ALBERT

Description: Multiple Myeloma (MM) is a genetically heterogeneous disease of plasma cells that generally exhibits chromosomal abnormalities and distinct gene expression signatures. Previous studies have sought to identify gene expression indices using microarray technology to discern genes associated with survival outcomes to predict whether a newly diagnosed patient has an aggressive form of the disease. One such MM-specific index is the UAMS 70 gene index, which is composed of 51 over- and 19 under-expressed genes. This index was developed using Affymetrix U133Plus2.0 microarray data from 532 MM patients at diagnosis by computing log-rank test statistics on gene expression quartiles. Despite consistently achieving a high performance across a variety of MM datasets, issues arise when applying this index to RNAseq data. Here we address those issues, deriving an independent index based on the RNAseq data from the Multiple Myeloma Research Foundation (MMRF) CoMMpass Study (NCT01454297), and benchmark its performance to an implementation of the UAMS 70 gene index. UAMS index scores are computed by taking the difference between the average log2-scale expression of the 51 over- and 19 under-expressed genes. We applied this calculation to RNAseq data analyzed using Sailfish, Salmon v7.2, and HTseq counts collected from 41 Multiple Myeloma Genomics Initiative samples and compared the results to scores from matching GCRMA, MAS5, RMA, and PLIER16 Affymetrix U133Plus2.0 microarray data. Differences in the distribution of index values across data types led to nonconforming classification of high-risk individuals. Additionally, when applied to RNAseq data, several Affymetrix probesets did not uniquely match to gene annotations from Ensembl-v74. This reduced the number of genes upon which our UAMS score was calculated to 61 genes. Of the original 51 over-expressed probes, only 44 uniquely mapped genes remained after 7 multi-mapped probes are removed and similarly, out of the 19 under-expressed genes only 17 were uniquely mapped. Given the complication of probe-gene mismatch and inconsistencies identifying high-risk individuals when applied to RNAseq data, we developed an independent index using the baseline RNAseq data from the MMRF CoMMpass Study IA13 dataset. From a training set (n=375) of RNAseq data measuring 56430 genes, we performed univariate log-rank tests on expression quartiles associated with disease-related survival while controlling for an FDR of 2.5%, resulting in 23 under- and 332 over-expressed genes. Subsequent multivariate Cox regression analysis and backward stepwise selection culminated in the identification of the CoMMpass RNAseq index, which is based on the ratio of mean expression values of 87 genes (19 under- and 68 over-expressed) predictive of high risk (hazard ratio [HR] = 8.7341, 95% CI = 5.615-13.58, p 〈 0.001). Validation on the test set (n=251) yielded a HR of 5.612 (95% CI = 3.066-10.27, p 〈 0.001) as compared to a HR of 4.753 (95% CI = 2.688-8.403, p 〈 0.001) achieved with the adapted UAMS index. Adjusting for a patient's International Staging System (ISS) stage revises these hazard ratios to 6.236 (95% CI = 3.345-11.627, p 〈 0.001) and 3.6420 (95% CI = 1.9726-6.724, p 〈 0.001) for the CoMMpass RNAseq and the adapted UAMS indices, respectively. Furthermore, the distribution of CoMMpass RNAseq index values across the training and test set show no observable bias with respect to three main therapy arms, suggesting it is predictive of high risk independent of treatment. Our newly derived CoMMpass RNAseq index shares one gene in common with the UAMS 61 gene index (CENPW) and recovers two over-expressed genes (FABP5, TAGLN2), which were removed from the UAMS 70 gene index due to probe multimapping. When the recovered genes are added back to the UAMS index, the unadjusted and adjusted hazard ratios measured for the test set are 5.173 (CI = 2.926-9.146, p 〈 0.001) and 4.022 (CI = 2.1840-7.408, p 〈 0.001), respectively. Of the original 70 genes in the UAMS index, 21 (30%) map to chromosome 1, which frequently exhibits copy number gains in MM. Only 11 of the 87 (13%) genes in our proposed index map to chr1, which indicates that, given its performance, the newly derived list of genes may represent a more diverse index to predict, and provide novel insights into, high risk MM. Altogether, the CoMMpass RNAseq index identifies a high risk signature in 13% of MM patients and outperforms the UAMS index. Disclosures Lonial: Amgen: Research Funding.

Description: Multiple myeloma (MM) is a hematological malignancy of plasma cells accounting for ~2% of new cancer cases each year in the United States. The Multiple Myeloma Research Foundation CoMMpass Study (NCT01454297) is a fully accrued, longitudinal, observational clinical trial with 1143 newly diagnosed MM patients from sites in the United States, Canada, Spain, and Italy. Tumor samples are collected and characterized using whole genome (WGS), exome (WES), and RNA (RNAseq) sequencing at diagnosis and each progression event. Clinical parameters are collected at baseline and every three months through the eight-year observation period. Although ongoing, longitudinal collection of molecular and clinical data from CoMMpass patients has aided in our understanding of the molecular mechanisms underpinning relapse in MM. The CoMMpass IA13 dataset includes 136 patients with longitudinal time points, including 25 patients with multiple progression events. We analyzed 100 patients with WES data at baseline and at least one progression event and identified 7 genes (KRAS, NRAS, SPEN, SRCAP, MACF1, ANK3, and RPRD2) with acquired non-synonymous mutations in at least 3% of patients at progression. We identified five patients with KRAS mutations at baseline in whom a clonal shift to NRAS Q61 mutations occurred at progression and four additional patients with novel NRAS Q61 mutations becoming detectable at progression. Patients with NRAS Q61 mutations at baseline exhibit poor OS outcomes as compared to patients with other NRAS mutations (p 〈 0.05), and exhibit no significant difference in outcome compared to patients with KRAS mutations, suggesting that clones with NRAS Q61 mutations have a competitive advantage over other NRAS mutations in MM. An integrated analysis leveraging WGS, WES, and RNAseq data identified gain- (GOF) and loss-of-function (LOF) genes for each sample. Longitudinal changes in gene functional status was determined for 47 patients with 57 paired time points. TRAF3 and CDKN2C/FAF1 were found to be the most common complete LOF events acquired at progression, found in 5 (10.6%) and 4 (8.5%) patients, respectively. Acquired complete LOF events are enriched for genes involved in cell cycle regulation (15% of patients, p 〈 0.001), including CDKN1B, CDKN2A, CDKN2C, PPP2R4, TP53, and RB1, indicating that novel events resulting in further destabilization of cell cycle control contribute to relapse in MM. Recurrent GOF events acquired at progression involving KRAS, PEAR1, and CDYL2 were observed in 〉4% of longitudinal patients. In addition, 5 (10.6%) patients acquired GOF events in genes either up- or downstream of RAS, including HIST2H3C, OSMR, PAK2, PIK3R6, and STAT3, highlighting the complexity of targeting RAS in MM. Unsupervised consensus clustering of RNAseq data for 714 patients at baseline identified 12 expression subtypes of MM, which generally correspond with known subgroups. The proliferation (PR) group consists of patients whose tumors have an array of genetic backgrounds but a similar RNA expression profile, and exhibit poor OS (HR = 3.996, 95% CI = 2.632 - 6.067, p 〈 0.001) and PFS (HR = 2.583, 95% CI = 1.817 - 3.67, p 〈 0.001) outcomes. We analyzed 50 patients with RNAseq data at multiple time points and identified 21 (42%) tumors that changed expression subtypes at progression, 12 (24%) of which transition to PR. Patients who transition to PR have extremely poor outcomes, with 75% of patients succumbing to their disease soon after progression (median = 2 months). Tumors with the PR subtype commonly possess del1p, gain1q, del13p, and LOF of RB1 (p 〈 0.001), and tumors that transition to PR at progression commonly acquire one or more of these abnormalities. Further, 4 (33%) patients that transition to PR acquire complete LOF of a cyclin-dependent kinase inhibitor, with 3 (25%) patients acquiring focal deletions of CDKN2C/FAF1 at progression. Although we observe multiple mechanisms driving the transition to PR, it is seemingly associated with acquired molecular alterations that result in further loss of cell cycle control. These observations suggest that progression in MM is often driven by marked shifts in gene expression and molecular events that further deregulate RAS and cell cycle pathways, highlighting the need for novel inhibitors in MM; protocols, such as MyDRUG, which aim to treat patients based on their tumor genetic profile; and molecular profiling of patients throughout their disease course. Disclosures Lonial: Amgen: Research Funding.

Description: The Multiple Myeloma Research Foundation (MMRF) CoMMpass trial (NCT145429) is a longitudinal study of 1147 patients with newly-diagnosed multiple myeloma from clinical sites in the United States, Canada, Spain, and Italy. Each patient receives a treatment regimen containing a proteasome inhibitor, immunomodulatory agent, or both. Clinical parameters are collected at study enrollment and every three months through the eight-year observation period. To identify molecular determinants of clinical outcome each baseline and progression tumor specimen is characterized using Whole Genome Sequencing, Exome Sequencing, and RNA sequencing. Data available as of January 1, 2016 is included in this first formal interim analysis, which includes 995 enrolled patients of whom 851 are molecularly characterized. This cohort of patients includes 74 patients with at least two sequential samples, plus 15 patients with characterized tumor samples from the bone marrow and peripheral blood. The median follow-up of the cohort is 66 weeks, which identified a median PFS of 36 months for the cohort. The median OS was not reached but 76% are still alive at 3 years. Although the age at enrollment by gender is uniform, there is a significant difference in PFS and OS, with males performing worse than females, p=0.001 and p=0.0004 respectively. Analysis of the exome sequencing data from the 746 baseline BM localized tumors identified a median of 122 non-immunoglobulin related mutations per patient, with an interquartile range of 96-155. There is a group of highly mutated (〉481 mutations [mean+1SD]) patients who frequently have MAF family translocations (66%) and/or mutations in the DNA repair genes MSH2, MSH3, MSH4, MSH6, or ATM (38%). Across the cohort 21/53 of the DNA repair gene mutations reside in these 21 patients compared to 14/47 MAF family translocations. Analysis of the somatic mutations identified 20 significant genes, which are recurrently mutated and the mutated allele is detectably expressed; BRAF, CYLD, DIS3, FAM46C, FCF1, FGFR3, FUBP1, KRAS, MAX, NFKBIA, NRAS, PRKD2, RASA2, RB1, SAMHD1, SP140, TGDS, TP53, TRAF2, and TRAF3. Integration of the copy number data and the mutation data identified an association between TP53 deletion and mutation, suggesting many patients present with homozygous loss of TP53. Patients with one or two functional TP53 alleles had similar PFS and OS but the patients with zero functional alleles had a significantly reduced OS (p

Description: The Multiple Myeloma Research Foundation (MMRF) CoMMpass trial (NCT0145429) is a longitudinal study of 1000 patients with newly-diagnosed multiple myeloma. The study opened July 2011 and now includes over 650 patients from 91 sites in the United States, Canada and European Union. Each patient is required to receive an approved proteasome inhibitor, immunumodulatory agent, or both. Enriched tumor and matched constitutional samples are comprehensively analyzed using Long-Insert Whole Genome Sequencing (WGS), Whole Exome Sequencing (WES) and RNA sequencing (RNAseq). Clinical parameters, Quality of Life measurements and health care resource utilization values are collected at study entry and every three months for a minimum of five years. Additional bone marrow aspirates are collected and analyzed at each recurrence or progression of disease. An extensive clinical and molecular database, the MMRF Researcher Gateway (https://research.themmrf.org), has been developed to facilitate the rapid dissemination of the results and provides the myeloma community with a mechanism to analyze the data. In this current interim analysis, we report on 195 patients that are fully characterized at the molecular level. We focused this analysis on immunoglobulin translocations and inter-chromosomal fusion transcripts. As expected we detected the classic canonical t(4;14), t(6;14), t(11;14), and t(14;16) translocations targeting FGFR3/MMSET, CCND3, CCND1, and MAF respectively. Seven patients presented with t(8;14) rearrangements correlating with high expression of MYC. Novel translocations were detected targeting MAP3K14/NIK in two patients and NFKB1, TOP1MT, TXNDC2, APOL3, FCHSD2, PRICKLE1, and BCL2L1 in individual patients. Importantly, the matched RNAseq data confirmed the high expression of MAP3K14, NFKB1, TOP1MT, APOL3 and BCL2L1. Moreover, the anti-apoptotic isoform of BCL2L1, Bcl-xL, was the prominent transcript isoform detected. In several patients we detected multiple IgH translocations. For instance the BCL2L1 translocation occurred in a downstream class switch recombination region from one associated with a co-occurring t(11:14). We also analyzed the RNAseq dataset for inter-chromosomal fusion transcripts and leveraged the independent long-insert WGS data to validate the predicted fusions. The only recurrent fusion partner identified was IgH-MMSET created by t(4:14). Fusion transcripts were detected in individual patients between IgH elements and MYEOV and WWOX along with several of the novel IgH translocation partners; NFKB1, TOP1MT, and APOL3. Several genes are involved in multiple fusions but with different partners. Three independent fusions were detected between the highly expressed gene FCHSD2 and MYC, MAP3K14, and ANKRD55. Three additional fusions were detected between MAP3K14 and ELL, PLCG2, and CDC27, which produce hybrid MAP3K14 isoforms lacking the N-terminal negative regulatory domain. We also detected three independent fusions involving BRF1, which is typically not expressed in myeloma tumors. These appear to be markers of translocations occurring just centromeric of the strong 3’ IgH enhancers. Interestingly, two of the partners are located in a region of chromosome 12 harboring MDM2 and spiked expression of MDM2 was observed. Additional genes with multiple fusion events included NEDD9 and ARHGEF12. Integrating the WES and RNAseq datasets, we identified 3518 variants (median 14 per patient) where the variant allele detected by WES, was also detected in the RNAseq data, suggesting it is potentially biologically relevant. Of these, 44 distinct genes were mutated in at least 2% of patients. The most common mutations (〉7 patients) occurred in KRAS, NRAS, IGLL5, DIS3, BRAF, ACTG1, EGR1, FAM46C, TRAF3, DUSP2, FGFR3, and PRR14L. We also identified a deletion of IKZF3/Aiolos in a patient who progressed rapidly on lenalidomide-dexamethasone. Alterations in Ikaros family members like Aiolos have recently been reported as a potential mechanism of resistance to IMiDs. As the study continues to mature, we expect it will provide unprecedented molecular characterization and correlating clinical datasets that will help define the determinants of response to anti-myeloma agents and facilitate future clinical trial designs, thus serving as a stepping-stone toward personalized medicine for myeloma patients. Disclosures Lonial: Millennium: The Takeda Oncology Company: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Onyx Pharmaceuticals: Consultancy, Research Funding.