Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF  〈  15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.

Abstract: Correction to this paper has been published: https://doi.org/10.1038/s41467-020-20128-w

Abstract: The genomic landscape of Waldenström macroglobulinemia (WM) is characterized by somatic mutations in MYD88, present from the precursor stages. Using the comprehensive resolution of whole genome sequencing (WGS) in 14 CD19-selected primary WM samples; comparing clonal and subclonal mutations revealed that germinal center (GC) mutational signatures SBS9 (poly-eta) and SBS84 (AID) have sustained activity, suggesting that the interaction between WM and the GC continues over time. Expanding our cohort size with 33 targeted sequencing samples, we interrogated the WM copy number aberration (CNA) landscape and chronology. Of interest, CNA prevalence progressively increased in symptomatic WM and relapsed disease when compared with stable precursor stages, with stable precursors lacking genomic complexity. Two MYD88 wild-type WGS contained a clonal gain affecting chromosome 12, which is typically an early event in chronic lymphocytic leukemia. Molecular time analysis demonstrated that both chromosomal 12 gain events occurred early in cancer development whereas other CNA changes tend to occur later in the disease course and are often subclonal. In summary, WGS analysis in WM allows the demonstration of sustained GC activity over time and allows the reconstruction of the temporal evolution of specific genomic features. In addition, our data suggest that, although MYD88-mutations are central to WM clone establishment and can be observed in precursor disease, CNA may contribute to later phases, and may be used as a biomarker for progression risk from precursor conditions to symptomatic disease.

Abstract: Minimal residual disease (MRD) tracking, by next generation sequencing of immunoglobulin sequences, is moving towards clinical implementation in multiple myeloma. However, there is only sparse information available to address whether clonal sequences remain stable for tracking over time, and to what extent light chain sequences are sufficiently unique for tracking. Here, we analyzed immunoglobulin repertoires from 905 plasma cell myeloma and healthy control samples, focusing on the third complementarity determining region (CDR3). Clonal heavy and/or light chain expression was identified in all patients at baseline, with one or more subclones related to the main clone in 3.2%. In 45 patients with 101 sequential samples, the dominant clonal CDR3 sequences remained identical over time, despite differential clonal evolution by whole exome sequencing in 49% of patients. The low frequency of subclonal CDR3 variants, and absence of evolution over time in active multiple myeloma, indicates that tumor cells at this stage are not under selective pressure to undergo antibody affinity maturation. Next, we establish somatic hypermutation and non‐templated insertions as the most important determinants of light chain clonal uniqueness, identifying a potentially trackable sequence in the majority of patients. Taken together, we show that dominant clonal sequences identified at baseline are reliable biomarkers for long‐term tracking of the malignant clone, including both IGH and the majority of light chain clones.

Abstract: 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.

Abstract: Introduction: Classical Hodgkin lymphoma (cHL) is characterized by a small fraction of Hodgkin and Reed-Sternberg (HRS) tumor cells (~1%) which are surrounded by an extensive immune infiltrate. The rare nature of HRS cells limits the ability to study the genomics of cHL using standard platforms. To circumvent this, our group has optimized fluorescence-activated cell sorting to isolate HRS cells and intratumor B- and T- cells and to perform whole exome sequencing (WES; Reichel, Blood 2015). To date, however, there have been no reports on whole genome sequencing (WGS) of cHL. Methods: We performed flow-sorting of HRS cells and WGS to define the genomic landscape of cHL including: i) mutational processes involved in pathogenesis, ii) large and focal copy number variants, iii) structural variants including complex events, iv) the sequence and evolution of molecular events in cHL. We interrogated WGS from 25 cases of cHL: 10 pediatric patients (age & lt;18), 9 adolescents and young adults (AYA, age 18-40), and 6 older adults (age & gt;40). Intra-tumoral T-cells were used as germline control. An additional 36 cHL cases were evaluated by WES. Results: The average depth of coverage among the 25 WGS cases was 27.5x. After having identified and removed amplification-based palindromic sequencing artifacts, we observed a median of 5006 single base substitutions (SBS; range 1763-18436). Pediatric and AYA patients had a higher SBS burden compared to older adults (median 5279 vs. 2945, p=0.009). Five main SBS signatures were identified: SBS1 and SBS5 (aging), SBS2 and SBS13 (APOBEC), and SBS25 (chemotherapy, in a relapsed case). A dNdScv driver discovery analysis performed on the combined WES and WGS cases identified 24 driver genes including BCL7A and CISH which had not been previously reported as drivers in cHL. An investigation of copy number alterations (CNAs) confirmed high ploidy in cHL (median 2.95, range 1.66-5.33). Whole genome duplication was identified in 64% cases. We also observed clear evidence of complex events such as chromothripsis (n=4), double minutes (dm, n=2), breakage-fusion-bridge (bfb; n=4). Some of these events were responsible for the acquisition of distinct drivers. For example, we observed one dm and one bfb responsible for CD274 and REL gains, respectively ( & gt;10 copies). Leveraging the high prevalence of large chromosomal gains, we performed an investigation of the relative timing of acquisition of driver mutations. Clonal mutations within chromosomal gains can be defined as duplicated (VAF~66%; acquired before the gain) or non-duplicated (VAF~33%; acquired before or after the gain). Sixty-one percent (152/249) of driver genes were duplicated suggesting that they were acquired prior to large chromosomal gains. Next, we used the corrected ratio between duplicated and non-duplicated mutations within large chromosomal gains to estimate the molecular time of each duplicated segment (Rustad, Nat Comm 2020). In 11/22 genomes the final CNA profile was acquired through at least two temporally distinct events. To convert these relative estimations into absolute timing (i.e., the age at which events occurred), we leveraged the clock-like mutation signatures (SBS1, SBS5). We first confirmed that the SBS1 and SBS5 mutation rate were constant over time (R 2=0.84; p & lt;0.0001 in Peds/AYA; R 2 =0.82; p=0.002 in older adults). We observed a higher mutation rate in Pediatric/AYA cases compared to older adults (p=0.01), which is consistent with the higher mutational burden observed in this age group. By estimating the SBS1- and SBS5-based molecular time for large chromosomal gains and converting relative estimates to absolute time, we are able to estimate the age in years at the time of the first multi-chromosomal gain event. We observed that the first multi-chromosomal gain in cHL is often acquired several years before the diagnosis/sample collection: median latency of 19.5 (range 12-27) and 5.6 (range 1.8-16) years in older adults and pediatric/AYA patients respectively. Conclusion: Here we report the first WGS in cHL. We identify novel drivers and genomic mechanisms involved in cHL pathogenesis. We found that mutations in driver genes are often acquired earlier then chromosomal gains, potentially preceding the cHL diagnosis by several years. In addition, we observed key differences in biology of cHL across age groups including accelerated mutagenesis and increased mutational burden among younger patients. Disclosures Maura: OncLive: Honoraria; Medscape: Consultancy, Honoraria. Oberley: Caris LIfe Science: Current Employment. Lim: EUSA Pharma: Honoraria. Landgren: Janssen: Other: IDMC; Celgene: Research Funding; Janssen: Honoraria; Amgen: Honoraria; Janssen: Research Funding; Amgen: Research Funding; Takeda: Other: IDMC; GSK: Honoraria. Moskowitz: Merck & Co., Inc.: Research Funding. Roshal: Celgene: Other: Provision of services; Auron Therapeutics: Other: Ownership / Equity interests; Provision of services; Physicians' Education Resource: Other: Provision of services. Elemento: Owkin: Consultancy, Other: Current equity holder; AstraZeneca: Research Funding; Champions Oncology: Consultancy; Volastra Therapeutics: Consultancy, Other: Current equity holder, Research Funding; One Three Biotech: Consultancy, Other: Current equity holder; Eli Lilly: Research Funding; Johnson and Johnson: Research Funding; Freenome: Consultancy, Other: Current equity holder in a privately-held company; Janssen: Research Funding. Roth: Janssen: Consultancy; Merck: Consultancy.

Abstract: Introduction Multiple Myeloma (MM) pathogenesis is characterised by extensive genetic and clonal evolution with frequent on-treatment progression. To date, most studies have focused on single diagnostic or paired diagnostic relapse biopsies, and the molecular mechanisms eventually resulting in treatment failure are poorly understood. To determine the molecular underpinnings of disease in its most advanced stage, we performed comprehensive genome profiling of 4 patients with extra-medullary metastatic disease. Methods A total of 8 patients with extramedullary myeloma with 188 (median = 22) distinct metastatic lesions were enrolled as part of the medical donation program at MSK. Here, we present results from 4 patients. Patients 1 and 2 had an indolent disease with a total survival of ~10 years whereas patients 3 and 4 had very aggressive disease and 2-3 years survival. All patients had received a sequence of multi-modal myeloma treatments. Targeted gene sequencing using a myeloma specific targeted panel myTYPE was performed in 28 samples from all 4 patients to a median coverage of 667x. Additionally, 6 tumors from patients 1 and 2 were subject to WGS to a median coverage 92x. Results Driver events: Aberrations across all 28 samples sequenced using myTYPE were examined. We found t(4;14) in Patients 3 and 4 across all the metastatic lesions consistent with previous knowledge that these are early initiating events. Besides IGH translocations, we found copy number changes involving 1p-, 1q+ and 13q-. Patient 1 and 2 had 17p- and 8p- shared across all the metastatic lesions. All patients had RAS/RAF pathway mutations and additional mutations were found in FAM46C, TP53 and BIRC3. In WGS, we observed a median SNV, Indel & SV burden of 12150, 1196 & 70.5 respectively. This mutational load is greater than two-fold higher to previously published estimates derived from primary diagnostic samples. Clonal structure using WGS: Clonal phylogeny was constructed using nDirichlet process clustering. Evaluation of mutation and clonal spectra showed evidence of clonal diversification amongst sites but within each sample all mutations had fully clonal cancer cell fractions, i.e. there were no subclones. For Patient 1, the phylogenetic tree was dominated by 9,099 truncal mutations, and 150-462 site specific yet clonal mutations. For Patient 2, the tree was dominated by 8,540 truncal mutations and site specific clonal mutations (n= 356; 1,186). However, evaluation of copy number alterations showed evidence of subclonal emergence of copy number aberrations implicating chromosomes 5, 8,16,18, 20, 21. This suggests that in these patients late stage tumor development and metastatic dissemination is further shaped by accrual of CNAs. Mutational processes using WGS: Signature analysis was performed by deconvolution of observed WGS mutations on the set of mutational signatures reported by Alexandrov et al. Consistent with previous reports, Signature 9 was identified as the dominant mutation signature, contributing to a median of 27% of all mutations in our cohort. Signature 9 is related to AID and has been previously implicated in early myeloma pathogenesis. Whilst canonical IGH translocations were not identified in Patient 1 or 2, Patient 1 showed evidence for chromoplexy with closed chain translocations having breakpoints spanning chromosomes 1, 4, 11, 16, 17, 19 across all 4 sites. Patient 2 presented with localised hypermutation on chromosomes 1, 5 and 22 which are shared between the 2 sites. These results suggest that subsequent clonal sweeps have acted upon the genome since disease initiation. Conclusion Preliminary data from multi-region WGS of the evolutionary end-stage in MM shows a single dominant clone with known driver events in each patient. This is in contrast to the subclonal heterogeneity characteristic of early disease, and presents opportunities for targeted therapies. Our observations are consistent with convergent evolution, where selective pressure from many years of therapy results in a relatively homogenous genomic landscape. A larger cohort of samples will ascertain patterns of biological processes we present here. These early investigations provides new insights on MM pathogenesis and metastatic dissemination. Disclosures Landgren: Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Research Funding; Karyopharm: Consultancy; Pfizer: Consultancy; Merck: Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Lesokhin:Genentech: Research Funding; Serametrix, inc.: Patents & Royalties: Royalties; Janssen: Research Funding; Takeda: Consultancy, Honoraria; Squibb: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding.

Abstract: The Multiple Myeloma Research Foundation (MMRF) CoMMpass trial is the keystone program working towards personalized medicine in multiple myeloma (MM). CoMMpass has characterized over 730 samples from 669 patients, a subset having time-course data, using multiple platforms and has made these data publicly available. Screening for druggable somatic mutations and gene expression outliers revealed great potential for repurposing existing pharmacotherapies. We have developed and curated a Database of Evidence for Precision Oncology (DEPO) that includes 442 mutations and 49 genes with expression changes implicated as drug targets from 34 cancer types. We found 43.6% of CoMMpass samples have one of 26 somatic mutations that could be targeted by 9 different drugs, suggesting many drugs may be repurposed for use in MM. HotSpot3D, a protein-structure-guided analysis tool, showed 3.3% (24/730) of samples have mutations involving BRAF and KRAS, clustering with known drug targets, and another 2.1%(15/730) have mutations in clusters formed from a prior TCGA pan-cancer analysis involving 22 cancer types. This indicates additional potential drug targets and functional mutations in MM. Interestingly, 11 samples (including relapse), have subclonal mutations in both KRAS and BRAF with variable allelic fractions, implicating different treatment requirements for tumor subpopulations and disease stages. Additionally, druggable gene expression outlier analysis of 591 samples reveals an average of nearly 5 outlier genes per sample from among 49 known target genes, with 18.1% (107/591) of samples with MYCN gene outliers as compared to MYC 1.4% (8/591), despite MYC being a known driver of MM. Other top outliers are DLL3 10.2%, EGFR 10.5%, FGFR1 10.2%, and FGFR2 12.5%. Our study highlights candidate drug targets previously omitted in MM. Taken together, it suggests that heterogeneity analysis and pharmaceutical repurposing could lead to substantially improved outcomes. Disclosures No relevant conflicts of interest to declare.

Abstract: Abstract 2920 Human myeloma cell lines (HMCL) provide both a discovery and validation platform to improve our understanding of the molecular pathogenesis of multiple myeloma. We have completed a project to characterize the underlying genetics of all commercially available HMCL with a primary goal of identifying appropriate model systems for findings from large scale patient studies like the multiple myeloma genomics initiative (MMGI). We first purchased all 33 commercially available HMCL from DSMZ, JCRB, ECACC, and ATCC. Subsequently each HMCL was thawed and cultured under strict parameters, which yielded cells for analysis, by Agilent 400k CGH, whole exome sequencing (Agilent 70Mb Exon+UTR), and mRNA sequencing. The combination of these three assays provides a detailed map of the genetic complexity underlying this deadly disease. For variant discovery, alignment was done using BWA followed by indel realignment, quality recalibration and duplicate removal. High quality calls were identified from the intersection of variants called by both Samtools and GATK. This identified a median of 32691 high confidence variants per sample with upper and lower quartile values of 34307.75 and 32241.25, respectively. To identify likely somatic mutations, we removed variants found in the 1000 genomes project and the NHLBI Exome Sequencing project. In addition, we removed variants present in dbSNP unless these mutations were also present in the COSMIC database. After these filtering steps a median of 702 potential mutations remained. From these lists we identified a median of 209.5 non-synonymous variants per sample and in genes which are typically expressed in the cohort, a median of 91 variants were found. Overall, these steps identified 2678 variants in 1978 genes. The primary goal was to identify appropriate models for novel findings from studies like the MMGI. For instance we identified HMCL with mutations in FAM46C and DIS3 among others. Secondarily, we focused on attempting to identify potential oncogenes and tumor suppressors through the integration of our three data types and data from published studies (Chapman et al. and Walker et al.). To identify potential oncogenes we focused on mutations that occurred at the same position in the genome or altered the same amino acid at a minimum. This identified 23 genes; including expected genes like KRAS (n=11) and, NRAS (n=7) but it also identified potentially activating mutations in IKBKB, SOX2, KDM4C, CD81, OSBP, NOTCH2, WDR92 and UBR2. To identify potential tumor suppressors we focused on genes that are typically expressed, which showed bi-allelic inactivation in two or more samples by either a homozygous deletion event, a deletion plus mutation, or two independent mutations. This identified 116 genes; including expected genes like TP53, CDKN2C, RB1, BIRC2/3, TRAF3, KDM6A, CDKN1B, FAM46C, and DIS3. Outside of the expected genes we identified recurrent inactivation in ANKRD11, ATP6AP1, ATXN1, BCL2L11, CDK8, RNF7, STS, TSPAN7, and TBL1XR1. These studies have highlighted the value in studying HMCL as most novel genes reported from recent studies were independently identified in this small cohort of samples. This is in large part because HMCL provide an unlimited DNA and RNA resource that allowed for multiple independent assays to be performed on each sample. Ultimately this study will provide the myeloma community with a detailed resource from which they can acquire appropriate model systems for their research goals from the various cell line repositories around the world. Disclosures: Keats: Tgen: Employment.