Accession PRJCA009519
Title Integration between MCL1 gene co-expression module and the RISS enables precise prognostication and prediction of response to proteasome inhibitor-based therapy in individual multiple myeloma
Relevance Medical
Data types Transcriptome or Gene expression
Organisms Homo sapiens
Description We recently identified a gene module of 87 genes co-expressed with MCL1 (MCL1-M), a critical regulator of plasma cell survival. MCL1-M captures both MM cell-intrinsically acting signals and the signals regulating the interaction between MM cells with bone marrow microenvironment. MM can be clustered into MCL1-M high and MCL1-M low subtypes. While the MCL1-M high MMs are enriched in a preplasmablast signature, the MCL1-M low MMs are enriched in B cell-specific genes. In multiple independent datasets, MCL1-M high MMs exhibited poorer prognosis compared to MCL1-M low MMs. Re-analysis of the phase III HOVON-65/GMMG-HD4 showed that only MCL1-M MMs, but not MCL1-M low MMs, benefited from bortezomib-based treatment. To translate the MCL1-M clustering scheme into a platform for individual diagnosis, we refined the classifier genes and developed a support vector machine-based algorithm.
Sample scope Multiisolate
Release date 2024-03-26
Publication
PubMed ID Article title Journal name DOI Year
Distinct Pathway Activities are Associated with Prognosis and Response to Bortezomib- containing Treatment in MCL1-M Based Molecular Subtypes of Multiple Myeloma Research Square 10.21203/rs.3.rs-3995303/v1 2024
Grants
Agency program Grant ID Grant title
Beijing Municipal Administration of Hospitals ZYLX201606 Multiple Myeloma
Submitter Yin Wu (wudxuan@126.com)
Organization Beijing Chaoyang Hospital, Capital Medical University
Submission date 2022-05-10

Project Data

Resource name Description