Distinct subtypes of diffuse large B-cell lymphoma defined by hypermutated genes.

Diffuse large B-cell lymphoma (DLBCL) is a biologically and clinically heterogeneous disease whose personalized clinical management requires robust molecular stratification. Here, we show that somatic hypermutation (SHM) patterns constitute a marker for DLBCL molecular classification. The activity of SHM mutational processes delineated the cell of origin (COO) in DLBCL. Expression of the herein identified 36 SHM target genes stratified DLBCL into four novel SHM subtypes. In a meta-analysis of patients with DLBCL treated with immunochemotherapy, the SHM subtypes were significantly associated with overall survival (1642 patients) and progression-free survival (795 patients). Multivariate analysis of survival indicated that the prognostic impact of the SHM subtypes is independent from the COO classification and the International Prognostic Index. Furthermore, the SHM subtypes had a distinct clinical outcome within each of the COO subtypes, and strikingly, even within unclassified DLBCL. The genetic landscape of the four SHM subtypes indicated unique associations with driver alterations and oncogenic signaling in DLBCL, which suggests a possibility for therapeutic exploitation. These findings provide a biologically driven classification system in DLBCL with potential clinical applications.

Anduril 2: upgraded large-scale data integration framework.

SUMMARY: Anduril is an analysis and integration framework that facilitates the design, use, parallelization and reproducibility of bioinformatics workflows. Anduril has been upgraded to use Scala for pipeline construction, which simplifies software maintenance, and facilitates design of complex pipelines. Additionally, Anduril's bioinformatics repository has been expanded with multiple components, and tutorial pipelines, for next-generation sequencing data analysis. AVAILABILITYAND IMPLEMENTATION: Freely available at http://anduril.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Drug-Sensitivity Screening and Genomic Characterization of 45 HPV-Negative Head and Neck Carcinoma Cell Lines for Novel Biomarkers of Drug Efficacy.

There is an unmet need for effective targeted therapies for patients with advanced head and neck squamous cell carcinoma (HNSCC). We correlated gene expression, gene copy numbers, and point mutations in 45 human papillomavirus-negative HNSCC cell lines with the sensitivity to 220 anticancer drugs to discover predictive associations to genetic alterations. The drug response profiles revealed diverse efficacy of the tested drugs across the cell lines. Several genomic abnormalities and gene expression differences were associated with response to mTOR, MEK, and EGFR inhibitors. NOTCH1 and FAT1 were the most commonly mutated genes after TP53 and also showed some association with response to MEK and/or EGFR inhibitors. MYC amplification and FAM83H overexpression associated with sensitivity to EGFR inhibitors, and PTPRD deletion with poor sensitivity to MEK inhibitors. The connection between high FAM83H expression and responsiveness to the EGFR inhibitor erlotinib was validated by gene silencing and from the data set at the Cancer Cell Line Encyclopedia. The data provide several novel genomic alterations that associated to the efficacy of targeted drugs in HNSCC. These findings require further validation in experimental models and clinical series. Mol Cancer Ther; 17(9); 2060-71. ©2018 AACR.

Let-7 microRNA controls invasion-promoting lysosomal changes via the oncogenic transcription factor myeloid zinc finger-1.

Cancer cells utilize lysosomes for invasion and metastasis. Myeloid Zinc Finger1 (MZF1) is an ErbB2-responsive transcription factor that promotes invasion of breast cancer cells via upregulation of lysosomal cathepsins B and L. Here we identify let-7 microRNA, a well-known tumor suppressor in breast cancer, as a direct negative regulator of MZF1. Analysis of primary breast cancer tissues reveals a gradual upregulation of MZF1 from normal breast epithelium to invasive ductal carcinoma and a negative correlation between several let-7 family members and MZF1 mRNA, suggesting that the inverse regulatory relationship between let-7 and MZF1 may play a role in the development of invasive breast cancer. Furthermore, we show that MZF1 regulates lysosome trafficking in ErbB2-positive breast cancer cells. In line with this, MZF1 depletion or let-7 expression inhibits invasion-promoting anterograde trafficking of lysosomes and invasion of ErbB2-expressing MCF7 spheres. The results presented here link MZF1 and let-7 to lysosomal processes in ErbB2-positive breast cancer cells that in non-cancerous cells have primarily been connected to the transcription factor EB. Identifying MZF1 and let-7 as regulators of lysosome distribution in invasive breast cancer cells, uncouples cancer-associated, invasion-promoting lysosomal alterations from normal lysosomal functions and thus opens up new possibilities for the therapeutic targeting of cancer lysosomes.

DNA methylation signature (SAM40) identifies subgroups of the Luminal A breast cancer samples with distinct survival.

Breast cancer patients with Luminal A disease generally have a good prognosis, but among this patient group are patients with good prognosis that are currently overtreated with adjuvant chemotherapy, and also patients that have a bad prognosis and should be given more aggressive treatment. There is no available method for subclassification of this patient group. Here we present a DNA methylation signature (SAM40) that segregates Luminal A patients based on prognosis, and identify one good prognosis group and one bad prognosis group. The prognostic impact of SAM40 was validated in four independent patient cohorts. Being able to subdivide the Luminal A patients may give the two-sided benefit of identifying one subgroup that may benefit from a more aggressive treatment than what is given today, and importantly, identifying a subgroup that may benefit from less treatment.

Language-Agnostic Reproducible Data Analysis Using Literate Programming.

A modern biomedical research project can easily contain hundreds of analysis steps and lack of reproducibility of the analyses has been recognized as a severe issue. While thorough documentation enables reproducibility, the number of analysis programs used can be so large that in reality reproducibility cannot be easily achieved. Literate programming is an approach to present computer programs to human readers. The code is rearranged to follow the logic of the program, and to explain that logic in a natural language. The code executed by the computer is extracted from the literate source code. As such, literate programming is an ideal formalism for systematizing analysis steps in biomedical research. We have developed the reproducible computing tool Lir (literate, reproducible computing) that allows a tool-agnostic approach to biomedical data analysis. We demonstrate the utility of Lir by applying it to a case study. Our aim was to investigate the role of endosomal trafficking regulators to the progression of breast cancer. In this analysis, a variety of tools were combined to interpret the available data: a relational database, standard command-line tools, and a statistical computing environment. The analysis revealed that the lipid transport related genes LAPTM4B and NDRG1 are coamplified in breast cancer patients, and identified genes potentially cooperating with LAPTM4B in breast cancer progression. Our case study demonstrates that with Lir, an array of tools can be combined in the same data analysis to improve efficiency, reproducibility, and ease of understanding. Lir is an open-source software available at github.com/borisvassilev/lir.

Data integration to prioritize drugs using genomics and curated data.

BACKGROUND: Genomic alterations affecting drug target proteins occur in several tumor types and are prime candidates for patient-specific tailored treatments. Increasingly, patients likely to benefit from targeted cancer therapy are selected based on molecular alterations. The selection of a precision therapy benefiting most patients is challenging but can be enhanced with integration of multiple types of molecular data. Data integration approaches for drug prioritization have successfully integrated diverse molecular data but do not take full advantage of existing data and literature. RESULTS: We have built a knowledge-base which connects data from public databases with molecular results from over 2200 tumors, signaling pathways and drug-target databases. Moreover, we have developed a data mining algorithm to effectively utilize this heterogeneous knowledge-base. Our algorithm is designed to facilitate retargeting of existing drugs by stratifying samples and prioritizing drug targets. We analyzed 797 primary tumors from The Cancer Genome Atlas breast and ovarian cancer cohorts using our framework. FGFR, CDK and HER2 inhibitors were prioritized in breast and ovarian data sets. Estrogen receptor positive breast tumors showed potential sensitivity to targeted inhibitors of FGFR due to activation of FGFR3. CONCLUSIONS: Our results suggest that computational sample stratification selects potentially sensitive samples for targeted therapies and can aid in precision medicine drug repositioning. Source code is available from http://csblcanges.fimm.fi/GOPredict/.

Identification of sample-specific regulations using integrative network level analysis.

BACKGROUND: Histologically similar tumors even from the same anatomical position may still show high variability at molecular level hindering analysis of genome-wide data. Leveling the analysis to a gene regulatory network instead of focusing on single genes has been suggested to overcome the heterogeneity issue although the majority of the network methods require large datasets. Network methods that are able to function at a single sample level are needed to overcome the heterogeneity and sample size issues. METHODS: We present a novel network method, Differentially Expressed Regulation Analysis (DERA) that integrates expression data to biological network information at a single sample level. The sample-specific networks are subsequently used to discover samples with similar molecular functions by identification of regulations that are shared between samples or are specific for a subgroup. RESULTS: We applied DERA to identify key regulations in triple negative breast cancer (TNBC), which is characterized by lack of estrogen receptor, progesterone receptor and HER2 expression and has poorer prognosis than the other breast cancer subtypes. DERA identified 110 core regulations consisting of 28 disconnected subnetworks for TNBC. These subnetworks are related to oncogenic activity, proliferation, cancer survival, invasiveness and metastasis. Our analysis further revealed 31 regulations specific for TNBC as compared to the other breast cancer subtypes and thus form a basis for understanding TNBC. We also applied DERA to high-grade serous ovarian cancer (HGS-OvCa) data and identified several common regulations between HGS-OvCa and TNBC. The performance of DERA was compared to two pathway analysis methods GSEA and SPIA and our results shows better reproducibility and higher sensitivity in a small sample set. CONCLUSIONS: We present a novel method called DERA to identify subnetworks that are similarly active for a group of samples. DERA was applied to breast cancer and ovarian cancer data showing our method is able to identify reliable and potentially important regulations with high reproducibility. R package is available at http://csbi.ltdk.helsinki.fi/pub/czliu/DERA/.

Identification of several potential chromatin binding sites of HOXB7 and its downstream target genes in breast cancer.

HOXB7 encodes a transcription factor that is overexpressed in a number of cancers and encompasses many oncogenic functions. Previous results have shown it to promote cell proliferation, angiogenesis, epithelial-mesenchymal transition, DNA repair and cell survival. Because of its role in many cancers and tumorigenic processes, HOXB7 has been suggested to be a potential drug target. However, HOXB7 binding sites on chromatin and its targets are poorly known. The aim of our study was to identify HOXB7 binding sites on breast cancer cell chromatin and to delineate direct target genes located nearby these binding sites. We found 1,504 HOXB7 chromatin binding sites in BT-474 breast cancer cell line that overexpresses HOXB7. Seventeen selected binding sites were validated by ChIP-qPCR in several breast cancer cell lines. Furthermore, we analyzed expression of a large number of genes located nearby HOXB7 binding sites and found several new direct targets, such as CTNND2 and SCGB1D2. Identification of HOXB7 chromatin binding sites and target genes is essential to understand better the role of HOXB7 in breast cancer and mechanisms by which it regulates tumorigenic processes.

MMP16 Mediates a Proteolytic Switch to Promote Cell-Cell Adhesion, Collagen Alignment, and Lymphatic Invasion in Melanoma.

Lymphatic invasion and accumulation of continuous collagen bundles around tumor cells are associated with poor melanoma prognosis, but the underlying mechanisms and molecular determinants have remained unclear. We show here that a copy-number gain or overexpression of the membrane-type matrix metalloproteinase MMP16 (MT3-MMP) is associated with poor clinical outcome, collagen bundle assembly around tumor cell nests, and lymphatic invasion. In cultured WM852 melanoma cells derived from human melanoma metastasis, silencing of MMP16 resulted in cell-surface accumulation of the MMP16 substrate MMP14 (MT1-MMP) as well as L1CAM cell adhesion molecule, identified here as a novel MMP16 substrate. When limiting the activities of these trans-membrane protein substrates toward pericellular collagen degradation, cell junction disassembly, and blood endothelial transmigration, MMP16 supported nodular-type growth of adhesive collagen-surrounded melanoma cell nests, coincidentally steering cell collectives into lymphatic vessels. These results uncover a novel mechanism in melanoma pathogenesis, whereby restricted collagen infiltration and limited mesenchymal invasion are unexpectedly associated with the properties of the most aggressive tumors, revealing MMP16 as a putative indicator of adverse melanoma prognosis.

Comparative analysis of methods for identifying somatic copy number alterations from deep sequencing data.

Somatic copy-number alterations (SCNAs) are an important type of structural variation affecting tumor pathogenesis. Accurate detection of genomic regions with SCNAs is crucial for cancer genomics as these regions contain likely drivers of cancer development. Deep sequencing technology provides single-nucleotide resolution genomic data and is considered one of the best measurement technologies to detect SCNAs. Although several algorithms have been developed to detect SCNAs from whole-genome and whole-exome sequencing data, their relative performance has not been studied. Here, we have compared ten SCNA detection algorithms in both simulated and primary tumor deep sequencing data. In addition, we have evaluated the applicability of exome sequencing data for SCNA detection. Our results show that (i) clear differences exist in sensitivity and specificity between the algorithms, (ii) SCNA detection algorithms are able to identify most of the complex chromosomal alterations and (iii) exome sequencing data are suitable for SCNA detection.

Deregulation of COMMD1 is associated with poor prognosis in diffuse large B-cell lymphoma.

BACKGROUND: Despite improved survival for the patients with diffuse large B-cell lymphoma (DLBCL), the prognosis after relapse is poor. The aim was to identify molecular events that contribute to relapse and treatment resistance in DLBCL. METHODS: We analysed 51 prospectively collected pretreatment tumour samples from clinically high risk patients treated in a Nordic phase II study with dose-dense chemoimmunotherapy and central nervous system prophylaxis with high resolution array comparative genomic hybridization (aCGH) and gene expression microarrays. Major finding was validated at the protein level immunohistochemically in a trial specific tissue microarray series of 70, and in an independent validation series of 146 patients. RESULTS: We identified 31 genes whose expression changes were strongly associated with copy number aberrations. In addition, gains of chromosomes 2p15 and 18q12.2 were associated with unfavourable survival. The 2p15 aberration harboured COMMD1 gene, whose expression had a significant adverse prognostic impact on survival. Immunohistochemical analysis of COMMD1 expression in two series confirmed the association of COMMD1 expression with poor prognosis. CONCLUSION: COMMD1 is a potential novel prognostic factor in DLBCLs. The results highlight the value of integrated comprehensive analysis to identify prognostic markers and genetic driver events not previously implicated in DLBCL. TRIAL REGISTRATION: ClinicalTrials.gov NCT01502982.

Identification of genetic markers with synergistic survival effect in cancer.

BACKGROUND: Cancers are complex diseases arising from accumulated genetic mutations that disrupt intracellular signaling networks. While several predisposing genetic mutations have been found, these individual mutations account only for a small fraction of cancer incidence and mortality. With large-scale measurement technologies, such as single nucleotide polymorphism (SNP) microarrays, it is now possible to identify combinatorial effects that have significant impact on cancer patient survival. RESULTS: The identification of synergetic functioning SNPs on genome-scale is a computationally daunting task and requires advanced algorithms. We introduce a novel algorithm, Geninter, to identify SNPs that have synergetic effect on survival of cancer patients. Using a large breast cancer cohort we generate a simulator that allows assessing reliability and accuracy of Geninter and logrank test, which is a standard statistical method to integrate genetic and survival data. CONCLUSIONS: Our results show that Geninter outperforms the logrank test and is able to identify SNP-pairs with synergetic impact on survival.

Individual and combined effects of DNA methylation and copy number alterations on miRNA expression in breast tumors.

BACKGROUND: The global effect of copy number and epigenetic alterations on miRNA expression in cancer is poorly understood. In the present study, we integrate genome-wide DNA methylation, copy number and miRNA expression and identify genetic mechanisms underlying miRNA dysregulation in breast cancer. RESULTS: We identify 70 miRNAs whose expression was associated with alterations in copy number or methylation, or both. Among these, five miRNA families are represented. Interestingly, the members of these families are encoded on different chromosomes and are complementarily altered by gain or hypomethylation across the patients. In an independent breast cancer cohort of 123 patients, 41 of the 70 miRNAs were confirmed with respect to aberration pattern and association to expression. In vitro functional experiments were performed in breast cancer cell lines with miRNA mimics to evaluate the phenotype of the replicated miRNAs. let-7e-3p, which in tumors is found associated with hypermethylation, is shown to induce apoptosis and reduce cell viability, and low let-7e-3p expression is associated with poorer prognosis. The overexpression of three other miRNAs associated with copy number gain, miR-21-3p, miR-148b-3p and miR-151a-5p, increases proliferation of breast cancer cell lines. In addition, miR-151a-5p enhances the levels of phosphorylated AKT protein. CONCLUSIONS: Our data provide novel evidence of the mechanisms behind miRNA dysregulation in breast cancer. The study contributes to the understanding of how methylation and copy number alterations influence miRNA expression, emphasizing miRNA functionality through redundant encoding, and suggests novel miRNAs important in breast cancer.

Comparative analysis of algorithms for integration of copy number and expression data.

Chromosomal instability is a hallmark of cancer, and genes that display abnormal expression in aberrant chromosomal regions are likely to be key players in tumor progression. Identifying such driver genes reliably requires computational methods that can integrate genome-scale data from several sources. We compared the performance of ten algorithms that integrate copy-number and transcriptomics data from 15 head and neck squamous cell carcinoma cell lines, 129 lung squamous cell carcinoma primary tumors and simulated data. Our results revealed clear differences between the methods in terms of sensitivity and specificity as well as in their performance in small and large sample sizes. Results of the comparison are available at http://csbi.ltdk.helsinki.fi/cn2gealgo/.

CNAmet: an R package for integrating copy number, methylation and expression data.

SUMMARY: Gene copy number and DNA methylation alterations are key regulators of gene expression in cancer. Accordingly, genes that show simultaneous methylation, copy number and expression alterations are likely to have a key role in tumor progression. We have implemented a novel software package (CNAmet) for integrative analysis of high-throughput copy number, DNA methylation and gene expression data. To demonstrate the utility of CNAmet, we use copy number, DNA methylation and gene expression data from 50 glioblastoma multiforme and 188 ovarian cancer primary tumor samples. Our results reveal a synergistic effect of DNA methylation and copy number alterations on gene expression for several known oncogenes as well as novel candidate oncogenes. AVAILABILITY: CNAmet R-package and user guide are freely available under GNU General Public License at http://csbi.ltdk.helsinki.fi/CNAmet.

Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme.

BACKGROUND: Coordinated efforts to collect large-scale data sets provide a basis for systems level understanding of complex diseases. In order to translate these fragmented and heterogeneous data sets into knowledge and medical benefits, advanced computational methods for data analysis, integration and visualization are needed. METHODS: We introduce a novel data integration framework, Anduril, for translating fragmented large-scale data into testable predictions. The Anduril framework allows rapid integration of heterogeneous data with state-of-the-art computational methods and existing knowledge in bio-databases. Anduril automatically generates thorough summary reports and a website that shows the most relevant features of each gene at a glance, allows sorting of data based on different parameters, and provides direct links to more detailed data on genes, transcripts or genomic regions. Anduril is open-source; all methods and documentation are freely available. RESULTS: We have integrated multidimensional molecular and clinical data from 338 subjects having glioblastoma multiforme, one of the deadliest and most poorly understood cancers, using Anduril. The central objective of our approach is to identify genetic loci and genes that have significant survival effect. Our results suggest several novel genetic alterations linked to glioblastoma multiforme progression and, more specifically, reveal Moesin as a novel glioblastoma multiforme-associated gene that has a strong survival effect and whose depletion in vitro significantly inhibited cell proliferation. All analysis results are available as a comprehensive website. CONCLUSIONS: Our results demonstrate that integrated analysis and visualization of multidimensional and heterogeneous data by Anduril enables drawing conclusions on functional consequences of large-scale molecular data. Many of the identified genetic loci and genes having significant survival effect have not been reported earlier in the context of glioblastoma multiforme. Thus, in addition to generally applicable novel methodology, our results provide several glioblastoma multiforme candidate genes for further studies.Anduril is available at http://csbi.ltdk.helsinki.fi/anduril/The glioblastoma multiforme analysis results are available at http://csbi.ltdk.helsinki.fi/anduril/tcga-gbm/