Genomic risk prediction of aromatase inhibitor‐related arthralgia in patients with breast cancer using a novel machine‐learning algorithm

Reinbolt, Raquel E.; Sonis, Stephen T.; Timmers, Cynthia; Fernández-Martínez, Juan Luis; Cernea, Ana; Andrés-Galiana, Enrique J. de; Hashemi, Sepehr; Miller, Kurt W.; Pilarski, Robert; Lustberg, Maryam B.

doi:10.1002/cam4.1256

Cited by 26 publications

(14 citation statements)

References 62 publications

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“…and those with clinically significant aromatase inhibitor-related arthralgia (AIA) resulting in aromatase inhibitor termination or therapy switch (n = 123) was performed. 14) This SNP group predicted AIA occurrence with a maximum accuracy of 75.93%. In these studies, drug-induced side effects have been predicted by machine learning using information including gene polymorphisms with an accuracy rate in the range of 70% to 80%.…”

Section: Discussionmentioning

confidence: 91%

A Genome-Wide Association Study Predicts the Onset of Dysgeusia Due to Anti-cancer Drug Treatment

Takei

Okada

Nakamura

et al. 2022

Biological & Pharmaceutical Bulletin

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Dysgeusia is a major side effect of anti-cancer drug treatment. Since dysgeusia significantly lowers the patient's quality of life, predicting and avoiding its onset in advance is desirable. Accordingly, aims of the present study were to use a genome-wide association study (GWAS) to identify genes associated with the development of dysgeusia in patients taking anti-cancer drugs and to predict the development of dysgeusia using associated SNPs. GWAS was conducted on 76 patients admitted to the Department of Hematology, Tokushima University Hospital. Using Sanger sequencing for 23 separately collected validation samples, the top two single nucleotide polymorphisms (SNPs) associated with the development of dysgeusia were determined.GWAS identified rs73049478 and rs41396146 SNPs on the RARB gene associated with dysgeusia development due to the administration of anti-cancer drugs. Evaluation of the two SNPs using 23 validation samples indicated that the accuracy rate of rs73049478 was relatively high (87.0%). Thus, the findings of the present study suggest that the rs73049478 SNP of RARB can be used to predict the onset of dysgeusia caused by the administration of anti-cancer drugs.

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Section: Discussionmentioning

confidence: 91%

A Genome-Wide Association Study Predicts the Onset of Dysgeusia Due to Anti-cancer Drug Treatment

Takei

Okada

Nakamura

et al. 2022

Biological & Pharmaceutical Bulletin

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“…Future studies can enhance the performance of ML algorithms through incorporation of additional clinical data, such as lifestyle, medications, breast images, exact histology of benign breast diseases and co-morbidities. 36,37,53 Future studies can also include resource rearrangement involving health policymakers and other stakeholders, in terms of cost-effectiveness and adaptability in different clinical settings. A prospective clinical trial would provide more functional and extended evaluation of the performance of ML algorithms, and findings can be compared with ongoing personalised breast cancer screening trials like 'My PeBS' and 'WISDOM'.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

“…32,33 A series of ML techniques, including our own work, have been developed and used in breast cancer prediction and prognosis, demonstrating that the application of ML methods could improve the prediction accuracy of cancer susceptibility, recurrence and survival models. [34][35][36][37][38][39] Previous studies presented the discriminatory accuracy, sensitivity, specificity and calibration performance of different ML algorithms. However, clinical utility, in terms of potential clinical consequences of using new ML prediction models, is rarely examined.…”

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confidence: 99%

Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations

et al. 2020

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BACKGROUND: The clinical utility of machine-learning (ML) algorithms for breast cancer risk prediction and screening practices is unknown. We compared classification of lifetime breast cancer risk based on ML and the BOADICEA model. We explored the differences in risk classification and their clinical impact on screening practices. METHODS: We used three different ML algorithms and the BOADICEA model to estimate lifetime breast cancer risk in a sample of 112,587 individuals from 2481 families from the Oncogenetic Unit, Geneva University Hospitals. Performance of algorithms was evaluated using the area under the receiver operating characteristic (AU-ROC) curve. Risk reclassification was compared for 36,146 breast cancer-free women of ages 20-80. The impact on recommendations for mammography surveillance was based on the Swiss Surveillance Protocol. RESULTS: The predictive accuracy of ML-based algorithms (0.843 ≤ AU-ROC ≤ 0.889) was superior to BOADICEA (AU-ROC = 0.639) and reclassified 35.3% of women in different risk categories. The largest reclassification (20.8%) was observed in women characterised as 'near population' risk by BOADICEA. Reclassification had the largest impact on screening practices of women younger than 50. CONCLUSION: ML-based reclassification of lifetime breast cancer risk occurred in approximately one in three women. Reclassification is important for younger women because it impacts clinical decision-making for the initiation of screening.

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“…A simplified version of this algorithm has been previously used to assess the genomic risk of aromatase inhibitor-related arthralgia in patients with breast cancer using SNPs [17], to perform the integration of genomic data in CLL patients [18,19], and to predict post-radiotherapy fatigue development in cancer patients [20]. The rationale of the HS algorithm is completely different from FRS; however, the purpose is the same: exploring the uncertainty space intrinsic to phenotype prediction problems.…”

Section: Sampling Algorithms Fisher's Ratio Sampler (Frs)mentioning

confidence: 99%

Robust pathway sampling in phenotype prediction. Application to triple negative breast cancer

et al. 2020

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Background: Phenotype prediction problems are usually considered ill-posed, as the amount of samples is very limited with respect to the scrutinized genetic probes. This fact complicates the sampling of the defective genetic pathways due to the high number of possible discriminatory genetic networks involved. In this research, we outline three novel sampling algorithms utilized to identify, classify and characterize the defective pathways in phenotype prediction problems, such as the Fisher's ratio sampler, the Holdout sampler and the Random sampler, and apply each one to the analysis of genetic pathways involved in tumor behavior and outcomes of triple negative breast cancers (TNBC). Altered biological pathways are identified using the most frequently sampled genes and are compared to those obtained via Bayesian Networks (BNs). Results: Random, Fisher's ratio and Holdout samplers were more accurate and robust than BNs, while providing comparable insights about disease genomics. Conclusions: The three samplers tested are good alternatives to Bayesian Networks since they are less computationally demanding algorithms. Importantly, this analysis confirms the concept of "biological invariance" since the altered pathways should be independent of the sampling methodology and the classifier used for their inference. Nevertheless, still some modifications are needed in the Bayesian networks to be able to sample correctly the uncertainty space in phenotype prediction problems, since the probabilistic parameterization of the uncertainty space is not unique and the use of the optimum network might falsify the pathways analysis. L Ã ðgÞ: g ∈ R s →C ¼ f1; 2g ð 1Þ The simplest case is to divide the phenotype in healthy controls and disease samples, but others problems

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Genomic risk prediction of aromatase inhibitor‐related arthralgia in patients with breast cancer using a novel machine‐learning algorithm

Cited by 26 publications

References 62 publications

A Genome-Wide Association Study Predicts the Onset of Dysgeusia Due to Anti-cancer Drug Treatment

A Genome-Wide Association Study Predicts the Onset of Dysgeusia Due to Anti-cancer Drug Treatment

Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations

Robust pathway sampling in phenotype prediction. Application to triple negative breast cancer

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