ArticleLeukemia2014
Gene expression profile alone is inadequate in predicting complete response in multiple myeloma.
Article in Leukemia, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genome-wide identification of potential biomarkers in multiple myeloma using meta-analysis of mRNA and miRNA expression data.Scientific reports · 2021Pooled it
- Construct prognostic models of multiple myeloma with pathway information incorporated.PLoS computational biology · 2024Article
- RNA-sequencing based first choice of treatment and determination of risk in multiple myeloma.Frontiers in immunology · 2023Article
- An overview of machine learning methods for monotherapy drug response prediction.Briefings in bioinformatics · 2022Article
- Radiomics Models Based on Magnetic Resonance Imaging for Prediction of the Response to Bortezomib-Based Therapy in Patients with Multiple Myeloma.BioMed research international · 2022Article
- Differential Splicing of Skipped Exons Predicts Drug Response in Cancer Cell Lines.Genomics, proteomics & bioinformatics · 2021Article
- Article
- Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles.Frontiers in oncology · 2021Article
- Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments.BMC medical genomics · 2020Article
- AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics.Bioinformatics (Oxford, England) · 2020Article
- Feature selection strategies for drug sensitivity prediction.Scientific reports · 2020Article
- Building generalized linear models with ultrahigh dimensional features: A sequentially conditional approach.Biometrics · 2020Article
- Flexible Data Trimming Improves Performance of Global Machine Learning Methods in Omics-Based Personalized Oncology.International journal of molecular sciences · 2020Article
- Leveraging RSF and PET images for prognosis of multiple myeloma at diagnosis.International journal of computer assisted radiology and surgery · 2020Review
- Prognostic model for multiple myeloma progression integrating gene expression and clinical features.GigaScience · 2019Article
- Decitabine shows potent anti-myeloma activity by depleting monocytic myeloid-derived suppressor cells in the myeloma microenvironment.Journal of cancer research and clinical oncology · 2019Article
- Machine learning and feature selection for drug response prediction in precision oncology applications.Biophysical reviews · 2019Review
- Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice.Frontiers in genetics · 2019Article
- Overcoming multiple myeloma drug resistance in the era of cancer 'omics'.Leukemia & lymphoma · 2018Review
- FLOating-Window Projective Separator (FloWPS): A Data Trimming Tool for Support Vector Machines (SVM) to Improve Robustness of the Classifier.Frontiers in genetics · 2018Article
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Authors and funding
17 authors.
Funding
Abstract
With advent of several treatment options in multiple myeloma (MM), a selection of effective regimen has become an important issue. Use of gene expression profile (GEP) is considered an important tool in predicting outcome; however, it is unclear whether such genomic analysis alone can adequately predict therapeutic response. We evaluated the ability of GEP to predict complete response (CR) in MM. GEP from pretreatment MM cells from 136 uniformly treated MM patients with response data on an IFM, France led study were analyzed. To evaluate variability in predictive power due to microarray platform or treatment types, additional data sets from three different studies (n=511) were analyzed using same methods. We used several machine learning methods to derive a prediction model using training and test subsets of the original four data sets. Among all methods employed for GEP-based CR predictive capability, we got accuracy range of 56-78% in test data sets and no significant difference with regard to GEP platforms, treatment regimens or in newly diagnosed or relapsed patients. Importantly, permuted P-value showed no statistically significant CR predictive information in GEP data. This analysis suggests that GEP-based signature has limited power to predict CR in MM, highlighting the need to develop comprehensive predictive model using integrated genomics approach.
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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.