The location of the t(4;14) translocation breakpoint within the NSD2 gene identifies a subset of patients with high-risk NDMM.

Although translocation events between chromosome 4 (NSD2 gene) and chromosome 14 (immunoglobulin heavy chain [IgH] locus) (t(4;14)) is considered high risk in newly diagnosed multiple myeloma (NDMM), only ∼30% to 40% of t(4;14) patients are clinically high risk. We generated and compared a large whole genome sequencing (WGS) and transcriptome (RNA sequencing) from 258 t(4;14) (n = 153 discovery, n = 105 replication) and 183 non-t(4;14) NDMM patients with associated clinical data. A landmark survival analysis indicated only ∼25% of t(4;14) patients had an overall survival (OS) <24 months, and a comparative analysis of the patient subgroups identified biomarkers associated with this poor outcome, including translocation breakpoints located in the NSD2 gene and expression of IgH-NSD2 fusion transcripts. Three breakpoints were identified and are designated as: "no-disruption" (upstream of NSD2), "early-disruption" (in the 5' UTR), and "late-disruption" (within the NSD2 gene). Our results show a significant difference in OS based on the location of DNA breakpoints (median OS 28.6 "late-disruption" vs 59.2 "early disruption" vs 75.1 months "no disruption"). These findings have been replicated in an independent replication dataset. Also, univariate and multivariate analysis suggest high-risk markers such as del17p, 1p independently contribute to poor outcome in t(4;14) MM patients.

Whole-genome analysis identifies novel drivers and high-risk double-hit events in relapsed/refractory myeloma.

Large-scale analyses of genomic data from patients with newly diagnosed multiple myeloma (ndMM) have been undertaken, however, large-scale analysis of relapsed/refractory MM (rrMM) has not been performed. We hypothesize that somatic variants chronicle the therapeutic exposures and clonal structure of myeloma from ndMM to rrMM stages. We generated whole-genome sequencing (WGS) data from 418 tumors (386 patients) derived from 6 rrMM clinical trials and compared them with WGS from 198 unrelated patients with ndMM in a population-based case-control fashion. We identified significantly enriched events at the rrMM stage, including drivers (DUOX2, EZH2, TP53), biallelic inactivation (TP53), noncoding mutations in bona fide drivers (TP53BP1, BLM), copy number aberrations (CNAs; 1qGain, 17pLOH), and double-hit events (Amp1q-ISS3, 1qGain-17p loss-of-heterozygosity). Mutational signature analysis identified a subclonal defective mismatch repair signature enriched in rrMM and highly active in high mutation burden tumors, a likely feature of therapy-associated expanding subclones. Further analysis focused on the association of genomic aberrations enriched at different stages of resistance to immunomodulatory agent (IMiD)-based therapy. This analysis revealed that TP53, DUOX2, 1qGain, and 17p loss-of-heterozygosity increased in prevalence from ndMM to lenalidomide resistant (LENR) to pomalidomide resistant (POMR) stages, whereas enrichment of MAML3 along with immunoglobulin lambda (IGL) and MYC translocations distinguished POM from the LEN subgroup. Genomic drivers associated with rrMM are those that confer clonal selective advantage under therapeutic pressure. Their role in therapy evasion should be further evaluated in longitudinal patient samples, to confirm these associations with the evolution of clinical resistance and to identify molecular subsets of rrMM for the development of targeted therapies.

Loss of COP9 signalosome genes at 2q37 is associated with IMiD resistance in multiple myeloma.

The acquisition of a multidrug refractory state is a major cause of mortality in myeloma. Myeloma drugs that target the cereblon (CRBN) protein include widely used immunomodulatory drugs (IMiDs), and newer CRBN E3 ligase modulator drugs (CELMoDs), in clinical trials. CRBN genetic disruption causes resistance and poor outcomes with IMiDs. Here, we investigate alternative genomic associations of IMiD resistance, using large whole-genome sequencing patient datasets (n = 522 cases) at newly diagnosed, lenalidomide (LEN)-refractory and lenalidomide-then-pomalidomide (LEN-then-POM)-refractory timepoints. Selecting gene targets reproducibly identified by published CRISPR/shRNA IMiD resistance screens, we found little evidence of genetic disruption by mutation associated with IMiD resistance. However, we identified a chromosome region, 2q37, containing COP9 signalosome members COPS7B and COPS8, copy loss of which significantly enriches between newly diagnosed (incidence 5.5%), LEN-refractory (10.0%), and LEN-then-POM-refractory states (16.4%), and may adversely affect outcomes when clonal fraction is high. In a separate dataset (50 patients) with sequential samples taken throughout treatment, we identified acquisition of 2q37 loss in 16% cases with IMiD exposure, but none in cases without IMiD exposure. The COP9 signalosome is essential for maintenance of the CUL4-DDB1-CRBN E3 ubiquitin ligase. This region may represent a novel marker of IMiD resistance with clinical utility.

Multiple cereblon genetic changes are associated with acquired resistance to lenalidomide or pomalidomide in multiple myeloma.

Emergence of drug resistance to all available therapies is the major challenge to improving survival in myeloma. Cereblon (CRBN) is the essential binding protein of the widely used immunomodulatory drugs (IMiDs) and novel CRBN E3 ligase modulator drugs (CELMoDs) in myeloma, as well as certain proteolysis targeting chimeras (PROTACs), in development for a range of diseases. Using whole-genome sequencing (WGS) data from 455 patients and RNA sequencing (RNASeq) data from 655 patients, including newly diagnosed (WGS, n = 198; RNASeq, n = 437), lenalidomide (LEN)-refractory (WGS, n = 203; RNASeq, n = 176), and pomalidomide (POM)-refractory cohorts (WGS, n = 54; RNASeq, n = 42), we found incremental increases in the frequency of 3 CRBN aberrations, namely point mutations, copy losses/structural variations, and a specific variant transcript (exon 10 spliced), with progressive IMiD exposure, until almost one-third of patients had CBRN alterations by the time they were POM refractory. We found all 3 CRBN aberrations were associated with inferior outcomes to POM in those already refractory to LEN, including those with gene copy losses and structural variations, a finding not previously described. This represents the first comprehensive analysis and largest data set of CBRN alterations in myeloma patients as they progress through therapy. It will help inform patient selection for sequential therapies with CRBN-targeting drugs.

Leveraging gene expression subgroups to classify DLBCL patients and select for clinical benefit from a novel agent.

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, commonly described by cell-of-origin (COO) molecular subtypes. We sought to identify novel patient subgroups through an unsupervised analysis of a large public dataset of gene expression profiles from newly diagnosed de novo DLBCL patients, yielding 2 biologically distinct subgroups characterized by differences in the tumor microenvironment. Pathway analysis and immune deconvolution algorithms identified higher B-cell content and a strong proliferative signal in subgroup A and enriched T-cell, macrophage, and immune/inflammatory signals in subgroup B, reflecting similar biology to published DLBCL stratification research. A gene expression classifier, featuring 26 gene expression scores, was derived from the public dataset to discriminate subgroup A (classifier-negative, immune-low) and subgroup B (classifier-positive, immune-high) patients. Subsequent application to an independent series of diagnostic biopsies replicated the subgroups, with immune cell composition confirmed via immunohistochemistry. Avadomide, a CRL4CRBN E3 ubiquitin ligase modulator, demonstrated clinical activity in relapsed/refractory DLBCL patients, independent of COO subtypes. Given the immunomodulatory activity of avadomide and the need for a patient-selection strategy, we applied the gene expression classifier to pretreatment biopsies from relapsed/refractory DLBCL patients receiving avadomide (NCT01421524). Classifier-positive patients exhibited an enrichment in response rate and progression-free survival of 44% and 6.2 months vs 19% and 1.6 months for classifier-negative patients (hazard ratio, 0.49; 95% confidence interval, 0.280-0.86; P = .0096). The classifier was not prognostic for rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone or salvage immunochemotherapy. The classifier described here discriminates DLBCL tumors based on tumor and nontumor composition and has potential utility to enrich for clinical response to immunomodulatory agents, including avadomide.

High subclonal fraction of 17p deletion is associated with poor prognosis in multiple myeloma.

Deletions of chromosome 17p (del17p) that span the TP53 gene are associated with poor outcome in multiple myeloma (MM), but the prognostic value of del17p cancer clonal fraction (CCF) remains unclear. We applied uniform cytogenetic assessments in a large cohort of newly diagnosed MM (NDMM) patients carrying varying levels of del17p. Incremental CCF change was associated with shorter survival, and a robust CCF threshold of 0.55 was established in discovery and replication data sets. After stratification on the 0.55-CCF threshold, high-risk patients had statistically significantly poorer outcomes compared with low-risk patients (median progression-free survival [PFS] and overall survival [OS], 14 and 32 vs 23.1 and 76.2 months, respectively). Analyses of a third data set comprising whole-exome sequencing data from NDMM patients identified presence of TP53 deletions/mutations as a necessary requirement for high-risk stratification in addition to exceeding the del17p CCF threshold. Meta-analysis conducted across 3 data sets confirmed the robustness of the CCF threshold for PFS and OS. Our analyses demonstrate the feasibility of fluorescence in situ hybridization- and sequencing-based methods to identify TP53 deletions, estimate CCF, and establish that both CCF threshold of 0.55 and presence of TP53 deletion are necessary to identify del17p-carrying NDMM patients with poor prognosis.

Identification of novel mutational drivers reveals oncogene dependencies in multiple myeloma.

Understanding the profile of oncogene and tumor suppressor gene mutations with their interactions and impact on the prognosis of multiple myeloma (MM) can improve the definition of disease subsets and identify pathways important in disease pathobiology. Using integrated genomics of 1273 newly diagnosed patients with MM, we identified 63 driver genes, some of which are novel, including IDH1, IDH2, HUWE1, KLHL6, and PTPN11 Oncogene mutations are significantly more clonal than tumor suppressor mutations, indicating they may exert a bigger selective pressure. Patients with more driver gene abnormalities are associated with worse outcomes, as are identified mechanisms of genomic instability. Oncogenic dependencies were identified between mutations in driver genes, common regions of copy number change, and primary translocation and hyperdiploidy events. These dependencies included associations with t(4;14) and mutations in FGFR3, DIS3, and PRKD2; t(11;14) with mutations in CCND1 and IRF4; t(14;16) with mutations in MAF, BRAF, DIS3, and ATM; and hyperdiploidy with gain 11q, mutations in FAM46C, and MYC rearrangements. These associations indicate that the genomic landscape of myeloma is predetermined by the primary events upon which further dependencies are built, giving rise to a nonrandom accumulation of genetic hits. Understanding these dependencies may elucidate potential evolutionary patterns and lead to better treatment regimens.

Laquinimod arrests experimental autoimmune encephalomyelitis by activating the aryl hydrocarbon receptor.

Laquinimod is an oral drug currently being evaluated for the treatment of relapsing, remitting, and primary progressive multiple sclerosis and Huntington's disease. Laquinimod exerts beneficial activities on both the peripheral immune system and the CNS with distinctive changes in CNS resident cell populations, especially astrocytes and microglia. Analysis of genome-wide expression data revealed activation of the aryl hydrocarbon receptor (AhR) pathway in laquinimod-treated mice. The AhR pathway modulates the differentiation and function of several cell populations, many of which play an important role in neuroinflammation. We therefore tested the consequences of AhR activation in myelin oligodendrocyte glycoprotein (MOG)-induced experimental autoimmune encephalomyelitis (EAE) using AhR knockout mice. We demonstrate that the pronounced effect of laquinimod on clinical score, CNS inflammation, and demyelination in EAE was abolished in AhR-/- mice. Furthermore, using bone marrow chimeras we show that deletion of AhR in the immune system fully abrogates, whereas deletion within the CNS partially abrogates the effect of laquinimod in EAE. These data strongly support the idea that AhR is necessary for the efficacy of laquinimod in EAE and that laquinimod may represent a first-in-class drug targeting AhR for the treatment of multiple sclerosis and other neurodegenerative diseases.

Pridopidine activates neuroprotective pathways impaired in Huntington Disease.

Pridopidine has demonstrated improvement in Huntington Disease (HD) motor symptoms as measured by secondary endpoints in clinical trials. Originally described as a dopamine stabilizer, this mechanism is insufficient to explain the clinical and preclinical effects of pridopidine. This study therefore explored pridopidine's potential mechanisms of action. The effect of pridopidine versus sham treatment on genome-wide expression profiling in the rat striatum was analysed and compared to the pathological expression profile in Q175 knock-in (Q175 KI) vs Q25 WT mouse models. A broad, unbiased pathway analysis was conducted, followed by testing the enrichment of relevant pathways. Pridopidine upregulated the BDNF pathway (P = 1.73E-10), and its effect on BDNF secretion was sigma 1 receptor (S1R) dependent. Many of the same genes were independently found to be downregulated in Q175 KI mice compared to WT (5.2e-7 < P < 0.04). In addition, pridopidine treatment upregulated the glucocorticoid receptor (GR) response, D1R-associated genes and the AKT/PI3K pathway (P = 1E-10, P = 0.001, P = 0.004, respectively). Pridopidine upregulates expression of BDNF, D1R, GR and AKT/PI3K pathways, known to promote neuronal plasticity and survival, as well as reported to demonstrate therapeutic benefit in HD animal models. Activation of S1R, necessary for its effect on the BDNF pathway, represents a core component of the mode of action of pridopidine. Since the newly identified pathways are downregulated in neurodegenerative diseases, including HD, these findings suggest that pridopidine may exert neuroprotective effects beyond its role in alleviating some symptoms of HD.

Genetic variants synthesize to produce paneth cell phenotypes that define subtypes of Crohn's disease.

BACKGROUND & AIMS: Genetic susceptibility loci for Crohn's disease (CD) are numerous, complex, and likely interact with undefined components of the environment. It has been a challenge to link the effects of particular loci to phenotypes of cells associated with pathogenesis of CD, such as Paneth cells. We investigated whether specific phenotypes of Paneth cells associated with particular genetic susceptibility loci can be used to define specific subtypes of CD. METHODS: We performed a retrospective analysis of 119 resection specimens collected from patients with CD at 2 separate medical centers. Paneth cell phenotypes were classified as normal or abnormal (with disordered, diminished, diffuse, or excluded granule phenotypes) based on lysozyme-positive secretory granule morphology. To uncover the molecular basis of the Paneth cell phenotypes, we developed methods to determine transcriptional profiles from whole-thickness and laser-capture microdissected, formalin-fixed, paraffin-embedded tissue sections. RESULTS: The proportion of abnormal Paneth cells was associated with the number of CD-associated NOD2 risk alleles. The cumulative number of NOD2 and ATG16L1 risk alleles had an additive effect on the proportion of abnormal Paneth cells. Unsupervised clustering analysis of demographic and Paneth cell data divided patients into 2 principal subgroups, defined by high and low proportions of abnormal Paneth cells. The disordered and diffuse abnormal Paneth cell phenotypes were associated with an altered transcriptional signature of immune system activation. We observed an inverse correlation between abnormal Paneth cells and presence of granuloma. In addition, high proportions of abnormal Paneth cells were associated with shorter time to disease recurrence after surgery. CONCLUSIONS: Histologic analysis of Paneth cell phenotypes can be used to divide patients with CD into subgroups with distinct pathognomonic and clinical features.

Genome-wide siRNA screen for mediators of NF-κB activation.

Although canonical NFκB is frequently critical for cell proliferation, survival, or differentiation, NFκB hyperactivation can cause malignant, inflammatory, or autoimmune disorders. Despite intensive study, mammalian NFκB pathway loss-of-function RNAi analyses have been limited to specific protein classes. We therefore undertook a human genome-wide siRNA screen for novel NFκB activation pathway components. Using an Epstein Barr virus latent membrane protein (LMP1) mutant, the transcriptional effects of which are canonical NFκB-dependent, we identified 155 proteins significantly and substantially important for NFκB activation in HEK293 cells. These proteins included many kinases, phosphatases, ubiquitin ligases, and deubiquinating enzymes not previously known to be important for NFκB activation. Relevance to other canonical NFκB pathways was extended by finding that 118 of the 155 LMP1 NF-κB activation pathway components were similarly important for IL-1ß-, and 79 for TNFα-mediated NFκB activation in the same cells. MAP3K8, PIM3, and six other enzymes were uniquely relevant to LMP1-mediated NFκB activation. Most novel pathway components functioned upstream of IκB kinase complex (IKK) activation. Robust siRNA knockdown effects were confirmed for all mRNAs or proteins tested. Although multiple ZC3H-family proteins negatively regulate NFκB, ZC3H13 and ZC3H18 were activation pathway components. ZC3H13 was critical for LMP1, TNFα, and IL-1ß NFκB-dependent transcription, but not for IKK activation, whereas ZC3H18 was critical for IKK activation. Down-modulators of LMP1 mediated NFκB activation were also identified. These experiments identify multiple targets to inhibit or stimulate LMP1-, IL-1ß-, or TNFα-mediated canonical NFκB activation.

Transcriptomic classification of diffuse large B-cell lymphoma identifies a high-risk activated B-cell-like subpopulation with targetable MYC dysregulation.

Immunochemotherapy has been the mainstay of treatment for newly diagnosed diffuse large B-cell lymphoma (ndDLBCL) yet is inadequate for many patients. In this work, we perform unsupervised clustering on transcriptomic features from a large cohort of ndDLBCL patients and identify seven clusters, one called A7 with poor prognosis, and develop a classifier to identify these clusters in independent ndDLBCL cohorts. This high-risk cluster is enriched for activated B-cell cell-of-origin, low immune infiltration, high MYC expression, and copy number aberrations. We compare and contrast our methodology with recent DLBCL classifiers to contextualize our clusters and show improved prognostic utility. Finally, using pre-clinical models, we demonstrate a mechanistic rationale for IKZF1/3 degraders such as lenalidomide to overcome the low immune infiltration phenotype of A7 by inducing T-cell trafficking into tumors and upregulating MHC I and II on tumor cells, and demonstrate that TCF4 is an important regulator of MYC-related biology in A7.

Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art.

BACKGROUND: RNA molecules play diverse functional and structural roles in cells. They function as messengers for transferring genetic information from DNA to proteins, as the primary genetic material in many viruses, as catalysts (ribozymes) important for protein synthesis and RNA processing, and as essential and ubiquitous regulators of gene expression in living organisms. Many of these functions depend on precisely orchestrated interactions between RNA molecules and specific proteins in cells. Understanding the molecular mechanisms by which proteins recognize and bind RNA is essential for comprehending the functional implications of these interactions, but the recognition 'code' that mediates interactions between proteins and RNA is not yet understood. Success in deciphering this code would dramatically impact the development of new therapeutic strategies for intervening in devastating diseases such as AIDS and cancer. Because of the high cost of experimental determination of protein-RNA interfaces, there is an increasing reliance on statistical machine learning methods for training predictors of RNA-binding residues in proteins. However, because of differences in the choice of datasets, performance measures, and data representations used, it has been difficult to obtain an accurate assessment of the current state of the art in protein-RNA interface prediction. RESULTS: We provide a review of published approaches for predicting RNA-binding residues in proteins and a systematic comparison and critical assessment of protein-RNA interface residue predictors trained using these approaches on three carefully curated non-redundant datasets. We directly compare two widely used machine learning algorithms (Naïve Bayes (NB) and Support Vector Machine (SVM)) using three different data representations in which features are encoded using either sequence- or structure-based windows. Our results show that (i) Sequence-based classifiers that use a position-specific scoring matrix (PSSM)-based representation (PSSMSeq) outperform those that use an amino acid identity based representation (IDSeq) or a smoothed PSSM (SmoPSSMSeq); (ii) Structure-based classifiers that use smoothed PSSM representation (SmoPSSMStr) outperform those that use PSSM (PSSMStr) as well as sequence identity based representation (IDStr). PSSMSeq classifiers, when tested on an independent test set of 44 proteins, achieve performance that is comparable to that of three state-of-the-art structure-based predictors (including those that exploit geometric features) in terms of Matthews Correlation Coefficient (MCC), although the structure-based methods achieve substantially higher Specificity (albeit at the expense of Sensitivity) compared to sequence-based methods. We also find that the expected performance of the classifiers on a residue level can be markedly different from that on a protein level. Our experiments show that the classifiers trained on three different non-redundant protein-RNA interface datasets achieve comparable cross-validation performance. However, we find that the results are significantly affected by differences in the distance threshold used to define interface residues. CONCLUSIONS: Our results demonstrate that protein-RNA interface residue predictors that use a PSSM-based encoding of sequence windows outperform classifiers that use other encodings of sequence windows. While structure-based methods that exploit geometric features can yield significant increases in the Specificity of protein-RNA interface residue predictions, such increases are offset by decreases in Sensitivity. These results underscore the importance of comparing alternative methods using rigorous statistical procedures, multiple performance measures, and datasets that are constructed based on several alternative definitions of interface residues and redundancy cutoffs as well as including evaluations on independent test sets into the comparisons.

Multiple Myeloma Patient Tumors With High Levels of Cereblon Exon-10 Deletion Splice Variant Upregulate Clinically Targetable Pro-Inflammatory Cytokine Pathways.

Immunomodulatory drugs (IMiDs), including lenalidomide and pomalidomide, are used in the routine treatment for multiple myeloma (MM) patients. Cereblon (CRBN) is the direct molecular target of IMiDs. While CRBN is not an essential gene for MM cell proliferation, the frequency of CRBN genetic aberrations, including mutation, copy number loss, and exon-10 (which includes a portion of the IMiD-binding domain) splicing, have been reported to incrementally increase in later-line patients. CRBN exon-10 splicing has also been shown to be associated with decreased progression-free survival in both newly diagnosed and relapsed refractory MM patients. Although we did not find significant general splicing defects among patients with CRBN exon-10 splice variant when compared to those expressing the full-length transcript, we identified upregulated TNFA signaling via NFKB, inflammatory response, and IL-10 signaling pathways in patients with exon-10 splice variant across various data sets-all potentially promoting tumor growth via chronic growth signals. We examined master regulators that mediate transcriptional programs in CRBN exon-10 splice variant patients and identified BATF, EZH2, and IKZF1 as the key candidates across the four data sets. Upregulated downstream targets of BATF, EZH2, and IKZF1 are components of TNFA signaling via NFKB, IL2/STAT5 signaling pathways, and IFNG response pathways. Previously, BATF-mediated transcriptional regulation was associated with venetoclax sensitivity in MM. Interestingly, we found that an EZH2 sensitivity gene expression signature also correlated with high BATF or venetoclax sensitivity scores in these tumors. Together, these data provide a rationale for investigating EZH2 inhibitors or venetoclax in combination with the next generation CRBN-targeting agents, such as CELMoDs, for patients overexpressing the CRBN exon-10 splice variant.

Interactome of Aiolos/Ikaros Reveals Combination Rationale of Cereblon Modulators with HDAC Inhibitors in DLBCL.

PURPOSE: Cereblon (CRBN), a substrate receptor of the E3 ubiquitin ligase complex CRL4CRBN, is the target of the small molecules lenalidomide and avadomide. Upon binding of the drugs, Aiolos and Ikaros are recruited to the E3 ligase, ubiquitylated, and subsequently degraded. In diffuse large B-cell lymphoma (DLBCL) cells, Aiolos and Ikaros are direct transcriptional repressors of interferon-stimulated genes (ISG) and degradation of these substrates results in increased ISG protein levels resulting in decreased proliferation and apoptosis. Herein, we aimed to uncover the mechanism(s) Aiolos and Ikaros use to repress ISG transcription and provide a mechanistic rationale for a combination strategy to enhance cell autonomous activities of CRBN modulators (CELMoD). EXPERIMENTAL DESIGN: We conducted paired RNA sequencing with histone modification and Aiolos/Ikaros chromatin immunoprecipitation sequencing to identify genes regulated by these transcription factors and to elucidate correlations to drug sensitivity. We confirmed Aiolos/Ikaros mediated transcriptional complex formation in DLBCL patient samples including those treated with avadomide. RESULTS: In DLBCL, the repression of ISG transcription is accomplished in part through recruitment of large transcriptional complexes such as the nucleosome remodeling and deacetylase, which modify the chromatin landscape of these promoters. A rational combination approach of avadomide with a specific histone deacetylase inhibitor leads to a significant increase in ISG transcription compared with either single agent, and synergistic antiproliferative activity in DLBCL cell lines. CONCLUSIONS: Our results provide a novel role for lineage factors Aiolos and Ikaros in DLBCL as well as further insight into the mechanism(s) of Aiolos and Ikaros-mediated transcriptional repression and unique therapeutic combination strategies.

Exploration of the relationship between topology and designability of conformations.

Protein structures are evolutionarily more conserved than sequences, and sequences with very low sequence identity frequently share the same fold. This leads to the concept of protein designability. Some folds are more designable and lots of sequences can assume that fold. Elucidating the relationship between protein sequence and the three-dimensional (3D) structure that the sequence folds into is an important problem in computational structural biology. Lattice models have been utilized in numerous studies to model protein folds and predict the designability of certain folds. In this study, all possible compact conformations within a set of two-dimensional and 3D lattice spaces are explored. Complementary interaction graphs are then generated for each conformation and are described using a set of graph features. The full HP sequence space for each lattice model is generated and contact energies are calculated by threading each sequence onto all the possible conformations. Unique conformation giving minimum energy is identified for each sequence and the number of sequences folding to each conformation (designability) is obtained. Machine learning algorithms are used to predict the designability of each conformation. We find that the highly designable structures can be distinguished from other non-designable conformations based on certain graphical geometric features of the interactions. This finding confirms the fact that the topology of a conformation is an important determinant of the extent of its designability and suggests that the interactions themselves are important for determining the designability.

Integrative multi-omics identifies high risk multiple myeloma subgroup associated with significant DNA loss and dysregulated DNA repair and cell cycle pathways.

BACKGROUND: Despite significant therapeutic advances in improving lives of multiple myeloma (MM) patients, it remains mostly incurable, with patients ultimately becoming refractory to therapies. MM is a genetically heterogeneous disease and therapeutic resistance is driven by a complex interplay of disease pathobiology and mechanisms of drug resistance. We applied a multi-omics strategy using tumor-derived gene expression, single nucleotide variant, copy number variant, and structural variant profiles to investigate molecular subgroups in 514 newly diagnosed MM (NDMM) samples and identified 12 molecularly defined MM subgroups (MDMS1-12) with distinct genomic and transcriptomic features. RESULTS: Our integrative approach let us identify NDMM subgroups with transversal profiles to previously described ones, based on single data types, which shows the impact of this approach for disease stratification. One key novel subgroup is our MDMS8, associated with poor clinical outcome [median overall survival, 38 months (global log-rank p-value < 1 × 10-6)], which uniquely presents a broad genomic loss (> 9% of entire genome, t-test p value < 1e-5) driving dysregulation of various transcriptional programs affecting DNA repair and cell cycle/mitotic processes. This subgroup was validated on multiple independent datasets, and a master regulator analyses identified transcription factors controlling MDMS8 transcriptomic profile, including CKS1B and PRKDC among others, which are regulators of the DNA repair and cell cycle pathways. CONCLUSION: Using multi-omics unsupervised clustering we were able to discover a new high-risk multiple myeloma patient segment. This high-risk group presents diverse previously known genetic markers, but also a new characteristic defined by accumulation of genomic loss which seems to drive transcriptional dysregulation of cell cycle, DNA repair and DNA damage. Finally, our work identified various master regulators, including E2F2 and CKS1B as the genes controlling these key biological pathways.

Detection of gene orthology from gene co-expression and protein interaction networks.

BACKGROUND: Ortholog detection methods present a powerful approach for finding genes that participate in similar biological processes across different organisms, extending our understanding of interactions between genes across different pathways, and understanding the evolution of gene families. RESULTS: We exploit features derived from the alignment of protein-protein interaction networks and gene-coexpression networks to reconstruct KEGG orthologs for Drosophila melanogaster, Saccharomyces cerevisiae, Mus musculus and Homo sapiens protein-protein interaction networks extracted from the DIP repository and Mus musculus and Homo sapiens and Sus scrofa gene coexpression networks extracted from NCBI's Gene Expression Omnibus using the decision tree, Naive-Bayes and Support Vector Machine classification algorithms. CONCLUSIONS: The performance of our classifiers in reconstructing KEGG orthologs is compared against a basic reciprocal BLAST hit approach. We provide implementations of the resulting algorithms as part of BiNA, an open source biomolecular network alignment toolkit.

Prognosis, Biology, and Targeting of TP53 Dysregulation in Multiple Myeloma.

Multiple myeloma (MM) is the second most common hematological cancer and is characterized by genetic features including translocations, chromosomal copy number aberrations, and mutations in key oncogene and tumor suppressor genes. Dysregulation of the tumor suppressor TP53 is important in the pathogenesis of many cancers, including MM. In newly-diagnosed MM patients, TP53 dysregulation occurs in three subsets: monoallelic deletion as part of deletion of chromosome 17p (del17p) (~8%), monoallelic mutations (~6%), and biallelic inactivation (~4%). Del17p is an established high-risk feature in MM and is included in current disease staging criteria. Biallelic inactivation and mutation have also been reported in MM patients but are not yet included in disease staging criteria for high-risk disease. Emerging clinical and genomics data suggest that the biology of high-risk disease is complex, and so far, traditional drug development efforts to target dysregulated TP53 have not been successful. Here we review the TP53 dysregulation literature in cancer and in MM, including the three segments of TP53 dysregulation observed in MM patients. We propose a reverse translational approach to identify novel targets and disease drivers from TP53 dysregulated patients to address the unmet medical need in this setting.

Multiple Myeloma DREAM Challenge reveals epigenetic regulator PHF19 as marker of aggressive disease.

While the past decade has seen meaningful improvements in clinical outcomes for multiple myeloma patients, a subset of patients does not benefit from current therapeutics for unclear reasons. Many gene expression-based models of risk have been developed, but each model uses a different combination of genes and often involves assaying many genes making them difficult to implement. We organized the Multiple Myeloma DREAM Challenge, a crowdsourced effort to develop models of rapid progression in newly diagnosed myeloma patients and to benchmark these against previously published models. This effort lead to more robust predictors and found that incorporating specific demographic and clinical features improved gene expression-based models of high risk. Furthermore, post-challenge analysis identified a novel expression-based risk marker, PHF19, which has recently been found to have an important biological role in multiple myeloma. Lastly, we show that a simple four feature predictor composed of age, ISS, and expression of PHF19 and MMSET performs similarly to more complex models with many more gene expression features included.