Big Data Analytics for Cancer Research and Precision Medicine

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (15 September 2020) | Viewed by 11763

Special Issue Editors

Cancer Genetics, Genomics and Systems Biology Laboratory, Basic and Translational Cancer Research Center (BTCRC), 1516 Nicosia, Cyprus
Interests: cancer genomics; precision medicine; data science in genomics; next-generation sequencing; translational oncology; tumor immunology
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Institute of Biology, Medicinal Chemistry & Biotechnology, National Hellenic Research Foundation (NHRF), 11635 Athens, Greece
Interests: bioinformatics; systems biology; metabolic engineering; transcriptomic analysis; biomedical imaging
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1. Department of Life Sciences, European University Cyprus, Nicosia 2404, Cyprus
2. School of Infection and Immunity, University of Glasgow, Glasgow G12 8TA, UK
Interests: tumor immunology; autoimmunity; RNA sequencing; immunotranscriptomics; translational oncology; immunoregulatory cytokines
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I invite you to contribute to the Special Issue of the journal Applied Sciences entitled “Big Data Analytics for Cancer Research and Precision Medicine”, which aims to present recent developments in the field of Big Data in cancer research and beyond.

Recent advances in high-throughput technologies at a constantly decreasing cost have led to an explosion of large amounts of data pertaining to the ‘omics field. These are called “Big Data”, and are so massive that we have both the potential for new discoveries, but also challenges in storing, transferring, and interpreting them. Big Data can contain information on different types of 'omes, including the genome, transcriptome, proteome, epigenome, phenome, metabolome, etc. of a disease, and they impose unprecedented challenges to be understood, analyzed, and interpreted.

Precision medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person. In contrast to the “one-size-fits-all” approach, precision medicine can allow doctors and researchers to more accurately predict which treatment and prevention strategy for a particular disease will work in specific groups of people.

As the field of ‘omics has sparked a revolution in medical discoveries, scientists are now focusing on better understanding the cancer genome and leveraging the data and information from genomic datasets. Although the analysis of Big Data is challenging, it offers unprecedented opportunities in precision medicine. Several applications of Big Data analytics have already reached the stage of clinical trials, while others are on their way.

We are thus inviting you to submit your research on this topic, in the form of original research papers, mini-reviews, and perspective articles.

Assoc. Prof. Dr. Apostolos Zaravinos
Dr. Aristotelis Chatziioannou
Dr. Marianna Christodoulou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Big Data
  • cancer genomics
  • precision medicine
  • high-throughput technologies
  • next-generation sequencing
  • ‘omics
  • genomics technologies in cancer research
  • transcriptomics technologies in cancer research
  • epigenomics technologies in cancer research
  • proteomics technologies in cancer research
  • metabolomics technologies in cancer research
  • cancer genomic datasets
  • computational analyses and annotation in genomics
  • tools for functional genomics
  • statistics for genomic data
  • impact of genomic technologies on personalized cancer medicine

Published Papers (4 papers)

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Research

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21 pages, 5771 KiB  
Article
Identification of Co-Deregulated Genes in Urinary Bladder Cancer Using High-Throughput Methodologies
by George I. Lambrou, Kleanthis Vichos, Dimitrios Koutsouris and Apostolos Zaravinos
Appl. Sci. 2021, 11(4), 1785; https://doi.org/10.3390/app11041785 - 18 Feb 2021
Viewed by 2257
Abstract
Although several genes are known to be deregulated in urinary bladder cancer (UBC), the list of candidate prognostic markers has expanded due to the advance of high-throughput methodologies, but they do not always accord from study to study. We aimed to detect global [...] Read more.
Although several genes are known to be deregulated in urinary bladder cancer (UBC), the list of candidate prognostic markers has expanded due to the advance of high-throughput methodologies, but they do not always accord from study to study. We aimed to detect global gene co-expressional profiles among a high number of UBC tumors. We mined gene expression data from 5 microarray datasets from GEO, containing 131 UBC and 15 normal samples. Data were analyzed using unsupervised classification algorithms. The application of clustering algorithms resulted in the isolation of 6 down-regulated genes (TMP2, ACTC1, TAGLN, MFAP4, SPARCL1, and GLP1R), which were mainly implicated in the proteasome, base excision repair, and DNA replication functions. We also detected 6 up-regulated genes (CDC20, KRT14, APOBEC3B, MCM5, STMN, and YWHAB) mainly involved in cancer pathways. We identified lists of drugs that could potentially associate with the Differentially Expressed Genes (DEGs), including Vardenafil, Pyridone 6, and Manganese (co-upregulated genes) or 1D-myo-inositol 1,4,5-triphosphate (co-down regulated genes). We propose 12 novel candidate markers for UBC, as well as potential drugs, shedding more light on the underlying cause of the development and progression of the disease. Full article
(This article belongs to the Special Issue Big Data Analytics for Cancer Research and Precision Medicine)
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18 pages, 1264 KiB  
Article
Entropic Ranks: A Methodology for Enhanced, Threshold-Free, Information-Rich Data Partition and Interpretation
by Hector-Xavier de Lastic, Irene Liampa, Alexandros G. Georgakilas, Michalis Zervakis and Aristotelis Chatziioannou
Appl. Sci. 2020, 10(20), 7077; https://doi.org/10.3390/app10207077 - 12 Oct 2020
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Abstract
Background: Here, we propose a threshold-free selection method for the identification of differentially expressed features based on robust, non-parametric statistics, ensuring independence from the statistical distribution properties and broad applicability. Such methods could adapt to different initial data distributions, contrary to statistical techniques, [...] Read more.
Background: Here, we propose a threshold-free selection method for the identification of differentially expressed features based on robust, non-parametric statistics, ensuring independence from the statistical distribution properties and broad applicability. Such methods could adapt to different initial data distributions, contrary to statistical techniques, based on fixed thresholds. This work aims to propose a methodology, which automates and standardizes the statistical selection, through the utilization of established measures like that of entropy, already used in information retrieval from large biomedical datasets, thus departing from classical fixed-threshold based methods, relying in arbitrary p-value and fold change values as selection criteria, whose efficacy also depends on degree of conformity to parametric distributions,. Methods: Our work extends the rank product (RP) methodology with a neutral selection method of high information-extraction capacity. We introduce the calculation of the RP entropy of the distribution, to isolate the features of interest by their contribution to its information content. Goal is a methodology of threshold-free identification of the differentially expressed features, which are highly informative about the phenomenon under study. Conclusions: Applying the proposed method on microarray (transcriptomic and DNA methylation) and RNAseq count data of varying sizes and noise presence, we observe robust convergence for the different parameterizations to stable cutoff points. Functional analysis through BioInfoMiner and EnrichR was used to evaluate the information potency of the resulting feature lists. Overall, the derived functional terms provide a systemic description highly compatible with the results of traditional statistical hypothesis testing techniques. The methodology behaves consistently across different data types. The feature lists are compact and rich in information, indicating phenotypic aspects specific to the tissue and biological phenomenon investigated. Selection by information content measures efficiently addresses problems, emerging from arbitrary thresh-holding, thus facilitating the full automation of the analysis. Full article
(This article belongs to the Special Issue Big Data Analytics for Cancer Research and Precision Medicine)
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15 pages, 2528 KiB  
Article
Performance of the OncomineTM Lung cfDNA Assay for Liquid Biopsy by NGS of NSCLC Patients in Routine Laboratory Practice
by Giuseppa De Luca, Sonia Lastraioli, Romana Conte, Marco Mora, Carlo Genova, Giovanni Rossi, Marco Tagliamento, Simona Coco, Maria Giovanna Dal Bello, Simona Zupo and Mariella Dono
Appl. Sci. 2020, 10(8), 2895; https://doi.org/10.3390/app10082895 - 22 Apr 2020
Cited by 8 | Viewed by 3527
Abstract
Targeted next-generation sequencing (NGS) based on molecular tagging technology allowed considerable improvement in the approaches of cell-free DNA (cfDNA) analysis. Previously, we demonstrated the feasibility of the OncomineTM Lung cell-free DNA Assay (OLcfA) NGS panel when applied on plasma samples of post-tyrosine [...] Read more.
Targeted next-generation sequencing (NGS) based on molecular tagging technology allowed considerable improvement in the approaches of cell-free DNA (cfDNA) analysis. Previously, we demonstrated the feasibility of the OncomineTM Lung cell-free DNA Assay (OLcfA) NGS panel when applied on plasma samples of post-tyrosine kinase inhibitors (TKIs) non-small cell lung cancer (NSCLC) patients. Here, we explored in detail the coverage metrics and variant calling of the assay and highlighted strengths and challenges by analyzing 92 plasma samples collected from a routine cohort of 76 NSCLC patients. First, performance of OLcfA was assessed using Horizon HD780 reference standards and sensitivity and specificity of 92.5% and 100% reported, respectively. The OLcfA was consequently evaluated in our plasma cohort and NGS technically successful in all 92 sequenced libraries. We demonstrated that initial cfDNA amount correlated positively with library yields (p < 0.0001) and sequencing performance (p < 0.0001). In addition, 0.1% limit of detection could be achieved even when < 10 ng cfDNA was employed. In contrast, the cfDNA amount seems to not affect the EGFR mutational status (p = 0.16). This study demonstrated an optimal performance of the OLcfA on routine plasma samples from NSCLC patients and supports its application in the liquid biopsy practice for cfDNA investigation in precision medicine laboratories. Full article
(This article belongs to the Special Issue Big Data Analytics for Cancer Research and Precision Medicine)
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Review

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28 pages, 1153 KiB  
Review
Information, Thermodynamics and Life: A Narrative Review
by George I. Lambrou, Apostolos Zaravinos, Penelope Ioannidou and Dimitrios Koutsouris
Appl. Sci. 2021, 11(9), 3897; https://doi.org/10.3390/app11093897 - 25 Apr 2021
Cited by 4 | Viewed by 2222
Abstract
Information is probably one of the most difficult physical quantities to comprehend. This applies not only to the very definition of information, but also to the physical entity of information, meaning how can it be quantified and measured. In recent years, information theory [...] Read more.
Information is probably one of the most difficult physical quantities to comprehend. This applies not only to the very definition of information, but also to the physical entity of information, meaning how can it be quantified and measured. In recent years, information theory and its function in systems has been an intense field of study, due to the large increase of available information technology, where the notion of bit dominated the information discipline. Information theory also expanded from the “simple” “bit” to the quantal “qubit”, which added more variables for consideration. One of the main applications of information theory could be considered the field of “autonomy”, which is the main characteristic of living organisms in nature since they all have self-sustainability, motion and self-protection. These traits, along with the ability to be aware of existence, make it difficult and complex to simulate in artificial constructs. There are many approaches to the concept of simulating autonomous behavior, yet there is no conclusive approach to a definite solution to this problem. Recent experimental results have shown that the interaction between machines and neural cells is possible and it consists of a significant tool for the study of complex systems. The present work tries to review the question on the interactions between information and life. It attempts to build a connection between information and thermodynamics in terms of energy consumption and work production, as well as present some possible applications of these physical quantities. Full article
(This article belongs to the Special Issue Big Data Analytics for Cancer Research and Precision Medicine)
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