In:
Blood, American Society of Hematology, Vol. 128, No. 22 ( 2016-12-02), p. 198-198
Abstract:
Introduction Although several novel drugs have recently been approved or are in development for multiple myeloma (MM), there are few molecular indicators to guide treatment selection. In addition, the impact of recurrent myeloma alterations on drug response is often unclear. To address these limitations and elucidate genotype to phenotype relationships in myeloma, we comprehensively analyzed 100 MM samples and compared genomic, transcriptomic, and cytogenetic information to ex vivo drug response profiles and clinical outcome of individual MM patients. Our results reveal novel insights on i) drug response and resistance mechanisms, ii) biomarkers for drug response, and iii) potential treatment combinations to overcome drug resistance. Methods Bone marrow aspirates were collected from MM patients (n=100; newly diagnosed n=34; relapsed/refractory n=66) and healthy individuals (n=14). CD138+ plasma cells were enriched from the mononuclear cell fraction by immunomagnetic bead selection. Cells were screened against 142 oncology drugs tested in a 10,000-fold concentration range and 12 different drug combinations Somatic alterations were identified by exome sequencing of DNA from CD138+ cells and skin biopsies from each patient (n=85). RNA sequencing derived read counts from CD138+ cells of MM samples (n=67) were used for differential gene expression. Karyotype was determined by fluorescence in situ hybridization. Results For most drugs tested, no significant difference in response was observed between samples from newly diagnosed and relapsed refractory patients except for signal transduction inhibitors targeting IGF1R-PI3K-mTOR, MAPK and HSP90. A positive correlation was observed between mutational burden and sensitivity to targeted therapies. The median number of somatic alterations was 118 in sensitive compared to 50 in resistant samples. 14% of the samples exhibited a multidrug resistant phenotype and were resistant to proteasome inhibitors, immunomodulatory drugs and glucocorticoids. 30% of the resistant samples were from del(17p) patients. In addition, gene expression analysis revealed elevated expression of cell adhesion and integrin signaling molecules including ITGB3, ITGA2B, VCL, TLN1, MMP8, MMP9, plus ABCC3, which encodes a transporter protein shown to be associated with multidrug resistance. A combination of the protein kinase C inhibitor bryostatin-1 and pan-BCL2 inhibitor navitoclax was highly effective against the resistant samples. 26% of the patient samples harbored mutations in genes involved in DNA damage repair signaling, namely TP53, TP73, ATM and BAX, in a mutually exclusive pattern. In addition, patients with these mutations had a high relapse rate and poor overall survival (HR=7.2,95%CI 3.2-16.08). Interestingly, CD138+ cells from these patients showed activation of IGF1R-PI3K-mTOR signaling and were highly susceptible to inhibitors targeting this signaling axis. These samples were also highly sensitive to HDAC inhibitors. While no strong correlation between RAS pathway mutations (NRAS, KRAS, NF1, BRAF) and MEK inhibitor sensitivity was observed, samples with clonal RAS mutations tended to be more sensitive to MEK inhibitors compared to samples with subclonal mutations. Summary Our results suggest that drug resistance in myeloma may occur either via accumulation of somatic alterations or via cell adhesion mediated cytoprotection. Driver alterations in DNA damage signaling pathways were found to contribute to poor prognosis, but samples with these mutations showed enhanced sensitivity to IGF1R-PI3K-mTOR and HDAC inhibitors. Using genomic and transcriptomic data we identified molecular events that may shape the drug response landscape and found drug combinations that can overcome resistance mechanisms. Our results demonstrate that molecular information and ex vivo drug profiling may be useful to develop tailored treatment strategies and guide treatment decision, especially for relapsed/refractory myeloma patients. Disclosures Silvennoinen: Sanofi: Honoraria, Other: Lecture fee; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Lecture fee; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Porkka:Bristol-Myers Squibb: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Heckman:Pfizer: Research Funding; Celgene: Research Funding.
Type of Medium:
Online Resource
ISSN:
0006-4971
,
1528-0020
DOI:
10.1182/blood.V128.22.198.198
Language:
English
Publisher:
American Society of Hematology
Publication Date:
2016
detail.hit.zdb_id:
1468538-3
detail.hit.zdb_id:
80069-7
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