ReviewHuman genomics2015
Cancer classification in the genomic era: five contemporary problems.
Review in Human genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 78 citations in OpenAlex.
- The impact of AI on modern oncology from early detection to personalized cancer treatment.NPJ precision oncology · 2026Review
- Special Issue "Immunomodulatory Molecules in Cancer".International journal of molecular sciences · 2026Article
- Exploring Natural Coumarins: Breakthroughs in Anticancer Therapeutics.Anti-cancer agents in medicinal chemistry · 2026Review
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- Unleashing the potential of ferroptosis, autophagy, and mitochondrial dynamics as emerging modalities in cancer treatment.World journal of clinical oncology · 2025Review
- MOGAN for LUAD Subtype Classification by Integrating Three Omics Data Types.Cancer innovation · 2025Article
- Multiple machine learning-based integrations of multi-omics data to identify molecular subtypes and construct a prognostic model for HNSCC.Hereditas · 2025Article
- Effects of Exercise on Cancer-Related Fatigue in Breast Cancer Patients: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.Life (Basel, Switzerland) · 2024Review
- Identification and Quantification of Extracellular Vesicles: Comparison of SDS-PAGE Analysis and Biosensor Analysis with QCM and IDT Chips.Biosensors · 2024Article
- Signal Amplification-Based Biosensors and Application in RNA Tumor Markers.Sensors (Basel, Switzerland) · 2023Review
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- Pharmacological properties of ginsenosides in inflammation-derived cancers.Molecular and cellular biochemistry · 2021Review
- Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics.Frontiers in oncology · 2021Review
- The Application of Prussian Blue Nanoparticles in Tumor Diagnosis and Treatment.Sensors (Basel, Switzerland) · 2020Review
- Disparity between Inter-Patient Molecular Heterogeneity and Repertoires of Target Drugs Used for Different Types of Cancer in Clinical Oncology.International journal of molecular sciences · 2020Article
- Article
- Article
- Influence of Pain and Analgesia on Cancer Research Studies.Comparative medicine · 2019Review
- Co-expression in Single-Cell Analysis: Saving Grace or Original Sin?Trends in genetics : TIG · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Classification is an everyday instinct as well as a full-fledged scientific discipline. Throughout the history of medicine, disease classification is central to how we develop knowledge, make diagnosis, and assign treatment. Here, we discuss the classification of cancer and the process of categorizing cancer subtypes based on their observed clinical and biological features. Traditionally, cancer nomenclature is primarily based on organ location, e.g., "lung cancer" designates a tumor originating in lung structures. Within each organ-specific major type, finer subgroups can be defined based on patient age, cell type, histological grades, and sometimes molecular markers, e.g., hormonal receptor status in breast cancer or microsatellite instability in colorectal cancer. In the past 15+ years, high-throughput technologies have generated rich new data regarding somatic variations in DNA, RNA, protein, or epigenomic features for many cancers. These data, collected for increasingly large tumor cohorts, have provided not only new insights into the biological diversity of human cancers but also exciting opportunities to discover previously unrecognized cancer subtypes. Meanwhile, the unprecedented volume and complexity of these data pose significant challenges for biostatisticians, cancer biologists, and clinicians alike. Here, we review five related issues that represent contemporary problems in cancer taxonomy and interpretation. (1) How many cancer subtypes are there? (2) How can we evaluate the robustness of a new classification system? (3) How are classification systems affected by intratumor heterogeneity and tumor evolution? (4) How should we interpret cancer subtypes? (5) Can multiple classification systems co-exist? While related issues have existed for a long time, we will focus on those aspects that have been magnified by the recent influx of complex multi-omics data. Exploration of these problems is essential for data-driven refinement of cancer classification and the successful application of these concepts in precision medicine.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.