Pancreatic cancer is one of the deadliest cancers, with extremely poor survival rates, highlighting the urgent need for new and more effective treatments. Immunotherapy, a promising cancer treatment that leverages the body's immune system to fight cancer, has shown limited success in pancreatic ductal adenocarcinoma (PDAC). Understanding why PDA tumors resist immunotherapy could help improve the effectiveness of existing treatments and lead to better outcomes for patients.

Tumors are not just cancer cells, but include many other cell types, including immune cells. A tumor’s DNA makeup can profoundly influence the number and types of immune cells surrounding the tumor, but the specific mutations directing immune response and their associated biochemical mechanisms are poorly understood. KRAS gene mutations are present in almost all pancreatic tumors, though there is considerable variation in patient outcomes depending on KRAS genotype. Our data using mouse models and publicly available and real-world human PDAC datasets indicate different immune cell populations are present in pancreas tumors based on tumor genotype. Our ultimate goal is to develop personalized, more effective treatment strategies for patients with PDA based on their genotype and/or immunophenotype.

About the speaker

Join us for an exciting September webinar where we delve into cutting-edge computing environments within the Cancer Genomics Cloud (CGC), led by Dave Roberson, Community Engagement Manager at Velsera.

The webinar features the Applied Chest Imaging Laboratory (ACIL) platform, a resource from Brigham & Women’s Hospital/Harvard Medical School, now integrated with the CGC. Discover how ACIL leverages transfer learning to evaluate vascularization and perform densitometry on chest CT images. This innovative approach is paving the way for improved diagnostics of Chronic Obstructive Pulmonary Disease (COPD) and other pulmonary conditions, offering valuable insights that transform patient care.

In addition to the ACIL platform, we will discuss the wealth of imaging data available through The Cancer Imaging Archive (TCIA) and the Imaging Data Commons (IDC). Be among the first to hear about the latest advancements on the Cancer Genomics Cloud, including new GPU hardware-accelerated instance types on Amazon Web Services and Google Cloud Platform to supercharge your research capabilities.

Join us for an enlightening session on groundbreaking advancements in pediatric oncology research and precision medicine. This webinar will delve into the innovative work behind the Open Pediatric Brain Tumor Atlas (OpenPBTA) and its expansion into the Open Pediatric Cancer (OpenPedCan) project. These initiatives offer a harmonized, open-source multi-omic dataset from approximately 6,000 pediatric cancer patients, encompassing over 100 histologies and 7,000 tumor events. Dr. Jo Lynne Rokita will showcase over 50 ready-to-use, reproducible analysis modules and more than 100 somatic outputs, designed to accelerate discovery, validation, and clinical translation in pediatric cancer research. This session promises to provide invaluable insights and practical tools for researchers dedicated to advancing the field.

About Our Speaker

Dr. Jo Lynne Rokita leads the Bioinformatics Translational Pediatric Oncology Team at the Center for Data-Driven Discovery in Biomedicine at the Children's Hospital of Philadelphia. With a focus on mechanisms of RNA splicing that drive tumorigenesis and the identification of novel splice-derived neoepitopes in pediatric brain tumors, her research also explores pathogenic germline risk variants and their influence on tumor development. Dr. Rokita is committed to advancing pediatric oncology through collaboration and the development of scalable, open-source analytical tools, frameworks, and data resources, utilizing platforms like Docker, GitHub, AWS, and CAVATICA. Her pioneering work is transforming pediatric cancer research, offering new avenues for discovery and clinical application.

Join us in June for the second part of our mini-symposium on canine cancer as an emerging model for cancer research.

Dogs experience spontaneously arising cancer at roughly five times the rate that humans do, and their immune systems and environmental exposures are very similar to those of humans. Recent work in both “wet” and “dry” labs have produced valuable new methods for sample collection and diagnosis; breed prediction and classification; and tumor microenvironment and genetic analysis. These advances promise to yield better outcomes for dogs and humans.

Dr. Shaying Zhao will speak on her lab’s development of new bioinformatic research methods in comparative oncology in her talk titled “MHC genotyping, tumor-specific neoantigen discovery, and T cell repertoire characterization for the dog.”

Dr. Heather Gardner will describe developments in minimally invasive diagnostic techniques in a talk titled “Considerations for longitudinal liquid biopsy analysis in spontaneous canine cancers.”

About our speakers

Join us in May for the first of a two-part series on the canine cancer research model.

Dogs are estimated to have a rate of spontaneous cancer incidence five times that of humans. As our webinar speakers noted in a recent review article[1], this provides a unique opportunity to study cancer causes and treatments in a mammal with a similar immune system to humans. The canine cancer model is emerging as a crucial tool for comparative oncology, yielding insights into human cancer pathophysiology and potential treatments. With recent advancements in both research techniques and genomic repositories and analyses, canine cancer has become an invaluable resource for cancer research.

Dr. Cheryl London will speak on Advances in the canine cancer genomics toolbox, followed by Gina Kuffel with a demonstration of the Integrated Canine Data Commons (ICDC) single click export button, From building cohorts to conducting meaningful analysis.

Then, Dr. Elaine Ostrander will present her lab’s recent research in the Genetic analysis of invasive bladder cancer risk in Shetland sheepdogs.

About the speakers

Dr. Cheryl London, the Anne Engen and Dusty Professor of Comparative Oncology at Tufts Cummings School of Veterinary Medicine, is a seasoned veterinary medical oncologist with a focus on translational and comparative oncology. As Associate Dean for Research and Graduate Education, she has expanded the university’s research footprint and increased DVM student engagement in research activities. Dr. London earned her DVM from Cummings School, completed her medical oncology residency at the University of Wisconsin-Madison, and received her PhD in Immunology from Harvard University.

Gina Kuffel of the Frederick National Laboratory is the Technical Project Manager and Product Owner for the ICDC. Gina’s professional interests include bioinformatics, open-source tools, cloud computing, and building web applications.

Dr. Elaine Ostrander is the Chief and Distinguished Senior Investigator and Head of the Section on Comparative Genetics, National Human Genome Research Institute, National Institutes of Health. Dr. Ostrander’s laboratory studies the canine genome and its utility as a system for informing human health and biology. Underlying her work are extensive worldwide studies of canine population structure. She has published over 400 papers and reviews, won several awards and in 2019 was elected to the National Academy of Sciences. Dr. Ostrander’s current work focuses on the identification of cancer susceptibility genes in dogs, particularly bladder cancer and histiocytic sarcoma, the role of genetic variants in canine behaviors, the origins of dog breeds, and genetic studies on dogs from Chernobyl.

1. London, C., et al. “Leading the pack: Best practices in comparative canine cancer genomics to inform human oncology.” Vet Comp Oncol. 2023; 21(4): 565-577. doi:10.1111/vco.12935.

Pancreatic cancer is a challenging disease with 5-year survival rates of ~10%. One contributing factor to these outcomes is that there are few biomarkers to guide chemotherapy decisions. We propose to discover predictive biomarkers of chemotherapy sensitivity by applying (a) first regulatory network and (b) then master regulator analysis to the annotated molecular-clinical dataset in the Pancreatic Cancer Action Network SPARK database. Regulatory network and master regulator analysis are complementary techniques that permit the generation of a reverse-engineered transcriptional network for a given tissue type and then the inference of critical protein signaling nodes – termed master regulators – from that network. 

About the Speaker

In the ever-expanding landscape of genomics research, efficient data access and analysis are paramount. Enter the SRA to DRS Converter, a new workflow in the Cancer Genomics Cloud SRA Tools Suite that bridges the gap between the Sequence Read Archive (SRA) and the Data Repository Service (DRS). 

Unlocking 14 Petabytes of Sequencing Data 

The SRA houses over 14 petabytes of invaluable sequencing data relevant to human health and medical research. However, traditional methods of accessing that data involve downloading files using the SRA Toolkit, resulting in redundant copies and significant time overhead. 

Our innovative workflow flips the script. Instead of downloading files locally, researchers can now directly link to the SRA data via a Data Repository Service (DRS) server. Imagine the convenience of pointing to the exact location of your data on Amazon Web Services or Google Cloud, eliminating the need for duplicate copies. 

With the SRA to DRS Converter, you can seamlessly integrate these data streams into your existing software pipelines. Run analyses, perform variant calling or transcript counting, and extract meaningful insights—all without the hassle of local file management. Researchers no longer have to pay storage for duplicate files, saving researchers and taxpayers money and allowing it to be reallocated to funding research.

Join us for an enlightening session with discussion 

• Jared Rozowsky will explore the GA4GH standards that underpin the DRS protocol, providing a deeper understanding of its architecture. 

• Cera Fisher will demonstrate the SRA to DRS Converter in action, showcasing its utility within real-world workflows. 

About the speakers

Our February webinar features work done by Isabel Rodriguez and Ayse Keskus from the Laboratory of Translational Genomics at the National Cancer Institute. Ms. Rodriguez and Dr. Keskus will present on methods and results from their recently published paper in the American Journal of Human Genetics (doi: 10.1016/j.ajhg.2024.01.002). This paper characterized 19 cervical and four head and neck cancer cell lines using long-read DNA and RNA sequencing and identified the HPV types, HPV integration sites, chromosomal alterations, and cancer driver mutations.

The authors analyzed the data with Severus, a complex structural variation analysis tool for cancer genomes. The findings revealed telomeric deletions associated with DNA inversions resulting from breakage-fusion-bridge (BFB) cycles. BFB is a common mechanism of chromosomal alterations in cancer, and this study applies long-read sequencing to this important chromosomal rearrangement type. Analysis of the inversion sites revealed staggered ends consistent with exonuclease digestion of the DNA after breakage. Some BFB events are complex, involving inter- or intra-chromosomal insertions or rearrangements. In summary, the team uncovered valuable insights into the mechanisms and consequences of BFB cycles in cervical cancer using long-read sequencing.

About the Speakers

Join us this January for an illuminating session with our presenters, Drs. Rowan Beck and Zelia Worman. Dive into the dynamic world of the Cancer Genomics Cloud (CGC) as we unveil a treasure trove of advancements and innovations from the past year and give you an exclusive sneak peek into the exciting features lined up for 2024.

What’s on the docket?

• New Data Studio Session Environments: Unlock the power of data!

  • The OHIF Image Viewer: Revolutionize how you view DICOM images.

  • Galaxy Studio: Experience the beloved Galaxy computation environment, now on CGC.

• Imaging Analysis with MC MICRO: Elevate your research with cutting-edge tools.

• Interactive RShiny Applications: Get hands-on with interactive data analysis.

• Microsoft Azure Cloud Support: Broaden your horizons with enhanced cloud capabilities.

Join us and be part of the conversation that shapes the future of cancer genomics research. Stay ahead, stay informed, and let's make 2024 a year of groundbreaking discoveries together!

About the Speakers

End-to-end microscopy imaging with MCMICRO and MCMICRO for HTAN

Our October webinar features the MCMICRO software tool on the Cancer Genomics Cloud, originally developed by collaborators in the Human Tumor Atlas Network (HTAN) consortium. MCMICRO is designed for detailed analysis of cellular structures through highly multiplexed tissue imaging – that is, whole-slide tissue imaging of samples with multiple channels of protein antibody staining. Analysis of these images makes the resolution of spatial sequencing data possible, underpinning techniques such as single-cell spatial transcriptomics, but presents several challenges to researchers. MCMICRO handles much of the processing in a single pipeline.  

Presenters Rowan Beck and Boris Majić will discuss the tool's development, including its availability in both NextFlow and Common Workflow Language formats on CGC and ongoing efforts for Galaxy integration. The discussion will include practical insights into MCMICRO's functionality in processing whole-slide images into single-cell data, using various tissue and tumor imaging platforms. Participants will learn about the software's specifications, operation, and application in research settings. 

For additional information, refer to the full paper: MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging 

About the speakers

Boris Majić is a Bioinformatics Analyst at Velsera. Mr. Majić holds a Master’s degree in Signal Processing and a Bachelor’s degree in Electrical Engineering.

Rowan Beck is a Community Engagement Manager at Velsera. Dr. Beck holds a PhD in Genetics and Molecular Biology from the University of North Carolina at Chapel Hill.

Advancing multi-modal research on the Cancer Genomics Cloud: deep learning applications of a pan-cancer PDX histology image repository

For our September webinar, Velsera scientist Dr. Phil Webster will present a case study in conducting multimodal research on the Cancer Genomics Cloud recently submitted for publication by the PDXNet consortium. The consortium demonstrated joint imaging and genomic analysis on the CGC by developing a genomics data repository, developing a haemotoxylin-eosin stain (H&E) image repository, and collaborating to bring new imaging tools to the CGC. Extensive studies of clinical H&E images demonstrating that deep learning can identify inter-cellular and morphological signals correlated with disease phenotype and therapeutic response motivated the Patient-Derived Xenograft (PDX) case study.  

In this webinar, we will present results demonstrating that deep learning can be applied to PDX H&E images to distinguish neoplastic, stromal, and necrotic regions and predict xenograft-transplant lymphoproliferative disorder. Lastly, we will present the strategies for making genomics and imaging repositories publicly available and the analytical tools used in the analysis. 

About the speaker

Dr. Phil Webster received his B.S. in Cell and Molecular Biology from San Diego State University, and completed his Ph.D. in Cell & Structural Biology at the University of Texas Health Science Center in San Antonio, TX. His research interests include functional genomics, metabolic dysfunction, cell signalling pathways, and the biology of aging. As a Data Scientist at Velsera, Dr. Webster has worked with various consortia including PDXnet to develop and implement deep learning algorithms for the analysis of data from patient-derived xenografts.

Enhancing Microbial Insights in Cancerous Tissue: Unveiling the Hidden Microbiome with an Advanced Host Depletion Pipeline

This July, our webinar features Ms. Caitlin Guccione of UCSD, who will give an insightful talk on the cancer microbiome, and a novel pipeline for extracting microbial communities from cancerous tissue samples. Ms. Guccione’s approach improves upon existing methods by effectively capturing more microbial reads while minimizing human contamination.

Key points of this webinar include:

• The novel pipeline’s effectiveness in filtering out irrelevant reads, providing a clearer understanding of the cancer microbiome.

• The significance of comprehending the tumor tissue microbiome, which opens avenues for analyzing disease trajectories along with potential therapeutic interventions.

More about the speaker

Ms. Guccione is a graduate of the University of Rhode Island (’22) with a bachelor’s degree in applied math and computer science and a master’s in applied math. Since then, she has pursued a PhD in bioninformatics and systems biology at UCSD, co-advised by Dr. Rob Knight and Dr. Kit Curtius. Her dissertation focuses on exploring mathematical modeling of the cancer microbiome.

Recurrent Repeat Expansions in Cancer

This June, our webinar features Dr. Gamze Gürsoy from the New York Genome Center. Dr. Gürsoy’s group has identified tandem repeat expansions in cancer genomes across 29 cancer types, revealing recurrent repeat expansions (rREs) that are subtype-specific. They found that these rREs, which are non-uniformly distributed in the genome and often located near regulatory elements, could play a role in gene regulation and contribute to genetic variation in human cancer. They further validated a set of rREs using long-read sequencing and developed small molecules targeting an intronic rREs that showed decrease in cell proliferation.

More about the speaker

Gamze Gürsoy, PhD, is an Assistant Professor in the Departments of Biomedical Informatics and Computer Science at Columbia University. She is also a core member at New York Genome Center. Dr. Gürsoy’s lab’s overarching research goal is to harmonize diverse fields such as biology, bioinformatics, molecular biology, engineering, and cryptography to achieve two fundamental aims:

(1) to increase biomedical data access to a wider group of scientists while preserving privacy of research participants; and

(2) to uncover the molecular underpinnings of gene dysregulation via knowledge gained from functional genomics data.

Dr. Gürsoy leads a group of computational and experimental scientists, creating opportunities for training in cross-disciplinary studies in her lab.

A computational framework for detecting low abundance clonal hematopoiesis mutations in large-scale tumor sequence datasets

This May, our webinar series features a talk by Dr. Vaidhyanathan Mahaganapathy, a computational biologist at the Ellison Institute for Transformative Medicine and a recent PhD graduate from the Khiabanian Lab of Rutgers University.

In this webinar, Dr. Mahaganapathy will summarize his work with Dr. Khiabanian’s group on developing the MERIT pipeline on the CGC. MERIT utilizes Samtools to identify all positions with alternate alleles from the aligned, indexed sequencing reads and then extracts additional information including alleles' depths, Phred quality, and position-in-read information for all variants. MERIT was originally developed by Mohammad Hadigol, but is updated and maintained by Viadhyanathan. He will speak about:

• Clonal hematopoiesis (CH), which is associated with inflammation, cardiovascular diseases, and a reduction in overall survival rate. 

• Building a computational pipeline that can reliably and reproducibly call low abundance somatic CH mutations, in pan-cancer primary tumor and matched normal blood exome sequencing samples from TCGA.

More about the speaker

Vaidhyanathan is a biotechnologist by training, who transitioned to a bioinformatician when he realized he could combine his passion for Biology with the puzzle-solving skills needed for scripting. During his dissertation, he explored different variant calling approaches on a variety of human genomic datasets and in the process developed skills for building, testing, and optimizing robust computational pipelines and workflows. In his current position at the Ellison Institute, he is working on establishing a sequence analysis toolbox for the different projects, and assisting on establishing a long-read sequencing protocol.

LinkedIn: linkedin.com/in/vaidhy-m

Mining for Alternative Polyadenylation Events in Cancer using Large Scale RNA-Seq Datasets

This April, our webinar series features a talk by Dr. Tolga Can, a Professor at the Colorado School of Mines in the Department of Computer Science.

In this webinar, Tolga will summarize his work with Dr. Elif Erson-Bensan’s group on screening for alternative polyadenylation events in cancer cells using publicly available RNA-seq datasets and utilizing the resources on the CGC. He will specifically talk about:

• How the tools on CGC can be utilized to speed up data upload, preprocessing, and alignment stages for hundreds of samples.

• How programmatic access allows you to automatically update metadata of individual samples to avoid manually entering the information for datasets containing hundreds of samples.

• Use of locally installed and cloud-based tools together for downstream analysis

More about the speaker

Tolga Can received his BSc degree in computer engineering from Middle East Technical University, Turkey, in 1998 and his PhD degree in computer science from the University of Santa Barbara in 2004 as a Fulbright scholar. He worked as a tenured professor at the Computer Engineering Department, Middle East Technical University (METU) until he joined the Colorado School of Mines as a Teaching Professor in Computer Science.

His main research interests include bioinformatics, graph theory, and algorithms. He has worked on protein structure analysis and on large-scale biological networks.

Interoperability and Integration: GA4GH Standards at work on the CGC

This March, our webinar series features a talk by Ms. Angela Page and Dr. Michele Mattioni, from GA4GH and Velsera respectively. The Global Alliance for Genomics and Health (GA4GH) is a policy-framing and technical standards-setting organization, seeking to enable responsible genomic data sharing within a human rights framework.

In the first part of the webinar, Ms. Page will introduce the GA4GH organization, its products, and how to get involved with both. Dr. Mattioni will showcase how the Cancer Genomics Cloud has used GA4GH standards to enable interoperability and connecting to other platforms. Using DRS clients, researchers on the CGC can access data from other Velsera platforms, such as CAVATICA and Biodata catalyst, as well as platforms in other ecosystems. The CGC enables cross-platform and cross-dataset analysis using this approach in a user-friendly way. 

Webinar Resources: https://doi.org/10.1016/j.xgen.2021.100029

More about the speakers

Angela Page is based at the Broad Institute in Cambridge, US, where she leads the communications and operations teams for the Global Alliance for Genomics and Health. She is responsible for ensuring GA4GH achieves its goals in the areas of international outreach and engagement. She has been with the organization since 2014.

LinkedIn: https://www.linkedin.com/in/angela-pagega4gh/

Michele Mattioni, PhD, leads the Integration and Interoperability strategy at Velsera. He is involved in several GA4GH standards and he is interested in making data actionable and findable by researchers. 

Identifying biomarkers of differential chemotherapy response in TNBC patient-derived xenografts with a CTD/WGCNA approach

This February, our webinar series features a talk by Dr. Varduhi Petrosyan at Baylor College of Medicine. Dr. Petrosyan will present work she and colleagues conducted on the Cancer Genomics Cloud using their novel network-based approach, CTD/WGCNA.

Although systemic chemotherapy remains the standard of care for triple-negative breast cancer (TNBC), even combination chemotherapy is often ineffective. The identification of biomarkers for differential chemotherapy response would allow for the selection of responsive patients, thus maximizing efficacy and minimizing toxicities. Here, we leverage TNBC patient-derived xenografts (PDXs) to identify biomarkers of response. To demonstrate their ability to function as a preclinical cohort, PDXs were characterized using DNA sequencing, transcriptomics, and proteomics to show consistency with clinical samples. We then developed a network-based approach (CTD/WGCNA) to identify biomarkers of response to carboplatin (MSI1, TMSB15A, ARHGDIB, GGT1, SV2A, SEC14L2, SERPINI1, ADAMTS20, DGKQ) and docetaxel (c, MAGED4, CERS1, ST8SIA2, KIF24, PARPBP). CTD/WGCNA multigene biomarkers are predictive in PDX datasets (RNAseq and Affymetrix) for both taxane- (docetaxel or paclitaxel) and platinum-based (carboplatin or cisplatin) response, thereby demonstrating cross-expression platform and cross-drug class robustness. These biomarkers were also predictive in clinical datasets, thus demonstrating translational potential.

More about the speaker

Varduhi Petrosyan is a senior bioinformatics analyst at Baylor College of Medicine (Texas Medical Center). She employs big data approaches to identify biological signal in complex and heterogenous disease. Her research interests include network analysis, deconvolution, and liquid biomarkers.

LinkedIn: https://www.linkedin.com/in/varduhi-petrosyan/

You can do it! Analyzing your data with custom workflows on the CGC

This January, the CGC Webinar Series features a talk by Dr. Rowan Beck from Seven Bridges.

Rowan is a Community Engagement Manager working on the Cancer Genomics Cloud.

In this upcoming webinar, Rowan will talk about Common Workflow Language (CWL), the CGC CWL editor, and its various applications.
She will then demonstrate how to create a custom app on the CGC using the CWL editor, and how to work with the CWL editor to tailor a workflow to fit your research needs.

More about the speaker

Dr. Beck’s primary interests lie at the intersection of public health and large scale “omics” analyses. She earned her PhD in 2019 from the University of North Carolina at Chapel Hill. Her graduate work focused on the identification of microRNAs and other regulatory mechanisms that underpin the association between environmental toxicant exposure and diabetes phenotypes. Prior to joining Seven Bridges, Rowan worked as a toxicologist at Reynolds American to evaluate the chemical, biological, and toxicological data generated across numerous products, materials, ingredients, technologies, and manufacturing processes.

LinkedIn: https://www.linkedin.com/in/rfbeck/

Analysis of multi-omics data using the Cancer Genomics Cloud

This October, the CGC Webinar Series features a talk by Dr. Min Zhang from Purdue University.

Dr. Min Zhang is a professor in the Department of Statistics and the Associate Director of Data Science at Purdue University Center for Cancer Research. By incorporating biological knowledge in statistical modeling, her research focuses on developing new statistical methods to analyze high-dimensional clinical and omics data that often occurs in biomedical research.  

In this presentation, Dr. Zhang will share her experience organizing a workshop on using CGC to access, share, and analyze multi-omics datasets with RNA-seq data as a case study. Dr. Zhang worked with the CGC Seven Bridges team to design a 4 part lecture that taught undergraduate and graduate students how to run a RNA-seq analysis, bulk and single-cell using the CGC. She will also share how she is using the CGC to implement SIGNET for genome-wide gene regulatory network construction.

References

1. Chen C, Zhang D*, Hazbun T*, Zhang M*. (*co-corresponding authors, 2019). Inferring gene regulatory networks from a population of yeast segregants. Scientific Reports. 9(1):1197. <https://www.nature.com/articles/s41598-018-37667-4>

2. Chen C, Ren M, Zhang M, and Zhang D. (2018). A two-stage penalized least squares method for constructing large systems of structural equations. Journal of Machine Learning Research. 19. <https://www.jmlr.org/papers/volume19/16-225/16-225.pdf>.

More about the speaker

Dr. Min Zhang is a professor in the Department of Statistics and the Associate Director of Data Science at Purdue University Center for Cancer Research. After obtaining an MD with a residency in cancer and a PhD in Neuroscience, she received another PhD from the Department of Biological Statistics and Computational Biology at Cornell University. By incorporating biological knowledge in statistical modeling, she has focused on developing new statistical methods to analyze high-dimensional clinical and omics data that often occurs in biomedical research.

LinkedIn: https://www.linkedin.com/in/min-zhang-80844a97

Democratizing Machine Learning and Drug Discovery Tool Access on the Cancer Genomics Cloud

This September, the CGC Webinar Series features a talk by Dr. Dennis A. Dean III from Seven Bridges.

Dr. Dean is a Principal Investigator at Seven Bridges, leading several national-scale genomic analysis projects. Dr. Dean leads the Translational Computation and Analytics Team and will present their progress in making machine learning models developed as part of the National Cancer Institute’s Predictive Oncology Model and Data Clearinghouse (MoDaC) program available for use on the Cancer Genomics Cloud. The NCI-sponsored MoDaC program aims to add sophisticated machine learning and drug discovery tool sets to the challenge of identifying novel treatments for Cancer Patients. The MoDaC model repository includes models from three programs: Accelerating Therapeutics for Opportunities in Medicine (ATOM), Advance Computing Solutions for Cancer, and ongoing NCI-DOE collaborations. 

In this presentation, he will demonstrate that translating the MoDaC tools into cloud-native resources on the CGC supports interactive and GUI-driven analysis. Consequently, allowing access to a broader user base than those who traditionally have the technical expertise required to work with machine learning models. He will present ML tools developed as part of the Predictive modeling for Pre-Clinical Screening and the Accelerating Therapeutics for opportunities in Cancer programs. And present details on porting the AMPL1 (A Data-Driven Modeling Pipeline for Drug Discovery) tools as a focal point for discussing standards that might promote portability and ensure equity as pertaining to use and access. 

Reference

1. Minnich, A. J. et al. AMPL: A Data-Driven Modeling Pipeline for Drug Discovery. J Chem Inf Model 60, 1955–1968 (2020).

More about the speaker

Dr. Dean is a Principal Investigator at Seven Bridges, leading several national-scale genomic analysis projects. He directs the Translational Computation and Analytics Team, which focuses on computational approaches to advancing the life sciences, including artificial intelligence and machine learning applications. He also leads Seven Bridges’ collaborations with the US Food and Drug Administration (FDA) through the BioCompute Object Initiative and the Patient-Derived Xenograft Network, funded by the National Cancer Institute (NCI).

LinkedIn: https://www.linkedin.com/in/dennis-a-dean-ii-ph-d-4b97b1b/

FragPipe enables one-stop proteomics data analysis

This August, the CGC Webinar Series features a talk by Dr. Fengchao Yu from University of Michigan and Dr. Rowan Beck from Seven Bridges.

In this upcoming webinar, Dr. Yu will present talk about FragPipe, a one-stop proteomics data analysis suite. Dr. Yu developed Fragpipe with other members of his lab. FragPipe supports both DDA and DIA data. MSFragger in FragPipe can perform closed, open, and mass-offset searches. FragPipe also supports label-free, isotopic- labeling, and isobaric labeling quantifications. Following his presentation, Dr. Beck will demonstrate how to run Fragpipe on the Cancer Genomics Cloud using publicly available data.

More about the speaker

Fengchao is a research investigator from Alexey Nesvizhskii's lab at University of Michigan. His research interests include Proteomics and Bioinformatics. Currently, Fengchao is the leading developer and maintainer of FragPipe, MSFragger, and IonQuant.

A Comparative Analysis of the Molecular Characteristics of Canine and Human Gliomas

This July, the CGC Webinar Series features a talk by Dr. Nadia Lanman from Purdue University.

In this upcoming webinar, Dr. Lanman will present her research studying human gliomas using dog as a model organism. Spontaneous gliomas in dogs are being used as a translational model for human glioma, but molecular characterization is not yet complete.  This work utilizes publicly available datasets to characterize the molecular characteristics of canine gliomas. Dr. Lanman shows how key canonical pathways altered in human gliomas are likewise altered in canine gliomas, and how the canine tumor microenvironment (TME), like that in humans, appears to be immunosuppressive. Gene expression profiles of astrocytomas and oligodendrogliomas show alterations in a number of signaling pathways, including several immune-related and TME-specific pathways.  Dr. Lanman and her team will show how they developed a Naïve Bayes classifier that accurately classifies canine glioma pathologies based on gene expression profiles alone. 

More about the speaker

Dr. Lanman is a research assistant professor in the Department of Comparative Pathobiology at Purdue University. In 2015, Dr. Lanman took a position with the Purdue University Center for Cancer Research, directing the Computational Genomics Shared Resource (CG-SR) and managing the Purdue side of the Collaborative Core for Cancer Bioinformatics (C3B), a joint bioinformatics core shared between IU and Purdue. Dr. Lanman’s work at the cancer center focuses on managing the bioinformatics core, training, and data analysis. Dr. Lanman’s research is focused on utilizing large genomics datasets to expand our knowledge of the molecular basis of cancer as well as immune and inflammatory diseases. Dr. Lanman is particularly interested in data integration and in developing methods for datasets that leverage temporal or spatial resolution.

Mapping the genome of chemoresistant ovarian cancer with multi-Omic profiling

This June, the CGC Webinar Series features a talk by Dr. Michelle R. Jones from Cedars-Sinai Medical Center.

In this upcoming webinar, Dr. Jones will present her research on ovarian cancer and how she has used the CGC to map the genome of chemoresistant ovarian cancer with multi-omic profiling. Ovarian cancer is the most lethal gynecologic cancer, with most patients diagnosed at late stage and succumbing to disease due to relapse and the development of chemo-resistance. Paired primary (chemo-naive) and relapse ovarian cancer tumors were profiled for the first time in this study, and revealed large scale conservation of gene regulation and expression throughout disease progression. Insights into the differences in Homologous recombination deficient and proficient tumors implicate differing immune evasion pathways and novel candidates for further study.

More about the speaker

Dr. Jones is a research scientist in the Center for Bioinformatics & Functional Genomics at Cedars-Sinai Medical Center. Her lab works on projects that apply genomics methods to improve our understanding of inherited risk for ovarian cancer, and mapping the somatic genome of ovarian cancer to improve our understanding of this lethal gynecological cancer.

Applications and workflows on the CGC: an update on available pipelines and how to run a GWAS on the cloud

This May, the CGC Webinar Series features a talk by Drs. Zélia Worman and Rowan Beck from Seven Bridges. Zelia is a Program Manager and Rowan is a Community Engagement Manager, both working on the Cancer Genomics Cloud.

In this upcoming webinar, they will talk about the new tools and datasets that are available on the Cancer Genomics Cloud (CGC), and will demonstrate how to run a Genome-wide association study (GWAS) on the CGC. With the growth of large-scale genomics, it’s hard to keep track of all the different tools and gold standards to analyze a particular dataset. The CGC not only hosts petabytes of public data integrated with the Cancer Research Data Commons ecosystem, but also provides the tools and pipelines to analyze it. As more data is added to the CGC, the public apps gallery has been updated to meet our users’ needs. In this webinar, we will highlight the new and updated applications and workflows to run variant calling, epigenetic studies, and long sequence reads, among others. We will also demonstrate how to perform a GWAS using the power of cloud computing.

Using the CGC, users have access to public genome cancer data without any requirement of software installation or system configuration. This setup  provides access to comprehensive pan-cancer genome analyses and facilitates data mining in wide research areas, such as therapeutic discovery process. As GWAS are fairly computationally intensive, using the cloud can provide an accessible platform for all researchers, particularly ones that do not have access to a high performance computing platform.

More about the speakers

Rowan’s primary interests lie at the intersection of public health and large scale “omics” analyses. She earned her PhD in 2019 from the University of North Carolina at Chapel Hill. Her graduate work focused on the identification of microRNAs and other regulatory mechanisms that underpin the association between environmental toxicant exposure and diabetes phenotypes. Prior to joining Seven Bridges, Rowan worked as a toxicologist at Reynolds American to evaluate the chemical, biological, and toxicological data generated across numerous products, materials, ingredients, technologies, and manufacturing processes.

Prior to Seven Bridges, Zelia was a scientific program manager for the Translational Research Institute for Space Health (TRISH), where she provided recommendations on research funding and strategies, peer-review procedures, and logistics. Zelia’s research interests are on human population genomics, space health, and transposable elements.

Zélia was born in Porto, Portugal where she received her bachelors in Biochemistry and PhD in Biodiversity, Genetics and Evolution from the University of Porto. Zelia was a postdoctoral fellow at the University of Pittsburgh and Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) .

MORE ABOUT US AND THE CGC

Seven Bridges is the leading biomedical data company, specializing in software and data analytics to drive public and private healthcare research. We deliver end-to-end bioinformatic solutions — including access to datasets, analytic workflows and algorithms, cloud-computing infrastructure, and scientific support — that speed the path from raw experimental data to new treatments and diagnostics.

The Cancer Genomics Cloud (CGC), powered by Seven Bridges and funded by the National Cancer Institute, is a flexible cloud resource platform that enables storage, analysis, and computation of large cancer datasets in the cloud. Since its launch in 2016, the platform has been continuously iterated with new applications and features to address the exponential growth and diversity of complex datasets. With the CGC, any user with an account can easily access petabytes of data, share it, analyze and use the computational power of the cloud without having to learn how to program and get familiar with several different data portals.

Bridging the gap between data scientist, clinicians and biologists using CRDC and CGC cloud resources

Below are the resources mentioned during the talk:

• Learn more about the Cancer Research Data Commons (CRDC).

• Watch the video explaining how the CRDC is making petabytes of cancer data available to researchers!

• Have some additional questions we didn’t get to? Join our Office Hours every Tuesday at 10am and Thursday at 2pm!

This April, the CGC Webinar Series features a talk by Dr. Daoud Meerzaman, Branch Chief of the Computational Genomics and Bioinformatics Branch (CGBB) at the Center for Biomedical Informatics & Information Technology (CBIIT), National Cancer Institute (NCI).

In this upcoming webinar, Dr. Meerzaman will talk about the Cancer Genomics Cloud (CGC) and the Cancer Research Data Commons (CRDC), and how these resources work together to bridge the gap between data scientists, clinicians and biologists. The recent explosion and ease of access to large-scale omics data provides a complex and vast framework for discovery. However, there are obstacles to optimal data management and analysis as the current bioinformatics tools require strong programming skills, but many scientists and clinicians do not have the expertise or the resources to go from data production, through bioinformatic and statistical analysis and ultimately clinical interpretation.

On April 28th, 2pm ET Dr. Meerzaman will highlight how the CGC can provide various approaches to mitigate these challenges. The CGC is a cloud based infrastructure that hosts data from different publicly available projects (such as the TCGA) and connects them with analytical tools to allow users to easily share, integrate, visualize and analyze these data. 

Dr. Meerzaman has published over 50 articles and served as an invited reviewer for multiple peer-reviewed scientific journals. You can find some of his work in the following review in the AACR Cancer Research Journal and research paper in Nature Biotechnology.

More about the speaker

MORE ABOUT US AND THE CGC

Seven Bridges is the leading biomedical data company, specializing in software and data analytics to drive public and private healthcare research. We deliver end-to-end bioinformatic solutions — including access to datasets, analytic workflows and algorithms, cloud-computing infrastructure, and scientific support — that speed the path from raw experimental data to new treatments and diagnostics.

The Cancer Genomics Cloud (CGC), powered by Seven Bridges and funded by the National Cancer Institute, is a flexible cloud resource platform that enables storage, analysis, and computation of large cancer datasets in the cloud. Since its launch in 2016, the platform has been continuously iterated with new applications and features to address the exponential growth and diversity of complex datasets. With the CGC, any user with an account can easily access petabytes of data, share it, analyze and use the computational power of the cloud without having to learn how to program and get familiar with several different data portals.

Genomic Alterations in Extraskeletal Myxoid Chondrosarcoma

The CGC webinar series is back in March, featuring a talk by Ms. Trudy Zou from the University of Pittsburgh School of Medicine, in Pittsburgh, PA, USA. Trudy conducts research primarily studying chondrosarcoma with Drs. Rebecca Watters and Kurt Weiss at the Musculoskeletal Oncology Laboratory (MOL). 

The Musculoskeletal Oncology Laboratory aims to better understand the biology of various primary and metastatic musculoskeletal tumors and translate discoveries into clinical solutions. Trudy’s project looks at extraskeletal myxoid chondrosarcoma (EMC), a rare musculoskeletal tumor with an indolent course and right rates of recurrence and metastasis to the lungs. In 70% of cases, it is characterized by the translocation of the NR4A3 gene on chromosome 9 to the EWSR1 N-terminal transactivation domain on chromosome 22.

In this webinar, Trudy will present an extremely rare primary EMC sample collected from a patient with matching lung metastasis, pelvic metastasis, normal lung, and blood available at the MOL lab. They used whole genome sequencing to identify structural variants that contribute to metastasis to the lungs and progression to pelvic metastases and that can be targeted for therapy. Trudy started using the Cancer Genomics Cloud in November 2021, and will be presenting the results of her and the lab’s work.

Trudy Zou is a medical student in the Physician Scientist Training Program at the University of Pittsburgh School of Medicine. She is primarily studying chondrosarcoma with Drs. Rebecca Watters and Kurt Weiss at the Musculoskeletal Oncology Laboratory (MOL). She earned her Bachelor of Science in cellular and molecular biology at Duke University, where she also studied drivers of metastasis and therapeutics for chondrosarcoma under Dr. Julia Visgauss. 

More about us and the CGC

Seven Bridges is the leading biomedical data company, specializing in software and data analytics to drive public and private healthcare research. We deliver end-to-end bioinformatic solutions — including access to datasets, analytic workflows and algorithms, cloud-computing infrastructure, and scientific support — that speed the path from raw experimental data to new treatments and diagnostics.

The Cancer Genomics Cloud (CGC), powered by Seven Bridges and funded by the National Cancer Institute, is a flexible cloud resource platform that enables storage, analysis, and computation of large cancer datasets in the cloud. Since its launch in 2016, the platform has been continuously iterated with new applications and features to address the exponential growth and diversity of complex datasets. With the CGC, any user with an account can easily access petabytes of data, share it, analyze and use the computational power of the cloud without having to learn how to program and get familiar with several different data portals.

Topic for January 2022

Understanding cervical cancer and papillomaviruses through long-read sequencing.

The CGC webinar series is back after a short break for the holiday season! This January, the CGC Webinar Series featured a talk by Dr. Michael Dean and Ms. Nicole Rossi from the Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics at the National Cancer Institute, NCI in Rockville, Maryland, USA. Dr. Dean is a senior investigator and is interested in inherited (germline) genetic variation, somatic mutations in tumors, and their effects on cancer risk, progression, and response to therapy. His lab, where Ms. Nicole Rossi is a postbac at, has a major focus on human papillomavirus (HPV) and cervical cancer and cancer health disparities in the U.S. and in Latin America.

During the webinar, Dr. Dean and Ms. Rossi will present on their work detecting the integration of HPV in the human genome through the use of long-sequencing reads. To characterize the most carcinogenic HPV, they sequenced 65 fresh-frozen HPV16-driven cervical cancer tumors, with episomal only (EP) or episomal and integrated (EP/INT) phenotypes. They identified monomer episomes as well as switches, deletions and rearrangements. Dr. Dean and his lab used the CGC platform and tools to perform bioinformatic analysis on their sequencing data. To learn more about their research and how they used the CGC, register for the webinar using the button below.

More about the speakers

Dr. Michael Dean is a senior investigator in the Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics at the National Cancer Institute. Dr. Dean is interested in inherited (germline) genetic variation, somatic mutations in tumors, and their effects on cancer risk, progression, and response to therapy. The lab has a major focus on human papillomavirus (HPV and cervical cancer and cancer health disparities in the U.S. and in Latin America.

Ms. Nicole Marie Rossi is a Post-Bacc fellow in the Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics at the National Cancer Institute, NCI in Rockville, Maryland, USA. She earned her Honors Bachelor of Science Degree in biological sciences from the University of Delaware. As an undergraduate, she worked in Dr. Melinda K Duncan’s lab, studying the role of Lactase-Like (LCTL) gene in lens cell homeostasis and cataract formation.

More about us and the CGC

Seven Bridges is the leading biomedical data company, specializing in software and data analytics to drive public and private healthcare research. We deliver end-to-end bioinformatic solutions — including access to datasets, analytic workflows and algorithms, cloud-computing infrastructure, and scientific support — that speed the path from raw experimental data to new treatments and diagnostics.

The Cancer Genomics Cloud (CGC), powered by Seven Bridges and funded by the National Cancer Institute, is a flexible cloud resource platform that enables storage, analysis, and computation of large cancer datasets in the cloud. Since its launch in 2016, the platform has been continuously iterated with new applications and features to address the exponential growth and diversity of complex datasets. With the CGC, any user with an account can easily access petabytes of data, share it, analyze and use the computational power of the cloud without having to learn how to program and get familiar with several different data portals.

Topic for October: HLA class II across TCGA cancer dataset

In October, the CGC Webinar Series featured a talk by Dr. Pascal Belleau. Dr. Belleau is a computational biology postdoctoral fellow in Krasnitz Laboratory, at the Quantitative Biology Department of Cold Spring Harbor Laboratory.

In this webinar, Dr. Belleau discussed HLA class II across The Cancer Genome Atlas dataset. The HLA class II complex is an essential molecular component of adaptive immunity in general, and of immune response to cancer. Using the Cancer Genomics Cloud, Dr. Belleau performed HLA class II typing across the entire TCGA collection of approximately 11,000 patient samples spanning 33 tumor types. Additionally, he investigated HLA class II pattern intra- and inter-different cancer types. The aim of this research is to clarify how HLA class II mediates immune response to cancer.

Topic for September: “Le grand et le petit”: splicing factors SF3B1 and SUGP1 and their cancer mutations leading to aberrant acceptor usage

This September, the CGC Webinar Series features a talk by Dr. Tatiana Popova, with the Institut Curie, in Paris, France. Tatiana is a senior scientist at the French Institute of Health and Medical Research (INSERM), in the Department of Genetics and Biology of Cancer at the Institut Curie. Tatiana also has a mathematics background and started working in the cancer genomics field in 2008.

During her webinar, Tatiana will present a recent discovery of SUGP1 (“Le petit”) as a splice factor, which experiences mutations in cancer leading to the same phenotype as missense mutations of SF3B1 (“Le grand”). The objective of the CGC-related part of her project was to discover all SF3B1-like splice aberrations in all cancer samples present in the TCGA. Tatiana used a Sequence Bloom Tree (SBT) structure built on TCGA RNA-seq data hosted on the CGC for fast and sensitive screening of aberrant splicing patterns. The discovery of cancer mutations in SUGP1 provides clues about the role of this small and low-abundance protein in splicing.

Topic for August: The Exploration of Pan-Cancer Dysregulated Pathways and PolyTherapy AI

This August, the CGC Webinar Series features a talk by Margaret Liñán, MPH MS, Bioinformatics Consultant with the College of Health Solutions, Arizona State University.

In this webinar, Margaret will discuss her translational bioinformatics research in collaboration with Dr. Valentin Dinu, an Associate Professor of Biomedical Informatics and the Principal Investigator of the Translational Genomics Lab at the College of Health Solutions at Arizona State University. Their collaborative research includes two previous pan-cancer studies: 1) Detecting and ranking mRNA dysregulated pathways using the Dinu Lab’s Pathways of Topological Rank Analysis (PoTRA) tool (Bioconductor) with HTSeq FPKM normalized mRNA data from the TCGA, and 2) identifying drivers of rank dispersion in mRNA mediated dysregulated pathways from invasive breast cancer. Finally, the most recent study, PolyTherapy AI, is a multi-institutional and multi-national collaboration with industry that will be developed as an interactive artificial intelligence platform for multi-omics combination drug treatment.

About our Featured Speaker:

Margaret is a Bioinformatics Consultant, currently collaborating with Dr. Valentin Dinu’s (PI) Translational Genomics Lab at the College of Health Solutions – Arizona State University, and others (academia/industry) on the PolyTherapy AI project. Margaret has previously worked in academia as a Computational Research Scientist and Consultant at the Icahn School of Medicine at Mount Sinai and in the biotechnology industry as a Bioinformatics Deep Learning Scientist. She has also worked as a bioinformatics research trainee in the Department of Health Sciences Research at the Mayo Clinic where she successfully executed a large-scale pathway analysis and graph networks project.