions in human squamous cell carcinoma (OSCC). Our approach provides complementary insights in tumorigenesis and offers an integrative tool for macroscale cancer tissues with spatial multi-omics assessments.

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