Baughn, Linda B.

Background: Multiple myeloma (MM) remains an incurable plasma cell (PC) malignancy. Despite improvements in clinical outcomes, variability in treatment response exists. Some patients experience innate resistance, while others develop resistance over the course of treatment. Identifying signatures of treatment response and resistance is essential to predicting effective treatment strategies. Given the phenotypic clonal heterogeneity observed in MM, we hypothesize that resistant and sensitive subpopulations have unique phenotypic signatures. We used mass cytometry (CyTOF) to characterize primary MM samples and correlated their phenotypic signatures with treatment response. Methods: We characterized 102 primary MM samples by CyTOF using 37 antibodies (see figure). Frozen whole BM specimens were thawed, stained with antibodies, barcoded, and analyzed on a Helios mass cytometer. Data was processed using Cytobank or Pathsetter and analyzed using t-distributed stochastic neighbor embedding (t-SNE) algorithm. T-SNE plots for CD45+ cells were created using lineage defining markers. T-SNE plots of neoplastic PCs (CD138+,CD3-,CD16-,CD19-,cytoplasmic kappa or lambda+) were created using all remaining markers. A differential analysis of each sample's clusters was performed against all other events. Z-score values, representing marker changes from the differential analysis, for each cluster were used to group similar clusters across all samples, allowing us to compare sub populations of PCs between patients (see figure). From these groups of clusters with similar changes in markers, we calculated the proportion of cells with increases or decreases in markers for each sample. Results were compared to IMWG 90-day response criteria using a random forest model. Results: We performed mass cytometry on 102 samples from 68 unique MM patients. Median age was 66 years (range 34-95) with a 1.5:1 male:female ratio. 37 (36%) had NDMM, 55 (54%) had RRMM, 2 (2%) had SMM and 8 cases (8%) had unknown status. FISH studies revealed 47 (46%) had hyperdiploidy without a recurrent primary IgH rearrangement, 31 (30%) had t(11;14), 13 (13%) had t(4;14), 5 (5%) had 14;16 or 14;20, 3 (3%) had hyperhaploidy and 3 (3%) had an undefined primary. 54 (53%) had 1q gain/amp, 22 (22%) had TP53 deletion, 22 (22%) had a MYC rearrangement and 71% had high mSMART risk. Nearly all patients received an immunomodulatory compound, a proteasome inhibitor and dexamethasone. 2 (2%) had complete response (CR), 10 (9.8%) very good partial response (VGPR), 36 (35%) had partial response (PR), 14 (14%) had minimal response (MR), 13 (13%) had stable disease (SD) and 17 (17%) had progressive disease (PD). Characterization of CD45+ cells identified a relatively conserved and reproducible map from each patient. Notable differences included an ~2-fold reduction in the percentage of total B cells (CD45+,CD3-,CD19+)/total CD45+ cells in cases with high risk mSMART, RRMM and TP53 deletion compared to cases with standard risk, NDMM or normal TP53 status. Similar B-cell reductions were found in RRMM vs. NDMM samples from matched-patients. In contrast to the conserved t-SNE structure of the CD45+ population, the neoplastic PC population displayed dramatically reduced t-SNE structural consistency. Heterogeneity between patient samples and between the same patient samples over time was observed. We focused our analysis on 22 patients with serial samples. The proportion of each t-SNE group present in each patient sample was calculated and then correlated to the 90-day response metric. We identified a phenotypic group whose proportion increased in relation to disease progression (R2=0.85). This group had elevated BCL-2, pSTAT3, CD56 levels and reduced CD45 and CD81 levels. Evaluating individual markers, we found an increase in the proportion of cells with elevated BCL-2, pS6, Ki67, and IKZF1 levels in relation to disease progression. We also observed an increase in the proportion of cells with reduced pCREB levels and a reduction in the proportion of cells with reduced CD147 levels in relation to disease progression. Conclusions: We have utilized CyTOF to characterize phenotypic and functional markers in MM cells on a single cell level and can identify unique subgroups that change in relation to disease progression. Future analysis will correlate phenotypic signatures with ISS stage, mSMART risk, cytogenetic abnormality and type of treatment. Figure 1 Disclosures Kumar: Kite Pharma: Consultancy, Research Funding; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Sanofi: Research Funding; MedImmune: Research Funding; Karyopharm: Consultancy; Merck: Consultancy, Research Funding; Carsgen: Other, Research Funding; Cellectar: Other; Novartis: Research Funding; Adaptive Biotechnologies: Consultancy; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Genecentrix: Consultancy; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Tenebio: Other, Research Funding; BMS: Consultancy, Research Funding; Dr. Reddy's Laboratories: Honoraria.

Background: Multiple myeloma (MM) is an incurable plasma cell malignancy and genetic abnormalities contribute to disease heterogeneity and outcome. Primary abnormalities, namely recurrent immunoglobulin (Ig) heavy chain translocations and hyperdiploidy, occur early in disease course. Secondary events, such as MYC abnormalities occur upon progression. Earlier studies showed MYC abnormalities detected by FISH or by capture sequencing were independently associated with poor outcome (Walker, et al., BCJ, 2014), while recent studies using WGS did not support this finding (excluding MYC/IGL) (Mikulasova, et al. Haematologica, 2020; Misund, et al. Leukemia, 2020). We hypothesize these discrepancies are due to differences in methods and sensitivities of detection of MYC abnormalities by FISH vs. WGS. Given that MYC abnormalities often display remarkable genomic heterogeneity with numerous gene partners, reduced detection of MYC abnormalities by FISH is not unexpected. This hypothesis is supported by lower frequencies of MYC abnormalities found by FISH (15%) vs. NGS (30-35%) consistent with ~50% false-negative rate of the MYC FISH probe (Smadbeck, et al. BCJ, 2019). To better understand the role of MYC in myeloma disease outcome, we compared the MYC abnormality subtype identified by FISH or NGS vs. MYC gene expression levels and overall survival. Methods: We performed a retrospective study of newly diagnosed MM patients seen at Mayo Clinic or enrolled in the MMRF CoMMpass trial. For Mayo cases, MYC FISH results (breakapart probe, Abbott) were obtained from the Mayo Clinic Genomics database (N=1342) and mate pair sequencing (MPseq) was performed on 140 cases. For CoMMpass cases, we obtained tumor long-insert whole genome sequencing (WGS), RNA sequencing (RNAseq) for gene expression and clinical outcome data. Overall survival (OS) was defined as time from diagnosis to death from any cause or to last follow up. Survival curves were estimated using Kaplan Meier and compared using the Log-Rank test. Statistical analyses performed using SPSS and JMP with significance determined when P <0.05. Results: We first evaluated the impact of MYC abnormalities on OS when detected by FISH or NGS. In Mayo cases, OS was significantly shorter in patients with MYC abnormalities compared to patients without MYC abnormalities using FISH (5.3 vs. 8.0 years, P<0.001, N=1342). In contrast, there was no significant difference in OS between patients with or without MYC or abnormalities using MPseq or WGS in both the Mayo and CoMMpass cohorts (Mayo: 6.4 vs. and 6.9 years P=0.78, N=140; CoMMpass: 4.9 vs. and 5.1 years P=0.74, N=546). Since FISH-detected MYC abnormalities were associated with poor outcome, we evaluated differences in the types of MYC abnormalities identified FISH and genome sequencing; 270 of 658 CoMMpass cases had a MYC abnormality and 12 abnormality subgroups were identified. In the Mayo cases, FISH preferentially detected translocations and complex abnormalities and missed insertions with flanking duplicating sequences or terminal tandem duplications (TTD) that occur telomeric to MYC. Since the level of MYC expression should be a consequence of the various genomic abnormalities altering the MYC gene region, we compared MYC expression levels in relation to MYC abnormality subgroups. Highest expression was seen with MYC amplification, followed by Ig abnormalities, non-Ig abnormalities, complex deletion/duplications, proximal deletions, non-Ig insertions, terminal deletions, TTD, trisomy 8, no MYC structural variation, monosomy 8 and cases with MAX mutations had the lowest expression. Abnormalities identified by FISH had higher MYC expression (83.5 TPM) compared to cases predicted to be missed by FISH (63.2 TPM). We tested if high MYC expression, irrespective of MYC structural abnormality, was associated with differences in OS. Boxplot analysis was used to categorize MYC expression in 631 CoMMpass patients as top quartile/high MYC expression (Q4≥ 75 TPM, n=159) and bottom quartile/low MYC expression (Q1≤ 16.5 TPM, n= 158) (see Figure). OS was significantly shorter in patients with high MYC expression compared to patients with low MYC expression (4.6 vs. 5.3 years, P <0.038). Conclusion: We show that FISH detects only a subset of the MYC abnormalities detected by genome sequencing, and that FISH-detected MYC abnormalities are associated with higher MYC gene expression and decreased survival. Figure 1 Disclosures Kumar: Kite Pharma: Consultancy, Research Funding; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Dr. Reddy's Laboratories: Honoraria; Cellectar: Other; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Novartis: Research Funding; Tenebio: Other, Research Funding; Carsgen: Other, Research Funding; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Merck: Consultancy, Research Funding; Genecentrix: Consultancy; BMS: Consultancy, Research Funding; Karyopharm: Consultancy; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Sanofi: Research Funding; MedImmune: Research Funding; Adaptive Biotechnologies: Consultancy.

AbstractAcute undifferentiated leukemia (AUL) is a very rare hematologic neoplasm that expresses no markers specific for either myeloid or lymphoid lineages. While commonly observed in several acute leukemias, KMT2A rearrangements in AUL have been rarely reported in the literature. We report the third case to our knowledge of AUL harboring a KMT2A rearrangement. Furthermore, the KMT2A/GIMAP8 gene fusion identified in this case represents a novel KMT2A rearrangement.

Abstract Purpose: Rearrangements involving the MYC protooncogene are common in newly diagnosed multiple myeloma, but their prognostic significance is still unclear. The purpose of this study was to assess the impact of MYC rearrangement on clinical characteristics, treatment response, and survival in patients with newly diagnosed multiple myeloma. Experimental Design: This is a retrospective study including 1,342 patients seen in Mayo Clinic in Rochester, MN, from January 2006 to January 2018, who had cytogenetic testing by FISH at diagnosis, including MYC testing using the break apart FISH probe (8q24.1). Results: A rearrangement involving MYC was found in 8% of patients and was associated with elevated β2-microglobulin, ≥50% bone marrow plasma cells, IgA multiple myeloma, and the cooccurrence of trisomies. There were no differences in overall response rates between patients with and without MYC rearrangement when induction chemotherapy was proteasome inhibitor (PI)-based, immunomodulatory drug (IMiD)-based or PI + IMiD-based. Overall survival was shorter in patients with MYC rearrangement compared with patients without MYC rearrangement (5.3 vs. 8.0 years, P < 0.001). MYC rearrangement was associated with increased risk of death on multivariate analysis when high-risk cytogenetic abnormalities, ISS stage III, and ≥70 years of age were included (risk ratio: 1.5; P = 0.007). Conclusions: MYC rearrangement is associated with high disease burden and is an independent adverse prognostic factor in patients with newly diagnosed multiple myeloma.

A fetus harboring a duplication of SMAD2 exons 1-6 presented with dextrocardia and pulmonary hypoplasia. Mate-pair sequencing revealed the duplication to be in inverted tandem orientation to the wild-type SMAD2 allele, disrupting its sequence and decreasing expression. These observations suggest SMAD2 to be responsible for the fetal dextrocardia.

Abstract Zinc-finger protein 384 (ZNF384) gene fusions with EP300 have recently been described as a recurrent fusion in B-cell acute lymphoblastic leukemia (B-ALL) with a good response to conventional chemotherapy, suggesting a favorable prognosis. Herein, we report on a female patient aged 12 years with uninformative conventional chromosome and B-ALL panel fluorescence in situ hybridization studies with chromosomal microarray showing multiple copy number gains, including relative gains in the ZNF384 (12p13.31) and EP300 (22q13.2) gene regions, suggesting a cryptic EP300/ZNF384 fusion. Ultimately, a next-generation sequencing assay, mate pair sequencing, was utilized to confirm EP300/ZNF384 fusion in this B-ALL clone, which may confer a favorable overall prognosis and potential targeted therapy.

Abstract A gain in chromosome 1q (+1q) is among the most common cytogenetic abnormalities in multiple myeloma (MM). It is unclear whether +1q is independently associated with decreased overall survival (OS). The objective of this study was to evaluate the impact of +1q on clinical characteristics, treatment response, and survival outcomes. We included 1376 Mayo Clinic patients diagnosed with MM from 2005 to 2018 who underwent fluorescence in situ hybridization testing at diagnosis with a panel including the +1q probe. A gain in 1q was found in 391 patients (28%) and was associated with anemia, hypercalcemia, high tumor burden, International Staging System (ISS) stage 3, high-risk (HR) translocations, and chromosome 13 abnormalities. There was no difference in overall response or deeper responses to proteasome inhibitor (PI)–, immunomodulatory drug (iMiD)–, or PI plus IMiD–based induction. Time to next treatment was shorter in patients with +1q compared with those without +1q (19.9 vs 27.7 months; P < .001). On univariate analysis, +1q was associated with increased risk of death (risk ratio [RR], 1.9; P < .001), and decreased OS was seen in all treatment groups. +1q was independently associated with decreased OS on multivariate analysis when other HR cytogenetic abnormalities, ISS stage 3, and age ≥70 years were included (RR, 1.5; P < .001). Gain of >1 copy of 1q was not associated with worse OS compared with gain of 1 copy (4.9 vs 4.3 years; P = .21). +1q was associated with high tumor burden, advanced disease stage, and HR translocations. It is independently associated with decreased OS, even in the setting of novel therapy and transplant.

e20520 Background: Cytogenetic abnormalities detected by FISH are found in the majority of multiple myeloma (MM) patients. Although their prognostic value has been studied extensively, less is known about their association with disease characteristics and treatment response. Methods: To address these questions, we designed a retrospective study including 2031 Mayo Clinic patients diagnosed with MM from 2004 to 2018. We compared baseline characteristics and treatment outcomes between primary cytogenetic groups: t(11;14), t(4;14), t(14;16), (14;20), t(6;14), unknown IgH translocation/del and trisomy (without IgH translocation). These included 373, 177, 78, 20, 18, 228 and 791 patients respectively. Kruskal-Wallis and Fisher’s exact tests were used for categorical and continuous variables respectively. Time to next treatment (TTNT) was estimated using Kaplan-Meier method and compared using Log-Rank test. Results: t(4;14), t(14;16), t(6;14) and t(14;20) groups were associated with hemoglobin < 10 g/dL, beta2microglobulin > 5.5 µg/ml, ISS stage 3 and ≥50% bone marrow plasma cells. The latter 3 groups were also associated with renal dysfunction (Cr ≥2 mg/dL) and higher urinary monoclonal protein. t(4;14) was associated with IgA isotype, serum monoclonal protein ≥1g/dL and plasma cell labeling index ≥1%. Light chain myeloma was more prevalent in patients with t(11;14). Overall response rate (ORR) to proteasome inhibitor (PI) induction was higher for those with IgH translocations (any) compared to trisomies (85% vs 77% P = 0.02), while ORR was higher for those with trisomies with immunomodulatory drug (IMiD) induction (90% vs 78% P < 0.01). The rate of ≥ very good partial response was higher for patients with high risk IgH translocations [t(4;14), t(14;16) or t(14;20)] compared to standard risk with PI-IMiD combination treatment (88% vs 65% P < 0.01). Otherwise, response rates did not differ between these 2 groups. TTNT was longer in patients with trisomies compared to those with IgH translocation with IMiD or PI-IMiD treatments (32.2 vs 19 and 44 vs 27.4 months, respectively P < 0.01). For all cytogenetic groups, better treatment responses and longer TTNT were seen with PI-IMiD combinations compared to other treatments. Conclusions: t(4;14), t(14;16), t(6;14), and t(14;20) are associated with high risk disease characteristics. Patients with IgH translocations may have better response to PI induction compared to those with trisomies, while those with trisomies may have better response to IMiD treatment, with best outcomes for both seen with PI-IMiD combinations.