HLA-A*03 and response to immune checkpoint blockade in cancer: an epidemiological biomarker study.

BACKGROUND: Predictive biomarkers could allow more precise use of immune checkpoint inhibitors (ICIs) in treating advanced cancers. Given the central role of HLA molecules in immunity, variation at the HLA loci could differentially affect the response to ICIs. The aim of this epidemiological study was to determine the effect of HLA-A*03 as a biomarker for predicting response to immunotherapy. METHODS: In this epidemiological study, we investigated the clinical outcomes (overall survival, progression free survival, and objective response rate) after treatment for advanced cancer in eight cohorts of patients: three observational cohorts of patients with various types of advanced tumours (the Memorial Sloan Kettering Integrated Mutation Profiling of Actionable Cancer Targets [MSK-IMPACT] cohort, the Dana-Farber Cancer Institute [DFCI] Profile cohort, and The Cancer Genome Atlas) and five clinical trials of patients with advanced bladder cancer (JAVELIN Solid Tumour) or renal cell carcinoma (CheckMate-009, CheckMate-010, CheckMate-025, and JAVELIN Renal 101). In total, these cohorts included 3335 patients treated with various ICI agents (anti-PD-1, anti-PD-L1, and anti-CTLA-4 inhibitors) and 10 917 patients treated with non-ICI cancer-directed therapeutic approaches. We initially modelled the association of HLA amino-acid variation with overall survival in the MSK-IMPACT discovery cohort, followed by a detailed analysis of the association between HLA-A*03 and clinical outcomes in MSK-IMPACT, with replication in the additional cohorts (two further observational cohorts and five clinical trials). FINDINGS: HLA-A*03 was associated in an additive manner with reduced overall survival after ICI treatment in the MSK-IMPACT cohort (HR 1·48 per HLA-A*03 allele [95% CI 1·20-1·82], p=0·00022), the validation DFCI Profile cohort (HR 1·22 per HLA-A*03 allele, 1·05-1·42; p=0·0097), and in the JAVELIN Solid Tumour clinical trial for bladder cancer (HR 1·36 per HLA-A*03 allele, 1·01-1·85; p=0·047). The HLA-A*03 effect was observed across ICI agents and tumour types, but not in patients treated with alternative therapies. Patients with HLA-A*03 had shorter progression-free survival in the pooled patient population from the three CheckMate clinical trials of nivolumab for renal cell carcinoma (HR 1·31, 1·01-1·71; p=0·044), but not in those receiving control (everolimus) therapies. Objective responses were observed in none of eight HLA-A*03 homozygotes in the ICI group (compared with 59 [26·6%] of 222 HLA-A*03 non-carriers and 13 (17·1%) of 76 HLA-A*03 heterozygotes). HLA-A*03 was associated with shorter progression-free survival in patients receiving ICI in the JAVELIN Renal 101 randomised clinical trial for renal cell carcinoma (avelumab plus axitinib; HR 1·59 per HLA-A*03 allele, 1·16-2·16; p=0·0036), but not in those receiving control (sunitinib) therapy. Objective responses were recorded in one (12·5%) of eight HLA-A*03 homozygotes in the ICI group (compared with 162 [63·8%] of 254 HLA-A*03 non-carriers and 40 [55·6%] of 72 HLA-A*03 heterozygotes). HLA-A*03 was associated with impaired outcome in meta-analysis of all 3335 patients treated with ICI at genome-wide significance (p=2·01 × 10-8) with no evidence of heterogeneity in effect (I2 0%, 95% CI 0-0·76) INTERPRETATION: HLA-A*03 is a predictive biomarker of poor response to ICI. Further evaluation of HLA-A*03 is warranted in randomised trials. HLA-A*03 carriage could be considered in decisions to initiate ICI in patients with cancer. FUNDING: National Institutes of Health, Merck KGaA, and Pfizer.

First-line avelumab in a cohort of 116 patients with metastatic Merkel cell carcinoma (JAVELIN Merkel 200): primary and biomarker analyses of a phase II study.

BACKGROUND: Avelumab (anti-programmed death ligand 1 (PD-L1)) is approved in multiple countries for the treatment of metastatic Merkel cell carcinoma (mMCC), a rare and aggressive skin cancer. We report efficacy and safety data and exploratory biomarker analyses from a cohort of patients with mMCC treated with first-line avelumab in a phase II trial. METHODS: Patients with treatment-naive mMCC received avelumab 10 mg/kg intravenously every 2 weeks. The primary endpoint was durable response, defined as objective response (complete or partial response; assessed by independent review) lasting ≥6 months. Additional assessments included progression-free survival (PFS), overall survival (OS), safety, and biomarker analyses. RESULTS: In 116 patients treated with avelumab, median follow-up was 21.2 months (range: 14.9-36.6). Thirty-five patients had a response lasting ≥6 months, giving a durable response rate of 30.2% (95% CI: 22.0% to 39.4%). The objective response rate was 39.7% (95% CI: 30.7% to 49.2%). Median PFS was 4.1 months (95% CI: 1.4 to 6.1) and median OS was 20.3 months (95% CI: 12.4 to not estimable). Response rates were numerically higher in patients with PD-L1+ tumors, Merkel cell polyomavirus (MCPyV)-negative tumors, and tumors with increased intratumoral CD8+ T-cell density. Exploratory analyses did not identify a biomarker that could reliably predict a response to first-line treatment with avelumab; however, a novel gene expression signature to identify the presence of MCPyV+ tumors was derived. Treatment-related adverse events (any grade) occurred in 94 (81.0%) patients, including grade 3/4 events in 21 (18.1%) patients; no treatment-related deaths occurred. CONCLUSION: In patients with mMCC, first-line treatment with avelumab led to responses in 40% and durable responses in 30%, and was associated with a low rate of grade 3/4 treatment-related adverse events.

Avelumab in patients with previously treated metastatic Merkel cell carcinoma: long-term data and biomarker analyses from the single-arm phase 2 JAVELIN Merkel 200 trial.

BACKGROUND: Merkel cell carcinoma (MCC) is a rare, aggressive skin cancer associated with a high risk of metastasis. In 2017, avelumab (anti-programmed death-ligand 1 (PD-L1)) became the first approved treatment for patients with metastatic MCC (mMCC), based on the occurrence of durable responses in a subset of patients. Here, we report long-term efficacy and safety data and exploratory biomarker analyses in patients with mMCC treated with avelumab. METHODS: In a cohort of this single-arm, phase 2 trial (JAVELIN Merkel 200), patients with mMCC and disease progression after prior chemotherapy received avelumab 10 mg/kg intravenously every 2 weeks. The primary endpoint was confirmed objective response rate (ORR) by independent review per Response Evaluation Criteria in Solid Tumors V.1.1. Other assessments included duration of response, progression-free survival, overall survival (OS), safety and biomarker analyses. RESULTS: As of 14 September 2018, 88 patients had been followed up for a median of 40.8 months (range 36.4-49.7 months). The ORR was 33.0% (95% CI 23.3% to 43.8%), including a complete response in 11.4% (10 patients), and the median duration of response was 40.5 months (95% CI 18.0 months to not estimable). As of 2 May 2019 (≥44 months of follow-up), the median OS was 12.6 months (95% CI 7.5 to 17.1 months) and the 42-month OS rate was 31% (95% CI 22% to 41%). Of long-term survivors (OS >36 months) evaluable for PD-L1 expression status (n=22), 81.8% had PD-L1+ tumors. In exploratory biomarker analyses, high tumor mutational burden (≥2 non-synonymous somatic variants per megabase) and high major histocompatibility complex class I expression (30% of tumors with highest expression) were associated with trends for improved ORR and OS. In long-term safety assessments (≥36 months of follow-up), no new or unexpected adverse events were reported, and no treatment-related deaths occurred. CONCLUSIONS: Avelumab showed continued durable responses and meaningful long-term survival outcomes in patients with mMCC, reinforcing avelumab as a standard-of-care treatment option for this disease. TRIAL REGISTRATION NUMBER: NCT02155647.

Applications of machine learning in drug discovery and development.

Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all stages of drug discovery. Examples include target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials. Applications have ranged in context and methodology, with some approaches yielding accurate predictions and insights. The challenges of applying ML lie primarily with the lack of interpretability and repeatability of ML-generated results, which may limit their application. In all areas, systematic and comprehensive high-dimensional data still need to be generated. With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential to speed up the process and reduce failure rates in drug discovery and development.

Cross-talk between chromatin acetylation and SUMOylation of tripartite motif-containing protein 24 (TRIM24) impacts cell adhesion.

Proteins with domains that recognize and bind post-translational modifications (PTMs) of histones are collectively termed epigenetic readers. Numerous interactions between specific reader protein domains and histone PTMs and their regulatory outcomes have been reported, but little is known about how reader proteins may in turn be modulated by these interactions. Tripartite motif-containing protein 24 (TRIM24) is a histone reader aberrantly expressed in multiple cancers. Here, our investigation revealed functional cross-talk between histone acetylation and TRIM24 SUMOylation. Binding of TRIM24 to chromatin via its tandem PHD-bromodomain, which recognizes unmethylated lysine 4 and acetylated lysine 23 of histone H3 (H3K4me0/K23ac), led to TRIM24 SUMOylation at lysine residues 723 and 741. Inactivation of the bromodomain, either by mutation or with a small-molecule inhibitor, IACS-9571, abolished TRIM24 SUMOylation. Conversely, inhibition of histone deacetylation markedly increased TRIM24's interaction with chromatin and its SUMOylation. Of note, gene expression profiling of MCF7 cells expressing WT versus SUMO-deficient TRIM24 identified cell adhesion as the major pathway regulated by the cross-talk between chromatin acetylation and TRIM24 SUMOylation. In conclusion, our findings establish a new link between histone H3 acetylation and SUMOylation of the reader protein TRIM24, a functional connection that may bear on TRIM24's oncogenic function and may inform future studies of PTM cross-talk between histones and epigenetic regulators.

TRIM28 and Interacting KRAB-ZNFs Control Self-Renewal of Human Pluripotent Stem Cells through Epigenetic Repression of Pro-differentiation Genes.

Reprogramming to induced pluripotent stem cells (iPSCs) and differentiation of pluripotent stem cells (PSCs) are regulated by epigenetic machinery. Tripartite motif protein 28 (TRIM28), a universal mediator of Krüppel-associated box domain zinc fingers (KRAB-ZNFs), is known to regulate both processes; however, the exact mechanism and identity of participating KRAB-ZNF genes remain unknown. Here, using a reporter system, we show that TRIM28/KRAB-ZNFs alter DNA methylation patterns in addition to H3K9me3 to cause stable gene repression during reprogramming. Using several expression datasets, we identified KRAB-ZNFs (ZNF114, ZNF483, ZNF589) in the human genome that maintain pluripotency. Moreover, we identified target genes repressed by these KRAB-ZNFs. Mechanistically, we demonstrated that these KRAB-ZNFs directly alter gene expression of important developmental genes by modulating H3K9me3 and DNA methylation of their promoters. In summary, TRIM28 employs KRAB-ZNFs to evoke epigenetic silencing of its target differentiation genes via H3K9me3 and DNA methylation.

TRIM28 multi-domain protein regulates cancer stem cell population in breast tumor development.

The expression of Tripartite motif-containing protein 28 (TRIM28)/Krüppel-associated box (KRAB)-associated protein 1 (KAP1), is elevated in at least 14 tumor types, including solid and hematopoietic tumors. High level of TRIM28 is associated with triple-negative subtype of breast cancer (TNBC), which shows higher aggressiveness and lower survival rates. Interestingly, TRIM28 is essential for maintaining the pluripotent phenotype in embryonic stem cells. Following on that finding, we evaluated the role of TRIM28 protein in the regulation of breast cancer stem cells (CSC) populations and tumorigenesis in vitro and in vivo. Downregulation of TRIM28 expression in xenografts led to deceased expression of pluripotency and mesenchymal markers, as well as inhibition of signaling pathways involved in the complex mechanism of CSC maintenance. Moreover, TRIM28 depletion reduced the ability of cancer cells to induce tumor growth when subcutaneously injected in limiting dilutions. Our data demonstrate that the downregulation of TRIM28 gene expression reduced the ability of CSCs to self-renew that resulted in significant reduction of tumor growth. Loss of function of TRIM28 leads to dysregulation of cell cycle, cellular response to stress, cancer cell metabolism, and inhibition of oxidative phosphorylation. All these mechanisms directly regulate maintenance of CSC population. Our original results revealed the role of the TRIM28 in regulating the CSC population in breast cancer. These findings may pave the way to novel and more effective therapies targeting cancer stem cells in breast tumors.

In Vivo Functional Platform Targeting Patient-Derived Xenografts Identifies WDR5-Myc Association as a Critical Determinant of Pancreatic Cancer.

Current treatment regimens for pancreatic ductal adenocarcinoma (PDAC) yield poor 5-year survival, emphasizing the critical need to identify druggable targets essential for PDAC maintenance. We developed an unbiased and in vivo target discovery approach to identify molecular vulnerabilities in low-passage and patient-derived PDAC xenografts or genetically engineered mouse model-derived allografts. Focusing on epigenetic regulators, we identified WDR5, a core member of the COMPASS histone H3 Lys4 (H3K4) MLL (1-4) methyltransferase complex, as a top tumor maintenance hit required across multiple human and mouse tumors. Mechanistically, WDR5 functions to sustain proper execution of DNA replication in PDAC cells, as previously suggested by replication stress studies involving MLL1, and c-Myc, also found to interact with WDR5. We indeed demonstrate that interaction with c-Myc is critical for this function. By showing that ATR inhibition mimicked the effects of WDR5 suppression, these data provide rationale to test ATR and WDR5 inhibitors for activity in this disease.

The SMARCA2/4 ATPase Domain Surpasses the Bromodomain as a Drug Target in SWI/SNF-Mutant Cancers: Insights from cDNA Rescue and PFI-3 Inhibitor Studies.

The SWI/SNF multisubunit complex modulates chromatin structure through the activity of two mutually exclusive catalytic subunits, SMARCA2 and SMARCA4, which both contain a bromodomain and an ATPase domain. Using RNAi, cancer-specific vulnerabilities have been identified in SWI/SNF-mutant tumors, including SMARCA4-deficient lung cancer; however, the contribution of conserved, druggable protein domains to this anticancer phenotype is unknown. Here, we functionally deconstruct the SMARCA2/4 paralog dependence of cancer cells using bioinformatics, genetic, and pharmacologic tools. We evaluate a selective SMARCA2/4 bromodomain inhibitor (PFI-3) and characterize its activity in chromatin-binding and cell-functional assays focusing on cells with altered SWI/SNF complex (e.g., lung, synovial sarcoma, leukemia, and rhabdoid tumors). We demonstrate that PFI-3 is a potent, cell-permeable probe capable of displacing ectopically expressed, GFP-tagged SMARCA2-bromodomain from chromatin, yet contrary to target knockdown, the inhibitor fails to display an antiproliferative phenotype. Mechanistically, the lack of pharmacologic efficacy is reconciled by the failure of bromodomain inhibition to displace endogenous, full-length SMARCA2 from chromatin as determined by in situ cell extraction, chromatin immunoprecipitation, and target gene expression studies. Furthermore, using inducible RNAi and cDNA complementation (bromodomain- and ATPase-dead constructs), we unequivocally identify the ATPase domain, and not the bromodomain of SMARCA2, as the relevant therapeutic target with the catalytic activity suppressing defined transcriptional programs. Taken together, our complementary genetic and pharmacologic studies exemplify a general strategy for multidomain protein drug-target validation and in case of SMARCA2/4 highlight the potential for drugging the more challenging helicase/ATPase domain to deliver on the promise of synthetic-lethality therapy.

Genetic events that limit the efficacy of MEK and RTK inhibitor therapies in a mouse model of KRAS-driven pancreatic cancer.

Mutated KRAS (KRAS*) is a fundamental driver in the majority of pancreatic ductal adenocarcinomas (PDAC). Using an inducible mouse model of KRAS*-driven PDAC, we compared KRAS* genetic extinction with pharmacologic inhibition of MEK1 in tumor spheres and in vivo. KRAS* ablation blocked proliferation and induced apoptosis, whereas MEK1 inhibition exerted cytostatic effects. Proteomic analysis evidenced that MEK1 inhibition was accompanied by a sustained activation of the PI3K-AKT-MTOR pathway and by the activation of AXL, PDGFRa, and HER1-2 receptor tyrosine kinases (RTK) expressed in a large proportion of human PDAC samples analyzed. Although single inhibition of each RTK alone or plus MEK1 inhibitors was ineffective, a combination of inhibitors targeting all three coactivated RTKs and MEK1 was needed to inhibit proliferation and induce apoptosis in both mouse and human low-passage PDAC cultures. Importantly, constitutive AKT activation, which may mimic the fraction of AKT2-amplified PDAC, was able to bypass the induction of apoptosis caused by KRAS* ablation, highlighting a potential inherent resistance mechanism that may inform the clinical application of MEK inhibitor therapy. This study suggests that combinatorial-targeted therapies for pancreatic cancer must be informed by the activation state of each putative driver in a given treatment context. In addition, our work may offer explanative and predictive power in understanding why inhibitors of EGFR signaling fail in PDAC treatment and how drug resistance mechanisms may arise in strategies to directly target KRAS.

Computational characterisation of cancer molecular profiles derived using next generation sequencing.

Our current understanding of cancer genetics is grounded on the principle that cancer arises from a clone that has accumulated the requisite somatically acquired genetic aberrations, leading to the malignant transformation. It also results in aberrent of gene and protein expression. Next generation sequencing (NGS) or deep sequencing platforms are being used to create large catalogues of changes in copy numbers, mutations, structural variations, gene fusions, gene expression, and other types of information for cancer patients. However, inferring different types of biological changes from raw reads generated using the sequencing experiments is algorithmically and computationally challenging. In this article, we outline common steps for the quality control and processing of NGS data. We highlight the importance of accurate and application-specific alignment of these reads and the methodological steps and challenges in obtaining different types of information. We comment on the importance of integrating these data and building infrastructure to analyse it. We also provide exhaustive lists of available software to obtain information and point the readers to articles comparing software for deeper insight in specialised areas. We hope that the article will guide readers in choosing the right tools for analysing oncogenomic datasets.

Targeting poly(ADP-ribose) polymerase and the c-Myb-regulated DNA damage response pathway in castration-resistant prostate cancer.

Androgen deprivation is the standard treatment for advanced prostate cancer (PCa), but most patients ultimately develop resistance and tumor recurrence. We found that MYB is transcriptionally activated by androgen deprivation therapy or genetic silencing of the androgen receptor (AR). MYB silencing inhibited PCa growth in culture and xenografts in mice. Microarray data revealed that c-Myb and AR shared a subset of target genes that encode DNA damage response (DDR) proteins, suggesting that c-Myb may supplant AR as the dominant regulator of their common DDR target genes in AR inhibition-resistant or AR-negative PCa. Gene signatures including AR, MYB, and their common DDR-associated target genes positively correlated with metastasis, castration resistance, tumor recurrence, and decreased survival in PCa patients. In culture and in xenograft-bearing mice, a combination strategy involving the knockdown of MYB, BRCA1, or TOPBP1 or the abrogation of cell cycle checkpoint arrest with AZD7762, an inhibitor of the checkpoint kinase Chk1, increased the cytotoxicity of the poly[adenosine 5'-diphosphate (ADP)-ribose] polymerase (PARP) inhibitor olaparib in PCa cells. Our results reveal new mechanism-based therapeutic approaches for PCa by targeting PARP and the DDR pathway involving c-Myb, TopBP1, ataxia telangiectasia mutated- and Rad3-related (ATR), and Chk1.

The shaping and functional consequences of the dosage effect landscape in multiple myeloma.

BACKGROUND: Multiple myeloma (MM) is a malignant proliferation of plasma B cells. Based on recurrent aneuploidy such as copy number alterations (CNAs), myeloma is divided into two subtypes with different CNA patterns and patient survival outcomes. How aneuploidy events arise, and whether they contribute to cancer cell evolution are actively studied. The large amount of transcriptomic changes resultant of CNAs (dosage effect) pose big challenges for identifying functional consequences of CNAs in myeloma in terms of specific driver genes and pathways. In this study, we hypothesize that gene-wise dosage effect varies as a result from complex regulatory networks that translate the impact of CNAs to gene expression, and studying this variation can provide insights into functional effects of CNAs. RESULTS: We propose gene-wise dosage effect score and genome-wide karyotype plot as tools to measure and visualize concordant copy number and expression changes across cancer samples. We find that dosage effect in myeloma is widespread yet variable, and it is correlated with gene expression level and CNA frequencies in different chromosomes. Our analysis suggests that despite the enrichment of differentially expressed genes between hyperdiploid MM and non-hyperdiploid MM in the trisomy chromosomes, the chromosomal proportion of dosage sensitive genes is higher in the non-trisomy chromosomes. Dosage-sensitive genes are enriched by genes with protein translation and localization functions, and dosage resistant genes are enriched by apoptosis genes. These results point to future studies on differential dosage sensitivity and resistance of pro- and anti-proliferation pathways and their variation across patients as therapeutic targets and prognosis markers. CONCLUSIONS: Our findings support the hypothesis that recurrent CNAs in myeloma are selected by their functional consequences. The novel dosage effect score defined in this work will facilitate integration of copy number and expression data for identifying driver genes in cancer genomics studies. The accompanying R code is available at http://www.canevolve.org/dosageEffect/.

Classify hyperdiploidy status of multiple myeloma patients using gene expression profiles.

Multiple myeloma (MM) is a cancer of antibody-making plasma cells. It frequently harbors alterations in DNA and chromosome copy numbers, and can be divided into two major subtypes, hyperdiploid (HMM) and non-hyperdiploid multiple myeloma (NHMM). The two subtypes have different survival prognosis, possibly due to different but converging paths to oncogenesis. Existing methods for identifying the two subtypes are fluorescence in situ hybridization (FISH) and copy number microarrays, with increased cost and sample requirements. We hypothesize that chromosome alterations have their imprint in gene expression through dosage effect. Using five MM expression datasets that have HMM status measured by FISH and copy number microarrays, we have developed and validated a K-nearest-neighbor method to classify MM into HMM and NHMM based on gene expression profiles. Classification accuracy for test datasets ranges from 0.83 to 0.88. This classification will enable researchers to study differences and commonalities of the two MM subtypes in disease biology and prognosis using expression datasets without need for additional subtype measurements. Our study also supports the advantages of using cancer specific characteristics in feature design and pooling multiple rounds of classification results to improve accuracy. We provide R source code and processed datasets at www.ChengLiLab.org/software.

canEvolve: a web portal for integrative oncogenomics.

BACKGROUND & OBJECTIVE: Genome-wide profiles of tumors obtained using functional genomics platforms are being deposited to the public repositories at an astronomical scale, as a result of focused efforts by individual laboratories and large projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium. Consequently, there is an urgent need for reliable tools that integrate and interpret these data in light of current knowledge and disseminate results to biomedical researchers in a user-friendly manner. We have built the canEvolve web portal to meet this need. RESULTS: canEvolve query functionalities are designed to fulfill most frequent analysis needs of cancer researchers with a view to generate novel hypotheses. canEvolve stores gene, microRNA (miRNA) and protein expression profiles, copy number alterations for multiple cancer types, and protein-protein interaction information. canEvolve allows querying of results of primary analysis, integrative analysis and network analysis of oncogenomics data. The querying for primary analysis includes differential gene and miRNA expression as well as changes in gene copy number measured with SNP microarrays. canEvolve provides results of integrative analysis of gene expression profiles with copy number alterations and with miRNA profiles as well as generalized integrative analysis using gene set enrichment analysis. The network analysis capability includes storage and visualization of gene co-expression, inferred gene regulatory networks and protein-protein interaction information. Finally, canEvolve provides correlations between gene expression and clinical outcomes in terms of univariate survival analysis. CONCLUSION: At present canEvolve provides different types of information extracted from 90 cancer genomics studies comprising of more than 10,000 patients. The presence of multiple data types, novel integrative analysis for identifying regulators of oncogenesis, network analysis and ability to query gene lists/pathways are distinctive features of canEvolve. canEvolve will facilitate integrative and meta-analysis of oncogenomics datasets. AVAILABILITY: The canEvolve web portal is available at http://www.canevolve.org/.

A small molecule inhibitor of ubiquitin-specific protease-7 induces apoptosis in multiple myeloma cells and overcomes bortezomib resistance.

Bortezomib therapy has proven successful for the treatment of relapsed/refractory, relapsed, and newly diagnosed multiple myeloma (MM); however, dose-limiting toxicities and the development of resistance limit its long-term utility. Here, we show that P5091 is an inhibitor of deubiquitylating enzyme USP7, which induces apoptosis in MM cells resistant to conventional and bortezomib therapies. Biochemical and genetic studies show that blockade of HDM2 and p21 abrogates P5091-induced cytotoxicity. In animal tumor model studies, P5091 is well tolerated, inhibits tumor growth, and prolongs survival. Combining P5091 with lenalidomide, HDAC inhibitor SAHA, or dexamethasone triggers synergistic anti-MM activity. Our preclinical study therefore supports clinical evaluation of USP7 inhibitor, alone or in combination, as a potential MM therapy.

Integrative analysis of gene and miRNA expression profiles with transcription factor-miRNA feed-forward loops identifies regulators in human cancers.

We describe here a novel method for integrating gene and miRNA expression profiles in cancer using feed-forward loops (FFLs) consisting of transcription factors (TFs), miRNAs and their common target genes. The dChip-GemiNI (Gene and miRNA Network-based Integration) method statistically ranks computationally predicted FFLs by their explanatory power to account for differential gene and miRNA expression between two biological conditions such as normal and cancer. GemiNI integrates not only gene and miRNA expression data but also computationally derived information about TF-target gene and miRNA-mRNA interactions. Literature validation shows that the integrated modeling of expression data and FFLs better identifies cancer-related TFs and miRNAs compared to existing approaches. We have utilized GemiNI for analyzing six data sets of solid cancers (liver, kidney, prostate, lung and germ cell) and found that top-ranked FFLs account for ∼20% of transcriptome changes between normal and cancer. We have identified common FFL regulators across multiple cancer types, such as known FFLs consisting of MYC and miR-15/miR-17 families, and novel FFLs consisting of ARNT, CREB1 and their miRNA partners. The results and analysis web server are available at http://www.canevolve.org/dChip-GemiNi.

Lessons from a decade of integrating cancer copy number alterations with gene expression profiles.

Over the last decade, multiple functional genomic datasets studying chromosomal aberrations and their downstream effects on gene expression have accumulated for several cancer types. A vast majority of them are in the form of paired gene expression profiles and somatic copy number alterations (CNA) information on the same patients identified using microarray platforms. In response, many algorithms and software packages are available for integrating these paired data. Surprisingly, there has been no serious attempt to review the currently available methodologies or the novel insights brought using them. In this work, we discuss the quantitative relationships observed between CNA and gene expression in multiple cancer types and biological milestones achieved using the available methodologies. We discuss the conceptual evolution of both, the step-wise and the joint data integration methodologies over the last decade. We conclude by providing suggestions for building efficient data integration methodologies and asking further biological questions.

Determinants of antigenicity and specificity in immune response for protein sequences.

BACKGROUND: Target specific antibodies are pivotal for the design of vaccines, immunodiagnostic tests, studies on proteomics for cancer biomarker discovery, identification of protein-DNA and other interactions, and small and large biochemical assays. Therefore, it is important to understand the properties of protein sequences that are important for antigenicity and to identify small peptide epitopes and large regions in the linear sequence of the proteins whose utilization result in specific antibodies. RESULTS: Our analysis using protein properties suggested that sequence composition combined with evolutionary information and predicted secondary structure, as well as solvent accessibility is sufficient to predict successful peptide epitopes. The antigenicity and the specificity in immune response were also found to depend on the epitope length. We trained the B-Cell Epitope Oracle (BEOracle), a support vector machine (SVM) classifier, for the identification of continuous B-Cell epitopes with these protein properties as learning features. The BEOracle achieved an F1-measure of 81.37% on a large validation set. The BEOracle classifier outperformed the classical methods based on propensity and sophisticated methods like BCPred and Bepipred for B-Cell epitope prediction. The BEOracle classifier also identified peptides for the ChIP-grade antibodies from the modENCODE/ENCODE projects with 96.88% accuracy. High BEOracle score for peptides showed some correlation with the antibody intensity on Immunofluorescence studies done on fly embryos. Finally, a second SVM classifier, the B-Cell Region Oracle (BROracle) was trained with the BEOracle scores as features to predict the performance of antibodies generated with large protein regions with high accuracy. The BROracle classifier achieved accuracies of 75.26-63.88% on a validation set with immunofluorescence, immunohistochemistry, protein arrays and western blot results from Protein Atlas database. CONCLUSIONS: Together our results suggest that antigenicity is a local property of the protein sequences and that protein sequence properties of composition, secondary structure, solvent accessibility and evolutionary conservation are the determinants of antigenicity and specificity in immune response. Moreover, specificity in immune response could also be accurately predicted for large protein regions without the knowledge of the protein tertiary structure or the presence of discontinuous epitopes. The dataset prepared in this work and the classifier models are available for download at https://sites.google.com/site/oracleclassifiers/.