Maura, Francesco

Introduction Monoclonal antibodies (MoAbs) directed against CD38 are a mainstay in multiple myeloma (MM). Downregulation of CD38 expression is a resistance mechanism of antigenic escape, however, cell surface expression may return. While this tumor cell plasticity is likely driven by epigenetic mechanisms, it is unknown if MM cells can develop permanent resistance to anti-CD38 MoAb by acquiring genomic events leading to biallelic disruption of CD38 thus precluding re-treatment. Here, we report biallelic loss of CD38 as a recurrent mechanism of resistance, often via convergent evolution where distinct subclones are advantaged towards the same selective pressure. Methods We interrogated two sets of patients (Mayo Clinic; n = 31, Calgary University; n =19) with relapse following anti-CD38 MoAbs. Two additional cases of interest were selected from Heidelberg University. Whole genome sequencing (WGS, 60-100x) or whole exome sequencing (WXS), flow cytometry (flow), and bulk RNAseq were used to validate the effect of genomic events on CD38. Missense mutations predicted to affect CD38 MoAb binding were validated with site-directed mutagenesis of a CD38 plasmid, transduction into K562 MM cells, and CD38 MoAb binding assays. Results The prevalence of biallelic inactivation of CD38 at anti-CD38 MoAb relapse was 6% (3/50). An additional selected biallelic event was noted in the Heidelberg set. Each case is described in detail: MM-19 (Calgary) received daratumumab (Dara), lenalidomide, and dexamethasone (Dex), and then had a second exposure to Dara, following relapse. At this second relapse, WGS was performed on two independent extramedullary disease localizations in the liver. Both samples had unique, large deletions (>3 Mb) on chromosome 4 coupled with additional unique focal deletions involving CD38. Flow confirmed the presence of CD138+/CD38- plasma cell populations in both sites. A second case of convergent evolution was seen in a patient (Mayo) after seventh-line Dara/Len/Dex. WGS revealed a common deletion encompassing CD38. On the remaining allele, one subclone had a structural variation-mediated loss of CD38(cancer cell fraction; CCF 40%) while another harbored an L153H missense mutation (CCF 60%). Flow confirmed presence of two CD138+ clones; one with diminished CD38 expression. L153H was functionally validated to abrogate Dara binding, while maintaining CD38 expression. The third sample was collected after fifth-line Dara and sixth-line Dara/Bortezomib/Dex (Heidelberg) and represents another case of convergent evolution with a common loss of chromosome 4 and two subclones detected by WGS (P98Lfs*12, CCF 39% resulting in biallelic inactivation; R140G, CCF 61%). Bulk RNAseq revealed, in comparison to NDMM from CoMMpass, CD38 expression below the first decile. Notably, the R140G variant reduced binding affinity to Dara but not to Isatuximab (Isa). A fourth case (Mayo) relapsing after 6 months of Dara-based therapy similarly had a focal deletion of CD38 with an L18Sfs*16 frameshift deletion on the remaining allele associated with a 4.35-fold reduction in CD38 expression by RNAseq compared to pre-treatment. Interestingly, in 3/4 cases the CD38-negative clone emerged after a second exposure to anti-CD38 MoAb, suggesting emergence with the first exposure and then selection during the second. One other monoallelic event (C275Y; Heidelberg) was detected post-Isa. Copy number of the alternate allele is not known given targeted sequencing methodology, but the variant was seen to reduce binding affinity to both Dara and Isa. The prevalence of monoallelic loss of CD38 in newly diagnosed MM from MMRF CoMMpass was 7% (50/701) and two patients had mutations in CD38, but there were no cases of biallelic loss suggesting these events are driven by treatment pressure. Conclusion Distinction of the mode of CD38 loss is valuable for predicting outcome of re-challenge to anti-CD38 MoAbs. We see here an estimated 5-10% of anti-CD38 MoAb relapses are characterized by genomic antigen escape. Results here are in line with antigen escape to BCMA- and GPRC5D-directed immunotherapies (Lee et al, Nat Med 2023), suggesting that acquired antigen escape resulting from prolonged exposure to the selective pressures of immunotherapy is a recurrent mechanism of resistance in MM. Moreover, in select cases, non-overlapping resistance mutations provide rationale for switching agents at second exposure.

Introduction: Multiple myeloma (MM) with t(11;14)(q13;q32), resulting in CCND1;IGH rearrangement, remains the only subset amenable to targeted therapy with the BCL2 inhibitor, Venetoclax. Nevertheless, not all patients with this translocation respond to treatment and some progress early after initial response. The mechanisms behind this clinical heterogeneity are largely unknown. Methods: To investigate mechanisms of heterogeneity in response to Venetoclax, we interrogated 44 whole genome sequencing (WGS) and 24 whole exome sequencing data from a training cohort including 33 patients with t(11;14) MM treated with Venetoclax at the Mayo Clinic. In a validation cohort, we extracted single nucleotide variants (SNV) and indels from 17 MM patients with t(11;14) with available RNAseq data treated with Venetoclax at Emory University. Results: In the training cohort the median number of lines of therapy was 4 and Venetoclax drug regimens included combinations with dexamethasone, proteasome inhibitors, anti-CD38 agents, and immunomodulatory agents. After a median follow up of 30 months, the median progression free survival (PFS) was 9 months, and 13 patients (39%) achieved a complete remission (CR) to therapy, while 9 (27%) were refractory (i.e. no response or progressed within the first 60 days). For our driver discovery analysis, we integrated SNV, indels, copy number variants (CNV) and structural variants (SV). Deletion 17p (del17p) or TP53 mutation, MCL1 gain/amp, and hyper-APOBEC did not correlate with PFS in our cohort (p=0.59, p=0.71, and p=0.05, respectively). In contrast, mutations involving the MAPK pathway (NRAS, KRAS, BRAF, FGFR3) displayed the strongest association with short PFS, which was retained after adjusting for del17p/TP53 mutations, and 1q gain/amp (HR: 4.76, CI: 1.8-12.9, p=0.002). The association between suboptimal response and resistance to Venetoclax in MM patients with MAPK pathway mutations (n=7) was also observed in our validation cohort from Emory University (p=0.046). This is in line with what has been reported in functional studies in acute myeloid leukemia, where RAS pathway promotes MCL1 under the selective pressure of Venetoclax (Zhang et al. Signal Transduct Target Ther 2022). While MAPK mutations explained most cases of refractory patients (86%), the mechanisms through which initially responding patients subsequently progressed remain unclear. To investigate this, we interrogated 10 patients with pre- and post-treatment samples collected at disease progression (PD). We identified genomic events acquired/selected at PD of genes involved in the anti-apoptotic MCL1/BCL2 and MAPK pathways in 6/10 cases. One patient was refractory to Venetoclax and had double minutes involving BCL2, which caused multiple focal amplifications (>30 copies), likely reflecting extrachromosomal DNA. Another patient progressed with an acquired, focal deletion on BCL2L11. In two patients, the progressing clones harbored mutations in MAPK pathways (one BRAF and the other WNK2). Two patients who relapsed after sustained CR had major branching evolution where a previously undetectable focal deletion of PMAIP1 (NOXA) was seen at PD. Importantly, one of these deletions was a biallelic event. Venetoclax sensitivity assays conducted on PMAIP1 knockout in the MM t(11;14) cell line, KMS12PE, demonstrated significantly reduced cell death across all drug concentrations (p<0.05), validating the loss of NOXA as a mechanism of resistance to Venetoclax in MM. Finally, similarly with what was previously reported in chronic lymphocyte leukemia treated with Venetoclax (e.g., Herling et al Nat Comm 2018), CDKN2A/B monoallelic and biallelic focal deletions were selected post-Venetoclax, at PD in 4 patients while TP53 SNV/deletions were selected from subclonal to clonal in 2 patients at PD. Conclusion: In this study, we demonstrate that mutations involving the MAPK pathway identify a population of MM patients with t(11;14)(CCND1;IGH) who are unlikely to respond to Venetoclax. We also show that genomic events involving the MAPK and BCL2/MCL1 pathways are primarily responsible for disease progression after initial response. These findings highlight the critical role of comprehensive genomic profiling through WGS in identifying mechanisms of resistance to targeted therapies in MM, aiding patient selection for Venetoclax, and guiding the development of novel drug combinations.

Background: Multiple myeloma (MM) is an incurable plasma cell malignancy with several recurrent primary cytogenetic abnormalities including t(11,14)(q13;q32), resulting in increased expression of CCND1. Although 40% of patients with t(11;14) MM respond favorably to the BCL-2 inhibitor venetoclax (ven), 60% fail to achieve an objective response. Previous studies have suggested upregulation of PMAIP1, encoding the pro-apoptotic NOXA protein, in association with ven sensitivity (Leblay, Blood, 2024). Here we sought to evaluate the role for NOXA in ven response in t(11;14) MM. Methods: We analyzed whole exome, whole genome and RNA sequencing data of CD138+ tumor cells from 1,359 patients with MM in the CoMMpass and Mayo Clinic cohorts. The histone mark H3K27ac was assessed in t(11;14)+ KMS12PE and U266B1 human myeloma cell lines using ChIP-seq. Gene expression and protein levels of PMAIP1 (NOXA) were also detected by qRT-PCR and Western blot, respectively. Live cell detection was evaluated by Annexin V and PI using flow cytometry and by YoYo3 dye using IncuCyte following treatment with ven. Results: The t(11;14) MM subtype was associated with a 1.6-5.5-fold increased expression of PMAIP1 compared to non-t(11,14) MM in the CoMMpass and Mayo Clinic cohorts (p<0.0001). These findings are consistent with a significant correlation between CCND1 and PMAIP1 expression in both cohorts (p<0.001). To evaluate whether cyclin D1 is directly associated with increasing expression of PMAIP1 in t(11;14) MM, we generated a cyclin D1 knock out (KO) version of the U266B1 line using CRISPR/cas9. Two independent U266B1 KO cell lines showed reduced expression of PMAIP1 and NOXA protein compared to the WT U266B1 consistent with a role for cyclin D1 in regulating PMAIP1 expression. In addition, the KO lines had a 4.8-fold reduced H3K27ac signal at the PMAIP1 locus in comparison to the WT control suggesting a role for cyclin D1 in promoting H3K27ac. We previously observed increased expression of CCND1 in association with the G allele compared to the A allele of rs9344 in both the CoMMpass and Mayo Clinic cohorts (Leukemia, in press). We next asked whether the G risk allele of rs9344 within CCND1 impacted gene expression and H3k27ac levels at the PMAIP1 locus. To study the allele specific effect of CCND1, we used the CRISPR/cas9 edited KMS12PE WT (rs9344 AA) and Knock In (KI) (rs9344 GG) cell lines. In the KI cells, the G allele was accompanied by an ~2-fold increased expression of PMAIP1 and NOXA protein detected by western blot (<0.05). Increased PMAIP1 expression in KI lines was also accompanied by a 2.2-fold increased H3K27ac further suggesting a role for the G allele of rs9344 in promoting H3K27ac and gene expression at the PMAIP1 locus. Finally, we evaluated the consequence of PMAIP1 expression levels in relation to ven response. Increased expression of PMAIP1 and elevated levels of NOXA protein in the KMS12PE KI lines was associated with increased sensitivity to ven resulting in ~50% increase in cell death of KI vs. WT lines (p<0.02). Conclusion: We demonstrate a role for cyclin D1 in regulating PMAIP1 expression through H3K27ac resulting in increased NOXA protein. Levels of NOXA are associated with the G allele of rs9344 and increased venetoclax response.

Introduction The pathogenic role of somatic non-coding (nc) single nucleotide variants (SNVs) is underexplored in multiple myeloma (MM) and additional characterization of these regions has the potential to provide significant new insights into disease progression. To investigate the role of ncSNVs and their relationship with MM pathogenesis, we have characterized a novel series of WGS from 294 cases of MGUS, SMM, and MM using computational tools optimized to identify functional driver SNVs in these regions. We hypothesize the ncSNVs that co-localize to sites of super-enhancers (SEs) active in plasma cells (PC) are sites of high functional impact SNVs and these mutations lead to the dysregulated expression of genes that should be silent at the terminally differentiated stage of PCs, providing a selective advantage that shapes the fitness of the MM genomic landscape. Methods We analyzed 294 high-coverage matched tumor/normal pairs (80x tumor and 40x normal) derived from 21 MGUS, 86 SMM, 167 NDMM and 40 RRMM. In addition to previously sequenced samples, newly sequenced patients were obtained from NYU Langone Health, Memorial Sloan Kettering Cancer Center, University of Alabama at Birmingham and University of Miami following IRB-approved informed consent. DNA was extracted from CD138+ purified bone marrow plasma cells using standard approaches. We used the MGP1000 (https://github.com/pblaney/mgp1000) to process sequencing data which employs a union-consensus mechanism for determining the final set of variants. We collected enhancer elements (E/SEs) active in GC B-cells, ABC-DLBCL and GBC-DLBCL (Bal, et al. Nature, 2022) and PC specific E/SEs (Loven, et al. Cell 2013). FunSeq2 was used to score ncSNVs. GRAM was used to predict how variants alter TF binding and gene expression in a subset of 6 cases with paired WGS in our series and will expand this analysis on a validation set of 263 NDMM and 93 RRMM cases from University-Hospital Heidelberg enrolled in the GMMG-HD6 clinical trial. Results We identified 1,952,232 high-quality SNVs with 18,199 (0.93%) accumulating in 280 focal sites we termed “mutationally enriched regions” or MERs. To discern SNVs in MERs with the highest potential for imparting a selective advantage, we clustered mutations based on the density of FunSeq2 impact scores which uses reference data contexts on sequence conservation, modification of transcription factor (TF) binding, and gene linkage to regulatory elements and network hubs from pan-cancer studies. We show that MERs are enriched for ncSNVs with high impact scores (mean=2.57) compared to random similarly-sized regions of the genome (Fisher's exact test P<9.7e-12, OR=10.10 [3.88-37.40]). The high impact ncSNVs (hi-ncSNVs) in MERs (n=220) account for 8.09% of all hi-ncSNVs and are present in 46.3% of cases (136/294) with some patients having up to 7. The proportion of cases carrying a MER hi-ncSNVs was similar across disease stages (MGUS=42.9%, SMM=46.5%, MM=40.7%, and RR=47.5%). Analysis of the VAF of MER hi-ncSNVs shows a minor shift towards more clonal versus subclonal mutations (53.6% vs 46.4% VAF>=0.25) suggesting some high impact mutations are acquired in the GC but additional hi-ncSNV are acquired later in PC development, providing evidence that hi-ncSNVs accumulate in MERs throughout disease progression. We mapped these hi-ncSNVs to associated genes then intersected the loci with E/SEs active in GCBs and PCs to determine their functional context. This approach identified 12 genes affected by hi-ncSNVs including the previously identified nc-drivers NEAT1, MIR142, ZCCHC7, and IGLL5 as well as the PC differentiation genes IRF8, BACH2, proliferation genes RHOH, SPRED2, known coding MM drivers CCND1, ZFP36L1, and chr1q genes BTG2 and CROCC. We predicted the effect of ncSNVs on expression through modulation of TF binding at this set of putative nc drivers in MM and identified a trend towards increase in expression of SPRED2, CCND1, NEAT1, and IGLL5 compared to non-mutated cases (P=0.089). We will present data on the validation of this set of genes on a larger set of data derived from the Heidelberg group. Conclusions We identified a set of 12 putative nc driver genes in MM that modify expression and have the potential to significantly alter the pathogenesis of MM by providing a selective survival advantage. We will present the impact of these by clinical stage and on outcome.

Introduction: Cyclin D1, encoded by the CCND1 gene, is primarily known as a major regulator of cell cycle progression. Emerging studies have demonstrated a role for cyclin D1 in regulating gene transcription. In mantle cell lymphoma with t(11;14), cyclin D1 strongly binds to the promoters of highly transcribed genes and its overexpression led to genome-wide transcriptional downregulation. However, the transcriptional role for cyclin D1 in t(11;14) MM remains undefined. This study aims to elucidate the global chromatin and transcriptional changes induced by cyclin D1 in t(11;14) MM. Methods: Knockout (KO) of CCND1 in U266B1, a MM cell line carrying t(11;14), was performed by the Synthego (Redwood City, CA) using CRISPR/Cas9, which generated a frameshift insertion of base pair (bp) “A” after 69,641,478 bp within exon 1. Two independent KO clones were used for downstream analysis. ChIP-seq and ATAC-seq were performed for KO (n=2) and wild-type clones (WT, n=2). ChIP-seq used antibodies against H3K4me3 (promoter) and H3K4me1 and H3K27ac (enhancer). Libraries were sequenced to 2x51 bp on the Illumina NextSeq 2000 platform. Total RNA libraries (in triplicate) were sequenced to 2x150 bp. ChIP-seq and ATAC-seq reads were mapped to hg38 with BWA, and peaks were identified by MACS2 at FDR 0.01. RNA-seq reads were mapped to hg38 with STAR and gene raw counts were estimated using featureCounts. Differential peaks and differentially expressed genes between KO and WT were identified using edgeR at FDR 0.05 and fold-change 2. Only peaks present in at least 2 samples and genes having reads-per-million value 0.1 in at least 1 sample were included, excluding those from chrY. Pathway analysis was performed with the Enrichr package. Results: Differential expression analysis identified 2-fold more genes that were down-regulated than up-regulated in KO compared to WT (467 vs. 233). Pathway analysis revealed an enrichment of TNF-alpha signaling via NF-kappaB, inflammatory response, and apoptosis in association with down-regulated genes, while up-regulated genes are enriched in myogenesis, KRAS signaling, and epithelial mesenchymal transition pathway. In parallel, differential chromatin analysis identified 2.5% (1,161) of the peaks from H3K27ac and 0.5-1% (359-610) of the peaks for the other 3 marks that showed significant changes of signal levels between KO and WT. For the promoter mark H3K4me3, there are largely a comparable number of peaks showing increased (191 peaks) and decreased (168 peaks) signals, respectively, in KO compared to WT. However, for ATAC-seq and H3K4me1, about 70% of the differential peaks showed an increased signal in KO, in contrast with H3K27ac for which 60% of differential peaks showed decreased signal in KO. The preferential loss of H3K27ac occupancy is consistent with the observation of a higher proportion of genes being down-regulated in KO. These results indicated that knockout of CCND1 led to a global increase of chromatin accessibility, but a reduction of H3K27ac and gene expression. Further annotation revealed that, for H3K27ac and H3K4me1/ATAC-seq, 92% and 97% of the differential peaks were located >2kb away from the transcription start sites (TSSs) of expressed genes. Even for H3K4me3, 88% of the differential peaks are >2kb away from TSSs. The results suggest that the changes of chromatin upon CCND1 knockout predominantly occurred at distal regulatory regions. For up- and down-regulated genes, we estimated ChIP-seq and ATAC-seq signals in the TSS 2kb regions, revealing that up-regulated genes were overall associated with increased signals. Further analysis found that, for ATAC-seq and H3K4me3, 44% and 59% of the differential peaks located in the promoter regions were associated with differentially expressed genes, which dropped to ~20% for the two enhancer marks. These data indicate that changes in chromatin state impact the expression of the associated genes. Conclusion: Knockout of CCND1 impacts local chromatin state, particularly at enhancer regions, and the transcriptional program. In promoter-proximal regulatory regions, the changes of chromatin accessibility and H3K4me3 are strongly correlated with the expression changes of nearby genes. Further studies will identify the genes targeted by the differential enhancers and their possible roles in MM pathogenesis mediated by cyclin D1 following the t(11;14) event.

Summary: While the current approach to precursor hematologic conditions is to “watch and wait,” this may change with the development of therapies that are safe and extend survival or delay the onset of symptomatic disease. The goal of future therapies in precursor hematologic conditions is to improve survival and prevent or delay the development of symptomatic disease while maximizing safety. Clinical trial considerations in this field include identifying an appropriate at-risk population, safety assessments, dose selection, primary and secondary trial endpoints including surrogate endpoints, control arms, and quality-of-life metrics, all of which may enable more precise benefit–risk assessment.

PURPOSE Outcomes for patients with newly diagnosed multiple myeloma (NDMM) are heterogenous, with overall survival (OS) ranging from months to over 10 years. METHODS To decipher and predict the molecular and clinical heterogeneity of NDMM, we assembled a series of 1,933 patients with available clinical, genomic, and therapeutic data. RESULTS Leveraging a comprehensive catalog of genomic drivers, we identified 12 groups, expanding on previous gene expression–based molecular classifications. To build a model predicting individualized risk in NDMM (IRMMa), we integrated clinical, genomic, and treatment variables. To correct for time-dependent variables, including high-dose melphalan followed by autologous stem-cell transplantation (HDM-ASCT), and maintenance therapy, a multi-state model was designed. The IRMMa model accuracy was significantly higher than all comparator prognostic models, with a c-index for OS of 0.726, compared with International Staging System (ISS; 0.61), revised-ISS (0.572), and R2-ISS (0.625). Integral to model accuracy was 20 genomic features, including 1q21 gain/amp, del 1p, TP53 loss, NSD2 translocations, APOBEC mutational signatures, and copy-number signatures (reflecting the complex structural variant chromothripsis). IRMMa accuracy and superiority compared with other prognostic models were validated on 256 patients enrolled in the GMMG-HD6 (ClinicalTrials.gov identifier: NCT02495922 ) clinical trial. Individualized patient risks were significantly affected across the 12 genomic groups by different treatment strategies (ie, treatment variance), which was used to identify patients for whom HDM-ASCT is particularly effective versus patients for whom the impact is limited. CONCLUSION Integrating clinical, demographic, genomic, and therapeutic data, to our knowledge, we have developed the first individualized risk-prediction model enabling personally tailored therapeutic decisions for patients with NDMM.