Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.0
4.5
Journals
Cancers
Special Issues
Genome Informatics and Cancers
share announcement

Genome Informatics and Cancers

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 (31 December 2021) | Viewed by 29533

Special Issue Editors

Dr. Krish Karuturi

E-Mail Website
Guest Editor
The Jackson Laboratory, Farmington, CT USA
Department of Computer Science & Engineering, University of Connecticut, Storrs, CT USA
Graduate School of Biomedical Sciences & Engineering, University of Maine, Orono, ME USA
Interests: Statistical Bioinformatics; Predictive Genomic Medicine; Data Science
Dr. Joshy George

E-Mail Website
Guest Editor
The Jackson Laboratory, Farmington, CT USA
Interests: Cancer Genomics; Bioinformatics; Machine Learning
Dr. Jeffrey Chuang

E-Mail Website
Guest Editor
The Jackson Laboratory, Farmington, CT USA
Department of Computer Science & Engineering, University of Connecticut, Storrs, CT USA
Interests: Cancer Genetics and Genomics; Computational Biology; Cancer Imaging

Special Issue Information

Dear Colleagues,

Genomic profiling has played a significant role in the rapid progress in our understanding of how cancers develop and progress, and contribute to precision in clinical care. However, the complexity and heterogeneity of cancer requires genomic profiling to be conducted along a number of orthogonal dimensions: single-cell to bulk tissue, model systems to human tissues, pediatric cancers to adult cancers, SNV to epigenetic to RNA profiling, etc. Such complex studies require an in-depth understanding of the opportunities and challenges of the complex heterogeneous data, developing associated analysis methods, and appropriate application of these methods to decipher the complexity of cancers. The journal Cancers invites articles in these broad areas of research for its Special Issue entitled “Genome Informatics in Cancers.” The articles may include original research, reviews, and perspectives on algorithms, statistical methods, software, and databases in all areas of cancer genomics (e.g., genomes, epigenomes, transcriptomes, regulomes, proteomes, and metabolomes). The specific areas of study include, but are not limited to,

  • Single cell and bulk genomics
  • Mammalian cancer genomics (e.g., mouse and canine models of cancer)
  • Patient derived xenograft (PDX) Genomics
  • Organoid analysis
  • Tumor Heterogeneity
  • Tumor microenvironment
  • Integrative genomics
  • X-species genomic analysis
  • Genome signatures for precision clinical care
  • Genome diagnostics
  • Cancer imaging

Dr. Krish Karuturi
Dr. Joshy George
Dr. Jeffrey Chuang
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 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
  • Tumor
  • Genomics
  • Single cell
  • Algorithms
  • Statistical Methods
  • Databases
  • Software
  • Organoids
  • PDX
  • Clinical assays
  • SNV
  • SV
  • ATAC-seq
  • Image Analysis

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 26076 KiB  
Article
Comprehensively Exploring the Mutational Landscape and Patterns of Genomic Evolution in Hypermutated Cancers
by Peng-Chan Lin, Yu-Min Yeh, Hui-Ping Hsu, Ren-Hao Chan, Bo-Wen Lin, Po-Chuan Chen, Chien-Chang Pan, Keng-Fu Hsu, Jenn-Ren Hsiao, Yan-Shen Shan and Meng-Ru Shen
Cancers 2021, 13(17), 4317; https://doi.org/10.3390/cancers13174317 - 26 Aug 2021
Cited by 3 | Viewed by 3368
Abstract
Tumor heterogeneity results in more than 50% of hypermutated cancers failing to respond to standard immunotherapy. There are numerous challenges in terms of drug resistance, therapeutic strategies, and biomarkers in immunotherapy. In this study, we analyzed primary tumor samples from 533 cancer patients [...] Read more.
Tumor heterogeneity results in more than 50% of hypermutated cancers failing to respond to standard immunotherapy. There are numerous challenges in terms of drug resistance, therapeutic strategies, and biomarkers in immunotherapy. In this study, we analyzed primary tumor samples from 533 cancer patients with six different cancer types using deep targeted sequencing and gene expression data from 78 colorectal cancer patients, whereby driver mutations, mutational signatures, tumor-associated neoantigens, and molecular cancer evolution were investigated. Driver mutations, including RET, CBL, and DDR2 gene mutations, were identified in the hypermutated cancers. Most hypermutated endometrial and pancreatic cancer patients carry genetic mutations in EGFR, FBXW7, and PIK3CA that are linked to immunotherapy resistance, while hypermutated head and neck cancer patients carry genetic mutations associated with better treatment responses, such as ATM and BRRCA2 mutations. APOBEC (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like) and DNA repair defects are mutational drivers that are signatures for hypermutated cancer. Cancer driver mutations and other mutational signatures are associated with sensitivity or resistance to immunotherapy, representing potential genetic markers in hypermutated cancers. Using computational prediction, we identified NF1 p.T700I and NOTCH1 p.V2153M as tumor-associated neoantigens, representing potential therapeutic targets for immunotherapy. Sequential mutations were used to predict hypermutated cancers based on genomic evolution. Using a logistic model, we achieved an area under the curve (AUC) = 0.93, accuracy = 0.93, and sensitivity = 0.81 in the testing set. The sequential patterns were distinct among the six cancer types, and the sequential mutation order of MSH2 and the coexisting BRAF genetic mutations influenced the hypermutated phenotype. The TP53~MLH1 and NOTCH1~TET2 sequential mutations impacted colorectal cancer survival (p-value = 0.027 and 0.0001, respectively) by reducing the expression of PTPRCAP (p-value = 1.06 × 10−6) and NOS2 (p-value = 7.57 × 10−7) in immunity. Sequential mutations are significant for hypermutated cancers, which are characterized by mutational heterogeneity. In addition to driver mutations and mutational signatures, sequential mutations in cancer evolution can impact hypermutated cancers. They characterize potential responses or predictive markers for hypermutated cancers. These data can also be used to develop hypermutation-associated drug targets and elucidate the evolutionary biology of cancer survival. In this study, we conducted a comprehensive analysis of mutational patterns, including sequential mutations, and identified useful markers and therapeutic targets in hypermutated cancer patients. Full article
(This article belongs to the Special Issue Genome Informatics and Cancers)
►▼ Show Figures

Figure 1

27 pages, 9747 KiB  
Article
Pan-Cancer Analysis of Immune Complement Signature C3/C5/C3AR1/C5AR1 in Association with Tumor Immune Evasion and Therapy Resistance
by Bashir Lawal, Sung-Hui Tseng, Janet Olayemi Olugbodi, Sitthichai Iamsaard, Omotayo B. Ilesanmi, Mohamed H. Mahmoud, Sahar H. Ahmed, Gaber El-Saber Batiha and Alexander T. H. Wu
Cancers 2021, 13(16), 4124; https://doi.org/10.3390/cancers13164124 - 16 Aug 2021
Cited by 26 | Viewed by 5855
Abstract
Despite the advances in our understanding of the genetic and immunological basis of cancer, cancer remains a major public health burden with an ever-increasing incidence rate globally. Nevertheless, increasing evidence suggests that the components of the complement system could regulate the tumor microenvironment [...] Read more.
Despite the advances in our understanding of the genetic and immunological basis of cancer, cancer remains a major public health burden with an ever-increasing incidence rate globally. Nevertheless, increasing evidence suggests that the components of the complement system could regulate the tumor microenvironment (TME) to promote cancer progression, recurrence, and metastasis. In the present study, we used an integrative multi-omics analysis of clinical data to explore the relationships between the expression levels of and genetic and epigenetic alterations in C3, C5, C3AR1, and C5AR1 and tumor immune evasion, therapy response, and patient prognosis in various cancer types. We found that the complements C3, C5, C3AR1, and C5AR1 have deregulated expression in human malignancies and are associated with activation of immune-related oncogenic processes and poor prognosis of cancer patients. Furthermore, we found that the increased expression levels of C3, C5, C3AR1, and C5AR1 were primarily predicted by copy number variation and gene methylation and were associated with dysfunctional T-cell phenotypes. Single nucleotide variation in the gene signature co-occurred with multiple oncogenic mutations and is associated with the progression of onco-immune-related diseases. Further correlation analysis revealed that C3, C5, C3AR1, and C5AR1 were associated with tumor immune evasion via dysfunctional T-cell phenotypes with a lesser contribution of T-cell exclusion. Lastly, we also demonstrated that the expression levels of C3, C5, C3AR1, and C5AR1 were associated with context-dependent chemotherapy, lymphocyte-mediated tumor killing, and immunotherapy outcomes in different cancer types. In conclusion, the complement components C3, C5, C3AR1, and C5AR1 serve as attractive targets for strategizing cancer immunotherapy and response follow-up. Full article
(This article belongs to the Special Issue Genome Informatics and Cancers)
►▼ Show Figures

Figure 1

28 pages, 9964 KiB  
Article
Co-Deregulated miRNA Signatures in Childhood Central Nervous System Tumors: In Search for Common Tumor miRNA-Related Mechanics
by George I. Lambrou, Apostolos Zaravinos and Maria Braoudaki
Cancers 2021, 13(12), 3028; https://doi.org/10.3390/cancers13123028 - 17 Jun 2021
Cited by 4 | Viewed by 2443
Abstract
Despite extensive experimentation on pediatric tumors of the central nervous system (CNS), related to both prognosis, diagnosis and treatment, the understanding of pathogenesis and etiology of the disease remains scarce. MicroRNAs are known to be involved in CNS tumor oncogenesis. We hypothesized that [...] Read more.
Despite extensive experimentation on pediatric tumors of the central nervous system (CNS), related to both prognosis, diagnosis and treatment, the understanding of pathogenesis and etiology of the disease remains scarce. MicroRNAs are known to be involved in CNS tumor oncogenesis. We hypothesized that CNS tumors possess commonly deregulated miRNAs across different CNS tumor types. Aim: The current study aims to reveal the co-deregulated miRNAs across different types of pediatric CNS tumors. Materials: A total of 439 CNS tumor samples were collected from both in-house microarray experiments as well as data available in public databases. Diagnoses included medulloblastoma, astrocytoma, ependydoma, cortical dysplasia, glioblastoma, ATRT, germinoma, teratoma, yoc sac tumors, ocular tumors and retinoblastoma. Results: We found miRNAs that were globally up- or down-regulated in the majority of the CNS tumor samples. MiR-376B and miR-372 were co-upregulated, whereas miR-149, miR-214, miR-574, miR-595 and miR-765 among others, were co-downregulated across all CNS tumors. Receiver-operator curve analysis showed that miR-149, miR-214, miR-574, miR-595 and miR765 could distinguish between CNS tumors and normal brain tissue. Conclusions: Our approach could prove significant in the search for global miRNA targets for tumor diagnosis and therapy. To the best of our knowledge, there are no previous reports concerning the present approach. Full article
(This article belongs to the Special Issue Genome Informatics and Cancers)
►▼ Show Figures

Figure 1

24 pages, 2367 KiB  
Article
Sequence Neighborhoods Enable Reliable Prediction of Pathogenic Mutations in Cancer Genomes
by Shayantan Banerjee, Karthik Raman and Balaraman Ravindran
Cancers 2021, 13(10), 2366; https://doi.org/10.3390/cancers13102366 - 14 May 2021
Cited by 4 | Viewed by 3946
Abstract
Identifying cancer-causing mutations from sequenced cancer genomes hold much promise for targeted therapy and precision medicine. “Driver” mutations are primarily responsible for cancer progression, while “passengers” are functionally neutral. Although several computational approaches have been developed for distinguishing between driver and passenger mutations, [...] Read more.
Identifying cancer-causing mutations from sequenced cancer genomes hold much promise for targeted therapy and precision medicine. “Driver” mutations are primarily responsible for cancer progression, while “passengers” are functionally neutral. Although several computational approaches have been developed for distinguishing between driver and passenger mutations, very few have concentrated on using the raw nucleotide sequences surrounding a particular mutation as potential features for building predictive models. Using experimentally validated cancer mutation data in this study, we explored various string-based feature representation techniques to incorporate information on the neighborhood bases immediately 5′ and 3′ from each mutated position. Density estimation methods showed significant distributional differences between the neighborhood bases surrounding driver and passenger mutations. Binary classification models derived using repeated cross-validation experiments provided comparable performances across all window sizes. Integrating sequence features derived from raw nucleotide sequences with other genomic, structural, and evolutionary features resulted in the development of a pan-cancer mutation effect prediction tool, NBDriver, which was highly efficient in identifying pathogenic variants from five independent validation datasets. An ensemble predictor obtained by combining the predictions from NBDriver with three other commonly used driver prediction tools (FATHMM (cancer), CONDEL, and MutationTaster) significantly outperformed existing pan-cancer models in prioritizing a literature-curated list of driver and passenger mutations. Using the list of true positive mutation predictions derived from NBDriver, we identified a list of 138 known driver genes with functional evidence from various sources. Overall, our study underscores the efficacy of using raw nucleotide sequences as features to distinguish between driver and passenger mutations from sequenced cancer genomes. Full article
(This article belongs to the Special Issue Genome Informatics and Cancers)
►▼ Show Figures

Figure 1

22 pages, 2992 KiB  
Article
Identification of a Candidate Gene Set Signature for the Risk of Progression in IgM MGUS to Smoldering/Symptomatic Waldenström Macroglobulinemia (WM) by a Comparative Transcriptome Analysis of B Cells and Plasma Cells
by Alessandra Trojani, Barbara Di Camillo, Luca Emanuele Bossi, Livia Leuzzi, Antonino Greco, Alessandra Tedeschi, Anna Maria Frustaci, Marina Deodato, Giulia Zamprogna, Alessandro Beghini and Roberto Cairoli
Cancers 2021, 13(8), 1837; https://doi.org/10.3390/cancers13081837 - 12 Apr 2021
Cited by 8 | Viewed by 2955
Abstract
Waldenström Macroglobulinemia (WM) is a B-cell lymphoma characterized by the precursor condition IgM monoclonal gammopathies of undetermined significance (IgM MGUS). We performed a gene expression profiling study to compare the transcriptome signatures of bone marrow (BM) B-cells and plasma cells of 36 WM [...] Read more.
Waldenström Macroglobulinemia (WM) is a B-cell lymphoma characterized by the precursor condition IgM monoclonal gammopathies of undetermined significance (IgM MGUS). We performed a gene expression profiling study to compare the transcriptome signatures of bone marrow (BM) B-cells and plasma cells of 36 WM patients, 13 IgM MGUS cases, and 7 healthy subjects used as controls (CTRLs) by Affymetrix microarray. We determined 2038 differentially expressed genes (DEGs) in CD19+ cells and 29 DEGs genes in CD138+ cells, respectively. The DEGs identified in B-cells were associated with KEGG pathways, mainly involved in hematopoietic cell lineage antigens, cell adhesion/focal adhesion/transmembrane proteins, adherens junctions, Wnt-signaling pathway, BCR-signaling pathway, calcium signaling pathway, complement/coagulation cascade, platelet activation, cytokine-cytokine receptor interactions, and signaling pathways responsible for cell cycle, apoptosis, proliferation and survival. In conclusion, we showed the deregulation of groups of genes belonging to KEGG pathways in the comparison among WM vs. IgM MGUS vs. CTRLs in B-cells. Interestingly, a small set of genes in B-cells displayed a common transcriptome expression profile between WM and IgM MGUS compared to CTRLs, suggesting its possible role in the risk of transformation of IgM MGUS to WM. Full article
(This article belongs to the Special Issue Genome Informatics and Cancers)
►▼ Show Figures

Graphical abstract

16 pages, 887 KiB  
Article
Network Analysis Identifies Drug Targets and Small Molecules to Modulate Apoptosis Resistant Cancers
by Samreen Fathima, Swati Sinha and Sainitin Donakonda
Cancers 2021, 13(4), 851; https://doi.org/10.3390/cancers13040851 - 18 Feb 2021
Cited by 8 | Viewed by 4055
Abstract
Programed cell death or apoptosis fails to induce cell death in many recalcitrant cancers. Thus, there is an emerging need to activate the alternate cell death pathways in such cancers. In this study, we analyzed the apoptosis-resistant colon adenocarcinoma, glioblastoma multiforme, and small [...] Read more.
Programed cell death or apoptosis fails to induce cell death in many recalcitrant cancers. Thus, there is an emerging need to activate the alternate cell death pathways in such cancers. In this study, we analyzed the apoptosis-resistant colon adenocarcinoma, glioblastoma multiforme, and small cell lung cancers transcriptome profiles. We extracted clusters of non-apoptotic cell death genes from each cancer to understand functional networks affected by these genes and their role in the induction of cell death when apoptosis fails. We identified transcription factors regulating cell death genes and protein–protein interaction networks to understand their role in regulating cell death mechanisms. Topological analysis of networks yielded FANCD2 (ferroptosis, negative regulator, down), NCOA4 (ferroptosis, up), IKBKB (alkaliptosis, down), and RHOA (entotic cell death, down) as potential drug targets in colon adenocarcinoma, glioblastoma multiforme, small cell lung cancer phenotypes respectively. We also assessed the miRNA association with the drug targets. We identified tumor growth-related interacting partners based on the pathway information of drug-target interaction networks. The protein–protein interaction binding site between the drug targets and their interacting proteins provided an opportunity to identify small molecules that can modulate the activity of functional cell death interactions in each cancer. Overall, our systematic screening of non-apoptotic cell death-related genes uncovered targets helpful for cancer therapy. Full article
(This article belongs to the Special Issue Genome Informatics and Cancers)
►▼ Show Figures

Figure 1

18 pages, 5878 KiB  
Article
Molecular Classification and Tumor Microenvironment Characterization of Gallbladder Cancer by Comprehensive Genomic and Transcriptomic Analysis
by Nobutaka Ebata, Masashi Fujita, Shota Sasagawa, Kazuhiro Maejima, Yuki Okawa, Yutaka Hatanaka, Tomoko Mitsuhashi, Ayako Oosawa-Tatsuguchi, Hiroko Tanaka, Satoru Miyano, Toru Nakamura, Satoshi Hirano and Hidewaki Nakagawa
Cancers 2021, 13(4), 733; https://doi.org/10.3390/cancers13040733 - 10 Feb 2021
Cited by 16 | Viewed by 5385
Abstract
Gallbladder cancer (GBC), a rare but lethal disease, is often diagnosed at advanced stages. So far, molecular characterization of GBC is insufficient, and a comprehensive molecular portrait is warranted to uncover new targets and classify GBC. We performed a transcriptome analysis of both [...] Read more.
Gallbladder cancer (GBC), a rare but lethal disease, is often diagnosed at advanced stages. So far, molecular characterization of GBC is insufficient, and a comprehensive molecular portrait is warranted to uncover new targets and classify GBC. We performed a transcriptome analysis of both coding and non-coding RNAs from 36 GBC fresh-frozen samples. The results were integrated with those of comprehensive mutation profiling based on whole-genome or exome sequencing. The clustering analysis of RNA-seq data facilitated the classification of GBCs into two subclasses, characterized by high or low expression levels of TME (tumor microenvironment) genes. A correlation was observed between gene expression and pathological immunostaining. TME-rich tumors showed significantly poor prognosis and higher recurrence rate than TME-poor tumors. TME-rich tumors showed overexpression of genes involved in epithelial-to-mesenchymal transition (EMT) and inflammation or immune suppression, which was validated by immunostaining. One non-coding RNA, miR125B1, exhibited elevated expression in stroma-rich tumors, and miR125B1 knockout in GBC cell lines decreased its invasion ability and altered the EMT pathway. Mutation profiles revealed TP53 (47%) as the most commonly mutated gene, followed by ELF3 (13%) and ARID1A (11%). Mutations of ARID1A, ERBB3, and the genes related to the TGF-β signaling pathway were enriched in TME-rich tumors. This comprehensive analysis demonstrated that TME, EMT, and TGF-β pathway alterations are the main drivers of GBC and provides a new classification of GBCs that may be useful for therapeutic decision-making. Full article
(This article belongs to the Special Issue Genome Informatics and Cancers)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop