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1. Methylation of CpG Sites as Biomarkers Predictive of Drug-Specific Patient Survival in Cancer

   https://doi.org/10.1177/11769351221131124  

   Neary, Bridget; Lin, Shuting; Qiu, Peng (January 2022, Cancer Informatics)

   Background: Though the development of targeted cancer drugs continues to accelerate, doctors still lack reliable methods for predicting patient response to standard-of-care therapies for most cancers. DNA methylation has been implicated in tumor drug response and is a promising source of predictive biomarkers of drug efficacy, yet the relationship between drug efficacy and DNA methylation remains largely unexplored. Method: In this analysis, we performed log-rank survival analyses on patients grouped by cancer and drug exposure to find CpG sites where binary methylation status is associated with differential survival in patients treated with a specific drug but not in patients with the same cancer who were not exposed to that drug. We also clustered these drug-specific CpG sites based on co-methylation among patients to identify broader methylation patterns that may be related to drug efficacy, which we investigated for transcription factor binding site enrichment using gene set enrichment analysis. Results: We identified CpG sites that were drug-specific predictors of survival in 38 cancer-drug patient groups across 15 cancers and 20 drugs. These included 11 CpG sites with similar drug-specific survival effects in multiple cancers. We also identified 76 clusters of CpG sites with stronger associations with patient drug response, many of which contained CpG sites in gene promoters containing transcription factor binding sites. Conclusion: These findings are promising biomarkers of drug response for a variety of drugs and contribute to our understanding of drug-methylation interactions in cancer. Investigation and validation of these results could lead to the development of targeted co-therapies aimed at manipulating methylation in order to improve efficacy of commonly used therapies and could improve patient survival and quality of life by furthering the effort toward drug response prediction. 

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2. A Pilot Study of Blood-Based Methylation Markers Associated With Pancreatic Cancer

   https://doi.org/10.3389/fgene.2022.849839  

   Jansen, Rick J.; Orr, Megan; Bamlet, William R.; Petersen, Gloria M. (March 2022, Frontiers in Genetics)

   Over the past several decades in the United States, incidence of pancreatic cancer (PCa) has increased, with the 5-year survival rate remaining extremely low at 10.8%. Typically, PCa is diagnosed at an advanced stage, with the consequence that there is more tumor heterogeneity and increased probability that more cells are resistant to treatments. Risk factors for PCa can serve as a way to select a high-risk population and develop biomarkers to improve early detection and treatment. We focus on blood-based methylation as an approach to identify a marker set that can be obtained in a minimally invasive way (through peripheral blood) and could be applied to a high-risk subpopulation [those with recent onset type 2 diabetes (DM)]. Blood samples were collected from 30 patients, 15 had been diagnosed with PCa and 15 had been diagnosed with recent onset DM. HumanMethylationEPIC Beadchip (Illumina, CA, United States) was used to quantify methylation of approximately 850,000 methylation sites across the genome and to analyze methylation markers associated with PCa or DM or both. Exploratory analysis conducted to propose importance of top CpG (5′—C—phosphate—G—3′) methylation site associated genes and visualized using boxplots. A methylation-based age predictor was also investigated for ability to distinguish disease groups from controls. No methylation markers were observed to be significantly associated with PCa or new onset diabetes compared with control the respective control groups. In our exploratory analysis, one methylation marker, CpG04969764, found in the Laminin Subunit Alpha 5 ( LAMA5 ) gene region was observed in both PCa and DM Top 100 methylation marker sets. Modification of LAMA5 methylation or LAMA5 gene function may be a way to distinguish those recent DM cases with and without PCa, however, additional studies with larger sample sizes and different study types (e.g., cohort) will be needed to test this hypothesis. 

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3. A comprehensive review of cancer survival prediction using multi-omics integration and clinical variables

   https://doi.org/10.1093/bib/bbaf150  

   Tran, Dao; Nguyen, Ha; Pham, Van-Dung; Nguyen, Phuong; Nguyen_Luu, Hung; Minh_Phan, Liem; Blair_DeStefano, Christin; Jim_Yeung, Sai-Ching; Nguyen, Tin (April 2025, Briefings in Bioinformatics)

   Abstract Cancer is an umbrella term that includes a wide spectrum of disease severity, from those that are malignant, metastatic, and aggressive to benign lesions with very low potential for progression or death. The ability to prognosticate patient outcomes would facilitate management of various malignancies: patients whose cancer is likely to advance quickly would receive necessary treatment that is commensurate with the predicted biology of the disease. Former prognostic models based on clinical variables (age, gender, cancer stage, tumor grade, etc.), though helpful, cannot account for genetic differences, molecular etiology, tumor heterogeneity, and important host biological mechanisms. Therefore, recent prognostic models have shifted toward the integration of complementary information available in both molecular data and clinical variables to better predict patient outcomes: vital status (overall survival), metastasis (metastasis-free survival), and recurrence (progression-free survival). In this article, we review 20 survival prediction approaches that integrate multi-omics and clinical data to predict patient outcomes. We discuss their strategies for modeling survival time (continuous and discrete), the incorporation of molecular measurements and clinical variables into risk models (clinical and multi-omics data), how to cope with censored patient records, the effectiveness of data integration techniques, prediction methodologies, model validation, and assessment metrics. The goal is to inform life scientists of available resources, and to provide a complete review of important building blocks in survival prediction. At the same time, we thoroughly describe the pros and cons of each methodology, and discuss in depth the outstanding challenges that need to be addressed in future method development. 

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4. An Interpretable Machine Learning Framework for Rare Disease: A Case Study to Stratify Infection Risk in Pediatric Leukemia

   https://doi.org/10.3390/jcm13061788  

   Al-Hussaini, Irfan; White, Brandon; Varmeziar, Armon; Mehra, Nidhi; Sanchez, Milagro; Lee, Judy; DeGroote, Nicholas P.; Miller, Tamara P.; Mitchell, Cassie S. (March 2024, Journal of Clinical Medicine)

   Background: Datasets on rare diseases, like pediatric acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), have small sample sizes that hinder machine learning (ML). The objective was to develop an interpretable ML framework to elucidate actionable insights from small tabular rare disease datasets. Methods: The comprehensive framework employed optimized data imputation and sampling, supervised and unsupervised learning, and literature-based discovery (LBD). The framework was deployed to assess treatment-related infection in pediatric AML and ALL. Results: An interpretable decision tree classified the risk of infection as either “high risk” or “low risk” in pediatric ALL (n = 580) and AML (n = 132) with accuracy of ∼79%. Interpretable regression models predicted the discrete number of developed infections with a mean absolute error (MAE) of 2.26 for bacterial infections and an MAE of 1.29 for viral infections. Features that best explained the development of infection were the chemotherapy regimen, cancer cells in the central nervous system at initial diagnosis, chemotherapy course, leukemia type, Down syndrome, race, and National Cancer Institute risk classification. Finally, SemNet 2.0, an open-source LBD software that links relationships from 33+ million PubMed articles, identified additional features for the prediction of infection, like glucose, iron, neutropenia-reducing growth factors, and systemic lupus erythematosus (SLE). Conclusions: The developed ML framework enabled state-of-the-art, interpretable predictions using rare disease tabular datasets. ML model performance baselines were successfully produced to predict infection in pediatric AML and ALL. 

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5. Colorectal Cancer Biomarker Identification via Joint DNA-Methylation and Transcriptomics Analysis Workflow

   https://doi.org/10.3390/genes16060620  

   Oladapo, Olajumoke B; Moussa, Marmar R (June 2025, Genes)

   Background: Colorectal cancer (CRC) is a term that refers to the combination of colon and rectal cancer as they are being treated as a single tumor. In CRC, 72% of tumors are colon cancer, while the other 28% represent rectal cancer. CRC is a multifactorial disease caused by both genetic and epigenetic changes in the colon mucosal cells, affecting the oncogenes, DNA repair genes, and tumor suppressor genes. Currently, two DNA methylation-based biomarkers for CRC have received FDA approval: SEPT9, used in blood-based screening tests, and a combination of NDRG4 and BMP3 for stool-based tests. Although DNA methylation biomarkers have been explored in colorectal cancer (CRC), the identification of robust and clinically valuable biomarkers remains a challenge, particularly for early-stage detection and precancerous lesions. Patients often receive diagnoses at the locally advanced stage, which limits the potential utility of current biomarkers in clinical settings. Methods: The datasets used in this study were retrieved from the GEO database, specifically GSE75548 and GSE75546 for rectal cancer and GSE50760 and GSE101764 for colon cancer, summing up to a total of 130 paired samples. These datasets represent expression profiling by array, methylation profiling by genome tiling array, and expression profiling by high-throughput sequencing and include rectal and colon cancer samples paired with adjacent normal tissue samples. Differential analysis was used to identify differentially methylated CPG sites (DMCs) and identify differentially expressed genes (DEGs). Results: From the integration of DMCs with DEGs in colorectal cancer, we identified 150 candidates for methylation-regulated genes (MRGs) with two genes common across all cohorts (GNG7 and PDX1) highlighted as candidate biomarkers in CRC. The functional enrichment analysis and protein–protein interactions (PPIs) identified relevant pathways involved in CRC, including the Wnt signaling pathway, extracellular matrix (ECM) organization, among other enriched pathways. Conclusions: Our findings show the strength of our in silco computational approach in jointly identifying methylation-regulated biomarkers for colon cancer and highlight several genes and pathways as biomarker candidates for further investigations. 

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