Submit to Cancers Review for Cancers Propose a Special Issue

Journal Browser

► Journal Browser

Applications of Machine Learning and Statistical Modeling in Precision Oncology

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Cancers (ISSN 2072-6694).

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 15577

Share This Special Issue

Special Issue Editor

Dr. Sandeep K. Singhal

E-Mail Website1 Website2 Website3
Guest Editor

1. Department of Pathology, School of Medicine and Health Sciences, University of North Dakota, Grand Forks, ND, USA
2. Department of Biomedical Engineering, School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND, USA
3. Bioinformatics Division, University of North Dakota, Grand Forks, ND, USA
Interests: bioinformatics; data mining; machine learning; statistical modeling; interaction network oncology; biomarkers

Special Issue Information

Dear Colleagues,

Molecular profiling (or tumor genomic profiling) of tumor biopsies plays an increasingly important role in cancer research, as well as in the treatment management of cancer patients. The introduction of next-generation sequencing technologies and the rising number of large-scale tumor molecular profiling programs across different cancer types hold the promise of improving diagnostics, prognostics and personalized treatment. As a result, we are generating tons of data, but these data are of no use unless we analyze them and find the biological patterns hidden within.

To deal with this, data science approaches—such as artificial intelligence, machine learning and statistical modeling, which help to turn information into knowledge in order to better understand human health—have become part of the vocabulary in biological and medical research. Due to this achievement, ways to pre-process, analyze, and infer knowledge have considerably changed in recent decades, whether in relation to transcriptomics, proteomics, epigenetics, sequencing data, clinical data, electronic health records, or medicine in general.

In this issue, we will discuss some aspects of this revolution, with a special emphasis on bioinformatics, machine learning, statistical modeling, how the omics data are being analyzed and used to improve cancer treatment and management.

Dr. Sandeep K. Singhal
Guest Editor

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

computational biology
bioinformatics
statistical modeling
machine learning
artificial intelligence
data mining
meta-analysis
network analysis
next-generation sequencing
precision medicine
oncology
algorithms
cancer
biomarkers

Published Papers (5 papers)

Download All Papers

Research

Jump to: Review

21 pages, 4721 KiB

Open AccessArticle

Schlafen 12 Slows TNBC Tumor Growth, Induces Luminal Markers, and Predicts Favorable Survival

by Sandeep K. Singhal, Sarmad Al-Marsoummi, Emilie E. Vomhof-DeKrey, Bo Lauckner, Trysten Beyer and Marc D. Basson

Cancers 2023, 15(2), 402; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers15020402 - 07 Jan 2023

Viewed by 1467

Abstract

The Schlafen 12 (SLFN12) protein regulates triple-negative breast cancer (TNBC) growth, differentiation, and proliferation. SLFN12 mRNA expression strongly correlates with TNBC patient survival. We sought to explore SLFN12 overexpression effects on in vivo human TNBC tumor xenograft growth and performed RNA-seq on xenografts to investigate related SLFN12 pathways. Stable SLFN12 overexpression reduced tumorigenesis, increased tumor latency, and reduced tumor volume. RNA-seq showed that SLFN12 overexpressing xenografts had higher luminal markers levels, suggesting that TNBC cells switched from an undifferentiated basal phenotype to a more differentiated, less aggressive luminal phenotype. SLFN12-overexpressing xenografts increased less aggressive BC markers, HER2 receptors ERBB2 and EGFR expression, which are not detectable by immunostaining in TNBC. Two cancer progression pathways, the NAD signaling pathway and the superpathway of cholesterol biosynthesis, were downregulated with SLFN12 overexpression. RNA-seq identified gene signatures associated with SLFN12 overexpression. Higher gene signature levels indicated good survival when tested on four independent BC datasets. These signatures behaved differently in African Americans than in Caucasian Americans, indicating a possible biological difference between these races that could contribute to the worse survival observed in African Americans with BC. These results suggest an increased SLFN12 expression modulates TNBC aggressiveness through a gene signature that could offer new treatment targets. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Statistical Modeling in Precision Oncology)

► Show Figures

Figure 1

21 pages, 3211 KiB

Open AccessArticle

External Validation of the Individualized Prediction of Breast Cancer Survival (IPBS) Model for Estimating Survival after Surgery for Patients with Breast Cancer in Northern Thailand

by Thanapat Charumporn, Nutcha Jarupanich, Chanawin Rinthapon, Kantapit Meetham, Napat Pattayakornkul, Teerapant Taerujjirakul, Krittai Tanasombatkul, Chagkrit Ditsatham, Wilaiwan Chongruksut, Areerak Phanphaisarn, Donsuk Pongnikorn and Phichayut Phinyo

Cancers 2022, 14(23), 5726; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers14235726 - 22 Nov 2022

Cited by 1 | Viewed by 1161

Abstract

The individualized prediction of breast cancer survival (IPBS) model was recently developed. Although the model showed acceptable performance during derivation, its external performance remained unknown. This study aimed to validate the IPBS model using the data of breast cancer patients in Northern Thailand. An external validation study was conducted based on female patients with breast cancer who underwent surgery at Maharaj Nakorn Chiang Mai hospital from 2005 to 2015. Data on IPBS predictors were collected. The endpoints were 5-year overall survival (OS) and disease-free survival (DFS). The model performance was evaluated in terms of discrimination and calibration. Missing data were handled with multiple imputation. Of all 3581 eligible patients, 1868 were included. The 5-year OS and DFS were 85.2% and 81.9%. The IPBS model showed acceptable discrimination: C-statistics 0.706 to 0.728 for OS and 0.675 to 0.689 for DFS at 5 years. However, the IPBS model minimally overestimated both OS and DFS predictions. These overestimations were corrected after model recalibration. In this external validation study, the IPBS model exhibited good discriminative ability. Although it may provide minimal overestimation, recalibrating the model to the local context is a practical solution to improve the model calibration. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Statistical Modeling in Precision Oncology)

► Show Figures

Figure 1

15 pages, 3134 KiB

Open AccessArticle

Machine Learning-Based Genome-Wide Salivary DNA Methylation Analysis for Identification of Noninvasive Biomarkers in Oral Cancer Diagnosis

by John Adeoye, Chi Ching Joan Wan, Li-Wu Zheng, Peter Thomson, Siu-Wai Choi and Yu-Xiong Su

Cancers 2022, 14(19), 4935; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers14194935 - 08 Oct 2022

Cited by 9 | Viewed by 2257

Abstract

This study aims to examine the feasibility of ML-assisted salivary-liquid-biopsy platforms using genome-wide methylation analysis at the base-pair and regional resolution for delineating oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMDs). A nested cohort of patients with OSCC and OPMDs was randomly selected from among patients with oral mucosal diseases. Saliva samples were collected, and DNA extracted from cell pellets was processed for reduced-representation bisulfite sequencing. Reads with a minimum of 10× coverage were used to identify differentially methylated CpG sites (DMCs) and 100 bp regions (DMRs). The performance of eight ML models and three feature-selection methods (ANOVA, MRMR, and LASSO) were then compared to determine the optimal biomarker models based on DMCs and DMRs. A total of 1745 DMCs and 105 DMRs were identified for detecting OSCC. The proportion of hypomethylated and hypermethylated DMCs was similar (51% vs. 49%), while most DMRs were hypermethylated (62.9%). Furthermore, more DMRs than DMCs were annotated to promoter regions (36% vs. 16%) and more DMCs than DMRs were annotated to intergenic regions (50% vs. 36%). Of all the ML models compared, the linear SVM model based on 11 optimal DMRs selected by LASSO had a perfect AUC, recall, specificity, and calibration (1.00) for OSCC detection. Overall, genome-wide DNA methylation techniques can be applied directly to saliva samples for biomarker discovery and ML-based platforms may be useful in stratifying OSCC during disease screening and monitoring. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Statistical Modeling in Precision Oncology)

► Show Figures

Figure 1

20 pages, 4342 KiB

Open AccessArticle

Clinical Categorization Algorithm (CLICAL) and Machine Learning Approach (SRF-CLICAL) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-World Evidence from the Istituto Nazionale Tumori IRCCS Fondazione Pascale, Napoli, Italy

by Gabriele Madonna, Giuseppe V. Masucci, Mariaelena Capone, Domenico Mallardo, Antonio Maria Grimaldi, Ester Simeone, Vito Vanella, Lucia Festino, Marco Palla, Luigi Scarpato, Marilena Tuffanelli, Grazia D'angelo, Lisa Villabona, Isabelle Krakowski, Hanna Eriksson, Felipe Simao, Rolf Lewensohn and Paolo Antonio Ascierto

Cancers 2021, 13(16), 4164; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers13164164 - 19 Aug 2021

Cited by 7 | Viewed by 3148

Abstract

The real-life application of immune checkpoint inhibitors (ICIs) may yield different outcomes compared to the benefit presented in clinical trials. For this reason, there is a need to define the group of patients that may benefit from treatment. We retrospectively investigated 578 metastatic melanoma patients treated with ICIs at the Istituto Nazionale Tumori IRCCS Fondazione “G. Pascale” of Napoli, Italy (INT-NA). To compare patients’ clinical variables (i.e., age, lactate dehydrogenase (LDH), neutrophil–lymphocyte ratio (NLR), eosinophil, BRAF status, previous treatment) and their predictive and prognostic power in a comprehensive, non-hierarchical manner, a clinical categorization algorithm (CLICAL) was defined and validated by the application of a machine learning algorithm—survival random forest (SRF-CLICAL). The comprehensive analysis of the clinical parameters by log risk-based algorithms resulted in predictive signatures that could identify groups of patients with great benefit or not, regardless of the ICI received. From a real-life retrospective analysis of metastatic melanoma patients, we generated and validated an algorithm based on machine learning that could assist with the clinical decision of whether or not to apply ICI therapy by defining five signatures of predictability with 95% accuracy. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Statistical Modeling in Precision Oncology)

► Show Figures

Figure 1

Review

Jump to: Research

16 pages, 862 KiB

Open AccessEditor’s ChoiceReview

Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection

by Alessandro Allegra, Alessandro Tonacci, Raffaele Sciaccotta, Sara Genovese, Caterina Musolino, Giovanni Pioggia and Sebastiano Gangemi

Cancers 2022, 14(3), 606; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers14030606 - 25 Jan 2022

Cited by 26 | Viewed by 6054

Abstract

Artificial intelligence has recently modified the panorama of oncology investigation thanks to the use of machine learning algorithms and deep learning strategies. Machine learning is a branch of artificial intelligence that involves algorithms that analyse information, learn from that information, and then employ their discoveries to make abreast choice, while deep learning is a field of machine learning basically represented by algorithms inspired by the organization and function of the brain, named artificial neural networks. In this review, we examine the possibility of the artificial intelligence applications in multiple myeloma evaluation, and we report the most significant experimentations with respect to the machine and deep learning procedures in the relevant field. Multiple myeloma is one of the most common haematological malignancies in the world, and among them, it is one of the most difficult ones to cure due to the high occurrence of relapse and chemoresistance. Machine learning- and deep learning-based studies are expected to be among the future strategies to challenge this negative-prognosis tumour via the detection of new markers for their prompt discovery and therapy selection and by a better evaluation of its relapse and survival. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Statistical Modeling in Precision Oncology)

► Show Figures