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: Chromosomal abnormalities are established prognostic markers in adult ALL. Analysis of the UKALLXII/ECOG2993 trial (Blood, 2007;109:3189) revealed that patients (15-65 years) with BCR-ABL1, KMT2A-AFF1, low hypodiploidy (HoL) or a complex karyotype (CK) had a significantly inferior outcome in multivariable analysis. In UKALL14 (ISRCTN 66541317), all patients (25-65 years) with these genetic abnormalities were stratified to the high-risk arm to receive allogeneic stem cell transplant (alloSCT), where possible, or an unrelated transplant. Patients with BCR-ABL1 received imatinib. The objectives of this study were to determine the frequency and outcome of patients with high-risk chromosomal abnormalities and identify new high-risk genetic abnormalities by screening for novel gene rearrangements and copy number alterations. All genetic screening was performed using diagnostic marrow samples and by cytogenetics, FISH and MLPA. Survival endpoints were defined according to the trial protocol and analysed using standard statistical methods. After a median follow-up time of 3.4 years the 3 year survival rates for the whole B-cell precursor ALL cohort were: overall survival (OS) 52% (95% 48-56), event-free survival (EFS), 45% (41-49), relapse rate (RR) 34% (30-39). Among 653 patients, 319 (49%) harboured high-risk primary chromosomal abnormalities: BCR-ABL1 (197/635, 31%), KMT2A-AFF1 (49/616, 8%), HoL (49/525, 9%), and CK (21/512, 5%). The OS of patients with CK and HoL was 24% (95% CI 9-43) & 19% (9-33) which was driven by high RR: 60% (36-85) & 56% (39-75), respectively, despite alloSCT in first remission. This outcome was not obviously improved compared to UKALLXII (28% and 22%); however, the patient population was older. Patients with KMT2A-AFF1 fusion had OS and RR similar to the small number of patients (n=9) with other KMT2A fusions: 44% (29-58) & 49% (34-65) v 42% (11-71) & 56% (22-93). The outcome of BCR-ABL1 positive patients was not different from the remaining BCP-ALL patients (hazard ratios 0.92 (0.72-1.18), p=0.5 & 0.93 (0.67-1.29), p=0.7). Additional primary chromosomal abnormalities were detected as follows: ABL-class fusions (ABL1, ABL2, PDGFRB, CSF1R) (n=6, 1.3%), JAK-STAT abnormalities (P2RY8-CRLF2, IGH-CRLF2, JAK2 fusions) (n=34, 1.3%) and ZNF384 fusions (n=12, 3%). Patients with JAK-STAT abnormalities had high rates of MRD positivity post phase 2 induction (76%), high RR (62% (42-82)) and lower OS (35% (18-52)) despite 82% being stratified as high-risk. Three of the six patients with an ABL-class fusion have had an event - 2 early remission deaths and 1 relapse. In contrast, among the 12 patients with a ZNF384 fusion only 2 have relapsed (both after 3 years) and none have died. Secondary copy number alterations affecting key genes/regions were determined by MLPA in 437 samples. 270 (62%) samples harboured one or more deletion at the following frequencies: IKZF1 (n=169, 39%), CDKN2A/B (n=163, 37%), PAX5 (n=95, 22%), BTG1 (n=48, 11%), ETV6 (n=33, 8%), EBF1 (n=15, 3%), RB1 (n=31, 7%) and PAR1 (n=6, 1%). None of the individual deletions nor the number of deletions were associated with survival, either in the cohort overall or when stratified by BCR-ABL1 status. In particular, IKZF1 deletions had no impact on RR: hazard ratios = 1.04 (0.72-1.49), p=0.850 (Overall); 0.84 (0.45-1.56), p=0.584 (BCR-ABL1); 1.23 (0.51-2.98), p=0.645 (B-Other). In addition, neither of the two recently validated paediatric CNA profiles - UKALL (Blood Advances 2019, 3:148) and IKZF1plus (JCO, 2018, 36:1240) - showed a significant association with outcome in the whole cohort or any relevant subgroup, e.g. BCR-ABL1 or B-other ALL. Collectively these data indicate that primary chromosomal abnormalities remain stronger prognostic markers in adult ALL than the more recently identified secondary deletions. On the basis of these results, we propose an amendment to the genetic risk classification for adult ALL wherein groups with distinct outcomes (see figure) comprise: (1) very high risk: CK, HoL or JAK-STAT abnormalities; (2) high risk: all KMT2A fusions; (3) Tyrosine kinase sensitive: BCR-ABL1 and ABL-class fusions; (4) low risk ZNF384 fusions; (5) standard risk: all other patients. The integration of these primary genetic risk factors with other risk factor such as age, white cell count and MRD into an overall risk score is a key goal of our current work. Figure Disclosures Papaemmanuil: Celgene: Research Funding. Menne:Bayer: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel grant, Research Funding, Speakers Bureau; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Astra Zeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Jazz: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel grant, Research Funding, Speakers Bureau; Kite/Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel grant, Research Funding, Speakers Bureau; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel grant, Research Funding, Speakers Bureau; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Kyowa Kirin: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel grant, Research Funding, Speakers Bureau; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel grant; Daiichi Sankyo: 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, Research Funding, Speakers Bureau; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel grant, Research Funding, Speakers Bureau. McMillan:BMS: Honoraria; Gilead: Honoraria; MSD: Honoraria; Novartis: Honoraria; Sandoz: Honoraria; F. Hoffmann-La Roche Ltd: Honoraria, Speakers Bureau; Pfizer: Honoraria, Research Funding; Celgene: Honoraria, Speakers Bureau. Rowntree:Novartis: Consultancy. Fielding:Novartis: Consultancy; Amgen: Consultancy; Pfizer: Consultancy; Incyte: Consultancy.