Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools

Nicora, Giovanna; Vitali, Francesca; Dagliati, Arianna; Geifman, Nophar; Bellazzi, Riccardo

doi:10.3389/fonc.2020.01030

Cited by 182 publications

(114 citation statements)

References 85 publications

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“…In recent years, high-throughput omics technologies, frequently defined as high-throughput biochemical assays, have provided an unprecedented opportunity to depict the biological system under investigation at multiple molecular levels [ 86 – 88 ]. However, the complexity of managing and integrating such multi-omics datasets continues to be a challenge, and the development of reliable strategies that can successfully tackle that complexity of multi-dimensional data is critical for gaining actionable knowledge in a precision medicine framework.…”

Section: Limitations Of the Studymentioning

confidence: 99%

“…However, the complexity of managing and integrating such multi-omics datasets continues to be a challenge, and the development of reliable strategies that can successfully tackle that complexity of multi-dimensional data is critical for gaining actionable knowledge in a precision medicine framework. Those recent data-driven methodologies/innovations (such as machine learning methodologies or network-based methods) have been developed/applied to respond to major challenges of stratified medicine, including patients’ phenotyping, biomarker discovery and drug repurposing; however, some constraints associated with the availability of the data for single omics individually, hosting options not dedicated for the multi-platform/multi-layered data, and the lack of relevant guidelines for quality and validation of data consistency should be improved before those tools might be deemed effective in a clinically meaningful way [ 86 – 88 ]. The authors are also aware of some limitations of the experimental design proposed as well as data accessibility in the current study.…”

Section: Limitations Of the Studymentioning

confidence: 99%

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Multi-omic signatures of atherogenic dyslipidaemia: pre-clinical target identification and validation in humans

et al. 2021

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Background Dyslipidaemia is a major risk factor for atherosclerosis and cardiovascular diseases. The molecular mechanisms that translate dyslipidaemia into atherogenesis and reliable markers of its progression are yet to be fully elucidated. To address this issue, we conducted a comprehensive metabolomic and proteomic analysis in an experimental model of dyslipidaemia and in patients with familial hypercholesterolemia (FH). Methods Liquid chromatography/mass spectrometry (LC/MS) and immunoassays were used to find out blood alterations at metabolite and protein levels in dyslipidaemic ApoE−/−/LDLR−/− mice and in FH patients to evaluate their human relevance. Results We identified 15 metabolites (inhibitors and substrates of nitric oxide synthase (NOS), low-molecular-weight antioxidants (glutamine, taurine), homocysteine, methionine, 1-methylnicotinamide, alanine and hydroxyproline) and 9 proteins (C-reactive protein, proprotein convertase subtilisin/kexin type 9, apolipoprotein C-III, soluble intercellular adhesion molecule-1, angiotensinogen, paraoxonase-1, fetuin-B, vitamin K-dependent protein S and biglycan) that differentiated FH patients from healthy controls. Most of these changes were consistently found in dyslipidaemic mice and were further amplified if mice were fed an atherogenic (Western or low-carbohydrate, high-protein) diet. Conclusions The alterations highlighted the involvement of an immune-inflammatory response system, oxidative stress, hyper-coagulation and impairment in the vascular function/regenerative capacity in response to dyslipidaemia that may also be directly engaged in development of atherosclerosis. Our study further identified potential biomarkers for an increased risk of atherosclerosis that may aid in clinical diagnosis or in the personalized treatment.

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Section: Limitations Of the Studymentioning

confidence: 99%

Section: Limitations Of the Studymentioning

confidence: 99%

Multi-omic signatures of atherogenic dyslipidaemia: pre-clinical target identification and validation in humans

et al. 2021

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“…This can be achieved by exploiting the multimodal nature of patient data (ie, personal, behavioral, professional, and health-related). ML techniques (ie, deep learning and hybrid neural networks) can fuse heterogeneous data in a common representation (ie, efficiently using very large data sets containing health care use data, clinical data, and data from personal devices and many other sources), as demonstrated by the recent deep learning systems used on multi-omics data sets to drive precision oncology care [ 56 ].…”

Section: Resultsmentioning

confidence: 99%

Status and Recommendations of Technological and Data-Driven Innovations in Cancer Care: Focus Group Study

Kondylakis¹,

Axenie²,

Bastola³

et al. 2020

J Med Internet Res

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Background The status of the data-driven management of cancer care as well as the challenges, opportunities, and recommendations aimed at accelerating the rate of progress in this field are topics of great interest. Two international workshops, one conducted in June 2019 in Cordoba, Spain, and one in October 2019 in Athens, Greece, were organized by four Horizon 2020 (H2020) European Union (EU)–funded projects: BOUNCE, CATCH ITN, DESIREE, and MyPal. The issues covered included patient engagement, knowledge and data-driven decision support systems, patient journey, rehabilitation, personalized diagnosis, trust, assessment of guidelines, and interoperability of information and communication technology (ICT) platforms. A series of recommendations was provided as the complex landscape of data-driven technical innovation in cancer care was portrayed. Objective This study aims to provide information on the current state of the art of technology and data-driven innovations for the management of cancer care through the work of four EU H2020–funded projects. Methods Two international workshops on ICT in the management of cancer care were held, and several topics were identified through discussion among the participants. A focus group was formulated after the second workshop, in which the status of technological and data-driven cancer management as well as the challenges, opportunities, and recommendations in this area were collected and analyzed. Results Technical and data-driven innovations provide promising tools for the management of cancer care. However, several challenges must be successfully addressed, such as patient engagement, interoperability of ICT-based systems, knowledge management, and trust. This paper analyzes these challenges, which can be opportunities for further research and practical implementation and can provide practical recommendations for future work. Conclusions Technology and data-driven innovations are becoming an integral part of cancer care management. In this process, specific challenges need to be addressed, such as increasing trust and engaging the whole stakeholder ecosystem, to fully benefit from these innovations.

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“…Systematic studies of multivariable clinical factors vs. molecular omics data may shed light on important correlations, but deeper relationships cannot be detected without sophisticated computational support. Computational approaches utilize, among others, machine learning, network-based methods, clustering, feature extraction and transformation and factorization [ 26 ]. Most often, machine learning (ML) techniques aim at classifying patients into cancer subtypes [ 27 , 28 , 29 ], supporting therapy decision-making.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Analysis of Human Serum N-glycome Alterations to Follow up Lung Tumor Surgery

Mészáros

Járvás

Kun

et al. 2020

Cancers

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The human serum N-glycome is a valuable source of biomarkers for malignant diseases, already utilized in multiple studies. In this paper, the N-glycosylation changes in human serum proteins were analyzed after surgical lung tumor resection. Seventeen lung cancer patients were involved in this study and the N-glycosylation pattern of their serum samples was analyzed before and after the surgery using capillary electrophoresis separation with laser-induced fluorescent detection. The relative peak areas of 21 N-glycans were evaluated from the acquired electropherograms using machine learning-based data analysis. Individual glycans as well as their subclasses were taken into account during the course of evaluation. For the data analysis, both discrete (e.g., smoker or not) and continuous (e.g., age of the patient) clinical parameters were compared against the alterations in these 21 N-linked carbohydrate structures. The classification tree analysis resulted in a panel of N-glycans, which could be used to follow up on the effects of lung tumor surgical resection.

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Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools

Cited by 182 publications

References 85 publications

Multi-omic signatures of atherogenic dyslipidaemia: pre-clinical target identification and validation in humans

Multi-omic signatures of atherogenic dyslipidaemia: pre-clinical target identification and validation in humans

Status and Recommendations of Technological and Data-Driven Innovations in Cancer Care: Focus Group Study

Machine Learning Based Analysis of Human Serum N-glycome Alterations to Follow up Lung Tumor Surgery

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