Genetic alterations alone cannot account for the diverse phenotypes of cancer cells. Even cancers with the same driver mutation show significant transcriptional heterogeneity and varied responses to therapy. However, the mechanisms underpinning this heterogeneity remain under-explored. Here, we find that novel enhancer usage is a common feature in acute lymphoblastic leukemia (ALL). In particular, KMT2A::AFF1 ALL, an aggressive leukemia with a poor prognosis and a low mutational burden, exhibits substantial transcriptional heterogeneity between individuals. Using single cell multiome analysis and extensive chromatin profiling, we reveal that much transcriptional heterogeneity in KMT2A::AFF1 ALL is driven by novel enhancer usage. By generating high resolution Micro Capture-C data in primary patient samples, we identify patient-specific enhancer activity at key oncogenes such as MEIS1 and RUNX2, driving high levels of expression of both oncogenes in a patient-specific manner. Overall, our data show that enhancer heterogeneity is highly prevalent in KMT2A::AFF1 ALL and may be a mechanism that drives transcriptional heterogeneity in cancer more generally.

Although immunomodulatory drugs (IMiDs, lenalidomide [LEN] & pomalidomide [POM]) have had a huge impact on the therapeutic landscape in multiple myeloma (MM), IMiD-induced neutropenia remains a clinical challenge. Understanding the molecular basis of IMiD-associated myelosuppression could improve the rational design of novel agents which mitigate this important side effect whilst retaining therapeutic efficacy against MM cells. We have previously established an experimental system to carry out single cell multiomic analyses of neutrophil differentiation ex vivo, allowing us to study the impact of exposure to IMiDs, which caused a maturation impairment and decrease in the abundance of differentiating granulocytes1. These findings aligned with previous clinical observations of a myeloid maturation arrest which is associated with IMiD-induced neutropenia in MM patients, with Cereblon (CRBN)-driven IKZF1 degradation previously implicated as the underlying cause. However, restoration of IKZF1 expression only partially rescues the IMiD-associated neutrophil maturation arrest, suggesting that other mechanisms might also contribute. The purpose of the current study was to interrogate our single cell multiomic dataset to characterise the impact of IMiDs on the dynamic genome regulatory landscape during neutrophil development and thereby identify putative novel neo-substrates that might contribute to myelosuppression. Analysis of single cell RNA seq (n=3 donors and 111,109 cells) and multiome datasets (n=3 donors and 47,452 cells) of ex vivo neutrophil differentiation and its perturbation by IMiDs, allowed us to construct a gene regulatory map to identify putative driver genes implicated in the pathobiology of IMiD-mediated neutrophil maturation arrest. Combined gene expression and chromatin accessibility analysis based on the detection of 330,334 peak-to-gene links correlated chromatin architecture to neutrophil terminal differentiation potential. Myeloid progenitors contained relaxed chromatin, whilst IMiD-associated abnormal myeloid precursors states were characterized by premature chromatin compaction (as reflected by a 30% decline in filtered peaks) and priming towards a pro-apoptotic state, despite bearing gene expression profiles largely reminiscent of intact neutrophils and precursors. This finding was corroborated by the reduction of accessible enhancer loci (4.5-14.8%), suggesting a repression of transcriptional activity in the treated populations. To enrich for candidate transcription factors, we selected motifs that were not altered in expression at the transcript level in the granulocyte-monocyte progenitor and metamyelocyte clusters but showed marked IMiD-induced changes in their associated regulons at the transcript level. Furthermore, ATAC-seq footprinting allowed us to infer altered transcription factor binding dynamics in the same cells. As expected, IKZF1 met these criteria alongside SPI1,CEBPA, ZBTB7A, ZBTB7B, KLF5, PPARA, PPARG additional candidate genes. To further prioritise candidate neo-substrates, we intersected our data with an extensive proteomic screening to enrich for CRBN-interacting small molecules. Amongst the genes overlapping in both our multiomic analysis and proteomic screen was KLF5, a known transcription factor involved in the regulation of neutrophil differentiation in mice. Moreover, we also identified downregulation of KLF5 binding partners C/EBPβ and C/EBPδ and the downstream target PPARG alongside a gradual switch from the overarching (during normal granulopoiesis) glycolytic metabolic machinery towards increased fatty acid oxidation. This surrogate pathway has been previously associated with skewed neutrophil effector functions and described in genome-wide association studies (GWAS) focusing on abnormal neutrophil counts. Intracellular FACS analysis confirmed rapid KLF5 degradation upon exposure to IMiDs, confirming that this is a likely IMiD-associated CRBN neo-substrate. Proximity assays, rescue studies and conformation capture assays to dissect the relevant key regulatory elements are underway. Our study has identified KLF5 as a putative novel regulator of IMiD-induced neutropenia which might inform the rational design of new therapeutic agents to mitigate this problematic side effect of widely used drugs in MM. 1.https://doi.org/10.1182/blood-2023-182041

Neutrophils are crucial immune cells with complex and heterogeneous transcriptional programs. To understand the dynamic changes governing human granulopoiesis, we previously developed an ex vivo myeloid cell differentiation assay using human mobilised peripheral blood CD34+ cells from healthy donors and conducted single cell multiomic analysis. We constructed a multimodal atlas of human granulopoiesis that captured the full spectrum of human myeloid cells spanning from early myeloid progenitors through to neutrophil populations, thus enabling the study the genome regulatory events underlying neutrophil maturation with unprecedented resolution1. Our aim was to design a computational analytical workflow enabling the exploration of the cellular and molecular drivers of normal human granulopoiesis alongside the precise characterisation of the dynamic changes in the genome regulatory landscape dictating myeloid cell commitment and terminal neutrophil differentiation. Our dataset included transcriptomic (scRNA; n=3 donors and 29,300 cells) and simultaneous profiled gene expression and chromatin accessibility (scGEX & scATAC; n=3 donors and 17,219 cells) analyses from mature neutrophils and myeloid precursors. We successfully co-embedded our data with a published reference dataset encompassing neutrophils and myeloid progenitors from heathy donors, confirming the overlap of the transcriptomic signatures. The transition from transcriptome-based to epigenome-based cluster labels improved annotation resolution, enabling precise identification of previously unexplored subtypes within neutrophil and precursor populations. Further investigation of both modalities revealed distinct chromatin conformations, but almost identical transcriptomic signatures, for clusters of band and mature neutrophils approaching the end of their lifespan. The exhausted pro-apoptotic neutrophils displayed dynamically increased chromatin compaction as evidenced by a 42.1% decline in accessible peaks detected, indicating an earlier stage towards programmed cell death commitment compared to robust neutrophils. Next, we used our resource atlas to interrogate the underlying genome regulatory networks and characterise cell-type-specific cis regulatory elements (CREs) involved in human granulopoiesis. We coupled transcription factor (TF) and target gene identification using Scanpy with SCENIC analysis, enabling a comparative study focusing on open chromatin regions. We then linked the pre-identified cell-type-specific activities for 178 differentially present TFs to chromatin accessibility changes using ArchR and performed motif enrichment and peak-to-gene linkage identification using the integrated scGEX dataset. As a result, we were able to underpin the connection between neutrophil terminal differentiation potential and the dynamic changes in chromatin architecture. We analysed the role of our candidate TFs by imputing regulatory single nucleotide polymorphisms (SNPs) from published genome-wide association studies (GWAS) focusing on neutrophil biology and absolute counts. This showed an increased transcriptional activity for our candidate TFs, with an overall enrichment frequency of detected regulatory SNPs, within the metamyelocyte compartment, thus further highlighting the role of this differentiation stage as a regulatory switch towards terminal neutrophil maturation. We used reference chromatin immunoprecipitation sequencing (ChIP-seq) data from mature neutrophils and progenitors focusing on histone modification marks and CTCF sites to further annotate de novo genome-wide cell-type-specific CREs using REgulamentary2. These findings were visualised using Multi-Dimensional Viewer, a comprehensive analytical interpretation tool, generating a publicly available resource atlas benefiting from the seamless interaction with this multiomic dataset. We present a computational analytical workflow that enables large-scale multiomic data mining to study the molecular underpinnings of normal neutrophil development, by precisely characterising the dynamical changes of the regulatory landscape during human granulopoiesis and generating testable hypothesis through regulatory SNPs assessment. 1. https://doi.org/10.1182/blood-2023-182041 2. https://doi.org/10.1101/2024.05.24.595662

Structural chromosomal changes including copy number aberrations (CNAs) are a major feature of multiple myeloma (MM), however their evolution in context of modern biological therapy is not well characterized. To investigate acquisition of CNAs and their prognostic relevance in context of first-line therapy, we profiled tumor diagnosis–relapse pairs from 178 NCRI Myeloma XI (ISRCTN49407852) trial patients using digital multiplex ligation-dependent probe amplification. CNA profiles acquired at relapse differed substantially between MM subtypes: hyperdiploid (HRD) tumors evolved predominantly in branching pattern vs. linear pattern in t(4;14) vs. stable pattern in t(11;14). CNA acquisition also differed between subtypes based on CCND expression, with a marked enrichment of acquired del(17p) in CCND2 over CCND1 tumors. Acquired CNAs were not influenced by high-dose melphalan or lenalidomide maintenance randomization. A branching evolution pattern was significantly associated with inferior overall survival (OS; hazard ratio (HR) 2.61, P = 0.0048). As an individual lesion, acquisition of gain(1q) at relapse was associated with shorter OS, independent of other risk markers or time of relapse (HR = 2.00; P = 0.021). There is an increasing need for rational therapy sequencing in MM. Our data supports the value of repeat molecular profiling to characterize disease evolution and inform management of MM relapse.

Background Treatment of relapsed/refractory multiple myeloma (RRMM) remains challenging as durable remissions are achieved in patient sub-groups only. Identifying patients that are likely to benefit prior to or early after starting relapse treatments remains an unmet need. MUKseven is a trial specifically designed to investigate and validate biomarkers for treatment optimization in a 'real-world' RRMM population. Design In the randomized multi-center phase 2 MUKseven trial, RRMM patients (≥2 prior lines of therapy, exposed to proteasome inhibitor and lenalidomide) were randomized 1:1 to cyclophosphamide (500 mg po d1, 8, 15), pomalidomide (4 mg days 1-21) and dexamethasone (40 mg; if ≥75 years 20 mg; d1, 8, 15, 21) (CPomD) or PomD and treated until progression. All patients were asked to undergo bone marrow (BM) and peripheral blood (PB) bio-sampling at baseline, cycle 1 day 14 (C1D14, on-treatment) and relapse. For biomarker discovery and validation, IGH translocations were profiled by qRT-PCR, copy number aberrations by digital MLPA (probemix D006; MRC Holland), GEP by U133plus2.0 array (Affymetrix), PD protein markers by IHC and PB T-cell subsets by flow cytometry for all patients with sufficient material. Primary endpoint was PFS, secondary endpoints included response, OS, safety/toxicity and biomarker validation. Original planned sample size was 250 patients but due to a change in UK standard of care during recruitment with pomalidomide becoming available, a decision was made to stop recruitment early. Results In total, 102 RRMM patients were randomized 1:1 between March 2016 and February 2018. Trial entry criteria were designed to include a real-world RRMM population, permitting transfusions and growth factor support. Median age at randomization was 69 years (range 42-88), 28% of patients had received ≥5 prior lines of therapy (median: 3). Median follow-up for this analysis was 13.4 months (95% CI: 12.0-17.5). 16 patients remained on trial at time of analysis (median number of cycles: 19.5; range 8-28). More patients achieved ≥PR with CPomD compared to PomD: 70.6% (95% CI: 56.2-82.5%) vs. 47.1% (CI: 32.9-61.5%) (P=0.006). Median PFS was 6.9 months (CI: 5.7-10.4) for CPomD vs. 4.6 months (CI: 3.5-7.4) for PomD, which was not significantly different as per pre-defined criteria. Follow-up for OS is ongoing and will be presented at the conference. High-risk genetic aberrations were found at following frequencies: t(4;14): 6%, t(14;16)/t(14;20): 2%, gain(1q): 45%, del(17p): 13%. Non-high risk lesions were present as follows: t(11;14): 22%, hyperdiploidy: 44%. Complete information on all high-risk genetic markers was available for 71/102 patients, of whom 12.7% had double-hit high-risk (≥2 adverse lesions), 46.5% single-hit high-risk (1 adverse lesion) and 40.8% no risk markers, as per our recent meta-analysis in NDMM (Shah V, et al., Leukemia 2018). Median PFS was significantly shorter for double-hit: 3.4 months (CI: 1.0-4.9) vs. single-hit: 5.8 months (CI: 3.7-9.0) or no hit: 14.1 months (CI: 6.9-17.3) (P=0.005) (Figure 1A). GEP was available for 48 patients and the EMC92 high-risk signature, present in 19% of tumors, was associated with significantly shorter PFS: 3.4 months (CI: 2.0-5.7) vs. 7.4 (CI: 3.9-15.1) for EMC92 standard risk (P=0.037). Pharmacodynamic (PD) profiling of cereblon and CRL4CRBN ubiquitination targets (including Aiolos, ZFP91) in BM clots collected at baseline and C1D14 is currently ongoing. Preliminary results for the first 10 patients demonstrate differential change of nuclear Aiolos (Figure 1C), with a major decrease in Aiolos H-scores in 7/10 patients from baseline to C1D14 and reconstitution at relapse. T-cell PB sub-sets were profiled at baseline and C1D14 by flow cytometry. Specific sub-sets increased with therapy from baseline to C1D14, e.g. activated (HLA-DR+) CD4+ T-cells, as reported at last ASH. CD4+ T-cell % at baseline was associated with shorter PFS in these analyses in a multi-variable Cox regression model (P=0.005). PD and T-cell biomarker results will be updated and integrated with molecular tumor characteristics and outcome. Discussion Our results demonstrate that molecular markers validated for NDMM predict treatment outcomes in RRMM, opening the potential for stratified delivery of novel treatment approaches for patients with a particularly high unmet need. Additional immunologic and PD biomarkers are currently being explored. Disclosures Croft: Celgene: Other: Travel expenses. Hall:Celgene, Amgen, Janssen, Karyopharm: Other: Research funding to Institution. Walker:Janssen, Celgene: Other: Research funding to Institution. Pawlyn:Amgen, Janssen, Celgene, Takeda: Other: Travel expenses; Amgen, Celgene, Janssen, Oncopeptides: Honoraria; Amgen, Celgene, Takeda: Consultancy. Flanagan:Amgen, Celgene, Janssen, Karyopharm: Other: Research funding to Institution. Garg:Janssen, Takeda, Novartis: Other: Travel expenses; Novartis, Janssen: Research Funding; Janssen: Honoraria. Couto:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Wang:Celgene Corporation: Employment, Equity Ownership. Boyd:Novartis: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Pierceall:Celgene: Employment. Thakurta:Celgene: Employment, Equity Ownership. Cook:Celgene, Janssen-Cilag, Takeda: Honoraria, Research Funding; Janssen, Takeda, Sanofi, Karyopharm, Celgene: Consultancy, Honoraria, Speakers Bureau; Amgen, Bristol-Myers Squib, GlycoMimetics, Seattle Genetics, Sanofi: Honoraria. Brown:Amgen, Celgene, Janssen, Karyopharm: Other: Research funding to Institution. Kaiser:Takeda, Janssen, Celgene, Amgen: Honoraria, Other: Travel Expenses; Celgene, Janssen: Research Funding; Abbvie, Celgene, Takeda, Janssen, Amgen, Abbvie, Karyopharm: Consultancy.

Background High-risk myeloma patients have unsatisfactory outcomes with current treatments and are in urgent need of improved diagnostic and therapeutic strategies. We have recently validated specific markers predicting high-risk disease in newly diagnosed MM (NDMM), in particular double-hit with presence of ≥2 consensus high-risk markers t(4;14), t(14;16), t(14;20), del(1p), gain(1q), del(17p) (Shah V, et al., Leukemia 2018) and diagnostic GEP SKY92 high risk signature (Sherborne A, et al., IMW 2017). Diagnostic tests for these markers were implemented in the UK multi-center OPTIMUM: MUK9 trial to prospectively stratify therapy for high-risk NDMM. Trial design OPTIMUM: MUK9 is a phase 2 trial for transplant eligible NDMM, consisting of two inter-related protocols: a molecular screening protocol (MUK9A) and an interventional trial (MUK9B) for high-risk MM identified in MUK9A. Patients with suspected or confirmed MM fit for intensive therapy enrolled in MUK9A have central molecular profiling at ICR, London, of CD138-selected BM MM cells for translocations, copy number aberrations (qRT-PCR; MLPA P425, MRC Holland) and SKY92 signature status (MMprofiler; SkylineDx). If clinically indicated SOC therapy (VTD, max. 2 cycles) can be given whilst central results are generated. Patients found to have high-risk MM by double-hit and/or SKY92 are offered enrolment into MUK9B. All other patients receive SOC (VTD, HD-MEL+ASCT) for which clinical data is collected. Patients diagnosed with plasma cell leukemia (PCL) can be enrolled directly in MUK9B. MUK9B treatment consists of quintuplet daratumumab, cyclophosphamide, bortezomib, lenalidomide, dexamethasone (Dara-CVRd) induction (up to 6 cycles), bortezomib-augmented single HD-MEL+ASCT, Dara-VRd consolidation 1 (6 cycles), Dara-VR consolidation 2 (12 cycles) and Dara-R maintenance (until PD). Dose adjustments are permitted in order to maximize tolerability of long-term therapy. Patient reported outcomes (PRO) are recorded at baseline and throughout treatment. Response and MRD are centrally assessed (Birmingham, Leeds). Primary endpoint for MUK9A is feasibility of central molecular testing within 56 days turnaround time, which we report on here. Primary endpoint of MUK9B is treatment efficacy, comparing MUK9B PFS to near-concurrent molecularly matched high-risk patient outcomes from UK NCRI Myeloma XI using a Bayesian design. Secondary endpoints include safety, PFS2, MRD and OS and study of molecular evolution in high-risk disease. Results The protocol recruited 29/Sep/17 - 31/Jul/19 at 39 UK sites, achieving the recruitment target of 105 high-risk patients treated on MUK9B ahead of projections. At the time of analysis (12/Jul/19), 430 patients with suspected or confirmed NDMM have been recruited to MUK9A across 39 UK NHS hospitals. Of these, 376 (87%) patients were confirmed to have symptomatic MM (60.9% male; median age 61y (range 29-79)) as per updated IMWG diagnostic criteria (2014), including 9 (2%) PCL patients, with the remainder diagnosed as SMM/MGUS (31; 7%) or other (14; 3%). For 371 of the 376 symptomatic MM patients BM was received by the central laboratory and was of sufficient quality for profiling in 331 (89%) patients. Repeat samples were requested for all others and a sufficient sample received for 20/45 (44%). Central results were successfully reported within the pre-specified 56 day interval for all patients (median 17 days; IQR 13-22). Of 346 patients with a reported result, 128 (37.0%) have high-risk MM, with molecular characteristics mirroring Myeloma XI patients (Figure 1). PCL patients show expected characteristics as listed in Table 1. Basic demographics were not different between high-risk vs. non-high-risk. 101 high-risk patients have or are planning to enter MUK9B, 10 pending decision; 17 high-risk patients did not enter MUK9B, the majority due to ineligibility. 92 patients have started Dara-CVRD therapy. There are currently no safety concerns, the majority of patients are completing induction successfully; 1 patient stopped induction therapy due to adverse events. Updated results will be presented. Discussion Our data demonstrate feasibility of multi-center molecular stratified trial delivery for high-risk NDMM patients. These early trial results strongly support accelerated trial strategies for MM patient groups with high unmet need and rational drug development specifically for high-risk MM. Disclosures Jenner: Abbvie, Amgen, Celgene, Novartis, Janssen, Sanofi Genzyme, Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Hall:Celgene, Amgen, Janssen, Karyopharm: Other: Research funding to Institution. Walker:Janssen, Celgene: Other: Research funding to Institution. Croft:Celgene: Other: Travel expenses. Jackson:Celgene, Amgen, Roche, Janssen, Sanofi: Honoraria. Flanagan:Amgen, Celgene, Janssen, Karyopharm: Other: Research funding to Institution. Drayson:Abingdon Health: Consultancy, Equity Ownership. Owen:Celgene, Janssen: Consultancy; Celgene: Research Funding; Janssen: Other: Travel expenses; Celgene, Janssen: Honoraria. Pratt:Binding Site, Amgen, Takeda, Janssen, Gilead: Consultancy, Honoraria, Other: Travel support. Cook:Celgene, Janssen-Cilag, Takeda: Honoraria, Research Funding; Janssen, Takeda, Sanofi, Karyopharm, Celgene: Consultancy, Honoraria, Speakers Bureau; Amgen, Bristol-Myers Squib, GlycoMimetics, Seattle Genetics, Sanofi: Honoraria. Brown:Amgen, Celgene, Janssen, Karyopharm: Other: Research funding to Institution. Kaiser:Celgene, Janssen: Research Funding; Abbvie, Celgene, Takeda, Janssen, Amgen, Abbvie, Karyopharm: Consultancy; Takeda, Janssen, Celgene, Amgen: Honoraria, Other: Travel Expenses.