Abstract Breast cancer in premenopausal women (preM) is frequently associated with worse prognosis compared to that in postmenopausal women (postM) even when controlling for prognostic variables. In particular, preM ER+ tumors have a poor prognosis on endocrine therapy. There is, however, a paucity of studies characterizing molecular alterations in premenopausal tumors, a potential avenue for finding personalized therapies for this group of women. We analyzed gene expression, CNV, methylation, and somatic mutations in tumors from preM (≤45; ER+ n = 110, and ER- n = 39) and postM (≥55, ER+ n = 392, and ER- n = 102) women in The Cancer Genome Atlas (TCGA). Unbiased hierarchical clustering of 2,900 most variably expressed genes (using both RNA-seq and Agilent expression array data) in the whole dataset (n = 643) identified four major subtypes which correlated highly with the PAM50 defined subtypes LumA, LumB, Basal and HER2; however, there wasn't any separation between preM and postM samples. Similarly, principal component analysis using 10,000 genes with the highest inter-quartile range (IQR) demonstrated high similarity across preM and postM samples. Direct examination of gene expression differences between PreM and PostM ER+ tumors using unpaired t-test (5% FDR) identified 3,044 differentially expressed genes. The genes most upregulated in premenopausal tumors included AREG, TFPI2, MSMB, TCN1, and GLRA3. Ingenuity Pathway Analysis revealed a highly significant enrichment for TGFb (p<1.9E-16) pathway activity in preM tumors. Intriguingly, no significant gene expression differences between preM and postM ER- tumors were identified. We thus then focused on genetic and epigenetic alterations that may underlie these transcriptomic changes in ER+ preM tumors. Comparison of methylation (450K Illumina array) between preM and postM ER+ tumors showed a difference in 1% (n = 1,738) of the probes. Genes with the largest difference included ESR1, SIM2, and KLF6. Significant differences in DNA copy number variation (Affymetrix SNP 6.0 array) were also identified in ER+ preM tumors. A number of somatic mutations were significantly enriched in preM ER+ tumors including DSPP and GATA3. Integrated analysis also showed that approximately half of the observed differences in gene expression are driven by CNVs. Conclusion: Our in silico study has identified a number of genes and pathways which are significantly altered between preM and postM ER+ breast cancer. Distinct genetic and epigenetic differences suggest unique etiology for some preM tumors. Currently ongoing Paradigm analysis, and confirmatory studies using METABRIC data are expected to further identify pathways that could specifically be targeted in premenopausal breast cancer. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P4-05-03.
Objective
Discover the sensitive genes of lung cancer and their relationships.
Methods
Searching the genes with differential expression and gene pairs with differential co-expression in lung cancer, through comparing the samples of lung cancer and control, then we mapped the genes into differential co-expressed networks, sorted the important node with degree, betweenness, vulnerability and closeness centrality respectively, and sorted the genes with the sum of these four characters to filter the sensitive genes.
Results
15 important lung associated genes were found in lung cancer, including 13 differential expressed genes. We also found that the expression of CCL19 and TNFSF13B could significantly decrease of expression of other genes even though they were not included in the list of differential expression genes.
Conclusion
These genes mined by this research provided an important supplementary to the etiology of lung cancer.
Key words:
Differential co-expression; Differential expression; Sensitive gene
<div>Abstract<p>Systematically tracking the tumor immunophenotype is required to understand the mechanisms of cancer immunity and improve clinical benefit of cancer immunotherapy. However, progress in current research is hindered by the lack of comprehensive immune activity resources and easy-to-use tools for biologists, clinicians, and researchers to conveniently evaluate immune activity during the “cancer-immunity cycle.” We developed a user-friendly one-stop shop web tool called TIP to comprehensively resolve tumor immunophenotype. TIP has the capability to rapidly analyze and intuitively visualize the activity of anticancer immunity and the extent of tumor-infiltrating immune cells across the seven-step cancer-immunity cycle. Also, we precalculated the pan-cancer immunophenotype for 11,373 samples from 33 The Cancer Genome Atlas human cancers that allow users to obtain and compare immunophenotype of pan-cancer samples. We expect TIP to be useful in a large number of emerging cancer immunity studies and development of effective immunotherapy biomarkers. TIP is freely available for use at <a href="http://biocc.hrbmu.edu.cn/TIP/" target="_blank">http://biocc.hrbmu.edu.cn/TIP/</a>.</p>Significance:<p>TIP is a one-stop shop platform that can help biologists, clinicians, and researchers conveniently evaluate anticancer immune activity with their own gene expression data.</p><p><i>See related commentary by Hirano, p. 6536</i></p></div>
Engineered organoids by sequential introduction of key mutations could help modeling the dynamic cancer progression. However, it remains difficult to determine gene paths which were sufficient to capture cancer behaviors and to broadly explain cancer mechanisms. Here, as a case study of colorectal cancer (CRC), functional and dynamic characterizations of five types of engineered organoids with different mutation combinations of five driver genes (APC, SMAD4, KRAS, TP53 and PIK3CA) showed that sequential introductions of all five driver mutations could induce enhanced activation of more hallmark signatures, tending to cancer. Comparative analysis of engineered organoids and corresponding CRC tissues revealed sequential introduction of key mutations could continually shorten the biological distance from engineered organoids to CRC tissues. Nevertheless, there still existed substantial biological gaps between the engineered organoid even with five key mutations and CRC samples. Thus, we proposed an integrative strategy to prioritize gene cascading paths for shrinking biological gaps between engineered organoids and CRC tissues. Our results not only recapitulated the well-known adenoma–carcinoma sequence model (e.g. AKST-organoid with driver mutations in APC, KRAS, SMAD4, and TP53), but also provided potential paths for delineating alternative pathogenesis underlying CRC populations (e.g. A-organoid with APC mutation). Our strategy also can be applied to both organoids with more mutations and other cancers, which can improve and innovate mechanism across cancer patients for drug design and cancer therapy.
One of the most fundamental questions in biology is what types of cells form different tissues and organs in a functionally coordinated fashion. Larger-scale single-cell sequencing and biology experiment studies are now rapidly opening up new ways to track this question by revealing substantial cell markers for distinguishing different cell types in tissues. Here, we developed the CellMarker database (http://biocc.hrbmu.edu.cn/CellMarker/ or http://bio-bigdata.hrbmu.edu.cn/CellMarker/), aiming to provide a comprehensive and accurate resource of cell markers for various cell types in tissues of human and mouse. By manually curating over 100 000 published papers, 4124 entries including the cell marker information, tissue type, cell type, cancer information and source, were recorded. At last, 13 605 cell markers of 467 cell types in 158 human tissues/sub-tissues and 9148 cell makers of 389 cell types in 81 mouse tissues/sub-tissues were collected and deposited in CellMarker. CellMarker provides a user-friendly interface for browsing, searching and downloading markers of diverse cell types of different tissues. Furthermore, a summarized marker prevalence in each cell type is graphically and intuitively presented through a vivid statistical graph. We believe that CellMarker is a comprehensive and valuable resource for cell researches in precisely identifying and characterizing cells, especially at the single-cell level.
Triple-negative breast cancer (TNBC) is a clinically aggressive disease with abundant variants that cause homologous recombination repair deficiency (HRD). Whether TNBC patients with HRD are sensitive to anthracycline, cyclophosphamide and taxane (ACT), and whether the combination of HRD and tumour immunity can improve the recognition of ACT responders are still unknown.Data from 83 TNBC patients in The Cancer Genome Atlas (TCGA) was used as a discovery cohort to analyse the association between HRD and ACT chemotherapy benefits. The combined effects of HRD and immune activation on ACT chemotherapy were explored at both the genome and the transcriptome levels. Independent cohorts from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO) were adopted to validate our findings.HRD was associated with a longer ACT chemotherapy failure-free interval (FFI) with a hazard ratio of 0.16 (P = 0.004) and improved patient prognosis (P = 0.0063). By analysing both HRD status and ACT response, we identified patients with a distinct TNBC subtype (ACT-S&HR-P) that showed higher tumour lymphocyte infiltration, IFN-γ activity and NK cell levels. Patients with ACT-S&HR-P had significantly elevated immune inhibitor levels and presented immune activation associated with the increased activities of both innate immune cells and adaptive immune cells, which suggested treatment with immune checkpoint blockade as an option for this subtype. Our analysis revealed that the combination of HRD and immune activation enhanced the efficiency of identifying responders to ACT chemotherapy (AUC = 0.91, P = 1.06e-04) and synergistically contributed to the clinical benefits of TNBC patients. A transcriptional HRD signature of ACT response-related prognostic factors was identified and independently validated to be significantly associated with improved survival in the GEO cohort (P = 0.0038) and the METABRIC dataset (P < 0.0001).These findings highlight that HR deficiency prolongs FFI and predicts intensified responses in TNBC patients by combining HRD and immune activation, which provides a molecular basis for identifying ACT responders.
T cells exhibit heterogeneous functional states, which correlate with responsiveness to immune checkpoint blockade and prognosis of tumor patients. However, the molecular regulatory mechanisms underlying the dynamic process of T cell state transition remain largely unknown. Based on single-cell transcriptome data of T cells in non-small cell lung cancer, we combined cell states and pseudo-times to propose a pipeline to construct dynamic regulatory networks for dissecting the process of T cell dysfunction. Candidate regulators at different stages were revealed in the process of tumor-infiltrating T cell dysfunction. Through comparing dynamic networks across the T cell state transition, we revealed frequent regulatory interaction rewiring and further refined critical regulators mediating each state transition. Several known regulators were identified, including TCF7, EOMES, ID2, and TOX. Notably, one of the critical regulators, TSC22D3, was frequently identified in the state transitions from the intermediate state to the pre-dysfunction and dysfunction state, exerting diverse roles in each state transition by regulatory interaction rewiring. Moreover, higher expression of TSC22D3 was associated with the clinical outcome of tumor patients. Our study embedded transcription factors (TFs) within the temporal dynamic networks, providing a comprehensive view of dynamic regulatory mechanisms controlling the process of T cell state transition.
Abstract Background Enhancer has been recognized as an important driver whose genetic alterations contribute to disease progression. However, there is still no easy-to-use tools to identify pathogenic enhancers, allowing for deciphering functional influence of genetic variants on enhancer. Results We developed a user-friendly one-stop shop platform, named inferring pathogenic enhancer with variant (IPEV), only requiring variants as input, to quickly infer the pathogenic enhancers that harbor variants affecting their activities. Results of IPEV are explored in an interactive, user-friendly web environment, which is designed to highlight the most probable pathogenic enhancers and their target genes. Furthermore, IPEV provides intuitive visualizations of how a variant affects the corresponding enhancer activity by mediating TF binding changes. Conclusions IPEV is specially designed to prioritize the potentially pathogenic enhancers with genetic variants, and provides intuitive visualizations how a variant affects the corresponding enhancer activity by mediating which transcription factor binding changes. The use of IPEV does not require any specialized computer skills. We believe that IPEV will be useful in interpreting non-coding variants by the inferring pathogenic enhancers. It is freely available at http://biocc.hrbmu.edu.cn/IPEV/ or http://210.46.80.168/IPEV and supports recent versions of all major browsers.
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