Faculty Bibliography

Analysis of the genetic basis for multiple myeloma (MM) has informed many of our current concepts of the biology that underlies disease initiation and progression. Studying these events in further detail is predicted to deliver important insights into its pathogenesis, prognosis and treatment. Information from whole genome sequencing of structural variation is revealing the role of these events as drivers of MM. In particular, we discuss how the insights we have gained from studying chromothripsis suggest that it can be used to provide information on disease initiation and that, as a consequence, it can be used for the clinical classification of myeloma precursor diseases allowing for early intervention and prognostic determination. For newly diagnosed MM, the integration of information on the presence of chromothripsis has the potential to significantly enhance current risk prediction strategies and to better characterize patients with high-risk disease biology. In this article we summarize the genetic basis for MM and the role played by chromothripsis as a critical pathogenic factor active at early disease phases.

The role of infection and chronic inflammation in plasma cell disorders (PCD) has been well-described. Despite not being a diagnostic criterion, infection is a common complication of most PCD and represents a significant cause of morbidity and mortality in this population. As immune-based therapeutic agents are being increasingly used in multiple myeloma, it is important to recognize their impact on the epidemiology of infections and to identify preventive measures to improve outcomes. This review outlines the multiple factors attributed to the high infectious risk in PCD (e.g. the underlying disease status, patient age and comorbidities, and myeloma-directed treatment), with the aim of highlighting future prophylactic and preventive strategies that could be implemented in the clinic. Beyond this, infection and pathogens as an entity are believed to also influence disease biology from initiation to response to treatment and progression through a complex interplay involving pathogen exposure, chronic inflammation, and immune response. This review will outline both the direct and indirect role played by oncogenic pathogens in PCD, highlight the requirement for large-scale studies to decipher the precise implication of the microbiome and direct pathogens in the natural history of myeloma and its precursor disease states, and understand how, in turn, pathogens shape plasma cell biology.

Sequencing studies have shed some light on the pathogenesis of progression from smouldering multiple myeloma (SMM) and symptomatic multiple myeloma (MM). Given the scarcity of smouldering samples, little data are available to determine which translational programmes are dysregulated and whether the mechanisms of progression are uniform across the main molecular subgroups. In this work, we investigated 223 SMM and 1348 MM samples from the University of Arkansas for Medical Sciences (UAMS) for which we had gene expression profiling (GEP). Patients were analysed by TC-7 subgroup for gene expression changes between SMM and MM. Among the commonly dysregulated genes in each subgroup, PHF19 and EZH2 highlight the importance of the PRC2.1 complex. We show that subgroup specific differences exist even at the SMM stage of disease with different biological features driving progression within each TC molecular subgroup. These data suggest that MMSET SMM has already transformed, but that the other precursor diseases are distinct clinical entities from their symptomatic counterpart.

Aminopeptidases, which catalyze the cleavage of amino acids from the amino terminus of proteins, are widely distributed in the natural world and play a crucial role in cellular processes and functions, including metabolism, signaling, angiogenesis, and immunology. They are also involved in the homeostasis of amino acids and proteins that are required for cellular proliferation. Tumor cells are highly dependent on the exogenous supply of amino acids for their survival, and overexpression of aminopeptidase facilitates rapid tumor cell proliferation. In addition, clinical studies have demonstrated that patients with cancers with high aminopeptidase expression often have poorer outcomes. Emerging evidence supports the rationale of inhibiting aminopeptidase activity as a targeted approach for novel treatment options, as limiting the availability of amino acids can be selectively lethal to tumor cells. While there are agents that directly target aminopeptidases that demonstrate potential as cancer therapies, such as bestatin and tosedostat, more selective and more targeted therapeutic approaches are needed. This article specifically looks at the biological role of aminopeptidases in both normal and cancer processes, and their potential as a biological target for future therapeutic strategies. When examining previous publications, most do not cover aminopeptidases and their role in cancer processes. Aminopeptidases play a vital role in cell processes and functions; however, their overexpression may lead to a rapid proliferation of tumor cells. Emerging evidence supports the rationale of leveraging aminopeptidase activity as a targeted approach for new oncological treatments. This article specifically looks at the biological role of aminopeptidases in both normal and cancer processes, and their potential as a biological target for future therapeutic strategies.

Introduction/UNASSIGNED:(4;14) and del(17p) in multiple myeloma (MM), but its impact on gain 1q (+1q) is unknown. Methods/UNASSIGNED:(4;14), and +1q. Presence of +1q was defined as the presence of at least three copies of 1q21 at the cut off level of 20% of bone marrow plasma cells. Results/UNASSIGNED: = 0.000069). Conclusion/UNASSIGNED:+1q is an adverse factor for OS in MM uniformly treated with bortezomib-based induction but was partially mitigated by ASCT. A risk scoring system comprising +1q, LDH, high-risk FISH, and ISS is a potential tool for risk stratification in MM.

Despite  improvements in outcome, 15-25% of newly diagnosed multiple myeloma (MM) patients have treatment resistant high-risk (HR) disease with a poor survival. The lack of a genetic basis for HR has focused attention on the role played by epigenetic changes. Aberrant expression and somatic mutations affecting genes involved in the regulation of tri-methylation of the lysine (K) 27 on histone 3 H3 (H3K27me3) are common in cancer. H3K27me3 is catalyzed by EZH2, the catalytic subunit of the Polycomb Repressive Complex 2 (PRC2). The deregulation of H3K27me3 has been shown to be involved in oncogenic transformation and tumor progression in a variety of hematological malignancies including MM. Recently we have shown that aberrant overexpression of the PRC2 subunit PHD Finger Protein 19 (PHF19) is the most significant overall contributor to HR status further focusing attention on the role played by epigenetic change in MM. By modulating both the PRC2/EZH2 catalytic activity and recruitment, PHF19 regulates the expression of key genes involved in cell growth and differentiation. Here we review the expression, regulation and function of PHF19 both in normal and the pathological contexts of solid cancers and MM. We present evidence that strongly implicates PHF19 in the regulation of genes important in cell cycle and the genetic stability of MM cells making it highly relevant to HR MM behavior. A detailed understanding of the normal and pathological functions of PHF19 will allow us to design therapeutic strategies able to target aggressive subsets of MM.

Introduction: The incidence of multiple myeloma (MM) varies by ethnicity with Black patients approximately twice as likely to develop MM compared to White or Asian (Black: White males 2.9:1, females 2.2:1). The National Cancer Registration and Analysis Service (NCRAS) in 2015 reported the incidence of MM by ethnicity in England over 10 years to be 85.5% White; 5.4% Black; 3.6% Asian and 1.9% Other. Ethnic minorities have been reported to be under-represented in clinical trials partly because of socio-economic factors; however, it is unknown if these disparities exist in state funded health care systems where access to healthcare is free and should be equitable.
Method(s): Ethnicity, baseline demographics, progression-free survival (PFS) and overall survival (OS) were collected from patients enrolled into 1 ^st line UK academic transplant eligible (TE) and transplant non-eligible (TNE) - Myeloma IX, XI and XIV trials, and at 1 ^st relapse - Myeloma X and XII clinical trials. These trials enrolled from 2003 to 2021. The Myeloma XII and XIV (FiTNEss) trials are currently enrolling, all other trials have closed. Ethnicity was coded by White, Black, Asian and Other in line with Office for National Statistics (ONS) categories. Patients were enrolled across 120 centres covering a wide geographical distribution in the UK. These studies were designed to have permissive eligibility criteria to enrol as close to real world patients as possible. Baseline characteristics were summarised descriptively and comparisons made using the chi-squared test. Comparisons with population-level data used one-sample chi-squared tests. Survivor functions were estimated using the Kaplan-Meier method and were compared using the logrank test. Cox proportional hazards models with suitable interaction terms were used to test for heterogeneity. All tests were called significant at the 5% level.
Result(s): 7,291 patients were enrolled across 5 randomised controlled trials over 18 years. Overall, the ethnic distribution was White 93.8%, Black 2.2%, Asian 1.8%, Other 0.6% and unknown 1.6%. The skew to enrolment of White patients was more apparent in the TNE studies (Myeloma IX non-intensive: White 97.4%, Black 1.3%, Asian 0.4%; Myeloma XI non-intensive: White 94.5%, Black 1.8%, Asian 1.6%, Myeloma XIV: White 94.2%, Black 0%, Asian 3.2%). This was different to the incidence of myeloma cases across the UK with the difference most apparent in TNE studies (TE trials (observed vs NCRAS, P < 0.0001); TNE trials (observed vs NCRAS, P < 0.0001); 1 ^st relapse trials (observed vs NCRAS, P = 0.035)). Enrolment distribution by ethnicity was consistent over the 18 years, with no change in diversity over time despite there being an increase in UK non-white populations. In the Myeloma IX trial, there was no significant difference in age at enrolment; however, the performance status in Black patients was worse than non-Black (P = 0.045), there was fewer cytogenetic high risk Black patients (P = 0.007) and less ISS 1 Black patients vs non-Black (P = 0.0416). There were no demographic differences by ethnicity in the Myeloma XI trial. The outcomes of patients by PFS or OS by ethnic group was similar within each trial (figure 1). An overall improvement in OS for was demonstrated over time from Myeloma IX to the Myeloma XI trial with the incorporation of novel agents (median OS MRC-Myeloma IX: 48 months vs. median OS NCRI Myeloma-XI: 70 months, P < 0.0001). There was no evidence of heterogeneity of effect with respect to ethnicity (P = 0.456) suggesting all ethnic sub-groups benefited from this improvement in OS.
Conclusion(s): Enrolment of ethnic minorities into academic clinical trials in the UK was below that expected despite enrolling from >100 geographically spread sites and intended equitable access to healthcare. All ethnic groups derived an OS benefit from novel agents within trials that were not otherwise routinely available; however, a substantial proportion of ethnic minorities were not enrolled particularly TNE patients, thereby limiting their survival gains. Understanding causes of inequality and addressing these is a priority for the UK-MRA to ensure that all groups can potentially benefit, and trial results are representative of the UK population. [Formula presented] Disclosures: Popat: Abbvie, Takeda, Janssen, and Celgene: Consultancy; AbbVie, BMS, Janssen, Oncopeptides, and Amgen: Honoraria; Takeda: Honoraria, Other: TRAVEL, ACCOMMODATIONS, EXPENSES; GlaxoSmithKline: Consultancy, Honoraria, Research Funding; Janssen and BMS: Other: travel expenses. Craig: Celgene: Research Funding; Merck Sharpe & Dohme: Research Funding; Amgen: Research Funding; Takeda: Research Funding. Davies: Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria. Cairns: Amgen: Research Funding; Merck Sharpe and Dohme: Research Funding; Takeda: Research Funding; Celgene / BMS: Other: travel support, Research Funding. Olivier: Merck Sharpe and Dohme: Research Funding; Takeda: Research Funding; Amgen: Research Funding; Celgene / BMS: Research Funding. 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. Cook: BMS/Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Sanofi: Consultancy; Karyopharm: Consultancy; Amgen: Consultancy. OffLabel Disclosure: Revlimid and carfilzomib combinations are used off label
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Introduction: A number of cytogenetic abnormalities (CAs) are associated with poorer prognosis in MM, including del(17p), t(4;14), t(14;16), and amp1q21. There is a general consensus that treatment with proteasome inhibitors (PIs) benefits pts carrying these CAs (Sonneveld Blood 2016). This meta-analysis of four phase 3 studies assesses PFS benefit in pts receiving the oral PI ixa vs pbo regarding the specific adverse CAs.
Method(s): Pts in TOURMALINE-MM1 (N=722; relapsed/refractory MM; Moreau N Engl J Med 2016) and MM2 (N=705; newly diagnosed MM; Facon Blood 2021) received ixa plus lenalidomide-dexamethasone (Rd) vs pbo-Rd (1:1). Pts in TOURMALINE-MM3 (N=656; Dimopoulos Lancet 2019) and TOURMALINE-MM4 (N=706; Dimopoulos J Clin Oncol 2020) received ixa vs pbo (3:2) as maintenance following autologous stem cell transplant or as post-induction maintenance in transplant-ineligible pts, respectively. In TOURMALINE-MM1/MM2, CAs were centrally assessed on CD138 positive sorted cells from bone marrow samples collected at study entry using fluorescence in situ hybridization (FISH). Cutoff values for defining the presence of del(17p), t(4;14), and t(14;16) were 5%, 3%, and 3% positive cells, respectively, based on the false-positive rates (technical cutoffs) of the FISH probes used, and cutoff values of 3% (MM1) and 20% (MM2) were used for amp1q21. In TOURMALINE-MM3/MM4, cytogenetic assessment was performed locally using FISH or conventional karyotyping with locally defined thresholds for positivity.
Result(s): 270/1227 (22%) vs 227/1019 (22%) evaluable pts receiving ixa-based vs pbo-based therapy had high-risk CAs [del(17p), t(4;14), t(14;16)]: 75 vs 62 in MM1, 60 vs 63 in MM2, 61 vs 54 in MM3, and 74 vs 48 in MM4. 957/1227 (78%) vs 792/1019 (78%) had complementary standard-risk CAs: 200 vs 216 in MM1, 231 vs 234 in MM2, 252 vs 152 in MM3, and 275 vs 190 in MM4. 555/1142 (49%) vs 479/955 (50%) evaluable pts receiving ixa-based vs pbo-based therapy had expanded high-risk CAs (high-risk CAs +/- amp1q21): 155 vs 154 in MM1, 134 vs 146 in MM2, 116 vs 88 in MM3, and 150 vs 91 in MM4. 587/1142 (51%) vs 476/955 (50%) had complementary standard-risk CAs: 122 vs 126 in MM1, 164 vs 153 in MM2, 154 vs 89 in MM3, and 148 vs 108 in MM4. After a median follow-up in the pooled analysis of 25.6 months (mos; 12.7, 54.6, 29.7, and 21.3 mos in MM1, MM2, MM3, and MM4, respectively), the hazard ratio (HR) for PFS with ixa-based vs pbo-based therapy in pts with high-risk CAs was 0.74 (95% confidence interval [CI] 0.59-0.93; median 17.8 vs 13.2 mos) and 0.70, (95% CI 0.62-0.80; median 26.3 vs 17.6 mos) in pts with standard-risk CAs. In the subgroup analyses of expanded high-risk CAs, the HR for PFS with ixa-based vs pbo-based therapy in pts in the expanded high-risk group was 0.75 (95% CI 0.64-0.87; median 18.1 vs 14.1 mos; Figure 1) and 0.71 (95% CI 0.59-0.85; median 36.1 vs 21.4 mos) in the complementary standard-risk group. Analyses of PFS according to the presence of individual CAs (Figure 2) indicated differing magnitudes of PFS benefit. Notably, in pts with t(4;14) (n=124 vs n=102), the HR for PFS with ixa-based vs pbo-based therapy was 0.68 (95% CI 0.48-0.96; median 22.4 vs 13.2 mos), while for pts with amp1q21 (n=380 vs n=312), the HR was 0.77 (95% CI 0.63-0.93; median 18.8 vs 14.5 mos) and for pts with del(17p) (n=141 vs n=107) the HR was 0.80 (95% CI 0.59-1.09; median 15.7 vs 13.2 mos).
Conclusion(s): This pooled analysis demonstrated a PFS benefit with ixa-based therapy vs pbo-based therapy regardless of the presence of specific adverse CAs, with a similar magnitude of benefit in pts with (expanded) high-risk CAs and the respective complementary standard-risk groups. However, due to the differences in study eligibility criteria and pt populations, ixa combined with Rd or as single-agent maintenance therapy may not abrogate the negative impact of high-risk CAs. Analyses of PFS in subgroups with specific CAs indicated that the greatest magnitudes of benefit (lowest HRs) with ixa-based vs pbo-based therapy were in pts with t(4;14) (HR 0.68) and pts with amp1q21 (HR 0.77), suggesting that the improved outcome with ixa-based vs pbo-based therapy in the expanded high-risk subgroup was primarily driven by PFS differences in pts with these more common CAs. Further study is needed, and additional sensitivity analyses will be presented in subsequent publications. [Formula presented] Disclosures: Chng: BMS/Celgene: Honoraria, Research Funding; Amgen: Honoraria; Takeda: Honoraria; Abbvie: Honoraria; Sanofi: Honoraria; Pfizer: Honoraria; Johnson and Johnson: Honoraria, Research Funding. Lonial: BMS/Celgene: Consultancy, Honoraria, Research Funding; AMGEN: Consultancy, Honoraria; GlaxoSmithKline: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; Merck: Honoraria. Morgan: Takeda: Honoraria. Iida: Amgen: Research Funding; Daiichi Sankyo: Research Funding; Glaxo SmithKlein: Research Funding; Ono: Honoraria, Research Funding; Takeda: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Chugai: Research Funding; Abbvie: Research Funding; Janssen: Honoraria, Research Funding; Bristol-Myers Squibb: Research Funding. Moreau: Sanofi: Honoraria; Celgene BMS: Honoraria; Abbvie: Honoraria; Amgen: Honoraria; Janssen: Honoraria; Oncopeptides: Honoraria. Kumar: Bluebird Bio: Consultancy; Carsgen: Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Antengene: Consultancy, Honoraria; Novartis: Research Funding; Oncopeptides: Consultancy; Tenebio: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche-Genentech: Consultancy, Research Funding; Beigene: Consultancy; Merck: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Twumasi-Ankrah: Takeda: Current Employment. Kumar: Takeda: Current Employment, Current holder of stock options in a privately-held company. Dash: Takeda: Current Employment, Current equity holder in publicly-traded company. Vorog: Takeda: Current Employment. Zhang: Takeda: Current Employment. Suryanarayan: Takeda: Current Employment. Labotka: Takeda: Current Employment. Dimopoulos: Janssen: Honoraria; Takeda: Honoraria; Beigene: Honoraria; BMS: Honoraria; Amgen: Honoraria. OffLabel Disclosure: Use of the oral proteasome inhibitor ixazomib for the initial treatment of multiple myeloma and as maintenance treatment following stem cell transplantation or induction therapy in newly diagnosed patients
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