Faculty Bibliography

We carried out a single-cell (sc) multiomic analysis on a series of MYD88 mutated Waldenström macroglobulinemia (WM) cases and identified two distinct subtypes of disease, memory B-cell-like (MBC-like) and plasma cell-like (PC-like), based on their expression of key lineage defining genes. Biologically, the subtypes are characterized by their variable capacity to differentiate fully towards a plasma cell (PC) and exhibit unique transcriptomic, chromatin accessibility, and genomic profiles. The MBC-like subtype is unable to differentiate beyond the memory B-cell (MBC) stage, upregulates key MBC genes, and is characterized by upregulated BCR and AKT/mTOR signaling. In contrast, the PC-like subtype can partially differentiate towards a PC, upregulates key PC genes, has enhanced NF-kB signaling, and has an upregulated unfolded protein response. Pseudotime trajectory analysis of combined scRNA-sequencing and scATAC-sequencing supports the variable differentiation capacity of each subtype and implicate key transcription factors SPI1, SPIB, BCL11A, and XBP1 in these features. The existence and generalizability of the two disease subtypes were validated further using hierarchical clustering of bulk RNA-seq data from a secondary set of cases. The biological significance of the subtypes was further established using whole genome sequencing, where it was shown that CXCR4, NIK, and ARID1A mutations occur predominantly in the MBC-like subtype and 6q deletions in the PC-like subtype. We conclude that the variable differentiation blockade seen in WM manifests itself clinically as two disease subtypes with distinct epigenetic, mutational, transcriptional, and clinical features with potential implications for WM treatment strategies.

Patients participating in clinical trials are highly selected and may not represent the general population. The pivotal study of teclistamab (MajesTEC-1), a B-cell maturation antigen (BCMA)xCD3 bispecific antibody, demonstrated impressive response rates and progression free survival in relapsed/refractory multiple myeloma (RRMM) with acceptable toxicity. We performed a retrospective study of 58 patients treated as standard of care at four US academic centers to determine how these results translated to the real-world. Most patients (87.9%) would not have been eligible for the MajesTEC-1 study due to either disease related factors, patient related comorbidities or socio-economic/geographical factors. Despite these 'less-favorable' characteristics we observed similar efficacy and toxicity to MajesTEC-1. A meta-analysis with six other published real-world series (n = 546) confirmed these results. These data support the significant clinical activity of teclistamab in RRMM and highlights the importance of real-world data to accompany the pivotal trial data to further inform daily clinical practice.

Immune cells express an incredible variety of proteins; by measuring combinations of these, cell types influencing disease can be precisely identified. We developed terraFlow, a platform that defines cell subsets exhaustively by combinatorial protein expression. Using high-parameter checkpoint-focused and function-focused panels, we studied classical Hodgkin's lymphoma (cHL), where systemic T cells have not been investigated in detail. terraFlow revealed immune perturbations in patients, including elevated activated, exhausted, and interleukin (IL)-17+ phenotypes, along with diminished early, interferon (IFN)γ+, and tumor necrosis factor (TNF)+ T cells before treatment; many perturbations remained after treatment. terraFlow identified more disease-associated differences than other tools, often with better predictive power, and included a non-gating approach, eliminating time-consuming and subjective manual thresholds. It also reports a method to identify the smallest set of markers distinguishing study groups. Our results provide mechanistic support for past reports of immune deficiency in cHL and demonstrate the value of terraFlow in immunotherapy and biomarker studies.

PURPOSE OF REVIEW/OBJECTIVE:The use of immune checkpoint inhibitors (ICIs) for oncologic indications is associated with immune-related adverse events (irAEs). Patients with pre-existing autoimmune diseases are at increased risk of irAEs and have largely been excluded from clinical trials of ICIs. Therefore, there is limited data on the safety of safety of ICIs in patients with pre-existing neurologic autoimmune diseases (nAIDs) such as myasthenia gravis and multiple sclerosis. This review aims to synthesize the literature on the post-marketing experience with ICI in patients with pre-existing nAID and to discuss possible strategies for mitigating the risk of post-ICI nAID relapses. RECENT FINDINGS/RESULTS:Patients with pre-existing myasthenia gravis (MG), myositis, and paraneoplastic encephalitis appear highly susceptible to neurologic relapses of their underlying neurologic disorder following ICI initiation; these relapses can cause considerable morbidity and mortality. In patients with multiple sclerosis (MS), the risk and severity of MS relapses following ICI appears to be relatively lower compared to MG. Preliminary evidence suggests that older MS patients with no recent focal neuroinflammatory activity may be safely treated with ICI. Among the several case reports of ICI in patients with a history of Guillain-Barre syndrome (GBS), neurologic worsening was only recorded in one patient who was in the acute phase of GBS at the time of ICI start. Initiating an ICI in a patient with pre-existing nAID involves a complex risk-benefit discussion between the patient, their oncologist, and neurologist. Relevant issues to consider before ICI include the choice of disease-modifying therapy for nAID (if any) and strategies for promptly identifying and managing nAID relapses should they occur. Currently, the literature consists mainly of case reports and case series, subject to publication bias. Prospective studies of ICI in patients with nAID are needed to improve the level of evidence.

PURPOSE/OBJECTIVE:Chromosome 1 (chr1) copy number abnormalities (CNAs) and structural variants (SV) are frequent in newly diagnosed multiple myeloma (NDMM) and associate with a heterogeneous impact on outcome the drivers of which are largely unknown. EXPERIMENTAL DESIGN/METHODS:A multiomic approach comprising CRISPR, gene mapping of CNA and SV, methylation, expression, and mutational analysis was used to document the extent of chr1 molecular variants and their impact on pathway utilisation. RESULTS:We identified two distinct groups of gain(1q): focal gains associated with limited gene expression changes and a neutral prognosis, and whole-arm gains, which associate with substantial gene expression changes, complex genetics and an adverse prognosis. CRISPR identified a number of dependencies on chr1 but only limited variants associated with acquired CNAs. We identified seven regions of deletion, nine of gain, three of chromothripsis (CT) and two of templated-insertion (TI), which contain a number of potential drivers. An additional mechanism involving hypomethylation of genes at 1q may contribute to the aberrant gene expression of a number of genes. Expression changes associated with whole-arm gains were substantial and gene set enrichment analysis identified metabolic processes, apoptotic resistance, signaling via the MAPK pathway, and upregulation of transcription factors as being key drivers of the adverse prognosis associated with these variants. CONCLUSIONS:Multiple layers of genetic complexity impact the phenotype associated with CNAs on chr1 to generate its associated clinical phenotype. Whole-arm gains of 1q are the critically important prognostic group that deregulate multiple pathways, which may offer therapeutic vulnerabilities.

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.

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.

Background Classical Hodgkin lymphoma (cHL) is characterized by rare, malignant Hodgkin/Reed Sternberg (HRS) cells that shape their microenvironment (TME) to inhibit anti-tumor immune response. Systemic immune dysregulation may influence treatment response and toxicity, but the systemic influence of the TME is less well described. The wide variety of proteins measured in high-parmater flow cytometry make it a powerful tool for immune monitoring, but presents challenges in immuno-monitoring. Combinatorial expression of these proteins defines cell types that may influence disease. TerraFlow is a fully automated data analysis platform that evaluates millions of phenotypes and selects the populations that best predict clinical variables. The analysis can be performed using classical Boolean gates or a non-gating approach that approximates gates without using manual thresholds, allowing immunophenotypes to be comprehensively surveyed for disease associations. The platform was used to find phenotypes that discriminate healthy versus cHL patients (AUC = 1) and pre versus post treatment patient phenotypes(AUC = 0.79). Methods Human Subjects: Informed consent was obtained from cHL patients (N=44) treated at the Perlmutter Cancer Center (PCC) at NYU Langone Health and New York Presbyterian Weil Cornell (NYP) between 2011 and 2016. Blood samples were drawn at multiple time-points, for this study pre-treatment and 3 month post-treatment samples were used. Age-matched, cryopreserved healthy donor PBMC (n=25) were obtained from STEMCELL Technologies (Cambridge, MA).Patient-derived blood was processed for isolation of PBMC, stained analyzed on a Symphony Flow Cytometer (BD Biosciences, San Jose, CA). Analysis: Data was analyzed using an original platform called terraFlow. Many immune cell subsets are defined by the combinations of proteins they express. TerraFlow systematically evaluates millions of cell types by generating every possible combination of 1 to 5 markers. A network-based algorithm then selects the "best" phenotype from each set of inter-related combinations based on statistical power and ease of interpretation. Each phenotype is defined using a minimal gating strategy that can be replicated in a diagnostic panel or cell sorter. Together, phenotypes describe all the major differences between patient groups. A new platform developed by Epistemic AI was used to mine scientific literature and interpret selected phenotypes. Results We observed clear perturbations in the cHL systemic T-cell compartment pre-treatment as shown in Figure 1. These include higher levels of activated (CD278+), exhausted (CD366+, PD1+, CD152+), and suppressive (GITR+) T-cells compared to healthy donors, and diminished levels of T-cells producing effector cytokines (like IFNgamma and IL4). Subsets of cytokine-producing cells that co-express markers of exhaustion (i.e., TNF+ CD366+ cells) are also elevated in cHL patients. Finally, T-cells expressing CD127 a receptor for IL7 involved in homeostatic renewal of cells and observed on naive and central memory T-cells are reduced. Taken together, these findings suggest that in cHL the systemic T-cell compartment is shifted toward a more exhausted profile, and away from less differentiated cells, with the potential for self-renewal. Our data also demonstrates a shift from T-helper 1 and T-helper 2 type toward T-helper 17 cells suggesting that T-cell effector function may be reduced. Conclusion Using a novel data analysis platform, TerraFlow we demonstrate dysregulation in systemic T cell function in cHL patients pre-treatment that persists within 3 months of completing therapy. Associations of phenotypes with clinical variables, and post-treatment phenotypes will be described in detail at the meeting. Our results detail new immunotherapy and biomarker research targets, and suggest novel strategies for combination therapies. [Formula presented] Disclosures: Li: BD Bioscience: Current Employment. Ruan: Kite Pharma: Consultancy; AstraZeneca: Research Funding; BMS: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; Pharmacyclics: Research Funding; Seagen: Consultancy. Diefenbach: Incyte: Research Funding; Trillium: Research Funding; Celgene: Research Funding; IGM Biosciences: Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding; Gilead: Current equity holder in publicly-traded company; AbbVie: Research Funding; Perlmutter Cancer Center at NYU Langone Health: Current Employment; MEI: Consultancy, Research Funding; Genentech, Inc./ F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; IMab: Research Funding; Morphosys: Consultancy, Honoraria, Research Funding; Merck Sharp & Dohme: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding.
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BACKGROUND:Selinexor is an oral, selective nuclear export inhibitor. STORM was a phase 2b, single-arm, open-label, multicenter trial of selinexor with low dose dexamethasone in patients with penta-exposed relapsed/refractory multiple myeloma (RRMM) that met its primary endpoint, with overall response of 26% (95% confidence interval [CI], 19 to 35%). Health-related quality of life (HRQoL) was a secondary endpoint measured using the Functional Assessment of Cancer Therapy - Multiple Myeloma (FACT-MM). This study examines impact of selinexor treatment on HRQoL of patients treated in STORM and reports two approaches to calculate minimal clinically important differences for the FACT-MM. METHODS:FACT-MM data were collected at baseline, on day 1 of each 4-week treatment cycle, and at end of treatment (EOT). Changes from baseline were analyzed for the FACT-MM total score, FACT-trial outcome index (TOI), FACT-General (FACT-G), and the MM-specific domain using mixed-effects regression models. Two approaches for evaluating minimal clinically important differences were explored: the first defined as 10% of the instrument range, and the second based on estimated mean baseline differences between Eastern Cooperative Oncology Group performance status (ECOG PS) scores. Post-hoc difference analysis compared change in scores from baseline to EOT for treatment responders and non-responders. RESULTS:Eighty patients were included in the analysis; the mean number of prior therapies was 7.9 (standard deviation [SD] 3.1), and mean duration of myeloma was 7.6 years (SD 3.4). Each exploratory minimal clinically important difference threshold yielded consistent results whereby most patients did not experience HRQoL decline during the first six cycles of treatment (range: 53.9 to 75.7% for the first approach; range: 52.6 to 72.9% for the second). Treatment responders experienced less decline in HRQoL from baseline to EOT than non-responders, which was significant for the FACT-G, but not for other scores. CONCLUSION/CONCLUSIONS:The majority of patients did not experience decline in HRQoL based on minimal clinically important differences during early cycles of treatment with selinexor and dexamethasone in the STORM trial. An anchor-based approach utilizing patient-level data (ECOG PS score) to define minimal clinically important differences for the FACT-MM gave consistent results with a distribution-based approach. TRIAL REGISTRATION/BACKGROUND:This trial was registered on ClinicalTrials.gov under the trial-ID NCT02336815 on January 8, 2015.