Abstract: Chromothripsis is emerging as a strong and independent prognostic factor in multiple myeloma (MM), predicting shorter progression-free (PFS) and overall survival (Rustad BioRxiv 2019). Reliable detection requires whole genome sequencing (WGS), with 24% prevalence in 752 newly diagnosed multiple myeloma (NDMM) from CoMMpass (NCT01454297, Rustad BioRxiv 2019) compared with 1.3% by array-based techniques (Magrangeas Blood 2011). In MM, chromothripsis presents differently to solid cancers. Although the biological impact is similar across malignancies, in MM the structural complexity of chromothriptic events is typically lower. In addition, chromothripsis can occur early in MM development and remain stable over time (Maura Nat Comm 2019). Computational algorithms for chromothripsis detection (e.g. ShatterSeek; Cortes-Ciriano Nat Gen 2018) were developed in solid cancers and are accurate in that setting. Running ShatterSeek on 752 NDMM patients with low coverage WGS from CoMMpass, we observed a high specificity for chromothripsis (98.3%) but poor sensitivity (30.2%). ShatterSeek detected chromothripsis in 64/752 samples (8.5%), with 85% confirmed on manual curation; however, missed 114 cases located by manual curation. This indicates that MM-specific computational methods are required. We hypothesized that a signature analysis approach using copy number variation (CNV) may provide an accurate estimation of chromothripsis. We adapted CNV signature analysis, developed in ovarian cancer (Macintyre Nat Gen 2018), to now detect MM-specific CNV and structural features. The analysis utilizes 6 fundamental CN features: i) absolute CN of segments, ii) difference in CN in adjacent segments, iii) breakpoints per 10 Mb, iv) breakpoints per chromosome arm, v) lengths of oscillating CN segment chains, and vi) the size of segments. The optimal number of categories in each CNV feature was established using a mixed effect model (mclust R package). Using CoMMpass low-coverage WGS, de novo extraction using the hierarchical dirichlet process defined 5 signatures, 2 of which (CNV-SIG 4 and CNV-SIG 5) contain features associated with chromothripsis: longer lengths of oscillating CN states, higher numbers of breakpoints / chromosome arm, and higher total numbers of small segments of CN change. Next, we demonstrate that CNV signatures are highly predictive of chromothripsis (average area-under-the-curve /AUC = 0.9, based on 10-fold cross validation). Chromothripsis-associated CNV signatures are correlated with biallelic TP53 inactivation (p= 0.01) and gain1q21 (p & lt;0.001) and show negative association with t(11;14) (p & lt;0.001). Chromothriptic signatures were associated with shorter PFS, with multivariate analysis after correction for ISS, age, biallelic TP53 inactivation, t(4;14) and gain1q21 producing a hazard ratio of 2.9 (95% CI 1.07-7.7, p = 0.036). A validation set of 29 NDMM WGS confirmed the ability of CNV signatures to predict chromothripsis (AUC 0.87). As WGS is currently too expensive and computationally intensive to employ in routine practice, we investigated if CNV signatures can predict chromothripsis without using WGS. First, we performed de novo signature extraction using whole exome data from 865 CoMMpass samples. CNV signatures extracted without reference to WGS produced an AUC = 0.81 for predicting chromothripsis (in those with WGS to confirm; n =752), and the chromothriptic-signatures confirmed the association with a shorter PFS (HR=7.2, 95%CI 1.32-39.4, p = 0.022). Second, we applied CNV signature analysis to NDMM having either the myTYPE targeted sequencing panel (n= 113; Yellapantula, Blood Can J 2019) or a single nucleotide polymorphism (SNP) array (n= 217). CNV signature assessment by each technology was predictive of clinical outcome, likely due to the detection of chromothripsis. As with WGS, multivariate analysis confirmed CNV signatures to be independently prognostic (myTYPE; p = 0.003, SNP; p = 0.004). Overall, we demonstrate that CNV signature analysis in NDMM provides a highly accurate prediction of chromothripsis. CNV signature assessment remains reliable by multiple surrogate measures, without requiring WGS. Chromothripsis-associated CNV signatures are an independent and adverse prognostic factor, potentially allowing refinement of standard prognostic scores for NDMM patients and providing a more accurate risk stratification for clinical trials. Disclosures Hultcrantz: Amgen: Research Funding; Daiichi Sankyo: Research Funding; GSK: Research Funding; Intellisphere LLC: Consultancy. Dogan:Takeda: Consultancy; National Cancer Institute: Research Funding; Roche: Consultancy, Research Funding; Seattle Genetics: Consultancy; AbbVie: Consultancy; EUSA Pharma: Consultancy; Physicians Education Resource: Consultancy; Corvus Pharmaceuticals: Consultancy. Morgan:Bristol-Myers Squibb: Consultancy, Honoraria; Janssen: Research Funding; Karyopharm: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria; GSK: Consultancy, Honoraria. Landgren:Cellectis: Consultancy, Honoraria; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; BMS: Consultancy, Honoraria; Adaptive: Consultancy, Honoraria; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Glenmark: Consultancy, Honoraria, Research Funding; Seattle Genetics: Research Funding; Binding Site: Consultancy, Honoraria; Karyopharma: Research Funding; Merck: Other; BMS: Consultancy, Honoraria; Karyopharma: Research Funding; Merck: Other; Pfizer: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Seattle Genetics: Research Funding; Juno: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Pfizer: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding; Cellectis: Consultancy, Honoraria; Glenmark: Consultancy, Honoraria, Research Funding; Binding Site: Consultancy, Honoraria.

Abstract: The World Trade Center (WTC) attack of September 11, 2001 created an unprecedented environmental exposure to known and suspected carcinogens. High incidence of multiple myeloma and precursor conditions has been reported among first responders to the WTC disaster. To expand on our prior screening studies, and to characterize the genomic impact of the exposure to known and potential carcinogens in the WTC debris, we were motivated to perform whole-genome sequencing (WGS) of WTC first responders and recovery workers who developed a plasma cell disorder after the attack. Experimental Design: We performed WGS of nine CD138-positive bone marrow mononuclear samples from patients who were diagnosed with plasma cell disorders after the WTC disaster. Results: No significant differences were observed in comparing the post-WTC driver and mutational signature landscapes with 110 previously published WGSs from 56 patients with multiple myeloma and the CoMMpass WGS cohort (n = 752). Leveraging constant activity of the single-base substitution mutational signatures 1 and 5 over time, we estimated that tumor-initiating chromosomal gains were windowed to both pre- and post-WTC exposure. Conclusions: Although limitations in sample size preclude any definitive conclusions, our findings suggest that the observed increased incidence of plasma cell neoplasms in this population is due to complex and heterogeneous effects of the WTC exposure that may have initiated or contributed to progression of malignancy.

Abstract: Background: Patients treated with cytotoxic chemotherapies and/or autologous stem-cell transplantation (ASCT) are at risk for therapy-related myeloid neoplasms (tMN). As these agents yield increased mutation burden in relapsed malignancies and leave evidence of exposure via mutational signatures, we studied the genomic and temporal relationship between chemo exposure and progression of clonal hematopoiesis (CH) to tMN. Methods: We analyzed 32 tMN whole genomes (WG) from 31 patients [27 acute myeloid leukemias (AML), 4 myelodysplastic syndromes]. For 7 patients with tMN post-high-dose melphalan/ASCT, we investigated the presence of antecedent CH using targeted sequencing (MSK-IMPACT; Bolton et al. Nat Gen 2020) on pre-melphalan blood mononuclear cells, granulocytes, or CD34+ apheresis samples. Results: TMN was diagnosed a median of 4.2 years (IQR, 2.6-6.6) following primary treatment. When compared to data from 200 de novo AML from TCGA (NEJM, 2013), tMNs had fewer mutations in FLT3 (9.7% v 28.0%; p = 0.028) and NPM1 (3.2% v 27.0%; p = 0.003). TP53 loss was enriched in tMNs (25.8% v 10.5%; p = 0.035 ). Mutational signature analysis revealed 5 known single base substitution (SBS) signatures in tMN: the hematopoietic stem-cell (SBS-HSC), aging (SBS1), melphalan (SBS-MM1), and platinum signatures (E-SBS1, E-SBS20) (Rustad et al. Nat Comm 2020, Pich et al. Nat Gen 2019). Complex structural variants (SV), defined as ≥3 breakpoint pairs involved in simultaneous copy number changes (Rustad et al. Blood Can Disc 2020), were observed in 7 tMNs; including chromothripsis in 6 tumors (19.4%), chromoplexy in 2 (6.5%), templated insertion in 1 (3.2%), and unspecified complex SV in 2 (6.5%). Chromothripsis has not been previously reported in de novo AML and, in 4 cases, involved chromosome 19 with hyper-amplification of the SMARCA4 locus (≥5 copies). CH variants that became clonal in tumor were seen in 5/7 pre-melphalan/ASCT samples and included mutations in TP53, RUNX1, NCOR1, NF1, CREBBP, DNMT3A, and PPM1D. Chemotherapy introduces hundreds of mutations, leaving each exposed cell with a unique catalogue (i.e., barcode). In fact, TMNs with evidence of chemo signatures had a higher mutation burden (median 1574 single nucleotide variants) than those without (median 938; p = 0.004). Detection of chemo signatures in bulk genome sequencing relies on one cell, with its catalogue of mutations, to expand to clonal dominance (Fig 1a, Landau et al. Nat Comm 2020). Given the long latency between exposure and tMN diagnosis, this single-cell expansion model was expected for all samples exposed to melphalan or platinum-based regimens (i.e., agents with a measurable signature). Strikingly, all patients with pre-tMN platinum exposure (n=7) had evidence of platinum SBS signatures whereas only 2 of 7 patients with prior melphalan/ASCT had a melphalan signature (SBS-MM1). As all platinum-exposed tMN had mutational evidence of exposure, a CH clone must have existed prior to exposure, supporting a single-cell expansion model. Absence of a chemo signature for 5/7 post-melphalan/ASCT tumors despite exposure implies tumor progression driven either by multiple clones in parallel (Fig 1b) or by an unexposed clone. As latency largely excludes the former, this suggests pre-tMN CH clones were re-infused during SCT, thus avoiding chemo exposure (Fig 1c). This is supported by two lines of evidence: 1) tMNs from 2 patients exposed to sequential platinum and melphalan/ASCT had platinum but not melphalan signatures confirming single-cell expansion of the pre-tMN CH clone post-platinum but with escape from exposure to melphalan in the ASCT (Fig 1d); 2) targeted sequencing of pre-tMN samples from melphalan/ASCT patients identified tMN genomic mutations at the CH level in 5/7 cases, including in all 3 tested apheresis samples - one of which (TP53) expanded to dominance without a melphalan signature. Conclusion: WG sequencing identified novel features of tMN revealing the key driver role of complex SV. Mutational signature analyses and targeted sequencing of pre-tMN samples can increase our understanding of tMN pathogenesis and demonstrate that tMNs arising post-ASCT are often driven by CH clones that re-engraft after escaping melphalan exposure. This mode of expansion suggests that a permissive, immunosuppressed, post-transplant environment might play a more important role than chemotherapy-induced mutagenesis in tMN pathogenesis. Figure 1 Figure 1. Disclosures Diamond: Sanofi: Honoraria; Medscape: Honoraria. Watts: Rafael Pharmaceuticals: Consultancy; Genentech: Consultancy; Bristol Myers Squibb: Consultancy; Takeda: Consultancy, Research Funding; Jazz Pharmaceuticals: Consultancy; Aptevo Therapeutices: Research Funding. Kazandjian: Arcellx: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees. Bradley: AbbVie: Consultancy, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Bolli: Amgen: Honoraria; Takeda: Honoraria; Janssen: Consultancy, Honoraria; Celgene/BMS: Consultancy, Honoraria. Papaemmanuil: Isabl Technologies: Divested equity in a private or publicly-traded company in the past 24 months; Kyowa Hakko Kirin Pharma: Consultancy. Scordo: Kite - A Gilead Company: Membership on an entity's Board of Directors or advisory committees; i3 Health: Other: Speaker; Omeros Corporation: Consultancy; Angiocrine Bioscience: Consultancy, Research Funding; McKinsey & Company: Consultancy. Lahoud: MorphoSys: Membership on an entity's Board of Directors or advisory committees. Stein: Jazz Pharmaceuticals: Consultancy; Foghorn Therapeutics: Consultancy; Blueprint Medicines: Consultancy; Gilead Sciences, Inc.: Consultancy; Abbvie: Consultancy; Janssen Pharmaceuticals: Consultancy; Genentech: Consultancy; Celgene: Consultancy; Bristol Myers Squibb: Consultancy; Agios Pharmaceuticals, Inc: Consultancy; Novartis: Consultancy; Astellas: Consultancy; Syndax Pharmaceuticals: Consultancy; PinotBio: Consultancy; Daiichi Sankyo: Consultancy; Syros Pharmaceuticals, Inc.: Consultancy. Sauter: Precision Biosciences: Consultancy; Kite/Gilead: Consultancy; Bristol-Myers Squibb: Research Funding; GSK: Consultancy; Gamida Cell: Consultancy; Celgene: Consultancy, Research Funding; Genmab: Consultancy; Novartis: Consultancy; Spectrum Pharmaceuticals: Consultancy; Juno Therapeutics: Consultancy, Research Funding; Sanofi-Genzyme: Consultancy, Research Funding. Hassoun: Celgene, Takeda, Janssen: Research Funding. Mailankody: Bristol Myers Squibb/Juno: Research Funding; Physician Education Resource: Honoraria; Plexus Communications: Honoraria; Takeda Oncology: Research Funding; Jansen Oncology: Research Funding; Fate Therapeutics: Research Funding; Allogene Therapeutics: Research Funding; Legend Biotech: Consultancy; Evicore: Consultancy. Korde: Medimmune: Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding. Hultcrantz: Daiichi Sankyo: Research Funding; Intellisphere LLC: Consultancy; Curio Science LLC: Consultancy; GlaxoSmithKline: Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Research Funding. Shah: Bristol Myers Squibb: Research Funding; Janssen: Research Funding. Shah: Janssen Pharmaceutica: Research Funding; Amgen: Research Funding. Park: Servier: Consultancy; Affyimmune: Consultancy; Autolus: Consultancy; Minerva: Consultancy; PrecisionBio: Consultancy; BMS: Consultancy; Novartis: Consultancy; Kura Oncology: Consultancy; Curocel: Consultancy; Artiva: Consultancy; Innate Pharma: Consultancy; Intellia: Consultancy; Amgen: Consultancy; Kite Pharma: Consultancy. Landau: Genzyme: Honoraria; Takeda, Janssen, Caelum Biosciences, Celgene, Pfizer, Genzyme: Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding. Sekeres: BMS: Membership on an entity's Board of Directors or advisory committees; Takeda/Millenium: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees. Ho: Blueprint Medicine: Membership on an entity's Board of Directors or advisory committees. Roshal: Celgene: Other: Provision of services; Auron Therapeutics: Other: Ownership / Equity interests; Provision of services; Physicians' Education Resource: Other: Provision of services. Lesokhin: pfizer: Consultancy, Research Funding; Janssen: Honoraria, Research Funding; Iteos: Consultancy; Serametrix, Inc: Patents & Royalties; Genetech: Research Funding; Trillium Therapeutics: Consultancy; bristol myers squibb: Research Funding; Behringer Ingelheim: Honoraria. Morgan: BMS: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees. Landgren: Janssen: Other: IDMC; Janssen: Research Funding; Amgen: Honoraria; Celgene: Research Funding; Janssen: Honoraria; Amgen: Research Funding; Takeda: Other: IDMC; GSK: Honoraria. Maura: OncLive: Honoraria; Medscape: Consultancy, Honoraria.

Abstract: PURPOSE : The World Trade Center (WTC) attack of September 11, 2001 created an unprecedented environmental exposure to known and suspected carcinogens. A higher incidence of multiple myeloma (MM) and precursor disease has been reported among first responders to the WTC disaster compared to the unexposed population (Landgren, 2018). To expand on prior screening studies, and to characterize the genomic impact of the exposure to known and potential carcinogens in the WTC debris, we were motivated to perform whole genome sequencing (WGS) of WTC first responders and recovery workers who were diagnosed with a plasma cell disorder after the attack. PATIENTS AND METHODS: We performed WGS of 9 CD138-positive bone marrow mononuclear samples from patients who were diagnosed with plasma cell disorders after exposure to the WTC disaster: 4 monoclonal gammopathy of undetermined significance (MGUS), 2 smoldering multiple myeloma (SMM), 2 MMs, and 1 patient with plasma cell leukemia (PCL). Eight patients (88%) were first responders and one was a recovery worker. Peripheral blood mononuclear cells were used as normal match. Median coverage for tumor and normal samples was 50.9X (range 47-76) and 37X (range 35-41), respectively. The landscape of genomic drivers and complex structural events was compared to 752 MM patients enrolled in the CoMMpass trial with available whole exome and low-coverage long-insert WGS data (IA15; NCT01454297). To characterize the mutational signature landscape we combined the WTC cohort with 110 whole genomes from 56 patients with multiple myeloma and myeloma precursor disease (Rustad et al. 2020; Landau et al. 2020) and we ran our three-step workflow: de novo extraction (i.e. sigprofiler), assignment, and fitting (i.e. mmsig). To exclude contribution of any environmental agents in the WTC debris with known mutational signatures (Kucab et al., 2019), we ran our fitting algorithm mmsig in each post-WTC case, including and forcing the extraction of these mutational signatures. RESULTS: No significant differences were observed in comparing the post-WTC driver and mutational signatures landscape with 110 previously published WGS from 56 patients with MM and the CoMMpass WGS cohort (n=752). Likewise, we did not observe any new or distinct mutational signatures among WTC-exposed patients. Following forced extraction of 5 mutational signatures associated with environmental agents detected in the WTC debris (e.g. PAHs), we did not find significant contributions from any of these described environmental mutational signatures. To reconstruct the temporal activity of each mutational process we divided all single nucleotide variants into subclonal and clonal. Clonal mutations were further subdivided into duplicated (acquired before a chromosomal gain) and unduplicated (Rustad et al. 2020). WTC-exposed patients had differing patterns in mutational signature timelines of AID and APOBEC activity. Overall, the mutational signature activity over time in post-WTC plasma cell dyscrasia reflects what has been previously observed in multiple myeloma without WTC-exposure (Rustad et al., 2020). Finally, leveraging constant activity of the clock-like single base substitution mutational signatures 1 and 5 over time and our molecular time workflow (Rustad et al., 2020), we estimated the age at which each evaluable patient acquired a tumor-initiating chromosomal gain and found that they were windowed to both pre- and post-WTC exposure across neoplasms (Figure 1). In some cases, clonal multi-chromosomal gain events were acquired decades before both the diagnosis and the WTC exposure. Specifically, of 6 patients with large clonal chromosomal gains, 1 MM case, 1 SMM, and 1 MGUS showed evidence of a pre-existing clone prior to WTC exposure, two MGUS showed evidence of multi-gain events following the exposure, and one MM case had a 1q gain in the same time window as the attack. CONCLUSIONS: Post-WTC plasma cell neoplasms had similar genomic landscapes to non-exposed cases. Although limitations in sample size preclude any definitive conclusions, our findings suggest that the observed increased incidence of plasma cell neoplasms in this population is due to complex and heterogeneous effects of the WTC exposure that may have initiated or contributed to progression of malignancy. The existence of pre-malignant clonal entities at time of WTC exposure may therefore be relevant for future WTC-related study. Figure 1 Disclosures Hultcrantz: Intellisphere LLC: Consultancy; Amgen: Research Funding; Daiichi Sankyo: Research Funding; GSK: Research Funding. Shah:Physicians Education Resource: Honoraria; Celgene/BMS: Research Funding. Iacobuzio-Donahue:BMS: Research Funding. Papaemmanuil:Isabl: Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Honoraria; Illumina: Consultancy, Honoraria; Kyowa Hakko Kirin: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Prime Oncology: Consultancy, Honoraria; MSKCC: Patents & Royalties. Verma:BMS: Consultancy, Research Funding; acceleron: Consultancy, Honoraria; stelexis: Current equity holder in private company; Janssen: Research Funding; Medpacto: Research Funding. Dogan:National Cancer Institute: Research Funding; EUSA Pharma: Consultancy; Takeda: Consultancy; Seattle Genetics: Consultancy; Corvus Pharmaceuticals: Consultancy; Physicians Education Resource: Consultancy; Roche: Consultancy, Research Funding; AbbVie: Consultancy. Landgren:Celgene: Consultancy, Honoraria, Research Funding; Seattle Genetics: Research Funding; Cellectis: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Merck: Other; Karyopharma: Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Pfizer: Consultancy, Honoraria; Merck: Other; Karyopharma: Research Funding; Binding Site: Consultancy, Honoraria; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Seattle Genetics: Research Funding; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding; Adaptive: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; Glenmark: Consultancy, Honoraria, Research Funding; Binding Site: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Glenmark: Consultancy, Honoraria, Research Funding.

Abstract: Introduction APOBEC mutational signatures are one of the most important mutational signatures in newly diagnosed multiple myeloma (NDMM). APOBEC mutagenesis primarily works through two enzymes, APOBEC3A (A3A) and APOBEC3B (A3B). While these genes are expressed in most NDMM patients, their influence on APOBEC mutagenesis appear to be extremely heterogeneous, with some patients having no detectable mutations and others having high contribution (hyper-APOBEC). Methods To decipher the mechanism of APOBEC mutagenesis, we interrogated 752 low coverage long insert whole genomes, 723 whole exomes (WES) and 767 RNA sequencing (RNA-Seq) from NDMM patients enrolled in the CoMMpass study. Results APOBEC mutational activity was identified in 416/723 (57.5%) patients using WES. Of these, 41 (5.8%) had MAF/MAFB translocations. Overall, 48/723 (6.6%) patients were defined as hyper-APOBEC (13 without MAF/MAFB events). We used differential expression (DE) analysis between three groups: hyper-APOBEC with MAF/MAFB (HA_TRA: n=31), hyper-APOBEC without MAF/MAFB (HA_NORM: n=8), and non-hyper-APOBEC without MAF/MAFB (WT: n=510). 534 genes were significantly associated with hyper-APOBEC independently from MAF/MAFB events. Importantly, A3A and A3B expression were higher in hyper-APOBEC compared to other cases (logFC=2.38, logFC=1.58 respectively). Using Spearman's correlation, we identified 50 significantly correlated genes (r-squared & gt;0.18; p & lt;0.01) with A3B. Most of the genes belonged to the APOBEC-associated group defined by the DE and were enriched for cell cycle/proliferation activity, suggesting a link between highly proliferative disease and hyper-APOBEC. To validate this finding, we evaluated the link between these APOBEC-associated genes and hyper-APOBEC among ICGC breast cancers, a tumor known to have high prevalence of APOBEC mutagenesis. Overall, 35/50 of these genes showed similar correlation between hyper-APOBEC breasts and MM. Interestingly, most of these genes appear to be negatively controlled by E2F4 (DREAM complex), and positively controlled by E2F1, FOXM1, and MYBL2 (i.e. cell cycle genes). In line with mouse models (Roelofs et al., eLife, 2020), our data confirmed that highly proliferating tumors suppress the DREAM complex leading to induction of A3B. Overall, these data suggest a strong link between proliferation and A3B expression and mutagenesis. However, not all high proliferating tumors had high APOBEC mutagenesis. To better investigate this aspect, we compared 139 known MM genomic events to identify potential differences between the hyper-APOBEC group (HA) and the rest of the cohort (WT). HA_TRA and HA_NORM showed similarities, including a highly complex genomic profile with enrichment for 1q gain/amp (p & lt; 0.02), 13q (p & lt; 0.05) and 16q deletions (p & lt;0.01). Interestingly, 5/13 (38.4%) of HA_NORM samples harbor CCND1 translocation. In terms of gene expression, both groups showed enrichment for GEP70 indicating a high-risk transcriptional profile apart from MAF/MAFB overexpression. Finally, HA_TRA and HA_NORM showed similar poor outcomes compared to WT. To further validate the association between APOBEC and complex genomic profile, we explored again the ICGC breast WGS, observing a strong correlation between homologous recombination deficiency (HRD) and hyper-APOBEC (p & lt; 0.05). To validate the relationship between the DREAM complex and APOBEC, we pharmacologically inhibited FOXM1 (FDI6, 20uM) and E2F4/6 (HLM00074, 20uM) in 8226, U266 and MM1.S multiple myeloma cell lines and showed increased A3B levels upon 72h of E2F4/6 inhibition. Simultaneously, FOXM1 inhibition confirmed A3B downregulation. Conclusion Overall, our data support a model where, APOBEC genes are variably expressed in all NDMM, but only patients with high proliferation rate and high level of complexity acquire mutations (Figure 1).

Abstract: Introduction Current clinical models for predicting the progression from myeloma precursor disease (smoldering multiple myeloma (SMM) and monoclonal gammopathy of undetermined significance (MGUS)) to multiple myeloma (MM) are based on tumor burden, and not designed to capture heterogeneity in tumor biology. With the advent of whole genome sequencing (WGS), complex genomic change including the catastrophic event of chromothripsis has been detected in a significant fraction of MM patients. Chromothripsis is associated with other features of aggressive biology (i.e. biallelic TP53 deletion and increased APOBEC activity), and in newly diagnosed MM (NDMM), patients harboring chromothripsis have a shorter progression free survival (PFS) (Rustad BioRxiv 2019). Chromothripsis has also been demonstrated in SMM which later progressed to MM (Maura Nat Comm 2019) and our preliminary results indicate that the absence of chromothripsis is associated with stable precursor disease (Oben ASH 2020). We have demonstrated that chromothripsis can be accurately predicted in NDMM using copy-number variation (CNV) signatures on both WGS and whole exome sequencing (Maclachlan ASH 2020). As with WGS, CNV signature analysis in less comprehensive assays (e.g. targeted sequencing panels and single nucleotide polymorphism (SNP) arrays) demonstrated that chromothripsis-associated CNV signatures are associated with shorter PFS. The aim of this study was to define the landscape of CNV signatures in myeloma precursor disease, and to compare the results with CNV signatures in NDMM. Methods CNV signature analysis uses 6 fundamental features: i) breakpoint count per 10 Mb, ii) absolute CN of segments, iii) difference in CN between adjacent segments, iv) breakpoint count per chromosome arm, v) lengths of oscillating CN segments, and vi) the size of segments (Macintyre Nat Gen 2018). The number of subcategories for each feature (which may differ between cancer and assay types) was established using a mixed effect model (mclust R package). For both targeted sequencing (myTYPE panel; (n=19, 4 MGUS, 15 SMM) and SNP array (n=78, 16 MGUS, 62 SMM), de novo CNV signature extraction was performed by hierarchical dirichlet process, running the analysis together with NDMM samples for reliable signature detection. Results Our analysis identified 4 and 6 CNV signatures from myTYPE and SNP array data respectively, with the extracted signatures being analogous to those from WGS, which are highly predictive of chromothripsis (Maclachlan ASH 2020). Compared with NDMM (myTYPE; n=113; SNP array; n=217), precursor samples contained significantly fewer breakpoints / chromosome arm (myTYPE; p= 0.0003, SNP; p & lt;0.0001), fewer breakpoints / 10 Mb (both; p & lt;0.0001), shorter lengths of oscillating CN (myTYPE; p= 0.013, SNP; p= 0.018), fewer jumps between CN states (myTYPE; p= 0.0043, SNP; p & lt; 0.0001), lower absolute CN (myTYPE; p= 0.0059, SNP; p & lt; 0.0001) and fewer small segments of CN change (myTYPE; p= 0.0007, SNP; p= 0.0008). Chromothripsis-associated CNV signatures were significantly enriched in NDMM compared to precursor disease (p & lt;0.0001), with only 8.2% of precursors having a significant contribution from these signatures (NDMM; 38.7%). Overall, every CNV feature consistent with chromothripsis was measured at a significantly lower level in precursors than NDMM. As & lt;5% of the precursors have progressed to MM, and given that we see heterogeneity in the pattern of CNV abnormalities both between MM and precursor disease, and within patients with precursor disease, we are currently investigating the role of CNV abnormalities in relation to clinical progression. As an interim measure; restricting analysis to patients with clinical stability & gt;5 years (n=11), we observed chromothripsis-associated signatures to be absent in all samples. Conclusion All individual CN features comprising chromothripsis-associated CNV signatures are significantly lower in stable myeloma precursor disease compared with NDMM when assessed by targeted sequencing and SNP array, along with a lower contribution from chromothripsis-associated signatures. Given the adverse impact of chromothripsis in MM, these data show great promise towards the future refinement of risk prediction estimation in myeloma precursor disease. Our ongoing work involves extending CNV analysis into larger datasets, including precursor patients who subsequently progressed to MM. Disclosures Hultcrantz: Intellisphere LLC: Consultancy; Amgen: Research Funding; Daiichi Sankyo: Research Funding; GSK: Research Funding. Dogan:Roche: Consultancy, Research Funding; Physicians Education Resource: Consultancy; Corvus Pharmaceuticals: Consultancy; Seattle Genetics: Consultancy; Takeda: Consultancy; EUSA Pharma: Consultancy; AbbVie: Consultancy; National Cancer Institute: Research Funding. Morgan:Bristol-Myers Squibb: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Janssen: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; GSK: Consultancy, Honoraria. Landgren:Amgen: Consultancy, Honoraria, Research Funding; Karyopharma: Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Seattle Genetics: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Glenmark: Consultancy, Honoraria, Research Funding; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Cellectis: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Binding Site: Consultancy, Honoraria; Adaptive: Consultancy, Honoraria; Merck: Other; Pfizer: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Binding Site: Consultancy, Honoraria; Karyopharma: Research Funding; Merck: Other; Pfizer: Consultancy, Honoraria; Seattle Genetics: Research Funding; Juno: Consultancy, Honoraria; Glenmark: Consultancy, Honoraria, Research Funding.

Abstract: Multiple myeloma (MM) progression is characterized by the seeding of cancer cells in different anatomic sites. To characterize this evolutionary process, we interrogated, by whole genome sequencing, 25 samples collected at autopsy from 4 patients with relapsed MM and an additional set of 125 whole exomes collected from 51 patients. Mutational signatures analysis showed how cytotoxic agents introduce hundreds of unique mutations in each surviving cancer cell, detectable by bulk sequencing only in cases of clonal expansion of a single cancer cell bearing the mutational signature. Thus, a unique, single-cell genomic barcode can link chemotherapy exposure to a discrete time window in a patient′s life. We leveraged this concept to show that MM systemic seeding is accelerated at relapse and appears to be driven by the survival and subsequent expansion of a single myeloma cell following treatment with high-dose melphalan therapy and autologous stem cell transplant.

Abstract: A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-20978-y.