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The Role of Artificial Intelligence in Cancer Treatment: Molecular Perspective

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: 20 January 2026 | Viewed by 2640

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Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA
Interests: rare tumors; data science and computational biology; brain and spine cancer; head and neck cancer; genitourinary tumors; re-irradiation
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Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) in its various forms—machine learning (ML), deep learning (DL), and hybrid techniques—has seamlessly integrated into our daily lives. This integration is especially evident in the medical field, where the explosion of data across different modalities and settings offers us unprecedented opportunities to explore and analyze diverse data types. However, despite the vast amount of data available in cancer research, we still lack clinically actionable conclusions that can guide personalized treatments through AI-approved methods.

But there is hope on the horizon. The intersection of advanced computational methods and the multi-dimensional data in cancer research is paving the way for a theoretical shift. We now have the potential to translate AI findings into clinical trials and practical applications, bridging the gap from bench to bedside. The Special Issue brings together cutting-edge research that demonstrates how AI is enabling the analysis of massive multi-omics data, including genomics, transcriptomics, proteomics, and metabolomics, to uncover novel molecular mechanisms underlying cancer progression and the response to therapies. By harnessing AI’s molecular insights, we can develop cancer treatments that are not only effective but also interpretable for clinical use. Your participation and interest can help drive this exciting evolution in cancer care.

Dr. Andra Valentina Krauze
Guest Editor

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Keywords

  • cancer treatment
  • artificial intelligence
  • machine learning
  • precision medicine
  • molecular insights

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Published Papers (2 papers)

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Research

17 pages, 2089 KiB  
Article
From Mutation to Prognosis: AI-HOPE-PI3K Enables Artificial Intelligence Agent-Driven Integration of PI3K Pathway Data in Colorectal Cancer Precision Medicine
by Ei-Wen Yang, Brigette Waldrup and Enrique Velazquez-Villarreal
Int. J. Mol. Sci. 2025, 26(13), 6487; https://doi.org/10.3390/ijms26136487 - 5 Jul 2025
Viewed by 298
Abstract
The rising incidence of early-onset colorectal cancer (EOCRC), particularly among underrepresented populations, highlights the urgent need for tools that can uncover clinically meaningful, population-specific genomic alterations. The phosphoinositide 3-kinase (PI3K) pathway plays a key role in tumor progression, survival, and therapeutic [...] Read more.
The rising incidence of early-onset colorectal cancer (EOCRC), particularly among underrepresented populations, highlights the urgent need for tools that can uncover clinically meaningful, population-specific genomic alterations. The phosphoinositide 3-kinase (PI3K) pathway plays a key role in tumor progression, survival, and therapeutic resistance in colorectal cancer (CRC), yet its impact in EOCRC remains insufficiently explored. To address this gap, we developed AI-HOPE-PI3K, a conversational artificial intelligence platform that integrates harmonized clinical and genomic data for real-time, natural language-based analysis of PI3K pathway alterations. Built on a fine-tuned biomedical LLaMA 3 model, the system automates cohort generation, survival modeling, and mutation frequency comparisons using multi-institutional cBioPortal datasets annotated with clinical variables. AI-HOPE-PI3K replicated known associations and revealed new findings, including worse survival in colon versus rectal tumors harboring PI3K alterations, enrichment of INPP4B mutations in Hispanic/Latino EOCRC patients, and favorable survival outcomes associated with high tumor mutational burden in FOLFIRI-treated patients. The platform also enabled context-specific survival analyses stratified by age, tumor stage, and molecular alterations. These findings support the utility of AI-HOPE-PI3K as a scalable and accessible tool for integrative, pathway-specific analysis, demonstrating its potential to advance precision oncology and reduce disparities in EOCRC through data-driven discovery. Full article
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25 pages, 5480 KiB  
Article
MetaWise: Combined Feature Selection and Weighting Method to Link the Serum Metabolome to Treatment Response and Survival in Glioblastoma
by Erdal Tasci, Michael Popa, Ying Zhuge, Shreya Chappidi, Longze Zhang, Theresa Cooley Zgela, Mary Sproull, Megan Mackey, Heather R. Kates, Timothy J. Garrett, Kevin Camphausen and Andra V. Krauze
Int. J. Mol. Sci. 2024, 25(20), 10965; https://doi.org/10.3390/ijms252010965 - 11 Oct 2024
Cited by 4 | Viewed by 1933
Abstract
Glioblastoma (GBM) is a highly malignant and devastating brain cancer characterized by its ability to rapidly and aggressively grow, infiltrating brain tissue, with nearly universal recurrence after the standard of care (SOC), which comprises maximal safe resection followed by chemoirradiation (CRT). The metabolic [...] Read more.
Glioblastoma (GBM) is a highly malignant and devastating brain cancer characterized by its ability to rapidly and aggressively grow, infiltrating brain tissue, with nearly universal recurrence after the standard of care (SOC), which comprises maximal safe resection followed by chemoirradiation (CRT). The metabolic triggers leading to the reprogramming of tumor behavior and resistance are an area increasingly studied in relation to the tumor molecular features associated with outcome. There are currently no metabolomic biomarkers for GBM. Studying the metabolomic alterations in GBM patients undergoing CRT could uncover the biochemical pathways involved in tumor response and resistance, leading to the identification of novel biomarkers and the optimization of the treatment response. The feature selection process identifies key factors to improve the model’s accuracy and interpretability. This study utilizes a combined feature selection approach, incorporating both Least Absolute Shrinkage and Selection Operator (LASSO) and Minimum Redundancy–Maximum Relevance (mRMR), alongside a rank-based weighting method (i.e., MetaWise) to link metabolomic biomarkers to CRT and the 12-month and 20-month overall survival (OS) status in patients with GBM. Our method shows promising results, reducing feature dimensionality when employed on serum-based large-scale metabolomic datasets (University of Florida) for all our analyses. The proposed method successfully identified a set of eleven serum biomarkers shared among three datasets. The computational results show that the utilized method achieves 96.711%, 92.093%, and 86.910% accuracy rates with 48, 46, and 33 selected features for the CRT, 12-month, and 20-month OS-based metabolomic datasets, respectively. This discovery has implications for developing personalized treatment plans and improving patient outcomes. Full article
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