scholarly journals Contribution of Immune Cells to Glucocorticoid Receptor Expression in Breast Cancer

2020 ◽  
Vol 21 (13) ◽  
pp. 4635 ◽  
Author(s):  
Shipra Gandhi ◽  
Ahmed Elkhanany ◽  
Masanori Oshi ◽  
Tao Dai ◽  
Mateusz Opyrchal ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Glucocorticoid Receptor ◽  
Single Cell ◽  
Immune Cells ◽  
Receptor Expression ◽  
Gene Set Enrichment Analysis ◽  
Proliferation Index ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Risk Of Cancer

Breast cancer (BC) patients experience increased stress with elevated cortisol levels, increasing risk of cancer recurrence. Cortisol binds to a cytoplasmic receptor, glucocorticoid receptor (GR) encoded by GR gene (NR3C1). We hypothesized that not only cancer cells, but even immune cells in the tumor microenvironment (TME) may contribute to GR expression in bulk tumor and influence prognosis. To test this, mRNA expression data was accessed from METABRIC and TCGA. “High” and “low” expression was based on highest and lowest quartiles of NR3C1 gene expression, respectively. Single-cell sequencing data were obtained from GSE75688 and GSE114725 cohorts. Computer algorithms CIBERSORT, Gene Set Enrichment Analysis and TIMER were used. GR-high BC has better median disease-free and disease-specific survival. Single cell sequencing data showed higher GR expression on immune cells compared to cancer and stromal cells. Positive correlation between GR-high BC and CD8+ T-cells was noted. In GR-high tumors, higher cytolytic activity (CYT) with decreased T-regulatory and T-follicular helper cells was observed. High GR expression was associated with lower proliferation index Ki67, enriched in IL-2_STAT5, apoptosis, KRAS, TGF-β signaling, and epithelial-to-mesenchymal transition. Immune cells significantly contribute to GR expression of bulk BC. GR-high BC has a favorable TME with higher CYT with favorable outcomes.

scholarly journals From Oncogenic Signaling Pathways to Single-Cell Sequencing of Immune Cells: Changing the Landscape of Cancer Immunotherapy

Molecules ◽  
2021 ◽  
Vol 26 (8) ◽  
pp. 2278
Author(s):  
Afshin Derakhshani ◽  
Zeinab Rostami ◽  
Hossein Safarpour ◽  
Mahdi Abdoli Shadbad ◽  
Niloufar Sadat Nourbakhsh ◽  
...  
Keyword(s):  
Single Cell ◽  
Signaling Pathways ◽  
Immune Cells ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Cancer Development ◽  
Immune Checkpoints ◽  
Single Cell Sequencing ◽  
Increased Risk

Over the past decade, there have been remarkable advances in understanding the signaling pathways involved in cancer development. It is well-established that cancer is caused by the dysregulation of cellular pathways involved in proliferation, cell cycle, apoptosis, cell metabolism, migration, cell polarity, and differentiation. Besides, growing evidence indicates that extracellular matrix signaling, cell surface proteoglycans, and angiogenesis can contribute to cancer development. Given the genetic instability and vast intra-tumoral heterogeneity revealed by the single-cell sequencing of tumoral cells, the current approaches cannot eliminate the mutating cancer cells. Besides, the polyclonal expansion of tumor-infiltrated lymphocytes in response to tumoral neoantigens cannot elicit anti-tumoral immune responses due to the immunosuppressive tumor microenvironment. Nevertheless, the data from the single-cell sequencing of immune cells can provide valuable insights regarding the expression of inhibitory immune checkpoints/related signaling factors in immune cells, which can be used to select immune checkpoint inhibitors and adjust their dosage. Indeed, the integration of the data obtained from the single-cell sequencing of immune cells with immune checkpoint inhibitors can increase the response rate of immune checkpoint inhibitors, decrease the immune-related adverse events, and facilitate tumoral cell elimination. This study aims to review key pathways involved in tumor development and shed light on single-cell sequencing. It also intends to address the shortcomings of immune checkpoint inhibitors, i.e., their varied response rates among cancer patients and increased risk of autoimmunity development, via applying the data from the single-cell sequencing of immune cells.

484 Bioturing browser: interactively explore public single cell sequencing data

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A520-A520
Author(s):  
Son Pham ◽  
Tri Le ◽  
Tan Phan ◽  
Minh Pham ◽  
Huy Nguyen ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Cell Types ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Data Formats ◽  
Cancer Types ◽  
Cell Data

BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual datasets and meta-analysis.MethodsN/AResultsWe present BioTuring Browser, an interactive platform for accessing and reanalyzing published single-cell omics data. The platform is currently hosting a curated database of more than 10 million cells from 247 projects, covering more than 120 immune cell types and subtypes, and 15 different cancer types. All data are processed and annotated with standardized labels of cell types, diseases, therapeutic responses, etc. to be instantly accessed and explored in a uniform visualization and analytics interface. Based on this massive curated database, BioTuring Browser supports searching similar expression profiles, querying a target across datasets and automatic cell type annotation. The platform supports single-cell RNA-seq, CITE-seq and TCR-seq data. BioTuring Browser is now available for download at www.bioturing.com.ConclusionsN/A

Predictive factors associated with a pathologic complete response in a Mexican HER2+ breast cancer population.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e12630-e12630
Author(s):  
Raul Alejandro Andrade Moreno ◽  
José Fabián Martínez-Herrera ◽  
Geovani Amador ◽  
Raquel Gerson Cwilich ◽  
Juan Alberto Serrano ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Predictive Factors ◽  
Hormone Receptor ◽  
Locally Advanced ◽  
Complete Response ◽  
Receptor Expression ◽  
Proliferation Index ◽  
Comprehensive Cancer Center ◽  
Lymph Node Status ◽  
Her2 Breast Cancer

e12630 Background: The current standard of treatment for locally advanced and early HER2+ breast cancer is the use of neoadjuvant chemotherapy (NAC) in combination with trastuzumab and pertuzumab. Mexican reports about its efficacy and predictive factors leading to pathological complete response (pCR) are scarce and few statistics are known. Methods: We performed a retrospective review of medical records of locally advanced and early HER2+ breast cancer patients who were treated with NAC in association with pertuzumab and trastuzumab. pCR was defined as the absence of residual invasive cancer cells in the breast and lymph nodes (ypT0/ypN0). Other histopathological features included Tumor type, estrogen, and progesterone receptor expression, HER2 status and Ki67. Clinical data included age, body mass index and number of metastatic nodes. Results: Thirty-five patients with early or locally advanced HER2+ breast cancer diagnosed and treated in a Comprehensive Cancer Center between January 2014 to June 2020 were included. The median age in the population was 47 years (range 28-79) with 20 patients under 50 years (57% of the total population). 40% of the patients were classified as overweight or obese at the time of diagnosis. The predominant histology was infiltrating ductal carcinoma (91%). The most frequent clinical stages were IIA, (34.2%) IIB (31.4%) and IIIA (22.8%). The population included patients with N0 (21.7%), N1 (56.5%), N2 (13%) and N3 (8.7%). Most tumors were larger than 2 centimeters at the time of diagnosis. T1 (17.4%), T2 (60.9%), T3 (17.4%) and T4 (4.3%). Most of the patients (77%) had a high proliferation index (Ki67 > 20). A total of 12 patients (34.3%) were hormone receptor (HR) negative and the rest (65.7%) were categorized as Triple Positive. The chemotherapy schemes used for NAC treatment were AC/THP (57.5%), THP (22.8%), TCHP (17.1%) and FEC/THP (2.7%) pCR was achieved in 60% of the patients. Patients with HR (-) achieved a pCR in 83% of the cases (10/12 patients) against 47.8% (11/23 patients) of the triple positive population. The Odds ratio (OR) for residual disease was 6.6 (95%CI 1.17-37.02) in the HR+ population. HR-/HER2+ tumors (p = 0.49) were independent predictors of pCR at multivariate logistic regression. No other variables including Ki67, BMI, age, tumor size, type of chemotherapy administered, and lymph node status were statistically significant. Conclusions: In this Mexican population there is a significant difference between the percentage of patients who achieve pCR in relation to the status of hormone receptors, favoring those patients with hormone receptor negative tumors. Nevertheless, most of the population achieves this benefit regardless of their hormone status, as HER2+ tumors showed sensitivity to chemotherapy and to the humanized anti-HER2 therapies. No other clinical or pathological variables were associated with pCR.

Multi tissue processing for single cell sequencing of human immune cells v1

2021 ◽  
Author(s):  
Daniel Rainbow ◽  
Sarah Howlett ◽  
Lorna Jarvis ◽  
Joanne Jones
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cells ◽  
Human Tissues ◽  
Single Cell Sequencing ◽  
Tissue Processing ◽  
Simultaneous Processing ◽  
Single Cell Rna Sequencing ◽  
Human Immune

This protocol has been developed for the simultaneous processing of multiple human tissues to extract immune cells for single cell RNA sequencing using the 10X platform, and ideal for atlasing projects. Included in this protocol are the steps needed to go from tissue to loading the 10X Chromium for single cell RNA sequencing and includes the hashtag and CiteSeq labelling of cells as well as the details needed to stimulate cells with PMA+I.

scholarly journals P02.10 FocuSCOPE: a single cell, multi-omics solution to simultaneously analyze tumor variants and microenvironment

2021 ◽  
Vol 9 (Suppl 1) ◽  
pp. A12.1-A12
Author(s):  
Y Arjmand Abbassi ◽  
N Fang ◽  
W Zhu ◽  
Y Zhou ◽  
Y Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Single Cell ◽  
High Throughput ◽  
Immune Cells ◽  
Genetic Variants ◽  
Expression Profiles ◽  
Single Cells ◽  
Gene Expression Profiles ◽  
Single Cell Sequencing

Recent advances of high-throughput single cell sequencing technologies have greatly improved our understanding of the complex biological systems. Heterogeneous samples such as tumor tissues commonly harbor cancer cell-specific genetic variants and gene expression profiles, both of which have been shown to be related to the mechanisms of disease development, progression, and responses to treatment. Furthermore, stromal and immune cells within tumor microenvironment interact with cancer cells to play important roles in tumor responses to systematic therapy such as immunotherapy or cell therapy. However, most current high-throughput single cell sequencing methods detect only gene expression levels or epigenetics events such as chromatin conformation. The information on important genetic variants including mutation or fusion is not captured. To better understand the mechanisms of tumor responses to systematic therapy, it is essential to decipher the connection between genotype and gene expression patterns of both tumor cells and cells in the tumor microenvironment. We developed FocuSCOPE, a high-throughput multi-omics sequencing solution that can detect both genetic variants and transcriptome from same single cells. FocuSCOPE has been used to successfully perform single cell analysis of both gene expression profiles and point mutations, fusion genes, or intracellular viral sequences from thousands of cells simultaneously, delivering comprehensive insights of tumor and immune cells in tumor microenvironment at single cell resolution.Disclosure InformationY. Arjmand Abbassi: None. N. Fang: None. W. Zhu: None. Y. Zhou: None. Y. Chen: None. U. Deutsch: None.

scholarly journals Effective clustering for single cell sequencing cancer data

2019 ◽  
Author(s):  
Simone Ciccolella ◽  
Murray Patterson ◽  
Paola Bonizzoni ◽  
Gianluca Della Vedova
Keyword(s):  
Single Cell ◽  
Categorical Data ◽  
Euclidean Distance ◽  
Missing Values ◽  
False Negative ◽  
Ground Truth ◽  
Sequencing Data ◽  
Large Space ◽  
Cancer Data ◽  
Single Cell Sequencing

AbstractBackgroundSingle cell sequencing (SCS) technologies provide a level of resolution that makes it indispensable for inferring from a sequenced tumor, evolutionary trees or phylogenies representing an accumulation of cancerous mutations. A drawback of SCS is elevated false negative and missing value rates, resulting in a large space of possible solutions, which in turn makes infeasible using some approaches and tools. While this has not inhibited the development of methods for inferring phylogenies from SCS data, the continuing increase in size and resolution of these data begin to put a strain on such methods.One possible solution is to reduce the size of an SCS instance — usually represented as a matrix of presence, absence and missing values of the mutations found in the different sequenced cells — and infer the tree from this reduced-size instance. Previous approaches have used k-means to this end, clustering groups of mutations and/or cells, and using these means as the reduced instance. Such an approach typically uses the Euclidean distance for computing means. However, since the values in these matrices are of a categorical nature (having the three categories: present, absent and missing), we explore techniques for clustering categorical data — commonly used in data mining and machine learning — to SCS data, with this goal in mind.ResultsIn this work, we present a new clustering procedure aimed at clustering categorical vector, or matrix data — here representing SCS instances, called celluloid. We demonstrate that celluloid clusters mutations with high precision: never pairing too many mutations that are unrelated in the ground truth, but also obtains accurate results in terms of the phylogeny inferred downstream from the reduced instance produced by this method.Finally, we demonstrate the usefulness of a clustering step by applying the entire pipeline (clustering + inference method) to a real dataset, showing a significant reduction in the runtime, raising considerably the upper bound on the size of SCS instances which can be solved in practice.AvailabilityOur approach, celluloid: clustering single cell sequencing data around centroids is available at https://github.com/AlgoLab/celluloid/ under an MIT license.

scholarly journals K-mer counting with low memory consumption enables fast clustering of single-cell sequencing data without read alignment

2019 ◽  
Author(s):  
Christina Huan Shi ◽  
Kevin Y. Yip
Keyword(s):  
Single Cell ◽  
State Of The Art ◽  
Rna Seq ◽  
Sequencing Data ◽  
Memory Consumption ◽  
Analysis Pipeline ◽  
Cell Clusters ◽  
Single Cell Sequencing ◽  
Sequencing Errors ◽  
Full Analysis

AbstractK-mer counting has many applications in sequencing data processing and analysis. However, sequencing errors can produce many false k-mers that substantially increase the memory requirement during counting. We propose a fast k-mer counting method, CQF-deNoise, which has a novel component for dynamically identifying and removing false k-mers while preserving counting accuracy. Compared with four state-of-the-art k-mer counting methods, CQF-deNoise consumed 49-76% less memory than the second best method, but still ran competitively fast. The k-mer counts from CQF-deNoise produced cell clusters from single-cell RNA-seq data highly consistent with CellRanger but required only 5% of the running time at the same memory consumption, suggesting that CQF-deNoise can be used for a preview of cell clusters for an early detection of potential data problems, before running a much more time-consuming full analysis pipeline.

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