Dynamics of circulating endothelial cells and endothelial progenitor cells in breast cancer patients receiving cytotoxic chemotherapy

scSID: A lightweight algorithm for identifying rare cell types by capturing differential expression from single-cell sequencing data

Single-cell RNA sequencing (scRNA-seq) is currently an important technology for identifying cell types and studying diseases at the genetic level. Identifying rare cell types is biologically important as one of the downstream data analyses of single-cell RNA sequencing. Although rare cell identification methods have been developed, most of these suffer from insufficient mining of intercellular similarities, low scalability, and being time-consuming. In this paper, we propose a single-cell similarity division algorithm (scSID) for identifying rare cells. It takes cell-to-cell similarity into consideration by analyzing both inter-cluster and intra-cluster similarities, and discovers rare cell types based on the similarity differences. We show that scSID outperforms other existing methods by benchmarking it on different experimental datasets. Application of scSID to multiple datasets, including 68K PBMC and intestine, highlights its exceptional scalability and remarkable ability to identify rare cell populations.

Connectivity Network Feature Sharing in Single-Cell RNA Sequencing Data Identifies Rare Cells

Single-cell RNA sequencing is a valuable technique for identifying diverse cell subtypes. A key challenge in this process is that the detection of rare cells is often missed by conventional methods due to low abundance and subtle features of these cells. To overcome this, we developed SCLCNF (Local Connectivity Network Feature Sharing in Single-Cell RNA sequencing), a novel approach that identifies rare cells by analyzing features uniquely expressed in these cells. SCLCNF creates a cellular connectivity network, considering how each cell relates to its neighbors. This network helps to pinpoint coexpression patterns unique to rare cells, utilizing a rarity score to confirm their presence. Our method performs better in detecting rare cells than existing techniques, offering enhanced robustness. It has proven to be effective in human gastrula data sets for accurately pinpointing rare cells, and in sepsis data sets where it uncovers previously unidentified rare cell populations.

Cooperation between Angiogenesis, Vasculogenesis, Chemotaxis, and Coagulation in Breast Cancer Metastases Development: Pathophysiological Point of View

Simple Summary

Breast cancer is one of the main causes of morbidity and mortality in women. Early breast cancer has a relatively good prognosis, in contrast to metastatic disease with rather poor outcomes. Metastasis formation in distant organs is a complex process requiring cooperation of numerous cells, growth factors, cytokines, and chemokines. Tumor growth, invasion, and finally systemic spread are driven by processes of angiogenesis, vasculogenesis, chemotaxis, and coagulation. This review summarizes their role in development of distant metastases in breast cancer, as well as explains the essential processes occurring throughout these actions.

Abstract

With almost 2.3 million new cases and 685 thousand fatal events in 2020 alone, breast cancer remains one of the main causes of morbidity and mortality in women worldwide. Despite the increasing prevalence of the disease in recent years, the number of deaths has dropped—this is mostly the result of better diagnostic and therapeutic opportunities, allowing to recognize and treat breast cancer earlier and more efficiently. However, metastatic disease still remains a therapeutic challenge. As mechanisms of tumor spread are being explored, new drugs can be implemented in clinical practice, improving the outcomes in patients with advanced disease. Formation of metastases is a complex process, which involves activation of angiogenesis, vasculogenesis, chemotaxis, and coagulation. The actions, which occur during metastatic spread are interrelated and complementary. This review summarizes their importance and mutual connections in formation of secondary tumors in breast cancer.

GapClust is a light-weight approach distinguishing rare cells from voluminous single cell expression profiles

Single cell RNA sequencing (scRNA-seq) is a powerful tool in detailing the cellular landscape within complex tissues. Large-scale single cell transcriptomics provide both opportunities and challenges for identifying rare cells playing crucial roles in development and disease. Here, we develop GapClust, a light-weight algorithm to detect rare cell types from ultra-large scRNA-seq datasets with state-of-the-art speed and memory efficiency. Benchmarking on diverse experimental datasets demonstrates the superior performance of GapClust compared to other recently proposed methods. When applying our algorithm to an intestine and 68 k PBMC datasets, GapClust identifies the tuft cells and a previously unrecognised subtype of monocyte, respectively.

Phase I dose escalation study of sorafenib plus S-1 for advanced solid tumors

S-1, an oral pyrimidine fluoride-derived agent, is effective against various cancers. Sorafenib, an oral multikinase inhibitor, was found to prolong the survival of various cancers and enhance the cytotoxicity of chemotherapeutic agents. We conducted a phase I dose escalation study to determine dose-limiting toxicity (DLT) and maximal tolerated dose (MTD) of S-1 when combined with sorafenib for refractory solid tumors. Eligible patients received escalating doses (30, 35, and 40 mg/m² bid) of S-1 Day 1 (D1)-D14 and continuous sorafenib 400 mg bid from cycle 1 D8 every 21 days in a standard 3 + 3 study design. Primary endpoint was MTD. Thirteen patients were enrolled between May 2010 and Feb 2012. DLT developed in two (one grade 3 erythema and one prolonged grade 2 hand-foot-skin reaction) of the 6 patients at 35 mg/m² dose level. One pancreatic neuroendocrine tumor (pNET) patient achieved a durable partial response (27.9 months). Four colon cancer patients had stable disease and 3 of them had progression-free survival greater than 6 months. This study determined the recommended (MTD) S-1 dose of 30 mg/m² bid for this regimen. This result warrants further phase II studies for advanced pNET and colon cancer to evaluate the efficacy of this combination.

Circulating Biomarkers for Tumor Angiogenesis: Where Are We?

BACKGROUND

In recent years, several anti-angiogenic drugs have been developed and their addition to standard treatment has been associated with clinical benefits. However, the response to anti-angiogenic therapy is characterized by considerable variability. In this context, the development of dynamic non-invasive biomarkers would be helpful to elucidate the emergence of anti-angiogenic resistance as well as to correctly address the treatment.

OBJECTIVES

The purpose of this review is to describe current reports on circulating diagnostic and prognostic biomarkers related to angiogenesis. We further discuss how this non-invasive strategy could improve the monitoring of tumor treatment and help clinical strategy.

RESULTS

We discuss the latest evidence in the literature regarding circulating anti-angiogenic markers. Besides growth factor proteins, different circulating miRNAs could exert a pro- or anti-angiogenic activity so as to represent suitable candidates for a non-invasive strategy. Recent reports indicate that tumor-derived exosomes, which are small membrane vesicles abundant in biological fluids, also have an impact on vascular remodeling.

CONCLUSION

Numerous circulating biomarkers related to angiogenesis have been recently identified. Their use will allow identifying patients who are more likely to benefit from a specific anti-angiogenic treatment, as well as detecting those who will develop resistance and/or adverse effects. Nonetheless, further studies are required to elucidate the role of these biomarkers in clinical settings.

Low Pre-Treatment Count of Circulating Endothelial Progenitors as a Prognostic Biomarker of the High Risk of Breast Cancer Recurrence

Neoangiogenesis is mediated by circulating bone marrow-derived endothelial progenitors (circulating EPCs). The aim of the study was the quantification of circulating EPCs from the peripheral blood mononuclear cells of invasive breast cancer (IBrC) patients by flow cytometry, before and after cancer adjuvant treatment. A total of 88 stage IA-IIB primary IBrC patients were enrolled prospectively. Circulating EPCs with the immune-phenotype CD45−CD34+CD133+CD31+ were assessed. Treatment significantly reduced the number of EPCs/µL in the general IBrC cohort. However, there was a relevant elevation in the number of circulating EPCs after nine months of adjuvant treatment in the group of patients aged ≥ 55 years, of T2 clinical type, with nodal involvement (N1) and Ki67 expression > 15%. Follow-up revealed a significantly higher incidence of disease relapse in breast cancer patients with low pre-treatment circulating EPCs levels compared with those with a high baseline circulating EPCs count. The receiver-operating characteristic curve identified a tumour diameter of 2.1 cm as the best cut-off value to discriminate between disease-relapse subjects and non-relapse disease cases. Our study strongly indicates that, next to tumour diameter and Ki67 expression, circulating bone marrow-derived EPCs may serve as useful markers for predicting therapeutic outcomes as well as a future prognosis.

Multi-Objective Optimized Fuzzy Clustering for Detecting Cell Clusters from Single-Cell Expression Profiles

Rapid advance in single-cell RNA sequencing (scRNA-seq) allows measurement of the expression of genes at single-cell resolution in complex disease or tissue. While many methods have been developed to detect cell clusters from the scRNA-seq data, this task currently remains a main challenge. We proposed a multi-objective optimization-based fuzzy clustering approach for detecting cell clusters from scRNA-seq data. First, we conducted initial filtering and SCnorm normalization. We considered various case studies by selecting different cluster numbers ( c l = 2 to a user-defined number), and applied fuzzy c-means clustering algorithm individually. From each case, we evaluated the scores of four cluster validity index measures, Partition Entropy ( P E ), Partition Coefficient ( P C ), Modified Partition Coefficient ( M P C ), and Fuzzy Silhouette Index ( F S I ). Next, we set the first measure as minimization objective (↓) and the remaining three as maximization objectives (↑), and then applied a multi-objective decision-making technique, TOPSIS, to identify the best optimal solution. The best optimal solution (case study) that had the highest TOPSIS score was selected as the final optimal clustering. Finally, we obtained differentially expressed genes (DEGs) using Limma through the comparison of expression of the samples between each resultant cluster and the remaining clusters. We applied our approach to a scRNA-seq dataset for the rare intestinal cell type in mice [GEO ID: GSE62270, 23,630 features (genes) and 288 cells]. The optimal cluster result (TOPSIS optimal score= 0.858) comprised two clusters, one with 115 cells and the other 91 cells. The evaluated scores of the four cluster validity indices, F S I , P E , P C , and M P C for the optimized fuzzy clustering were 0.482, 0.578, 0.607, and 0.215, respectively. The Limma analysis identified 1240 DEGs (cluster 1 vs. cluster 2). The top ten gene markers were Rps21, Slc5a1, Crip1, Rpl15, Rpl3, Rpl27a, Khk, Rps3a1, Aldob and Rps17. In this list, Khk (encoding ketohexokinase) is a novel marker for the rare intestinal cell type. In summary, this method is useful to detect cell clusters from scRNA-seq data.

Discovery of rare cells from voluminous single cell expression data

Single cell messenger RNA sequencing (scRNA-seq) provides a window into transcriptional landscapes in complex tissues. The recent introduction of droplet based transcriptomics platforms has enabled the parallel screening of thousands of cells. Large-scale single cell transcriptomics is advantageous as it promises the discovery of a number of rare cell sub-populations. Existing algorithms to find rare cells scale unbearably slowly or terminate, as the sample size grows to the order of tens of thousands. We propose Finder of Rare Entities (FiRE), an algorithm that, in a matter of seconds, assigns a rareness score to every individual expression profile under study. We demonstrate how FiRE scores can help bioinformaticians focus the downstream analyses only on a fraction of expression profiles within ultra-large scRNA-seq data. When applied to a large scRNA-seq dataset of mouse brain cells, FiRE recovered a novel sub-type of the pars tuberalis lineage.

Predicting the response to neoadjuvant therapy for early-stage breast cancer: tumor-, blood-, and imaging-related biomarkers

Neoadjuvant therapy (NAT) has been used increasingly in patients with locally advanced or early-stage breast cancer. However, the accurate evaluation and prediction of response to NAT remain the great challenge. Biomarkers could prove useful to identify responders or nonresponders, or even to distinguish between early and delayed responses. These biomarkers could include markers from the tumor itself, such as versatile proteins, genes, and ribonucleic acids, various biological factors or peripheral blood cells, and clinical and pathological features. Possible predictive markers could also include multiple features from functional imaging, such as standard uptake values in positron emission tomography, apparent diffusion coefficient in magnetic resonance, or radiomics imaging biomarkers. In addition, cells that indirectly present the immune status of tumor cells and/or their host could also potentially be used as biomarkers, eg, tumor-infiltrating lymphocytes, tumor-associated macrophages, and myeloid-derived suppressor cells. Though numerous biomarkers have been widely investigated, only estrogen and/or progesterone receptors and human epidermal growth factor receptor have been proven to be reliable biomarkers to predict the response to NAT. They are the only biomarkers recommended in several international guidelines. The other aforementioned biomarkers warrant further validation studies. Some multigene profiling assays that are commercially available, eg, Oncotype DX and MammaPrint, should be used with caution when extrapolated to NAT settings. A panel of combined multilevel biomarkers might be able to predict the response to NAT more robustly than individual biomarkers. To establish such a panel and its prediction model, reliable methods and extensive clinical validation are warranted.

Endothelial progenitor cells contribute to neovascularization of non-small cell lung cancer via histone deacetylase 7-mediated cytoskeleton regulation and angiogenic genes transcription

To supply tumor tissues with nutrients and oxygen, endothelial progenitor cells (EPCs) home to tumor sites and contribute to neovascularization. Although the precise mechanism of EPCs-induced neovascularization remains poorly understood in non-small cell lung cancer (NSCLC), histone deacetylase 7 (HDAC7) is considered as a critical regulator. To explore the function of HDAC7 in neovascularization induced by EPCs, tube formation assay, immunofluorescence, microarray, Western blot analysis and animal models were performed. In vitro, HDAC7 abrogation led to the activation of Rho-associated coiled-coil containing protein kinase/myosin light chain 2 pathway concomitant with ERK dephosphorylation, causing the instability of cytoskeleton and collapse of tube formation. In vivo, absence of HDAC7 impaired the vascular lumen integrity and decreased the functional blood perfusion, inhibiting the growth of tumor. At the level of transcription, HDAC7 silencing upregulated antiangiogenic genes and suppressed proangiogenic genes collectively, turning off the angiogenic switch during vessel formation. Taken together, HDAC7 plays a dual role in maintaining the structural and nonstructural functions of EPCs. Our work demonstrates the molecular mechanism by which HDAC7 contributes to the angiogenic property of EPCs and provides a rational basis for specific targeting of antiangiogenic strategies in lung cancer.

Flow cytometric analysis of circulating endothelial cells and endothelial progenitors for clinical purposes in oncology: A critical evaluation

Malignant tumors are characterized by uncontrolled cell growth and metastatic spread, with a pivotal importance of the phenomenon of angiogenesis. For this reason, research has focused on the development of agents targeting the vascular component of the tumor microenvironment and regulating the angiogenic switch. As a result, the therapeutic inhibition of angiogenesis has become an important component of anticancer treatment, however, its utility is partly limited by the lack of an established methodology to assess its efficacy in vivo. Circulating endothelial cells (CECs), which are rare in healthy subjects and significantly increased in different tumor types, represent a promising tool for monitoring the tumor clinical outcome and the treatment response. A cell population circulating into the blood also able to form endothelial colonies in vitro and to promote vasculogenesis is represented by endothelial progenitor cells (EPCs). The number of both of these cell types is extremely low and they cannot be identified using a single marker, therefore, in absence of a definite consensus on their phenotype, require discrimination using combinations of antigens. Multiparameter flow cytometry (FCM) is ideal for rapid processing of high numbers of cells per second and is commonly utilized to quantify CECs and EPCs, however, remains technically challenging since there is as yet no standardized protocol for the identification and enumeration of these rare events. Methodology in studies on CECs and/or EPCs as clinical biomarkers in oncology is heterogeneous and data have been obtained from different studies leading to conflicting conclusions. The present review presented a critical review of the issues that limit the comparability of results of the most significant studies employing FCM for CEC and/or EPC detection in patients with cancer.

Significance and therapeutic implications of endothelial progenitor cells in angiogenic-mediated tumour metastasis

Cancer conveys profound social and economic consequences throughout the world. Metastasis is responsible for approximately 90% of cancer-associated mortality and, when it occurs, cancer becomes almost incurable. During metastatic dissemination, cancer cells pass through a series of complex steps including the establishment of tumour-associated angiogenesis. The human endothelial progenitor cells (hEPCs) are a cell population derived from the bone marrow which are required for endothelial tubulogenesis and neovascularization. They also express abundant inflammatory cytokines and paracrine angiogenic factors. Clinically hEPCs are highly correlated with relapse, disease progression, metastasis and treatment response in malignancies such as breast cancer, ovarian cancer and non-small-cell lung carcinoma. It has become evident that the hEPCs are involved in the angiogenesis-required progression and metastasis of tumours. However, it is not clear in what way the signalling pathways, controlling the normal cellular function of human BM-derived EPCs, are hijacked by aggressive tumour cells to facilitate tumour metastasis. In addition, the actual roles of hEPCs in tumour angiogenesis-mediated metastasis are not well characterised. In this paper we reviewed the clinical relevance of the hEPCs with cancer diagnosis, progression and prognosis. We further summarised the effects of tumour microenvironment on the hEPCs and underlying mechanisms. We also hypothesized the roles of altered hEPCs in tumour angiogenesis and metastasis. We hope this review may enhance our understanding of the interaction between hEPCs and tumour cells thus aiding the development of cellular-targeted anti-tumour therapies.

Simultaneous detection of circulating immunological parameters and tumor biomarkers in early stage breast cancer patients during adjuvant chemotherapy

BACKGROUND

Chemotherapy-induced immune suppression has mainly been studied in patients with advanced cancer, but the influence of chemotherapy on the immune system in early stage cancer patients has so far not been studied systematically. The aim of the present study was to monitor the immune system during anthracycline- and taxane-based adjuvant chemotherapy in early stage breast cancer patients, to assess the impact of circulating tumor cells on selected immune parameters and to reveal putative angiogenic effects of circulating endothelial cells.

METHODS

Peripheral blood samples from 20 early stage breast cancer patients were analyzed using a flow cytometric multi-color of antibodies to enumerate lymphocyte and dendritic cell subsets, as well as endothelial and tumor cells. An enzyme-linked immunosorbent assay (ELISA) was used to measure the levels of various serological factors.

RESULTS

During chemotherapy, all immunological parameters and angiogenesis surrogate biomarkers showed significant decreases. The numbers of circulating tumor cells showed significant inverse correlations with the numbers of T helper cells, a lymphocyte subset directly related to effective anti-tumor responses. Reduced T helper cell numbers may contribute to systemic immunosuppression and, as such, the activation of dormant tumor cells.

CONCLUSIONS

From our results we conclude that adjuvant chemotherapy suppresses immune function in early stage breast cancer patients. In addition, we conclude that the presence of circulating tumor cells, defined as pan-cytokeratin(+), CD326(+), CD45(-) cells, may serve as an important indicator of a patient's immune status. Further investigations are needed to firmly define circulating tumor cells as a predictor for the success of breast cancer adjuvant chemotherapy.