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A comprehensive RNA and DNA sequencing platform can guide treatment decisions for late-stage and drug-resistant multiple myeloma (MM), according to a study published in JCO Precision Oncology.
Researchers used the platform to generate treatment suggestions for 64 MM patients who had exhausted all approved treatment options.
Of the 21 evaluable patients who received the sequencing-recommended therapies, 67% achieved a response. Five patients had ongoing responses at the end of the trial.
“Our study shows how a precision medicine approach incorporating RNA sequencing may identify viable and effective therapeutic options beyond the current FDA-approved armamentarium for multiple myeloma patients,” said study author Samir Parekh, MD, of the Icahn School of Medicine at Mount Sinai in New York, New York.
“The trial has allowed us to test the accuracy of our platform, laying the foundation for our next-generation precision medicine framework.”
Dr Parekh and his colleagues used DNA and RNA sequencing data to generate personalized treatment recommendations for 64 heavily pretreated MM patients.
The patients had received a median of 7 lines of therapy. Most patients (61%) were male, their median age was 59 (range, 40-85), and 67% had high-risk cytogenetics.
The sequencing data yielded treatment recommendations for 63 patients. Twenty-six patients (42%) actually received at least 1 of the recommended treatments.
The treatments (given alone or in combination) were:
- Trametinib (n=16)—recommended because of mutations in NRAS or KRAS
- Venetoclax (n=8)—recommended because of high BCL2 expression
- Panobinostat (n=6)—recommended due to activation of the HDAC pathway and/or by RNA-based drug repurposing selecting the pan-HDAC inhibitor vorinostat
- Dabrafenib (n=1)—recommended because of concurrent BRAF and RAS mutations
- Etoposide (n=2)—selected by RNA-based drug repurposing.
Twenty-one patients were evaluable for response. The researchers noted that 11 of these patients received treatment based on RNA findings, 8 based on DNA, and 2 based on both.
One patient achieved a complete response, 3 had a very good partial response, 10 had a partial response, 2 had a minimal response (25% reduction of disease marker), 3 had stable disease, and 2 progressed.
That means the overall response rate was 66.6% (14/21), and the clinical benefit rate (minimal response or better) was 76.2% (16/21).
The median duration of response was 131 days (range, 37-372), and 5 patients were still in response at the end of the study (September, 1, 2017).
Mount Sinai researchers have received funding to develop a clinical trial that will incorporate machine learning algorithms into this precision medicine platform, which will implement interactive learning techniques to refine the predictions based on a patient’s success with the therapies and a physician’s opinion of the treatment plan.
“This research is part of an accelerating paradigm shift in cancer therapy where treatment may be given based on the specific genomic alterations observed in a patient’s tumor rather than on the tumor histology or tissue type,” said study author Joel Dudley, PhD, of the Icahn School of Medicine at Mount Sinai.
“RNA sequencing will likely complement current precision medicine strategies in the near future due to its ability to capture more dynamic aspects of unique tumor biology and provide information beyond what is capable with DNA alone.”
A comprehensive RNA and DNA sequencing platform can guide treatment decisions for late-stage and drug-resistant multiple myeloma (MM), according to a study published in JCO Precision Oncology.
Researchers used the platform to generate treatment suggestions for 64 MM patients who had exhausted all approved treatment options.
Of the 21 evaluable patients who received the sequencing-recommended therapies, 67% achieved a response. Five patients had ongoing responses at the end of the trial.
“Our study shows how a precision medicine approach incorporating RNA sequencing may identify viable and effective therapeutic options beyond the current FDA-approved armamentarium for multiple myeloma patients,” said study author Samir Parekh, MD, of the Icahn School of Medicine at Mount Sinai in New York, New York.
“The trial has allowed us to test the accuracy of our platform, laying the foundation for our next-generation precision medicine framework.”
Dr Parekh and his colleagues used DNA and RNA sequencing data to generate personalized treatment recommendations for 64 heavily pretreated MM patients.
The patients had received a median of 7 lines of therapy. Most patients (61%) were male, their median age was 59 (range, 40-85), and 67% had high-risk cytogenetics.
The sequencing data yielded treatment recommendations for 63 patients. Twenty-six patients (42%) actually received at least 1 of the recommended treatments.
The treatments (given alone or in combination) were:
- Trametinib (n=16)—recommended because of mutations in NRAS or KRAS
- Venetoclax (n=8)—recommended because of high BCL2 expression
- Panobinostat (n=6)—recommended due to activation of the HDAC pathway and/or by RNA-based drug repurposing selecting the pan-HDAC inhibitor vorinostat
- Dabrafenib (n=1)—recommended because of concurrent BRAF and RAS mutations
- Etoposide (n=2)—selected by RNA-based drug repurposing.
Twenty-one patients were evaluable for response. The researchers noted that 11 of these patients received treatment based on RNA findings, 8 based on DNA, and 2 based on both.
One patient achieved a complete response, 3 had a very good partial response, 10 had a partial response, 2 had a minimal response (25% reduction of disease marker), 3 had stable disease, and 2 progressed.
That means the overall response rate was 66.6% (14/21), and the clinical benefit rate (minimal response or better) was 76.2% (16/21).
The median duration of response was 131 days (range, 37-372), and 5 patients were still in response at the end of the study (September, 1, 2017).
Mount Sinai researchers have received funding to develop a clinical trial that will incorporate machine learning algorithms into this precision medicine platform, which will implement interactive learning techniques to refine the predictions based on a patient’s success with the therapies and a physician’s opinion of the treatment plan.
“This research is part of an accelerating paradigm shift in cancer therapy where treatment may be given based on the specific genomic alterations observed in a patient’s tumor rather than on the tumor histology or tissue type,” said study author Joel Dudley, PhD, of the Icahn School of Medicine at Mount Sinai.
“RNA sequencing will likely complement current precision medicine strategies in the near future due to its ability to capture more dynamic aspects of unique tumor biology and provide information beyond what is capable with DNA alone.”
A comprehensive RNA and DNA sequencing platform can guide treatment decisions for late-stage and drug-resistant multiple myeloma (MM), according to a study published in JCO Precision Oncology.
Researchers used the platform to generate treatment suggestions for 64 MM patients who had exhausted all approved treatment options.
Of the 21 evaluable patients who received the sequencing-recommended therapies, 67% achieved a response. Five patients had ongoing responses at the end of the trial.
“Our study shows how a precision medicine approach incorporating RNA sequencing may identify viable and effective therapeutic options beyond the current FDA-approved armamentarium for multiple myeloma patients,” said study author Samir Parekh, MD, of the Icahn School of Medicine at Mount Sinai in New York, New York.
“The trial has allowed us to test the accuracy of our platform, laying the foundation for our next-generation precision medicine framework.”
Dr Parekh and his colleagues used DNA and RNA sequencing data to generate personalized treatment recommendations for 64 heavily pretreated MM patients.
The patients had received a median of 7 lines of therapy. Most patients (61%) were male, their median age was 59 (range, 40-85), and 67% had high-risk cytogenetics.
The sequencing data yielded treatment recommendations for 63 patients. Twenty-six patients (42%) actually received at least 1 of the recommended treatments.
The treatments (given alone or in combination) were:
- Trametinib (n=16)—recommended because of mutations in NRAS or KRAS
- Venetoclax (n=8)—recommended because of high BCL2 expression
- Panobinostat (n=6)—recommended due to activation of the HDAC pathway and/or by RNA-based drug repurposing selecting the pan-HDAC inhibitor vorinostat
- Dabrafenib (n=1)—recommended because of concurrent BRAF and RAS mutations
- Etoposide (n=2)—selected by RNA-based drug repurposing.
Twenty-one patients were evaluable for response. The researchers noted that 11 of these patients received treatment based on RNA findings, 8 based on DNA, and 2 based on both.
One patient achieved a complete response, 3 had a very good partial response, 10 had a partial response, 2 had a minimal response (25% reduction of disease marker), 3 had stable disease, and 2 progressed.
That means the overall response rate was 66.6% (14/21), and the clinical benefit rate (minimal response or better) was 76.2% (16/21).
The median duration of response was 131 days (range, 37-372), and 5 patients were still in response at the end of the study (September, 1, 2017).
Mount Sinai researchers have received funding to develop a clinical trial that will incorporate machine learning algorithms into this precision medicine platform, which will implement interactive learning techniques to refine the predictions based on a patient’s success with the therapies and a physician’s opinion of the treatment plan.
“This research is part of an accelerating paradigm shift in cancer therapy where treatment may be given based on the specific genomic alterations observed in a patient’s tumor rather than on the tumor histology or tissue type,” said study author Joel Dudley, PhD, of the Icahn School of Medicine at Mount Sinai.
“RNA sequencing will likely complement current precision medicine strategies in the near future due to its ability to capture more dynamic aspects of unique tumor biology and provide information beyond what is capable with DNA alone.”