Computational Biology Helps Understand How Polyploid Giant Cancer Cells Drive Tumor Success DOI Open Access
Matheus Correia Casotti, Débora Dummer Meira, Aléxia Stefani Siqueira Zetum

et al.

Genes, Journal Year: 2023, Volume and Issue: 14(4), P. 801 - 801

Published: March 26, 2023

Precision and organization govern the cell cycle, ensuring normal proliferation. However, some cells may undergo abnormal divisions (neosis) or variations of mitotic cycles (endopolyploidy). Consequently, formation polyploid giant cancer (PGCCs), critical for tumor survival, resistance, immortalization, can occur. Newly formed end up accessing numerous multicellular unicellular programs that enable metastasis, drug recurrence, self-renewal diverse clone formation. An integrative literature review was carried out, searching articles in several sites, including: PUBMED, NCBI-PMC, Google Academic, published English, indexed referenced databases without a publication time filter, but prioritizing from last 3 years, to answer following questions: (i) "What is current knowledge about polyploidy tumors?"; (ii) are applications computational studies understanding polyploidy?"; (iii) "How do PGCCs contribute tumorigenesis?"

Language: Английский

Biomarkers for immunotherapy of hepatocellular carcinoma DOI
Tim F. Greten, Augusto Villanueva, Firouzeh Korangy

et al.

Nature Reviews Clinical Oncology, Journal Year: 2023, Volume and Issue: 20(11), P. 780 - 798

Published: Sept. 19, 2023

Language: Английский

Citations

101
Artificial intelligence for digital and computational pathology DOI
Andrew H. Song, Guillaume Jaume, Drew F. K. Williamson

et al.

Nature Reviews Bioengineering, Journal Year: 2023, Volume and Issue: 1(12), P. 930 - 949

Published: Oct. 2, 2023

Language: Английский

Citations

92
Advancing CAR T cell therapy through the use of multidimensional omics data DOI
Jingwen Yang, Yamei Chen, Ying Jing

et al.

Nature Reviews Clinical Oncology, Journal Year: 2023, Volume and Issue: 20(4), P. 211 - 228

Published: Jan. 31, 2023

Language: Английский

Citations

66
Multimodal data integration for oncology in the era of deep neural networks: a review DOI Creative Commons
Asim Waqas, Aakash Tripathi, Ravi P. Ramachandran

et al.

Frontiers in Artificial Intelligence, Journal Year: 2024, Volume and Issue: 7

Published: July 25, 2024

Cancer research encompasses data across various scales, modalities, and resolutions, from screening diagnostic imaging to digitized histopathology slides types of molecular clinical records. The integration these diverse for personalized cancer care predictive modeling holds the promise enhancing accuracy reliability screening, diagnosis, treatment. Traditional analytical methods, which often focus on isolated or unimodal information, fall short capturing complex heterogeneous nature data. advent deep neural networks has spurred development sophisticated multimodal fusion techniques capable extracting synthesizing information disparate sources. Among these, Graph Neural Networks (GNNs) Transformers have emerged as powerful tools learning, demonstrating significant success. This review presents foundational principles learning including oncology taxonomy strategies. We delve into recent advancements in GNNs oncology, spotlighting key studies their pivotal findings. discuss unique challenges such heterogeneity complexities, alongside opportunities it a more nuanced comprehensive understanding cancer. Finally, we present some latest pan-cancer By surveying landscape our goal is underline transformative potential Transformers. Through technological methodological innovations presented this review, aim chart course future promising field. may be first that highlights current state applications using transformers, sources, sets stage evolution, encouraging further exploration care.

Language: Английский

Citations

25
MoDAFold: a strategy for predicting the structure of missense mutant protein based on AlphaFold2 and molecular dynamics DOI Creative Commons
Lingyan Zheng, Shuiyang Shi, Xiuna Sun

et al.

Briefings in Bioinformatics, Journal Year: 2024, Volume and Issue: 25(2)

Published: Jan. 22, 2024

Abstract Protein structure prediction is a longstanding issue crucial for identifying new drug targets and providing mechanistic understanding of protein functions. To enhance the progress in this field, spectrum computational methodologies has been cultivated. AlphaFold2 exhibited exceptional precision predicting wild-type structures, with performance exceeding that other methods. However, structures missense mutant proteins using remains challenging due to intricate substantial structural alterations caused by minor sequence variations proteins. Molecular dynamics (MD) validated precisely capturing changes amino acid interactions attributed mutations. Therefore, first time, strategy entitled ‘MoDAFold’ was proposed improve accuracy reliability combining MD. Multiple case studies have confirmed superior MoDAFold compared methods, particularly AlphaFold2.

Language: Английский

Citations

19
Omics-Based Investigations of Breast Cancer DOI Creative Commons

Anca-Narcisa Neagu,

Danielle Whitham, Pathea Bruno

et al.

Molecules, Journal Year: 2023, Volume and Issue: 28(12), P. 4768 - 4768

Published: June 14, 2023

Breast cancer (BC) is characterized by an extensive genotypic and phenotypic heterogeneity. In-depth investigations into the molecular bases of BC phenotypes, carcinogenesis, progression, metastasis are necessary for accurate diagnoses, prognoses, therapy assessments in predictive, precision, personalized oncology. This review discusses both classic as well several novel omics fields that involved or should be used modern investigations, which may integrated a holistic term, onco-breastomics. Rapid recent advances profiling strategies analytical techniques based on high-throughput sequencing mass spectrometry (MS) development have generated large-scale multi-omics datasets, mainly emerging from three ”big omics”, central dogma biology: genomics, transcriptomics, proteomics. Metabolomics-based approaches also reflect dynamic response cells to genetic modifications. Interactomics promotes view research constructing characterizing protein–protein interaction (PPI) networks provide hypothesis pathophysiological processes progression subtyping. The emergence new omics- epiomics-based multidimensional opportunities gain insights heterogeneity its underlying mechanisms. main epiomics (epigenomics, epitranscriptomics, epiproteomics) focused epigenetic DNA changes, RNAs modifications, posttranslational modifications (PTMs) affecting protein functions in-depth understanding cell proliferation, migration, invasion. Novel fields, such epichaperomics epimetabolomics, could investigate interactome induced stressors PPI metabolites, drivers BC-causing phenotypes. Over last years, proteomics-derived omics, matrisomics, exosomics, secretomics, kinomics, phosphoproteomics, immunomics, provided valuable data deep dysregulated pathways their tumor microenvironment (TME) immune (TIMW). Most these datasets still assessed individually using distinct approches do not generate desired expected global-integrative knowledge with applications clinical diagnostics. However, hyphenated approaches, proteo-genomics, proteo-transcriptomics, phosphoproteomics-exosomics useful identification putative biomarkers therapeutic targets. To develop non-invasive diagnostic tests discover BC, omics-based allow significant blood/plasma-based omics. Salivaomics, urinomics, milkomics appear integrative high potential early diagnoses BC. Thus, analysis circulome considered frontier liquid biopsy. Omics-based modeling, classification subtype characterization. future single-cell analyses.

Language: Английский

Citations

36
A systematic review of computational approaches to understand cancer biology for informed drug repurposing DOI Creative Commons
Faheem Ahmed,

Anupama Samantasinghar,

Afaque Manzoor Soomro

et al.

Journal of Biomedical Informatics, Journal Year: 2023, Volume and Issue: 142, P. 104373 - 104373

Published: April 27, 2023

Cancer is the second leading cause of death globally, trailing only heart disease. In United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, success rate drug development remains less than 10%, making disease particularly challenging. This low largely attributed to complex poorly understood nature etiology. Therefore, it critical find alternative approaches understanding biology developing effective treatments. One such approach repurposing, which offers a shorter timeline lower costs while increasing likelihood success. this review, we provide comprehensive analysis computational biology, including systems multi-omics, pathway analysis. Additionally, examine use these methods repurposing in cancer, databases tools that are used research. Finally, present case studies discussing their limitations offering recommendations future research area.

Language: Английский

Citations

35
Computational approaches to modelling and optimizing cancer treatment DOI
Thomas O. McDonald, Yu-Chen Cheng, Christopher Graser

et al.

Nature Reviews Bioengineering, Journal Year: 2023, Volume and Issue: 1(10), P. 695 - 711

Published: July 19, 2023

Language: Английский

Citations

26
Big data and artificial intelligence in cancer research DOI
Xifeng Wu, Wenyuan Li, Huakang Tu

et al.

Trends in cancer, Journal Year: 2023, Volume and Issue: 10(2), P. 147 - 160

Published: Nov. 16, 2023

Language: Английский

Citations

24
Integration of pan-omics technologies and three-dimensional in vitro tumor models: an approach toward drug discovery and precision medicine DOI Creative Commons

Anmi Jose,

Pallavi Kulkarni,

Jaya Thilakan

et al.

Molecular Cancer, Journal Year: 2024, Volume and Issue: 23(1)

Published: March 9, 2024

Abstract Despite advancements in treatment protocols, cancer is one of the leading cause deaths worldwide. Therefore, there a need to identify newer and personalized therapeutic targets along with screening technologies combat cancer. With advent pan-omics technologies, such as genomics, transcriptomics, proteomics, metabolomics, lipidomics, scientific community has witnessed an improved molecular metabolomic understanding various diseases, including In addition, three-dimensional (3-D) disease models have been efficiently utilized for pathophysiology tools drug discovery. An integrated approach utilizing 3-D vitro tumor led intricate network encompassing signalling pathways cross-talk solid tumors. present review, we underscore current trends omics highlight their role genotypic-phenotypic co-relation respect models. We further discuss challenges associated provide our outlook on future applications these discovery precision medicine management Graphical

Language: Английский

Citations

16