Comprehensive Analysis of MYC Translocations in Multiple Myeloma By Whole Genome Sequencing and Whole Transcriptome Sequencing

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1774-1774 ◽  
Author(s):  
Sonja Seliger ◽  
Verena Geirhos ◽  
Torsten Haferlach ◽  
Wolfgang Kern ◽  
Wencke Walter ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Genome Sequencing ◽  
Transcriptome Sequencing ◽  
Comprehensive Analysis ◽  
Whole Genome ◽  
Employment Equity ◽  
Whole Transcriptome Sequencing ◽  
Equity Ownership ◽  
Whole Transcriptome

Background 8q24 translocations leading to overexpression of MYC are an established prognostic marker in multiple myeloma (MM). Currently FISH (fluorescence in situ hybridization) on CD138+ enriched cell population is the standard diagnostic approach to evaluate the presence of 8q24 translocations. Due to the heterogeneity of breakpoints and technical issues the design of FISH probes is challenging and so far no single FISH assay is capable of detecting each translocation. Aims (1) Evaluation of the frequency of 8q24 translocations in MM by whole genome sequencing (WGS). (2) Determination of the breakpoints on 8q24 and partners. (3) Correlation of WGS data with FISH and MYC expression determined by whole transcriptome sequencing (WTS). Patient cohort and methods CD138+ cell fractions were selected by MACS from bone marrow aspirate samples of 264 patients diagnosed with MM. FISH, WGS and WTS were performed in all cases. For WGS, 151bp paired-end sequences where generated on NovaSeq 6000 machines (Illumina, San Diego, CA). All reported p-values are two-sided and were considered significant at p<0.05. For gene expression (GE) analysis by WTS, estimated gene counts were normalized and the resulting log2 counts per million were used as a proxy of gene expression in each sample. For artefact exclusion, structural variants were checked against 4386 cases covering the spectrum of hematological malignancies. Results In 91/264 (34%) of cases, at least one rearrangement involving the MYC locus (MYCr) was detected by WGS. In 18 of these samples (20%), >1 MYCr was present (114 MYCr in total). Out of these 91 patients, in 32 (35%) the MYCr had been identified by FISH, in 46 cases (51%) it was not detected due to the heterogeneity of breakpoints, while in 13 (14%) patients FISH could not be evaluated (e.g. due to insufficient patient material). Of the 114 MYCr encountered in WGS, 42 involved one of the immunoglobulin loci (IGH n=25, IGK n=9, IGL n=8). The remaining 72 MYCr involved other rare partners. In 29 of these rearrangements, as well as in four complex rearrangements involving IGH or IGK, recurrent rare partners were identified, comprising 1p12/FAM46C (n=6), 6p24.3/BMP6 (n=10), 6q21/FOXO3 (n=4), 7p21.3 (n=3), 11q13/CCND1 (n=5), 20q11.22 (n=5). 43 MYCr involved non-recurrent (single) rare partners, for 4 of these a MYCr was also detected by FISH. The MYCr detected were rather complex: only 34 (30%) showed a simple reciprocal translocation (IGH n=7, IGL n=2, IGK n=4, rare partners n=21), 60 (53%) showed more complex rearrangements (IGH n=12, IGL n=4, IGK n=2, rare partners n=42) and in 20 cases (18%) at least one additional chromosome was involved (IGH n=6, IGL n=3, IGK n=2, rare partners n=9). In 80% of MYCr, breakpoints were located between genomic positions 128.203.605 and 129.375.490 encompassing the pre-described MYC surrounding locus PVT1. IGH-MYC rearrangements showed a tendency to cluster towards the centromere. MYCr involving rare partners showed the broadest breakpoint spectrum and clustered in both directions of the hotspot (Fig 1A). Regarding expression of MYC, all cases showed an overexpression (median GE: 6.9 vs 4.5 in normal controls). Median GE was similar in cases with Ig partners (IGH: 7.1, IGL: 6.7, IGK: 6.6) and non Ig partners (6.8) and also in cases with MYCr detected by FISH (7.0) and cases in which it was not detected by FISH (6.5). Analysis of additional chromosomal aberrations revealed that hyperdiploidy was significantly more frequent in MYCr (n=68/91, 75% vs n=76/173, 44%; p=0.001), while t(11;14) was found significantly less frequent (n=11/91, 12% vs n=49/173, 28%; p=0.003) (Fig 1B). No associations were found between MYCr and other frequent chromosomal abnormalities. Furthermore, molecular mutations frequently occurring in MM (ATM, BRAF, KRAS, NRAS, TP53, IRF4) were analyzed, revealing that patients with MYCr were significantly less frequently associated with mutations in the IRF4 gene (MYCr patients n=1/91; non-MYCr patients n=13/173; p =0.028) (Fig 1C). Conclusions (1) WGS detects ~3x more MYCr compared to FISH. (2) The complexity on the genomic level of MYCr is high, therefore the detection with targeted assays is limited while WGS allows a more comprehensive analysis. (3) MYC expression in cases with MYCr with non Ig partners is comparably high as for Ig-MYC translocations. (4) MYCr are associated with hyperdiploidy, whereas t(11;14) and IRF4 mutations were detected at a lower frequency. Disclosures Seliger: MLL Munich Leukemia Laboratory: Employment. Geirhos:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Walter:MLL Munich Leukemia Laboratory: Employment. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Baer:MLL Munich Leukemia Laboratory: Employment. Stengel:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.

scholarly journals Whole Genome Sequencing and RNA Sequencing of 27 Patients with Persistent Polyclonal B-Cell Lymphocytosis Reveals a High Mutation Frequency/Overexpression of Lymphoma Associated Genes: Really a Benign Disorder?

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 924-924
Author(s):  
Anna Stengel ◽  
Alexander Höllein ◽  
Wolfgang Kern ◽  
Manja Meggendorfer ◽  
Claudia Haferlach ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Whole Genome Sequencing ◽  
Rna Sequencing ◽  
Genome Sequencing ◽  
Splice Site ◽  
Mutation Frequency ◽  
Whole Genome ◽  
Employment Equity ◽  
Equity Ownership

Abstract Background: Persistent polyclonal B-cell lymphocytosis (PPBL) is a rare disorder, occurs almost exclusively in smoking women and is characterized by a chronic polyclonal lymphocytosis with circulating binucleated lymphocytes, clonal cytogenetic abnormalities involving chromosome 3, and chromosomal instability. Outcome of PPBL patients is mostly benign, but subsequent malignancies (non-Hodgkin´s lymphomas and solid tumors) were described. Potential molecular factors leading to their development are yet unclear. Aims: Detailed molecular genetic characterization of PPBL by whole genome sequencing (WGS) and RNA sequencing (RNAseq) in comparison to the well-characterized lymphoid malignancy CLL. Patient cohorts and methods: The total cohort comprised 27 PPBL (3 male, 24 female) and 250 CLL cases (163 male, 87 female). WGS was performed for all patients: 150bp paired-end reads where generated on Illumina HiseqX and NovaSeq 6000 machines (Illumina, San Diego, CA). A mixture genomic DNA from multiple anonymous donors was used as normal controls. To remove potential germline variants, each variant was queried against the gnomAD database, variants with global population frequencies >1% where excluded. Final analysis was performed only on protein-altering and splice-site variants. For further analysis, a virtual panel of 355 lymphoid genes was selected. All reported p-values are two-sided and were considered significant at p<0.05. For gene expression analysis, estimated gene counts were normalized applying Trimmed mean of M-values (TMM) normalization method and the resulting log2 counts per million (CPMs) were used as a proxy of gene expression in each sample. Genes were kept if they were expressed (> 5 CPM) in at least 66% of the samples. Genes with FDR (false discovery rate) < 0.05 and an absolute logFC > 1.5 were considered differentially expressed (DE). Results: Median age was 46 years for PPBL patients (range: 23-67 years) and 67 years for CLL patients (range: 39-94 years). Mean number of mutations per patient was 18 for PPBL and 20 for CLL. For both entities, the majority of mutations were missense mutations (88% in PPBL vs. 81% in CLL), followed by splice-site mutations (7% vs. 10%), other mutation types were only rarely detected. In PPBL, 42 genes were found to be mutated at a frequency of >15%, including ATM (22%), CREBBP (19%), NCOR2 (19%), AHNAK2 (15%), JAK3 (15%), NOTCH2 (15%) and TRAF1 (15%), all of which have been associated with a variety of cancers. Moreover, ATM, NOTCH2 and TRAF1 mutations were described before to be associated with lymphomas. In PPBL patients, mutations in TRAF1 and ATM as well as mutations in TRAF1 and NOTCH2 were found to be mutually exclusive. For CLL patients, 29 genes showed a mutation frequency of >15%, comprising ATM (26%), KMT2D (23%), NOTCH1 (23%), LRP1B (19%), TP53 (16%) and CREBBP (15%). Comparison of the mutation frequencies between the two entities revealed several genes with significant differences: whereas mutations in CKAP5 (11% vs. 2%, p=0.022), DNMT3A (11% vs. 3%, p=0.033), MAP2 (19% vs. 4%, p=0.009), ROBO1 (15% vs. 4%, p=0.046) and TRAF1 (15% vs. 2%, p=0.006) were found to be more frequent in PPBL cases compared to CLL cases, KMT2D (4% vs. 23%, p=0.014), TDRD6 (0% vs. 14%, p=0.032) and TP53 (4% vs. 16%, p=0.048) mutations were more abundantly detected in CLL patients. Moreover, NOTCH1 was mutated more frequently in CLL cases (7% vs. 23%, p=0.082), whereas mutated NOTCH2 (known to be frequently mutated in splenic marginal zone lymphoma), was more abundant in PPBL patients (15% vs. 6%, p=0.116), although both correlations were not statistically significant. Gene expression analyses by RNAseq revealed 337 genes to be differentially expressed between the entities. 207 genes were upregulated in PPBL, including PTPRK, CXCR1, BCL11B, CEPBA, CCR4 and MYC, whereas 130 genes were found to be upregulated in CLL cases, comprising ID3, BCL2, FGF2 and FLT1. Conclusions: 1) WGS analysis identifies high frequencies of cancer/lymphoma-associated gene mutations in PPBL, including mutated ATM, NOTCH2 and TRAF1. 2) Five genes showed a higher mutation frequency compared to CLL including TRAF1,DNMT3A, CKAP5 and MAP2. 3) Lymphoma associated genes (BCL11B and MYC) were overexpressed in PPBL vs CLL. 4) Taken together our results question PPBL as a benign entity and identify molecular markers that might contribute to development of subsequent malignancies. Disclosures Stengel: MLL Munich Leukemia Laboratory: Employment. Höllein:MLL Munich Leukemia Laboratory: Employment. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Walter:MLL Munich Leukemia Laboratory: Employment. Hutter:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.

scholarly journals Chromoplexy and Chromothripsis Are Important Prognostically in Myeloma and Deregulate Gene Function By a Range of Mechanisms

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 3767-3767 ◽  
Author(s):  
Cody Ashby ◽  
Eileen M Boyle ◽  
Brian A Walker ◽  
Michael A Bauer ◽  
Katie Rose Ryan ◽  
...  
Keyword(s):  
Board Of Directors ◽  
Genome Sequencing ◽  
Gene Function ◽  
Research Funding ◽  
Whole Genome ◽  
Structural Variants ◽  
Employment Equity ◽  
Advisory Committees ◽  
Equity Ownership ◽  
Research Grant

Background: Structural variants are key recurrent molecular features of myeloma (MM) with two types of complex rearrangement, chromoplexy and chromothripsis, having been described recently. The contribution of these to MM prognosis, rapid changes in clinical behavior and punctuated evolution is currently unknown as is the mechanism by which they deregulate gene function. Methods: We analyzed two sets of newly diagnosed MM data: 85 cases with phased whole genome sequencing; and 812 cases from CoMMpass where long-insert whole-genome sequencing was available. Patient derived xenografts from five MM cases were used to generate epigenetic maps for the histone marks, BRD4, MED1, H3K27Ac, H3K4me1, H3K4me3, H3K9me3, H3K36me3 and H3K27me3. Results: In the 10X data the median number of structural events per case was 25 (range 1 - 182); with a median of 14 intra-chromosomal events (range 1 - 179; P<0.001) and 7 inter-chromosomal events (range 0 - 29). Structural events were seen most frequently on chromosomes 14 (64%), 8 (53%), 1 (44%) and 6 (42%). Complex chromosomal rearrangements involving 3 or more chromosomal sites were seen in 46%, 4 or more sites in 20%, 5 or more in 10% and 6 or more in 5% of samples. There were significantly more structural events in the t(4;14) subgroup compared to the t(11;14) subgroup. Significantly more events were also seen in the bi-allelically inactivated TP53 cases. Using an elbow test defined cutoff, we identified cases with high structural variant load in 10% of cases. Chromoplexy called by "Chainfinder" was seen in 18% of cases. Chromothripsis called by "Shatterseek" was seen in 9% of cases. Cases with a high structural load alone were not associated with an adverse outcome whereas cases with chromoplexy or chromothripsis were associated with adverse PFS and OS, p=0.001. A new high-risk subgroup comprising approximately 5% of cases was identified with chromoplexy, chromothripsis and a high structural load. Gene set enrichment analysis of cases with chromoplexy and chromothripsis showed an excess of MYC, E2F and G2M targets, and a reduction in RAS signaling. Interferon a and g responses, an excess of TP53 and reduction in TRAF3 mutations was associated predominantly with chromothripsis. How chromoplexy and chromothripsis are tolerated by the cell is unknown and the association with the cGAS/STING response is further being explored. To determine how chromoplexy may deregulate multiple genes we identified the full spectrum of structural variants to the immunoglobulin (Ig) and non-Ig loci. A range of genes are deregulated by Ig loci including MAP3K14 at a frequency of 2% confirming the importance of non-canonical NFkB signaling. A novel intra-chromosomal rearrangement to ZFP36L1 was upregulated in 10% of cases but was not prognostic. Gene upregulation by non-Ig super enhancers is frequent and targets include PAX5, GLI3, CD40, NFKB1, MAP3K14, LRRC37A, LIPG, PHLDA3, ZNF267, CENPF, SLC44A2, MIER1, SOX30, TMEM258, PPIL1, and BUB3. The topologically associating domain (TADs) containing super enhancers bringing about gene deregulation include TXNDC5, FOXO3, FCHSD2, SP2, FAM46C, CACNA1C, TLCD2 and PIK3C2G. These super enhancers frequently contain important MM genes, the coding sequence of which are disrupted by the rearrangement and could contribute to the clinical phenotype. Accurately reconstructing the structure of the complex rearrangements will allow us to identify the mechanism of gene deregulation and to distinguish between either gene stacking, receptor stacking or both. Conclusions: Upregulation of gene expression by super enhancer rearrangement is a major mechanism of gene deregulation in MM and complex structural events contribute significantly to adverse prognosis by a range of mechanisms as well as simple gene overexpression. Disclosures Boyle: Amgen, Abbvie, Janssen, Takeda, Celgene Corporation: Honoraria; Amgen, Janssen, Takeda, Celgene Corporation: Other: Travel expenses. Walker:Celgene: Research Funding. Thakurta:Celgene: Employment, Equity Ownership. Flynt:Celgene Corporation: Employment, Equity Ownership. Davies:Amgen, Celgene, Janssen, Oncopeptides, Roche, Takeda: Membership on an entity's Board of Directors or advisory committees, Other: Consultant/Advisor; Janssen, Celgene: Other: Research Grant, Research Funding. Morgan:Amgen, Roche, Abbvie, Takeda, Celgene, Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Other: research grant, Research Funding.

scholarly journals Baseline and on-Treatment Bone Marrow Microenvironments Predict Myeloma Patient Outcomes and Inform Potential Intervention Strategies

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1882-1882 ◽  
Author(s):  
Samuel A Danziger ◽  
Mark McConnell ◽  
Jake Gockley ◽  
Mary Young ◽  
Adam Rosenthal ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Bone Marrow ◽  
Board Of Directors ◽  
Research Funding ◽  
Expression Profiles ◽  
Cell Types ◽  
Employment Equity ◽  
Advisory Committees ◽  
Equity Ownership

Abstract Introduction The multiple myeloma (MM) tumor microenvironment (TME) strongly influences patient outcomes as evidenced by the success of immunomodulatory therapies. To develop precision immunotherapeutic approaches, it is essential to identify and enumerate TME cell types and understand their dynamics. Methods We estimated the population of immune and other non-tumor cell types during the course of MM treatment at a single institution using gene expression of paired CD138-selected bone marrow aspirates and whole bone marrow (WBM) core biopsies from 867 samples of 436 newly diagnosed MM patients collected at 5 time points: pre-treatment (N=354), post-induction (N=245), post-transplant (N=83), post-consolidation (N=51), and post-maintenance (N=134). Expression profiles from the aspirates were used to infer the transcriptome contribution of immune and stromal cells in the WBM array data. Unsupervised clustering of these non-tumor gene expression profiles across all time points was performed using the R package ConsensusClusterPlus with Bayesian Information Criterion (BIC) to select the number of clusters. Individual cell types in these TMEs were estimated using the DCQ algorithm and a gene expression signature matrix based on the published LM22 leukocyte matrix (Newman et al., 2015) augmented with 5 bone marrow- and myeloma-specific cell types. Results Our deconvolution approach accurately estimated percent tumor cells in the paired samples compared to estimates from microscopy and flow cytometry (PCC = 0.63, RMSE = 9.99%). TME clusters built on gene expression data from all 867 samples resulted in 5 unsupervised clusters covering 91% of samples. While the fraction of patients in each cluster changed during treatment, no new TME clusters emerged as treatment progressed. These clusters were associated with progression free survival (PFS) (p-Val = 0.020) and overall survival (OS) (p-Val = 0.067) when measured in pre-transplant samples. The most striking outcomes were represented by Cluster 5 (N = 106) characterized by a low innate to adaptive cell ratio and shortened patient survival (Figure 1, 2). This cluster had worse outcomes than others (estimated mean PFS = 58 months compared to 71+ months for other clusters, p-Val = 0.002; estimate mean OS = 105 months compared with 113+ months for other clusters, p-Val = 0.040). Compared to other immune clusters, the adaptive-skewed TME of Cluster 5 is characterized by low granulocyte populations and high antigen-presenting, CD8 T, and B cell populations. As might be expected, this cluster was also significantly enriched for ISS3 and GEP70 high risk patients, as well as Del1p, Del1q, t12;14, and t14:16. Importantly, this TME persisted even when the induction therapy significantly reduced the tumor load (Table 1). At post-induction, outcomes for the 69 / 245 patients in Cluster 5 remain significantly worse (estimate mean PFS = 56 months compared to 71+ months for other clusters, p-Val = 0.004; estimate mean OS = 100 months compared to 121+ months for other clusters, p-Val = 0.002). The analysis of on-treatment samples showed that the number of patients in Cluster 5 decreases from 30% before treatment to 12% after transplant, and of the 63 patients for whom we have both pre-treatment and post-transplant samples, 18/20 of the Cluster 5 patients moved into other immune clusters; 13 into Cluster 4. The non-5 clusters (with better PFS and OS overall) had higher amounts of granulocytes and lower amounts of CD8 T cells. Some clusters (1 and 4) had increased natural killer (NK) cells and decreased dendritic cells, while other clusters (2 and 3) had increased adipocytes and increases in M2 macrophages (Cluster 2) or NK cells (Cluster 3). Taken together, the gain of granulocytes and adipocytes was associated with improved outcome, while increases in the adaptive immune compartment was associated with poorer outcome. Conclusions We identified distinct clusters of patient TMEs from bulk transcriptome profiles by computationally estimating the CD138- fraction of TMEs. Our findings identified differential immune and stromal compositions in patient clusters with opposing clinical outcomes and tracked membership in those clusters during treatment. Adding this layer of TME to the analysis of myeloma patient baseline and on-treatment samples enables us to formulate biological hypotheses and may eventually guide therapeutic interventions to improve outcomes for patients. Disclosures Danziger: Celgene Corporation: Employment, Equity Ownership. McConnell:Celgene Corporation: Employment. Gockley:Celgene Corporation: Employment. Young:Celgene Corporation: Employment, Equity Ownership. Schmitz:Celgene Corporation: Employment, Equity Ownership. Reiss:Celgene Corporation: Employment, Equity Ownership. Davies:MMRF: Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; TRM Oncology: Honoraria; Abbvie: Consultancy; ASH: Honoraria; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria. Copeland:Celgene Corporation: Employment, Equity Ownership. Fox:Celgene Corporation: Employment, Equity Ownership. Fitch:Celgene Corporation: Employment, Equity Ownership. Newhall:Celgene Corporation: Employment, Equity Ownership. Barlogie:Celgene: Consultancy, Research Funding; Dana Farber Cancer Institute: Other: travel stipend; Multiple Myeloma Research Foundation: Other: travel stipend; International Workshop on Waldenström's Macroglobulinemia: Other: travel stipend; Millenium: Consultancy, Research Funding; European School of Haematology- International Conference on Multiple Myeloma: Other: travel stipend; ComtecMed- World Congress on Controversies in Hematology: Other: travel stipend; Myeloma Health, LLC: Patents & Royalties: : Co-inventor of patents and patent applications related to use of GEP in cancer medicine licensed to Myeloma Health, LLC. Trotter:Celgene Research SL (Spain), part of Celgene Corporation: Employment, Equity Ownership. Hershberg:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Dervan:Celgene Corporation: Employment, Equity Ownership. Ratushny:Celgene Corporation: Employment, Equity Ownership. Morgan:Takeda: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Research Funding.

scholarly journals Gene Expression and Genomic Markers Identify a Subpopulation of Poor Prognosis t(4;14) Patients in Newly Diagnosed Multiple Myeloma

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 366-366
Author(s):  
Maria Ortiz ◽  
Fadi Towfic ◽  
Erin Flynt ◽  
Nicholas Stong ◽  
Sneh Lata ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Poor Prognosis ◽  
Newly Diagnosed ◽  
Employment Equity ◽  
Gene Set ◽  
Combining Data ◽  
Genomic Markers ◽  
Equity Ownership

Cytogenetics is an important prognostic marker in multiple myeloma (MM). Patients with t(4;14) (~15% of newly diagnosed MM patients) are known to have short progression free survival (PFS) and overall survival (OS). This feature, measured by FISH, is used in combination with ISS=3 as a selection marker for patients with high risk (HR) of progression. Only a subset of patients grouped by t(4;14) and ISS=3 display genuinely poor survival, however, with ~25% dying within 24 months after diagnosis (similar to the Double Hit subgroup defined by Walker et al1). To elucidate this observation, we created the largest dataset of MM t(4;14) patients to date by combining data from the Myeloma Genome Project (MGP, n=73) and data from TOUL (n=100, patients analyzed in routine practice) to identify transcriptomic and/or genomic markers associated with HR t(4;14). Gene expression (GE), copy number aberration (CNA), single nucleotide variant (SNV) and translocations were derived from RNAseq and WGS/WES profiling of biopsies from patients aged less than 75 years who received transplant, and integrated with clinical information (including Age, PFS and OS). Demographics: MGP median age=61; 30% female; median PFS (mPFS)=26.2months (m) and median OS (mOS) not reached. TOUL median age=60; 35% female, mPFS=23.7m and mOS = 86.1m. Our previous work (Ortiz ASH 2018, Ortiz EHA 2018) identified a molecularly-defined HR MM patient subgroup (MDMS8, mPFS&lt;20m, m0S&lt;35m) defined by GE patterns related to cell cycle dysregulation. In that analysis, 24% of t(4;14) patients were identified as MDMS8 (mPFS&lt;13m, mOS&lt;30m), the rest (76%) were grouped in other lower risk molecular segments (mPFS&lt;30m, mOS NR). A GE classifier for t(4;14) in MDMS8 vs the rest of t(4;14) patients was created on the MGP dataset and applied to identify similar patients in the TOUL data, obtaining a significant difference between MDMS8-like t(4;14) patients (20% prevalence, mPFS&lt;15m, mOS&lt;26m) in the TOUL dataset and non-HR t(4;14) (mPFS&lt;26m, mOS&lt;103m) in both PFS (p.value&lt;1e-3) and OS (p.value&lt;1e-5). Although there are some conventional t(4;14) gene expression surrogates, they do not identify the HR t(4:14) subgroup. Comparison of known t(4;14) gene expression markers MMSET and FGFR3 in HR t(4;14) (OS &lt; 24ms & not_alive, N=34) versus non-HR t(4;14) patients (N=94) across both datasets combined did not yield significant differential expression of either gene (p.value&gt;0.10). MMSET was over-expressed in all t(4;14) patients, while FGFR3 displayed a binomial distribution (two groups of patients with high (N=37, median value=10 log2CPM) and low (N=91, median value=2 log2CPM) FGFR3 expression) within t(4;14) patients (p.value&lt;0.05) without association with outcome (p.value&gt;0.10). GE analysis of HR t(4;14) vs non-HR t(4;14) patients aligned with MDMS8 biology, but identified new pathways also including DNA repair, MYC targets and Oxidative Phosphorylation being up-regulated in the HR t(4;14) group. A gene-set variation analysis based on the MSigDb C1 gene-set, wherein genes are grouped based on their genomic location, was performed to identify GE changes of potentially epigenomic origin. Results highlighted chr9q22, chr9q33, and chr13q13 as down-regulated in the HR t(4;14) group, while genes in 16q24 were significantly up-regulated. CNA analysis identified amplifications in chromosomes 3 and 19 and deletions in chr12p as significantly associated with the HR t(4;14) population (p.value &lt; 0.05); while deletions in chr14q (preceding the translocated region) occurred more frequently in the non-HR t(4;14) group. Our results provide new insights into identification of these patients and underlying biology that could drive poor prognosis in t(4;14) patients. Molecular identification of HR t(4;14) patients would enable proper risk classification for this MM patient group and understanding differences in HR t(4;14) biology could provide the basis for identification of a specific therapeutic target for this HR subpopulation. An ongoing aim of this work is development of a clinically applicable classifier that accurately identifies this subpopulation of MM patients and the biological drivers of their high-risk disease. Disclosures Ortiz: Celgene Corporation: Employment, Equity Ownership. Towfic:Celgene Corporation: Employment, Equity Ownership. Flynt:Celgene Corporation: Employment, Equity Ownership. Stong:Celgene Corporation: Employment, Equity Ownership. Lata:Celgene Corporation: Employment, Equity Ownership. Sampath:Celgene Corporation: Employment, Equity Ownership. Rozelle:Celgene Corporation: Other: Contractor for Celgene. Trotter:Celgene Corporation: Employment, Equity Ownership. Thakurta:Celgene: Employment, Equity Ownership.

Clinical interpretation of whole-genome and whole-transcriptome sequencing for precision oncology

2021 ◽  
Author(s):  
Vaidehi Jobanputra ◽  
Kazimierz O. Wrzeszczynski ◽  
Reinhard Buttner ◽  
Carlos Caldas ◽  
Edwin Cuppen ◽  
...  
Keyword(s):  
Transcriptome Sequencing ◽  
Precision Oncology ◽  
Whole Genome ◽  
Clinical Interpretation ◽  
Whole Transcriptome Sequencing ◽  
Whole Transcriptome
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