Prognostic Validation of SKY92 and Its Combination With ISS in an Independent Cohort of Patients With Multiple Myeloma

Enhancing multiple myeloma staging: a novel cell death risk model approach

The prognostication of survival trajectories in multiple myeloma (MM) patients presents a substantial clinical challenge. Leveraging transcriptomic and clinical profiles from an expansive cohort of 2,088 MM patients, sourced from the Gene Expression Omnibus and The Cancer Genome Atlas repositories, we applied a sophisticated nested lasso regression technique to construct a prognostic model predicated on 28 gene pairings intrinsic to cell death pathways, thereby deriving a quantifiable risk stratification metric. Employing a threshold of 0.15, we dichotomized the MM samples into discrete high-risk and low-risk categories. Notably, the delineated high-risk cohort exhibited a statistically significant diminution in survival duration, a finding which consistently replicated across both training and external validation datasets. The prognostic acumen of our cell death signature was further corroborated by TIME ROC analyses, with the model demonstrating robust performance, evidenced by AUC metrics consistently surpassing the 0.6 benchmark across the evaluated arrays. Further analytical rigor was applied through multivariate COX regression analyses, which ratified the cell death risk model as an independent prognostic determinant. In an innovative stratagem, we amalgamated this risk stratification with the established International Staging System (ISS), culminating in the genesis of a novel, refined ISS categorization. This tripartite classification system was subjected to comparative analysis against extant prognostic models, whereupon it manifested superior predictive precision, as reflected by an elevated C-index. In summation, our endeavors have yielded a clinically viable gene pairing model predicated on cellular mortality, which, when synthesized with the ISS, engenders an augmented prognostic tool that exhibits pronounced predictive prowess in the context of multiple myeloma.

Multi-dimensional scaling techniques unveiled gain1q&loss13q co-occurrence in Multiple Myeloma patients with specific genomic, transcriptional and adverse clinical features

The complexity of Multiple Myeloma (MM) is driven by several genomic aberrations, interacting with disease-related and/or -unrelated factors and conditioning patients' clinical outcome. Patient's prognosis is hardly predictable, as commonly employed MM risk models do not precisely partition high- from low-risk patients, preventing the reliable recognition of early relapsing/refractory patients. By a dimensionality reduction approach, here we dissect the genomic landscape of a large cohort of newly diagnosed MM patients, modelling all the possible interactions between any MM chromosomal alterations. We highlight the presence of a distinguished cluster of patients in the low-dimensionality space, with unfavorable clinical behavior, whose biology was driven by the co-occurrence of chromosomes 1q CN gain and 13 CN loss. Presence or absence of these alterations define MM patients overexpressing either CCND2 or CCND1, fostering the implementation of biology-based patients' classification models to describe the different MM clinical behaviors.

Daratumumab, Cyclophosphamide, Bortezomib, Lenalidomide, and Dexamethasone as Induction and Extended Consolidation Improves Outcome in Ultra-High-Risk Multiple Myeloma

PURPOSE

The multicenter OPTIMUM (MUKnine) phase II trial investigated daratumumab, low-dose cyclophosphamide, lenalidomide, bortezomib, and dexamethasone (Dara-CVRd) before and after autologous stem-cell transplant (ASCT) in newly diagnosed patients with molecularly defined ultra-high-risk (UHiR) multiple myeloma (NDMM) or plasma cell leukemia (PCL). To provide clinical context, progression-free survival (PFS) and overall survival (OS) were referenced to contemporaneous outcomes seen in patients with UHiR NDMM treated in the recent Myeloma XI (MyeXI) trial.

METHODS

Transplant-eligible all-comers NDMM patients were profiled for UHiR disease, defined by presence of ≥2 genetic risk markers t(4;14)/t(14;16)/t(14;20), del(1p), gain(1q), and del(17p), and/or SKY92 gene expression risk signature. Patients with UHiR MM/PCL were offered treatment with Dara-CVRd induction, V-augmented ASCT, extended Dara-VR(d) consolidation, and Dara-R maintenance. UHiR patients treated in MyeXI with carfilzomib, lenalidomide, dexamethasone, and cyclophosphamide, or lenalidomide, dexamethasone, and cyclophosphamide, ASCT, and R maintenance or observation were identified by mirrored molecular screening. OPTIMUM PFS at 18 months (PFS18m) was compared against MyeXI using a Bayesian framework, and patients were followed up to the end of consolidation for PFS and OS.

RESULTS

Of 412 screened NDMM OPTIMUM patients, 103 were identified as UHiR or PCL and subsequently treated on trial with Dara-CVRd; 117 MyeXI patients identified as UHiR formed the external comparator arm, with comparable clinical and molecular characteristics to OPTIMUM. Comparison of PFS18m per Bayesian framework resulted in a 99.5% chance of OPTIMUM being superior to MyeXI. At 30 months' follow-up, PFS was 77% for OPTIMUM versus 39.8% for MyeXI, and OS 83.5% versus 73.5%, respectively. Extended post-ASCT Dara-VRd consolidation therapy was highly deliverable, with limited toxicity.

CONCLUSION

Our results suggest that Dara-CVRd induction and extended post-ASCT Dara-VRd consolidation markedly improve PFS for UHiR NDMM patients over conventional management, supporting further evaluation of this strategy.

High or low? Assessing disease risk in multiple myeloma

Based upon the development of highly effective therapies such as immunomodulatory drugs, proteasome inhibitors, and monoclonal antibodies that target plasma cell biology, a dramatic improvement in overall survival has been observed for most patients with multiple myeloma (MM) over the past 2 decades. Although it is now commonplace for many patients with myeloma to live in excess of 10 years after diagnosis, unfortunately a large subset of patients continues to experience an aggressive disease course marked by substantial morbidity and early mortality. Many clinical biomarkers and staging systems in use today can help with prognostication, but accurate risk assessment can be difficult due to the presence of many different biomarkers with variable prognostic value. Furthermore, with the implementation of novel therapies and unprecedented rates of deep and durable responses, it is becoming apparent that risk assessment is best envisioned as a dynamic process that requires ongoing reevaluation. As risk and response-adapted approaches are becoming more commonplace, it is essential that clinicians understand the biological and prognostic implications of clinical, genomic, and response-based biomarkers in order to promote management strategies that will help improve both survival and quality of life for patients across the risk spectrum.

ERBB1/EGFR and JAK3 Tyrosine Kinases as Potential Therapeutic Targets in High-Risk Multiple Myeloma

Our main objective was to identify abundantly expressed tyrosine kinases in multiple myeloma (MM) as potential therapeutic targets. We first compared the transcriptomes of malignant plasma cells from newly diagnosed MM patients who were risk-categorized based on the patient-specific EMC-92/SKY-92 gene expression signature values vs. normal plasma cells from healthy volunteers using archived datasets from the HOVON65/GMMG-HD4 randomized Phase 3 study evaluating the clinical efficacy of bortezomib induction/maintenance versus classic cytotoxic drugs and thalidomide maintenance. In particular, ERBB1/EGFR was significantly overexpressed in MM cells in comparison to normal control plasma cells, and it was differentially overexpressed in MM cells from high-risk patients. Amplified expression of EGFR/ERBB1 mRNA in MM cells was positively correlated with increased expression levels of mRNAs for several DNA binding proteins and transcription factors with known upregulating activity on EGFR/ERBB1 gene expression. MM patients with the highest ERBB1/EGFR expression level had significantly shorter PFS and OS times than patients with the lowest ERBB1/EGFR expression level. High expression levels of EGFR/ERBB1 were associated with significantly increased hazard ratios for unfavorable PFS and OS outcomes in both univariate and multivariate Cox proportional hazards models. The impact of high EGFR/ERBB1 expression on the PFS and OS outcomes remained significant even after accounting for the prognostic effects of other covariates. These results regarding the prognostic effect of EGFR/ERBB1 expression were validated using the MMRF-CoMMpass RNAseq dataset generated in patients treated with more recently applied drug combinations included in contemporary induction regimens. Our findings provide new insights regarding the molecular mechanism and potential clinical significance of upregulated EGFR/ERBB1 expression in MM.

An emerging prognosis prediction model for multiple myeloma: Hypoxia-immune related microenvironmental gene signature

Multiple myeloma (MM), a hematologic malignancy, is characterized by malignant plasma cells clonal proliferation. Many evidences indicated the indirect interaction between hypoxic environment and immune state in MM tumorigenesis, but the underlying mechanism remains unclear. MM-related datasets were downloaded from the Gene Expression Omnibus (GEO) database. The R packages were applied for screening protective differentially expressed genes (DEGs) and risk DEGs. The signature was constructed based the most prognostic gene signature in the training and assessed in the validation cohorts. The immune cell infiltration, the expression of the HLA family and immune checkpoint genes inside the low- and high-risk groups were compared to determine the differences in immune infiltration and immunotherapy responses. Moreover, the expression of HLA families and immune checkpoints inside the low- and high-risk groups was markedly disordered. The results indicated hypoxia- and immune-related genes, including CHRDL1, DDIT4, DNTT, FAM133A, MYB, PRR15, QTRT1, and ZNF275, were identified and used to construct a prognostic signature. Role of DDIT4 in multiple myeloma was confirmed in vivo and in vitro. DDIT4 knockdown inhibited MM cell viability, migration and invasion potential as well as promoted myeloma cells apoptosis under hypoxia. Taken together, our study may contribute to the treatment and prognosis prediction of MM.

Prognostic Stratification of Multiple Myeloma Using Clinicogenomic Models: Validation and Performance Analysis of the IAC-50 Model

A growing need to evaluate risk-adapted treatments in multiple myeloma (MM) exists. Several clinical and molecular scores have been developed in the last decades, which individually explain some of the variability in the heterogeneous clinical behavior of this neoplasm. Recently, we presented Iacobus-50 (IAC-50), which is a machine learning-based survival model based on clinical, biochemical, and genomic data capable of risk-stratifying newly diagnosed MM patients and predicting the optimal upfront treatment scheme. In the present study, we evaluated the prognostic value of the IAC-50 gene expression signature in an external cohort composed of patients from the Total Therapy trials 3, 4, and 5. The prognostic value of IAC-50 was validated, and additionally we observed a better performance in terms of progression-free survival and overall survival prediction compared with the UAMS70 gene expression signature. The combination of the IAC-50 gene expression signature with traditional prognostic variables (International Staging System [ISS] score, baseline B2-microglobulin, and age) improved the performance well above the predictability of the ISS score. IAC-50 emerges as a powerful risk stratification model which might be considered for risk stratification in newly diagnosed myeloma patients, in the context of clinical trials but also in real life.

High Levels of Circulating Tumor Plasma Cells as a Key Hallmark of Aggressive Disease in Transplant-Eligible Patients With Newly Diagnosed Multiple Myeloma

PURPOSE

High levels of circulating tumor plasma cells (CTC-high) in patients with multiple myeloma are a marker of aggressive disease. We aimed to confirm the prognostic impact and identify a possible cutoff value of CTC-high for the prediction of progression-free survival (PFS) and overall survival (OS), in the context of concomitant risk features and minimal residual disease (MRD) achievement.

METHODS

CTC were analyzed at diagnosis with two-tube single-platform flow cytometry (sensitivity 4 × 10^-5) in patients enrolled in the multicenter randomized FORTE clinical trial (ClinicalTrials.gov identifier: NCT02203643). MRD was assessed by second-generation multiparameter flow cytometry (sensitivity 10^-5). We tested different cutoff values in series of multivariate (MV) Cox proportional hazards regression analyses on PFS outcome and selected the value that maximized the Harrell's C-statistic. We analyzed the impact of CTC on PFS and OS in a MV analysis including baseline features and MRD negativity.

RESULTS

CTC analysis was performed in 401 patients; the median follow-up was 50 months (interquartile range, 45-54 months). There was a modest correlation between the percentage of CTC and bone marrow plasma cells (r = 0.38). We identified an optimal CTC cutoff of 0.07% (approximately 5 cells/µL, C-index 0.64). In MV analysis, CTC-high versus CTC-low patients had significantly shorter PFS (hazard ratio, 2.61; 95% CI, 1.49 to 2.97, P < .001; 4-year PFS 38% v 69%) and OS (hazard ratio, 2.61; 95% CI, 1.49 to 4.56; P < .001; 4-year OS 68% v 92%). The CTC levels, but not the bone marrow plasma cell levels, affected the outcome. The only factor that reduced the negative impact of CTC-high was the achievement of MRD negativity (interaction P = .039).

CONCLUSION

In multiple myeloma, increasing levels of CTC above an optimal cutoff represent an easy-to-assess, robust, and independent high-risk factor. The achievement of MRD negativity is the most important factor that modulates their negative prognostic impact.

High-risk disease in newly diagnosed multiple myeloma: beyond the R-ISS and IMWG definitions

Multiple myeloma (MM) is an acquired malignant plasma cell disorder that develops late in life. Although progression free and overall survival has improved across all age, race, and ethnic groups, a subset of patients have suboptimal outcomes and are labeled as having high risk disease. A uniform approach to risk in NDMM remains elusive despite several validated risk stratification systems in clinical use. While we attempt to capture risk at diagnosis, the reality is that many important prognostic characteristics remain ill-defined as some patients relapse early who were defined as low risk based on their genomic profile at diagnosis. It is critical to establish a definition of high risk disease in order to move towards risk-adapted treatment approaches. Defining risk at diagnosis is important to both effectively design future clinical trials and guide which clinical data is needed in routine practice. The goal of this review paper is to summarize and compare the various established risk stratification systems, go beyond the R-ISS and international myeloma working group risk stratifications to evaluate specific molecular and cytogenetic abnormalities and how they impact prognosis independently. In addition, we explore the wealth of new genomic information from recent whole genome/exome sequencing as well as gene expression data and review known clinical factors affecting outcome such as disease burden and early relapse as well as patient related factors such as race. Finally, we provide an outlook on developing a new high risk model system and how we might make sense of co-occurrences, oncogenic dependencies, and mutually exclusive mutations.

Unsupervised machine learning improves risk stratification in newly diagnosed multiple myeloma: an analysis of the Spanish Myeloma Group

The International Staging System (ISS) and the Revised International Staging System (R-ISS) are commonly used prognostic scores in multiple myeloma (MM). These methods have significant gaps, particularly among intermediate-risk groups. The aim of this study was to improve risk stratification in newly diagnosed MM patients using data from three different trials developed by the Spanish Myeloma Group. For this, we applied an unsupervised machine learning clusterization technique on a set of clinical, biochemical and cytogenetic variables, and we identified two novel clusters of patients with significantly different survival. The prognostic precision of this clusterization was superior to those of ISS and R-ISS scores, and appeared to be particularly useful to improve risk stratification among R-ISS 2 patients. Additionally, patients assigned to the low-risk cluster in the GEM05 over 65 years trial had a significant survival benefit when treated with VMP as compared with VTD. In conclusion, we describe a simple prognostic model for newly diagnosed MM whose predictions are independent of the ISS and R-ISS scores. Notably, the model is particularly useful in order to re-classify R-ISS score 2 patients in 2 different prognostic subgroups. The combination of ISS, R-ISS and unsupervised machine learning clusterization brings a promising approximation to improve MM risk stratification.

Risk Stratification Before and During Treatment in Newly Diagnosed Multiple Myeloma: From Clinical Trials to the Real-World Setting

Multiple Myeloma (MM) is a hematologic malignancy characterized by a wide clinical and biological heterogeneity leading to different patient outcomes. Various prognostic tools to stratify newly diagnosed (ND)MM patients into different risk groups have been proposed. At baseline, the standard-of-care prognostic score is the Revised International Staging System (R-ISS), which stratifies patients according to widely available serum markers (i.e., albumin, β 2-microglobulin, lactate dehydrogenase) and high-risk cytogenetic abnormalities detected by fluorescence in situ hybridization. Though this score clearly identifies a low-risk and a high-risk population, the majority of patients are categorized as at “intermediate risk”. Although new prognostic factors identified through molecular assays (e.g., gene expression profiling, next-generation sequencing) are now available and may improve risk stratification, the majority of them need specialized centers and bioinformatic expertise that may preclude their broad application in the real-world setting. In the last years, new tools to monitor response and measurable residual disease (MRD) with very high sensitivity after the start of treatment have been developed. MRD analyses both inside and outside the bone marrow have a strong prognostic impact, and the achievement of MRD negativity may counterbalance the high-risk behavior identified at baseline. All these techniques have been developed in clinical trials. However, their efficient application in real-world clinical practice and their potential role to guide treatment-decision making are still open issues. This mini review will cover currently known prognostic factors identified before and during first-line treatment, with a particular focus on their potential applications in real-world clinical practice.

Gene Expression Profiling in Multiple Myeloma: Redefining the Paradigm of Risk-Adapted Treatment

Multiple myeloma is a blood cancer characterized by clonal proliferation of plasma cells in the bone marrow. In recent years, several new drugs have been added to the therapeutic landscape of multiple myeloma, which have contributed to increased survival rates. However, while the use of therapeutics has evolved, there is still a group of high-risk patients who do not benefit from current treatment strategies. Risk stratification and risk-adapted treatment are crucial to identify the group of patients with urgent need for novel therapies. Gene expression profiling has been introduced as a tool for risk stratification in multiple myeloma based on the genetic make-up of myeloma cells. In this review we discuss the challenge of defining the high-risk multiple myeloma patient. We focus on the standardized analysis of myeloma cancer cells by gene expression profiling and describe how gene expression profiling provides additional insights for optimal risk-adapted treatment of patients suffering from multiple myeloma.

Prognostic gene expression analysis in a retrospective, multinational cohort of 155 multiple myeloma patients treated outside clinical trials

OBJECTIVES

Typically, prognostic capability of gene expression profiling (GEP) is studied in the context of clinical trials, for which 50%-80% of patients are not eligible, possibly limiting the generalizability of findings to routine practice. Here, we evaluate GEP analysis outside clinical trials, aiming to improve clinical risk assessment of multiple myeloma (MM) patients.

METHODS

A total of 155 bone marrow samples from MM patients were collected from which RNA was analyzed by microarray. Sixteen previously developed GEP-based markers were evaluated, combined with survival data, and studied using Cox proportional hazard regression.

RESULTS

Gene expression profiling-based markers SKY92 and the PR-cluster were shown to be independent prognostic factors for survival, with hazard ratios and 95% confidence interval of 3.6 [2.0-6.8] (P < .001) and 5.8 [2.7-12.7] (P < .01) for overall survival (OS). A multivariate model proved only SKY92 and the PR-cluster to be independent prognostic factors compared to cytogenetic high-risk patients, the International Staging System (ISS), and revised ISS. A substantial number of high-risk individuals could be further identified when SKY92 was added to the cytogenetic, ISS, or R-ISS. In the cytogenetic standard-risk group, ISS I/II, and R-ISS I/II, 13%, 23%, and 23% of patients with adverse survivals were identified.

CONCLUSIONS

For the first time, this study confirmed the prognostic value of GEP markers outside clinical trials. Conventional prognostic models to define high-risk MM are improved by the incorporation of GEP markers.

Prognostic and predictive biomarker developments in multiple myeloma

New approaches to stratify multiple myeloma patients based on prognosis and therapeutic decision-making, or prediction, are needed since patients are currently managed in a similar manner regardless of individual risk factors or disease characteristics. However, despite new and improved biomarkers for determining the prognosis of patients, there is currently insufficient information to utilise biomarkers to intensify, reduce or altogether change treatment, nor to target patient-specific biology in a so-called predictive manner. The ever-increasing number and complexity of drug classes to treat multiple myeloma have improved response rates and so clinically useful biomarkers will need to be relevant in the era of such novel therapies. Therefore, the field of multiple myeloma biomarker development is rapidly progressing, spurred on by new technologies and therapeutic approaches, and underpinned by a deeper understanding of tumour biology with individualised patient management the goal. In this review, we describe the main biomarker categories in multiple myeloma and relate these to diagnostic, prognostic and predictive applications.

Knowing the unknowns in high risk multiple myeloma

High risk multiple myeloma (HRMM) continues to portend worse outcomes despite the many advances in anti-myeloma therapeutics. The optimal approach to treatment is not clearly defined on account of the variable definitions of HRMM and the paucity of studies dedicated to the treatment of HRMM. In this review, we use a case-based approach to review the definitions of HRMM, and evaluate the evidence for induction, stem cell transplantation, and post-transplant therapy approaches for HRMM.

Disease heterogeneity, prognostication and the role of targeted therapy in multiple myeloma

Multiple myeloma (MM) is a clonal plasma cell malignancy with a heterogeneous disease course. Insights into the genetics of the disease have identified certain high-risk cytogenetic features that are associated with adverse outcomes. While the advances in therapy have translated into dramatic improvements in the outcome of patients with MM, those with high-risk genetic features continue to perform poorly. This has resulted in a need for clinical trials focusing on the high-risk subgroup of MM as they search for additional biomarkers and therapeutic targets continue. In this review, we discuss the currently existing data on prognostic and predictive biomarkers in MM and speculate the role of treatment stratification based on the genetic features of the disease.

Prognostic and predictive performance of R-ISS with SKY92 in older patients with multiple myeloma: the HOVON-87/NMSG-18 trial

The standard prognostic marker for multiple myeloma (MM) patients is the revised International Staging System (R-ISS). However, there is room for improvement in guiding treatment. This applies particularly to older patients, in whom the benefit/risk ratio is reduced because of comorbidities and subsequent side effects. We hypothesized that adding gene-expression data to R-ISS would generate a stronger marker. This was tested by combining R-ISS with the SKY92 classifier (SKY-RISS). The HOVON-87/NMSG-18 trial (EudraCT: 2007-004007-34) compared melphalan-prednisone-thalidomide followed by thalidomide maintenance (MPT-T) with melphalan-prednisone-lenalidomide followed by lenalidomide maintenance (MPR-R). From this trial, 168 patients with available R-ISS status and gene-expression profiles were analyzed. R-ISS stages I, II, and III were assigned to 8%, 75%, and 7% of patients, respectively (3-year overall survival [OS] rates: 80%, 65%, 33%, P = 8 × 10-3). Using the SKY92 classifier, 13% of patients were high risk (HR) (3-year OS rates: standard risk [SR], 70%; HR, 28%; P < .001). Combining SKY92 with R-ISS resulted in 3 risk groups: SKY-RISS I (SKY-SR + R-ISS-I; 15%), SKY-RISS III (SKY-HR + R-ISS-II/III; 11%), and SKY-RISS II (all other patients; 74%). The 3-year OS rates for SKY-RISS I, II, and III are 88%, 66%, and 26%, respectively (P = 6 × 10-7). The SKY-RISS model was validated in older patients from the CoMMpass dataset. Moreover, SKY-RISS demonstrated predictive potential: HR patients appeared to benefit from MPR-R over MPT-T (median OS, 55 and 14 months, respectively). Combined, SKY92 and R-ISS classify patients more accurately. Additionally, benefit was observed for MPR-R over MPT-T in SKY92-RISS HR patients only.

Gene expression profiling impacts treatment decision making in newly diagnosed multiple myeloma patients in the prospective PROMMIS trial

Multiple myeloma (MM) is a heterogeneous hematologic malignancy associated with several risk factors including genetic aberrations which impact disease response and survival. Thorough risk classification is essential to select the best clinical strategy to optimize outcomes. The SKY92 molecular signature classifies patients as standard‐ or high‐risk for progression. The PRospective Observational Multiple Myeloma Impact Study (PROMMIS; NCT02911571) measures impact of SKY92 on risk classification and treatment plan. Newly diagnosed MM patients had bone marrow aspirates analyzed for SKY92. Physicians completed a questionnaire for each patient capturing risk classification, hypothetical treatment plan, and physician confidence in the treatment plan, before and after unblinding SKY92. One hundred forty seven MM patients were enrolled. Before unblinding SKY92, physicians regarded 74 (50%) patients as clinical standard‐risk. After unblinding SKY92, 16 patients were re‐assigned as high‐risk by the physician, and for 15 of them treatment strategy was impacted, resulting in an escalated treatment plan. For the 73 (50%) clinical high‐risk patients, SKY92 indicated 46 patients to be standard‐risk; for 31 of these patients the treatment strategy was impacted consistent with a de‐escalation of risk. Overall, SKY92 impacted treatment decisions in 37% of patients (p < 0.001). For clinical decision‐making, physicians incorporated SKY92, and the final assigned clinical risk was in line with SKY92 for 89% of patients. Furthermore, SKY92 significantly increased the confidence of the physicians’ treatment decisions (p < 0.001). This study shows potential added value of SKY92 in MM for treatment decision making.

Multi-omics tumor profiling technologies to develop precision medicine in multiple myeloma

Multiple myeloma (MM), the second most common hematologic cancer, is caused by accumulation of aberrant plasma cells in the bone marrow. Its molecular causes are not fully understood and its great heterogeneity among patients complicates therapeutic decision-making. In the past decades, development of new therapies and drugs have significantly improved survival of MM patients. However, resistance to drugs and relapse remain the most common causes of mortality and are the major challenges to overcome. The advent of high throughput omics technologies capable of analyzing big amount of clinical and biological data has changed the way to diagnose and treat MM. Integration of omics data (gene mutations, gene expression, epigenetic information, and protein and metabolite levels) with clinical histories of thousands of patients allows to build scores to stratify the risk at diagnosis and predict the response to treatment, helping clinicians to make better educated decisions for each particular case. There is no doubt that the future of MM treatment relies on personalized therapies based on predictive models built from omics studies. This review summarizes the current treatments and the use of omics technologies in MM, and their importance in the implementation of personalized medicine.

Multi-omics tumor profiling technologies to develop precision medicine in multiple myeloma

Multiple myeloma (MM), the second most common hematologic cancer, is caused by accumulation of aberrant plasma cells in the bone marrow. Its molecular causes are not fully understood and its great heterogeneity among patients complicates therapeutic decision-making. In the past decades, development of new therapies and drugs have significantly improved survival of MM patients. However, resistance to drugs and relapse remain the most common causes of mortality and are the major challenges to overcome. The advent of high throughput omics technologies capable of analyzing big amount of clinical and biological data has changed the way to diagnose and treat MM. Integration of omics data (gene mutations, gene expression, epigenetic information, and protein and metabolite levels) with clinical histories of thousands of patients allows to build scores to stratify the risk at diagnosis and predict the response to treatment, helping clinicians to make better educated decisions for each particular case. There is no doubt that the future of MM treatment relies on personalized therapies based on predictive models built from omics studies. This review summarizes the current treatments and the use of omics technologies in MM, and their importance in the implementation of personalized medicine.

Analytical Validation of SKY92 for the Identification of High-Risk Multiple Myeloma

Multiple myeloma (MM) is an incurable plasma cell cancer with a large variability in survival. Patients with MM classified as high risk by the SKY92 gene expression classifier are at high risk of relapse and short survival. Analytical validation of the SKY92 assay was performed with primary bone marrow specimens from 12 patients with MM and 7 reference cell line specimens. The SKY92 results were 100% concordant with the reference and/or their expected result for sensitivity, specificity, microarray stability, and RLT buffer stability. The SKY92 results were 90% concordant for primary specimen stability, 96.4% concordant for intermediate precision, and 80% to 100% concordant for RNA stability. For the cell-line reproducibility, the concordance was at least 92.9%, except for one near-cut point specimen. For the clinical specimen reproducibility, the concordance was 100%, except for two near-cut point specimens. Three independent laboratories were concordant in ≥77.8% and ≥92.9% of experiments for patient specimens and cell lines, respectively. Statistical acceptance thresholds were developed as Δ ≤1.48 (change in SKY92 score) and SD ≤0.45 (SD across SKY92 scores). Using the Clinical and Laboratory Standards Institute method of choice (EP05-A2/A3), restricted maximum likelihood, the observed Δ values (0 to 1.14) and SDs (0.22 to 0.31) passed acceptance criteria. Thus, we successfully present analytical validation for the SKY92 assay as a prognostic molecular test for individual patients with MM.

Next-Generation Sequencing for Clinical Management of Multiple Myeloma: Ready for Prime Time?

Personalized treatment is an attractive strategy that promises increased efficacy with reduced side effects in cancer. The feasibility of such an approach has been greatly boosted by next-generation sequencing (NGS) techniques, which can return detailed information on the genome and on the transcriptome of each patient's tumor, thus highlighting biomarkers of response or druggable targets that may differ from case to case. However, while the number of cancers sequenced is growing exponentially, much fewer cases are amenable to a molecularly-guided treatment outside of clinical trials to date. In multiple myeloma, genomic analysis shows a variety of gene mutations, aneuploidies, segmental copy-number changes, translocations that are extremely heterogeneous, and more numerous than other hematological malignancies. Currently, in routine clinical practice we employ reduced FISH panels that only capture three high-risk features as part of the R-ISS. On the contrary, recent advances have suggested that extending genomic analysis to the full spectrum of recurrent mutations and structural abnormalities in multiple myeloma may have biological and clinical implications. Furthermore, increased efficacy of novel treatments can now produce deeper responses, and standard methods do not have enough sensitivity to stratify patients in complete biochemical remission. Consequently, NGS techniques have been developed to monitor the size of the clone to a sensitivity of up to a cell in a million after treatment. However, even these techniques are not within reach of standard laboratories. In this review we will recapitulate recent advances in multiple myeloma genomics, with special focus on the ones that may have immediate translational impact. We will analyze the benefits and pitfalls of NGS-based diagnostics, highlighting crucial aspects that will need to be taken into account before this can be implemented in most laboratories. We will make the point that a new era in myeloma diagnostics and minimal residual disease monitoring is close and conventional genetic testing will not be able to return the required information. This will mandate that even in routine practice NGS should soon be adopted owing to a higher informative potential with increasing clinical benefits.

Multiple myeloma: an overview of management

Multiple myeloma represents 2% of all new cancer diagnoses in the United Kingdom and accounts for 2% of all cancer deaths. In the past few decades, there have been huge improvements in life expectancy which have been driven by novel therapeutic agents, autologous stem cell transplants and intensified supportive care. This review will discuss the pathogenesis of multiple myeloma, current management approaches and the direction of future treatments. In addition, this review will highlight the high burden of symptoms that patients experience and therefore the great benefits that can be gained from specialist palliative care input.

High-Risk Multiple Myeloma: Integrated Clinical and Omics Approach Dissects the Neoplastic Clone and the Tumor Microenvironment

Multiple myeloma (MM) is a genetically heterogeneous disease that includes a subgroup of 10–15% of patients facing dismal survival despite the most intensive treatment. Despite improvements in biological knowledge, MM is still an incurable neoplasia, and therapeutic options able to overcome the relapsing/refractory behavior represent an unmet clinical need. The aim of this review is to provide an integrated clinical and biological overview of high-risk MM, discussing novel therapeutic perspectives, targeting the neoplastic clone and its microenvironment. The dissection of the molecular determinants of the aggressive phenotypes and drug-resistance can foster a better tailored clinical management of the high-risk profile and therapy-refractoriness. Among the current clinical difficulties in MM, patients’ management by manipulating the tumor niche represents a major challenge. The angiogenesis and the stromal infiltrate constitute pivotal mechanisms of a mutual collaboration between MM and the non-tumoral counterpart. Immuno-modulatory and anti-angiogenic therapy hold great efficacy, but variable and unpredictable responses in high-risk MM. The comprehensive understanding of the genetic heterogeneity and MM high-risk ecosystem enforce a systematic bench-to-bedside approach. Here, we provide a broad outlook of novel druggable targets. We also summarize the existing multi-omics-based risk profiling tools, in order to better select candidates for dual immune/vasculogenesis targeting.

Toward personalized treatment in multiple myeloma based on molecular characteristics

To date, the choice of therapy for an individual multiple myeloma patient has been based on clinical factors such as age and comorbidities. The widespread evolution, validation, and clinical utilization of molecular technologies, such as fluorescence in situ hybridization and next-generation sequencing has enabled the identification of a number of prognostic and predictive biomarkers for progression-free survival, overall survival, and treatment response. In this review, we argue that in order to continue to improve myeloma patient outcomes incorporating such biomarkers into the routine diagnostic workup of patients will allow for the use of personalized, biologically based treatments.