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.

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.

Patients with hematologic malignancies are a high priority for SARS-CoV-2 vaccination, yet the benefit they will derive is uncertain. We investigated the humoral response to vaccination in 53 non-Hodgkin lymphoma (NHL), Hodgkin lymphoma (HL), or CLL patients. Peripheral blood was obtained 2 weeks after first vaccination and 6 weeks after second vaccination for antibody profiling using the multiplex bead-binding assay. Serum IgG, IgA, and IgM antibody levels to the spike specific receptor binding domain (RBD) were evaluated as a measure of response. Subsequently, antibody-positive serum were assayed for neutralization capacity against authentic SARS-CoV-2. Histology was 68% lymphoma and 32% CLL; groups were: patients receiving anti-CD20-based therapy (45%), monitored with disease (28%), receiving BTK inhibitors (19%), or chemotherapy (all HL) (8%). SARS-CoV-2 specific RBD IgG antibody response was decreased across all NHL and CLL groups: 25%, 73%, and 40%, respectively. Antibody IgG titers were significantly reduced (p < 0.001) for CD20 treated and targeted therapy patients, and (p = 0.003) for monitored patients. In 94% of patients evaluated after first and second vaccination, antibody titers did not significantly boost after second vaccination. Only 13% of CD20 treated and 13% of monitored patients generated neutralizing antibodies to SARS-CoV-2 with ICD50s 135 to 1767, and 445 and > 10240. This data has profound implications given the current guidance relaxing masking restrictions and for timing of vaccinations. Unless immunity is confirmed with laboratory testing, these patients should continue to mask, socially distance, and to avoid close contact with non-vaccinated individuals.

Purpose/UNASSIGNED:To evaluate pulmonary embolism (PE) prevalence at CT pulmonary angiography in patients testing positive for coronavirus disease 2019 (COVID-19) and factors associated with PE severity. Materials and Methods/UNASSIGNED:value < .05 was considered significant. Results/UNASSIGNED:< .001). One additional patient with negative results at CT pulmonary angiography had deep venous thrombosis, thus resulting in 38.7% with PE or deep venous thrombosis, despite 40% receiving prophylactic anticoagulation. Other factors did not demonstrate significant PE association. Conclusion/UNASSIGNED:© RSNA, 2020.