DNA copy number aberrations (CNAs) are genetic alterations common in cancer cells. Their transcriptional consequences are still poorly understood. Based on the fact that DNA copy number (CN) is highly correlated with the genomic position, we have applied a segmentation algorithm to gene expression (GE) to explore its relation with CN. We have found a strong correlation between segmented CN (sCN) and segmented GE (sGE), corroborating that CNAs have clear effects on genome-wide expression. We have found out that most of the recurrent regions of sGE are common to those obtained from sCN analysis. Results for two cancer datasets confirm the known targets of aberrations and provide new candidates to study. The suggested methodology allows to find recurrent aberrations specific to sGE, revealing loci where the expression of the genes is independent from their CNs. R code and additional files are available as supplementary material.

Motivation Patient and sample diversity is one of the main challenges when dealing with clinical cohorts in biomedical genomics studies. During last decade, several methods have been developed to identify biomarkers assigned to specific individuals or subtypes of samples. However, current methods still fail to discover markers in complex scenarios where heterogeneity or hidden phenotypical factors are present. Here, we propose a method to analyze and understand heterogeneous data avoiding classical normalization approaches of reducing or removing variation. Results DEcomposing heterogeneous Cohorts using Omic data profiling (DECO) is a method to find significant association among biological features (biomarkers) and samples (individuals) analyzing large-scale omic data. The method identifies and categorizes biomarkers of specific phenotypic conditions based on a recurrent differential analysis integrated with a non-symmetrical correspondence analysis. DECO integrates both omic data dispersion and predictor–response relationship from non-symmetrical correspondence analysis in a unique statistic (called h-statistic), allowing the identification of closely related sample categories within complex cohorts. The performance is demonstrated using simulated data and five experimental transcriptomic datasets, and comparing to seven other methods. We show DECO greatly enhances the discovery and subtle identification of biomarkers, making it especially suited for deep and accurate patient stratification. Availability and implementation DECO is freely available as an R package (including a practical vignette) at Bioconductor repository (http://bioconductor.org/packages/deco/). Supplementary information Supplementary data are available at Bioinformatics online.

The method is available on CRAN (http://cran.r-project.org/) in the open-source R package calmate, which also includes an add-on to the Aroma Project framework (http://www.aroma-project.org/).

The method is available in the open-source R package ACNE, which also includes an add on to the aroma.affymetrix framework (http://www.aroma-project.org/).

BackgroundRNA-seq is a reference technology for determining alternative splicing at genome-wide level. Exon arrays remain widely used for the analysis of gene expression, but show poor validation rate with regard to splicing events. Commercial arrays that include probes within exon junctions have been developed in order to overcome this problem.We compare the performance of RNA-seq (Illumina HiSeq) and junction arrays (Affymetrix Human Transcriptome array) for the analysis of transcript splicing events. Three different breast cancer cell lines were treated with CX-4945, a drug that severely affects splicing. To enable a direct comparison of the two platforms, we adapted EventPointer, an algorithm that detects and labels alternative splicing events using junction arrays, to work also on RNA-seq data. Common results and discrepancies between the technologies were validated and/or resolved by over 200 PCR experiments.ResultsAs might be expected, RNA-seq appears superior in cases where the technologies disagree and is able to discover novel splicing events beyond the limitations of physical probe-sets. We observe a high degree of coherence between the two technologies, however, with correlation of EventPointer results over 0.90. Through decimation, the detection power of the junction arrays is equivalent to RNA-seq with up to 60 million reads.ConclusionsOur results suggest, therefore, that exon-junction arrays are a viable alternative to RNA-seq for detection of alternative splicing events when focusing on well-described transcriptional regions.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-5082-2) contains supplementary material, which is available to authorized users.

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.

BackgroundThe selection of the reference to scale the data in a copy number analysis has paramount importance to achieve accurate estimates. Usually this reference is generated using control samples included in the study. However, these control samples are not always available and in these cases, an artificial reference must be created. A proper generation of this signal is crucial in terms of both noise and bias.We propose NSA (Normality Search Algorithm), a scaling method that works with and without control samples. It is based on the assumption that genomic regions enriched in SNPs with identical copy numbers in both alleles are likely to be normal. These normal regions are predicted for each sample individually and used to calculate the final reference signal. NSA can be applied to any CN data regardless the microarray technology and preprocessing method. It also finds an optimal weighting of the samples minimizing possible batch effects.ResultsFive human datasets (a subset of HapMap samples, Glioblastoma Multiforme (GBM), Ovarian, Prostate and Lung Cancer experiments) have been analyzed. It is shown that using only tumoral samples, NSA is able to remove the bias in the copy number estimation, to reduce the noise and therefore, to increase the ability to detect copy number aberrations (CNAs). These improvements allow NSA to also detect recurrent aberrations more accurately than other state of the art methods.ConclusionsNSA provides a robust and accurate reference for scaling probe signals data to CN values without the need of control samples. It minimizes the problems of bias, noise and batch effects in the estimation of CNs. Therefore, NSA scaling approach helps to better detect recurrent CNAs than current methods. The automatic selection of references makes it useful to perform bulk analysis of many GEO or ArrayExpress experiments without the need of developing a parser to find the normal samples or possible batches within the data. The method is available in the open-source R package NSA, which is an add-on to the aroma.cn framework. http://www.aroma-project.org/addons.

Introduction: Lenalidomide (Len) is indicated for the treatment of relapsed/refractory (R/R) Mantle Cell Lymphoma (MCL) in the United States and Switzerland. Len binds to the cullin 4 ring E3 ubiquitin ligase complex resulting in ubiquitination and subsequent proteasomal degradation of lymphoid transcription factors Aiolos and Ikaros leading to stimulation of immune cells, such as T-cells. Clinical trial CC-5013-MCL-002 (NCT00875667) is a randomized open-label phase II study in R/R MCL patients in which Len was given orally at 25 mg/day on days 1-21 of each 28-day cycle until progression (N=170). The control arm consisted of investigator choice of single-agent rituximab, gemcitabine, fludarabine, chlorambucil, or cytarabine (N=84). We explored the immune effects of Len treatment in MCL patients enrolled in CC-5013-MCL-002 and further investigated our findings in in vitro MCL co-culture models. Methods: Peripheral blood samples for exploratory analysis were collected at Cycle 1 Day 1 (C1D1, pre-treatment), Cycle 1 Day 4 (C1D4), Cycle 2 Day 15 (C2D15) and at treatment discontinuation. Flow cytometric profiling of T, B and natural killer (NK) cell subsets was performed and differences were analyzed for correlation with clinical outcomes (response rate and progression free survival [PFS]). Cell dependent cytotoxicity was measured in 1) anti-CD3 stimulated peripheral blood mononuclear cells (PBMC) treated with vehicle or 1-10000 nM Len for 3 days and incubated with target tumor cells for an additional 4 hours followed by an apoptosis assay as measured by Annexin V/ToPro-3 flow cytometry and 2) negatively selected CD56+ NK cells stimulated with IL-2 and treated with Len (1 nM to 10 μM) for 18 hrs and incubated with target tumor cells for an additional 4 hours followed by apoptosis assay. Results: At baseline, no significant differences were observed in the absolute levels of immune subsets when comparing non-responders (NR) and responders (R) in either Len (NR=11, R=23) or control (NR=4, R=5) arms. However, in the Len arm, significantly elevated (adj. p < 0.05) proportions of CD3-CD56+CD16+ NK cells (difference of means = 8.73; 95%CI [4.48, 12.98]) were observed at C1D4 compared to baseline in the R (N=19) outcome sub-group compared to NR (N=11). A similar trend in levels of NK subsets was observed at C2D15, however the difference was not significant. In addition, elevated proportions of CD3-CD56+CD16+ NK cells (p≤0.016) at C1D4 relative to total lymphocytes correlated significantly to longer PFS in the Len arm. Immune subset analysis in the control arm did not show any correlation to response or PFS at any visit. The mechanism whereby NK cell modulation contributes to clinical benefit demonstrated by Len in patients was further explored in in vitro co-culture systems with MCL cell lines. Len treated PBMC co-cultured with Jeko-1, Granta-519, and Mino MCL cell lines resulted in 38-47.5% more apoptosis compared to DMSO (p≤0.001). We examined the effect of Len on Aiolos and Ikaros protein expression in CD56+ NK and CD3+ T cells within anti-CD3 antibody stimulated PBMCs treated with DMSO or various concentrations of Len (1 nM to 10 μM) for 72 hours. Degradation of both Aiolos (40%) and Ikaros (95%) was observed after drug treatment in CD56+ NK cells. Aiolos and Ikaros levels were also monitored in CD3+ T cells and showed decreased levels after Len treatment, consistent with previous reports (Gandhi, 2014; Kronke, 2014). Furthermore, purified CD56+ NK cell mediated cytotoxicity produced a similar pro-apoptotic effect as the PBMC assay in all MCL cell lines versus DMSO (p≤0.01). Supernatants from co-cultures of NK cells with MCL cell lines showed significantly elevated granzyme B levels as compared to DMSO controls (p≤0.0001), suggesting that the apoptotic effects observed are induced by granzyme B. Conclusions: Lenalidomide is an immune modulating agent and NK cell modulation in particular may play a role in its clinical activity in MCL. A significant increase in proportions of NK cell subsets (vs total lymphocytes) at C1D4 versus baseline was observed and is a potential response indicator of favorable clinical outcome in R/R MCL patients treated with Len. In vitro, Len enhances cell mediated cytotoxicity of MCL cell lines in two co-culture model systems. Understanding NK cell mediated mechanism(s) has potential to enhance guiding patient selection strategies and rational combination therapies of lenalidomide in MCL. Disclosures Hagner: Celgene: Employment, Equity Ownership. Chiu:Celgene: Employment, Equity Ownership. Ortiz-Estevez:Celgene: Employment, Equity Ownership. Biyukov:Celgene: Employment, Equity Ownership. Brachman:Celgene: Employment, Equity Ownership. Trneny:Celgene: Consultancy, Honoraria, Other: Travel, accommodations, expenses, Research Funding. Morschhauser:Genentech Inc./Roche: Other: Advisory boards. Stilgenbauer:AbbVie, Amgen, Boehringer-Ingelheim, Celgene, Genentech, Genzyme, Gilead, GSK, Janssen, Mundipharma, Novartis, Pharmacyclics, Roche: Consultancy, Honoraria, Research Funding. Milpied:Celgene: Honoraria, Research Funding. Musto:Sandoz: Consultancy; Celgene: Honoraria; Roche: Honoraria; Sanofi: Consultancy; Genzyme: Consultancy; Novartis: Honoraria; Janssen: Honoraria; Mundipharma: Honoraria. Martinelli:AMGEN: Consultancy; Ariad: Consultancy; Pfizer: Consultancy; ROCHE: Consultancy; BMS: Consultancy, Speakers Bureau; Novartis: Consultancy, Speakers Bureau; MSD: Consultancy. Heise:Celgene: Employment, Equity Ownership. Daniel:Celgene: Employment, Equity Ownership. Chopra:Celgene: Employment, Equity Ownership. Carmichael:Celgene: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment. Gandhi:Celgene: Employment, Equity Ownership. Thakurta:Celgene Corporation: Employment, Equity Ownership.