MCL1 gene co-expression module stratifies multiple myeloma and predicts response to proteasome inhibitor-based therapy

Multiple myeloma (MM) is the second most common hematologic cancer, characterized by abnormal accumulation of plasma cells in the bone marrow. The extensive biological and clinical heterogeneity of MM hinders effective treatment and etiology research. Several molecular classification systems of prognostic impact have been proposed, but they do not predict the response to treatment nor do they correlate to plasma cell development pathways. Here we describe the classification of MM into two distinct subtypes based on the expression levels of a gene module coexpressed with MCL1 (MCL1-M), a regulator of plasma cell survival. The classification system enabled prediction of the prognosis and the response to bortezomib-based therapy. Moreover, the two MM subtypes were associated with two different plasma cell differentiation pathways (enrichment of a preplasmablast signature versus aberrant expression of B cell genes). 1q gain, harboring 63 of the 87 MCL1-M members including MCL1, was found in about 80% of the MM with upregulated MCL1-M expression. Clonal analysis showed that 1q gain tended to occur as an early clonal event. Members of MCL1-M captured both MM cell-intrinsically acting signals and the signals regulating the interaction between MM cells with bone marrow microenvironment. MCL1-M members were co-expressed in mouse germinal center B cells. Together, these findings indicate that MCL1-M may play previously inadequately recognized, initiating role in the pathogenesis of MM. Our findings suggest that MCL1-M signature-based molecular clustering of MM constitutes a solid framework toward understanding the etiology of this disease and establishing personalized care. Article Summary: A pathogenic mechanism-guided molecular classification would facilitate treatment decision and etiology research of multiple myeloma. On the basis of the expression levels of a gene module coexpressed with MCL1, w

A glioma classification scheme based on coexpression modules of EGFR and PDGFRA

We hypothesized that key signaling pathways of glioma genesis might enable the molecular classification of gliomas. Gene coexpression modules around epidermal growth factor receptor (EGFR) (EM, 29 genes) or platelet derived growth factor receptor A (PDGFRA) (PM, 40 genes) in gliomas were identified. Based on EM and PM expression signatures, nonnegative matrix factorization reproducibly clustered 1,369 adult diffuse gliomas WHO grades II-IV from four independent databases generated in three continents, into the subtypes (EM, PM and EMlowPMlow gliomas) in a morphology-independent manner. Besides their distinct patterns of genomic alterations, EM gliomas were associated with higher age at diagnosis, poorer prognosis, and stronger expression of neural stem cell and astrogenesis genes. Both PM and EMlowPMlow gliomas were associated with younger age at diagnosis and better prognosis. PM gliomas were enriched in the expression of oligodendrogenesis genes, whereas EMlowPMlow gliomas were enriched in the signatures of mature neurons and oligodendrocytes. The EM/PM-based molecular classification scheme is applicable to adult low-grade and high-grade diffuse gliomas, and outperforms existing classification schemes in assigning diffuse gliomas to subtypes with distinct transcriptomic and genomic profiles. The majority of the EM/PM classifiers, including regulators of glial fate decisions, have not been extensively studied in glioma biology. Subsets of these classifiers were coexpressed in mouse glial precursor cells, and frequently amplified or lost in an EM/PM glioma subtypespecific manner, resulting in somatic copy number alteration-dependent gene expression that contributes to EM/PM signatures in glioma samples. EM/PM-based molecular classification provides a molecular diagnostic framework to expedite the search for new glioma therapeutic targets