A gene expression signature for high-risk multiple myeloma

AbstractMany cancer drugs only benefit a subset of the patients that receive them, but are often associated with serious side effects. Predictive classification methods that can identify which patients will benefit from a specific treatment are therefore of great clinical utility. We here introduce a novel machine learning method to identify predictive gene expression signatures, based on the idea that patients who received different treatments but exhibit similar expression profiles can be used to model response to the alternative treatment. We use this method to predict proteasome inhibitor benefit in Multiple Myeloma (MM). In a dataset of 910 MM patients we identify a 14-gene expression signature that can successfully predict benefit to the proteasome inhibitor bortezomib, with a hazard ratio of 0.47 (p = 0.04) in class ‘benefit’, while in class ‘no benefit’ the hazard ratio is 0.91 (p = 0.68). Importantly, we observe a similar classification performance (HR class benefit = 0.46, p = 0.04) in an independent patient cohort which was moreover measured on a different platform, demonstrating the robustness of the signature. Moreover, we find that the genes in the discovered signature are essential, as no equivalent signature can be found when they are excluded from the analysis. Multiple genes in the signature are linked to working mechanisms of proteasome inhibitors or MM disease progression. In conclusion, our method allows for identification of gene expression signatures that can aid in treatment decisions for MM patients and provide insight into the biological mechanism behind treatment benefit.

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Identification of a Gene Signature Associated to Treatment Response to Palbociclib in Multiple Myeloma

Blood ◽

10.1182/blood.v128.22.5666.5666 ◽

2016 ◽

Vol 128 (22) ◽

pp. 5666-5666

Author(s):

Angelique Bruyer ◽

Alboukadel Kassambara ◽

Paul Anziani ◽

Donia El Bahlagui ◽

Nicolas Robert ◽

...

Keyword(s):

Gene Expression ◽

Cell Cycle ◽

Multiple Myeloma ◽

Rna Sequencing ◽

Cell Lines ◽

Molecular Mechanisms ◽

Conflicts Of Interest ◽

Gene Expression Signature ◽

G1 Arrest ◽

Expression Signature

Abstract Background: Inpatients with relaspsed/refractoryMultiple Myeloma (MM), outcomes are far from optimal, especially in patients refractory to current treatments Recent studies and clinical trials have highlighted the therapeutic potential of Palbociclib, a CDK4/6 inhibitor, in various cancers including MM. Deregulation of CDK4/6 is involved in the loss of cell cycle control in MM. Response to Palbociclib combined with bortezomib and dexamethasone was acquired in 20% of the relapsed/refractory MM patients, suggesting that biomarkers to identify patients that could benefit from this treatment are needed. Additional studies are required to understand the biological pathways associated with sensitivity or resistance of MM cells to Palbociclib. Methods: 14 human MM cell lines and 12 primary MM samples were tested for response to Palbociclib treatment. The concentration required to inhibit growth by 50% (IC50) was calculated. Gene expression signature associated with multiple myeloma response to Palbociclib, as well as, genes deregulated by the treatment have been analyzed using microarray and RNA-sequencing methods. Results: Palbociclib had an heterogeneous in vitro activity among the 14 human myeloma cell lines tested, which aggregated into three groups based on the distribution of the IC50 values: sensitive (n = 5, IC50: 0.2 - 0.3µM), intermediate (n = 3, IC50: 0.5 - 0.7µM) or more resistant group (n = 6, IC50: 0.9 - 2.4µM). The same holds true when testing the Palbociclib on primary multiple myeloma samples. The evaluation of the Palbociclib effect on cell cycle progression and the induction of the apoptosis, reveals that Palbociclib is essentially cytostatic, inducing prolonged G1 arrest in sensitive cell lines with a strong reduction of the percentage of cells in S phase. To better understand the molecular mechanisms associated with Palbociclib response, we identified a gene expression signature correlated with the response in both MM cell lines and primary myeloma cells from patients. Additionally, we have analyzed differentially expressed genes after Palbociclib treatment in human MM cell lines using RNA sequencing (n = 4). The physiological role of the downregulated genes after Palbociclib treatment is associated with cell cycle, mitosis and E2F mediated regulation of DNA replication. Significantly upregulated genes, after Palbociclib treatment, were enriched in genes encoding proteins involved in glutathione synthesis and recycling, and biological oxidations. Conclusion: Altogether, our data demonstrated a high heterogeneity in the response of MM cells to Palbociclib. We identified a gene expression signature associated with Palbociclib response in MM. These genes could help to identify MM patients that could benefit from Palbociclib treatment and provide novel targets for efficient combination therapy. Disclosures No relevant conflicts of interest to declare.

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