skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Methylation of CpG Sites as Biomarkers Predictive of Drug-Specific Patient Survival in Cancer
Background: Though the development of targeted cancer drugs continues to accelerate, doctors still lack reliable methods for predicting patient response to standard-of-care therapies for most cancers. DNA methylation has been implicated in tumor drug response and is a promising source of predictive biomarkers of drug efficacy, yet the relationship between drug efficacy and DNA methylation remains largely unexplored. Method: In this analysis, we performed log-rank survival analyses on patients grouped by cancer and drug exposure to find CpG sites where binary methylation status is associated with differential survival in patients treated with a specific drug but not in patients with the same cancer who were not exposed to that drug. We also clustered these drug-specific CpG sites based on co-methylation among patients to identify broader methylation patterns that may be related to drug efficacy, which we investigated for transcription factor binding site enrichment using gene set enrichment analysis. Results: We identified CpG sites that were drug-specific predictors of survival in 38 cancer-drug patient groups across 15 cancers and 20 drugs. These included 11 CpG sites with similar drug-specific survival effects in multiple cancers. We also identified 76 clusters of CpG sites with stronger associations with patient drug response, many of which contained CpG sites in gene promoters containing transcription factor binding sites. Conclusion: These findings are promising biomarkers of drug response for a variety of drugs and contribute to our understanding of drug-methylation interactions in cancer. Investigation and validation of these results could lead to the development of targeted co-therapies aimed at manipulating methylation in order to improve efficacy of commonly used therapies and could improve patient survival and quality of life by furthering the effort toward drug response prediction.  more » « less
Award ID(s):
2007029
PAR ID:
10378519
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Cancer Informatics
Volume:
21
ISSN:
1176-9351
Page Range / eLocation ID:
117693512211311
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; (, Scientific Reports)
    null (Ed.)
    Abstract The ability to predict the efficacy of cancer treatments is a longstanding goal of precision medicine that requires improved understanding of molecular interactions with drugs and the discovery of biomarkers of drug response. Identifying genes whose expression influences drug sensitivity can help address both of these needs, elucidating the molecular pathways involved in drug efficacy and providing potential ways to predict new patients’ response to available therapies. In this study, we integrated cancer type, drug treatment, and survival data with RNA-seq gene expression data from The Cancer Genome Atlas to identify genes and gene sets whose expression levels in patient tumor biopsies are associated with drug-specific patient survival using a log-rank test comparing survival of patients with low vs. high expression for each gene. This analysis was successful in identifying thousands of such gene–drug relationships across 20 drugs in 14 cancers, several of which have been previously implicated in the respective drug’s efficacy. We then clustered significant genes based on their expression patterns across patients and defined gene sets that are more robust predictors of patient outcome, many of which were significantly enriched for target genes of one or more transcription factors, indicating several upstream regulatory mechanisms that may be involved in drug efficacy. We identified a large number of genes and gene sets that were potentially useful as transcript-level biomarkers for predicting drug-specific patient survival outcome. Our gene sets were robust predictors of drug-specific survival and our results included both novel and previously reported findings, suggesting that the drug-specific survival marker genes reported herein warrant further investigation for insights into drug mechanisms and for validation as biomarkers to aid cancer therapy decisions. 
    more » « less
  2. ; (, Cancer Informatics)
    Background:Transcriptomics can reveal much about cellular activity, and cancer transcriptomics have been useful in investigating tumor cell behaviors. Patterns in transcriptome-wide gene expression can be used to investigate biological mechanisms and pathways that can explain the variability in patient response to cancer therapies. Methods:We identified gene expression patterns related to patient drug response by clustering tumor gene expression data and selecting from the resulting gene clusters those where expression of cluster genes was related to patient survival on specific drugs. We then investigated these gene clusters for biological meaning using several approaches, including identifying common genomic locations and transcription factors whose targets were enriched in these clusters and performing survival analyses to support these candidate transcription factor-drug relationships. Results:We identified gene clusters related to drug-specific survival, and through these, we were able to associate observed variations in patient drug response to specific known biological phenomena. Specifically, our analysis implicated 2 stem cell-related transcription factors, HOXB4 and SALL4, in poor response to temozolomide in brain cancers. In addition, expression of SNRNP70 and its targets were implicated in cetuximab response by 3 different analyses, although the mechanism remains unclear. We also found evidence that 2 cancer-related chromosomal structural changes may impact drug efficacy. Conclusion:In this study, we present the gene clusters identified and the results of our systematic analysis linking drug efficacy to specific transcription factors, which are rich sources of potential mechanistic relationships impacting patient outcomes. We also highlight the most promising of these results, which were supported by multiple analyses and by previous research. We report these findings as promising avenues for independent validation and further research into cancer treatments and patient response. 
    more » « less
  3. ; ; (, BMC Genomics)
    Abstract The effect of DNA methylation on the regulation of gene expression has been extensively discussed in the literature. However, the potential association between DNA methylation and alternative splicing is not understood well. In this study, we integrated multiple omics data types from The Cancer Genome Atlas (TCGA) and systematically examined the relationship between DNA methylation and alternative splicing. Using the methylation data and exon expression data, we identified many CpG sites significantly associated with exon expression in various types of cancers. We further observed that the direction and strength of significant CpG-exon correlation tended to be consistent across different cancer contexts, indicating that some CpG-exon correlation patterns reflect fundamental biological mechanisms that transcend tissue- and cancer- types. We also discovered that CpG sites correlated with exon expressions were more likely to be associated with patient survival outcomes compared to CpG sites that did not correlate with exon expressions. Furthermore, we found that CpG sites were more strongly correlated with exon expression than expression of isoforms harboring the corresponding exons. This observation suggests that a major effect of CpG methylation on alternative splicing may be related to the inclusion or exclusion of exons, which subsequently impacts the relative usage of various isoforms. Overall, our study revealed correlation patterns between DNA methylation and alternative splicing, which provides new insights into the role of methylation in the transcriptional process. 
    more » « less
  4. ; ; ; (, Frontiers in Genetics)
    Similar molecular and genetic aberrations among diseases can lead to the discovery of jointly important treatment options across biologically similar diseases. Oncologists closely looked at several hormone-dependent cancers and identified remarkable pathological and molecular similarities in their DNA repair pathway abnormalities. Although deficiencies in Homologous Recombination (HR) pathway plays a significant role towards cancer progression, there could be other DNA-repair pathway deficiencies that requires careful investigation. In this paper, through a biomarker-driven drug repurposing model, we identified several potential drug candidates for breast and prostate cancer patients with DNA-repair deficiencies based on common specific biomarkers and irrespective of the organ the tumors originated from. Normalized discounted cumulative gain (NDCG) and sensitivity analysis were used to assess the performance of the drug repurposing model. Our results showed that Mitoxantrone and Genistein were among drugs with high therapeutic effects that significantly reverted the gene expression changes caused by the disease (FDR adjusted p-values for prostate cancer =1.225e-4 and 8.195e-8, respectively) for patients with deficiencies in their homologous recombination (HR) pathways. The proposed multi-cancer treatment framework, suitable for patients whose cancers had common specific biomarkers, has the potential to identify promising drug candidates by enriching the study population through the integration of multiple cancers and targeting patients who respond poorly to organ-specific treatments. 
    more » « less
  5. ; ; ; ; ; (, In: Wei, Y., Li, M., Skums, P., Cai, Z. (eds) Bioinformatics Research and Applications. ISBRA 2021. Lecture Notes in Computer Science)
    Wei, Yanjie; Li, Min; Skums, Pavel; Cai, Zhipeng (Ed.)
    Novel discoveries of biomarkers predictive of drug-specific responses not only play a pivotal role in revealing the drug mechanisms in cancers, but are also critical to personalized medicine. In this study, we identified drug-specific biomarkers by integrating protein expression data, drug treatment data and survival outcome of 7076 patients from The Cancer Genome Atlas (TCGA). We first defined cancer-drug groups, where each cancer-drug group contains patients with the same cancer and treated with the same drug. For each protein, we stratified the patients in each cancer-drug group by high or low expression of the protein, and applied log-rank test to examine whether the stratified patients show significant survival difference. We examined 336 proteins in 98 cancer-drug groups and identified 65 protein-cancer-drug combinations involving 55 unique proteins, where the protein expression levels are predictive of drug-specific survival outcomes. Some of the identified proteins were supported by published literature. Using the gene expression data from TCGA, we found the mRNA expression of ∼11% of the drug-specific proteins also showed significant correlation with drug-specific survival, and most of these drug-specific proteins and their corresponding genes are strongly correlated. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email