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Patrício A, Costa RS, Henriques R. Pattern-centric transformation of omics data grounded on discriminative gene associations aids predictive tasks in TCGA while ensuring interpretability. Biotechnol Bioeng 2024; 121:2881-2892. [PMID: 38859573 DOI: 10.1002/bit.28758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 02/07/2024] [Accepted: 05/18/2024] [Indexed: 06/12/2024]
Abstract
The increasing prevalence of omics data sources is pushing the study of regulatory mechanisms underlying complex diseases such as cancer. However, the vast quantities of molecular features produced and the inherent interplay between them lead to a level of complexity that hampers both descriptive and predictive tasks, requiring custom-built algorithms that can extract relevant information from these sources of data. We propose a transformation that moves data centered on molecules (e.g., transcripts and proteins) to a new data space focused on putative regulatory modules given by statistically relevant co-expression patterns. To this end, the proposed transformation extracts patterns from the data through biclustering and uses them to create new variables with guarantees of interpretability and discriminative power. The transformation is shown to achieve dimensionality reductions of up to 99% and increase predictive performance of various classifiers across multiple omics layers. Results suggest that omics data transformations from gene-centric to pattern-centric data supports both prediction tasks and human interpretation, notably contributing to precision medicine applications.
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Affiliation(s)
- André Patrício
- INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
- LAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, NOVA University Lisbon, Caparica, Portugal
| | - Rafael S Costa
- LAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, NOVA University Lisbon, Caparica, Portugal
| | - Rui Henriques
- INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
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2
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Irrera O, Marchesin S, Silvello G. MetaTron: advancing biomedical annotation empowering relation annotation and collaboration. BMC Bioinformatics 2024; 25:112. [PMID: 38486137 PMCID: PMC10941452 DOI: 10.1186/s12859-024-05730-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 03/04/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND The constant growth of biomedical data is accompanied by the need for new methodologies to effectively and efficiently extract machine-readable knowledge for training and testing purposes. A crucial aspect in this regard is creating large, often manually or semi-manually, annotated corpora vital for developing effective and efficient methods for tasks like relation extraction, topic recognition, and entity linking. However, manual annotation is expensive and time-consuming especially if not assisted by interactive, intuitive, and collaborative computer-aided tools. To support healthcare experts in the annotation process and foster annotated corpora creation, we present MetaTron. MetaTron is an open-source and free-to-use web-based annotation tool to annotate biomedical data interactively and collaboratively; it supports both mention-level and document-level annotations also integrating automatic built-in predictions. Moreover, MetaTron enables relation annotation with the support of ontologies, functionalities often overlooked by off-the-shelf annotation tools. RESULTS We conducted a qualitative analysis to compare MetaTron with a set of manual annotation tools including TeamTat, INCEpTION, LightTag, MedTAG, and brat, on three sets of criteria: technical, data, and functional. A quantitative evaluation allowed us to assess MetaTron performances in terms of time and number of clicks to annotate a set of documents. The results indicated that MetaTron fulfills almost all the selected criteria and achieves the best performances. CONCLUSIONS MetaTron stands out as one of the few annotation tools targeting the biomedical domain supporting the annotation of relations, and fully customizable with documents in several formats-PDF included, as well as abstracts retrieved from PubMed, Semantic Scholar, and OpenAIRE. To meet any user need, we released MetaTron both as an online instance and as a Docker image locally deployable.
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Affiliation(s)
- Ornella Irrera
- Department of Information Engineering, University of Padova, Padua, Italy.
| | - Stefano Marchesin
- Department of Information Engineering, University of Padova, Padua, Italy
| | - Gianmaria Silvello
- Department of Information Engineering, University of Padova, Padua, Italy
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3
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Marchesin S, Menotti L, Giachelle F, Silvello G, Alonso O. Building a large gene expression-cancer knowledge base with limited human annotations. Database (Oxford) 2023; 2023:baad061. [PMID: 37768281 PMCID: PMC10533344 DOI: 10.1093/database/baad061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 07/27/2023] [Accepted: 08/25/2023] [Indexed: 09/29/2023]
Abstract
Cancer prevention is one of the most pressing challenges that public health needs to face. In this regard, data-driven research is central to assist medical solutions targeting cancer. To fully harness the power of data-driven research, it is imperative to have well-organized machine-readable facts into a knowledge base (KB). Motivated by this urgent need, we introduce the Collaborative Oriented Relation Extraction (CORE) system for building KBs with limited manual annotations. CORE is based on the combination of distant supervision and active learning paradigms and offers a seamless, transparent, modular architecture equipped for large-scale processing. We focus on precision medicine and build the largest KB on 'fine-grained' gene expression-cancer associations-a key to complement and validate experimental data for cancer research. We show the robustness of CORE and discuss the usefulness of the provided KB. Database URL https://zenodo.org/record/7577127.
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Affiliation(s)
- Stefano Marchesin
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6b, Padova 35131, Italy
| | - Laura Menotti
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6b, Padova 35131, Italy
| | - Fabio Giachelle
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6b, Padova 35131, Italy
| | - Gianmaria Silvello
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6b, Padova 35131, Italy
| | - Omar Alonso
- Applied Science, Amazon, 3075 Olcott St., Santa Clara, California 95054, USA
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4
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Lee RH, Roy R, Li H, Hechmer A, Zhu TR, Izgutdina A, Olshen AB, Johnson DE, Grandis JR. Therapeutic implications of transcriptomics in head and neck cancer patient-derived xenografts. PLoS One 2023; 18:e0282177. [PMID: 36857322 PMCID: PMC9977000 DOI: 10.1371/journal.pone.0282177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 02/08/2023] [Indexed: 03/02/2023] Open
Abstract
There are currently no clinical strategies utilizing tumor gene expression to inform therapeutic selection for patients with head and neck squamous cell carcinoma (HNSCC). One of the challenges in developing predictive biomarkers is the limited characterization of preclinical HNSCC models. Patient-derived xenografts (PDXs) are increasingly recognized as translationally relevant preclinical avatars for human tumors; however, the overall transcriptomic concordance of HNSCC PDXs with primary human HNSCC is understudied, especially in human papillomavirus-associated (HPV+) disease. Here, we characterized 64 HNSCC PDXs (16 HPV+ and 48 HPV-) at the transcriptomic level using RNA-sequencing. The range of human-specific reads per PDX varied from 64.6%-96.5%, with a comparison of the most differentially expressed genes before and after removal of mouse transcripts revealing no significant benefit to filtering out mouse mRNA reads in this cohort. We demonstrate that four previously established HNSCC molecular subtypes found in The Cancer Genome Atlas (TCGA) are also clearly recapitulated in HNSCC PDXs. Unsupervised hierarchical clustering yielded a striking natural division of HNSCC PDXs by HPV status, with C19orf57 (BRME1), a gene previously correlated with positive response to cisplatin in cervical cancer, among the most significantly differentially expressed genes between HPV+ and HPV- PDXs. In vivo experiments demonstrated a possible relationship between increased C19orf57 expression and superior anti-tumor responses of PDXs to cisplatin, which should be investigated further. These findings highlight the value of PDXs as models for HPV+ and HPV- HNSCC, providing a resource for future discovery of predictive biomarkers to guide treatment selection in HNSCC.
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Affiliation(s)
- Rex H. Lee
- Department of Otolaryngology, Head and Neck Surgery, University of California, San Francisco, San Francisco, California, United States of America
| | - Ritu Roy
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, United States of America
| | - Hua Li
- Department of Otolaryngology, Head and Neck Surgery, University of California, San Francisco, San Francisco, California, United States of America
| | - Aaron Hechmer
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, United States of America
| | - Tian Ran Zhu
- Department of Otolaryngology, Head and Neck Surgery, University of California, San Francisco, San Francisco, California, United States of America
| | - Adila Izgutdina
- Department of Otolaryngology, Head and Neck Surgery, University of California, San Francisco, San Francisco, California, United States of America
| | - Adam B. Olshen
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, United States of America
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, United States of America
| | - Daniel E. Johnson
- Department of Otolaryngology, Head and Neck Surgery, University of California, San Francisco, San Francisco, California, United States of America
| | - Jennifer R. Grandis
- Department of Otolaryngology, Head and Neck Surgery, University of California, San Francisco, San Francisco, California, United States of America
- * E-mail:
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Neary B, Lin S, Qiu P. Methylation of CpG Sites as Biomarkers Predictive of Drug-Specific
Patient Survival in Cancer. Cancer Inform 2022; 21:11769351221131124. [PMID: 36340286 PMCID: PMC9634212 DOI: 10.1177/11769351221131124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 09/18/2022] [Indexed: 11/06/2022] Open
Abstract
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.
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Affiliation(s)
- Bridget Neary
- School of Biological Sciences, Georgia
Institute of Technology, Atlanta, GA, USA
| | - Shuting Lin
- School of Biological Sciences, Georgia
Institute of Technology, Atlanta, GA, USA
| | - Peng Qiu
- Department of Biomedical Engineering,
Georgia Institute of Technology and Emory University, Atlanta, GA, USA,Peng Qiu, Department of Biomedical
Engineering, Georgia Institute of Technology and Emory University, 950 Atlantic
Dr. NW, Atlanta, GA 30332 USA.
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Lin S, Zhou J, Xiao Y, Neary B, Teng Y, Qiu P. Integrative analysis of TCGA data identifies miRNAs as drug-specific survival biomarkers. Sci Rep 2022; 12:6785. [PMID: 35474090 PMCID: PMC9042876 DOI: 10.1038/s41598-022-10662-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 04/11/2022] [Indexed: 01/03/2023] Open
Abstract
Biomarkers predictive of drug-specific outcomes are important tools for personalized medicine. In this study, we present an integrative analysis to identify miRNAs that are predictive of drug-specific survival outcome in cancer. Using the clinical data from TCGA, we defined subsets of cancer patients who suffered from the same cancer and received the same drug treatment, which we call cancer-drug groups. We then used the miRNA expression data in TCGA to evaluate each miRNA’s ability to predict the survival outcome of patients in each cancer-drug group. As a result, the identified miRNAs are predictive of survival outcomes in a cancer-specific and drug-specific manner. Notably, most of the drug-specific miRNA survival markers and their target genes showed consistency in terms of correlations in their expression and their correlations with survival. Some of the identified miRNAs were supported by published literature in contexts of various cancers. We explored several additional breast cancer datasets that provided miRNA expression and survival data, and showed that our drug-specific miRNA survival markers for breast cancer were able to effectively stratify the prognosis of patients in those additional datasets. Together, this analysis revealed drug-specific miRNA markers for cancer survival, which can be promising tools toward personalized medicine.
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Affiliation(s)
- Shuting Lin
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, USA
| | - Jie Zhou
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, USA
| | - Yiqiong Xiao
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, USA
| | - Bridget Neary
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, USA
| | - Yong Teng
- Department of Hematology and Medical Oncology, Winship Cancer Institute, Emory University School of Medicine, Atlanta, USA
| | - Peng Qiu
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, USA.
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