RNA-seq of newly diagnosed patients in the PADIMAC study leads to a bortezomib/lenalidomide decision signature

Activated interferon response from DNA damage in multiple myeloma cells contributes to the chemotherapeutic effects of anthracyclines

Introduction

Multiple myeloma (MM) is a malignant plasma cell disease caused by abnormal proliferation of clonal plasma cells in bone marrow. Upfront identification of tumor subgroups with specific biological markers has the potential to improve biologically-driven therapy. Previously, we established a molecular classification by stratifying multiple myeloma into two subtypes with a different prognosis based on a gene module co-expressed with MCL-1 (MCL1-M).

Methods

Gene Ontology (GO) analysis with differentially expressed genes was performed to identify signal pathway. Drug sensitivity was analyzed using the OncoPredict algorithm. Drug sensitivity of different myeloma cell lines was detected by CCK8 and flow cytometry. RNA-seq was performed on drug-sensitive cell lines before and after adriamycin treatment. RT-qPCR was used to further verify the sequencing results. The expression of γ-H2AX and dsDNA in sensitive and resistant cell lines was detected by immunofluorescence method.

Results

In our study, we demonstrated that MCL1-M low MM were more sensitive to anthracyclines. We treated different myeloma cell lines with doxorubicin in vitro and discovered the association of drug sensitivity with IFN signaling. Herein, we demonstrate that the doxorubicin-sensitive myeloma cell line showed significant DNA damage and up-regulated expression of genes related to the IFN response, which was not observed in drug-insensitive cell lines.

Discussion

Our results suggest that the active IFN signaling pathway may serve as a marker for predicting chemotherapy sensitivity in patients with myeloma. With our MCL1-M molecular classification system, we can screen patients with a potentially good response to the interferon signaling pathway and provide individualized treatment for MM. We propose IFN-a as adjuvant therapy for patients with myeloma sensitive to anthracyclines to further improve the therapeutic effect and prolong the survival of patients.

Dynamic single-cell RNA-seq analysis reveals distinct tumor program associated with microenvironmental remodeling and drug sensitivity in multiple myeloma

BACKGROUND

Multiple myeloma (MM) is a hematological malignancy characterized by clonal proliferation of malignant plasma cells. Despite extensive research, molecular mechanisms in MM that drive drug sensitivity and clinic outcome remain elusive.

RESULTS

Single-cell RNA sequencing was applied to study tumor heterogeneity and molecular dynamics in 10 MM individuals before and after 2 cycles of bortezomib-cyclophosphamide-dexamethasone (VCD) treatment, with 3 healthy volunteers as controls. We identified that unfolded protein response and metabolic-related program were decreased, whereas stress-associated and immune reactive programs were increased after 2 cycles of VCD treatment. Interestingly, low expression of the immune reactive program by tumor cells was associated with unfavorable drug response and poor survival in MM, which probably due to downregulation of MHC class I mediated antigen presentation and immune surveillance, and upregulation of markers related to immune escape. Furthermore, combined with immune cells profiling, we uncovered a link between tumor intrinsic immune reactive program and immunosuppressive phenotype in microenvironment, evidenced by exhausted states and expression of checkpoint molecules and suppressive genes in T cells, NK cells and monocytes. Notably, expression of YBX1 was associated with downregulation of immune activation signaling in myeloma and reduced immune cells infiltration, thereby contributed to poor prognosis.

CONCLUSIONS

We dissected the tumor and immune reprogramming in MM during targeted therapy at the single-cell resolution, and identified a tumor program that integrated tumoral signaling and changes in immune microenvironment, which provided insights into understanding drug sensitivity in MM.

Single-cell analysis of multiple myelomas refines the molecular features of bortezomib treatment responsiveness

Both the tumor and tumor microenvironment (TME) are crucial for pathogenesis and chemotherapy resistance in multiple myeloma (MM). Bortezomib, commonly used for MM treatment, works on both MM and TME cells, but innate and acquired resistance easily develop. By single-cell RNA sequencing (scRNA-seq), we investigated bone marrow aspirates of 18 treatment-naïve MM patients who later received bortezomib-based treatments. Twelve plasma and TME cell types and their subsets were identified. Suboptimal responders (SORs) to bortezomib exhibited higher copy number alteration burdens than optimal responders (ORs). Forty-four differentially expressed genes for SORs based on scRNA-seq data were further analyzed in an independent cohort of 90 treatment-naïve MMs, where 24 genes were validated. A combined model of three clinical variables (older age, low absolute lymphocyte count, and no autologous stem cell transplantation) and 24 genes was associated with bortezomib responsiveness and poor prognosis. In T cells, cytotoxic memory, proliferating, and dysfunctional subsets were significantly enriched in SORs. Moreover, we identified three monocyte subsets associated with bortezomib responsiveness and an MM-specific NK cell trajectory that ended with an MM-specific subset. scRNA-seq predicted the interaction of the GAS6-MERTK, ALCAM-CD6, and BAG6-NCR gene networks. Of note, tumor cells from ORs and SORs were the most prominent sources of ALCAM on effector T cells and BAG6 on NK cells, respectively. Our results indicate that the complicated compositional and molecular changes of both tumor and immune cells in the bone marrow (BM) milieu are important in the development and acquisition of resistance to bortezomib-based treatment of MM.

Transcriptional profiles define drug refractory disease in myeloma

Identifying biomarkers associated with disease progression and drug resistance are important for personalized care. We investigated the expression of 121 curated genes, related to immunomodulatory drugs (IMiDs) and proteasome inhibitors (PIs) responsiveness. We analyzed 28 human multiple myeloma (MM) cell lines with known drug sensitivities and 130 primary MM patient samples collected at different disease stages, including newly diagnosed (ND), on therapy (OT), and relapsed and refractory (RR, collected within 12 months before the patients' death) timepoints. Our findings led to the identification of a subset of genes linked to clinical drug resistance, poor survival, and disease progression following combination treatment containing IMIDs and/or PIs. Finally, we built a seven-gene model (MM-IMiD and PI sensitivity-7 genes [IP-7]) using digital gene expression profiling data that significantly separates ND patients from IMiD- and PI-refractory RR patients. Using this model, we retrospectively analyzed RNA sequcencing (RNAseq) data from the Mulltiple Myeloma Research Foundation (MMRF) CoMMpass (n = 578) and Mayo Clinic MM patient registry (n = 487) to divide patients into probabilities of responder and nonresponder, which subsequently correlated with overall survival, disease stage, and number of prior treatments. Our findings suggest that this model may be useful in predicting acquired resistance to treatments containing IMiDs and/or PIs.

Highly expressed genes in multiple myeloma cells - what can they tell us about the disease?

Cancer cells can convert proto‐oncoproteins into oncoproteins by increasing the expression of genes that are oncogenic when expressed at high levels. Such genes can promote oncogenesis without being mutated. To find overexpressed genes in cancer cells from patients with multiple myeloma, we retrieved mRNA expression data from the CoMMpass database and ranked genes by their expression levels. We grouped the most highly expressed genes based on a set of criteria and we discuss the role a selection of them can play in the disease pathophysiology. The list was highly concordant with a similar list based on mRNA expression data from the PADIMAC study. Many well‐known “myeloma genes” such as MCL1, CXCR4, TNFRSF17, SDC1, SLAMF7, PTP4A3, and XBP1 were identified as highly expressed, and we believe that hitherto unrecognized key players in myeloma pathogenesis are also enriched on the list. Highly expressed genes in malignant plasma cells that were absent or expressed at only a low level in healthy plasma cells included IFI6, IFITM1, PTP4A3, SIK1, ALDOA, ATP5MF, ATP5ME, and PSMB4. The ambition of this article is not to validate the role of each gene but to serve as a guide for studies aiming at identifying promising treatment targets.

Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data

Multiple myeloma (MM) remains mostly an incurable disease with a heterogeneous clinical evolution. Despite the availability of several prognostic scores, substantial room for improvement still exists. Promising results have been obtained by integrating clinical and biochemical data with gene expression profiling (GEP). In this report, we applied machine learning algorithms to MM clinical and RNAseq data collected by the CoMMpass consortium. We created a 50-variable random forests model (IAC-50) that could predict overall survival with high concordance between both training and validation sets (c-indexes, 0.818 and 0.780). This model included the following covariates: patient age, ISS stage, serum B2-microglobulin, first-line treatment, and the expression of 46 genes. Survival predictions for each patient considering the first line of treatment evidenced that those individuals treated with the best-predicted drug combination were significantly less likely to die than patients treated with other schemes. This was particularly important among patients treated with a triplet combination including bortezomib, an immunomodulatory drug (ImiD), and dexamethasone. Finally, the model showed a trend to retain its predictive value in patients with high-risk cytogenetics. In conclusion, we report a predictive model for MM survival based on the integration of clinical, biochemical, and gene expression data with machine learning tools.

Treatment Strategies Considering Micro-Environment and Clonal Evolution in Multiple Myeloma

Simple Summary

Multiple myeloma is an uncurable hematological malignancy, although the prognosis of myeloma patients is getting better using proteasome inhibitors (PIs), immune modulatory drugs (IMiDs), monoclonal antibodies (MoAbs), and cytotoxic agents. Drug resistance makes myeloma difficult to treat and it can be subdivided into two broad categories: de novo and acquired. De novo drug resistance is associated with the bone marrow microenvironment including bone marrow stromal cells, the vascular niche and endosteal niche. Acquired drug resistance is related to clonal evolution and non-genetic diversity. The initial treatment plays the most important role considering de novo and acquired drug resistance and should contain PIs, IMIDs, MoAbs, and autologous stem cell transplantation because these treatments improve the bone marrow microenvironment and might prevent clonal evolution via sustained deep response including minimal residual disease negativity.

Abstract

Multiple myeloma is an uncurable hematological malignancy because of obtained drug resistance. Microenvironment and clonal evolution induce myeloma cells to develop de novo and acquired drug resistance, respectively. Cell adhesion-mediated drug resistance, which is induced by the interaction between myeloma and bone marrow stromal cells, and soluble factor-mediated drug resistance, which is induced by cytokines and growth factors, are two types of de novo drug resistance. The microenvironment, including conditions such as hypoxia, vascular and endosteal niches, contributes toward de novo drug resistance. Clonal evolution was associated with acquired drug resistance and classified as branching, linear, and neutral evolutions. The branching evolution is dependent on the microenvironment and escape of immunological surveillance while the linear and neutral evolution is independent of the microenvironment and associated with aggressive recurrence and poor prognosis. Proteasome inhibitors (PIs), immunomodulatory drugs (IMiDs), monoclonal antibody agents (MoAbs), and autologous stem cell transplantation (ASCT) have improved prognosis of myeloma via improvement of the microenvironment. The initial treatment plays the most important role considering de novo and acquired drug resistance and should contain PIs, IMIDs, MoAb and ASCT. This review summarizes the role of anti-myeloma agents for microenvironment and clonal evolution and treatment strategies to overcome drug resistance.

The Emerging Treatment Options of Plasmablastic Lymphoma: Analysis of 173 Individual Patient Outcomes

Plasmablastic lymphoma (PBL) is a newly recognized aggressive subtype of non-Hodgkin lymphoma. Its rarity hinders testing effective treatment options in clinical trials. We conducted a systematic review of PubMed and our internal records to retrieve patients with a PBL diagnosis with evaluable treatment outcomes. Aggressive chemotherapy was defined as more intense regimens than CHOP (cyclophosphamide, doxorubicin, vincristine, and prednisone). We compiled a meta-dataset of 173 patients. The median age at diagnosis was 48.5 years, 75% of patients were male, and stages III/IV accounted for 47% of the cohort. Of 138 patients with known response status after first-line chemotherapy, 63 (45%) achieved a complete response with a 2-year relapse-free survival of 71.6%. Sixty-nine (50%) patients received first-line CHOP. There was no significant difference in the objective response rate among the 2 most commonly used regimens, CHOP and DA-EPOCH (dose-adjusted etoposide, prednisone, vincristine, cyclophosphamide, and doxorubicin) (69% vs. 79%; P = .4). The median follow-up was 9 months, and the 2-year overall survival (OS) was 47.4%. A univariate analysis identified factors associated with worse OS, including stage III/IV (hazard ratio [HR], 2.82; P < .001), human herpes virus-8-positive (HR, 3.30; P = .01), bone marrow (HR, 1.07; P = .035), and cardiorespiratory involvement (HR, 2.26; P = .015). Meanwhile, Epstein-Varr virus-encoded small RNA-positivity (HR, 0.31; P < .001) and involvement of head and neck (HR, 0.44; P = .009) were associated with better OS. Multivariate analysis showed that aggressive chemotherapy was significantly associated with better OS (HR, 0.22; P = .016). Patients with PBL with high-risk features, such as advanced stage, human herpes virus-8-positivity, bone marrow, and cardiorespiratory involvement, require more aggressive chemotherapy. Bortezomib and lenalidomide are promising add-on agents.

Gene Networks Constructed Through Simulated Treatment Learning can Predict Proteasome Inhibitor Benefit in Multiple Myeloma

PURPOSE

Proteasome inhibitors are widely used in treating multiple myeloma, but can cause serious side effects and response varies among patients. It is, therefore, important to gain more insight into which patients will benefit from proteasome inhibitors.

EXPERIMENTAL DESIGN

We introduce simulated treatment learned signatures (STLsig), a machine learning method to identify predictive gene expression signatures. STLsig uses genetically similar patients who have received an alternative treatment to model which patients will benefit more from proteasome inhibitors than from an alternative treatment. STLsig constructs gene networks by linking genes that are synergistic in their ability to predict benefit.

RESULTS

In a dataset of 910 patients with multiple myeloma, STLsig identified two gene networks that together can predict benefit to the proteasome inhibitor, bortezomib. In class "benefit," we found an HR of 0.47 (P = 0.04) in favor of bortezomib, while in class "no benefit," the HR was 0.91 (P = 0.68). Importantly, we observed a similar performance (HR class benefit, 0.46; P = 0.04) in an independent patient cohort. Moreover, this signature also predicts benefit for the proteasome inhibitor, carfilzomib, indicating it is not specific to bortezomib. No equivalent signature can be found when the genes in the signature are excluded from the analysis, indicating that they are essential. Multiple genes in the signature are linked to working mechanisms of proteasome inhibitors or multiple myeloma disease progression.

CONCLUSIONS

STLsig can identify gene signatures that could aid in treatment decisions for patients with multiple myeloma and provide insight into the biological mechanism behind treatment benefit.

The combination of WGS and RNA-Seq is superior to conventional diagnostic tests in multiple myeloma: Ready for prime time?

The diagnosis and risk stratification of multiple myeloma (MM) is based on clinical and cytogenetic tests. Magnetic CD138 enrichment followed by interphase FISH (fluorescence in situ hybridisation) is the gold standard to identify prognostic translocations and copy number alterations (CNA). Although clinical implications of gene expression profiling (GEP) or panel based sequencing results are evident, those tests have not yet reached routine clinical application. We set up a single workflow to analyse MM of 211 patients at first diagnosis by whole genome sequencing (WGS) and RNA-Seq and validate the results by FISH analysis. We observed a 96% concordance of FISH and WGS results when assessing translocations involving the IGH locus and an overall concordance of FISH and WGS of 92% when assessing CNA. WGS analysis resulted in the identification of 17 additional MYC-translocations that were missed by FISH analysis. RNA-Seq followed by supervised clustering grouped patients in their expected genetically defined subgroup and prompted the assessment of WGS data in cases that were not congruent with FISH. This allowed the identification of additional IGH-translocations and hyperdiploid cases. We show the reliability of WGS an RNA-Seq in a clinical setting, which is a prerequisite for a novel routine diagnostic test.

A Network Analysis of Multiple Myeloma Related Gene Signatures

Multiple myeloma (MM) is the second most prevalent hematological cancer. MM is a complex and heterogeneous disease, and thus, it is essential to leverage omics data from large MM cohorts to understand the molecular mechanisms underlying MM tumorigenesis, progression, and drug responses, which may aid in the development of better treatments. In this study, we analyzed gene expression, copy number variation, and clinical data from the Multiple Myeloma Research Consortium (MMRC) dataset and constructed a multiple myeloma molecular causal network (M3CN). The M3CN was used to unify eight prognostic gene signatures in the literature that shared very few genes between them, resulting in a prognostic subnetwork of the M3CN, consisting of 178 genes that were enriched for genes involved in cell cycle (fold enrichment = 8.4, p value = 6.1 × 10⁻²⁶). The M3CN was further used to characterize immunomodulators and proteasome inhibitors for MM, demonstrating the pleiotropic effects of these drugs, with drug-response signature genes enriched across multiple M3CN subnetworks. Network analyses indicated potential links between these drug-response subnetworks and the prognostic subnetwork. To elucidate the structure of these important MM subnetworks, we identified putative key regulators predicted to modulate the state of these subnetworks. Finally, to assess the predictive power of our network-based models, we stratified MM patients in an independent cohort, the MMRF-CoMMpass study, based on the prognostic subnetwork, and compared the performance of this subnetwork against other signatures in the literature. We show that the M3CN-derived prognostic subnetwork achieved the best separation between different risk groups in terms of log-rank test p-values and hazard ratios. In summary, this work demonstrates the power of a probabilistic causal network approach to understanding molecular mechanisms underlying the different MM signatures.

Pharmacodynamics and pharmacokinetics of proteasome inhibitors for the treatment of multiple myeloma

Introduction: Multiple myeloma (MM) is the second most commonly diagnosed hematologic malignancy and has an increasing incidence and prevalence globally, and proteasome inhibitors (PIs) form the backbone of some of our most effective regimens for all phases of this disease in fit and frail patients. Areas covered: Strong understanding of the proteasome complex is increasingly important as the rapid development of new PIs and innovative myeloma therapies complicate the use of old and new combination regimens. We focus herein on the pharmacodynamics and pharmacokinetics of the approved PIs and others in development, including their safety and efficacy in corresponding clinical studies. Expert opinion: Advancements such as the first oral PI, ixazomib, with a more convenient route of administration and improved toxicity profile led to an improved quality of life, patient compliance, and all-oral combination regimens which are efficacious for long-term management of standard and high-risk MM. Novel pan-PIs, such as marizomib, hold the promise of superior clinical activity due to irreversible targeting of all multicatalytic proteinase complex subunits. Development of clinically validated biomarkers of PI sensitivity/resistance is required to inform utilization of the most optimal and effective, rationally targeted PI treatments for all MM patients.