Robust metabolic transcriptional components in 34,494 patient-derived cancer-related samples and cell lines

Abstract Background Patient-derived bulk expression profiles of cancers can provide insight into the transcriptional changes that underlie reprogrammed metabolism in cancer. These profiles represent the average expression pattern of all heterogeneous tumor and non-tumor cells present in biopsies of tumor lesions. Hence, subtle transcriptional footprints of metabolic processes can be concealed by other biological processes and experimental artifacts. However, consensus independent component analyses (c-ICA) can capture statistically independent transcriptional footprints of both subtle and more pronounced metabolic processes. Methods We performed c-ICA with 34,494 bulk expression profiles of patient-derived tumor biopsies, non-cancer tissues, and cell lines. Gene set enrichment analysis with 608 gene sets that describe metabolic processes was performed to identify the transcriptional components enriched for metabolic processes (mTCs). The activity of these mTCs was determined in all samples to create a metabolic transcriptional landscape. Results A set of 555 mTCs was identified of which many were robust across different datasets, platforms, and patient-derived tissues and cell lines. We demonstrate how the metabolic transcriptional landscape defined by the activity of these mTCs in samples can be used to explore the associations between the metabolic transcriptome and drug sensitivities, patient outcomes, and the composition of the immune tumor microenvironment. Conclusions To facilitate the use of our transcriptional metabolic landscape, we have provided access to all data via a web portal (www.themetaboliclandscapeofcancer.com). We believe this resource will contribute to the formulation of new hypotheses on how to metabolically engage the tumor or its (immune) microenvironment.

Download Full-text

Robust Metabolic Transcriptional Components in 34,494 Patient-Derived Samples and Cell Lines

10.21203/rs.3.rs-659596/v1 ◽

2021 ◽

Author(s):

Vincent Christiaan Leeuwenburgh ◽

Carlos G. Urzúa-Traslaviña ◽

Arkajyoti Bhattacharya ◽

Marthe T.C. Walvoort ◽

Mathilde Jalving ◽

...

Keyword(s):

Cell Lines ◽

Expression Profiles ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Metabolic Processes ◽

Gene Sets ◽

Transcriptional Changes ◽

Cancer Tissues ◽

Tumor Biopsies ◽

Transcriptional Landscape

Abstract Background: Patient-derived bulk expression profiles of cancers can provide insight into transcriptional changes that underlie reprogrammed metabolism in cancer. These profiles represent the average expression pattern of all heterogeneous tumor and non-tumor cells present in biopsies of tumor lesions. Hence, subtle transcriptional footprints of metabolic processes can be concealed by other biological processes and experimental artifacts. However, consensus Independent Component Analyses (c-ICA) can capture statistically independent transcriptional footprints, of both subtle and more pronounced metabolic processes. Methods: We performed c-ICA with 34,494 bulk expression profiles of patient-derived tumor biopsies, non-cancer tissues, and cell lines. Gene set enrichment analysis with 608 gene sets that describe metabolic processes was performed to identify transcriptional components enriched for metabolic processes (mTCs). The activity of these mTCs were determined in all samples to create a metabolic transcriptional landscape. Results: A set of 555 mTCs were identified of which many were robust across different datasets, platforms, and patient-derived tissues and cell lines. We demonstrate how the metabolic transcriptional landscape defined by the activity of these mTCs in samples can be used to explore associations between the metabolic transcriptome and drug sensitivities, patient outcomes, and the composition of the immune tumor microenvironment. Conclusions: To facilitate the use of our transcriptional metabolic landscape, we have provided access to all data via a web portal ( www.themetaboliclandscapeofcancer.com ). We believe this resource will contribute to the formulation of new hypotheses on how to metabolically engage the tumor or its (immune) microenvironment.

Download Full-text

Robust and independent metabolic transcriptional components in 34,494 patient-derived samples and cell lines: a resource for translational cancer research

10.1101/2020.10.01.321950 ◽

2020 ◽

Author(s):

V.C. Leeuwenburgh ◽

C.G. Urzúa-Traslaviña ◽

A. Bhattacharya ◽

M.T.C. Walvoort ◽

M. Jalving ◽

...

Keyword(s):

Cell Lines ◽

Patient Outcomes ◽

Expression Profiles ◽

Biological Processes ◽

Metabolic Processes ◽

Translational Cancer Research ◽

Transcriptional Changes ◽

Cancer Tissues ◽

Tumor Biopsies ◽

Insight Into

SUMMARYPatient-derived bulk expression profiles of cancers can provide insight into transcriptional changes that underlie reprogrammed metabolism in cancer. However, these bulk profiles represent the average expression pattern of all heterogeneous tumor and non-tumor cells present in the biopsy. Therefore, subtle transcriptional footprints of metabolic processes can be concealed by other biological processes and experimental artifacts. We therefore performed consensus Independent Component Analyses (c-ICA) with 34,494 bulk expression profiles of patient-derived tumor biopsies, non-cancer tissues and cell lines. c-ICA enabled us to create a transcriptional metabolic landscape in which many robust metabolic transcriptional components (mTCs) and their activation score in individual samples were defined. Here we demonstrate that this metabolic landscape can be used to explore associations between metabolic processes and drug sensitivities, patient outcomes, and the composition of the immune tumor microenvironment. The metabolic landscape can be explored at http://www.themetaboliclandscapeofcancer.com.

Download Full-text

Transcriptomic Analysis of the Differential Nephrotoxicity of Diverse Brominated Flame Retardants in Rat and Human Renal Cells

International Journal of Molecular Sciences ◽

10.3390/ijms221810044 ◽

2021 ◽

Vol 22 (18) ◽

pp. 10044

Author(s):

Lillie Marie A. Barnett ◽

Naomi E. Kramer ◽

Amanda N. Buerger ◽

Deirdre H. Love ◽

Joseph H. Bisesi ◽

...

Keyword(s):

Cell Lines ◽

Flame Retardants ◽

Molecular Mechanisms ◽

Annexin V ◽

Enrichment Analysis ◽

Brominated Flame Retardants ◽

Human Cells ◽

Gene Set Enrichment Analysis ◽

Coagulation Cascade ◽

Transcriptional Changes

Brominated flame retardants (BFRs) are environmentally persistent, are detected in humans, and some have been banned due to their potential toxicity. BFRs are developmental neurotoxicants and endocrine disruptors; however, few studies have explored their potential nephrotoxicity. We addressed this gap in the literature by determining the toxicity of three different BFRs (tetrabromobisphenol A (TBBPA), hexabromocyclododecane (HBCD), and tetrabromodiphenyl ether (BDE-47)) in rat (NRK 52E) and human (HK-2 and RPTEC) tubular epithelial cells. All compounds induced time- and concentration-dependent toxicity based on decreases in MTT staining and changes in cell and nuclear morphology. The toxicity of BFRs was chemical- and cell-dependent, and human cells were more susceptible to all three BFRs based on IC50s after 48 h exposure. BFRs also had chemical- and cell-dependent effects on apoptosis as measured by increases in annexin V and PI staining. The molecular mechanisms mediating this toxicity were investigated using RNA sequencing. Principal components analysis supported the hypothesis that BFRs induce different transcriptional changes in rat and human cells. Furthermore, BFRs only shared nine differentially expressed genes in rat cells and five in human cells. Gene set enrichment analysis demonstrated chemical- and cell-dependent effects; however, some commonalities were also observed. Namely, gene sets associated with extracellular matrix turnover, the coagulation cascade, and the SNS-related adrenal cortex response were enriched across all cell lines and BFR treatments. Taken together, these data support the hypothesis that BFRs induce differential toxicity in rat and human renal cell lines that is mediated by differential changes in gene expression.

Download Full-text

Connecting gene expression subtypes of colorectal cancer (CRC) with cell lines and drug resistance.

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.15_suppl.e14544 ◽

2013 ◽

Vol 31 (15_suppl) ◽

pp. e14544-e14544

Author(s):

Eva Budinska ◽

Jenny Wilding ◽

Vlad Calin Popovici ◽

Edoardo Missiaglia ◽

Arnaud Roth ◽

...

Keyword(s):

Gene Expression ◽

Cell Line ◽

Clinical Significance ◽

Cell Lines ◽

Drug Response ◽

Drug Sensitivity ◽

Expression Profiles ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis

e14544 Background: We identified CRC gene expression subtypes (ASCO 2012, #3511), which associate with established parameters of outcome as well as relevant biological motifs. We now substantiate their biological and potentially clinical significance by linking them with cell line data and drug sensitivity, primarily attempting to identify models for the poor prognosis subtypes Mesenchymal and CIMP-H like (characterized by EMT/stroma and immune-associated gene modules, respectively). Methods: We analyzed gene expression profiles of 35 publicly available cell lines with sensitivity data for 82 drug compounds, and our 94 cell lines with data on sensitivity for 7 compounds and colony morphology. As in vitro, stromal and immune-associated genes loose their relevance, we trained a new classifier based on genes expressed in both systems, which identifies the subtypes in both tissue and cell cultures. Cell line subtypes were validated by comparing their enrichment for molecular markers with that of our CRC subtypes. Drug sensitivity was assessed by linking original subtypes with 92 drug response signatures (MsigDB) via gene set enrichment analysis, and by screening drug sensitivity of cell line panels against our subtypes (Kruskal-Wallis test). Results: Of the cell lines 70% could be assigned to a subtype with a probability as high as 0.95. The cell line subtypes were significantly associated with their KRAS, BRAF and MSI status and corresponded to our CRC subtypes. Interestingly, the cell lines which in matrigel created a network of undifferentiated cells were assigned to the Mesenchymal subtype. Drug response studies revealed potential sensitivity of subtypes to multiple compounds, in addition to what could be predicted based on their mutational profile (e.g. sensitivity of the CIMP-H subtype to Dasatinib, p<0.01). Conclusions: Our data support the biological and potentially clinical significance of the CRC subtypes in their association with cell line models, including results of drug sensitivity analysis. Our subtypes might not only have prognostic value but might also be predictive for response to drugs. Subtyping cell lines further substantiates their significance as relevant model for functional studies.

Download Full-text

Improved cancer biomarkers identification using network-constrained infinite latent feature selection

PLoS ONE ◽

10.1371/journal.pone.0246668 ◽

2021 ◽

Vol 16 (2) ◽

pp. e0246668

Author(s):

Lihua Cai ◽

Honglong Wu ◽

Ke Zhou

Keyword(s):

Feature Selection ◽

Expression Profiles ◽

Biological Significance ◽

Feature Selection Method ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Gene Sets ◽

Significant Gene

Identifying biomarkers that are associated with different types of cancer is an important goal in the field of bioinformatics. Different researcher groups have analyzed the expression profiles of many genes and found some certain genetic patterns that can promote the improvement of targeted therapies, but the significance of some genes is still ambiguous. More reliable and effective biomarkers identification methods are then needed to detect candidate cancer-related genes. In this paper, we proposed a novel method that combines the infinite latent feature selection (ILFS) method with the functional interaction (FIs) network to rank the biomarkers. We applied the proposed method to the expression data of five cancer types. The experiments indicated that our network-constrained ILFS (NCILFS) provides an improved prediction of the diagnosis of the samples and locates many more known oncogenes than the original ILFS and some other existing methods. We also performed functional enrichment analysis by inspecting the over-represented gene ontology (GO) biological process (BP) terms and applying the gene set enrichment analysis (GSEA) method on selected biomarkers for each feature selection method. The enrichments analysis reports show that our network-constraint ILFS can produce more biologically significant gene sets than other methods. The results suggest that network-constrained ILFS can identify cancer-related genes with a higher discriminative power and biological significance.

Download Full-text

Integrative DNA Methylation and Gene Expression Analysis Reveals Candidate Biomarkers Associated with Dichotomized Response to Chemoimmunotherapy in Diffuse Large B-Cell Lymphoma

Blood ◽

10.1182/blood-2020-137180 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 22-22

Author(s):

Ellen K. Kendall ◽

Manishkumar S. Patel ◽

Sarah Ondrejka ◽

Agrima Mian ◽

Yazeed Sawalha ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Research Funding ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

B Cell Lymphoma ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Gene Sets

Background: Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma. While 60% of DLBCL patients achieve complete remission with frontline therapy, relapsed/refractory (R/R) DLBCL patients have a poor prognosis with median overall survival below one year, necessitating investigation into the biological principles that distinguish cured from R/R DLBCL. Recent analyses have identified unfavorable molecular signatures when accounting for gene expression, copy number alterations and mutational profiles in R/R DLBCL. However, an integrative analysis of the relationship between epigenetic and transcriptomic changes has yet to be described. In this study, we compared baseline methylation and gene expression profiles of DLBCL patients with dichotomized clinical outcomes. Methods: Diagnostic DLBCL biopsies were obtained from two patient cohorts: patients who relapsed or were refractory following chemoimmunotherapy ("R/R"), and patients who entered durable clinical remission following therapy ("cured"). The median age for R/R and cured cohorts were 62 (range 35-86) years vs. 64 (range 28-83) years (P= 0.27). High-intermediate or high IPI scores were present in 14 vs. 6 patients (P= 0.08) in the R/R and cured cohorts, respectively. All patients were treated with frontline R-CHOP or R-EPOCH. DNA and RNA were extracted simultaneously from formalin-fixed, paraffin embedded biopsy samples. An Illumina 850k Methylation Array was used to identify DNA methylation levels in 29 R/R patients and 20 cured patients. RNA sequencing was performed on 9 R/R patients and 7 cured patients at diagnosis using Illumina HiSeq4000. Differentially methylated probes were identified using the DMRcate package, and differentially expressed genes were identified using the DESeq2 package. Gene set enrichment analysis was performed using canonical pathway gene sets from MSigDB. Results: At the time of diagnosis, we found significant epigenetic and transcriptomic differences between cured and R/R patients. Comparing cured to R/R samples, there were 8,159 differentially methylated probes (FDR<0.05). Differentially methylated regions between R/R and cured cohorts overlap with genes previously identified as mutation hotspots in DLBCL. Upon comparing transcriptomic profiles between R/R and cured, 267 genes were found to be differentially expressed (Log2FC>|1| and FDR<0.05). Gene set enrichment analysis revealed gene sets related to cell cycle, membrane trafficking, Rho and Rab family GTPase function, and transcriptional regulation were upregulated in the R/R samples. Gene sets related to innate immune signaling, Type I and II interferon signaling, fatty acid and carbohydrate metabolism were upregulated in the cured samples. To identify genes likely to be regulated by specific changes in methylation, we selected genes that were both differentially expressed and differentially methylated between the R/R and cured cohorts. In the R/R samples, 13 genes (ARMC5, ARRDC1, C12orf57, CCSER1, D2HGDH, DUOX2, FAM189B, FKBP2, KLF5, MFSD10, NEK8, NT5C, and WDR18) were significantly hypermethylated and underexpressed when compared to cured specimens, suggesting that epigenetic silencing of these genes is associated with lack of response to chemoimmunotherapy. In contrast, 12 genes (ATP2B1, C15orf41, FAM102B, FAM3C, FHOD3, FYTTD1, GPR180, KIAA1841, LRMP, MEF2A, RRAS2, and TPD52) were significantly hypermethylated and underexpressed in cured patients, suggesting that epigenetic silencing of these genes is favorable for treatment response. Many of these epigenetically modified genes have been previously implicated in cancer biology, including roles in NOTCH signaling, chromosomal instability, and biomarkers of prognosis. Conclusions: This is the first integrative epigenetic and transcriptomic analysis of diagnostic biopsies from cured and R/R DLBCL patients following chemoimmunotherapy. At the time of diagnosis, both the methylation and gene expression profiles significantly differ between patients that enter durable remission as opposed to those who are R/R to therapy. Soon, the hypomethylating agent CC-486 (i.e. oral azacitidine) will be explored in combination with mini-R-CHOP for older DLBCL patients in whom DNA methylation is likely increased. These data support the use of hypomethylating agents to potentially restore sensitivity of DLBCL to chemoimmunotherapy. Disclosures Hsi: Eli Lilly: Research Funding; Abbvie: Research Funding; Miltenyi: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; CytomX: Consultancy, Honoraria. Hill:Celgene: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria; Kite, a Gilead Company: Consultancy, Honoraria, Research Funding; AstraZenica: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria, Research Funding; Takeda: Research Funding; Beigene: Consultancy, Honoraria, Research Funding; Genentech: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding.

Download Full-text

Application of biclustering of gene expression data and gene set enrichment analysis methods to identify potentially disease causing nanomaterials

Beilstein Journal of Nanotechnology ◽

10.3762/bjnano.6.252 ◽

2015 ◽

Vol 6 ◽

pp. 2438-2448 ◽

Cited By ~ 14

Author(s):

Andrew Williams ◽

Sabina Halappanavar

Keyword(s):

Gene Expression ◽

Pulmonary Fibrosis ◽

Expression Profiles ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Data Driven ◽

Gene Set Enrichment ◽

Gene Set ◽

Analysis Methods ◽

Gene Sets

Background: The presence of diverse types of nanomaterials (NMs) in commerce is growing at an exponential pace. As a result, human exposure to these materials in the environment is inevitable, necessitating the need for rapid and reliable toxicity testing methods to accurately assess the potential hazards associated with NMs. In this study, we applied biclustering and gene set enrichment analysis methods to derive essential features of altered lung transcriptome following exposure to NMs that are associated with lung-specific diseases. Several datasets from public microarray repositories describing pulmonary diseases in mouse models following exposure to a variety of substances were examined and functionally related biclusters of genes showing similar expression profiles were identified. The identified biclusters were then used to conduct a gene set enrichment analysis on pulmonary gene expression profiles derived from mice exposed to nano-titanium dioxide (nano-TiO2), carbon black (CB) or carbon nanotubes (CNTs) to determine the disease significance of these data-driven gene sets. Results: Biclusters representing inflammation (chemokine activity), DNA binding, cell cycle, apoptosis, reactive oxygen species (ROS) and fibrosis processes were identified. All of the NM studies were significant with respect to the bicluster related to chemokine activity (DAVID; FDR p-value = 0.032). The bicluster related to pulmonary fibrosis was enriched in studies where toxicity induced by CNT and CB studies was investigated, suggesting the potential for these materials to induce lung fibrosis. The pro-fibrogenic potential of CNTs is well established. Although CB has not been shown to induce fibrosis, it induces stronger inflammatory, oxidative stress and DNA damage responses than nano-TiO2 particles. Conclusion: The results of the analysis correctly identified all NMs to be inflammogenic and only CB and CNTs as potentially fibrogenic. In addition to identifying several previously defined, functionally relevant gene sets, the present study also identified two novel genes sets: a gene set associated with pulmonary fibrosis and a gene set associated with ROS, underlining the advantage of using a data-driven approach to identify novel, functionally related gene sets. The results can be used in future gene set enrichment analysis studies involving NMs or as features for clustering and classifying NMs of diverse properties.

Download Full-text

modEnrichr: a suite of gene set enrichment analysis tools for model organisms

Nucleic Acids Research ◽

10.1093/nar/gkz347 ◽

2019 ◽

Vol 47 (W1) ◽

pp. W183-W190 ◽

Cited By ~ 7

Author(s):

Maxim V Kuleshov ◽

Jennifer E L Diaz ◽

Zachary N Flamholz ◽

Alexandra B Keenan ◽

Alexander Lachmann ◽

...

Keyword(s):

Data Integration ◽

Expression Profiles ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Protein Domain ◽

Model Organisms ◽

Gene Set Enrichment ◽

Gene Set ◽

Analysis Tools ◽

Gene Sets

Abstract High-throughput experiments produce increasingly large datasets that are difficult to analyze and integrate. While most data integration approaches focus on aligning metadata, data integration can be achieved by abstracting experimental results into gene sets. Such gene sets can be made available for reuse through gene set enrichment analysis tools such as Enrichr. Enrichr currently only supports gene sets compiled from human and mouse, limiting accessibility for investigators that study other model organisms. modEnrichr is an expansion of Enrichr for four model organisms: fish, fly, worm and yeast. The gene set libraries within FishEnrichr, FlyEnrichr, WormEnrichr and YeastEnrichr are created from the Gene Ontology, mRNA expression profiles, GeneRIF, pathway databases, protein domain databases and other organism-specific resources. Additionally, libraries were created by predicting gene function from RNA-seq co-expression data processed uniformly from the gene expression omnibus for each organism. The modEnrichr suite of tools provides the ability to convert gene lists across species using an ortholog conversion tool that automatically detects the species. For complex analyses, modEnrichr provides API access that enables submitting batch queries. In summary, modEnrichr leverages existing model organism databases and other resources to facilitate comprehensive hypothesis generation through data integration.

Download Full-text

Differential Gene Set Enrichment Analysis: A statistical approach to quantify the relative enrichment of two gene sets

10.1101/860460 ◽

2019 ◽

Author(s):

James H. Joly ◽

William E. Lowry ◽

Nicholas A. Graham

Keyword(s):

Cell Lines ◽

Cancer Cell ◽

Enrichment Analysis ◽

Cancer Cell Lines ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Gene Set ◽

Relative Enrichment ◽

Gene Sets ◽

Differential Gene

AbstractGene Set Enrichment Analysis (GSEA) is an algorithm widely used to identify statistically enriched gene sets in transcriptomic data. However, to our knowledge, there exists no method for examining the enrichment of two gene sets relative to one another. Here, we present Differential Gene Set Enrichment Analysis (DGSEA), an adaptation of GSEA that assesses the relative enrichment of two gene sets. Using the metabolic pathways glycolysis and oxidative phosphorylation as an example, we demonstrate that DGSEA accurately captures the hypoxia-induced shift towards glycolysis. We also show that DGSEA is more predictive than GSEA of the metabolic state of cancer cell lines, including lactate secretion and intracellular concentrations of lactate and AMP. Furthermore, we demonstrate that DGSEA identifies novel metabolic dependencies not found by GSEA in cancer cell lines. Together, these data demonstrate that DGSEA is a novel tool to examine the relative enrichment of two gene sets.

Download Full-text

Utilizing Cancer - Functional Gene Set - Compound Networks to Identify Putative Drugs for Breast Cancer

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1574888x13666180105125347 ◽

2018 ◽

Vol 21 (2) ◽

pp. 74-83

Author(s):

Tzu-Hung Hsiao ◽

Yu-Chiao Chiu ◽

Yu-Heng Chen ◽

Yu-Ching Hsu ◽

Hung-I Harry Chen ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Cancer Therapy ◽

Cancer Treatment ◽

Cancer Survival ◽

Expression Profiles ◽

Functional Gene ◽

Gene Set Enrichment Analysis ◽

Gene Set ◽

Gene Sets

Aim and Objective: The number of anticancer drugs available currently is limited, and some of them have low treatment response rates. Moreover, developing a new drug for cancer therapy is labor intensive and sometimes cost prohibitive. Therefore, “repositioning” of known cancer treatment compounds can speed up the development time and potentially increase the response rate of cancer therapy. This study proposes a systems biology method for identifying new compound candidates for cancer treatment in two separate procedures. Materials and Methods: First, a “gene set–compound” network was constructed by conducting gene set enrichment analysis on the expression profile of responses to a compound. Second, survival analyses were applied to gene expression profiles derived from four breast cancer patient cohorts to identify gene sets that are associated with cancer survival. A “cancer–functional gene set– compound” network was constructed, and candidate anticancer compounds were identified. Through the use of breast cancer as an example, 162 breast cancer survival-associated gene sets and 172 putative compounds were obtained. Results: We demonstrated how to utilize the clinical relevance of previous studies through gene sets and then connect it to candidate compounds by using gene expression data from the Connectivity Map. Specifically, we chose a gene set derived from a stem cell study to demonstrate its association with breast cancer prognosis and discussed six new compounds that can increase the expression of the gene set after the treatment. Conclusion: Our method can effectively identify compounds with a potential to be “repositioned” for cancer treatment according to their active mechanisms and their association with patients’ survival time.

Download Full-text