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A simulation could help personalize therapy for multiple myeloma (MM), according to researchers.
With the help of gene expression signatures, a simulated treatment learning model identified which MM patients would have the greatest progression-free survival (PFS) benefit from treatment with bortezomib or lenalidomide.
Joske Ubels, of University Medical Center Utrecht in the Netherlands, and her colleagues described this research in Nature Communications.
“The key idea of simulated treatment learning is that a patient’s treatment benefit can be estimated by comparing [his or her] survival to a set of genetically similar patients [who] received the comparator treatment,” the researchers noted.
For this study, the team applied a simulated treatment learning model called GESTURE to two MM gene expression datasets. One set included patients who received bortezomib (and other therapies). The other included patients who received lenalidomide (and other therapies).
For the bortezomib dataset, the researchers combined data from three randomized, phase 3 trials of 910 MM patients (total therapy 2, total therapy 3, and HOVON-65/GMMG-HD4).
The model suggested that, in 19.8% of these patients, bortezomib would produce a two-fold greater PFS benefit than in the patient population as a whole.
For the lenalidomide dataset, the researchers obtained data on 662 MM patients in the CoMMpass trial.
The model suggested that, in 31.1% of these patients, lenalidomide would produce a three-fold greater PFS benefit than that observed in the patient population as a whole.
Based on these results, the researchers concluded that GESTURE “can derive clinically actionable gene expression signatures that enable a more personalized approach to treatment.”
The method requires a large dataset but could be useful for trials that have missed their primary endpoint, according to the researchers.
The team’s next step is to see if GESTURE makes useful treatment predictions for other cancers. The code needed to train and validate the model is available at github.com/jubels/GESTURE.
The Van Herk Fellowship provided support for this research. The lenalidomide dataset was created as part of the Multiple Myeloma Research Foundation Personalized Medicine Initiative.
Dr. Ubels and one co-investigator are employees of SkylineDx. Another co-investigator served on the company’s advisory board. All other study authors reported having no relevant conflicts of interest.
A simulation could help personalize therapy for multiple myeloma (MM), according to researchers.
With the help of gene expression signatures, a simulated treatment learning model identified which MM patients would have the greatest progression-free survival (PFS) benefit from treatment with bortezomib or lenalidomide.
Joske Ubels, of University Medical Center Utrecht in the Netherlands, and her colleagues described this research in Nature Communications.
“The key idea of simulated treatment learning is that a patient’s treatment benefit can be estimated by comparing [his or her] survival to a set of genetically similar patients [who] received the comparator treatment,” the researchers noted.
For this study, the team applied a simulated treatment learning model called GESTURE to two MM gene expression datasets. One set included patients who received bortezomib (and other therapies). The other included patients who received lenalidomide (and other therapies).
For the bortezomib dataset, the researchers combined data from three randomized, phase 3 trials of 910 MM patients (total therapy 2, total therapy 3, and HOVON-65/GMMG-HD4).
The model suggested that, in 19.8% of these patients, bortezomib would produce a two-fold greater PFS benefit than in the patient population as a whole.
For the lenalidomide dataset, the researchers obtained data on 662 MM patients in the CoMMpass trial.
The model suggested that, in 31.1% of these patients, lenalidomide would produce a three-fold greater PFS benefit than that observed in the patient population as a whole.
Based on these results, the researchers concluded that GESTURE “can derive clinically actionable gene expression signatures that enable a more personalized approach to treatment.”
The method requires a large dataset but could be useful for trials that have missed their primary endpoint, according to the researchers.
The team’s next step is to see if GESTURE makes useful treatment predictions for other cancers. The code needed to train and validate the model is available at github.com/jubels/GESTURE.
The Van Herk Fellowship provided support for this research. The lenalidomide dataset was created as part of the Multiple Myeloma Research Foundation Personalized Medicine Initiative.
Dr. Ubels and one co-investigator are employees of SkylineDx. Another co-investigator served on the company’s advisory board. All other study authors reported having no relevant conflicts of interest.
A simulation could help personalize therapy for multiple myeloma (MM), according to researchers.
With the help of gene expression signatures, a simulated treatment learning model identified which MM patients would have the greatest progression-free survival (PFS) benefit from treatment with bortezomib or lenalidomide.
Joske Ubels, of University Medical Center Utrecht in the Netherlands, and her colleagues described this research in Nature Communications.
“The key idea of simulated treatment learning is that a patient’s treatment benefit can be estimated by comparing [his or her] survival to a set of genetically similar patients [who] received the comparator treatment,” the researchers noted.
For this study, the team applied a simulated treatment learning model called GESTURE to two MM gene expression datasets. One set included patients who received bortezomib (and other therapies). The other included patients who received lenalidomide (and other therapies).
For the bortezomib dataset, the researchers combined data from three randomized, phase 3 trials of 910 MM patients (total therapy 2, total therapy 3, and HOVON-65/GMMG-HD4).
The model suggested that, in 19.8% of these patients, bortezomib would produce a two-fold greater PFS benefit than in the patient population as a whole.
For the lenalidomide dataset, the researchers obtained data on 662 MM patients in the CoMMpass trial.
The model suggested that, in 31.1% of these patients, lenalidomide would produce a three-fold greater PFS benefit than that observed in the patient population as a whole.
Based on these results, the researchers concluded that GESTURE “can derive clinically actionable gene expression signatures that enable a more personalized approach to treatment.”
The method requires a large dataset but could be useful for trials that have missed their primary endpoint, according to the researchers.
The team’s next step is to see if GESTURE makes useful treatment predictions for other cancers. The code needed to train and validate the model is available at github.com/jubels/GESTURE.
The Van Herk Fellowship provided support for this research. The lenalidomide dataset was created as part of the Multiple Myeloma Research Foundation Personalized Medicine Initiative.
Dr. Ubels and one co-investigator are employees of SkylineDx. Another co-investigator served on the company’s advisory board. All other study authors reported having no relevant conflicts of interest.