Identification of multiple risk loci and regulatory mechanisms influencing susceptibility to multiple myeloma

Multiple myeloma

Multiple myeloma (MM) is a haematological lymphoid malignancy involving tumoural plasma cells and is usually characterized by the presence of a monoclonal immunoglobulin protein. MM is the second most common haematological malignancy, with an increasing global incidence. It remains incurable because most patients relapse or become refractory to treatments. MM is a genetically complex disease with high heterogeneity that develops as a multistep process, involving acquisition of genetic alterations in the tumour cells and changes in the bone marrow microenvironment. Symptomatic MM is diagnosed using the International Myeloma Working Group criteria as a bone marrow infiltration of ≥10% clonal plasma cells, and the presence of at least one myeloma-defining event, either standard CRAB features (hypercalcaemia, renal failure, anaemia and/or lytic bone lesions) or biomarkers of imminent organ damage. Younger and fit patients are considered eligible for transplant. They receive an induction, followed by consolidation with high-dose melphalan and autologous haematopoietic cell transplantation, and maintenance therapy. In older adults (ineligible for transplant), the combination of daratumumab, lenalidomide and dexamethasone is the preferred option. If relapse occurs and requires further therapy, the choice of therapy will be based on previous treatment and response and now includes immunotherapies, such as bi-specific monoclonal antibodies and chimeric antigen receptor T cell therapy.

Identification of COQ2 as a regulator of proliferation and lipid peroxidation through genome-scale CRISPR-Cas9 screening in myeloma cells

Multiple myeloma (MM) is the second most common malignant haematological disease with a poor prognosis. The limit therapeutic progress has been made in MM patients with cancer relapse, necessitating deeper research into the molecular mechanisms underlying its occurrence and development. A genome-wide CRISPR-Cas9 loss-of-function screening was utilized to identify potential therapeutic targets in our research. We revealed that COQ2 plays a crucial role in regulating MM cell proliferation and lipid peroxidation (LPO). Knockout of COQ2 inhibited cell proliferation, induced cell cycle arrest and reduced tumour growth in vivo. Mechanistically, COQ2 promoted the activation of the MEK/ERK cascade, which in turn stabilized and activated MYC protein. Moreover, we found that COQ2-deficient MM cells increased sensitivity to the LPO activator, RSL3. Using an inhibitor targeting COQ2 by 4-CBA enhanced the sensitivity to RSL3 in primary CD138+ myeloma cells and in a xenograft mouse model. Nevertheless, co-treatment of 4-CBA and RSL3 induced cell death in bortezomib-resistant MM cells. Together, our findings suggest that COQ2 promotes cell proliferation and tumour growth through the activation of the MEK/ERK/MYC axis and targeting COQ2 could enhance the sensitivity to ferroptosis in MM cells, which may be a promising therapeutic strategy for the treatment of MM patients.

Epigenomic Characterization of Lymphoid Neoplasms

Lymphoid neoplasms represent a heterogeneous group of disease entities and subtypes with markedly different molecular and clinical features. Beyond genetic alterations, lymphoid tumors also show widespread epigenomic changes. These severely affect the levels and distribution of DNA methylation, histone modifications, chromatin accessibility, and three-dimensional genome interactions. DNA methylation stands out as a tracer of cell identity and memory, as B cell neoplasms show epigenetic imprints of their cellular origin and proliferative history, which can be quantified by an epigenetic mitotic clock. Chromatin-associated marks are informative to uncover altered regulatory regions and transcription factor networks contributing to the development of distinct lymphoid tumors. Tumor-intrinsic epigenetic and genetic aberrations cooperate and interact with microenvironmental cells to shape the transcriptome at different phases of lymphoma evolution, and intraclonal heterogeneity can now be characterized by single-cell profiling. Finally, epigenetics offers multiple clinical applications, including powerful diagnostic and prognostic biomarkers as well as therapeutic targets.

Exploring the genetic and molecular basis of differences in multiple myeloma of individuals of African and European descent

Multiple Myeloma is a typical example of a neoplasm that shows significant differences in incidence, age of onset, type, and frequency of genetic alterations between patients of African and European ancestry. This perspective explores the hypothesis that both genetic polymorphisms and spontaneous somatic mutations in the TP53 tumor suppressor gene are determinants of these differences. In the US, the rates of occurrence of MM are at least twice as high in African Americans (AA) as in Caucasian Americans (CA). Strikingly, somatic TP53 mutations occur in large excess (at least 4-6-fold) in CA versus AA. On the other hand, TP53 contains polymorphisms specifying amino-acid differences that are under natural selection by the latitude of a population and have evolved during the migrations of humans over several hundred thousand years. The p53 protein plays important roles in DNA strand break repair and, therefore, in the surveillance of aberrant DNA recombination, leading to the B-cell translocations that are causal in the pathogenesis of MM. We posit that polymorphisms in one region of the TP53 gene (introns 2 and 3, and the proline-rich domain) specify a concentration of the p53 protein with a higher capacity to repress translocations in CA than AA patients. This, in turn, results in a higher risk of acquiring inactivating, somatic mutations in a different region of the TP53 gene (DNA binding domain) in CA than in AA patients. Such a mechanism, by which the polymorphic status of a gene influencing its own "spontaneous" mutation frequency, may provide a genetic basis to address ethnicity-related differences in the incidence and phenotypes of many different forms of cancer.

Sunlight, vitamin D, vitamin D receptor polymorphisms, and risk of multiple myeloma: A systematic review

This systematic review examines the relationship with multiple myeloma (MM) risk for sunlight and vitamin D related exposures, including vitamin D supplementation, circulating 25-hydroxyvitamin D concentration, personal ultraviolet B radiation exposure, ambient solar irradiance and vitamin D receptor (VDR) gene polymorphisms We conducted a search for terms related to multiple myeloma, vitamin D, vitamin D receptor, ultraviolet radiation, sunlight, and single nucleotide polymorphism (SNP) using Ovid MEDLINE, Ovid EMBASE, Web of Science and Cochrane CENTRAL. Studies were assessed for risk of bias and quality using the RoB 2.0, ROBINS-E or Q-Genie tools. We identified 13 eligible studies: one randomised controlled trial, two cohort studies, and ten case-control studies, including one nested case-control study and one meta-analysis of genome-wide association studies. We conducted a qualitative synthesis; quantitative synthesis was not appropriate due to study heterogeneity and the small number of studies identified. There was insufficient evidence to support an effect of any sunlight or vitamin D related exposure on MM risk. No polymorphisms in VDR were found to be strongly related to risk for people of European ancestry. Of the identified studies, many had high risk of bias or were of lower quality. Few studies have investigated the association between sunlight and vitamin D related exposures and multiple myeloma risk. The scarcity of high-quality studies makes it difficult to evaluate potential effects of these exposures on MM risk. Further research is necessary to investigate the influence of vitamin D related exposures on risk of multiple myeloma..

Polygenic Risk and Chemotherapy-Related Subsequent Malignancies in Childhood Cancer Survivors: A Childhood Cancer Survivor Study and St Jude Lifetime Cohort Study Report

PURPOSE

Chemotherapeutic exposures are associated with subsequent malignant neoplasm (SMN) risk. The role of genetic susceptibility in chemotherapy-related SMNs should be defined as use of radiation therapy (RT) decreases.

PATIENTS AND METHODS

SMNs among long-term childhood cancer survivors of European (EUR; N = 9,895) and African (AFR; N = 718) genetic ancestry from the Childhood Cancer Survivor Study and St Jude Lifetime Cohort Study were evaluated. An externally validated 179-variant polygenic risk score (PRS) associated with pleiotropic adult cancer risk from the UK Biobank Study (N > 400,000) was computed for each survivor. SMN cumulative incidence comparing top and bottom PRS quintiles was estimated, along with hazard ratios (HRs) from proportional hazards models.

RESULTS

A total of 1,594 survivors developed SMNs, with basal cell carcinomas (n = 822), breast cancers (n = 235), and thyroid cancers (n = 221) being the most frequent. Although SMN risk associations with the PRS were extremely modest in RT-exposed EUR survivors (HR, 1.22; P = .048; n = 4,630), the increase in 30-year SMN cumulative incidence and HRs comparing top and bottom PRS quintiles was statistically significant among nonirradiated EUR survivors (n = 4,322) treated with alkylating agents (17% v 6%; HR, 2.46; P < .01), anthracyclines (20% v 8%; HR, 2.86; P < .001), epipodophyllotoxins (23% v 1%; HR, 12.20; P < .001), or platinums (46% v 7%; HR, 8.58; P < .01). This PRS also significantly modified epipodophyllotoxin-related SMN risk among nonirradiated AFR survivors (n = 414; P < .01). Improvements in prediction attributable to the PRS were greatest for epipodophyllotoxin-exposed (AUC, 0.71 v 0.63) and platinum-exposed (AUC,0.68 v 0.58) survivors.

CONCLUSION

A pleiotropic cancer PRS has strong potential for improving SMN clinical risk stratification among nonirradiated survivors treated with specific chemotherapies. A polygenic risk screening approach may be a valuable complement to an early screening strategy on the basis of treatments and rare cancer-susceptibility mutations.

Proteasome inhibition in combination with immunotherapies: State-of-the-Art in multiple myeloma

Multiple myeloma (MM) is a malignant plasma cell disorder accounting for around 1.8% of all neoplastic diseases. Nowadays, clinicians have a broad arsenal of drugs at their disposal for the treatment of MM, such as proteasome inhibitors, immunomodulatory drugs, monoclonal antibodies, bispecific antibodies, CAR T-cell therapies and antibody-drug conjugates. In this paper we briefly highlight essential clinical elements relating to proteasome inhibitors, such as bortezomib, carfilzomib and ixazomib. Studies suggest that the early use of immunotherapy may improve outcomes significantly. Therefore, in our review we specifically focus on the combination therapy of proteasome inhibitors with novel immunotherapies and/or transplant. A high number of patients develop PI resistance. Thus, we also review new generation PIs, such as marizomib, oprozomib (ONX0912) and delanzomib (CEP-18770) and their combinations with immunotherapies.

Identification and validation of a cellular senescence-related lncRNA signature for prognostic prediction in patients with multiple myeloma

Multiple myeloma (MM) is the second most common hematologic malignancy, which primarily occurs in the elderly. Cellular senescence is considered to be closely associated with the occurrence and progression of malignant tumors including MM, and lncRNA can mediate the process of cellular senescence by regulating key signaling pathways such as p53/p21 and p16/RB. However, the role of cellular senescence related lncRNAs (CSRLs) in MM development has never been reported. Herein, we identified 11 CSRLs (AC004918.5, AC103858.1, AC245100.4, ACBD3-AS1, AL441992.2, ATP2A1-AS1, CCDC18-AS1, LINC00996, TMEM161B-AS1, RP11-706O15.1, and SMURF2P1) to build the CSRLs risk model, which was confirmed to be highly associated with overall survival (OS) of MM patients. We further demonstrated the strong prognostic value of the risk model in MM patients receiving different regimens, especially for those with three-drug combination of bortezomib, lenalidomide, and dexamethasone (VRd) as first-line therapy. Not only that, our risk model also excels in predicting the OS of MM patients at 1, 2, and 3 years. In order to verify the function of these CSRLs in MM, we selected the lncRNA ATP2A1-AS1 which presented the largest expression difference between high-risk groups and low-risk groups for subsequent analysis and validation. Finally, we found that down-regulation of ATP2A1-AS1 can promote cellular senescence in MM cell lines. In conclusion, the CSRLs risk model established in present study provides a novel and more accurate method for predicting MM patients' prognosis and identifies a new target for MM therapeutic intervention.

Polymorphisms within Autophagy-Related Genes as Susceptibility Biomarkers for Multiple Myeloma: A Meta-Analysis of Three Large Cohorts and Functional Characterization

Multiple myeloma (MM) arises following malignant proliferation of plasma cells in the bone marrow, that secrete high amounts of specific monoclonal immunoglobulins or light chains, resulting in the massive production of unfolded or misfolded proteins. Autophagy can have a dual role in tumorigenesis, by eliminating these abnormal proteins to avoid cancer development, but also ensuring MM cell survival and promoting resistance to treatments. To date no studies have determined the impact of genetic variation in autophagy-related genes on MM risk. We performed meta-analysis of germline genetic data on 234 autophagy-related genes from three independent study populations including 13,387 subjects of European ancestry (6863 MM patients and 6524 controls) and examined correlations of statistically significant single nucleotide polymorphisms (SNPs; p < 1 × 10^-9) with immune responses in whole blood, peripheral blood mononuclear cells (PBMCs), and monocyte-derived macrophages (MDM) from a large population of healthy donors from the Human Functional Genomic Project (HFGP). We identified SNPs in six loci, CD46, IKBKE, PARK2, ULK4, ATG5, and CDKN2A associated with MM risk (p = 4.47 × 10^-4-5.79 × 10^-14). Mechanistically, we found that the ULK4_rs6599175 SNP correlated with circulating concentrations of vitamin D3 (p = 4.0 × 10^-4), whereas the IKBKE_rs17433804 SNP correlated with the number of transitional CD24⁺CD38⁺ B cells (p = 4.8 × 10^-4) and circulating serum concentrations of Monocyte Chemoattractant Protein (MCP)-2 (p = 3.6 × 10^-4). We also found that the CD46_rs1142469 SNP correlated with numbers of CD19⁺ B cells, CD19⁺CD3^- B cells, CD5⁺IgD^- cells, IgM^- cells, IgD^-IgM^- cells, and CD4^-CD8^- PBMCs (p = 4.9 × 10^-4-8.6 × 10^-4) and circulating concentrations of interleukin (IL)-20 (p = 0.00082). Finally, we observed that the CDKN2A_rs2811710 SNP correlated with levels of CD4⁺EMCD45RO⁺CD27^- cells (p = 9.3 × 10^-4). These results suggest that genetic variants within these six loci influence MM risk through the modulation of specific subsets of immune cells, as well as vitamin D3^-, MCP-2^-, and IL20-dependent pathways.

GSTP1-mediated S-glutathionylation of Pik3r1 is a redox hub that inhibits osteoclastogenesis through regulating autophagic flux

Glutathione S-transferase P1(GSTP1) is known for its transferase and detoxification activity. Based on disease-phenotype genetic associations, we found that GSTP1 might be associated with bone mineral density through Mendelian randomization analysis. Therefore, this study was performed both in vitro cellular and in vivo mouse model to determine how GSTP1 affects bone homeostasis. In our research, GSTP1 was revealed to upregulate the S-glutathionylation level of Pik3r1 through Cys498 and Cys670, thereby decreasing its phosphorylation, further controlling the alteration of autophagic flux via the Pik3r1-AKT-mTOR axis, and lastly altering osteoclast formation in vitro. In addition, knockdown and overexpression of GSTP1 in vivo also altered bone loss outcomes in the OVX mice model. In general, this study identified a new mechanism by which GSTP1 regulates osteoclastogenesis, and it is evident that the cell fate of osteoclasts is controlled by GSTP1-mediated S-glutathionylation via a redox-autophagy cascade.

Clinical characteristics and outcome of 318 families with familial monoclonal gammopathy: A multicenter Intergroupe Francophone du Myélome study

Familial forms of monoclonal gammopathy, defined as multiple myeloma (MM) or Monoclonal Gammopathy of Undetermined Significance (MGUS), are relatively infrequent and most series reported in the literature describe a limited number of families. MM rarely occurs in a familial context. MGUS is observed much more commonly, which can in some cases evolve toward full-blown MM. Although recurrent cytogenetic abnormalities have been described in tumor cells of sporadic cases of MM, the pathogenesis of familial MM remains largely unexplained. In order to identify genetic factors predisposing to familial monoclonal gammopathy, the Intergroupe Francophone du Myélome identified 318 families with at least two confirmed cases of monoclonal gammopathy. There were 169 families with parent/child pairs and 164 families with cases in at least two siblings, compatible with an autosomal transmission. These familial cases were compared with sporadic cases who were matched for age at diagnosis, sex and immunoglobulin isotype, with 10 sporadic cases for each familial case. The gender distribution, age and immunoglobulin subtypes of familial cases were unremarkable in comparison to sporadic cases. With a median follow-up of 7.4 years after diagnosis, the percentage of MGUS cases having evolved to MM was 3%. The median overall survival of the 148 familial MM cases was longer than that of matched sporadic cases, with projected values of 7.6 and 16.1 years in patients older and younger than 65 years, respectively. These data suggest that familial cases of monoclonal gammopathy are similar to sporadic cases in terms of clinical presentation and carry a better prognosis.

A pleiotropic variant in DNAJB4 is associated with multiple myeloma risk

Pleiotropy, which consists of a single gene or allelic variant affecting multiple unrelated traits, is common across cancers, with evidence for genome-wide significant loci shared across cancer and noncancer traits. This feature is particularly relevant in multiple myeloma (MM) because several susceptibility loci that have been identified to date are pleiotropic. Therefore, the aim of this study was to identify novel pleiotropic variants involved in MM risk using 28 684 independent single nucleotide polymorphisms (SNPs) from GWAS Catalog that reached a significant association (P < 5 × 10^-8 ) with their respective trait. The selected SNPs were analyzed in 2434 MM cases and 3446 controls from the International Lymphoma Epidemiology Consortium (InterLymph). The 10 SNPs showing the strongest associations with MM risk in InterLymph were selected for replication in an independent set of 1955 MM cases and 1549 controls from the International Multiple Myeloma rESEarch (IMMEnSE) consortium and 418 MM cases and 147 282 controls from the FinnGen project. The combined analysis of the three studies identified an association between DNAJB4-rs34517439-A and an increased risk of developing MM (OR = 1.22, 95%CI 1.13-1.32, P = 4.81 × 10^-7 ). rs34517439-A is associated with a modified expression of the FUBP1 gene, which encodes a multifunctional DNA and RNA-binding protein that it was observed to influence the regulation of various genes involved in cell cycle regulation, among which various oncogenes and oncosuppressors. In conclusion, with a pleiotropic scan approach we identified DNAJB4-rs34517439 as a potentially novel MM risk locus.

Investigation of Rare Non-Coding Variants in Familial Multiple Myeloma

Multiple myeloma (MM) is a plasma cell malignancy whereby a single clone of plasma cells over-propagates in the bone marrow, resulting in the increased production of monoclonal immunoglobulin. While the complex genetic architecture of MM is well characterized, much less is known about germline variants predisposing to MM. Genome-wide sequencing approaches in MM families have started to identify rare high-penetrance coding risk alleles. In addition, genome-wide association studies have discovered several common low-penetrance risk alleles, which are mainly located in the non-coding genome. Here, we further explored the genetic basis in familial MM within the non-coding genome in whole-genome sequencing data. We prioritized and characterized 150 upstream, 5' untranslated region (UTR) and 3' UTR variants from 14 MM families, including 20 top-scoring variants. These variants confirmed previously implicated biological pathways in MM development. Most importantly, protein network and pathway enrichment analyses also identified 10 genes involved in mitogen-activated protein kinase (MAPK) signaling pathways, which have previously been established as important MM pathways.

A germline exome analysis reveals harmful POT1 variants in multiple myeloma patients and families

Observations of inherited susceptibility to multiple myeloma have led to active research in defining predisposing genes to the disease. Here, we analysed 128 plasma cell dyscrasia patients' germline whole-exome sequencing data. Rare dominantly inherited pathogenic or likely pathogenic (P/LP) variant was found in 9.4% of the patients. Among the P/LP variants, CHEK2 (p. Thr410MetfsTer15) was the most prevalent (n = 5, 3.9%). Interestingly, P/LP variants in POT1 were identified in three patients (2.3%). Our findings broaden the spectrum of POT1-related cancers and demonstrate the importance of the germline genetic analysis in hematological malignancies.

Artificial Intelligence Predicted Overall Survival and Classified Mature B-Cell Neoplasms Based on Immuno-Oncology and Immune Checkpoint Panels

Artificial intelligence (AI) can identify actionable oncology biomarkers. This research integrates our previous analyses of non-Hodgkin lymphoma. We used gene expression and immunohistochemical data, focusing on the immune checkpoint, and added a new analysis of macrophages, including 3D rendering. The AI comprised machine learning (C5, Bayesian network, C&R, CHAID, discriminant analysis, KNN, logistic regression, LSVM, Quest, random forest, random trees, SVM, tree-AS, and XGBoost linear and tree) and artificial neural networks (multilayer perceptron and radial basis function). The series included chronic lymphocytic leukemia, mantle cell lymphoma, follicular lymphoma, Burkitt, diffuse large B-cell lymphoma, marginal zone lymphoma, and multiple myeloma, as well as acute myeloid leukemia and pan-cancer series. AI classified lymphoma subtypes and predicted overall survival accurately. Oncogenes and tumor suppressor genes were highlighted (MYC, BCL2, and TP53), along with immune microenvironment markers of tumor-associated macrophages (M2-like TAMs), T-cells and regulatory T lymphocytes (Tregs) (CD68, CD163, MARCO, CSF1R, CSF1, PD-L1/CD274, SIRPA, CD85A/LILRB3, CD47, IL10, TNFRSF14/HVEM, TNFAIP8, IKAROS, STAT3, NFKB, MAPK, PD-1/PDCD1, BTLA, and FOXP3), apoptosis (BCL2, CASP3, CASP8, PARP, and pathway-related MDM2, E2F1, CDK6, MYB, and LMO2), and metabolism (ENO3, GGA3). In conclusion, AI with immuno-oncology markers is a powerful predictive tool. Additionally, a review of recent literature was made.

Autophagy in Hematological Malignancies

Simple Summary

Autophagy is a dynamic and tightly regulated process that seems to have dual effects in cancer. In some contexts, it can induce carcinogenesis and promote cancer cell survival, whereas in others, it acts preventing tumor cell growth and tumor progression. Thus, autophagy functions seem to strictly depend on cancer ontogenesis, progression, and type. Here, we will dive into the current knowledge of autophagy in hematological malignancies and will highlight the main genetic components involved in each cancer type.

Abstract

Autophagy is a highly conserved metabolic pathway via which unwanted intracellular materials, such as unfolded proteins or damaged organelles, are digested. It is activated in response to conditions of oxidative stress or starvation, and is essential for the maintenance of cellular homeostasis and other vital functions, such as differentiation, cell death, and the cell cycle. Therefore, autophagy plays an important role in the initiation and progression of tumors, including hematological malignancies, where damaged autophagy during hematopoiesis can cause malignant transformation and increase cell proliferation. Over the last decade, the importance of autophagy in response to standard pharmacological treatment of hematological tumors has been observed, revealing completely opposite roles depending on the tumor type and stage. Thus, autophagy can promote tumor survival by attenuating the cellular damage caused by drugs and/or stabilizing oncogenic proteins, but can also have an antitumoral effect due to autophagic cell death. Therefore, autophagy-based strategies must depend on the context to create specific and safe combination therapies that could contribute to improved clinical outcomes. In this review, we describe the process of autophagy and its role on hematopoiesis, and we highlight recent research investigating its role as a potential therapeutic target in hematological malignancies. The findings suggest that genetic variants within autophagy-related genes modulate the risk of developing hemopathies, as well as patient survival.

Implementation of individualised polygenic risk score analysis: a test case of a family of four

Background

Polygenic risk scores (PRS) have been widely applied in research studies, showing how population groups can be stratified into risk categories for many common conditions. As healthcare systems consider applying PRS to keep their populations healthy, little work has been carried out demonstrating their implementation at an individual level.

Case presentation

We performed a systematic curation of PRS sources from established data repositories, selecting 15 phenotypes, comprising an excess of 37 million SNPs related to cancer, cardiovascular, metabolic and autoimmune diseases. We tested selected phenotypes using whole genome sequencing data for a family of four related individuals. Individual risk scores were given percentile values based upon reference distributions among 1000 Genomes Iberians, Europeans, or all samples. Over 96 billion allele effects were calculated in order to obtain the PRS for each of the individuals analysed here.

Conclusions

Our results highlight the need for further standardisation in the way PRS are developed and shared, the importance of individual risk assessment rather than the assumption of inherited averages, and the challenges currently posed when translating PRS into risk metrics.

Multiple Myeloma: Bioinformatic Analysis for Identification of Key Genes and Pathways

Multiple myeloma (MM) is a hematological malignancy in which monoclonal plasma cells multiply in the bone marrow and monoclonal immunoglobulins are overproduced in older people. Several molecular and cytogenetic advances allow scientists to identify several genetic and chromosomal abnormalities that cause the disease. The comprehension of the pathophysiology of MM requires an understanding of the characteristics of malignant clones and the changes in the bone marrow microenvironment. This study aims to identify the central genes and to determine the key signaling pathways in MM by in silico approaches. A list of 114 differentially expressed genes (DEGs) is important in the prognosis of MM. The DEGs are collected from scientific publications and databases (https://www.ncbi.nlm.nih.gov/). These data are analyzed by Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) software (https://string-db.org/) through the construction of protein-protein interaction (PPI) networks and enrichment analysis of the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, by CytoHubba, AutoAnnotate, Bingo Apps plugins in Cytoscape software (https://cytoscape.org/) and by DAVID database (https://david.ncifcrf.gov/). The analysis of the results shows that there are 7 core genes, including TP53; MYC; CDND1; IL6; UBA52; EZH2, and MDM2. These top genes appear to play a role in the promotion and progression of MM. According to functional enrichment analysis, these genes are mainly involved in the following signaling pathways: Epstein-Barr virus infection, microRNA pathway, PI3K-Akt signaling pathway, and p53 signaling pathway. Several crucial genes, including TP53, MYC, CDND1, IL6, UBA52, EZH2, and MDM2, are significantly correlated with MM, which may exert their role in the onset and evolution of MM.

Meiotic drive in chronic lymphocytic leukemia compared with other malignant blood disorders

The heredity of the malignant blood disorders, leukemias, lymphomas and myeloma, has so far been largely unknown. The present study comprises genealogical investigations of one hundred and twelve Scandinavian families with unrelated parents and two or more cases of malignant blood disease. For comparison, one large family with related family members and three hundred and forty-one cases of malignant blood disease from the Faroese population was included. The inheritance is non-Mendelian, a combination of genomic parental imprinting and feto-maternal microchimerism. There is significantly more segregation in maternal than in paternal lines, predominance of mother-daughter combinations in maternal lines, and father-son combinations in paternal lines. Chronic lymphocytic leukemia is the most frequent diagnosis in the family material, and chronic lymphocytic leukemia has a transgenerational segregation that is unique in that inheritance of susceptibility to chronic lymphocytic leukemia is predominant in males of paternal lines. Male offspring with chronic lymphocytic leukemia in paternal lines have a birth-order effect, which is manifest by the fact that there are significantly more male patients late in the sibling line. In addition, there is contravariation in chronic lymphocytic leukemia, i.e. lower occurrence than expected in relation to other diagnoses, interpreted in such a way that chronic lymphocytic leukemia remains isolated in the pedigree in relation to other diagnoses of malignant blood disease. Another non-Mendelian function appears in the form of anticipation, i.e. increased intensity of malignancy down through the generations and a lower age at onset of disease than otherwise seen in cases from the Cancer Registers, in acute lymphoblastic leukemia, for example. It is discussed that this non-Mendelian segregation seems to spread the susceptibility genes depending on the gender of the parents and not equally to all children in the sibling line, with some remaining unaffected by susceptibility i.e. "healthy and unaffected", due to a birth order effect. In addition, anticipation is regarded as a non-Mendelian mechanism that can amplify, «preserve» these vital susceptibility genes in the family. Perhaps this segregation also results in a sorting of the susceptibility, as the percentage of follicular lymphoma and diffuse large B-cell lymphoma is lower in the family material than in an unselected material. Although leukemias, lymphomas and myelomas are potentially fatal diseases, this non-Mendelian distribution and amplification hardly play any quantitative role in the survival of Homo sapiens, because these diseases mostly occur after fertile age.

Family history of plasma cell disorders is associated with improved survival in MGUS, multiple myeloma, and systemic AL amyloidosis

The association between familial plasma cell disorders (PCD) and prognosis in patients with MGUS, multiple myeloma (MM), and systemic light chain (AL) amyloidosis has not been well described. This study retrospectively reviewed outcomes of 25,423 patients (16,744 MGUS, 6194 MM, 2955 AL amyloidosis). Overall, 2.7% of patients reported having a family member with a PCD (defined as MGUS, MM, or AL amyloidosis). Family history was documented in 94% of MGUS, 92% of MM, and 88% of AL amyloidosis patients. The overall survival was consistently longer in patients with versus without familial PCD (crude hazard ratios: 0.52, 95% CI 0.40-0.67, p < 0.001 for MGUS patients; 0.68, 95% CI 0.57-0.79, p < 0.001 for MM patients; 0.60, 95% CI 0.43-0.84, p = 0.003 for AL patients). This association remained consistent when adjusting for baseline patient and disease characteristics. In MGUS patients, the risk of progression to MM, AL amyloidosis, or a lymphoproliferative disorder was higher in patients with familial PCD when accounting for death as a competing risk (cause-specific HR 1.9, 95% 1.3-2.7, p < 0.001). This is the first study to demonstrate that in a cohort of MGUS, MM, and systemic AL amyloidosis, patients with a PCD family history have an improved overall survival.

A polygenic risk score for multiple myeloma risk prediction

There is overwhelming epidemiologic evidence that the risk of multiple myeloma (MM) has a solid genetic background. Genome-wide association studies (GWAS) have identified 23 risk loci that contribute to the genetic susceptibility of MM, but have low individual penetrance. Combining the SNPs in a polygenic risk score (PRS) is a possible approach to improve their usefulness. Using 2361 MM cases and 1415 controls from the International Multiple Myeloma rESEarch (IMMEnSE) consortium, we computed a weighted and an unweighted PRS. We observed associations with MM risk with OR = 3.44, 95% CI 2.53-4.69, p = 3.55 × 10-15 for the highest vs. lowest quintile of the weighted score, and OR = 3.18, 95% CI 2.1 = 34-4.33, p = 1.62 × 10-13 for the highest vs. lowest quintile of the unweighted score. We found a convincing association of a PRS generated with 23 SNPs and risk of MM. Our work provides additional validation of previously discovered MM risk variants and of their combination into a PRS, which is a first step towards the use of genetics for risk stratification in the general population.

A genetic risk score of alleles related to MGUS interacts with socioeconomic position in a population-based cohort

Environmental, genetic, and social factors are suggested to jointly influence monoclonal gammopathy of undetermined significance (MGUS), a precursor of multiple myeloma. Aim of this study was to investigate interactions between MGUS-related genetic variants and socioeconomic position (SEP) indicators education and income on MGUS in a population-based study. Two different MGUS-related genetic risk allele sum scores (GRS) were calculated based on recent genome-wide meta-analyses. Odds Ratios (OR) were estimated in 4329 participants including 238 MGUS cases to assess associations and multiplicative interaction. The relative excess risk due to interaction (RERI) was calculated to assess additive interaction. Both GRSs were associated with MGUS. A multiplicative interaction between one GRS and education was observed with genetic effects of OR 1.34 (95% CI 1.11–1.62) per risk allele in the highest and OR 1.06 (95% CI 0.86–1.31) in the lowest education group. A RERI of 0.10 (95% CI 0.05–0.14) also indicated additive interaction. Further, additive GRS by income interaction (RERI 0.07; 95% CI 0.01–0.13) for the same GRS was also indicated. Results indicate interaction between MGUS-related genetic risk and SEP. Non-genetic MGUS risk factors more common in higher education groups may influence the expression of MGUS-related genetic variants.

Genetic Analysis of Acid β-Glucosidase in Patients with Multiple Myeloma from Central Taiwan: A Small-Cohort Case-Control Study

Introduction

Multiple myeloma (MM) is an incurable, biologically heterogeneous disease of the plasma cells, associated with older age and is more common in men. Gaucher disease, caused by mutation in acid β-glucosidase (glucocerebrosidase, GBA) gene, has been linked to multiple cancers, especially MM. Pathological accumulation of glucosylceramide and complex glycosphingolipids coupled with chronic inflammation may be the cause of cancer in patients with Gaucher disease. In this study, we hypothesized patients with MM have mutations in the GBA gene and analyzed patients with MM to determine whether they have a higher frequency of GBA variants.

Methods

Twenty-four MM samples were acquired from the Human Biobank, China Medical University Hospital, Taichung, Taiwan. GBA mutations were detected by polymerase chain reaction-directed DNA sequencing.

Results

We found no mutations in the coding regions of GBA in any of the 24 study subjects. However, two single-nucleotide polymorphisms, rs2070679 and rs2361534, were identified. A significant difference was observed between the study and control groups (p = 0.0028) in rs2361534 allele distribution, with the C allele frequency being higher in patients (1/48, 2.1%) than in the control group (5/3030, 0.16%, Taiwan Biobank).

Conclusion

In this study, the sample size was limited and GBA enzyme activity was not measured; therefore, we could not establish a direct correlation between MM and GBA mutations. However, the association of rs2361534 suggests that regions around this single-nucleotide polymorphism may be involved in MM. The relationship between MM and GBA mutations remains unclear. A large sample is required for a detailed analysis of this potential relationship.

Functional dissection of inherited non-coding variation influencing multiple myeloma risk

Thousands of non-coding variants have been associated with increased risk of human diseases, yet the causal variants and their mechanisms-of-action remain obscure. In an integrative study combining massively parallel reporter assays (MPRA), expression analyses (eQTL, meQTL, PCHiC) and chromatin accessibility analyses in primary cells (caQTL), we investigate 1,039 variants associated with multiple myeloma (MM). We demonstrate that MM susceptibility is mediated by gene-regulatory changes in plasma cells and B-cells, and identify putative causal variants at six risk loci (SMARCD3, WAC, ELL2, CDCA7L, CEP120, and PREX1). Notably, three of these variants co-localize with significant plasma cell caQTLs, signaling the presence of causal activity at these precise genomic positions in an endogenous chromosomal context in vivo. Our results provide a systematic functional dissection of risk loci for a hematologic malignancy.

Risk Alleles for Multiple Myeloma Susceptibility in ADME Genes

The cause of multiple myeloma (MM) remains largely unknown. Several pieces of evidence support the involvement of genetic and multiple environmental factors (i.e., chemical agents) in MM onset. The inter-individual variability in the bioactivation, detoxification, and clearance of chemical carcinogens such as asbestos, benzene, and pesticides might increase the MM risk. This inter-individual variability can be explained by the presence of polymorphic variants in absorption, distribution, metabolism, and excretion (ADME) genes. Despite the high relevance of this issue, few studies have focused on the inter-individual variability in ADME genes in MM risk. To identify new MM susceptibility loci, we performed an extended candidate gene approach by comparing high-throughput genotyping data of 1936 markers in 231 ADME genes on 64 MM patients and 59 controls from the CEU population. Differences in genotype and allele frequencies were validated using an internal control group of 35 non-cancer samples from the same geographic area as the patient group. We detected an association between MM risk and ADH1B rs1229984 (OR = 3.78; 95% CI, 1.18–12.13; p = 0.0282), PPARD rs6937483 (OR = 3.27; 95% CI, 1.01–10.56; p = 0.0479), SLC28A1 rs8187737 (OR = 11.33; 95% CI, 1.43–89.59; p = 0.005), SLC28A2 rs1060896 (OR = 6.58; 95% CI, 1.42–30.43; p = 0.0072), SLC29A1 rs8187630 (OR = 3.27; 95% CI, 1.01–10.56; p = 0.0479), and ALDH3A2 rs72547554 (OR = 2.46; 95% CI, 0.64–9.40; p = 0.0293). The prognostic value of these genes in MM was investigated in two public datasets showing that shorter overall survival was associated with low expression of ADH1B and SLC28A1. In conclusion, our proof-of-concept findings provide novel insights into the genetic bases of MM susceptibility.

Targeting Chemokine Receptor CCR1 as a Potential Therapeutic Approach for Multiple Myeloma

Multiple myeloma is an incurable plasma B-cell malignancy with 5-year survival rates approximately 10-30% lower than other hematologic cancers. Treatment options include combination chemotherapy followed by autologous stem cell transplantation. However, not all patients are eligible for autologous stem cell transplantation, and current pharmacological agents are limited in their ability to reduce tumor burden and extend multiple myeloma remission times. The "chemokine network" is comprised of chemokines and their cognate receptors, and is a critical component of the normal bone microenvironment as well as the tumor microenvironment of multiple myeloma. Antagonists targeting chemokine-receptor 1 (CCR1) may provide a novel approach for treating multiple myeloma. In vitro CCR1 antagonists display a high degree of specificity, and in some cases signaling bias. In vivo studies have shown they can reduce tumor burden, minimize osteolytic bone damage, deter metastasis, and limit disease progression in multiple myeloma models. While multiple CCR1 antagonists have entered the drug pipeline, none have entered clinical trials for treatment of multiple myeloma. This review will discuss whether current CCR1 antagonists are a viable treatment option for multiple myeloma, and studies aimed at identifying which CCR1 antagonist(s) are most appropriate for this disease.

Genome Instability in Multiple Myeloma: Facts and Factors

Multiple myeloma (MM) is a malignant neoplasm of terminally differentiated immunoglobulin-producing B lymphocytes called plasma cells. MM is the second most common hematologic malignancy, and it poses a heavy economic and social burden because it remains incurable and confers a profound disability to patients. Despite current progress in MM treatment, the disease invariably recurs, even after the transplantation of autologous hematopoietic stem cells (ASCT). Biological processes leading to a pathological myeloma clone and the mechanisms of further evolution of the disease are far from complete understanding. Genetically, MM is a complex disease that demonstrates a high level of heterogeneity. Myeloma genomes carry numerous genetic changes, including structural genome variations and chromosomal gains and losses, and these changes occur in combinations with point mutations affecting various cellular pathways, including genome maintenance. MM genome instability in its extreme is manifested in mutation kataegis and complex genomic rearrangements: chromothripsis, templated insertions, and chromoplexy. Chemotherapeutic agents used to treat MM add another level of complexity because many of them exacerbate genome instability. Genome abnormalities are driver events and deciphering their mechanisms will help understand the causes of MM and play a pivotal role in developing new therapies.

Prevalence of the GFI1-36N SNP in Multiple Myeloma Patients and Its Impact on the Prognosis

Transcription factor Growth Factor Independence 1 (GFI1) regulates the expression of genes important for survival, proliferation and differentiation of hematopoietic cells. A single nucleotide polymorphism (SNP) variant of GFI1 (GFI1-36N: serine replaced by asparagine at position 36), has a prevalence of 5-7% among healthy Caucasians and 10-15% in patients with myelodysplastic syndrome (MDS) and acute myeloid leukaemia (AML) predisposing GFI-36N carriers to these diseases. Since GFI1 is implicated in B cell maturation and plasma cell (PC) development, we examined its prevalence in patients with multiple myeloma (MM), a haematological malignancy characterized by expansion of clonal PCs. Strikingly, as in MDS and AML, we found that the GFI1-36N had a higher prevalence among MM patients compared to the controls. In subgroup analyses, GFI1-36N correlates to a shorter overall survival of MM patients characterized by the presence of t(4;14) translocation and gain of 1q21 (≤3 copies). MM patients carrying gain of 1q21 (≥3 copies) demonstrated poor progression free survival. Furthermore, gene expression analysis implicated a role for GFI1-36N in epigenetic regulation and metabolism, potentially promoting the initiation and progression of MM.

The Significance of mRNA in the Biology of Multiple Myeloma and Its Clinical Implications

Multiple myeloma (MM) is a genetically complex disease that results from a multistep transformation of normal to malignant plasma cells in the bone marrow. However, the molecular mechanisms responsible for the initiation and heterogeneous evolution of MM remain largely unknown. A fundamental step needed to understand the oncogenesis of MM and its response to therapy is the identification of driver mutations. The introduction of gene expression profiling (GEP) in MM is an important step in elucidating the molecular heterogeneity of MM and its clinical relevance. Since some mutations in myeloma occur in non-coding regions, studies based on the analysis of mRNA provide more comprehensive information on the oncogenic pathways and mechanisms relevant to MM biology. In this review, we discuss the role of gene expression profiling in understanding the biology of multiple myeloma together with the clinical manifestation of the disease, as well as its impact on treatment decisions and future directions.

From Bench to Bedside: The Evolution of Genomics and Its Implications for the Current and Future Management of Multiple Myeloma

ABSTRACT

The summation of 20 years of biological studies and the comprehensive analysis of more than 1000 multiple myeloma genomes with data linked to clinical outcome has enabled an increased understanding of the pathogenesis of multiple myeloma in the context of normal plasma cell biology. This novel data have facilitated the identification of prognostic markers and targets suitable for therapeutic manipulation. The challenge moving forward is to translate this genetic and biological information into the clinic to improve patient care. This review discusses the key data required to achieve this and provides a framework within which to explore the use of response-adapted, biologically targeted, molecularly targeted, and risk-stratified therapeutic approaches to improve the management of patients with multiple myeloma.

Autophagy in Hematological Malignancies: Molecular Aspects in Leukemia and Lymphoma

The organization of the hematopoietic system is dependent on hematopoietic stem cells (HSCs) that are capable of self-renewal and multilineage differentiation to produce different blood cell lines. Autophagy has a central role in energy production and metabolism of the cells during starvation, cellular stress adaption, and removing mechanisms for aged or damaged organelles. The role and importance of autophagy pathways are becoming increasingly recognized in the literature because these pathways can be useful in organizing intracellular circulation, molecular complexes, and organelles to meet the needs of various hematopoietic cells. There is supporting evidence in the literature that autophagy plays an emerging role in the regulation of normal cells and that it also has important features in malignant hematopoiesis. Understanding the molecular details of the autophagy pathway can provide novel methods for more effective treatment of patients with leukemia. Overall, our review will emphasize the role of autophagy and its different aspects in hematological malignant neoplasms.

Epidemiology, genetics and treatment of multiple myeloma and precursor diseases

Multiple myeloma (MM) is a hematological malignancy caused by the clonal expansion of plasma cells. The incidence of MM worldwide is increasing with greater than 140 000 people being diagnosed with MM per year. Whereas 5-year survival after a diagnosis of MM has improved from 28% in 1975 to 56% in 2012, the disease remains essentially incurable. In this review, we summarize our current understanding of MM including its epidemiology, genetics and biology. We will also provide an overview of MM management that has led to improvements in survival, including recent changes to diagnosis and therapies. Areas of unmet need include the management of patients with high-risk MM, those with reduced performance status and those refractory to standard therapies. Ongoing research into the biology and early detection of MM as well as the development of novel therapies, such as immunotherapies, has the potential to influence MM practice in the future.

FDG-PET/CT Variants and Pitfalls in Haematological Malignancies

Hematologic malignancies represent a vast group of hematopoietic and lymphoid cancers that typically involve the blood, the bone marrow, and the lymphatic organs. Due to extensive research and well defined and standardized response criteria, the role of [¹⁸F]FDG-PET/CT is well defined in these malignancies. Never the less, the reliability of visual and quantitative interpretation of PET/CT may be impaired by several factors including inconsistent scanning protocols and image reconstruction methods. Furthermore, the uptake of [¹⁸F]FDG not only reflects tissue glucose consumption by malignant lesions, but also in other situations such as in inflammatory lesions, local and systemic infections, benign tumors, reactive thymic hyperplasia, histiocytic infiltration, among others; or following granulocyte colony stimulating factors therapy, radiation therapy, chemotherapy or surgical interventions, all of which are a potential source of false-positive or negative interpretations. Therefore it is of paramount importance for the Nuclear Medicine Physician to be familiar with, not only the normal distribution of [¹⁸F]FDG in the body, but also with the most frequent findings that may hamper a correct interpretation of the scan, which could ultimately alter the patients management. In this review, we describe these myriad of situations so the interpreting physician can be familiar with them, providing tools for their correct identification and interpretation when possible.

Search for AL amyloidosis risk factors using Mendelian randomization

In amyloid light chain (AL) amyloidosis, amyloid fibrils derived from immunoglobulin light chain are deposited in many organs, interfering with their function. The etiology of AL amyloidosis is poorly understood. Summary data from genome-wide association studies (GWASs) of multiple phenotypes can be exploited by Mendelian randomization (MR) methodology to search for factors influencing AL amyloidosis risk. We performed a 2-sample MR analyzing 72 phenotypes, proxied by 3461 genetic variants, and summary genetic data from a GWAS of 1129 AL amyloidosis cases and 7589 controls. Associations with a Bonferroni-defined significance level were observed for genetically predicted increased monocyte counts (P = 3.8 × 10-4) and the tumor necrosis factor receptor superfamily member 17 (TNFRSF17) gene (P = 3.4 × 10-5). Two other associations with the TNFRSF (members 6 and 19L) reached a nominal significance level. The association between genetically predicted decreased fibrinogen levels may be related to roles of fibrinogen other than blood clotting. be related to its nonhemostatic role. It is plausible that a causal relationship with monocyte concentration could be explained by selection of a light chain-producing clone during progression of monoclonal gammopathy of unknown significance toward AL amyloidosis. Because TNFRSF proteins have key functions in lymphocyte biology, it is entirely plausible that they offer a potential link to AL amyloidosis pathophysiology. Our study provides insight into AL amyloidosis etiology, suggesting high circulating levels of monocytes and TNFRSF proteins as risk factors.

Genetic determinants of multiple myeloma risk within the Wnt/beta-catenin signaling pathway

BACKGROUND

Aberrant Wnt/beta-catenin pathway activation is implicated in Multiple Myeloma (MM) development, but little is known if genetic variants within this pathway contribute to MM susceptibility.

METHODS

We performed a discovery candidate pathway analysis in 269 non-Hispanic white MM cases and 272 controls focusing on 171 variants selected from 26 core genes within the Wnt/beta-catenin pathway. Significant candidate variants (P < 0.05) were selected for validation in internal and external non-Hispanic white populations totaling 818 cases and 1209 controls. We also examined significant variants in non-Hispanic black and Hispanic case/control study populations to identify potential differences by race/ethnicity. Possible biological functions of candidate variants were predicted in silico.

RESULTS

Seven variants were significantly associated with MM risk in non-Hispanic whites in the discovery population, of which LRP6:rs7966410 (OR: 0.57; 95 % CI: 0.38-0.88; P = 9.90 × 10^-3) and LRP6:rs7956971 (OR: 0.64; 95 % CI: 0.44-0.95; P = 0.027) remained significant in the internal and external populations. CSNK1D:rs9901910 replicated among all three racial/ethnic groups, with 2-6 fold increased risk of MM (OR: 2.40; 95 % CI: 1.67-3.45; P = 2.43 × 10^-6 - non-Hispanic white; OR: 6.42; 95 % CI: 2.47-16.7; P = 3.14 × 10^-4 - non-Hispanic black; OR: 4.31; 95 % CI: 1.83-10.1; P = 8.10 × 10^-4 - Hispanic). BTRC:rs7916830 was associated with a significant 37 % and 24 % reduced risk of MM in the non-Hispanic white (95 % CI: 0.49-0.82; P = 5.60 × 10^-4) and non-Hispanic Black (95 % CI: 0.60-0.97; P = 0.028) population, respectively. In silico tools predicted that these loci altered function through via gene regulation.

CONCLUSION

We identified several variants within the Wnt/beta-catenin pathway associated with MM susceptibility. Findings of this study highlight the potential genetic role of Wnt/beta-catenin signaling in MM etiology among a diverse patient population.

Sequencing at lymphoid neoplasm susceptibility loci maps six myeloma risk genes

Inherited genetic risk factors play a role in multiple myeloma (MM), yet considerable missing heritability exists. Rare risk variants at genome-wide association study (GWAS) loci are a new avenue to explore. Pleiotropy between lymphoid neoplasms (LNs) has been suggested in family history and genetic studies, but no studies have interrogated sequencing for pleiotropic genes or rare risk variants. Sequencing genetically enriched cases can help discover rarer variants. We analyzed exome sequencing in familial or early-onset MM cases to identify rare, functionally relevant variants near GWAS loci for a range of LNs. A total of 149 distinct and significant LN GWAS loci have been published. We identified six recurrent, rare, potentially deleterious variants within 5 kb of significant GWAS single nucleotide polymorphisms in 75 MM cases. Mutations were observed in BTNL2, EOMES, TNFRSF13B, IRF8, ACOXL and TSPAN32. All six genes replicated in an independent set of 255 early-onset MM or familial MM or precursor cases. Expansion of our analyses to the full length of these six genes resulted in a list of 39 rare and deleterious variants, seven of which segregated in MM families. Three genes also had significant rare variant burden in 733 sporadic MM cases compared with 935 control individuals: IRF8 (P = 1.0 × 10-6), EOMES (P = 6.0 × 10-6) and BTNL2 (P = 2.1 × 10-3). Together, our results implicate six genes in MM risk, provide support for genetic pleiotropy between LN subtypes and demonstrate the utility of sequencing genetically enriched cases to identify functionally relevant variants near GWAS loci.

Duo Shared Genomic Segment analysis identifies a genome-wide significant risk locus at 18q21.33 in myeloma pedigrees

AIM

High-risk pedigrees (HRPs) are a powerful design to map highly penetrant risk genes. We previously described Shared Genomic Segment (SGS) analysis, a mapping method for single large extended pedigrees that also addresses genetic heterogeneity inherent in complex diseases. SGS identifies shared segregating chromosomal regions that may inherit in only a subset of cases. However, single large pedigrees that are individually powerful (at least 15 meioses between studied cases) are scarce. Here, we expand the SGS strategy to incorporate evidence from two extended HRPs by identifying the same segregating risk locus in both pedigrees and allowing for some relaxation in the size of each HRP.

METHODS

Duo-SGS is a procedure to combine single-pedigree SGS evidence. It implements statistically rigorous duo-pedigree thresholding to determine genome-wide significance levels that account for optimization across pedigree pairs. Single-pedigree SGS identifies optimal segments shared by case subsets at each locus across the genome, with nominal significance assessed empirically. Duo-SGS combines the statistical evidence for SGS segments at the same genomic location in two pedigrees using Fisher's method. One pedigree is paired with all others and the best duo-SGS evidence at each locus across the genome is established. Genome-wide significance thresholds are determined through distribution-fitting and the Theory of Large Deviations. We applied the duoSGS strategy to eleven extended, myeloma HRPs.

RESULTS

We identified one genome-wide significant region at 18q21.33 (0.85 Mb, P = 7.3 × 10-9) which contains one gene, CDH20. Thirteen regions were genome-wide suggestive: 1q42.2, 2p16.1, 3p25.2, 5q21.3, 5q31.1, 6q16.1, 6q26, 7q11.23, 12q24.31, 13q13.3, 18p11.22, 18q22.3 and 19p13.12.

CONCLUSION

Our results provide novel risk loci with segregating evidence from multiple HRPs and offer compelling targets and specific segment carriers to focus a future search for functional variants involved in inherited risk formyeloma.

Genotype-immunophenotype analysis reveals the immunogenomic subtype and prognosis of multiple myeloma

Immune dysfunction plays an important role in tumour development, recurrence, therapeutic responses and overall survival (OS). Multiple myeloma (MM) is a clonal B-cell malignancy which characterized by anti-tumoural immune dysfunction. In this study, we analysed 28 tumour-immune-related pathways and calculated the immune pathway score through published microarray data from the Gene Expression Omnibus (GEO) data portal. A training set of 345 patients and a validation set of 214 patients with primary MM were chosen. We performed least absolute shrinkage and selection operator (LASSO) analysis to identify prognostic factors. Then, we used cluster analysis to divide patients into three immunogenomic subtypes, which named abnormal immune activated type, common type and anti-myeloma immune activated type. Log‑rank tests showed that anti-myeloma immune activated type had the best prognosis and abnormal immune activated type had the shortest OS (P = 0.000) and event-free survival (EFS) (P = 0.000). Multivariate Cox also indicated that the immunogenomic subtype was an independent predictor of OS (P = 0.001) and EFS (P = 0.000). We also analysed the characteristics and the immune-response patterns of different subtypes. Then, we established a mathematical model to classify patients in the validation set. In the validation set, patients with different immunogenomic subtypes also had a significantly different OS (P = 0.001) and EFS (P = 0.005). Our study explored tumour-immune-related pathways at a multi-dimensional level and found the immunogenomic subtype of MM. Potential mechanisms on the genetic level of how tumour-immunity influences the prognosis and therapeutic responses are provided. The immunogenomic subtype may be feasible for deciding clinical treatment in the future.

Familial aggregation of early-onset haematological malignancies

Population-based studies on familial aggregation of haematological malignancies (HM) have rarely focused specifically on early-onset HMs. We estimated standardized incidence ratios (SIR) and cumulative risks of relatives with Hodgkin lymphoma (HL), non-Hodgkin lymphomas (NHL), acute lymphoblastic leukaemia/lymphoma (ALL/LBL) and acute myeloid leukaemia (AML) when index persons and relatives were diagnosed with early-onset HM. A total of 8791 patients aged ≤40 years and diagnosed with primary HM in Finland from 1970 to 2012 were identified from the Finnish Cancer Registry and their 75 774 family members were retrieved from the population registry. SIRs for concordant HMs were elevated among first-degree relatives in all of the most common HMs of children and adolescents and young adults (AYA). The risk was highest among siblings with HL (SIR 9·09, 95% confidence interval 5·55-14·04) and AML (8·29, 1·00-29·96). HL also had the highest cumulative risk for siblings at ≤40 years of age (0·92% vs. 0·11% in the population). In conclusion, significantly elevated SIRs indicate a role of shared aetiological factors in some families, which should be noted in the clinical setting when caring for patients with early-onset HMs.

A meta-analysis of genome-wide association studies of multiple myeloma among men and women of African ancestry

Persons of African ancestry (AA) have a twofold higher risk for multiple myeloma (MM) compared with persons of European ancestry (EA). Genome-wide association studies (GWASs) support a genetic contribution to MM etiology in individuals of EA. Little is known about genetic risk factors for MM in individuals of AA. We performed a meta-analysis of 2 GWASs of MM in 1813 cases and 8871 controls and conducted an admixture mapping scan to identify risk alleles. We fine-mapped the 23 known susceptibility loci to find markers that could better capture MM risk in individuals of AA and constructed a polygenic risk score (PRS) to assess the aggregated effect of known MM risk alleles. In GWAS meta-analysis, we identified 2 suggestive novel loci located at 9p24.3 and 9p13.1 at P < 1 × 10-6; however, no genome-wide significant association was noted. In admixture mapping, we observed a genome-wide significant inverse association between local AA at 2p24.1-23.1 and MM risk in AA individuals. Of the 23 known EA risk variants, 20 showed directional consistency, and 9 replicated at P < .05 in AA individuals. In 8 regions, we identified markers that better capture MM risk in persons with AA. AA individuals with a PRS in the top 10% had a 1.82-fold (95% confidence interval, 1.56-2.11) increased MM risk compared with those with average risk (25%-75%). The strongest functional association was between the risk allele for variant rs56219066 at 5q15 and lower ELL2 expression (P = 5.1 × 10-12). Our study shows that common genetic variation contributes to MM risk in individuals with AA.

Search for multiple myeloma risk factors using Mendelian randomization

The etiology of multiple myeloma (MM) is poorly understood. Summary data from genome-wide association studies (GWASs) of multiple phenotypes can be exploited in a Mendelian randomization (MR) phenome-wide association study (PheWAS) to search for factors influencing MM risk. We performed an MR-PheWAS analyzing 249 phenotypes, proxied by 10 225 genetic variants, and summary genetic data from a GWAS of 7717 MM cases and 29 304 controls. Odds ratios (ORs) per 1 standard deviation increase in each phenotype were estimated under an inverse variance weighted random effects model. A Bonferroni-corrected threshold of P = 2 × 10-4 was considered significant, whereas P < .05 was considered suggestive of an association. Although no significant associations with MM risk were observed among the 249 phenotypes, 28 phenotypes showed evidence suggestive of association, including increased levels of serum vitamin B6 and blood carnitine (P = 1.1 × 10-3) with greater MM risk and ω-3 fatty acids (P = 5.4 × 10-4) with reduced MM risk. A suggestive association between increased telomere length and reduced MM risk was also noted; however, this association was primarily driven by the previously identified risk variant rs10936599 at 3q26 (TERC). Although not statistically significant, increased body mass index was associated with increased risk (OR, 1.10; 95% confidence interval, 0.99-1.22), supporting findings from a previous meta-analysis of prospective observational studies. Our study did not provide evidence supporting any modifiable factors examined as having a major influence on MM risk; however, it provides insight into factors for which the evidence has previously been mixed.

Coinherited genetics of multiple myeloma and its precursor, monoclonal gammopathy of undetermined significance

So far, 23 germline susceptibility loci have been associated with multiple myeloma (MM) risk. It is unclear whether the genetic variation associated with MM susceptibility also predisposes to its precursor, monoclonal gammopathy of undetermined significance (MGUS). Leveraging 2434 MM cases, 754 MGUS cases, and 2 independent sets of controls (2567/879), we investigated potential shared genetic susceptibility of MM and MGUS by (1) performing MM and MGUS genome-wide association studies (GWAS); (2) validating the association of a polygenic risk score (PRS) based on 23 established MM loci (MM-PRS) with risk of MM, and for the first time with MGUS; and (3) examining genetic correlation of MM and MGUS. Heritability and genetic estimates yielded 17% (standard error [SE] ±0.04) and 15% (SE ±0.11) for MM and MGUS risk, respectively, and a 55% (SE ±0.30) genetic correlation. The MM-PRS was associated with risk of MM when assessed continuously (odds ratio [OR], 1.17 per SD; 95% confidence interval [CI], 1.13-1.21) or categorically (OR, 1.70; 95% CI, 1.38-2.09 for highest; OR, 0.71; 95% CI, 0.55-0.90 for lowest compared with middle quintile). The MM-PRS was similarly associated with MGUS (OR, 1.19 per SD; 95% CI, 1.14-1.26 as a continuous measure, OR, 1.77, 95%CI: 1.29-2.43 for highest and OR, 0.70, 95%CI: 0.50-0.98 for lowest compared with middle quintile). MM and MGUS associations did not differ by age, sex, or MM immunoglobulin isotype. We validated a 23-SNP MM-PRS in an independent series of MM cases and provide evidence for its association with MGUS. Our results suggest shared common genetic susceptibility to MM and MGUS.

Germline variants at SOHLH2 influence multiple myeloma risk

Multiple myeloma (MM) is caused by the uncontrolled, clonal expansion of plasma cells. While there is epidemiological evidence for inherited susceptibility, the molecular basis remains incompletely understood. We report a genome-wide association study totalling 5,320 cases and 422,289 controls from four Nordic populations, and find a novel MM risk variant at SOHLH2 at 13q13.3 (risk allele frequency = 3.5%; odds ratio = 1.38; P = 2.2 × 10-14). This gene encodes a transcription factor involved in gametogenesis that is normally only weakly expressed in plasma cells. The association is represented by 14 variants in linkage disequilibrium. Among these, rs75712673 maps to a genomic region with open chromatin in plasma cells, and upregulates SOHLH2 in this cell type. Moreover, rs75712673 influences transcriptional activity in luciferase assays, and shows a chromatin looping interaction with the SOHLH2 promoter. Our work provides novel insight into MM susceptibility.

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.

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.