Trial reportNature communications2018
Predicting treatment benefit in multiple myeloma through simulation of alternative treatment effects.
Trial report in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Schlafen 11 Is Overexpressed in Multiple Myeloma and Undergoes Nucleolar Translocation in Response to Bortezomib.Cancer research communications · 2026Article
- Plasma cell development-based prediction of prognosis and treatment response in individual multiple myeloma.Frontiers of medicine · 2025Article
- Circulating extracellular vesicle isomiR signatures predict therapy response in patients with multiple myeloma.Cell reports. Medicine · 2025Article
- Machine Learning-Based Identification of Target Groups for Thrombectomy in Acute Stroke.Translational stroke research · 2023Article
- Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection.Cancers · 2022Review
- Carfilzomib modulates tumor microenvironment to potentiate immune checkpoint therapy for cancer.EMBO molecular medicine · 2022Article
- Using MMRFBiolinks R-Package for Discovering Prognostic Markers in Multiple Myeloma.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Article
- Treatment Strategies Considering Micro-Environment and Clonal Evolution in Multiple Myeloma.Cancers · 2021Review
- Article
- Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles.Frontiers in oncology · 2021Article
- COVID-19 and myeloma: what are the implications for now and in the future?British journal of haematology · 2020Article
- Article
- NRF2 Is One of the Players Involved in Bone Marrow Mediated Drug Resistance in Multiple Myeloma.International journal of molecular sciences · 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many cancer treatments are associated with serious side effects, while they often only benefit a subset of the patients. Therefore, there is an urgent clinical need for tools that can aid in selecting the right treatment at diagnosis. Here we introduce simulated treatment learning (STL), which enables prediction of a patient's treatment benefit. STL uses the idea that patients who received different treatments, but have similar genetic tumor profiles, can be used to model their response to the alternative treatment. We apply STL to two multiple myeloma gene expression datasets, containing different treatments (bortezomib and lenalidomide). We find that STL can predict treatment benefit for both; a twofold progression free survival (PFS) benefit is observed for bortezomib for 19.8% and a threefold PFS benefit for lenalidomide for 31.1% of the patients. This demonstrates that STL can derive clinically actionable gene expression signatures that enable a more personalized approach to treatment.
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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.