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Machine learning-assisted classification of responders and non-responders to bortezomib treatment regimens PAD and VCD using new experimental dataset of 58 multiple myeloma RNA sequencing profiles

ABSTRACT: Machine learning-assisted classification of responders and non-responders to bortezomib treatment regimens PAD and VCD using new experimental dataset of 58 multiple myeloma RNA sequencing profiles

PROVIDER: PRJNA668906 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR12817000.fastq.gz	Fastqsanger.gz
	SRR12817001.fastq.gz	Fastqsanger.gz
	SRR12817002.fastq.gz	Fastqsanger.gz
	SRR12817003.fastq.gz	Fastqsanger.gz
	SRR12817004.fastq.gz	Fastqsanger.gz

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Machine learning-assisted classification of responders and non-responders to bortezomib treatment regimens PAD and VCD using new experimental dataset of 58 multiple myeloma RNA sequencing profiles

Project description:Multiple myeloma (MM) is a hematological cancer which arises from abnormal antibody producing white blood plasma cells. MM affected approximately 500 000 people and results in ~100 000 deaths annually, being currently considered treatable but uncurable. There are several MM chemotherapy treatment regimens, among which ten include bortezomib, a proteasome-targeted drug. Patients with MM respond differently on bortezomib, and new prognostic biomarkers are needed to personalize treatments. However, there is a shortage of clinically annotated molecular profiles of MM that could be used to establish novel molecular diagnostics. In this study we report new molecular profiles for 58 MM patients annotated with data on two chemotherapy regimens which include bortezomib, doxorubicin, and dexamethasone (PAD) or bortezomib, cyclophosphamide, and dexamethasone (VCD). MM biosamples were taken prior to first line chemotherapy treatments and subjected to RNA sequencing.

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