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Predictors of breast cancer cell types and their prognostic power in breast cancer patients.

ABSTRACT: Comprehensive understanding of intratumor heterogeneity requires identification of molecular markers, which are capable of differentiating different subpopulations and which also have clinical significance. One important tool that has been addressing this issue is single cell RNA-Sequencing (scRNASeq) that allows the quantification of expression profiles of transcripts in individual cells in a population of cancer cells. Using the expression profiles from scRNASeq, current studies conduct analysis to group cells into different subpopulations using clustering algorithms. In this study, we explore scRNASeq cancer data from a different perspective. We focus on scRNASeq data originating from cancer cells pertaining to a particular cancer type, where the cell type or the subpopulation to which each cell belongs is known. We investigate if the "cell type" of a cancer cell can be predicted based on the expression profiles of a small set of transcripts.We outline a predictive analytics pipeline to accurately predict 6 breast cancer cell types using single cell gene expression profiles. Instead of building predictive models using the complete human transcripts, the pipeline first eliminates predictors with low expression and low variance. A multinomial penalized logistic regression further reduces the size of the predictors to only 308, out of which 34 are long non-coding RNAs. Tuning of predictive models shows support vector machines and neural networks as the most accurate models achieving close to 98% prediction accuracies. We also find that mixture of protein coding genes and long non-coding RNAs are better predictors compared to when the two sets of transcripts are treated separately. A signature risk score originating from 65 protein coding genes and 5 lncRNA predictors is associated with prognostic survival of TCGA breast cancer patients. This association was maintained when the risk scores were generated using 65 PCGs and 5 lncRNA separately. We further show that predictors restricted to a particular cell type serve as better prognostic markers for the respective patient subtype.Our results show that in general, the breast cancer cell type predictors are also associated with patient survivability and hence have clinical significance.

SUBMITTER: Wang F

PROVIDER: S-EPMC5809864 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Predictors of breast cancer cell types and their prognostic power in breast cancer patients.

Wang Fan F Dohogne Zachariah Z Yang Jin J Liu Yu Y Soibam Benjamin B

BMC genomics 20180213 1

<h4>Background</h4>Comprehensive understanding of intratumor heterogeneity requires identification of molecular markers, which are capable of differentiating different subpopulations and which also have clinical significance. One important tool that has been addressing this issue is single cell RNA-Sequencing (scRNASeq) that allows the quantification of expression profiles of transcripts in individual cells in a population of cancer cells. Using the expression profiles from scRNASeq, current stu ...[more]

PMID: 29433432

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Project description:BACKGROUND:Breast cancer is a highly heterogeneous disease, this poses challenges for classification and management. Long non-coding RNAs play acrucial role in the breast cancersdevelopment and progression, especially in tumor-related immune processes which have become the most rapidly investigated area. Therefore, we aimed at developing an immune-related lncRNA signature to improve the prognosis prediction of breast cancer. METHODS:We obtained breast cancer patient samples and corresponding clinical data from The Cancer Genome Atlas (TCGA) database. Immune-related lncRNAs were screened by co-expression analysis of immune-related genes which were downloaded from the Immunology Database and Analysis Portal (ImmPort). Clinical patient samples were randomly separated into training and testing sets. In the training set, univariate Cox regression analysis and LASSO regression were utilized to build a prognostic immune-related lncRNA signature. The signature was validated in the training set, testing set, and whole cohorts by the Kaplan-Meier log-rank test, time-dependent ROC curve analysis, principal component analysis, univariate andmultivariate Cox regression analyses. RESULTS:A total of 937 immune- related lncRNAs were identified, 15 candidate immune-related lncRNAs were significantly associated with overall survival (OS). Eight of these lncRNAs (OTUD6B-AS1, AL122010.1, AC136475.2, AL161646.1, AC245297.3, LINC00578, LINC01871, AP000442.2) were selected for establishment of the risk prediction model. The OS of patients in the low-risk group was higher than that of patients in the high-risk group (p?=?1.215e?-?06 in the training set; p?=?0.0069 in the validation set; p?=?1.233e?-?07 in whole cohort). The time-dependent ROC curve analysis revealed that the AUCs for OS in the first, eighth, and tenth year were 0.812, 0.81, and 0.857, respectively, in the training set, 0.615, 0.68, 0.655 in the validation set, and 0.725, 0.742, 0.741 in the total cohort. Multivariate Cox regression analysis indicated the model was a reliable and independent indicator for the prognosis of breast cancer in the training set (HR?=?1.432; 95% CI 1.204-1.702, p?<?0.001), validation set (HR?=?1.162; 95% CI 1.004-1.345, p?=?0.044), and whole set (HR?=?1.240; 95% CI 1.128-1.362, p?<?0.001). GSEA analysis revealed a strong connection between the signature and immune-related biological processes and pathways. CONCLUSIONS:We constructed and verified a robust signature of 8 immune-related lncRNAs for the prediction of breast cancer patient survival.

Project description:BackgroundBreast cancer clinical outcome relies on its intrinsic molecular subtype and mortality is almost exclusively due to metastasis, whose mechanism remains unclear. We recently revealed the specific contribution of plasma membrane cholesterol to the invasion of malignant MCF10CAIa but not premalignant MCF10AT and normal MCF10A cell lines in 2D, through invadopodia formation and extracellular matrix (ECM) degradation. In the present study, we address the impact of breast cancer subtypes, mutations and aggressiveness on cholesterol implication in breast cancer cell invasion and 3D spheroid invasion and growth.MethodsWe used nine breast cancer cell lines grouped in four subtypes matching breast tumor classification. Four of these cell lines were also used to generate 3D spheroids. These cell lines were compared for cell invasion in 2D and 3D, spheroid growth in 3D, gelatin degradation, cortactin expression, activation and subcellular distribution as well as cell surface cholesterol distribution and lipid droplets. The effect of plasma membrane cholesterol depletion on all these parameters was determined in parallel and systematically compared with the impact of global matrix metalloproteinase (MMP) inhibition.ResultsThe six invasive cell lines in 2D were sensitive to partial cholesterol depletion, independently of their subtype, aggressiveness or mutation. Nevertheless, the effect was stronger in the three cell lines able to degrade gelatin. 3D spheroid invasion was also reduced after cholesterol depletion in all breast cancer subtypes tested. Notably, targeting cholesterol was more powerful than MMP inhibition in reducing invasion in both 2D and 3D culture models. Moreover, cholesterol depletion in the six invasive cell lines impaired cortactin distribution in the perinuclear region where invadopodia localized. Breast cancer cell line aggressiveness relied on cholesterol-enriched domains at the ECM-free side and intracellular lipid droplets. Furthermore, the three gelatin-degrading cell lines were characterized by increased cholesterol-enriched submicrometric domains at their ECM-contact side.ConclusionTogether, our data suggest cell surface cholesterol combined with lipid droplet labeling as a breast cancer cell aggressiveness marker. They also open the way to test other cholesterol-targeting drugs in more complex models to further evaluate whether cholesterol could represent a strategy in breast cancer therapy.

Project description:Background: The most common metastatic site in cervical cancers is lung. The aim of this study is to identify cervical cancer patients with high risk for developing lung metastasis and further explore their prognosis. Materials and Methods: At first, patients diagnosed with cervical cancer from 2010 to 2015 were identified from The Surveillance, Epidemiology, and End Results (SEER) database. Multivariate logistic and Cox regression models were used to identify risk and prognostic factors in cervical cancer patients with lung metastasis. Besides, the clinical characteristics of 14 cervical cancer patients followed up for five years with only lung metastases treated at the First Affiliated Hospital of Wenzhou Medical University were retrospectively analyzed. Results: 19,377 cervical cancer patients were selected from the SEER database; the incidence of lung metastases is 4.33%. Multivariable analysis indicated that advantage age (p<0.001), non-squamous type (p<0.001), late stage (p<0.001), lymph nodes metastases (p<0.001), and poor differentiation (p<0.003) were associated with increased risks for developing lung metastasis. Survival analysis showed that adenocarcinoma, as well as bone and liver metastases were associated with shorter survival in multivariate Cox regression. Among 14 cervical cancer patients with only lung metastasis treated in our hospital, seven patients died within median follow-up time of 16.5 months, including six patients with multiple lung metastasis lesions and one patient with solitary lesion. Seven patients received pulmonary metastasectomy and the following cisplatin-based chemotherapy, among whom one patient died during follow up. Conclusions: Lung metastasis has poor prognosis. Senior age, non-squamous type, late stage, lymph nodes metastases, and poor differentiation are associated with an increased risk for lung metastasis. We recommend pulmonary imaging assessment within 2 years after primary treatment. Adenocarcinoma and multiple pulmonary lesions might predict poor prognosis. To those patients with resectable lung metastasis lesion and no other organ involvement, metastasectomy might improve survival.

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Predictors of breast cancer cell types and their prognostic power in breast cancer patients.

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Predictors of breast cancer cell types and their prognostic power in breast cancer patients.

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