Tang, Hongwei

Abstract Multiple myeloma (MM) is the most common blood cancer in African American (AA) individuals. AAs have an increased risk of developing Monoclonal Gammopathy of Undetermined Significance (MGUS) and MM compared to non-Hispanic White (NHW) individuals. While several studies have shown a pivotal role for the immune tumor microenvironment (iTME) in MM pathogenesis, the composition of the iTME in AAs with MM is under investigated. Viably frozen whole bone marrow (BM) aspirates from 128 individuals (53 AA and 75 NHW) with either MM (n=65), MGUS (n=28), smoldering MM (SMM, n=13), or healthy controls (n=22) were evaluated by single cell RNA-sequencing (scRNA-seq) or mass cytometry (CyTOF). We characterized 34 (14 AA and 20 NHW) by scRNA-seq and 94 (39 AA and 55 NHW) by CyTOF. CyTOF samples were stained with Maxpar Direct Immune Profiling Assay Kit (Fluidigm), acquired on a Helios mass cytometer and analyzed in Cytobank. Data were analyzed by logistic equivalent regression model with p<0.05 for significance. ScRNA-seq samples were processed as in Yao, 2022. Data were normalized using scvi-tools and clustered by Uniform manifold approximation and projection (UMAP). Differentially expressed markers between clusters were identified using Model-based Analysis of Single-cell Transcriptomics (MAST) in Seurat R package at FDR < 0.05 and log2 fold change > 0.55. ScRNA-seq of the CD45+ non-tumor lymphocytes revealed 17 immune populations (HSC, erythroblast, MDSC, GMP, CD1c+ DC, CD4+ and CD8+ T cell, CD14+ M1 macrophage, CD16+monocyte, monocyte, pDC, naïve B, pre-B, pro-B, memory B, NK/MAIT). CD3E/D+cells were clustered into naïve/central memory (CCR7+, LEF1+, SELL+), memory (GZMK+), effector (GZMB+, GZMH+, FGFBP2+), g/d (TRDC+), MAIT (SLC4A10+, KLRB1+) and IFN CD8+ T cells (IFIT1+, IFIT2+,IFIT3+). Disease progression was associated with increases in median CD4+ and CD8+ GZMK+ T cells, memory B cells and a reduction in all monocyte clusters. Within the diseased cohort in comparison to NHW patients, AA patients had increased CD3+ T cells (CD4 and CD8+) (AA 57.5% vs. NHW 44.3%) and a reduction in overall monocytes (AA 10.5% vs. 26.5%). These results were validated by CyTOF, which confirmed an increase in CD3+ T cells (AA 53.7% vs. NHW 35.7%, p<0.001) including increases in CD8+ (AA 7.0% vs. NHW 3.2%, p<0.05) and CD4+ terminal effector T cells (AA 5.7% vs. NHW 2.7%, p<0.05) and a reduction in monocytes (AA 18.3% vs. NHW 26.6%) among AAs. After adjusting for age and sex, AAs retained an increase in T cells (OR 1.59 (95%CI 1.2-2.01, p=0.00023)) and a reduction in monocytes (OR 0.88 (95%CI 0.78-1, p=0.045)). Despite the increase in CD8+ T cells in AA patients, we identified reduced GZMK+ T cells and reductions in exhaustion markers (TIGIT, LAG3, TOX) in CD8+ T cells in comparison to NHWs. Nevertheless, the distribution of the immune populations was similar between healthy AA and NHW individuals. Our multiomics approach to characterize the iTME identified significant differences among AA compared to NHW patients with MM. Citation Format: Suganti Shivaram, Hongwei Tang, Huihuang Yan, Shulan Tian, Guyu Qin, Emilie Anderson, Neeraj Sharma, Abiola Bolarinwa, Cinthya Zepeda-Mendoza, Stacey Lehman, Felicia Gomez, Amit Mitra, Surendra Dasari, Taxiarchis Kourelis, Shaji Kumar, Linda Baughn. Characterization of the Immune Tumor Microenvironment in a Diverse Cohort of Patients with Multiple Myeloma [abstract]. In: Proceedings of the Blood Cancer Discovery Symposium; 2024 Mar 4-6; Boston, MA. Philadelphia (PA): AACR; Blood Cancer Discov 2024;5(2_Suppl):Abstract nr P06.

Abstract Purpose: Multiple myeloma (MM) is a plasma cell malignancy characterized by a collection of cytogenetic abnormalities that drive clinical presentation, response to treatment, and prognosis. The most common immunoglobulin heavy chain (IGH) translocation found in MM, t(11;14), results in CCND1 overexpression. Although t(11;14) is considered a standard risk cytogenetic abnormality, heterogeneity in patient outcomes and response to therapy within the t(11;14) group has been reported. In contrast to t(11;14) mantle cell lymphoma, where most cases (73%) have a typical, balanced t(11;14) rearrangement, only 47% of t(11;14) MM have a typical, balanced t(11;14) FISH pattern; the remaining 53% of MM are characterized by an atypical t(11;14) FISH result (Dalland, Genes Chromosomes Cancer, 2021). Here, we compared the genetic characteristics of typical vs. atypical t(11;14) MM and the association with other genomic abnormalities, CCND1 expression, IGH breakpoint location and patient outcomes. Methods: We performed a retrospective study of newly diagnosed t(11;14) MM patients seen at Mayo Clinic (N=411) or enrolled in the MMRF CoMMpass trial (N=167). The t(11;14) FISH results of the Mayo Clinic samples were segmented into two groups: typical CCND1/IGH signal pattern (n=179, 3 red, 3 green, 2 fusion signals) and atypical CCND1/IGH signal pattern (n=232, any FISH pattern deviating from typical). The CoMMpass cases had both RNAseq and long-insert WGS data and were analyzed to detect gene expression, structural and copy number variants and IGH breakpoint locations. These data were used to classify the t(11;14) as typical (n=63) or atypical (n=104) and whether there was a single t(11;14) fusion (n=107) or a gain of the t(11;14) fusion (n=60), and whether these cases were associated with changes in gene expression or breakpoint location. Differences between the groups were measured using χ2 and Wilcoxon tests. Survival curves were estimated using Kaplan Meier and compared using the Log-Rank test. Statistical significance was determined at p<0.05. Results: Of the Mayo t(11;14) cohort, 232/411 (56%) had an atypical t(11;14) FISH pattern and 179/411 (44%) had a typical t(11;14) FISH pattern. Although an atypical t(11;14) FISH pattern was not significantly associated with monosomy 17/17p deletion, MYC rearrangements, 1q gain or ISS stage III, it was significantly associated with the presence of any trisomy in comparison to typical t(11;14) cases (40/222=18% vs.12/166=7%, p=0.002). Atypical t(11;14) cases had an increase in CCND1 gene expression compared to typical t(11;14) cases (median 375.0 RPKM vs. 266.4 RPKM, p<0.0001) and cases with a gain of the t(11;14) fusion were associated with even greater CCND1 expression compared to cases with a single t(11;14) fusion (median 459.4 RPKM vs. 263.2 RPKM, p<0.0001). We next evaluated whether the t(11:14) FISH pattern was associated with differences in IGH breakpoint locations. Nearly 80.0% of atypical t(11;14) cases had a breakpoint within the constant region of the IGH locus commonly reported for MM, while only 61.0% of the typical cases had constant region breakpoints (p=0.035); the remaining cases had breakpoints involving the VDJ region. We next evaluated the impact of atypical t(11;14) FISH on OS. In Mayo cases, OS was significantly shorter in patients with atypical FISH patterns compared to typical t(11;14) FISH patterns (6.9 years [95% CI: 5.1-7.8] vs. 9.0 years [95% CI: 7.0-11.6], p=0.019). Although atypical t(11;14) was associated with increased risk of death (RR 1.45, p=0.021), this finding was not retained in a multivariate model including high risk abnormalities such as age, ISS stage, 17p deletion and 1q gain demonstrating atypical t(11;14) is not an independent predictor of poor outcome when treated with standard therapies. Conclusions: Atypical t(11;14) FISH in MM is found in approximately half of t(11;14) cases, is associated with trisomies, higher CCND1 expression and is more likely to have a breakpoint involving the constant region of the IGH locus. The enrichment of VDJ breakpoints within typical t(11;14) MM is consistent with enrichment of VDJ IGH breakpoints in MCL, which are mostly characterized by typical t(11;14) FISH patterns. While not an independent predictor of outcome with standard therapies, it remains to be evaluated whether patients with typical or atypical t(11;14) have a differential response to novel agents, such as venetoclax. Disclosures Fonseca: Janssen: Consultancy; Novartis: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Merck: Consultancy; Juno: Consultancy; Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy; Aduro: Consultancy; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy; AbbVie: Consultancy; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; Mayo Clinic in Arizona: Current Employment; Amgen: Consultancy; BMS: Consultancy; Celgene: Consultancy; Takeda: Consultancy; Bayer: Consultancy; Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees. Bergsagel: Pfizer: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; GSK: Consultancy, Honoraria; Oncopeptides: Consultancy, Honoraria; Genetech: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Patents & Royalties: human CRBN mouse. Kumar: Merck: Research Funding; Carsgen: Research Funding; Roche-Genentech: Consultancy, Research Funding; Beigene: Consultancy; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bluebird Bio: Consultancy; Antengene: Consultancy, Honoraria; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Sanofi: Research Funding.

Abstract Purpose: Multiple myeloma (MM) is a plasma cell (PC) malignancy with an increasing incidence in the United States. Fluorescence in situ hybridization (FISH) is currently the gold-standard diagnostic assay to detect recurrent genomic abnormalities of prognostic and therapeutic significance in MM. Hyperdiploid MM, a standard risk abnormality, is characterized by gains of odd-numbered chromosomes including chromosome 15. While gains of chromosome 15 have been reported in approximately 53% of standard risk MM patients (Chretien, Blood, 2015), losses of chromosome 15 are rarely reported. Enumeration of chromosome 15 centromere is detected using the alpha satellite FISH probe. We previously discovered ~9% of cases had false-negative chromosome 15 FISH results when compared to Mate Pair Sequencing (MPseq) (Smadbeck, BCJ, 2019). To our knowledge, no previous studies have reported a reduced performance of the alpha satellite D15Z4 FISH probe in PC FISH studies. A false-negative result for D15Z4 could result in an underreporting of trisomy 15 and overreporting of monosomy 15. Here, we investigated the incidence of false-negative D15Z4 FISH results by examining the presence of monosomy 15 from patients with a monoclonal gammopathy. Methods: We identified 1231 samples from patients with a monoclonal gammopathy who had undergone uniform testing to identify MM-specific cytogenetic abnormalities. DNA from bone marrow samples was genotyped on the Precision Medicine Research Array and biogeographical ancestry was assessed using the Geographic Population Structure Origins tool (Elhaik, Nat Commun, 2014; Home DNA 2016). FISH of immunoglobulin (cIg)-stained PCs was performed as described (Baughn, BCJ, 2018). Chromosome 15 centromere probe (Vysis CEP 15 D15Z4, Abbott Molecular, Abbott Park, IL) was used for chromosome 15 enumeration. Samples were divided into two groups: Cases with or without evidence of monosomy 15 by FISH. Mann-Whitney U test was applied to compare difference between groups for data deviating from normal distribution. A Chi-squared test evaluating the differences across these two groups was used. Results: We identified monosomy 15 in 31 cases (2.5% of cohort). Monosomy 15 was observed in nearly 100% of both the PC (cIg+) and non-PC (cIg-) populations, by contrast to the other recurrent PC-specific genomic abnormalities which were restricted to only the PC population. All samples with a banded analysis revealed most metaphases had two normal chromosome 15s demonstrating that the D15Z4 FISH probe failed to correctly enumerate the number of chromosome 15 centromere signals. Of the samples with a monosomy 15 result, the median African ancestry was 76.2% (95% percentiles: 77.5%-89.0%), while the median African ancestry of the non-monosomy 15 cohort was 2.3% (95% percentiles: 0%-87.7%) (). Although not all patients with >50% African ancestry had evidence of monosomy 15, 26/31 (83.9%) of patients with monosomy 15 had >50% African ancestry. Accordingly, patients with African ancestry (similarity ≥ 0.70) had 8.02-fold (95% confidence interval: 3.73-17.25, P = 9.92 X10 -8) increased possibility to have monosomy 15. Of cases with monosomy 15, 22 (71.0%) had an IGH rearrangement with the most prevalent IGH partner being CCND1. The incidence of t(11;14) was greater in the monosomy 15 group (13/31:41.9%), in contrast to the non-monosomy 15 group (330/1200=27.5%) (P = 0.12). Conclusions: We report a polymorphic variant of the chromosome 15 centromere resulting in a false-negative FISH result using the D15Z4 FISH probe. Surprisingly, the false-negative FISH result was not uniformly distributed among patients and was highly enriched in individuals of African ancestry. This may be due to a variation of the chromosome 15 alpha satellite in some individuals that prevents the D15Z4 FISH probe from sufficiently binding (O'Keefe, Genome Research, 2000). Although this study focused on identification of monosomy 15, the incidence of a false-negative result is likely greater than we have reported in this study since we did not evaluate cases that had a true trisomy 15 that was reported as normal chromosome 15 enumeration by FISH. This study highlights a critical need to ensure diagnostic reagents have equal performance for all patients independent of their ancestry. Evaluation of genomic events using unbiased approaches such as whole genome sequencing may circumvent some of these limitations. Disclosures Elhaik: DDC: Consultancy. Baird: DDC: Current Employment. Kumar: Antengene: Consultancy, Honoraria; Bluebird Bio: Consultancy; Beigene: Consultancy; BMS: Consultancy, Research Funding; Tenebio: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Novartis: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche-Genentech: Consultancy, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Carsgen: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.