The biological and clinical impact of deletions before and after large chromosomal gains in multiple myeloma.

Acquisition of a hyperdiploid (HY) karyotype or immunoglobulin heavy chain (IGH) translocations are considered key initiating events in multiple myeloma (MM). To explore if other genomic events can precede these events, we analyzed whole-genome sequencing (WGS) data from 1173 MM samples. Integrating molecular time and structural variants (SV) within early chromosomal duplications, we indeed identified pre-gain deletions in 9.4% of HY patients without IGH translocations, challenging HY as the earliest somatic event. Remarkably, these deletions affected tumor suppressor genes (TSG) and/or oncogenes in 2.4% of HY patients without IGH translocations, supporting their role in MM pathogenesis. Furthermore, our study points to post-gain deletions as novel driver mechanisms in MM. Using multi-omics approaches to investigate their biological impact, we found associations with poor clinical outcome in newly diagnosed patients and profound effects on both oncogene and TSG activity, despite the diploid gene status. Overall, this study provides novel insights into the temporal dynamics of genomic alterations in MM.

Sequence diversity of kappa light chains from patients with AL amyloidosis and multiple myeloma.

BACKGROUND: AL amyloidosis (AL) results from the misfolding of immunoglobulin light chains (IG LCs). Aim of this study was to comprehensively analyse kappa LC sequences from AL patients in comparison with multiple myeloma (MM). OBJECTIVE: We analysed IGKV/IGKJ usage and associated organ tropism and IGKV1/D-33 in terms of mutational analysis and theoretical biochemical properties. MATERIAL AND METHODS: cDNA and bulk RNA sequencing of the LCs of AL and MM patients. RESULTS: We studied 41 AL and 83 MM patients showing that IGKV1 was most expressed among kappa AL and MM, with higher frequency in AL (80% vs. 53%, p = .002). IGKV3 was underrepresented in AL (10% vs. 30%, p = .014). IGKJ2 was more commonly used in AL than in MM (39% vs. 29%). Patients with IGKV1/D-33 were associated with heart involvement (75%, p = .024). IGKV1/D-33-segments of AL had a higher mutation count (AL = 12.0 vs. MM = 10.0). FR3 and CDR3 were most frequently mutated in both, with a median mutation count in FR3 being the highest (AL = 4.0; MM = 3.5) and one mutation hotspot (FR3 (83I)) for IGKV1/D-33/IGKJ2 was associated with cardiac involvement. CONCLUSION: This study confirmed that germline usage has an influence on AL amyloidosis risk and organ involvement.

Genomic Classification and Individualized Prognosis in Multiple Myeloma.

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.

Resolving the spatial architecture of myeloma and its microenvironment at the single-cell level.

In multiple myeloma spatial differences in the subclonal architecture, molecular signatures and composition of the microenvironment remain poorly characterized. To address this shortcoming, we perform multi-region sequencing on paired random bone marrow and focal lesion samples from 17 newly diagnosed patients. Using single-cell RNA- and ATAC-seq we find a median of 6 tumor subclones per patient and unique subclones in focal lesions. Genetically identical subclones display different levels of spatial transcriptional plasticity, including nearly identical profiles and pronounced heterogeneity at different sites, which can include differential expression of immunotherapy targets, such as CD20 and CD38. Macrophages are significantly depleted in the microenvironment of focal lesions. We observe proportional changes in the T-cell repertoire but no site-specific expansion of T-cell clones in intramedullary lesions. In conclusion, our results demonstrate the relevance of considering spatial heterogeneity in multiple myeloma with potential implications for models of cell-cell interactions and disease progression.

Resolving therapy resistance mechanisms in multiple myeloma by multiomics subclone analysis.

Intratumor heterogeneity as a clinical challenge becomes most evident after several treatment lines, when multidrug-resistant subclones accumulate. To address this challenge, the characterization of resistance mechanisms at the subclonal level is key to identify common vulnerabilities. In this study, we integrate whole-genome sequencing, single-cell (sc) transcriptomics (scRNA sequencing), and chromatin accessibility (scATAC sequencing) together with mitochondrial DNA mutations to define subclonal architecture and evolution for longitudinal samples from 15 patients with relapsed or refractory multiple myeloma. We assess transcriptomic and epigenomic changes to resolve the multifactorial nature of therapy resistance and relate it to the parallel occurrence of different mechanisms: (1) preexisting epigenetic profiles of subclones associated with survival advantages, (2) converging phenotypic adaptation of genetically distinct subclones, and (3) subclone-specific interactions of myeloma and bone marrow microenvironment cells. Our study showcases how an integrative multiomics analysis can be applied to track and characterize distinct multidrug-resistant subclones over time for the identification of molecular targets against them.

Comparison of IGLV2-14 light chain sequences of patients with AL amyloidosis or multiple myeloma.

Light chain amyloidosis (AL) is one of the most common forms of systemic amyloidosis and is caused by the deposition of insoluble fibrils derived from misfolded and aggregated immunoglobulin light chains (LC). To uncover the causes leading to this aggregation, we compared AL LC sequences with those of patients with the related disease multiple myeloma (MM), which do not aggregate in insoluble fibrils in vivo. IGLV2-14 is one of the most common AL-associated IGLV subfamilies. Here, we analysed IGLV2-14 LC sequences of 13 AL and eight MM patients in detail. We found that AL-associated LCs presented a lower median mutation count (7.0 vs. 11.5 in MM; P = 0.045), as well as an overall composition of less charged amino acids than MM LCs. However, we did not find a mutation that was present in ≥ 50% of the AL and not in the MM sequences. Furthermore, we did not find a significant difference in the isoelectric point (pI) in general, suggesting similar stability of the LCs in AL and MM. However, the subgroup of patients without a detectable heavy chain stood out. Surprisingly, they are characterized by an increase in mutation count (median 7.0 vs. 5.5) and pI (median 7.82 vs. 6.44, P = 0.043). In conclusion, our data suggest that the amount of mutations and the introduction of charges play a crucial role in AL fibril formation, as well as the absence or presence of a potential heavy chain binding partner.

Implications and prognostic impact of mass spectrometry in patients with newly-diagnosed multiple myeloma.

Mass spectrometry (MS) is a promising tool for monitoring monoclonal protein in plasma cell dyscrasias. We included 480 transplant-eligible newly-diagnosed multiple myeloma (MM) patients from the GMMG-MM5 trial (EudraCT No. 2010-019173-16) and performed a retrospective MS analysis at baseline (480 patients) and at the pre-defined, consecutive time points after induction (444 patients), prior to maintenance (305 patients) and after one year of maintenance (227 patients). We found that MS negativity was significantly associated with improved progression-free survival (PFS) even in patients with complete response (CR) at all investigated follow-up time points. The prognostic impact was independent of established risk factors, such as the revised International Staging System. Combining MS and baseline cytogenetics improved the prediction of outcome: MS-positive patients with high-risk cytogenetics had a dismal PFS of 1.9 years (95% confidence interval [CI]: 1.6-2.3 years) from the start of maintenance. Testing the value of sequential MS prior to and after one year of maintenance, patients converting from MS positivity to negativity had an excellent PFS (median not reached) while patients converting from MS negativity to positivity progressed early (median 0.6 years, 95% CI: 0.3-not reached). Among patients with sustained MS positivity, the baseline high-risk cytogenetic status had a significant impact and defined a group with poor PFS. Combining minimal residual disease (MRD) in the bone marrow and MS allowed the identification of double negative patients with a favorable PFS (median 3.33 years, 95% CI: 3.08-not reached) and no overall survival events. Our study provides strong evidence that MS is superior to conventional response monitoring, highlighting the potential of MS to become a new standard. Our data indicate that MS should be performed sequentially and combined with baseline disease features and MRD to improve its clinical value.Clinical Trials Register: EudraCT No. 2010-019173-16.

A phase 2 clinical trial of combined BRAF/MEK inhibition for BRAFV600E-mutated multiple myeloma.

Activating BRAF mutations are found in a small subset of patients with newly diagnosed multiple myeloma, but prevalence increases in late-stage, refractory disease, and the mutations are associated with adverse outcome. This prospective single-arm, open-label, multicenter phase 2 trial assessed the efficacy and safety of combined BRAF/MEK inhibition, using encorafenib and binimetinib, in patients with relapsed/refractory multiple myeloma (RRMM) carrying a BRAFV600E mutation. Patients received 450 mg encorafenib once daily and binimetinib 45 mg twice daily. The primary end point was the overall response rate achieved within the first year after start of treatment according to International Myeloma Working Group criteria. Twelve RRMM patients with a median of 5 prior lines of therapy were enrolled. The overall response rate was 83.3%, with 10 patients achieving at least a partial response. The median progression-free survival was 5.6 months, and overall survival was 55% at 24 months. Emerging resistance to therapy was driven by RAS mutations and structural variants involving the BRAF locus. This is the first prospective clinical trial to demonstrate that combined BRAF/MEK inhibition is highly effective in patients with BRAFV600E-mutated RRMM, and it represents a successful targeted precision medicine approach in this disease. This trial was registered at www.clinicaltrials.gov as #NCT02834364.

An mTORC1 to HRI signaling axis promotes cytotoxicity of proteasome inhibitors in multiple myeloma.

Multiple myeloma (MM) causes approximately 20% of deaths from blood cancers. Notwithstanding significant therapeutic progress, such as with proteasome inhibitors (PIs), MM remains incurable due to the development of resistance. mTORC1 is a key metabolic regulator, which frequently becomes dysregulated in cancer. While mTORC1 inhibitors reduce MM viability and synergize with other therapies in vitro, clinically, mTORC1 inhibitors are not effective for MM. Here we show that the inactivation of mTORC1 is an intrinsic response of MM to PI treatment. Genetically enforced hyperactivation of mTORC1 in MM was sufficient to compromise tumorigenicity in mice. In vitro, mTORC1-hyperactivated MM cells gained sensitivity to PIs and hypoxia. This was accompanied by increased mitochondrial stress and activation of the eIF2α kinase HRI, which initiates the integrated stress response. Deletion of HRI elevated the toxicity of PIs in wt and mTORC1-activated MM. Finally, we identified the drug PMA as a robust inducer of mTORC1 activity, which synergized with PIs in inducing MM cell death. These results help explain the clinical inefficacy of mTORC1 inhibitors in MM. Our data implicate mTORC1 induction and/or HRI inhibition as pharmacological strategies to enhance MM therapy by PIs.

Characterizing the role of the immune microenvironment in multiple myeloma progression at a single-cell level.

Early alterations within the bone marrow microenvironment that contribute to the progression of multiple myeloma (MM) from its precursor stages could be the key to identifying novel therapeutic approaches. However, the intrinsic variability in cellular populations between patients and the differences in sample processing and analysis methods have made it difficult to identify consistent changes between data sets. Here, we used single-cell RNA sequencing of bone marrow cells from precursor stages, monoclonal gammopathy of unknown significance, smoldering MM, and newly diagnosed MM and analyzed our data in combination with a previously published data set that used a similar patient population and sample processing. Despite the vast interpatient heterogeneity, some alterations were consistently observed in both data sets. We identified changes in immune cell populations as the disease progressed, which were characterized by a substantial decrease in memory and naïve CD4 T cells, and an increase in CD8+ effector T cells and T-regulatory cells. These alterations were further accompanied by an enrichment of nonclonal memory B cells and an increase in CD14 and CD16 monocytes in MM compared with its precursor stages. These results provide crucial information on the immune changes associated with the progression to clinical MM and can help to develop immune-based strategies for patient stratification and early therapeutic intervention.

The spatio-temporal evolution of multiple myeloma from baseline to relapse-refractory states.

Deciphering Multiple Myeloma evolution in the whole bone marrow is key to inform curative strategies. Here, we perform spatial-longitudinal whole-exome sequencing, including 140 samples collected from 24 Multiple Myeloma patients during up to 14 years. Applying imaging-guided sampling we observe three evolutionary patterns, including relapse driven by a single-cell expansion, competing/co-existing sub-clones, and unique sub-clones at distinct locations. While we do not find the unique relapse sub-clone in the baseline focal lesion(s), we show a close phylogenetic relationship between baseline focal lesions and relapse disease, highlighting focal lesions as hotspots of tumor evolution. In patients with ≥3 focal lesions on positron-emission-tomography at diagnosis, relapse is driven by multiple distinct sub-clones, whereas in other patients, a single-cell expansion is typically seen (p < 0.01). Notably, we observe resistant sub-clones that can be hidden over years, suggesting that a prerequisite for curative therapies would be to overcome not only tumor heterogeneity but also dormancy.

Inhibiting PI3K-AKT-mTOR Signaling in Multiple Myeloma-Associated Mesenchymal Stem Cells Impedes the Proliferation of Multiple Myeloma Cells.

The microenvironment of cancer cells is receiving increasing attention as an important factor influencing the progression and prognosis of tumor diseases. In multiple myeloma (MM), a hematological cancer of plasma cells, mesenchymal stem cells (MSCs) represent an integral part of the bone marrow niche and tumor microenvironment. It has been described that MM cells alter MSCs in a way that MM-associated MSCs promote the proliferation and survival of MM cells. Yet, our understanding of the molecular mechanisms governing the interaction between MM cells and MSCs and whether this can be targeted for therapeutic interventions is limited. To identify potential molecular targets, we examined MSCs by RNA sequencing and Western blot analysis. We report that MSCs from MM patients with active disease (MM-Act-MSCs) show a distinct gene expression profile as compared with MSCs from patients with other (non-) malignant diseases (CTR-MSCs). Of note, we detected a significant enrichment of the PI3K-AKT-mTOR hallmark gene set in MM-Act-MSCs and further confirmed the increased levels of related proteins in these MSCs. Pictilisib, a pan-PI3K inhibitor, selectively reduced the proliferation of MM-Act-MSCs as compared with CTR-MSCs. Furthermore, pictilisib treatment impaired the MM-promoting function of MM-Act-MSCs. Our data thus provide a deeper insight into the molecular signature and function of MSCs associated with MM and show that targeting PI3K-AKT-mTOR signaling in MSCs may represent an additional therapeutic pathway in the treatment of MM patients.

PITX1 Is a Regulator of TERT Expression in Prostate Cancer with Prognostic Power.

The current risk stratification in prostate cancer (PCa) is frequently insufficient to adequately predict disease development and outcome. One hallmark of cancer is telomere maintenance. For telomere maintenance, PCa cells exclusively employ telomerase, making it essential for this cancer entity. However, TERT, the catalytic protein component of the reverse transcriptase telomerase, itself does not suit as a prognostic marker for prostate cancer as it is rather low expressed. We investigated if, instead of TERT, transcription factors regulating TERT may suit as prognostic markers. To identify transcription factors regulating TERT, we developed and applied a new gene regulatory modeling strategy to a comprehensive transcriptome dataset of 445 primary PCa. Six transcription factors were predicted as TERT regulators, and most prominently, the developmental morphogenic factor PITX1. PITX1 expression positively correlated with telomere staining intensity in PCa tumor samples. Functional assays and chromatin immune-precipitation showed that PITX1 activates TERT expression in PCa cells. Clinically, we observed that PITX1 is an excellent prognostic marker, as concluded from an analysis of more than 15,000 PCa samples. PITX1 expression in tumor samples associated with (i) increased Ki67 expression indicating increased tumor growth, (ii) a worse prognosis, and (iii) correlated with telomere length.

Subclone-specific microenvironmental impact and drug response in refractory multiple myeloma revealed by single-cell transcriptomics.

Virtually all patients with multiple myeloma become unresponsive to treatment over time. Relapsed/refractory multiple myeloma (RRMM) is accompanied by the clonal evolution of myeloma cells with heterogeneous genomic aberrations and profound changes of the bone marrow microenvironment (BME). However, the molecular mechanisms that drive drug resistance remain elusive. Here, we analyze the heterogeneous tumor cell population and its complex interaction network with the BME of 20 RRMM patients by single cell RNA-sequencing before/after treatment. Subclones with chromosome 1q-gain express a specific transcriptomic signature and frequently expand during treatment. Furthermore, RRMM cells shape an immune suppressive BME by upregulation of inflammatory cytokines and close interaction with the myeloid compartment. It is characterized by the accumulation of PD1+ γδ T-cells and tumor-associated macrophages as well as the depletion of hematopoietic progenitors. Thus, our study resolves transcriptional features of subclones in RRMM and mechanisms of microenvironmental reprogramming with implications for clinical decision-making.

Expression of CCCTC-binding factor (CTCF) is linked to poor prognosis in prostate cancer.

The chromatin-organizing factor CCCTC-binding factor (CTCF) is involved in transcriptional regulation, DNA-loop formation, and telomere maintenance. To evaluate the clinical impact of CTCF in prostate cancer, we analyzed CTCF expression by immunohistochemistry on a tissue microarray containing 17 747 prostate cancers. Normal prostate tissue showed negative to low CTCF expression, while in prostate cancers, CTCF expression was seen in 7726 of our 12 555 (61.5%) tumors and was considered low in 44.6% and high in 17% of cancers. Particularly, high CTCF expression was significantly associated with the presence of the transmembrane protease, serine 2:ETS-related gene fusion: Only 10% of ERG-negative cancers, but 30% of ERG-positive cancers had high-level CTCF expression (P < 0.0001). CTCF expression was significantly associated with advanced pathological tumor stage, high Gleason grade (P < 0.0001 each), nodal metastasis (P = 0.0122), and early biochemical recurrence (P < 0.0001). Multivariable modeling revealed that the prognostic impact of CTCF was independent from established presurgical parameters such as clinical stage and Gleason grade of the biopsy. Comparison with key molecular alterations showed strong associations with the expression of the Ki-67 proliferation marker and presence of phosphatase and tensin homolog deletions (P < 0.0001 each). The results of our study identify CTCF expression as a candidate biomarker for prognosis assessment in prostate cancer.

Linking aberrant chromatin features in chronic lymphocytic leukemia to transcription factor networks.

In chronic lymphocytic leukemia (CLL), a diverse set of genetic mutations is embedded in a deregulated epigenetic landscape that drives cancerogenesis. To elucidate the role of aberrant chromatin features, we mapped DNA methylation, seven histone modifications, nucleosome positions, chromatin accessibility, binding of EBF1 and CTCF, as well as the transcriptome of B cells from CLL patients and healthy donors. A globally increased histone deacetylase activity was detected and half of the genome comprised transcriptionally downregulated partially DNA methylated domains demarcated by CTCF CLL samples displayed a H3K4me3 redistribution and nucleosome gain at promoters as well as changes of enhancer activity and enhancer linkage to target genes. A DNA binding motif analysis identified transcription factors that gained or lost binding in CLL at sites with aberrant chromatin features. These findings were integrated into a gene regulatory enhancer containing network enriched for B-cell receptor signaling pathway components. Our study predicts novel molecular links to targets of CLL therapies and provides a valuable resource for further studies on the epigenetic contribution to the disease.

Modelling TERT regulation across 19 different cancer types based on the MIPRIP 2.0 gene regulatory network approach.

BACKGROUND: Reactivation of the telomerase reverse transcriptase gene TERT is a central feature for unlimited proliferation of the majority of cancers. However, the underlying regulatory processes are only partly understood. RESULTS: We assembled regulator binding information from serveral sources to construct a generic human and mouse gene regulatory network. Advancing our "Mixed Integer linear Programming based Regulatory Interaction Predictor" (MIPRIP) approach, we identified the most common and cancer-type specific regulators of TERT across 19 different human cancers. The results were validated by using the well-known TERT regulation by the ETS1 transcription factor in a subset of melanomas with mutations in the TERT promoter. Our improved MIPRIP2 R-package and the associated generic regulatory networks are freely available at https://github.com/KoenigLabNM/MIPRIP. CONCLUSION: MIPRIP 2.0 identified common as well as tumor type specific regulators of TERT. The software can be easily applied to transcriptome datasets to predict gene regulation for any gene and disease/condition under investigation.

Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast.

Understanding telomere length maintenance mechanisms is central in cancer biology as their dysregulation is one of the hallmarks for immortalization of cancer cells. Important for this well-balanced control is the transcriptional regulation of the telomerase genes. We integrated Mixed Integer Linear Programming models into a comparative machine learning based approach to identify regulatory interactions that best explain the discrepancy of telomerase transcript levels in yeast mutants with deleted regulators showing aberrant telomere length, when compared to mutants with normal telomere length. We uncover novel regulators of telomerase expression, several of which affect histone levels or modifications. In particular, our results point to the transcription factors Sum1, Hst1 and Srb2 as being important for the regulation of EST1 transcription, and we validated the effect of Sum1 experimentally. We compiled our machine learning method leading to a user friendly package for R which can straightforwardly be applied to similar problems integrating gene regulator binding information and expression profiles of samples of e.g. different phenotypes, diseases or treatments.