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Cancers
Special Issues
From Biobanking to Artificial Intelligence for Personalized Medicine of Cancer...
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From Biobanking to Artificial Intelligence for Personalized Medicine of Cancer Therapy

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".

Deadline for manuscript submissions: closed (15 July 2022) | Viewed by 13563

Special Issue Editors

Prof. Dr. Fiorella Guadagni

E-Mail Website
Guest Editor
1. BioBIM (InterInstitutional Multidisciplinary Biobank), IRCCS San Raffaele Roma, 00166 Rome, Italy
2. Department of Human Sciences & Quality of Life Promotion, San Raffaele Roma Open University, 00166 Rome, Italy
Interests: biomarker discovery; cancer biomarkers; decision support systems; predictive medicine; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Patrizia Ferroni

E-Mail Website
Guest Editor
1. Interinstitutional Multidisciplinary Biobank (BioBIM), IRCCS San Raffaele Roma, 00166 Rome, Italy
2. Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Roma Open University, 00166 Rome, Italy
Interests: biomarker discovery; decision support systems; predictive medicine; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As we enter the era of precision medicine, biobanking is increasingly recognized as a key area for health care development, and biobanks are no longer considered just sites for the cold storage of biospecimens, but also as data repositories, treasures of information often unrecognized. The development of high-throughput, data-intensive biomedical research assays and technologies, in fact, has greatly contributed to a substantial shift from the concept of simple biobank toward a real databank.

On the one side, this has led to increased attention to data management and data sharing, as access to scientific data is fundamental for validation of research results, enabling researchers to combine data to strengthen analyses or to facilitate the reuse of hard-to-generate data. Sharing, however, requires that ethics and privacy issues are respected, and that confidential/proprietary data are appropriately protected.

On the other hand, the massive amounts of collected data have posed new challenging possibilities in terms of data management/analysis, as they exceed the concept of "statistical sampling" in favor of artificial intelligence (AI) techniques for the construction of predictive models. AI can play, indeed, an important role in the field of personalized medicine, but we must not forget that its ability will critically depend on the ways of storing, aggregating, accessing and ultimately integrating the available data.

This Special Issue will focus on recent advances in the field of biobanking and biospecimen research and the application of AI techniques in the field of personalized medicine for cancer therapy. Emphasis will be put on ethics and privacy issues on data management and data sharing policies of cancer therapy.

Prof. Dr. Fiorella Guadagni
Prof. Dr. Patrizia Ferroni
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. Cancers 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 2900 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

  • cancer therapy
  • biobanks
  • data science
  • data management
  • artificial intelligence
  • decision support systems
  • predictive medicine

Published Papers (5 papers)

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Research

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9 pages, 593 KiB  
Article
Identification and Assessment of Risks in Biobanking: The Case of the Cancer Institute of Bari
by Giuseppe De Palma, Giulia Bolondi, Antonio Tufaro, Giuseppe Pelagio, Giuseppe Brando, Daniela Vitale and Angelo Virgilio Paradiso
Cancers 2022, 14(14), 3460; https://doi.org/10.3390/cancers14143460 - 16 Jul 2022
Cited by 1 | Viewed by 1519
Abstract
Although research biobanks are among the most promising tools to fight disease and improve public health, there are a range of risks biobanks may face that mainly need to be assessed in an attempt to be relieved. We conducted a strategic insurance review [...] Read more.
Although research biobanks are among the most promising tools to fight disease and improve public health, there are a range of risks biobanks may face that mainly need to be assessed in an attempt to be relieved. We conducted a strategic insurance review of an institutional cancer biobank with the aim of both identifying the insurable risks of our own Biobank and gathering useful evidence of primary exposure to insurable risks. In this practical scenario, risks have been outlined and categorized into inherent and residual risks, along with their possible impact on biobank maintenance. Results at the Biobank of the Cancer Institute of Bari showed evidence of potentially significant and intrinsic risk due to highly relevant threats, along with already implemented improvements that significantly reduce risks to a range of relative acceptability. Full article
(This article belongs to the Special Issue From Biobanking to Artificial Intelligence for Personalized Medicine of Cancer Therapy)
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16 pages, 3031 KiB  
Article
Is High Expression of Claudin-7 in Advanced Colorectal Carcinoma Associated with a Poor Survival Rate? A Comparative Statistical and Artificial Intelligence Study
by Victor Ianole, Mihai Danciu, Constantin Volovat, Cipriana Stefanescu, Paul-Corneliu Herghelegiu, Florin Leon, Adrian Iftene, Ciprian-Gabriel Cusmuliuc, Bogdan Toma, Vasile Drug and Delia Gabriela Ciobanu Apostol
Cancers 2022, 14(12), 2915; https://doi.org/10.3390/cancers14122915 - 13 Jun 2022
Cited by 1 | Viewed by 1925
Abstract
Aim: The need for predictive and prognostic biomarkers in colorectal carcinoma (CRC) brought us to an era where the use of artificial intelligence (AI) models is increasing. We investigated the expression of Claudin-7, a tight junction component, which plays a crucial role in [...] Read more.
Aim: The need for predictive and prognostic biomarkers in colorectal carcinoma (CRC) brought us to an era where the use of artificial intelligence (AI) models is increasing. We investigated the expression of Claudin-7, a tight junction component, which plays a crucial role in maintaining the integrity of normal epithelial mucosa, and its potential prognostic role in advanced CRCs, by drawing a parallel between statistical and AI algorithms. Methods: Claudin-7 immunohistochemical expression was evaluated in the tumor core and invasion front of CRCs from 84 patients and correlated with clinicopathological parameters and survival. The results were compared with those obtained by using various AI algorithms. Results: the Kaplan–Meier univariate survival analysis showed a significant correlation between survival and Claudin-7 intensity in the invasive front (p = 0.00), a higher expression being associated with a worse prognosis, while Claudin-7 intensity in the tumor core had no impact on survival. In contrast, AI models could not predict the same outcome on survival. Conclusion: The study showed through statistical means that the immunohistochemical overexpression of Claudin-7 in the tumor invasive front may represent a poor prognostic factor in advanced stages of CRCs, contrary to AI models which could not predict the same outcome, probably because of the small number of patients included in our cohort. Full article
(This article belongs to the Special Issue From Biobanking to Artificial Intelligence for Personalized Medicine of Cancer Therapy)
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18 pages, 3391 KiB  
Article
Gene-Mutation-Based Algorithm for Prediction of Treatment Response in Colorectal Cancer Patients
by Heather Johnson, Zahra El-Schich, Amjad Ali, Xuhui Zhang, Athanasios Simoulis, Anette Gjörloff Wingren and Jenny L. Persson
Cancers 2022, 14(8), 2045; https://doi.org/10.3390/cancers14082045 - 18 Apr 2022
Cited by 4 | Viewed by 2263
Abstract
Purpose: Despite the high mortality of metastatic colorectal cancer (mCRC), no new biomarker tools are available for predicting treatment response. We developed gene-mutation-based algorithms as a biomarker classifier to predict treatment response with better precision than the current predictive factors. Methods: Random forest [...] Read more.
Purpose: Despite the high mortality of metastatic colorectal cancer (mCRC), no new biomarker tools are available for predicting treatment response. We developed gene-mutation-based algorithms as a biomarker classifier to predict treatment response with better precision than the current predictive factors. Methods: Random forest machine learning (ML) was applied to identify the candidate algorithms using the MSK Cohort (n = 471) as a training set and validated in the TCGA Cohort (n = 221). Logistic regression, progression-free survival (PFS), and univariate/multivariate Cox proportional hazard analyses were performed and the performance of the candidate algorithms was compared with the established risk parameters. Results: A novel 7-Gene Algorithm based on mutation profiles of seven KRAS-associated genes was identified. The algorithm was able to distinguish non-progressed (responder) vs. progressed (non-responder) patients with AUC of 0.97 and had predictive power for PFS with a hazard ratio (HR) of 16.9 (p < 0.001) in the MSK cohort. The predictive power of this algorithm for PFS was more pronounced in mCRC (HR = 16.9, p < 0.001, n = 388). Similarly, in the TCGA validation cohort, the algorithm had AUC of 0.98 and a significant predictive power for PFS (p < 0.001). Conclusion: The novel 7-Gene Algorithm can be further developed as a biomarker model for prediction of treatment response in mCRC patients to improve personalized therapies. Full article
(This article belongs to the Special Issue From Biobanking to Artificial Intelligence for Personalized Medicine of Cancer Therapy)
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Review

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45 pages, 2328 KiB  
Review
Use of Personalized Biomarkers in Metastatic Colorectal Cancer and the Impact of AI
by Simona-Ruxandra Volovat, Iolanda Augustin, Daniela Zob, Diana Boboc, Florin Amurariti, Constantin Volovat, Cipriana Stefanescu, Cati Raluca Stolniceanu, Manuela Ciocoiu, Eduard Alexandru Dumitras, Mihai Danciu, Delia Gabriela Ciobanu Apostol, Vasile Drug, Sinziana Al Shurbaji, Lucia-Georgiana Coca, Florin Leon, Adrian Iftene and Paul-Corneliu Herghelegiu
Cancers 2022, 14(19), 4834; https://doi.org/10.3390/cancers14194834 - 03 Oct 2022
Cited by 3 | Viewed by 2882
Abstract
Colorectal cancer is a major cause of cancer-related death worldwide and is correlated with genetic and epigenetic alterations in the colonic epithelium. Genetic changes play a major role in the pathophysiology of colorectal cancer through the development of gene mutations, but recent research [...] Read more.
Colorectal cancer is a major cause of cancer-related death worldwide and is correlated with genetic and epigenetic alterations in the colonic epithelium. Genetic changes play a major role in the pathophysiology of colorectal cancer through the development of gene mutations, but recent research has shown an important role for epigenetic alterations. In this review, we try to describe the current knowledge about epigenetic alterations, including DNA methylation and histone modifications, as well as the role of non-coding RNAs as epigenetic regulators and the prognostic and predictive biomarkers in metastatic colorectal disease that can allow increases in the effectiveness of treatments. Additionally, the intestinal microbiota’s composition can be an important biomarker for the response to strategies based on the immunotherapy of CRC. The identification of biomarkers in mCRC can be enhanced by developing artificial intelligence programs. We present the actual models that implement AI technology as a bridge connecting ncRNAs with tumors and conducted some experiments to improve the quality of the model used as well as the speed of the model that provides answers to users. In order to carry out this task, we implemented six algorithms: the naive Bayes classifier, the random forest classifier, the decision tree classifier, gradient boosted trees, logistic regression and SVM. Full article
(This article belongs to the Special Issue From Biobanking to Artificial Intelligence for Personalized Medicine of Cancer Therapy)
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Other

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38 pages, 1002 KiB  
Systematic Review
Artificial Intelligence Predictive Models of Response to Cytotoxic Chemotherapy Alone or Combined to Targeted Therapy for Metastatic Colorectal Cancer Patients: A Systematic Review and Meta-Analysis
by Valentina Russo, Eleonora Lallo, Armelle Munnia, Miriana Spedicato, Luca Messerini, Romina D’Aurizio, Elia Giuseppe Ceroni, Giulia Brunelli, Antonio Galvano, Antonio Russo, Ida Landini, Stefania Nobili, Marcello Ceppi, Marco Bruzzone, Fabio Cianchi, Fabio Staderini, Mario Roselli, Silvia Riondino, Patrizia Ferroni, Fiorella Guadagni, Enrico Mini and Marco Pelusoadd Show full author list remove Hide full author list
Cancers 2022, 14(16), 4012; https://doi.org/10.3390/cancers14164012 - 19 Aug 2022
Cited by 11 | Viewed by 4080
Abstract
Tailored treatments for metastatic colorectal cancer (mCRC) have not yet completely evolved due to the variety in response to drugs. Therefore, artificial intelligence has been recently used to develop prognostic and predictive models of treatment response (either activity/efficacy or toxicity) to aid in [...] Read more.
Tailored treatments for metastatic colorectal cancer (mCRC) have not yet completely evolved due to the variety in response to drugs. Therefore, artificial intelligence has been recently used to develop prognostic and predictive models of treatment response (either activity/efficacy or toxicity) to aid in clinical decision making. In this systematic review, we have examined the ability of learning methods to predict response to chemotherapy alone or combined with targeted therapy in mCRC patients by targeting specific narrative publications in Medline up to April 2022 to identify appropriate original scientific articles. After the literature search, 26 original articles met inclusion and exclusion criteria and were included in the study. Our results show that all investigations conducted on this field have provided generally promising results in predicting the response to therapy or toxic side-effects. By a meta-analytic approach we found that the overall weighted means of the area under the receiver operating characteristic (ROC) curve (AUC) were 0.90, 95% C.I. 0.80–0.95 and 0.83, 95% C.I. 0.74–0.89 in training and validation sets, respectively, indicating a good classification performance in discriminating response vs. non-response. The calculation of overall HR indicates that learning models have strong ability to predict improved survival. Lastly, the delta-radiomics and the 74 gene signatures were able to discriminate response vs. non-response by correctly identifying up to 99% of mCRC patients who were responders and up to 100% of patients who were non-responders. Specifically, when we evaluated the predictive models with tests reaching 80% sensitivity (SE) and 90% specificity (SP), the delta radiomics showed an SE of 99% and an SP of 94% in the training set and an SE of 85% and SP of 92 in the test set, whereas for the 74 gene signatures the SE was 97.6% and the SP 100% in the training set. Full article
(This article belongs to the Special Issue From Biobanking to Artificial Intelligence for Personalized Medicine of Cancer Therapy)
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