ArticleFrontiers in oncology2021
Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 33 citations in OpenAlex.
- Artificial intelligence for risk assessment and outcome prediction in malignant haematology.British journal of haematology · 2026Review
- A quantum machine learning framework for predicting drug sensitivity in multiple myeloma using proteomic data.Scientific reports · 2025Article
- Predicting progression events in multiple myeloma from routine blood work.NPJ digital medicine · 2025Article
- Construction of a treatment response prediction model for multiple myeloma based on multi-omics and machine learning.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Article
- Increased RNA and Protein Degradation Is Required for Counteracting Transcriptional Burden and Proteotoxic Stress in Human Aneuploid Cells.Cancer discovery · 2024Article
- Review
- Bioinformatics in Russia: history and present-day landscape.Briefings in bioinformatics · 2024Review
- Uniformly shaped harmonization combines human transcriptomic data from different platforms while retaining their biological properties and differential gene expression patterns.Frontiers in molecular biosciences · 2023Article
- Computational approaches in rheumatic diseases - Deciphering complex spatio-temporal cell interactions.Computational and structural biotechnology journal · 2023Review
- Expression levels of NONO, a nuclear protein primarily involved in paraspeckles function, are associated with several deregulated molecular pathways and poor clinical outcome in multiple myeloma.Discover oncology · 2022Article
- Experimentally Deduced Criteria for Detection of Clinically Relevant Fusion 3' Oncogenes from FFPE Bulk RNA Sequencing Data.Biomedicines · 2022Article
- Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection.Cancers · 2022Review
- Ablation of VLA4 in multiple myeloma cells redirects tumor spread and prolongs survival.Scientific reports · 2022Article
- Clinically relevant fusion oncogenes: detection and practical implications.Therapeutic advances in medical oncology · 2022Review
- Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer.Frontiers in genetics · 2022Review
- RNA Sequencing Data for FFPE Tumor Blocks Can Be Used for Robust Estimation of Tumor Mutation Burden in Individual Biosamples.Frontiers in oncology · 2021Article
- Activation of the ERK1/2 Molecular Pathways and Its Relation to the Pathogenicity of Human Malignant Tumors.Acta naturaeArticle
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Authors and funding
14 authors at 6 institutions in 2 countries.
Funding
Abstract
Multiple myeloma (MM) affects ~500,000 people and results in ~100,000 deaths annually, being currently considered treatable but incurable. There are several MM chemotherapy treatment regimens, among which eleven include bortezomib, a proteasome-targeted drug. MM patients respond differently to bortezomib, and new prognostic biomarkers are needed to personalize treatments. However, there is a shortage of clinically annotated MM molecular data that could be used to establish novel molecular diagnostics. We report new RNA sequencing profiles for 53 MM patients annotated with responses on two similar chemotherapy regimens: bortezomib, doxorubicin, dexamethasone (PAD), and bortezomib, cyclophosphamide, dexamethasone (VCD), or with responses to their combinations. Fourteen patients received both PAD and VCD; six received only PAD, and 33 received only VCD. We compared profiles for the good and poor responders and found five genes commonly regulated here and in the previous datasets for other bortezomib regimens (all upregulated in the good responders):
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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.