ArticleCancer medicine2018
Genomic risk prediction of aromatase inhibitor-related arthralgia in patients with breast cancer using a novel machine-learning algorithm.
Article in Cancer medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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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
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 30 citations in OpenAlex.
- Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review.Journal of pain and symptom management · 2024Pooled it
- Prediction of mucositis risk secondary to cancer therapy: a systematic review of current evidence and call to action.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2020Pooled it
- Genome-wide association study of aromatase inhibitor discontinuation due to musculoskeletal symptoms.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2022Trial
- The mechanisms and therapeutic advances of interactions between breast cancer and cardiovascular diseases.Frontiers in pharmacology · 2026Review
- Aromatase Inhibitors as Adjuvant Therapy in Early Breast Cancer: Insights into Toxicities and Their Management.Cancers · 2025Review
- OATP1B-type Transport Function Is a Determinant of Aromatase Inhibitor-Associated Arthralgia Susceptibility.Cancer research communications · 2025Article
- Musculoskeletal symptoms associated with aromatase inhibitors in the treatment of early breast cancer: A scoping review of risk factors and outcomes.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025Article
- Unlocking the potential of big data and AI in medicine: insights from biobanking.Frontiers in medicine · 2024Article
- Leveraging GWAS data derived from a large cooperative group trial to assess the risk of taxane-induced peripheral neuropathy (TIPN) in patients being treated for breast cancer: Part 2-functional implications of a SNP cluster associated with TIPN risk in patients being treated for breast cancer.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2023Article
- Identification of a SNP cluster associated with taxane-induced peripheral neuropathy risk in patients being treated for breast cancer using GWAS data derived from a large cooperative group trial.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2023Article
- A Survey on the Role of Artificial Intelligence in Biobanking Studies: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review.International journal of molecular sciences · 2022Review
- Aromatase inhibitor-associated musculoskeletal pain: An overview of pathophysiology and treatment modalities.SAGE open medicine · 2022Review
- Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations.British journal of cancer · 2020Article
- Robust pathway sampling in phenotype prediction. Application to triple negative breast cancer.BMC bioinformatics · 2020Article
- Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models.Breast cancer research : BCR · 2019Article
- Genomic risk prediction of aromatase inhibitor-related arthralgia in patients with breast cancer using a novel machine-learning algorithm.Cancer medicine · 2018Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
Abstract
Many breast cancer (BC) patients treated with aromatase inhibitors (AIs) develop aromatase inhibitor-related arthralgia (AIA). Candidate gene studies to identify AIA risk are limited in scope. We evaluated the potential of a novel analytic algorithm (NAA) to predict AIA using germline single nucleotide polymorphisms (SNP) data obtained before treatment initiation. Systematic chart review of 700 AI-treated patients with stage I-III BC identified asymptomatic patients (n = 39) and those with clinically significant AIA resulting in AI termination or therapy switch (n = 123). Germline DNA was obtained and SNP genotyping performed using the Affymetrix UK BioBank Axiom Array to yield 695,277 SNPs. SNP clusters that most closely defined AIA risk were discovered using an NAA that sequentially combined statistical filtering and a machine-learning algorithm. NCBI PhenGenI and Ensemble databases defined gene attribution of the most discriminating SNPs. Phenotype, pathway, and ontologic analyses assessed functional and mechanistic validity. Demographics were similar in cases and controls. A cluster of 70 SNPs, correlating to 57 genes, was identified. This SNP group predicted AIA occurrence with a maximum accuracy of 75.93%. Strong associations with arthralgia, breast cancer, and estrogen phenotypes were seen in 19/57 genes (33%) and were functionally consistent. Using a NAA, we identified a 70 SNP cluster that predicted AIA risk with fair accuracy. Phenotype, functional, and pathway analysis of attributed genes was consistent with clinical phenotypes. This study is the first to link a specific SNP/gene cluster to AIA risk independent of candidate gene bias.
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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.