Search Results - EGA European Genome-Phenome Archive

Exome-sequencing identifies new oncogenes and tumor suppressor genes recurrently altered in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy. High-resolution copy number analysis of 125 tumors of which 24 were subjected to whole-exome sequencing identified 135 homozygous deletions and 994 somatic gene mutations with predicted functional consequences. We identified new recurrent alterations in 4 genes (ARID1A, RPS6KA3, NFE2L2 and IRF2) not previously described in HCC. Functional analyses demonstrated tumor suppressor properties for IRF2 whose inactivation, exclusively found in hepatitis B virus related tumors, leads to impaired TP53 function. Alternatively, inactivation of proteins involved in chromatin remodeling was frequent and predominant in alcohol related tumors. Moreover, activation of the oxidative stress metabolism and inactivation of RPS6KA3 were new pathways associated with WNT/beta-catenin activation, thereby suggesting a cooperative effect in tumorigenesis. This study shows the dramatic somatic genetic diversity in HCC, it reveals interactions between oncogene and tumor suppressor gene mutations markedly related to specific risk factors.

Study EGAS00001000217

Mutations in the RAS/MAPK pathway drive replication repair deficient hypermutated tumors and confer sensitivity to MEK inhibition

The RAS/MAPK pathway is an emerging targeted pathway across a spectrum of both adult and pediatric cancers. Typically, this is associated with a single, well-characterized point mutation in an oncogene. Hypermutant tumors which harbor many somatic mutations may obscure the interpretation of such targetable genomic events. We find that replication repair deficient (RRD) cancers which are universally hypermutant and affect children born with RRD cancer predisposition, are enriched for RAS/MAPK mutations (p=10-8). These mutations are not random, exist in subclones, and increase in allelic frequency over time. The RAS/MAPK pathway is activated both transcriptionally and at the protein level in patient derived RRD tumors and these tumors responded to MEK inhibition in vitro and in vivo. Treatment of patients with RAS/MAPK hypermutant gliomas reveal durable responses to MEK inhibition. Our observations suggest that hypermutant tumors may be addicted to oncogenic pathways resulting in favorable response to targeted therapies.

Study EGAS00001005008

Enhancer Mapping in Chronic Lymphocytic Leukemia

In this study we profile the epigenomic enhancer landscapes of CLL B cells (CD19+/CD5+) harvested from peripheral blood of patients from our Center. Included are results of ChIPseq profiling using chromatin immunoprecipitation of the enhancer histone mark H3K27ac (acetylated lysine 27 on histone H3), and open chromatin profiles using ATAC-seq (assay for transposase accessible chromatin). These profiles are used to define the global enhancer and super enhancer landscape of CLL B cells, and to derive active transcription factor networks associated with this disease. Also included are H3K27ac ChIP-seq and ATAC-seq datasets for non-CLL B cells obtained from the peripheral blood of normal adult donors.

Study phs001704

scRNA-seq of HSPC treated with gemcitabine and carboplatin

4 samples of 2000000 HSPCs in 2 ml media each were then prepared and treated for 24 hours: carboplatin-high: 150 µg/ml carboplatin carboplatin-low: 18.75 µg/ml carboplatin gemcitabine: 25 ng/ml gemcitabine control: no drug Single-cells were subsequently extracted using the ddSEQ™ Single-Cell Isolator (Bio-Rad) and later sequenced using the SureCell™ Whole Transcriptome Analysis 3' Library Prep Kit (Illumina) on the NextSeq 500 System (Illumina) using the NextSeq 500/550 High Output Kit v2.5 150 Cycles (Illumina) all following the manufacturer's instructions. The raw FASTQ-files are available as read 1 and read 2 files from 4 lanes for each sample giving a total of 16 FASTQ-files. The FASTQ-files were also processed following the ddSeeker (Romagnoli, D., et al., BMC Genomics 2018, doi:10.1186/s12864-018-5249-x) and Drop-seq (Macosko, E.Z., et al., Cell 2015, doi:10.1016/j.cell.2015.05.002) protocols for processing scRNA-seq data to yield the final digital gene expression (dge) for each cell of each sample. This has resulted in one dge text file and one dge summary text file per sample. These 8 text-files with dge data are found in the zip-compressed analysis data-file.

Dataset EGAD00001006080

Distribution of data using Live Distribution

Live Distribution Welcome to the documentation for using the Live Distribution feature for distributing data files securely through the EGA platform. This guide will walk you through the process of downloading encrypted files and decrypting them using Crypt4GH. Please follow the steps below to ensure a smooth experience. Before Downloading Create an EGA user. Make sure that you have the permissions to download the dataset of interest. In case you don’t have access, request access to the dataset. Add your Crypt4GH-compatible public key to your EGA account. Please allow a few hours for your public key to be synced with your profile. Afterwards, you will be able to connect to your EGA outbox using the SFTP protocol. Download Graphical User Interface (GUI) You can use any GUI that supports SFTP connections, such as FileZilla, an open-source FTP client. For Filezilla as your GUI, follow these steps to download files: Open FileZilla and access Site Manager (File > Site Manager). Create a new connection with the following settings (Figure 1): Protocol: SFTP - SSH File Transfer Protocol Host: __EGA_OUTBOX_DOMAIN__ Logon Type: Key file User: your EGA username Key file: path/to/your/private_key Figure 1: Process of establishing a new connection to __EGA_OUTBOX_DOMAIN__ using a key file as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the EGA outbox, ensuring seamless data transfers. Click Connect to access your Outbox. This folder serves as your storage space within the EGA cloud, containing files accessible for download in a secure way. Browse the remote directory on the right side of the FileZilla screen. Select the files you wish to download, right-click, and choose Download (Figure 2). Figure 2: Step-by-step process of manually downñoad files from __EGA_OUTBOX_DOMAIN__ using FileZilla, with FileZilla version 3.52.2 operating on IOS v11.2.3. The figure demonstrates how users can downñoad data from the EGA outbox to their local storage by following the depicted steps SFTP command line You can also use the SFTP command line to securely download files from the EGA Outbox. Using SFTP command line client in Linux/Unix Open a terminal window Enter the following command to connect: sftp username@hostname Enter your EGA password To see a list of available sftp commands type help sftp> put – Upload file sftp> get – Download file sftp> cd path – Change remote directory to ‘path’ sftp> pwd – Display remote working directory sftp> lcd path – Change the local directory to ‘path’ sftp> lpwd – Display local working directory sftp> ls – Display the contents of the remote working directory sftp> lls – Display the contents of the local working directory Type get command to download files. For example: get encrypted_file.c4gh Use the bye command to close the connection (SFTP session). Convenient SSH settings Include the following settings in your SSH config file, located in ~/.ssh/config Host __EGA_OUTBOX_DOMAIN__ EGA-outbox hostname __EGA_OUTBOX_DOMAIN__ User username IdentityFile path/to/the/private/key Replace username and path/to/the/private/key with the appropriate settings, and you will be able to connect to the __EGA_OUTBOX_DOMAIN__ simply using sftp EGA-outbox. How to decrypt Files archived at the EGA are encrypted based on Crypt4GH. Hence, to decrypt the files you need to install Crypt4GH. You can install a python implementation of it, with pip install crypt4gh or directly from the Github repository pip install git+https://github.com/EGA-archive/crypt4gh.git After installing Crypt4GH, decrypt files using the following command: crypt4gh decrypt --sk /path/private/key < encrypted_file.c4gh > decrypted_filename The command reads the encrypted file from stdin (with <) and output the decrypted version to stdout (with >). Replace encrypted_file.c4gh and decrypted_filename with the appropriate filenames but make sure to not use the same filename for both reading and writing because your SHELL would then truncate both files before you even read or write. Frequently Asked Questions What username should I use to log in to my outbox? The authentication process for logging in to the EGA website, as well as accessing your inbox and outbox, requires the use of your username, not your email address. Therefore, if you registered a username different from your email address when creating your EGA account, you must use that username to log in. If you have forgotten your registered username, please, contact our Helpdesk team for assistance. I see that some files in my dataset have 'unavailable' as extension. What should I do? Within your Outbox, you'll find a list of all the datasets available for download. Occasionally, certain files may be marked as "unavailable". These unavailable files can be identified by the "unavailable" extension added to their filenames (e.g. filename.fastq.gz.unavailable.c4gh). If you encounter an unavailable file that you need, please reach out to our Helpdesk. We'll promptly work on making the file accessible for download as soon as possible. Specific to using keys Can I access one EGA account from different devices? Yes, you can access your account from different devices by linking several public keys to your EGA account. Each device can generate a unique public-private key pair, and the corresponding public keys can be linked to the same account. This way, you can use different public keys on different devices and still have access to the same account and data. I have several keys and I don't remember which one is which When generating SSH keys, it's a good practice to add a comment using the -C flag. This will allow you to add a descriptive tag to your key, making it easier to identify later on. Here's an example command that generates an SSH key with a comment: ssh-keygen -t ed25519 -C work-pass In this example, we're generating an ed25519 SSH key with the comment work-pass. Once you have multiple keys with different comments, you can use the comments to easily identify each key. To view the comments for your existing SSH keys, you can use the following command: ssh-keygen -l -f /path/to/key This will display the key fingerprint and the associated comment. By checking the comments, you should be able to identify which key is which. What if I can't find my SSH keys for uploading files with a key file, and how can I use new keys? If you can't find your SSH keys, don't worry - you can make new ones. To do this, open your terminal or command prompt and type a command to make a new SSH key. You can pick a name for the key, and choose a password to keep it safe. After making the key, you can add the new key to your account or server where you want to upload files using the key file. This usually involves copying and pasting the key's "public" (e.g. file.pub) part to the right place. If you lose track of the key again, just make a new one and add it again. Keep in mind that SSH keys belong to you and your computer, so if you switch computers or accounts, you'll need to make new keys. I don't want to type the passphrase every time I use the key. What can I do? You can use an ssh-agent to avoid typing the passphrase every time you use the key. An ssh-agent is a program that stores your private keys in memory and provides them to ssh when needed. You can add your key to the ssh-agent using the command ssh-add followed by the path to your key file.Here's an example of the steps to follow: Open a terminal window.Start the ssh-agent by typing the command eval $(ssh-agent).Add your key to the ssh-agent by typing the command ssh-add [key filepath]. For instance, if your key file is located in the home directory with the name mykey, the command will look like this: ssh-add ~/mykey After adding your, key to the ssh-agent, you should be able to use ssh without having to enter your passphrase every time. Can I use my password for authentication (without my private key)? If you prefer to use your username and password for authentication instead of your private key, you can still do so. When using a Graphical User Interface (GUI) such as FileZilla, you can select Ask for password as your Logon Type (Figure 3). This option will prompt you to enter your password when you click Connect, instead of using your private key. Figure 3: This option will prompt you to enter your password when you click "Connect", instead of using your private key. Figure 3: Process of establishing a new connection to __EGA_OUTBOX_DOMAIN__ using your password as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the inbox, ensuring seamless data transfers. It's worth noting that using a password for authentication can be less secure than using an SSH key, as passwords can be more easily compromised through various means. However, if you choose to use your password for authentication, selecting "Ask for password" as your Logon Type is a good way to do so securely via a GUI. Why is it better to use my key and not my password? SSH keys for authentication is generally considered to be more secure and convenient than using passwords. SSH keys are more difficult to crack than passwords, and they can be restricted to specific users and machines, giving you more control over access. Once you set up your SSH keys, you can use them to authenticate quickly and easily, without having to enter a password every time. This makes automation of tasks, such as uploading encrypted files, much simpler. Additionally, SSH keys provide better logging, allowing you to keep track of who is accessing your systems and when. All in all, using SSH keys is a good practice for improving security and convenience in your authentication process.

Documentation access/download/files/live-outbox

Transcriptional and DNA binding profiling of CEBPE in REH acute lymphoblastic leukaemia cells using shRNA RNA-Seq and ChIP-Seq

ChIP-Seq - CEBPE - REH. The ETV6/RUNX1 translocated acute lymphoblastic leukaemia cell line. REH was used to perform ChIP-Seq using a CEBPE antibody. Cells were fixed in 1% formaldehyde for 10mins, prior to preparation of chromatin using Active Motif Express ChIP-IT. 2ug of antibody (anti CEBPE Atlas Antibodies HPA002928)was added to 25ug of chromatin O/N at 4C with rotation. Duplicate reactions were pooled and purified. 10ng of ChIP’d and input DNA used for Illumina NGS preparation (NEBNext ChIP-Seq Library kit; New England Biolabs), CEBPE and Input DNA ChIP samples were sequenced on a MiSeq using 150bp Kit v3 paired end and a HiSeq 2500 using 2x101 version 4 paired end (Illumina) respectively. Reactions performed in duplicate. shCEBPE RNA-Seq - REH. REH cells were lentivirally transduced with a pTRIPZ shRNA vector for transcriptional profiling of CEBPE. Two controls (empty and non-targeting) and two CEBPE shRNAs (V3THS_150517(A13), V3THS_404312(G3) Dharmacon, GE) were transduced into REH cells. Cells were treated with 1ug/ml doxycyclin for 144hrs and total RNA purified using Qiagen RNeasy. Knock down of CEBPE was validated by qRT. RNA integrity >7.7 for all samples. Libraries were prepared using NEBNext Ultra II Directional RNA Library Prep Kit and sequenced on an Illuimna HiSeq 2500 using 2x101 version 4 paired end chemistry. 3 biological replicates of each samples were prepared.

Dataset EGAD00001004084

Dataset for "HPV integration induces gene fusions" (Illumina)

Illumina whole-genome sequencing data for "HPV integration induces gene fusions" We performed short read whole-genome sequencing of five HPV+ head and neck cancer samples using Illumina. The reads are submitted in bam or fastq file format.

Dataset EGAD50000001305

Raw scRNA-seq data for “Characterization of intestinal immune responses in generalized human and murine lipodystrophy”

Raw 10X 5' v3 scRNA-seq data (GEX) for PBMC of 3 helthy donors and two AGLCD patient timepoints as well as of LPMC of 3 Crohns disease patients and one timepoint of the AGLCD patient.

Dataset EGAD50000002190

Raw scTCR-seq data for “Characterization of intestinal immune responses in generalized human and murine lipodystrophy”

Raw 10X 5' v3 scTCR-seq data (TCR) for PBMC of 3 helthy donors and two AGLCD patient timepoints as well as of LPMC of 3 Crohns disease patients and one timepoint of the AGLCD patient.

Dataset EGAD50000002191

ChIP-seq for 10 samples

This dataset contains ChIP-seq data generated from two patient-derived xenograft (PDX) models. For each model, sequencing data are provided from input control (n=1), IgG control (n=2), and FOXF1 immunoprecipitation (n=2). All the 10 samples are in bam file type.

Dataset EGAD50000001786

WGS of NPC268_Tumor and NPC268_Cell_line

This dataset contains WGS data in the form of BAM files for NPC268 - "Tumor" derived from snap-frozen tissue while "Cell line" derived from late passage NPC268 cell line. Extracted DNA was sent for 100x and 60x WGS with Novogene via Apical Scientific Sdn Bhd.

Dataset EGAD00001010292

Oncoprint GSCCs

This dataset contains DNA sequencing data from 7 GSCCs (data for BT569 is not available). We performed focused exome sequencing (43 most mutated genes in GBM), combined with OneSeq analysis (Agilent) on the GSCCs and identified both shared and GSCC-specific mutations. For CME038, whole genome sequencing was performed.

Dataset EGAD00001011276

GenomeDenmark Phase 2 - variants called on chrY for 62 males.

Variants on the Y chromosome for 62 danish males in VCF format from the GenomeDenmark Phase 2 cohort. Variants were called using reference based approaches such as the haplotype-caller module from GATK and using alignment of denovo assemblies to the reference using ASMvar.

Dataset EGAD00001003186

H3Africa ReMAC Shotgun Metagenomic Phenotype

Phenotype data for shotgun metagenomic sequencing data of nasopharyngeal fluid for studying nasopharyngeal colonization dynamics with Streptococcus pneumoniae and associated antimicrobial-resistance in a South African birth cohort. https://www.ebi.ac.uk/ena/data/view/PRJEB37312

Dataset EGAD00001006244

A Single-cell Atlas of Progressive TKI Resistance in Chronic Myeloid Leukemia

57 Bone marrow specimens for 5 healthy bone marrow and 24 CML samples profiled with 10X scRNA-seq 5' upon separation using MACS for CD34.

Dataset EGAD00001007946

Profiling RNA Translation in Pediatric Medulloblastoma

This study provides RNA-sequencing and ribosome profiling data for patient-derived cell lines and patient tissue samples for children with medulloblastoma. Ribosome profiling is a variant protocol of RNA-sequencing that directly sequences ribosome-bound RNA fragments only. Associated RNA-seq and Ribo-seq data obtained separately for some cancer cell lines can be found on the NCBI SRA as PRJNA957428. Samples were processed for poly-A mRNA sequencing using the Roche Kapa Kit. Ribosome profiling was performed as described in the manuscript referenced below (Hofman et al.) and based on the article by McGlincy et al., Methods (2017). Ribo-seq data were analyzed for sample quality using RiboseQC (Calviello, Nat Struct Mol Biol, 2020). These data were used to quantify P-sites of open reading frames. RNAseq and ribo-seq data were integrated and compared to determine translational efficiency values using TPM (Ribo-seq) over TPM (RNA-seq) as the metric. Ribo-seq data on 14 samples and RNAseq data on 21 samples derived from 16 fresh-frozen surgical samples for medulloblastoma and 5 autopsy samples are available through dbGaP. A second cohort of 4 medulloblastoma samples (RNAseq, Ribo-seq) related to this study can be found on the EGA at EGAS00001007426.

Study phs003446

Interactions between the tumor and the systemic response of breast cancer patients

Breast cancer (BC) research has largely focused on the molecular properties of the tumor proper. However, to understand how cancer progresses and ultimately modulates patient outcome, we also require an understanding of the molecular changes in the patient systemic response (SR). Toward this end, we generated and analyzed RNA profiles from tumor and matched blood samples in 173 BC patients. We designed a system (MIxT) to explore and link tightly co-expressed gene sets (modules) across matched tissues. The gene composition of modules, and their expression, largely vary across tissues. Distinct patterns of expression in the SR are predominantly detected in highly immunogenic BC subtypes. We also find that the expression pattern of a module in one tissue is correlated to the expression pattern of a module in the other tissue in a fashion that depends on subtype. For example, systemic immunosuppression is detected in basalL patients in a fashion that is proportional to the level of expression of several tumor modules highlighting immune evasion mechanisms active in these particular cancers.

Study EGAS00001001804

Sequencing to Guide Cancer Care (CanSeq)

The overall goal of the CanSeq U01 project is to study the impact of whole-exome sequencing (WES) on the clinical care of cancer patients and oncology provider practices. The aims of Project 1 are to implement and establish the feasibility of germline and somatic WES in patients with advanced solid tumors (lung and colon); to develop a framework for interpreting and reporting for exome sequencing data; to determine the proportion of patients with "actionable items" compared to existing technologies; and to report on the percentage of patients in whom unique WES findings led to a clinical action. The aims of Project 2 are to implement a production-scale platform for WES from archival (FFPE) material; to identify biologically relevant somatic and germline alterations existing in tumor/normal DNA from individual patients; to produce an evidence-based list of clinically "actionable" genetic alterations; and to develop inferential models that predict the utility of tumor genomic data within the larger clinical context. The goals of Project 3 are to describe the impact of information derived through WES on cancer patients; to test the hypothesis that patients will want to receive information about all potentially informative somatic and germline variants; to study patients' understanding of disclosed genomic information; and to describe the experiences of oncology providers as they implement WES into cancer care delivery.

Study phs001075

Differential Gene Expression in Cryptorchid Testes

Differential Gene Expression in Cryptorchid Testes Study uses whole-genome RNA profiling of testicular biopsies to determine a potential causative role of isolated congenital cryptorchidism in azoospermia and/or infertility in the context of our previously published GeneChip data. Cryptorchid patients, aged 7 months to 5 years and otherwise healthy, were enrolled in this prospective study. During surgery, testicular tissue biopsies were obtained for histological examination and RNA-sequencing (RNA-seq). Fifteen patients were selected based on the histological results and were divided into two groups. Seven were classified as belonging to the high infertility risk (HIR) and eight to the low infertility risk (LIR) group. Cryptorchid boys in the high infertility risk group lacked transformation of gonocytes into Ad spermatogonia due to impaired mini puberty. This group of patients will be infertile despite successful surgery.

Study phs001275

International Collaboration of Incident HIV and HCV in Injecting Cohorts (InC3)

This study represents a merged international multi-cohort project of pooled biological and behavioral data from ten prospective cohorts of injection drug users (Grebely J, Morris MD, Rice TM, Bruneau J, Cox AL, Kim AY, et al., 2012, PMID: 23203695). Of the 10 cohorts, six provided data and samples for the genome wide association study (GWAS) entitled “Genetic Analysis of Opioid Addiction in People Who Inject Drugs”. The InC3 cohorts included in the GWAS were: i) Australian Trial in Acute Hepatitis C (ATAHC-I, Sydney); ii) Hepatitis C Incidence and Transmission Study in Prison (HITS-p) (Sydney); iii) Hepatitis C Incidence and Transmission Study in Community (HITS-c) (Sydney); iv) HepCo Study (Montreal); v) SuperMIX Study (combining the Networks II & MIX studies, Melbourne); and vi) the UFO Study (San Francisco).

Study phs002310

Combined PDCD1, BRAF and MAP2K7 Inhibition in BRAFV600E Colorectal Cancer: A Phase 2 Trial

In this study, we evaluated the clinical efficacy of combined PDCD1, BRAF, and MAP2K7 inhibition in metastatic BRAFV600E Colorectal Cancer (CRC) patients (NCT03668431). We performed bulk whole exome sequencing (WXS) and bulk RNA sequencing (RNA-Seq) in 35 pre-treatment tumor biopsies and matching germline for tumor mutational burden (TMB), BM1/BM2 transcriptional subtypes, and consensus molecular subtypes (CMS) analysis, which were previously reported to affect prognosis and response to therapy (PMIDs: 32047001, 27354468, and 26457756). Analysis of bulk data showed that TMB, BM1/BM2 subtypes, and CMS did not correlate with clinical response in this study. Analysis of single cell RNA sequencing (scRNA-Seq) data of tumor epithelial cells from 23 paired day 0 and day 15 tumor biopsies revealed greater induction of tumor cell-intrinsic immune programs and more complete MAPK inhibition in patients with better clinical outcome.

Study phs003178

RNA-seq, WGS and WES of Hepatocellular carcinomas, enriched in fibrolamellar carcinomas

DNAJB1-PRKACA fusion is a specific driver event in fibrolamellar carcinoma (FLC), a rare subtype of hepatocellular carcinoma (HCC) occurring in adolescents and young adults. In older patients, molecular determinants of HCC with mixed histological features of HCC and FLC (mixed-FLC/HCC) remain to be discovered.This study aims to molecularly caracterize mixed fibrolamellar hepatocellular carcinoma (mixed FLC/HCC) within the molecular liver tumors diversity, in respect of pure FLC and different HCC subtypes. This study reunites WGS data (n=3, Tumoral/Non-Tumoral pairs), WES data (n=43, Tumoral/Non-Tumoral pairs) and RNAseq data (n=66, Tumoral only). On a trascriptomic level, most of the mixed-FLC/HCC RNAseq clustered in a robust subgroup of tumors all showing mutation or translocation inactivating BAP1 that codes for the BRCA1 associated protein-1.

Study EGAS00001003837

RNA stability controlled by m6A methylation contributes to X-to-autosome dosage compensation in mammals

In mammals, X-chromosomal genes are expressed from a single copy since males (XY) possess a single X chromosome, while females (XX) undergo X inactivation. To compensate for this reduction in dosage compared to two active copies of autosomes, it has been proposed that genes from the active X chromosome exhibit dosage compensation. However, the existence and mechanism of X-to-autosome dosage compensation are still under debate. Here, we show that X-chromosomal transcripts are reduced in m6A modifications and more stable compared to their autosomal counterparts. Acute depletion of m6A selectively stabilises autosomal transcripts, resulting in perturbed dosage compensation in mouse embryonic stem cells. We propose that higher stability of X-chromosomal transcripts is directed by lower levels of m6A, indicating that mammalian dosage compensation is partly regulated by epitranscriptomic RNA modifications.

Study EGAS00001007112

Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma

Background: Cancer patients with advanced disease exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates sequencing data with functional assay data to develop patient-specific combination cancer treatments. Methods: Tissue taken from a murine model of alveolar rhabdomyosarcoma was used to perform single agent drug screening and DNA/RNA sequencing experiments; results integrated via our computational modeling approach identified a synergistic personalized two-drug combination. Cells derived from the tumor were allografted into mouse models and used to validate the personalized two-drug combination. Computational modeling of single agent drug screening and RNA sequencing of multiple heterogenous sites from a single patient’s epithelioid sarcoma identified a personalized two-drug combination effective across all tumor regions. The heterogeneity-consensus combination was validated in a xenograft model derived from the patient’s primary tumor. Cell cultures derived from human and canine undifferentiated pleomorphic sarcoma were assayed by drug screen; computational modeling identified a resistance-abrogating two-drug combination common to both cell cultures. This combination was validated in vitro via a cell regrowth assay. Results Our computational modeling approach addresses three major challenges in personalized cancer therapy: synergistic drug combination predictions (validated in vitro and in vivo in a genetically engineered murine cancer model), identification of unifying therapeutic targets to overcome intra-tumor heterogeneity (validated in vivo in a human cancer xenograft), and mitigation of cancer cell resistance and rewiring mechanisms (validated in vitro in a human and canine cancer model). Conclusions These proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy.

Study EGAS00001003564

NEI CIDR Methylation Profiling of Primary Open Angle Glaucoma in GLAUGEN Samples

This is a case-control study of Primary Open Angle Glaucoma (POAG). POAG is a progressive optic neuropathy that eventually leads to blindness. More than 30 million people worldwide have Primary Open Angle Glaucoma (POAG), of which greater than 3 million are blind. Gene expression changes in the retina have been observed for POAG. Several recent studies, including the GLAUGEN (Glaucoma Gene Environment) initiative have used GWAS to identify correlative regions of the genome. Despite this, the genetic basis of Glaucoma is not well understood. Epigenetic variation may account for low heritability and environmental effects on human disease. Despite the significant advances being made in understanding the role of epigenetics in gene regulation in other fields, little is known about the relationship between DNA methylation patterns, retinal gene expression, and retinal disease. The goal of this study is to identify differentially methylated regions in the peripheral blood of patients with POAG. The case identification is as per the GLAUGEN description: Cases and controls were recruited from ophthalmology clinics and were examined by ophthalmologists. For cases the clinical exam included intraocular pressure measurements, optic nerve assessment and visual field evaluation. Controls had no family history of glaucoma, normal intraocular pressure and normal optic nerves. POAG and control samples come from Massachusetts Eye and Ear Infirmary (MEEI). These samples were collected and genotyped as part of the GLAUGEN study. From the MEEI, 50 patients were selected from POAG pedigrees, to include all the probands genotyped by CIDR and are currently undergoing exome sequencing, and to include additional genotyped probands who belong to pedigrees of sufficient size and structure that parent of origin effects could be evaluated. Additional individual-level phenotype and genotype data may be obtained through the authorized access portal of phs000308 (Geneva - Glaugen GWAS Study).

Study phs000461

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