Jackson, Graham

PURPOSE Outcomes for patients with newly diagnosed multiple myeloma (NDMM) are heterogenous, with overall survival (OS) ranging from months to over 10 years. METHODS To decipher and predict the molecular and clinical heterogeneity of NDMM, we assembled a series of 1,933 patients with available clinical, genomic, and therapeutic data. RESULTS Leveraging a comprehensive catalog of genomic drivers, we identified 12 groups, expanding on previous gene expression–based molecular classifications. To build a model predicting individualized risk in NDMM (IRMMa), we integrated clinical, genomic, and treatment variables. To correct for time-dependent variables, including high-dose melphalan followed by autologous stem-cell transplantation (HDM-ASCT), and maintenance therapy, a multi-state model was designed. The IRMMa model accuracy was significantly higher than all comparator prognostic models, with a c-index for OS of 0.726, compared with International Staging System (ISS; 0.61), revised-ISS (0.572), and R2-ISS (0.625). Integral to model accuracy was 20 genomic features, including 1q21 gain/amp, del 1p, TP53 loss, NSD2 translocations, APOBEC mutational signatures, and copy-number signatures (reflecting the complex structural variant chromothripsis). IRMMa accuracy and superiority compared with other prognostic models were validated on 256 patients enrolled in the GMMG-HD6 (ClinicalTrials.gov identifier: NCT02495922 ) clinical trial. Individualized patient risks were significantly affected across the 12 genomic groups by different treatment strategies (ie, treatment variance), which was used to identify patients for whom HDM-ASCT is particularly effective versus patients for whom the impact is limited. CONCLUSION Integrating clinical, demographic, genomic, and therapeutic data, to our knowledge, we have developed the first individualized risk-prediction model enabling personally tailored therapeutic decisions for patients with NDMM.

Abstract Introduction Over the last 15 years gene expression profiling (GEP) has been used to define myeloma molecular subgroups and to determine clinical prognosis. Two major molecular subgroup classifications have been used: the UAMS which determines 7 subgroups and the TC classification based on the presence of IgH translocations and expression of D group cyclins. For prognosis, although a number of different GEP signatures have been defined, the widely used GEP70 identifies 15% of patients with high risk (HR) disease who have a median PFS and OS of 1.75 and 2.83 years. An ideal classification system would identify clinically relevant subgroups with distinct etiology and biology using standardized techniques. We have examined a large group of patients characterized at multiple genetic levels to optimize the diagnostic approach of newly diagnosed patients going forward. Materials and methods Study subjects included 1349 cases enrolled in Total Therapy trials (median follow up 7.5 years). Gene expression profiling was used to determine GEP70 risk status, molecular subgroup by UAMS and TC classifications, and to devise a new and extended TC classification (TC10). Interphase FISH associated with IgH translocations and 1q+ and 17p- were used to build GEP proxies. Data from mutational analysis generated by the FoundationOne targeted sequence panel was also incorporated. Results were validated on the UK MRC MyelomaIX and Hovon65/GMMG-4 studies. Results An initial agnostic analysis of GEP data using sparse k-means clustering verified the existence of TC based groups. Six groups were identified that corresponded overwhelmingly with known TC subgroups; CCND1-t(11;14), D1-HRD, D2-HRD, MMSET, MAF/CCND2, and CCND3. Further comparisons between the molecular subgroup and TC classifier revealed that the UAMS 7 subgroups clustered strongly within one predominant TC group: CD-1 and CD-2 to t(11;14), HY to D1, LB and PR to D2, MF to t(14;16) or t(14;20), and MS to t(4;14). As the UAMS molecular subgroups are largely contained within the TC framework, we aimed to extend the TC by developing the TC10. To extend the known TC subgroups, unsupervised clustering was applied to the 3 largest subgroups [t(11;14), D1, and D2] to determine the strongest single divisor within each respective subgroup. The dominant feature within the t(11;14) cases was CD20 expression, while the D1 and D2 subgroups both split according to RRAS2. CD20 is associated with PAX5 and VPREB3 expression, and RRAS2 is associated with decreased PTP4A3 and increased TNFAIP3 and BIRC3 expression. RRAS2 activation within D1 subgroup and CD20 activation within t(11;14) cases corresponds to an increased time to response to induction therapy suggesting they constitute important biological subgroups. The TC10 combines the known etiologic subgroups of the TC with functionally relevant subdivisions to create 10 novel subgroups: t(11;14) CD20+/-, D1: RRAS2+/-, D2: RRAS2+/-, t(4;14), t(14;16), t(14;20), and t(6;14). Analysis of mutational data revealed that RRAS2 and CD20 activation within the D1, D2, and t(11;14) subgroups reduced the number of mutations in the MAPK pathway. Further mutational analysis revealed that median mutational load was highest in t(14;16) and lowest in D2: RRAS2+ subgroups. The GEP70 score identifies 15% of patients with HR disease and is specific for this purpose. In an analysis of risk assessment methods, we compared GEP detected adverse lesions [t(4;14), t(14;16), t(14;20), 17p- and 1q+] with the GEP70 and revealed that GEP70 HR identified samples have lower OS rates than cases with more than one adverse lesion (validated in external sets). GEP70 HR segregates non-uniformly across molecular subgroups as over 40% of all HR cases are found in the TC10 t(4;14), t(14;16), and t(14;20) subgroups. GEP70 HR cases also have a higher mutational load than low risk cases. Furthermore, GEP70 HR is uniquely associated with 1q+ and 17p- as cases with at least one of these adverse lesions are 4.9 times as likely to be GEP70 HR as cases with neither. Conclusion GEP profiling has a central role in simplifying and standardizing the molecular subgroup designation and risk stratifying of MM patients. The GEP70 risk score reliably identifies HR cases and outperforms FISH in risk assessment, even in validation data sets. The TC10 provides a classification system that improves upon previous methods by defining both etiological and functionally meaningful subgroups. Disclosures Stein: University of Arkansas for Medical Sciences: Employment. Davies:University of Arkansas for Medical Sciences: Employment; Celgene: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Onyx: Consultancy. Heuck:University of Arkansas for Medical Sciences: Employment; Celgene: Consultancy; Janssen: Other: Advisory Board; Millenium: Other: Advisory Board; Foundation Medicine: Honoraria. Weinhold:University of Arkansas for Medical Sciences: Employment; Janssen Cilag: Other: Advisory Board. Chavan:University of Arkansas for Medical Sciences: Employment. Thanendrarajan:University of Arkansas for Medical Sciences: Employment. Epstein:University of Arkansas for Medical Sciences: Employment. Yaccoby:University of Arkansas for Medical Sciences: Employment. Zangari:University of Arkansas for Medical Sciences: Employment; Novartis: Research Funding; Onyx: Research Funding; Millennium: Research Funding. van Rhee:University of Arkansa for Medical Sciences: Employment. Kaiser:Janssen: Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; BristolMyerSquibb: Consultancy; Chugai: Consultancy. Sonneveld:Janssen-Cilag, Celgene, Onyx, Karyopharm: Honoraria, Research Funding; novartis: Honoraria. Goldschmidt:Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Onyx: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen-Cilag: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Millenium: Honoraria, Research Funding, Speakers Bureau; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Chugai: Honoraria, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Barlogie:University of Arkansas for Medical Sciences: Employment. Morgan:Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; MMRF: Honoraria; CancerNet: Honoraria; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; University of Arkansas for Medical Sciences: Employment; Weismann Institute: Honoraria; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Abstract Introduction High expression of the H3K27 histone methyltransferase EZH2 mRNA in myeloma (MM) patient samples is associated with molecular features of high risk disease, including increased proliferation, and adverse outcomes (1). Mutations or deletions in the H3K27 demethylase KDM6A are associated with similar findings (2) and would be expected to have the same epigenetic effect, increasing H3K27me3 levels, a mark associated with repression of gene expression. We, therefore, sought to identify the role EZH2 plays in controlling myeloma cell proliferation. Methods A panel of MM cell lines and primary patient samples (CD138 selected from bone marrow with consent) representing a variety of different MM molecular subgroups were used. Cell viability (WST-1), cell cycle (PI) and apoptosis (AnnexinV/PI, Caspase-Glo 3/7) assays were performed. Affymetrix gene expression arrays followed by validation with RT-PCR were used to identify patterns of gene expression change with EZH2i. Western blotting confirmed changes at the protein level and Chip-PCR was performed using a validated antibody and isotype control to identify H3K27me3 changes at the relevant gene promotors. Affymetrix gene expression data for 1213 patients enrolled in the Total Therapy studies were used to investigate the relevance of our findings in myeloma patient samples. Results We confirmed a reduction in viability following EZH2i using two chemically distinct, specific small molecule inhibitors (EPZ005687 and UNC1999) and the negative control compound UNC2400. There was a reduction in viability in 6/8 cell lines and 5/6 patient samples. Response to inhibition was not related to molecular subgroup or the presence of high-risk molecular features including del17p. Global levels of H3K27me3 measured by Western blot were reduced in all cell lines regardless of response to EZH2i. In responding cell lines EZH2i induced cell cycle arrest at G1/S followed by induction of apoptosis. Gene expression arrays performed using mRNA from KMS11 and KMM1 cell lines highlighted a change in expression of cell cycle control genes associated with EZH2i. This finding was validated using qRT-PCR, which demonstrated upregulation of the cyclin dependent kinase inhibitors CDKN2B, CDKN1A or both. These findings were confirmed at the protein level by Western blotting. Chip-PCR experiment using cell lysates from KMS11 cells following incubation with EZH2i over 6 days identified changes in H3K27me3 at the promoter and transcriptional start site (PROM/TSS) regions of the CDKN2B and CDKN1A genes. The most specific changes occurred at the CDKN1A PROM/TSS, which were more heavily marked with H3K27me3 at baseline compared to a region approx. 5KB upstream. Given these results, which suggest that CDKN1A expression may be controlled by changes in H3K27me3, we explored the effect of CDKN1A mRNA expression in our patient datasets. We found the expression of EZH2 and CDKN1A to be inversely correlated (R=-0.170, p<0.0001) and that low expression of CDKN1A was associated with a significantly shorter progression free and overall survival (p<0.001). In order to confirm whether these gene expression changes could be used as a potential biomarker of response we looked at our panel of cell lines with variable responses to EZH2i. We identified a consistent increase in expression of CDKN1A only in responding cell lines suggesting it could be used as a biomarker of efficacy in the clinic. Conclusions These data support the hypothesis that CDKN1A expression is suppressed by increased H3K27me3, due to high expression of EZH2 and that this can be reversed with pharmacological EZH2 inhibition leading to a reduction in proliferation of myeloma cells. We provide data which supports the investigation of EZH2i in clinical trials of myeloma patients, which has the potential to be an effective therapeutic strategy even for those with high-risk disease, for whom current treatment approaches are ineffective.Pawlyn et al, EZH2 Overexpression in Myeloma Patients Shortens Survival and in-vitro Data Supports a Potential New Targeted Treatment Strategy. AACR and IMW abstracts, 2015Pawlyn et al, The Spectrum and Clinical Impact of Epigenetic Modifier Mutations in Myeloma. Clinical Cancer Research, 2016 Disclosures Pawlyn: Celgene: Consultancy, Honoraria, Other: Travel Support; Takeda Oncology: Consultancy. Kaiser:Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; BMS: Consultancy, Other: Travel Support; Takeda: Consultancy, Other: Travel Support; Chugai: Consultancy. Jones:Celgene: Honoraria, Research Funding. Jackson:Amgen: Consultancy, Honoraria, Speakers Bureau; Roche: Consultancy, Honoraria, Speakers Bureau; MSD: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau. Bergsagel:Novartis: Research Funding; Amgen, BMS, Novartis, Incyte: Consultancy. Morgan:Univ of AR for Medical Sciences: Employment; Janssen: Research Funding; Bristol Meyers: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding. Davies:Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria.

Key Points Using the largest set of patients with newly diagnosed myeloma, we identified 63 mutated driver genes. We identified oncogenic dependencies, particularly relating to primary translocations, indicating a nonrandom accumulation of genetic hits.

AbstractA venous thromboembolism (VTE) with the subsequent risk of pulmonary embolism is a major concern in the treatment of patients with multiple myeloma with thalidomide. The susceptibility to developing a VTE in response to thalidomide therapy is likely to be influenced by both genetic and environmental factors. To test genetic variation associated with treatment related VTE in patient peripheral blood DNA, we used a custom-built molecular inversion probe (MIP)–based single nucleotide polymorphism (SNP) chip containing 3404 SNPs. SNPs on the chip were selected in “functional regions” within 964 genes spanning 67 molecular pathways thought to be involved in the pathogenesis, treatment response, and side effects associated with myeloma therapy. Patients and controls were taken from 3 large clinical trials: Medical Research Council (MRC) Myeloma IX, Hovon-50, and Eastern Cooperative Oncology Group (ECOG) EA100, which compared conventional treatments with thalidomide in patients with myeloma. Our analysis showed that the set of SNPs associated with thalidomide-related VTE were enriched in genes and pathways important in drug transport/metabolism, DNA repair, and cytokine balance. The effects of the SNPs associated with thalidomide-related VTE may be functional at the level of the tumor cell, the tumor-related microenvironment, and the endothelium. The clinical trials described in this paper have been registered as follows: MRC Myeloma IX: ISRCTN68454111; Hovon-50: NCT00028886; and ECOG EA100: NCT00033332.