Multi-omics data integration approaches for precision oncology

Correa-Aguila, Raidel; Alonso-Pupo, Niuxia; Hernández-Rodríguez, Erix W.

doi:10.1039/d1mo00411e

Cited by 12 publications

(5 citation statements)

References 119 publications

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“…Understanding tumor heterogeneity, its temporal evolution over time, and the outcomes of targeted treatment are strictly dependent on accurate data collection in the context of routine clinical care 21 . In this respect, the "multi-omics" approach is gaining momentum in oncologic predictive pathology 22 . The traditional tissuebased biomarker assessment is being integrated using multiple new bioanalytes, such as blood, plasma, urines, saliva, and stool 23 , 24 , 25 , 26 biobanks and some freeware can be obtained for biobank managing 27 , 28 , 29 , 30 , 31 .…”

Section: Discussionmentioning

confidence: 99%

Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management

Bonizzi

Capra

Cassi

et al. 2022

JoVE

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Biobanks are key research infrastructures aimed at the collection, storage, processing, and sharing of high-quality human biological samples and associated data for research, diagnosis, and personalized medicine. The Biobank for Translational and Digital Medicine Unit at the European Institute of Oncology (IEO) is a landmark in this field. Biobanks collaborate with clinical divisions, internal and external research groups, and industry, supporting patients' treatment and scientific progress, including innovative diagnostics, biomarker discovery, and clinical trial design. Given the central role of biobanks in modern research, biobanking standard operating procedures (SOPs) should be extremely precise. SOPs and controls by certified specialists ensure the highest quality of samples for the implementation of science-based, diagnostic, prognostic, and therapeutic personalized strategies. However, despite numerous efforts to standardize and harmonize biobanks, these protocols, which follow a strict set of rules, quality controls, and guidelines based on ethical and legal principles, are not easily accessible. This paper presents the biobank standard operating procedures of a large cancer center.

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Section: Discussionmentioning

confidence: 99%

Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management

Bonizzi

Capra

Cassi

et al. 2022

JoVE

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“…Unsupervised learning is an analytical approach that eliminates the need for prelabeled training data. The main objective of unsupervised learning is to unveil hidden patterns and establish new connections between variables within a dataset [ 107 ]. In the study of enhancers, unsupervised learning methods can be divided into distance-based methods and correlation-based methods [ 108 ].…”

Section: Multi-omics Integration Methodsmentioning

confidence: 99%

“…In supervised learning, an incorrect label definition can lead to inaccurate prediction results. Consequently, it is essential to enlist biological expertise to properly define the labels [ 107 ]. On the other hand, models, such as the ABC model and eNet model, predict the functions of thousands of enhancers.…”

Section: Challenges In Multi-omics Approachesmentioning

confidence: 99%

Integrative approaches based on genomic techniques in the functional studies on enhancers

Wang,

Zhang,

Liu

et al. 2023

Briefings in Bioinformatics

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With the development of sequencing technology and the dramatic drop in sequencing cost, the functions of noncoding genes are being characterized in a wide variety of fields (e.g. biomedicine). Enhancers are noncoding DNA elements with vital transcription regulation functions. Tens of thousands of enhancers have been identified in the human genome; however, the location, function, target genes and regulatory mechanisms of most enhancers have not been elucidated thus far. As high-throughput sequencing techniques have leapt forwards, omics approaches have been extensively employed in enhancer research. Multidimensional genomic data integration enables the full exploration of the data and provides novel perspectives for screening, identification and characterization of the function and regulatory mechanisms of unknown enhancers. However, multidimensional genomic data are still difficult to integrate genome wide due to complex varieties, massive amounts, high rarity, etc. To facilitate the appropriate methods for studying enhancers with high efficacy, we delineate the principles, data processing modes and progress of various omics approaches to study enhancers and summarize the applications of traditional machine learning and deep learning in multi-omics integration in the enhancer field. In addition, the challenges encountered during the integration of multiple omics data are addressed. Overall, this review provides a comprehensive foundation for enhancer analysis.

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“…Combining multiple experimental approaches and then integrating the results is a valuable strategy to generalize human cancer’s complexity from experimental models [ 197 ]. The integration of multiple omics, such as genomics, proteomics, and metabolomics, will help us understand tumours and advance antitumor drug developments [ 198 , 199 , 200 ]. In addition, numerous studies have confirmed that developing high-throughput sequencing technologies has revolutionized multi-omics research.…”

Section: Future Perspectives and Conclusionmentioning

confidence: 99%

Multi-Omics Approaches in Colorectal Cancer Screening and Diagnosis, Recent Updates and Future Perspectives

Ullah

Yang

Yin

et al. 2022

Cancers

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Colorectal cancer (CRC) is common Cancer as well as the third leading cause of mortality around the world; its exact molecular mechanism remains elusive. Although CRC risk is significantly correlated with genetic factors, the pathophysiology of CRC is also influenced by external and internal exposures and their interactions with genetic factors. The field of CRC research has recently benefited from significant advances through Omics technologies for screening biomarkers, including genes, transcripts, proteins, metabolites, microbiome, and lipidome unbiasedly. A promising application of omics technologies could enable new biomarkers to be found for the screening and diagnosis of CRC. Single-omics technologies cannot fully understand the molecular mechanisms of CRC. Therefore, this review article aims to summarize the multi-omics studies of Colorectal cancer, including genomics, transcriptomics, proteomics, microbiomics, metabolomics, and lipidomics that may shed new light on the discovery of novel biomarkers. It can contribute to identifying and validating new CRC biomarkers and better understanding colorectal carcinogenesis. Discovering biomarkers through multi-omics technologies could be difficult but valuable for disease genotyping and phenotyping. That can provide a better knowledge of CRC prognosis, diagnosis, and treatments.

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Multi-omics data integration approaches for precision oncology

Abstract: Next-generation sequencing (NGS) has been pivotal to enhance the molecular characterization of human malignancies, allowing multiple omics data types to be available for cancer researchers and practitioners. In this context,...

Cited by 12 publications

References 119 publications

Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management

Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management

Integrative approaches based on genomic techniques in the functional studies on enhancers

Multi-Omics Approaches in Colorectal Cancer Screening and Diagnosis, Recent Updates and Future Perspectives

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