ALBERT

Description: B-other ALL represents a working definition for patients with B cell precursor (BCP) ALL without a known primary chromosomal abnormality. In this study we use whole genome sequencing (WGS) to characterize adult B-other cases (age ≥ 25yrs) from the UKALL14 trial (NCT01085617). Of 652 patients aged 25-65yrs enrolled onto UKALL14, 333 (51%) had B-other ALL. Sufficient material was available to screen 156/333 B-other cases for recurrent Ph-like fusion events (CLRF2, JAK2, ABL1, ABL2PDGFRB) using FISH and MLPA (kit P335). This identified 28 (18%) Ph-like fusion events (21 CRLF2, 5 ABL-class fusions and 2 JAK). Of the remaining 128 B-other cases 57 had available samples for tumor normal paired WGS (read depth 60x and 30x respectively). Bioinformatic analysis was performed to determine small somatic mutations (SSMs); single nucleotide variants (SNVs) and insertion/deletions (INDELs) as well as copy number aberrations (CNA) and structural variants (SV). We also undertook de novo motif analysis to identify RAG mediated deletions. We present data for 30/57 cases (median age of diagnosis 44, range 25-65), the remainder are undergoing sequencing. Within this cohort we identified 784 SVs, 49,244 SNVs and 2,881 INDELs, with each case having a median representation of 23 SVs, 1,674 SNVs and 86 INDELs. The Median SSM burden was 0.55 per megabase (range 0.31-0.81), which is in the upper third of previous ALL estimates (median 0.26 range 0.03-2.9) but low compared to most other cancer types (Alexandrov et al. 2013 Nature). Fusion gene analysis identified 11/30 (37%) cases with recurrent rearrangements (2 ZNF384r, 2 PAX5r, 5 DUX4r and FGFR1r); this identified a novel 5' partner for ZNF384r (AKAP8) and MYO18A-FGFR1 a fusion previously seen in a single case of 8p11 myeloproliferative syndrome. A further 7 clonal coding drivers were identified, 3 PAX5alt, 3 Ph-like candidates and a single ETV6-RUNX1-like candidate (ETV6 R399C, a dominant negative mutation). We classified our cohort into 6 driver subgroups as shown in table 1 using Gu et al. as a framework (Gu et al. 2019 Nat Gen). We found no evidence of driver subgroups clustering with age, although the Ph-like candidates were all identified in patients 39yrs or older. There was evidence that known driver subgroups have differing mutation burdens but these were not significant in this preliminary cohort; the single MEF2Dr case had a high SV burden compared to all other known subgroups (137 vs. cohort median 22); Favorable outcome subgroups (ZNF384r and DUX4r n=7) had a lower SNV coding mutation burden (median 14 vs. 20; U = 60, p = 0.057; Mann-Whitney U test); PAX5alt (n=5) cases had a higher INDEL burden (median 174 vs. 82; U = 16.5, p =0.057). As expected we found recurrent CNA in CDKN2A/B (63%; 19/30), PAX5 (33%; 10/30), IKZF1 (27%; 8/30), ETV6 (11%; 3/30) and BTG1 (7%; 2/30) across the cohort. IKZF1 deletions were enriched in the Ph-like candidate (n=4) subgroup compared to all other known groups (75% vs. 12%; p = 0.028; Fisher's Exact). RAG mediated deletions have been established as an oncogenic driver mechanism in childhood ALL, so we sought to ascertain its role in adults. We identified 54% (128/236) of the breakpoint resolved deletions had a RAG heptamer site. There was a significant difference (U = 167.5, p = 0.021) in RAG deletion burden between patients over 40yrs and under 40yrs at diagnosis (median 1.5 vs 3). Within the unknown driver subgroup; three cases carried ZEB2 H1038R mutations, two with concurrent IGH-CEBPA/B fusions, identifying them as likely belonging to the G12 cluster identified by Li et al. (Li et al. 2018 PNAS); one case had a high translocation burden (37 cohort median 2) and a single case had a high RAG deletion burden (45, cohort median 2); of the remaining 7 cases, four had either a NRAS or FLT3 subclonal hotspot mutation. Here we present the WGS analyses of 30 cases classified as B-other on the basis of no cytogenetic findings by standard clinical assays. The landscape of adult B-other ALL is highly heterogeneous but with WGS we were able to find at least one disease defining event in 60% of our cohort. These events often encompass novel fusion partners of established genomic subtypes or a cytogenetically cryptic lesion such as IGH-DUX4. Taken together our findings demonstrate the clinical utility of WGS as a diagnostic assay to inform and improve the management of adult B-other ALL patients in the future. Disclosures Fielding: Pfizer: Consultancy; Incyte: Consultancy; Amgen: Consultancy; Novartis: Consultancy. Papaemmanuil:Celgene: Research Funding.

Description: Whole genome sequencing (WGS) studies have started to reveal the critical role of structural variants (SVs) in multiple myeloma (MM) pathogenesis and evolution. We have recently revealed the existence of three main classes of complex events in 30 MM patients: chromothripsis, chromoplexy and templated insertions (Maura F et al, Nat Comm, 2019). Here, drawing on a large cohort of 768 MM patients enrolled in the MMRF CoMMpass study (NCT01454297), we comprehensively characterized the landscape of SVs and their functional implications. Low coverage long-insert WGS (median 4-8X) was available from all patients, of whom 591 also had RNAseq data. Overall, we identified a median of 15 total SVs (range 1-253). Fifty-one percent of SVs (n = 8766) were defined as part of complex events, with a median of one per patient (range 0-14). Chromothripsis, chromoplexy and templated insertions involving 〉2 chromosomes were observed in 21%, 11% and 21 %, respectively. Chromothripsis was the only SV class with clear prognostic implications after adjustment for molecular and clinical features, resulting in adverse PFS (adjusted HR = 1.57; 95% CI 1.13-2.22; p = 0.008) and OS (adjusted HR = 2.4; 95% CI 1.5-3.83; p 〈 0.001). Templated insertions emerged as the cause of CCND1-IGH and MYC translocations in 34 % and 73 % of cases, respectively. This is particularly important given the capability of templated insertions to connect and amplify multiple regions of the genome, involving several oncogenes and regulatory regions (e.g. super enhancers). Twenty-four patients (3.1 %) had translocation between an immunoglobulin locus and a non-canonical driver gene (e.g. PAX5, CD40 and MAP3K14), showing outlier expression by RNAseq where available. SV hotspot analysis was carried out using the Piecewise Constant Fitting algorithm, comparing the local SV breakpoint density to an empirical background model (Glodzik et al, Nat Genet, 2017). To identify functionally important hotspots, we integrated: 1) local cumulative copy number data, 2) amplification and deletion peaks identified by GISTIC v2 (q 〈 0.1), 3) gene fusion data and 4) differential expression analysis with adjustment for main molecular subgroups (limma; Bonferroni-Holm adjusted p-values 〈 0.01). Ninety-eight hotspots were identified (Figure 1), of which 71 (72%) have not previously been reported. Among these novel hotspots, 23 (33 %) contained a known or suspected driver gene, including TNFRSF17 (encoding CAR-T target BCMA), SYK (BCR signal transduction) and KLF2 (key myeloma transcription factor and germline predisposition locus). Active enhancer regions were present in 29 of the novel hotspots (41 %), including 65 % of those with a concurrent putative driver gene involved. For 34 hotspots (48 %) no clear target gene or regulatory region emerged. SV hotspots and GISTIC peaks covered 13 % of the genome. Overall 38 % of simple and complex SVs had at least one breakpoint falling within a recurrently involved region. The majority of chromoplexy, chromothripsis and templated insertions involved recurrent regions (64, 76 and 86 %, respectively). Simple events were most commonly rare, ranging from 74 % of deletions to 45 % for translocations. Quantifying the global functional impact of the remaining 72 % of non-recurrent or rare SVs, we observed that genes involved by a rare SV were significantly enriched for outlier expression (z-score +/- 2) compared to a permutation background model. Rare deletions and duplications exerted their effects within 10 Kb of the gene body. Translocations and templated insertions were associated with overexpression up to 1 Mb from the gene, but had no effect when involving the gene body, consistent with a major enhancer hijacking mechanism. Finally, we sought to understand the role of recurrent and rare SVs in evolutionary dynamics, analyzing 27 patients that progressed with branching evolution. Seventy-two acquired SVs involved a hotspot region (42 driver and/or enhancer; 48 unknown), while 328 were rare. In conclusion, the SV landscape in multiple myeloma is characterized by multiple recurrently involved genes and regulatory regions. These regions account for the majority of complex SVs, indicating strong positive selection of these events. Nonetheless, the majority of SVs remain unaccounted for. Rare SVs were associated with outlier gene expression and may contribute to the tumor evolutionary trajectory of individual patients. Disclosures Papaemmanuil: Celgene: Research Funding. Landgren:Karyopharm: Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Other: IDMC; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Membership on an entity's Board of Directors or advisory committees; Theradex: Other: IDMC; Adaptive: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees.

Description: INTRODUCTION: Cancer pathogenesis is usually characterized by a long evolutionary process where genomic driver events accumulate over time, conferring advantage to distinct subclones, allowing their expansion and progression. METHODS: To investigate the multiple myeloma (MM) evolutionary history, we characterized the mutational processes' landscape and activity over time utilizing a large cohort of 89 whole genomes and 973 exomes. To improve the accuracy of mutational signatures analysis, we analyzed both the 3' and 5' nucleotide context of each mutation and we developed the novel fitting algorithm mmSig, which fits the entire mutational catalogue of each patient with the mutational signatures involved in MM pathogenesis. The contribution of each mutational signature was then corrected based on the cosine similarity between the original 96-mutational profile and the reconstructed profile generated without that signature. To reconstruct the genetic evolutionary history of each patient's cancer, we integrated two approaches. First dividing all mutations into clonal (early) or subclonal (late), then subdivided the clonal mutations into duplicated mutations (present on two alleles and therefore acquired before the duplication) or non-duplicated mutations (detected on a single allele), reflecting either pre-gain and post-gain mutations on the minor allele, or post-gain mutations acquired on one of the duplicated alleles. RESULTS Eight mutational signatures were identified, seven of which showed significant similarity with the most recent mutational signature catalogue (i.e SBS1, SBS2, SBS5, SBS8, SBS9, SBS13 and SBS18). The new mutational signature (named SBS-MM1) was observed only among relapsed patients exposed to alkylating agents (i.e melphalan). The etiology of this specific signature was further confirmed by analyzing recent whole genomes public data from human-induced pluripotent stem cells exposed to melphalan (Kucab et al, Cell 2019). Reconstructing the chronological activity of each mutational signature, we identified four different routes to acquire the full mutational spectrum in MM based on the differential temporal activity of AID (SBS9) and APOBEC (SBS2 and SBS13). Our data indicate that AID activity is not limited to the first contact with the GC, but persists in the majority of patients, behaving similarly to a B-memory cells, capable of re-entering the germinal center upon antigen stimulation to undergo clonal expansion several times before MM diagnosis. Next, we confirmed the clock-like nature (i.e constant mutation rate) of SBS5 in MM and other post-germinal center disorders such as chronic lymphocytic leukemia and B-cell lymphomas. Based on the SBS5 mutation rates and the corrected ratio between duplicated and non-duplicated mutations within large chromosomal gains, we could time the acquisition of the first copy number gain during the life history of each MM patient. Intriguingly, the first MM chromosomal duplication was acquired on average 38 years (ranges 11-64) before sample collection. In 23/27 (85%) cases the first multi gain event occurred before 30 years of age, and in 13/27 (48%) before 20 years reflecting a long and slow process potentially influenced and accelerated by extrinsic and intrinsic factors. DISCUSSION Our analysis provides a glimpse into the early stages of myelomagenesis, where acquisition of the first key drivers precedes cancer diagnosis by decades. Defining the time window when transformation occurs opens up for new avenues of research: to identify causal mechanisms of disease initiation and evolution, to better define the optimal time to start therapy, and ultimately develop early prevention strategies. Disclosures Bolli: CELGENE: Honoraria; JANSSEN: Honoraria; GILEAD: Other: Travel expenses. Corradini:Janssen: Honoraria, Other: Travel Costs; Jazz Pharmaceutics: Honoraria; KiowaKirin: Honoraria; Servier: Honoraria; Takeda: Honoraria, Other: Travel Costs; Kite: Honoraria; Novartis: Honoraria, Other: Travel Costs; Gilead: Honoraria, Other: Travel Costs; Roche: Honoraria; Sanofi: Honoraria; BMS: Other: Travel Costs. Anderson:Janssen: Consultancy, Speakers Bureau; Takeda: Consultancy, Speakers Bureau; Celgene: Consultancy, Speakers Bureau; Bristol-Myers Squibb: Other: Scientific Founder; Oncopep: Other: Scientific Founder; Amgen: Consultancy, Speakers Bureau; Sanofi-Aventis: Other: Advisory Board. Moreau:Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria. Papaemmanuil:Celgene: Research Funding. Avet-Loiseau:takeda: Consultancy, Other: travel fees, lecture fees, Research Funding; celgene: Consultancy, Other: travel fees, lecture fees, Research Funding. Munshi:Adaptive: Consultancy; Amgen: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Takeda: Consultancy; Oncopep: Consultancy; Abbvie: Consultancy. Landgren:Karyopharm: Membership on an entity's Board of Directors or advisory committees; Theradex: Other: IDMC; Adaptive: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Other: IDMC; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.

Description: Multiple Myeloma (MM) initiation and progression is driven by recurrent cytogenetic events, i.e. multiple trisomies or translocations within the immunoglobulin locus. Gene mutations have been extensively studied, and they are generally involved in late phases of disease development. On the contrary, very little is known about non-recurrent structural variations (SV), which are increasingly emerging as critical driver in several cancers. To determine the extent to which the MM genome is shaped by such events, we performed whole genome sequencing (WGS) on 67 CD138+ purified bone marrow MM samples from 30 patients (median of 2 samples per patient; range 1-4), to which we added 22 previously published cases (Chapman et al, Nature 2011) for a total of 52 patients and 89 tumor samples. We defined SVs as inversions, translocations, internal tandem duplications and deletions, which we analysed using publicly available tools developed at the Wellcome Sanger Institute. We found a stunning 1887 unique SVs in the whole cohort, with a variable distribution across the entire series (median 29 per patient, range 0-156). To derive a homogeneous catalogue of SV across the different MM patients and biological subgroups, we annotated events according to the recently proposed classification on 〉2500 cancer genomes (Li Y. et al BioRxiv 2018). IGH and MYC translocations were the most frequent recurrent events and accounted for just 5.3% of the entire SV catalogue. We defined as complex events the following SV classes: chromothripsis, chromoplexy, multiple inversions (distinct between Local_n_Jumps and Local_and_distant_n_jumps in case of translocation involvement), templated insertion between more than 2 chromosomes. According to this classification, in 93% of patients a single, private complex SV was responsible for multiple and simultaneous CNAs across different chromosomes, thus providing a novel pathogenetic explanation for many recurrent CNAs in MM. Overall, 136 complex events were observed in 43/52 patients (83%). We found 34 instances of chromotripsis (Korbel J.O. et al., Cell 2013) in 18/52 (34%) patients. The vast majority (30/34) were clonal and conserved during evolution, suggesting an early role in MM pathogenesis. In addition, we observed 5 chromoplexy events (Korbel J.O. et al., Cell 2013) acquired in 5 patients. More interestingly, evidence of templated insertion on more than 2 chromosomes was observed in 13 patients (25%). This event is composed by multiple concatenated translocations causing small CNAs (mostly gains) and in 77% it resulted in a translocation involving an important MM oncogene (8 MYC and 2 CCND1), suggesting that this is a novel relevant driver mechanism in MM. Given the variety of the landscape of SV between patients, we investigated the presence of SV patterns (SV signatures) by the hierarchical dirichlet process (hdp) (https://github.com/nicolaroberts/hdp). Six main SV signatures were extracted: SV signature #1 was characterized by multiple isolated deletions; SV signature #2 was associated with chromotripsis; SV signature #3 was characterized by reciprocal translocation, local_and_distant_n_jumps and templete insertion between 2 chromosomes. Signature #4 was mostly characterized by local_n_jumps; Signature #5 and #6 were associated with temple insertions on multiple chromosomes with or without large tandem duplication, respectively. Different patients showed differential contribution from different signatures, and based on this we observed 5 distinct clusters. Interestingly some of these clusters were associated with distinct and known MM drivers. For example t(4;14)(MMSET;IGH) cases were enriched for SV signature #1. A significant fraction of patients without any recurrent IGH translocation were characterized by high prevalence of chromothripsis. SV signatures #5 and #6 were mostly associated with hyperdiploid patients with MYC translocation and low genomic impairment. In this study, we describe the landscape of SVs and complex events in MM, suggesting that this notation may represent an important step forward in disentangling the genomic complexity and heterogeneity of MM. Disclosures Moreau: Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees. Corradini:Roche: Honoraria, Other: Advisory Board & Lecturer; Janssen: Honoraria, Other: Lecturer; Sandoz: Other: Advisory Board; Novartis: Honoraria, Other: Advisory Board & Lecturer; Abbvie: Honoraria, Other: Advisory Board & Lecturer; Celgene: Honoraria, Other: Advisory Board & Lecturer; Gilead: Honoraria, Other: Advisory Board & Lecturer; Takeda: Honoraria, Other: Advisory Board & Lecturer; Sanofi: Honoraria, Other: Advisory Board & Lecturer; Amgen: Honoraria, Other: Advisory Board & Lecturer. Anderson:Celgene: Consultancy; Oncopep: Equity Ownership; C4 Therapeutics: Equity Ownership; Takeda Millennium: Consultancy; Gilead: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Consultancy. Avet-Loiseau:Sanofi: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Membership on an entity's Board of Directors or advisory committees. Munshi:OncoPep: Other: Board of director. Bolli:Celgene: Honoraria.