The genetic and genomic background of multiple myeloma patients achieving complete response after induction therapy with bortezomib, thalidomide and dexamethasone (VTD)

Genomic hallmarks and therapeutic implications of G0 cell cycle arrest in cancer

BACKGROUND

Therapy resistance in cancer is often driven by a subpopulation of cells that are temporarily arrested in a non-proliferative G0 state, which is difficult to capture and whose mutational drivers remain largely unknown.

RESULTS

We develop methodology to robustly identify this state from transcriptomic signals and characterise its prevalence and genomic constraints in solid primary tumours. We show that G0 arrest preferentially emerges in the context of more stable, less mutated genomes which maintain TP53 integrity and lack the hallmarks of DNA damage repair deficiency, while presenting increased APOBEC mutagenesis. We employ machine learning to uncover novel genomic dependencies of this process and validate the role of the centrosomal gene CEP89 as a modulator of proliferation and G0 arrest capacity. Lastly, we demonstrate that G0 arrest underlies unfavourable responses to various therapies exploiting cell cycle, kinase signalling and epigenetic mechanisms in single-cell data.

CONCLUSIONS

We propose a G0 arrest transcriptional signature that is linked with therapeutic resistance and can be used to further study and clinically track this state.

BDNF/TrkB confers bortezomib resistance in multiple myeloma by inducing BRINP3

BACKGROUND

The proteasome inhibitor bortezomib (BTZ) has significantly improved the survival of multiple myeloma (MM) patients. However, most MM patients still relapse and have drug resistance after BTZ treatment.

METHODS

siRNA transfection was performed to knock down BDNF and TrkB expression. ELISA, western blot, quantitative polymerase chain reaction, CCK-8 assay, and flow cytometry analysis were performed to analyze the functions of BDNF/TrkB signaling in MM cells.

RESULTS

We identified a cell-autonomous mechanism that promotes BTZ resistance in MM, prolongs their RPMI 8226/BTZ resistant cell survival and optimizes their proliferating function. Specifically, RPMI 8226/BTZ cells produced the brain derived neurotrophic factor (BDNF) and its receptor TrkB, which served as a survival factor in the RPMI 8226/BTZ resistant environment. BDNF/TrkB induced phosphorylation of STAT3 that upregulated the bone morphogenetic protein/retinoic acid inducible neural-specific 3 (BRINP3).

CONCLUSIONS

BDNF/TrkB enhanced downstream pathway expression of phosphorylation STAT3 and BRINP3 molecules, promoting RPMI 8226/BTZ cell proliferation and survival.

GENERAL SIGNIFICANCE

These data place BDNF/TrkB at the top of a pSTAT3-BRINP3 survival pathway and link adaptability to BTZ resistant conditions in MM disease.

A network approach for low dimensional signatures from high throughput data

One of the main objectives of high-throughput genomics studies is to obtain a low-dimensional set of observables-a signature-for sample classification purposes (diagnosis, prognosis, stratification). Biological data, such as gene or protein expression, are commonly characterized by an up/down regulation behavior, for which discriminant-based methods could perform with high accuracy and easy interpretability. To obtain the most out of these methods features selection is even more critical, but it is known to be a NP-hard problem, and thus most feature selection approaches focuses on one feature at the time (k-best, Sequential Feature Selection, recursive feature elimination). We propose DNetPRO, Discriminant Analysis with Network PROcessing, a supervised network-based signature identification method. This method implements a network-based heuristic to generate one or more signatures out of the best performing feature pairs. The algorithm is easily scalable, allowing efficient computing for high number of observables ([Formula: see text]-[Formula: see text]). We show applications on real high-throughput genomic datasets in which our method outperforms existing results, or is compatible with them but with a smaller number of selected features. Moreover, the geometrical simplicity of the resulting class-separation surfaces allows a clearer interpretation of the obtained signatures in comparison to nonlinear classification models.

Multiple Myeloma Relapse Is Associated with Increased NFκB Pathway Activity and Upregulation of the Pro-Survival BCL-2 Protein BFL-1

Multiple myeloma (MM) is a hematological malignancy that is still considered incurable due to the development of therapy resistance and subsequent relapse of disease. MM plasma cells (PC) use NFκB signaling to stimulate cell growth and disease progression, and for protection against therapy-induced apoptosis. Amongst its diverse array of target genes, NFκB regulates the expression of pro-survival BCL-2 proteins BCL-XL, BFL-1, and BCL-2. A possible role for BFL-1 in MM is controversial, since BFL-1, encoded by BCL2A1, is downregulated when mature B cells differentiate into antibody-secreting PC. NFκB signaling can be activated by many factors in the bone marrow microenvironment and/or induced by genetic lesions in MM PC. We used the novel signal transduction pathway activity (STA) computational model to quantify the functional NFκB pathway output in primary MM PC from diverse patient subsets at multiple stages of disease. We found that NFκB pathway activity is not altered during disease development, is irrespective of patient prognosis, and does not predict therapy outcome. However, disease relapse after treatment resulted in increased NFκB pathway activity in surviving MM PC, which correlated with increased BCL2A1 expression in a subset of patients. This suggests that BFL-1 upregulation, in addition to BCL-XL and BCL-2, may render MM PC resistant to therapy-induced apoptosis, and that BFL-1 targeting could provide a new approach to reduce therapy resistance in a subset of relapsed/refractory MM patients.

Feature-weighted Ordinal Classification for Predicting Drug Response in Multiple Myeloma

MOTIVATION

Ordinal classification problems arise in a variety of real-world applications, in which samples need to be classified into categories with a natural ordering. An example of classifying high-dimensional ordinal data is to use gene expressions to predict the ordinal drug response, which has been increasingly studied in pharmacogenetics. Classical ordinal classification methods are typically not able to tackle high-dimensional data and standard high-dimensional classification methods discard the ordering information among the classes. Existing work of high-dimensional ordinal classification approaches usually assume a linear ordinality among the classes. We argue that manually-labeled ordinal classes may not be linearly arranged in the data space, especially in high-dimensional complex problems.

RESULTS

We propose a new approach that can project high-dimensional data into a lower discriminating subspace, where the innate ordinal structure of the classes is uncovered. The proposed method weights the features based on their rank correlations with the class labels and incorporates the weights into the framework of linear discriminant analysis. We apply the method to predict the response to two types of drugs for patients with Multiple Myeloma, respectively. A comparative analysis with both ordinal and nominal existing methods demonstrates that the proposed method can achieve a competitive predictive performance while honoring the intrinsic ordinal structure of the classes. We provide interpretations on the genes that are selected by the proposed approach to understand their drug-specific response mechanisms.

AVAILABILITY AND IMPLEMENTATION

The data underlying this article are available in the Gene Expression Omnibus Database at https://www.ncbi.nlm.nih.gov/geo/ and can be accessed with accession number GSE9782 and GSE68871. The source code for FWOC can be accessed at https://github.com/pisuduo/Feature-Weighted-Ordinal-Classification-FWOC.

Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles

Multiple myeloma (MM) affects ~500,000 people and results in ~100,000 deaths annually, being currently considered treatable but incurable. There are several MM chemotherapy treatment regimens, among which eleven include bortezomib, a proteasome-targeted drug. MM patients respond differently to bortezomib, and new prognostic biomarkers are needed to personalize treatments. However, there is a shortage of clinically annotated MM molecular data that could be used to establish novel molecular diagnostics. We report new RNA sequencing profiles for 53 MM patients annotated with responses on two similar chemotherapy regimens: bortezomib, doxorubicin, dexamethasone (PAD), and bortezomib, cyclophosphamide, dexamethasone (VCD), or with responses to their combinations. Fourteen patients received both PAD and VCD; six received only PAD, and 33 received only VCD. We compared profiles for the good and poor responders and found five genes commonly regulated here and in the previous datasets for other bortezomib regimens (all upregulated in the good responders): FGFR3, MAF, IGHA2, IGHV1-69, and GRB14. Four of these genes are linked with known immunoglobulin locus rearrangements. We then used five machine learning (ML) methods to build a classifier distinguishing good and poor responders for two cohorts: PAD + VCD (53 patients), and separately VCD (47 patients). We showed that the application of FloWPS dynamic data trimming was beneficial for all ML methods tested in both cohorts, and also in the previous MM bortezomib datasets. However, the ML models build for the different datasets did not allow cross-transferring, which can be due to different treatment regimens, experimental profiling methods, and MM heterogeneity.

Synthetic lethality-mediated precision oncology via the tumor transcriptome

Precision oncology has made significant advances, mainly by targeting actionable mutations in cancer driver genes. Aiming to expand treatment opportunities, recent studies have begun to explore the utility of tumor transcriptome to guide patient treatment. Here, we introduce SELECT (synthetic lethality and rescue-mediated precision oncology via the transcriptome), a precision oncology framework harnessing genetic interactions to predict patient response to cancer therapy from the tumor transcriptome. SELECT is tested on a broad collection of 35 published targeted and immunotherapy clinical trials from 10 different cancer types. It is predictive of patients' response in 80% of these clinical trials and in the recent multi-arm WINTHER trial. The predictive signatures and the code are made publicly available for academic use, laying a basis for future prospective clinical studies.

Expression quantitative trait loci of genes predicting outcome are associated with survival of multiple myeloma patients

Gene expression profiling can be used for predicting survival in multiple myeloma (MM) and identifying patients who will benefit from particular types of therapy. Some germline single nucleotide polymorphisms (SNPs) act as expression quantitative trait loci (eQTLs) showing strong associations with gene expression levels. We performed an association study to test whether eQTLs of genes reported to be associated with prognosis of MM patients are directly associated with measures of adverse outcome. Using the genotype-tissue expression portal, we identified a total of 16 candidate genes with at least one eQTL SNP associated with their expression with P < 10^-7 either in EBV-transformed B-lymphocytes or whole blood. We genotyped the resulting 22 SNPs in 1327 MM cases from the International Multiple Myeloma rESEarch (IMMEnSE) consortium and examined their association with overall survival (OS) and progression-free survival (PFS), adjusting for age, sex, country of origin and disease stage. Three polymorphisms in two genes (TBRG4-rs1992292, TBRG4-rs2287535 and ENTPD1-rs2153913) showed associations with OS at P < .05, with the former two also associated with PFS. The associations of two polymorphisms in TBRG4 with OS were replicated in 1277 MM cases from the International Lymphoma Epidemiology (InterLymph) Consortium. A meta-analysis of the data from IMMEnSE and InterLymph (2579 cases) showed that TBRG4-rs1992292 is associated with OS (hazard ratio = 1.14, 95% confidence interval 1.04-1.26, P = .007). In conclusion, we found biologically a plausible association between a SNP in TBRG4 and OS of MM patients.

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.

Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments

Background

Machine learning (ML) methods still have limited applicability in personalized oncology due to low numbers of available clinically annotated molecular profiles. This doesn’t allow sufficient training of ML classifiers that could be used for improving molecular diagnostics.

Methods

We reviewed published datasets of high throughput gene expression profiles corresponding to cancer patients with known responses on chemotherapy treatments. We browsed Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Tumor Alterations Relevant for GEnomics-driven Therapy (TARGET) repositories.

Results

We identified data collections suitable to build ML models for predicting responses on certain chemotherapeutic schemes. We identified 26 datasets, ranging from 41 till 508 cases per dataset. All the datasets identified were checked for ML applicability and robustness with leave-one-out cross validation. Twenty-three datasets were found suitable for using ML that had balanced numbers of treatment responder and non-responder cases.

Conclusions

We collected a database of gene expression profiles associated with clinical responses on chemotherapy for 2786 individual cancer cases. Among them seven datasets included RNA sequencing data (for 645 cases) and the others – microarray expression profiles. The cases represented breast cancer, lung cancer, low-grade glioma, endothelial carcinoma, multiple myeloma, adult leukemia, pediatric leukemia and kidney tumors. Chemotherapeutics included taxanes, bortezomib, vincristine, trastuzumab, letrozole, tipifarnib, temozolomide, busulfan and cyclophosphamide.

Combinatorial Discriminant Analysis Applied to RNAseq Data Reveals a Set of 10 Transcripts as Signatures of Exposure of Cattle to Mycobacterium avium subsp. paratuberculosis

Paratuberculosis or Johne's disease in cattle is a chronic granulomatous gastroenteritis caused by infection with Mycobacterium avium subspecies paratuberculosis (MAP). Paratuberculosis is not treatable; therefore, the early identification and isolation of infected animals is a key point to reduce its incidence. In this paper, we analyse RNAseq experimental data of 5 ELISA-negative cattle exposed to MAP in a positive herd, compared to 5 negative-unexposed controls. The purpose was to find a small set of differentially expressed genes able to discriminate between exposed animals in a preclinical phase from non-exposed controls. Our results identified 10 transcripts that differentiate between ELISA-negative, clinically healthy, and exposed animals belonging to paratuberculosis-positive herds and negative-unexposed animals. Of the 10 transcripts, five (TRPV4, RIC8B, IL5RA, ERF, CDC40) showed significant differential expression between the three groups while the remaining 5 (RDM1, EPHX1, STAU1, TLE1, ASB8) did not show a significant difference in at least one of the pairwise comparisons. When tested in a larger cohort, these findings may contribute to the development of a new diagnostic test for paratuberculosis based on a gene expression signature. Such a diagnostic tool could allow early interventions to reduce the risk of the infection spreading.

Flexible Data Trimming Improves Performance of Global Machine Learning Methods in Omics-Based Personalized Oncology

(1) Background: Machine learning (ML) methods are rarely used for an omics-based prescription of cancer drugs, due to shortage of case histories with clinical outcome supplemented by high-throughput molecular data. This causes overtraining and high vulnerability of most ML methods. Recently, we proposed a hybrid global-local approach to ML termed floating window projective separator (FloWPS) that avoids extrapolation in the feature space. Its core property is data trimming, i.e., sample-specific removal of irrelevant features. (2) Methods: Here, we applied FloWPS to seven popular ML methods, including linear SVM, k nearest neighbors (kNN), random forest (RF), Tikhonov (ridge) regression (RR), binomial naïve Bayes (BNB), adaptive boosting (ADA) and multi-layer perceptron (MLP). (3) Results: We performed computational experiments for 21 high throughput gene expression datasets (41–235 samples per dataset) totally representing 1778 cancer patients with known responses on chemotherapy treatments. FloWPS essentially improved the classifier quality for all global ML methods (SVM, RF, BNB, ADA, MLP), where the area under the receiver-operator curve (ROC AUC) for the treatment response classifiers increased from 0.61–0.88 range to 0.70–0.94. We tested FloWPS-empowered methods for overtraining by interrogating the importance of different features for different ML methods in the same model datasets. (4) Conclusions: We showed that FloWPS increases the correlation of feature importance between the different ML methods, which indicates its robustness to overtraining. For all the datasets tested, the best performance of FloWPS data trimming was observed for the BNB method, which can be valuable for further building of ML classifiers in personalized oncology.

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.

FLOating-Window Projective Separator (FloWPS): A Data Trimming Tool for Support Vector Machines (SVM) to Improve Robustness of the Classifier

Here, we propose a heuristic technique of data trimming for SVM termed FLOating Window Projective Separator (FloWPS), tailored for personalized predictions based on molecular data. This procedure can operate with high throughput genetic datasets like gene expression or mutation profiles. Its application prevents SVM from extrapolation by excluding non-informative features. FloWPS requires training on the data for the individuals with known clinical outcomes to create a clinically relevant classifier. The genetic profiles linked with the outcomes are broken as usual into the training and validation datasets. The unique property of FloWPS is that irrelevant features in validation dataset that don’t have significant number of neighboring hits in the training dataset are removed from further analyses. Next, similarly to the k nearest neighbors (kNN) method, for each point of a validation dataset, FloWPS takes into account only the proximal points of the training dataset. Thus, for every point of a validation dataset, the training dataset is adjusted to form a floating window. FloWPS performance was tested on ten gene expression datasets for 992 cancer patients either responding or not on the different types of chemotherapy. We experimentally confirmed by leave-one-out cross-validation that FloWPS enables to significantly increase quality of a classifier built based on the classical SVM in most of the applications, particularly for polynomial kernels.

Pathway-structured predictive modeling for multi-level drug response in multiple myeloma

Motivation

Molecular analyses suggest that myeloma is composed of distinct sub-types that have different molecular pathologies and various response rates to certain treatments. Drug responses in multiple myeloma (MM) are usually recorded as a multi-level ordinal outcome. One of the goals of drug response studies is to predict which response category any patients belong to with high probability based on their clinical and molecular features. However, as most of genes have small effects, gene-based models may provide limited predictive accuracy. In that case, methods for predicting multi-level ordinal drug responses by incorporating biological pathways are desired but have not been developed yet.

Results

We propose a pathway-structured method for predicting multi-level ordinal responses using a two-stage approach. We first develop hierarchical ordinal logistic models and an efficient quasi-Newton algorithm for jointly analyzing numerous correlated variables. Our two-stage approach first obtains the linear predictor (called the pathway score) for each pathway by fitting all predictors within each pathway using the hierarchical ordinal logistic approach, and then combines the pathway scores as new predictors to build a predictive model. We applied the proposed method to two publicly available datasets for predicting multi-level ordinal drug responses in MM using large-scale gene expression data and pathway information. Our results show that our approach not only significantly improved the predictive performance compared with the corresponding gene-based model but also allowed us to identify biologically relevant pathways.

Availability and implementation

The proposed approach has been implemented in our R package BhGLM, which is freely available from the public GitHub repository https://github.com/abbyyan3/BhGLM.

Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression

Background

Multiple myeloma (MM), like other cancers, is caused by the accumulation of genetic abnormalities. Heterogeneity exists in the patients’ response to treatments, for example, bortezomib. This urges efforts to identify biomarkers from numerous molecular features and build predictive models for identifying patients that can benefit from a certain treatment scheme. However, previous studies treated the multi-level ordinal drug response as a binary response where only responsive and non-responsive groups are considered.

Methods

It is desirable to directly analyze the multi-level drug response, rather than combining the response to two groups. In this study, we present a novel method to identify significantly associated biomarkers and then develop ordinal genomic classifier using the hierarchical ordinal logistic model. The proposed hierarchical ordinal logistic model employs the heavy-tailed Cauchy prior on the coefficients and is fitted by an efficient quasi-Newton algorithm.

Results

We apply our hierarchical ordinal regression approach to analyze two publicly available datasets for MM with five-level drug response and numerous gene expression measures. Our results show that our method is able to identify genes associated with the multi-level drug response and to generate powerful predictive models for predicting the multi-level response.

Conclusions

The proposed method allows us to jointly fit numerous correlated predictors and thus build efficient models for predicting the multi-level drug response. The predictive model for the multi-level drug response can be more informative than the previous approaches. Thus, the proposed approach provides a powerful tool for predicting multi-level drug response and has important impact on cancer studies.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4483-6) contains supplementary material, which is available to authorized users.

Drug response prediction in high-risk multiple myeloma

A Drug Response Prediction (DRP) score was developed based on gene expression profiling (GEP) from cell lines and tumor samples. Twenty percent of high-risk patients by GEP70 treated in Total Therapy 2 and 3A have a progression-free survival (PFS) of more than 10years. We used available GEP data from high-risk patients by GEP70 at diagnosis from Total Therapy 2 and 3A to predict the response by the DRP score of drugs used in the treatment of myeloma patients. The DRP score stratified patients further. High-risk myeloma with a predicted sensitivity to melphalan by the DRP score had a prolonged PFS, HR=2.4 (1.2-4.9, P=0.014) and those with predicted sensitivity to bortezomib had a HR 5.7 (1.2-27, P=0.027). In case of predicted sensitivity to bortezomib, a better response to treatment was found (P=0.022). This method may provide us with a tool for identifying candidates for effective personalized medicine and spare potential non-responders from suffering toxicity.

Standardisation of minimal residual disease in multiple myeloma

The assessment of the effectiveness of chemotherapy in oncology cannot disregard the concept of minimal residual disease (MRD). In fact, the efforts of numerous scientific groups all over the world are currently focusing on this issue, with the sole purpose of defining sensitive, effective assessment criteria that are, above all, able to give acceptable, easily repeatable results worldwide. Regarding this issue, especially with the advent of new drugs, multiple myeloma is one of the haematologic malignancies for which a consensus has not yet been reached. In this review, we analyse various techniques that have been used to improve the sensitivity of response, aimed at reducing the cut-off values previously allowed, as well as serological values like serum-free light chain, or immunophenotypic tools on bone marrow or peripheral blood, like multi-parameter flow cytometry, or molecular ones such as allele-specific oligonucleotide (ASO)-qPCR and next-generation/high-throughput sequencing technologies (NGS). Moreover, our discussion makes a brief reference to promising techniques, such as mass spectrometry for identifying Ig light chain (LC) in peripheral blood, and the assessment of gene expression profile not only in defining prognostic risk at the diagnosis but also as a tool for evaluation of response.

IKAROS expression in distinct bone marrow cell populations as a candidate biomarker for outcome with lenalidomide-dexamethasone therapy in multiple myeloma

Immunomodulatory drugs (IMiDs) are a cornerstone in the treatment of multiple myeloma (MM), but specific markers to predict outcome are still missing. Recent work pointed to a prognostic role for IMiD target genes (e.g. CRBN). Moreover, indirect activity of IMiDs on immune cells correlated with outcome, raising the possibility that cell populations in the bone marrow (BM) microenvironment could serve as biomarkers. We therefore analysed gene expression levels of six IMiD target genes in whole BM samples of 44 myeloma patients treated with lenalidomide-dexamethasone. Expression of CRBN (R = 0.30, P = .05), IKZF1 (R = 0.31, P = .04), IRF4 (R = 0.38, P = .01), MCT-1 (R = 0.30, P = .05), and CD147 (R = 0.38, P = .01), but not IKZF3 (R = -0.15, P = .34), was significantly associated with response. Interestingly, IKZF1 expression was elevated in BM environmental cells and thus selected for further investigation by multicolor flow cytometry. High IKAROS protein levels in total BM mononuclear cells (median OS 83.4 vs. 32.2 months, P = .02), CD19⁺ B cells (median OS 71.1 vs. 32.2 months, P = .05), CD3⁺ CD8⁺ T cells (median OS 83.4 vs 19.0 months, P = .008) as well as monocytes (median OS 53.9 vs 18.0 months, P = .009) were associated with superior overall survival (OS). In contrast, IKAROS protein expression in MM cells was not predictive for OS. Our data therefore corroborate the central role of immune cells for the clinical activity of IMiDs and built the groundwork for prospective analysis of IKAROS protein levels in distinct cell populations as a potential biomarker for IMiD based therapies.

RPL5 on 1p22.1 is recurrently deleted in multiple myeloma and its expression is linked to bortezomib response

Chromosomal region 1p22 is deleted in ≥20% of multiple myeloma (MM) patients, suggesting the presence of an unidentified tumor suppressor. Using high-resolution genomic profiling, we delimit a 58 kb minimal deleted region (MDR) on 1p22.1 encompassing two genes: ectopic viral integration site 5 (EVI5) and ribosomal protein L5 (RPL5). Low mRNA expression of EVI5 and RPL5 was associated with worse survival in diagnostic cases. Patients with 1p22 deletion had lower mRNA expression of EVI5 and RPL5, however, 1p22 deletion status is a bad predictor of RPL5 expression in some cases, suggesting that other mechanisms downregulate RPL5 expression. Interestingly, RPL5 but not EVI5 mRNA levels were significantly lower in relapsed patients responding to bortezomib and; both in newly diagnosed and relapsed patients, bortezomib treatment could overcome their bad prognosis by raising their progression-free survival to equal that of patients with high RPL5 expression. In conclusion, our genetic data restrict the MDR on 1p22 to EVI5 and RPL5 and although the role of these genes in promoting MM progression remains to be determined, we identify RPL5 mRNA expression as a biomarker for initial response to bortezomib in relapsed patients and subsequent survival benefit after long-term treatment in newly diagnosed and relapsed patients.

Gene Expression Profiles in Myeloma: Ready for the Real World?

Multiple myeloma is a plasma cell malignancy characterized by molecular and clinical heterogeneity. The outcome of the disease has been dramatically improved with the advent of new drugs in the past few years. However, even in this context of increasing therapeutic options, important challenges remain, such as accurately evaluating patients' prognosis and predicting sensitivity to specific treatments and drug combinations. Transcriptomic studies have largely contributed to help decipher multiple myeloma complexity, characterizing multiple myeloma subgroups distinguished by different outcomes. Microarrays and, more recently, RNA sequencing allow evaluation of expression of coding and noncoding genes, alternate splicing events, mutations, and novel transcriptome modifiers, providing new information regarding myeloma biology, prognostication, and therapy. In this review, we discuss the role and impact of gene expression profiling studies in myeloma. Clin Cancer Res; 22(22); 5434-42. ©2016 AACR SEE ALL ARTICLES IN THIS CCR FOCUS SECTION, "MULTIPLE MYELOMA MULTIPLYING THERAPIES".