ArticleJournal of translational medicine2022
A molecular approach integrating genomic and DNA methylation profiling for tissue of origin identification in lung-specific cancer of unknown primary.
Article in Journal of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- New techniques to identify the tissue of origin for cancer of unknown primary in the era of precision medicine: progress and challenges.Briefings in bioinformatics · 2024Pooled it
- Cancer of unknown primary: the evolution of tissue of origin identification in the artificial intelligence era.Biomarker research · 2026Review
- An AI Approach to Differentiating Lung Squamous Cell Carcinoma From Metastases of Other Origins.JAMA network open · 2026Article
- Rethinking cancer of unknown primary: from diagnostic challenge to targeted treatment.Nature reviews. Clinical oncology · 2025Review
- A platform-independent AI tumor lineage and site (ATLAS) classifier.Communications biology · 2024Article
- Biomarkers for Early Cancer Detection: A Landscape View of Recent Advancements, Spotlighting Pancreatic and Liver Cancers.ACS pharmacology & translational science · 2024Review
- Accurate prediction of pan-cancer types using machine learning with minimal number of DNA methylation sites.Journal of molecular cell biology · 2023Article
- The diagnosis and treatment for a patient with cancer of unknown primary: A case report.Frontiers in genetics · 2023Article
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDetermining the tissue of origin (TOO) is essential for managing cancer of unknown primary (CUP). In this study, we evaluated the concordance between genome profiling and DNA methylation analysis in determining TOO for lung-specific CUP and assessed their performance by comparing the clinical responses and survival outcomes of patients predicted with multiple primary or with metastatic cancer.
methodsWe started by retrospectively screening for CUP patients who presented with both intra- and extrathoracic tumors. Tumor samples from included patients were analyzed with targeted sequencing with a 520-gene panel and targeted bisulfite sequencing. TOO inferences were made in parallel via an algorithm using genome profiles and time interval between tumors and via machine learning-based classification of DNA methylation profiles.
resultsFour hundred patients were screened retrospectively. Excluding patients definitively diagnosed with conventional diagnostic work-up or without available samples, 16 CUP patients were included. Both molecular approaches alone enabled inference of clonality for all analyzed patients. Genome profile enabled TOO inference for 43.8% (7/16) patients, and the percentage rose to 68.8% (11/16) after considering inter-tumor time lag. On the other hand, DNA methylation analysis was conclusive for TOO prediction for 100% (14/14) patients with available samples. The two approaches gave 100% (9/9) concordant inferences regarding clonality and TOO identity. Moreover, patients predicted with metastatic disease showed significantly shorter overall survival than those with multiple primary tumors.
conclusionsGenome and DNA methylation profiling have shown promise as individual analysis for TOO identification. This study demonstrated the feasibility of incorporating the two methods and proposes an integrative scheme to facilitate diagnosing and treating lung-specific CUPs.
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