Identification of cancer subtypes from single-cell RNA-seq data using a consensus clustering method

Background

Human cancers are complex ecosystems composed of cells with distinct molecular signatures. Such intratumoral heterogeneity poses a major challenge to cancer diagnosis and treatment. Recent advancements of single-cell techniques such as scRNA-seq have brought unprecedented insights into cellular heterogeneity. Subsequently, a challenging computational problem is to cluster high dimensional noisy datasets with substantially fewer cells than the number of genes.

Methods

In this paper, we introduced a consensus clustering framework conCluster, for cancer subtype identification from single-cell RNA-seq data. Using an ensemble strategy, conCluster fuses multiple basic partitions to consensus clusters.

Results

Applied to real cancer scRNA-seq datasets, conCluster can more accurately detect cancer subtypes than the widely used scRNA-seq clustering methods. Further, we conducted co-expression network analysis for the identified melanoma subtypes.

Conclusions

Our analysis demonstrates that these subtypes exhibit distinct gene co-expression networks and significant gene sets with different functional enrichment.

     

Study of the tumor microenvironment during breast cancer progression

Background

Different cells and mediators in the tumor microenvironment play important roles in the progression of breast cancer. The aim of this study was to determine the composition of the microenvironment during tumor progression in order to discover new related biomarkers and potentials for targeted therapy.

Methods

In this study, breast cancer biopsies from four different stages, and control breast biopsies were collected. Then, the mRNA expression of several markers related to different CD4⁺ T cell subsets including regulatory T cells (Treg), T helper (Th) type 1, 2 and 17 were determined. In addition, we investigated the expression of two inflammatory cytokines (TNF-α and IL-6) and inflammatory mediators including FASL, IDO, SOCS1, VEGF, and CCR7.

Results

The results showed that the expression of Th1 and Th17 genes was decreased in tumor tissues compared to control tissues. In addition, we found that the gene expression related to these two cell subsets decreased during cancer progression. Moreover, the expression level of TNF-α increased with tumor progression.

Conclusion

We conclude that the expression of genes related to immune response and inflammation is different between tumor tissues and control tissues. In addition, this difference was perpetuated through the different stages of cancer.

     

Monitoring cancer prognosis,diagnosis and treatment efficacy using metabolomics and lipidomics

Introduction

Cellular metabolism is altered during cancer initiation and progression, which allows cancer cells to increase anabolic synthesis, avoid apoptosis and adapt to low nutrient and oxygen availability. The metabolic nature of cancer enables patient cancer status to be monitored by metabolomics and lipidomics. Additionally, monitoring metabolic status of patients or biological models can be used to greater understand the action of anticancer therapeutics.

Objectives

Discuss how metabolomics and lipidomics can be used to (i) identify metabolic biomarkers of cancer and (ii) understand the mechanism-of-action of anticancer therapies. Discuss considerations that can maximize the clinical value of metabolic cancer biomarkers including case–control, prognostic and longitudinal study designs.

Methods

A literature search of the current relevant primary research was performed.

Results

Metabolomics and lipidomics can identify metabolic signatures that associate with cancer diagnosis, prognosis and disease progression. Discriminatory metabolites were most commonly linked to lipid or energy metabolism. Case–control studies outnumbered prognostic and longitudinal approaches. Prognostic studies were able to correlate metabolic features with future cancer risk, whereas longitudinal studies were most effective for studying cancer progression. Metabolomics and lipidomics can help to understand the mechanism-of-action of anticancer therapeutics and mechanisms of drug resistance.

Conclusion

Metabolomics and lipidomics can be used to identify biomarkers associated with cancer and to better understand anticancer therapies.

     

Molecular profiling of thyroid cancer subtypes using large-scale text mining

Background

Thyroid cancer is the most common endocrine tumor with a steady increase in incidence. It is classified into multiple histopathological subtypes with potentially distinct molecular mechanisms. Identifying the most relevant genes and biological pathways reported in the thyroid cancer literature is vital for understanding of the disease and developing targeted therapeutics.

Results

We developed a large-scale text mining system to generate a molecular profiling of thyroid cancer subtypes. The system first uses a subtype classification method for the thyroid cancer literature, which employs a scoring scheme to assign different subtypes to articles. We evaluated the classification method on a gold standard derived from the PubMed Supplementary Concept annotations, achieving a micro-average F1-score of 85.9% for primary subtypes. We then used the subtype classification results to extract genes and pathways associated with different thyroid cancer subtypes and successfully unveiled important genes and pathways, including some instances that are missing from current manually annotated databases or most recent review articles.

Conclusions

Identification of key genes and pathways plays a central role in understanding the molecular biology of thyroid cancer. An integration of subtype context can allow prioritized screening for diagnostic biomarkers and novel molecular targeted therapeutics. Source code used for this study is made freely available online at https://github.com/chengkun-wu/GenesThyCan.

     

Male breast cancer: thirteen years experience of a single center

Background

This retrospective study analysed the epidemiological, clinical, and therapeutic profiles of breast cancer in males.

Methods

We report our experience at the Hospital of the University of Baskent, where 20 cases of male breast cancer were observed and treated between 1995–2008.

Results

Median age at presentation was 66,7 ± 10,9 years. Average follow-up was 63 ± 18,5 months. The main presenting symptom was a mass in 65% of cases (13 patients). Ýnvasive ductal carcinoma was the most frequent pathologic type (70% of cases).

Conclusion

Male breast cancer patients have an incidence of prostate cancer higher than would be predicted in the general population. Cause of men have a higher rate of ER positivity the responses with hormonal agents are good.

     

Untargeted and stable isotope-assisted metabolomic analysis of MDA-MB-231 cells under hypoxia

Introduction

Hypoxia commonly occurs in cancers and is highly related with the occurrence, development and metastasis of cancer. Treatment of triple negative breast cancer remains challenge. Knowledge about the metabolic status of triple negative breast cancer cell lines in hypoxia is valuable for the understanding of molecular mechanisms of this tumor subtype to develop effective therapeutics.

Objectives

Comprehensively characterize the metabolic profiles of triple negative breast cancer cell line MDA-MB-231 in normoxia and hypoxia and the pathways involved in metabolic changes in hypoxia.

Methods

Differences in metabolic profiles affected pathways of MDA-MB-231 cells in normoxia and hypoxia were characterized using GC–MS based untargeted and stable isotope assisted metabolomic techniques.

Results

Thirty-three metabolites were significantly changed in hypoxia and nine pathways were involved. Hypoxia increased glycolysis, inhibited TCA cycle, pentose phosphate pathway and pyruvate carboxylation, while increased glutaminolysis in MDA-MB-231 cells.

Conclusion

The current results provide metabolic differences of MDA-MB-231 cells in normoxia and hypoxia conditions as well as the involved metabolic pathways, demonstrating the power of combined use of untargeted and stable isotope-assisted metabolomic methods in comprehensive metabolomic analysis.

     

Characterizing biomarkers in osteosarcoma metastasis based on an ego-network

Objectives

To characterize biomarkers that underlie osteosarcoma (OS) metastasis based on an ego-network.

Results

From the microarray data, we obtained 13,326 genes. By combining PPI data and microarray data, 10,520 shared genes were found and constructed into ego-networks. 17 significant ego-networks were identified with p < 0.05. In the pathway enrichment analysis, seven ego-networks were identified with the most significant pathway.

Conclusions

These significant ego-modules were potential biomarkers that reveal the potential mechanisms in OS metastasis, which may contribute to understanding cancer prognoses and providing new perspectives in the treatment of cancer.

     

Applications of metabolomics in the study and management of preeclampsia: a review of the literature

Introduction

Preeclampsia represents a major public health burden worldwide, but predictive and diagnostic biomarkers are lacking. Metabolomics is emerging as a valuable approach to generating novel biomarkers whilst increasing the mechanistic understanding of this complex condition.

Objectives

To summarize the published literature on the use of metabolomics as a tool to study preeclampsia.

Methods

PubMed and Web of Science were searched for articles that performed metabolomic profiling of human biosamples using either Mass-spectrometry or Nuclear Magnetic Resonance based approaches and which included preeclampsia as a primary endpoint.

Results

Twenty-eight studies investigating the metabolome of preeclampsia in a variety of biospecimens were identified. Individual metabolite and metabolite profiles were reported to have discriminatory ability to distinguish preeclamptic from normal pregnancies, both prior to and post diagnosis. Lipids and carnitines were among the most commonly reported metabolites. Further work and validation studies are required to demonstrate the utility of such metabolites as preeclampsia biomarkers.

Conclusion

Metabolomic-based biomarkers of preeclampsia have yet to be integrated into routine clinical practice. However, metabolomic profiling is becoming increasingly popular in the study of preeclampsia and is likely to be a valuable tool to better understand the pathophysiology of this disorder and to better classify its subtypes, particularly when integrated with other omic data.

     

Successful pregnancy after breast cancer therapy: dream or reality?

Background

Nowadays, more breast cancer patients want to have children after the diagnosis of cancer. The purpose of this study is to review the possibility and risks of giving birth among women with breast cancer previously treated by chemotherapy.

Case presentation

Two young women aged 28 and 34 respectively, were treated in our clinic for breast cancer, the first (negative hormonal receptors) by surgery, chemotherapy and radiotherapy and the second (positive hormonal receptors) by surgery, radiotherapy and tamoxifen. They both became pregnant, 1 and 8 years after completion of the therapy respectively.

Results

Laboratory testing during pregnancy was negative in both cases and after an uneventful course each woman gave birth to a perfectly healthy child. The first patient breastfed her baby for three months, while the second one did not breastfeed her baby at all.

Conclusion

Women undergoing chemotherapy for breast cancer can maintain their fertility and get pregnant. Previous chemotherapy for breast cancer does not present any supplementary risks for the child's mental or physical health.

     

Potential plasma lipid biomarkers in early-stage breast cancer

Objective

To find new biomarkers for early diagnosis of breast cancer.

Results

847 lipid species were identified from 78 plasma samples (37 breast cancer samples and 41 healthy controls) by ultra HPLC coupled with quadrupole time-of-flight tandem mass spectrometry. These include 321 glycerophospholipids (GPs), 265 glycerolipids (GLs), 91 sphingolipids (SPs), 77 fatty acyls (FAs), 68 sterol lipids (STs), 18 prenol lipids (PRs), 6 polyketides (PKs), and 1 saccharolipid (SL). Separation was observed from an orthogonal signal correction Partial Least Square Discrimination Analysis model. Based on this analysis, six differentiating lipids were identified: PC (20:2/20:5), PC (22:0/24:1), TG (12:0/14:1), and DG (18:1/18:2) had high levels, whereas PE (15:0/19:1) and N-palmitoyl proline had low levels in the breast cancer samples compared with the healthy controls. Furthermore, significant differences in metabolites were found among some clinical characteristics.

Conclusions

Our results reveal that six specific lipids could serve as potential biomarkers for early diagnosis of breast cancer.

     

Three stage axillary lymphatic massage optimizes sentinel lymph node localisation using blue dye

Aims

This paper describes a simple technique of axillary and breast massage which improves the successful identification of blue sentinel nodes using patent blue dye alone.

Methods

Patent blue dye was injected in the subdermal part of the retroaroelar area in 167 patients having surgical treatment for invasive breast cancer. Three stage axillary lymphatic massage was performed prior to making the axillary incision for sentinel lymph node biopsy. All patients had completion axillary sampling or clearance.

Results

A blue lymphatic duct leading to lymph nodes of the first drainage was identified in 163 (97%) of the patients. Results are compared with 168 patients who had sentinel lymph node biopsy using blue dye without axillary massage. Allergic reactions were observed in four patients (1.2%).

Conclusion

Three stage axillary lymphatic massage improves the successful identification of a blue sentinel lymph node in breast cancer patients.

     

Akt1 inhibition promotes breast cancer metastasis through EGFR-mediated β-catenin nuclear accumulation

Background

Knockdown of Akt1 promotes Epithelial-to-Mesenchymal Transition in breast cancer cells. However, the mechanisms are not completely understood.

Methods

Western blotting, immunofluorescence, luciferase assay, real time PCR, ELISA and Matrigel invasion assay were used to investigate how Akt1 inhibition promotes breast cancer cell invasion in vitro. Mouse model of lung metastasis was used to measure in vivo efficacy of Akt inhibitor MK2206 and its combination with Gefitinib.

Results

Knockdown of Akt1 stimulated β-catenin nuclear accumulation, resulting in breast cancer cell invasion. β-catenin nuclear accumulation induced by Akt1 inhibition depended on the prolonged activation of EGFR signaling pathway in breast cancer cells. Mechanistic experiments documented that knockdown of Akt1 inactivates PIKfyve via dephosphorylating of PIKfyve at Ser³¹⁸ site, resulting in a decreased degradation of EGFR signaling pathway. Inhibition of Akt1 using MK2206 could induce an increase in the expression of EGFR and β-catenin in breast cancer cells. In addition, MK2206 at a low dosage enhance breast cancer metastasis in a mouse model of lung metastasis, while an inhibitor of EGFR tyrosine kinase Gefitinib could potentially suppress breast cancer metastasis induced by Akt1 inhibition.

Conclusion

EGFR-mediated β-catenin nuclear accumulation is critical for Akt1 inhibition-induced breast cancer metastasis.

     

Network-based logistic regression integration method for biomarker identification

Background

Many mathematical and statistical models and algorithms have been proposed to do biomarker identification in recent years. However, the biomarkers inferred from different datasets suffer a lack of reproducibilities due to the heterogeneity of the data generated from different platforms or laboratories. This motivates us to develop robust biomarker identification methods by integrating multiple datasets.

Methods

In this paper, we developed an integrative method for classification based on logistic regression. Different constant terms are set in the logistic regression model to measure the heterogeneity of the samples. By minimizing the differences of the constant terms within the same dataset, both the homogeneity within the same dataset and the heterogeneity in multiple datasets can be kept. The model is formulated as an optimization problem with a network penalty measuring the differences of the constant terms. The L₁ penalty, elastic penalty and network related penalties are added to the objective function for the biomarker discovery purpose. Algorithms based on proximal Newton method are proposed to solve the optimization problem.

Results

We first applied the proposed method to the simulated datasets. Both the AUC of the prediction and the biomarker identification accuracy are improved. We then applied the method to two breast cancer gene expression datasets. By integrating both datasets, the prediction AUC is improved over directly merging the datasets and MetaLasso. And it’s comparable to the best AUC when doing biomarker identification in an individual dataset. The identified biomarkers using network related penalty for variables were further analyzed. Meaningful subnetworks enriched by breast cancer were identified.

Conclusion

A network-based integrative logistic regression model is proposed in the paper. It improves both the prediction and biomarker identification accuracy.

     

Silencing CDK4 radiosensitizes breast cancer cells by promoting apoptosis

Background

The discovery of molecular markers associated with various breast cancer subtypes has greatly improved the treatment and outcome of breast cancer patients. Unfortunately, breast cancer cells acquire resistance to various therapies. Mounting evidence suggests that resistance is rooted in the deregulation of the G1 phase regulatory machinery.

Methods

To address whether deregulation of the G1 phase regulatory machinery contributes to radiotherapy resistance, the MCF10A immortalized human mammary epithelial cell line, ER-PR-Her2+ and ER-PR-Her2- breast cancer cell lines were irradiated. Colony formation assays measured radioresistance, while immunocytochemistry, Western blots, and flow cytometry measured the cell cycle, DNA replication, mitosis, apoptosis, and DNA breaks.

Results

Molecular markers common to all cell lines were overexpressed, including cyclin A1 and cyclin D1, which impinge on CDK2 and CDK4 activities, respectively. We addressed their potential role in radioresistance by generating cell lines stably expressing small hairpin RNAs (shRNA) against CDK2 and CDK4. None of the cell lines knocked down for CDK2 displayed radiosensitization. In contrast, all cell lines knocked down for CDK4 were significantly radiosensitized, and a CDK4/CDK6 inhibitor sensitized MDA-MB-468 to radiation induced apoptosis. Our data showed that silencing CDK4 significantly increases radiation induced cell apoptosis in cell lines without significantly altering cell cycle progression, or DNA repair after irradiation. Our results indicate lower levels of phospho-Bad at ser136 upon CDK4 silencing and ionizing radiation, which has been shown to signal apoptosis.

Conclusion

Based on our data we conclude that knockdown of CDK4 activity sensitizes breast cancer cells to radiation by activating apoptosis pathways.

     

Predicting ovarian cancer recurrence by plasma metabolic profiles before and after surgery

Background

Previous metabolomic studies have revealed that plasma metabolic signatures may predict epithelial ovarian cancer (EOC) recurrence. However, few studies have performed metabolic profiling of pre- and post-operative specimens to investigate EOC prognostic biomarkers.

Objective

The aims of our study were to compare the predictive performance of pre- and post-operative specimens and to create a better model for recurrence by combining biomarkers from both metabolic signatures.

Methods

Thirty-five paired plasma samples were collected from 35 EOC patients before and after surgery. The patients were followed-up until December, 2016 to obtain recurrence information. Metabolomics using rapid resolution liquid chromatography–mass spectrometry was performed to identify metabolic signatures related to EOC recurrence. The support vector machine model was employed to predict EOC recurrence using identified biomarkers.

Results

Global metabolomic profiles distinguished recurrent from non-recurrent EOC using both pre- and post-operative plasma. Ten common significant biomarkers, hydroxyphenyllactic acid, uric acid, creatinine, lysine, 3-(3,5-diiodo-4-hydroxyphenyl) lactate, phosphohydroxypyruvic acid, carnitine, coproporphyrinogen, l-beta-aspartyl-l-glutamic acid and 24,25-hydroxyvitamin D3, were identified as predictive biomarkers for EOC recurrence. The area under the receiver operating characteristic (AUC) values in pre- and post-operative plasma were 0.815 and 0.909, respectively; the AUC value after combining the two sets reached 0.964.

Conclusion

Plasma metabolomic analysis could be used to predict EOC recurrence. While post-operative biomarkers have a predictive advantage over pre-operative biomarkers, combining pre- and post-operative biomarkers showed the best predictive performance and has great potential for predicting recurrent EOC.

     

Evaluation of insulin-like growth factor-I in postmenopausal women with breast cancer treated with raloxifene

Background

The objective of this study was to evaluate serum IGF-I levels in postmenopausal women with breast cancer treated primarily with raloxifene.

Methods

Twenty-two postmenopausal patients with operable, stage I or II, estrogen receptor-positive carcinomas participated in this study. Following confirmation of diagnosis, the patients received 60 mg of raloxifene for 28 days prior to definitive surgery. Blood samples were collected for evaluation of serum IGF-I levels prior to initiating medication and following a 28-day treatment course. Student's t-test for paired samples was used in the statistical analysis. Significance was established at p < 0.05.

Results

Mean serum IGF-I levels pre- and post-raloxifene treatment were 143.7 ± 9.7 ng/ml and 94.8 ± 7.6 ng/ml, respectively. This reduction in serum IGF-I levels following treatment with raloxifene was statistically significant (p < 0.001).

Conclusion

Raloxifene significantly reduced serum IGF-I levels in postmenopausal women with breast cancer.

     

A review of metabolism-associated biomarkers in lung cancer diagnosis and treatment

Introduction

Lung cancer continues to be the leading cause of cancer-related mortality worldwide. Early detection has proven essential to extend survival. Genomic and proteomic advances have provided impetus to the effort dedicated to detect and diagnose the disease at an earlier stage. Recently, the study of metabolites associated with tumor formation and progression has inaugurated the era of cancer metabolomics to aid in this effort.

Objectives

This review summarizes recent work regarding novel metabolites with the potential to serve as biomarkers for early lung tumor detection, evaluation of disease progression, and prediction of patient outcomes.

Method

We compare the metabolite profiling of cancer patients with that of healthy individuals, and the metabolites identified in tissue and biofluid samples and their usefulness as lung cancer biomarkers. We discuss metabolite alterations in tumor versus paired non-tumor lung tissues, as well as metabolite alterations in different stages of lung cancers and their usefulness as indicators of disease progression and overall survival. We evaluate metabolite dysregulation in different types of lung cancers, and those associated with lung cancer versus other lung diseases. We also examine metabolite differences between lung cancer patients and smokers/risk-factor individuals.

Result

Although an extensive list of metabolites has been evaluated to distinguish between these cases, refinement of methods is further required for adequate patient diagnosis and treatment.

Conclusion

We conclude that with technological advancement, metabolomics may be able to replace more invasive and costly diagnostic procedures while also providing the means to more effectively tailor treatment to patient-specific tumors.

     

Comprehensive quantitative lipidomic approach to investigate serum phospholipid alterations in breast cancer

Introduction

Globally, breast cancer is the most common cancer in women and ranks second most common cause of cancer related mortality. Although efforts are made by researchers in molecular characterization of breast cancer using “-OMIC’S” approaches, limited work has explored to understand the phospholipid alterations in breast cancer.

Objectives

This study aims to explore five classes of serum phospholipid alterations in breast cancer towards discrimination of breast cancer from benign and healthy controls.

Methods

Twenty eight each of breast cancer patients and age-matched benign and healthy control serum samples were used to identify alterations of phospholipids using liquid chromatography-multiple reaction monitoring-mass spectrometry. Both multivariate and univariate statistical analyses were applied to investigate breast cancer associated phospholipid alterations. Differentially expressed phospholipid species were further confirmed by liquid chromatography-tandem mass spectrometry (LC-MS/MS).

Results

Among the identified and quantified 200 phospholipids, 25 phospholipids were found to be statistically significant (VIP > 1.4 and ANOVA p < 0.05) in the serum of women with breast cancer when compared with benign and healthy controls. Comparison of serum phospholipids of breast cancer patients and healthy controls revealed 12 phospholipids were found to be differentially expressed in which six were up-regulated and six were down-regulated. While comparative analysis of breast cancer serum against benign showed an increased concentration of six phospholipids in breast cancer samples. Further, significantly altered phospholipids were structurally characterized by enhanced product ion scanning.

Conclusion

Our results demonstrate that some of the differentially regulated phospholipids identified in our study such as PE (14:1/16:0), PC (18:0/18:0), LPE 14:0, PE (20:0/22:2) could be a panel of potential signature which can discriminate breast cancer from benign and healthy controls. These findings also provide insight into lipidomic information that can be used for monitoring of breast cancer progression.

     

Research on the relationship between fibrinogen level and subtypes of the TOAST criteria in the acute ischemic stroke

Background

Cerebral infarction caused by different reasons seems differ in fibrinogen levels, so the current work intends to explore the relationship between the fibrinogen level and subtypes of the TOAST criteria in the acute stage of ischemic stroke.

Methods

A total of 577 case research objects were treated acute ischemic stroke patients in our hospital from December 2008 to December 2010, and blood samples within 72 hours of the onset were processed with the fibrinogen (PT-der) measurement. Classification of selected patients according to the TOAST Criteria was conducted to study the distribution of fibrinogen levels in the stroke subtypes.

Results

The distribution of fibrinogen levels in the subtypes was observed to be statistically insignificant.

Conclusions

In the acute stage of ischemic stroke, fibrinogen level was not related to the subtypes of the TOAST criteria.

     

Good practice guidelines for biomarker discovery from array data: a case study for breast cancer prognosis

Background

Biomarker discovery holds the promise for advancing personalized medicine as the biomarkers can help match patients to optimal treatment to improve patient outcomes. However, serious concerns have been raised because very few molecular biomarkers or signatures discovered from high dimensional array data can be successfully validated and applied to clinical use. We propose good practice guidelines as well as a novel tool for biomarker discovery and use breast cancer prognosis as a case study to illustrate the proposed approach.

Results

We applied the proposed approach to a publicly available breast cancer prognosis dataset and identified small numbers of predictive markers for patient subpopulations stratified by clinical variables. Results from an independent cross-platform validation set show that our model compares favorably to other gene signature and clinical variable based prognostic tools. About half of the discovered candidate markers can individually achieve very good performance, which further demonstrate the high quality of feature selection. These candidate markers perform extremely well for young patient with estrogen receptor-positive, lymph node-negative early stage breast cancers, suggesting a distinct subset of these patients identified by these markers is actually at high risk of recurrence and may benefit from more aggressive treatment than cur-rent practice.

Conclusion

The results show that by following good practice guidelines, we can identify highly predictive genes in high dimensional breast cancer array data. These predictive genes have been successfully validated using an independent cross-platform dataset.