Integrative network analysis identifies novel drivers of pathogenesis and progression in newly diagnosed multiple myeloma.

Multiple myeloma (MM) is an incurable malignancy of bone marrow plasma cells characterized by wide clinical and molecular heterogeneity. In this study we applied an integrative network biology approach to molecular and clinical data measured from 450 patients with newly diagnosed MM from the MMRF (Multiple Myeloma Research Foundation) CoMMpass study. A novel network model of myeloma (MMNet) was constructed, revealing complex molecular disease patterns and novel associations between clinical traits and genomic markers. Genomic alterations and groups of coexpressed genes correlate with disease stage, tumor clonality and early progression. We validated CDC42BPA and CLEC11A as novel regulators and candidate therapeutic targets of MMSET-related myeloma. We then used MMNet to discover novel genes associated with high-risk myeloma and identified a novel four-gene prognostic signature. We identified new patient classes defined by network features and enriched for clinically relevant genetic events, pathways and deregulated genes. Finally, we demonstrated the ability of deep sequencing techniques to detect relevant structural rearrangements, providing evidence that encourages wider use of such technologies in clinical practice. An integrative network analysis of CoMMpass data identified new insights into multiple myeloma disease biology and provided improved molecular features for diagnosing and stratifying patients, as well as additional molecular targets for therapeutic alternatives.

High somatic mutation and neoantigen burden are correlated with decreased progression-free survival in multiple myeloma.

Tumor-specific mutations can result in immunogenic neoantigens, both of which have been correlated with responsiveness to immune checkpoint inhibitors in highly mutagenic cancers. However, early results of single-agent checkpoint inhibitors in multiple myeloma (MM) have been underwhelming. Therefore, we sought to understand the relationship between mutation and neoantigen landscape of MM patients and responsiveness to therapies. Somatic mutation burden, neoantigen load, and response to therapy were determined using interim data from the MMRF CoMMpass study (NCT01454297) on 664 MM patients. In this population, the mean somatic and missense mutation loads were 405.84(s=608.55) and 63.90(s=95.88) mutations per patient, respectively. There was a positive linear relationship between mutation and neoantigen burdens (R2=0.862). The average predicted neoantigen load was 23.52(s=52.14) neoantigens with an average of 9.40(s=26.97) expressed neoantigens. Survival analysis revealed significantly shorter progression-free survival (PFS) in patients with greater than average somatic missense mutation load (N=163, 0.493 vs 0.726 2-year PFS, P=0.0023) and predicted expressed neoantigen load (N=214, 0.555 vs 0.729 2-year PFS, P=0.0028). This pattern is maintained when stratified by disease stage and cytogenetic abnormalities. Therefore, high mutation and neoantigen load are clinically relevant risk factors that negatively impact survival of MM patients under current standards of care.

Correlation between array-comparative genomic hybridization-defined genomic gains and losses and survival: identification of 1p31-32 deletion as a prognostic factor in myeloma.

In this study, we correlated array-comparative genomic hybridization-defined abnormalities with survival in two different cohorts of patients treated with therapy based on high-dose melphalan with autologous stem-cell transplantation (64 from the Mayo Clinic and 67 from the University of Arkansas Medical School) and identified that several regions of genomic gains and losses were significantly associated with poorer survival. Three noncontiguous survival relevant regions covering 1p31-33 and two noncontiguous regions covering 20p12.3-12.1 were common between the two datasets. The prognostic relevance of these hotspots was validated in an independent cohort using fluorescent in situ hybridization, which showed that 1p31-32 loss is significantly associated with shorter survival (24.5 months versus 40 months, log-rank P-value=0.01), whereas 20p12 loss has a trend toward shorter survival (26.3 months versus 40 months, log-rank P-value=0.06). On multivariate analysis, 1p31-32 loss is an independent prognostic factor. On further analysis, the prognostic impact of 1p31-32 loss is due to shortening of post-relapse survival as there is no impact on complete response rates and progression-free survival.

Rearrangements and amplification of IER3 (IEX-1) represent a novel and recurrent molecular abnormality in myelodysplastic syndromes.

IER3 (formerly IEX-1) encodes a 27-kDa glycoprotein that regulates death receptor-induced apoptosis, interacts with NF-kappaB pathways, and increases expression rapidly in response to cellular stresses such as irradiation. Animal models, gene expression microarray experiments, and functional studies in cell lines have suggested a potential role for IER3 in oncogenesis, but, to date, no abnormalities of IER3 at the DNA level have been reported in patients with neoplasia. Here, we describe breakpoint cloning of a t(6;9)(p21;q34) translocation from a patient with a myelodysplastic syndrome (MDS), facilitated by conversion technology and array-based comparative genomic hybridization, which revealed a rearrangement translocating the IER3 coding region away from critical flanking/regulatory elements and to a transcript-poor chromosomal region, markedly decreasing expression. Using split-signal and locus-specific fluorescence in situ hybridization (FISH) probes, we analyzed 204 patients with diverse hematological malignancies accompanied by clonal chromosome 6p21 abnormalities, and found 8 additional patients with MDS with IER3 rearrangements (translocations or amplification). Although FISH studies on 157 additional samples from patients with MDS and a normal-karyotype were unrevealing, and sequencing the IER3 coding and proximal promoter regions of 74 MDS patients disclosed no point mutations, reverse transcription-PCR results suggested that dysregulated expression of IER3 is common in MDS (61% >4-fold increase or decrease in expression with decreased expression primarily in early MDS and increased expression primarily in later MDS progressing toward leukemia), consistent with findings in previous microarray experiments. These data support involvement of IER3 in the pathobiology of MDS.

Personality and sociodemographic influences on adolescents' substance use: a path analysis.

Five hundred and seven students 14- to 16-years-old gave self-report responses to a substance use questionnaire, the Norwicki-Strickland Locus of Control Scale, and The Piers-Harris Children's Self-Concept Scale. Path analysis revealed that the influence of peers' and parents' use and norms on adolescents' alcohol and tobacco use is mediated by personality, age, sex, and social status effects. Adolescents with external locus of control or low self-esteem "behavior" were more influenced by their peers to smoke. Younger students and girls were more influenced by parental norms than were older students and boys, but only for alcohol and not for tobacco use. Other findings were that girls were not as strongly influenced by their own norms and that girls and low social status adolescents were more influenced by their friend's smoking and drinking. The peers' findings are discussed in relation to substance prevention strategies.

Peer and parental influences on adolescents' substance use: a path analysis.

Five hundred and seven 14-to-16-year-old students gave self-report responses to a substance use questionnaire. The questionnaire assessed adolescents' use, preferences, and norms and also their perceptions of their parents' and peers' use and norms in relation to alcohol, tobacco, and tea/coffee. Path analysis revealed that adolescents' internalization of parental and peer pressures is a stronger predictor of substance use than are direct effects. Internalized effects occur by means of preferences rather than norms, and peer pressure is predominantly through modeling behavior, whereas parental influence is through perceived normative standards. Peers' influence is stronger in relation to tobacco use, parental influence is stronger in relation to tea/coffee use, and both are equally important in relation to alcohol use. These findings are discussed in relation to preventive strategies.