Q: Tell us about Data4Cure.

 

A: Data4Cure develops and provides its partners with an integrative platform that combines systems biology, machine learning and AI technologies to turn vast amounts of omics, phenotypic and clinical data (both public and proprietary) into a data-driven biomedical knowledge graph. The platform uses this information to build comprehensive maps of diseases to identify new drug targets and biomarkers, find new indications for existing drugs, and match drugs to patient subtypes. We are proud to work with some of the largest and most innovative pharmaceutical companies and leading research institutions who use our platform to gain new insights from their data and build their internal knowledge networks.

Q: What led you to found Data4Cure?

 

A: As a computer scientist and computational biologist, I am always interested in how we can combine and model different types of data together to better understand complex systems and make better decisions. Around 2013, I was working at UC San Diego, developing and publishing on new network and systems approaches which worked well for simple organisms such as yeast but weren’t yet as effective for human disease. I thought we can do better by bringing in a lot more diverse types of data and merging systems biology with some of the new machine learning and semantic learning technologies that were becoming available. That would require a new set of tools and a computational platform that would start to tie all of the pieces together. I thought the work could be more effective done through a company. So instead of looking to start my own academic lab, I left to start Data4Cure helped by our scientific co-founder Dr. Trey Ideker, key scientific advisors Drs. Lee Hood and Napoleone Ferrara, as well as early investors. We were very fortunate to be joined by extraordinary computational biologists and engineers and began working with pharmaceutical companies to apply some of the integrative systems biology machinery toward patient stratification and drug development. It gave us a good view into the problems people are facing both in terms of specific analytical challenges, as well as the bigger problem of growing knowledge from more and more data.

What we were able to develop over the last three to four years is an integrated system that not only applies many advanced analytical methods to the data but provides a technology platform to iteratively update knowledge based on new data and literature. The resulting information is stored in a dynamic data-driven knowledge graph that is unique to each organization. This technology became the basis for the Biomedical Intelligence Cloud — our SaaS offering for pharmaceutical R&D which we launched last year. We are continuously improving it by curating and feeding in new large datasets and building new applications to address a wider range of analytical problems.

Q: What are some of the key unique ideas behind the Biomedical Intelligence Cloud? What contextual knowledge do you bring in?

 

A: The key idea is convergence of evidence and information coming from multiple sources like multidimensional omics data, phenotypic data, molecular networks and pathways, literature, clinical trials, and other structured and non-structured data that may exist both within the organization and outside in the public domain. Typically information coming from each one of these sources is noisy, incomplete, and difficult to interpret. By putting the data together in context we are able to extract the most robust pieces of information and make better sense of it.

The Biomedical Intelligence Cloud uses a proprietary knowledge graph technology called CURIE to continuously aggregate various pieces of evidence, learn how they fit together and apply them toward interesting problems in the pharmaceutical and clinical R&D. We think of it also as convergence of systems biology and ML/AI because the technology leverages recent methods from machine learning and AI but is also heavily informed by the developments in systems biology and incorporates a lot of prior knowledge from the biomedical domain.

Q: What types of problems in pharmaceutical R&D do you help address and how does systems biology and ML help?

 

A: The platform has applications throughout the drug development pipeline from early target discovery and validation, to mechanism of action and efficacy studies, to patient selection and developing companion diagnostics. In each of these areas there is an opportunity for integrative systems-based modeling but there are also unique challenges. The platform helps address these individual areas using a set of dedicated applications.

For instance, to support target discovery and validation, users might start by building integrative molecular maps of diseases which integrate omics, phenotypic and literature information with molecular networks and pathways to identify the key system components with potential for therapeutic intervention. Precision medicine is all about precisely mapping disease subtypes and predicting how they might respond to therapy either as single agent or as combinations. This is where some of the other applications become useful. We have an application that performs a deep molecular subtyping of disease based on multidimensional omics data and specific disease pathways perturbed in each patient. Other applications couple machine learning algorithms with knowledge of molecular networks and pathways to predict sensitivity or resistance to drugs in specific patient subsets. We currently have over 10 applications available on the platform and are expanding with new ones in the next months.

Q: You will be speaking in the Immunotherapy Track as well as the AI showcase at PMWC 2018. What are the applications of the platform toward immune oncology? Can you give some use case examples of cancer biomarkers that the Biomedical Intelligence Cloud helped discover?

 

A: Immuno-oncology research and clinical trials is one the areas in which our platform has seen the most growth in 2017. We are particularly interested in understanding and predicting which patients across different histologies will respond to specific types of immunotherapy (e.g. PD1 or PD-L1 inhibitors) and which other patients may respond to combination therapies in which IO drugs might be combined with other therapies.

The power of the platform is that we can effectively combine multiple types of data, such as are often generated by the new IO clinical trials and apply a range of systems and ML approaches to make sense of these data. What is emerging is a view of the tumor-immune system interactions in which multiple factors such as cancer subtypes, immune infiltration, neoantigen load, checkpoint expression and tumor clonal evolution each may together determine response.

Our system can extract multidimensional information about disease biology coming from the analysis of large research cohorts and use it to boost signal and increase the ability to make accurate predictions for new clinical trial cohorts. In my talk I plan to discuss several case studies using recent trials in lung adenocarcinoma and melanoma.

We are very excited to contribute to the PMWC 2018 program. We’ve been part of the conference since the very beginning of Data4Cure. Every year the quality of the program and level of participants makes it a phenomenal event for anyone working in the field. The 2018 edition will be particularly interesting for anyone interested in immuno-oncology, and applications of machine learning and AI in medicine.

Interview with Gabriel Bien-Willner of Palmetto GBA

Q: What does your role entail as the director of the MolDX program at Palmetto GBA?

A: The job directing MolDX is multifaceted; first and foremost the MolDX program is responsible for assessing molecular diagnostic tests on the market and makes coverage and pricing determinations for such tests and technology. This is usually done through local coverage determination policies or technical assessments.

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Interview with Peter Marks of FDA

Q: The CBER’s Regenerative Medicine Advanced Therapy Designation program has been very successful, with about 100 requests for designation in the two years of its existence. Can you please tell us about the program and how it was put together?

A: The Regenerative Medicine Advanced Therapy (RMAT) Designation program came into being as part of the 21st Century Cures Act that was signed into law on December 13, 2016.

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Interview with Calum MacRae of Harvard Medical School

Q: What patient data do we need to better understand the underlying cause of disease and how to prevent it?

A: Medicine at present is highly underdetermined and data poor. To be precise, one must be comprehensive, so medicine (with our consent) will use not only what we currently conceive of as biomedical information, but also data from across our lives.

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Headlines from PMWC 2019 Silicon Valley

A big ‘Thank You’ to all of our presenters and attendees for celebrating 10 years of precision medicine progress with us! PMWC 2019 Silicon Valley was attended by 2000 participants from 35 countries, which included over 400 speakers in 5 parallel tracks!

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Interview with Ken Bloom of Ambry Genetics

Q: Tell us more about your organization/company. What patient population are you serving and which services are you specializing in?

A: Ambry Genetics is a recognized leader in high quality complex genetic testing. We seek to find the genomic cause or contributors to rare diseases, abnormal phenotypes and hereditary disorders.

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Interview with Lee Pierce of Sirius Computer Solutions

Q: What is the state of big data and analytics in healthcare, and how to best use the reams of data available?

A: More than ever, Healthcare organizations are achieving measurable value through use of their data and analytics assets. There is more raw material available than ever to create value. This raw material is the data flowing from internal systems and applications and also from devices and systems external to healthcare organizations.

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Interview with Anita Nelsen of PAREXEL

Q: There are various new, emerging technologies that bring us closer towards a cure for life-threatening disorders such as cancer, HIV, or Huntington’s disease. Prominent examples include the popular gene editing tool CRISPR or new and improved cell and gene therapies. By when can we expect these new technologies being part of routine clinical care?

A: Today’s emerging technologies are making the promise of individualized treatment a reality.

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Interview with Ilan Kirsch of Adaptive Biotechnologies

Q: The Nobel Prize in Medicine was awarded recently to James Allison and Tasuku Honjo for their work on unleashing the body’s immune system to attack cancer, a breakthrough that has led to an entirely new class of drugs and brought lasting remissions to many patients who had run out of options. The Nobel committee hailed their accomplishments as establishing “an entirely new principle for cancer therapy.” What is your first-hand experience the impact that those new drugs had on patients?

A: For decades cancer was viewed as solely a cell-autonomous condition.

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BMS buys Celgene | Lilly buys Loxo Oncology – Does this Signal a Return to Strong Deal-Making Activities in 2019?

Bristol-Myers Squibb’s blockbuster $74B deal to buy Celgene creates an oncology powerhouse amid industrywide excitement about the rapidly evolving science and explosive growth of the sector. The agreement could signal a return to deal-making for the pharmaceutical industry in the $133B global oncology therapeutics market.

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Interview with Gini Deshpande of NuMedii

Q: What need is NuMedii addressing?

A: NuMedii, has been pioneering the use of Big Data, artificial intelligence (AI) and systems biology since 2010 to accelerate the discovery of precision therapies to address high unmet medical needs. Artificial Intelligence approaches are a natural fit to harness Big Data as they provide a framework to ‘train’ computers to recognize patterns and sift through vast amounts of new and existing genomic

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Interview with Minnie Sarwal of UCSF

Q: Genomic medicine is entering more hospitals and bringing with it non-invasive technology that can be used to better target and treat diseases. What are some key milestones that contributed to this trend?

A: Completion of complete sequence data from the human genome project, and the advances in proteomic, microRNA and epigenetic assays added a layer of pathway biology to the understanding of human diseases.

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Interview with Shidong Jia of Predicine

Q: Once sequencing has been validated as a clinical solution via trusted workflows, and coinciding with the technological developments driving costs lower, we can expect accelerated human genome profiling for clinical Dx. How soon, do you think, will we see accelerated growth and what can we expect?

A: We will see accelerated human genome profiling for clinical Dx in 2019 and the coming years as more biomarker-based cancer drugs are gaining approval.

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Interview with Iya Khalil of GNS Healthcare

Q: Artificial intelligence (AI) techniques have sent vast waves across healthcare, even fueling an active discussion of whether AI doctors will eventually replace human physicians in the future. Do you believe that human physicians will be replaced by machines in the foreseeable future? What are your thoughts?

A: I think that there’s a lot of speculation and uncertainty around AI, but I don’t foresee a time when we won’t need physicians.

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Interview with Ilya Michael Rachman of Immix Biopharma Inc.

Q: The Nobel Price in Medicine was awarded recently to James Allison and Tasuku for their work on unleashing the body’s immune system to attack cancer, a breakthrough that has led to an entirely new class of drugs and brought lasting remissions to many patients who had run out of options. The Nobel committee hailed their accomplishments as establishing “an entirely new principle for cancer therapy.” Besides CAR T-cell therapy what do you think next generation immunotherapies will look like to successfully combat cancer?

A: The next generation of immunotherapies will build on the insights discovered by immunologists like James Allison and Tasuku Honjo and extend them to modify the body’s response to tumors.

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Join me to Kick off PMWC Silicon Valley in the Santa Clara Convention Center, Focusing on Every Element of Precision Medicine

My team worked in collaboration with Bill Dalton, Kim Blackwell, Atul Butte / India Hook Barnard, Nancy Davidson and Sharon Terry to create a program that touches every component of precision medicine while bringing together all of its key stakeholders. Leading participating institutions including Stanford Health Care, UCSF, Duke Health, Duke University, John Hopkins University, University of Michigan and more will share their learnings and experiences and their successes and challenges, as they make precision medicine the new standard of care for all.

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Johns Hopkins
University Of Michigan

The Precision Medicine World Conference (PMWC), in its 17th installment, will take place in the Santa Clara Convention Center (Silicon Valley) on January 21-24, 2020. The program will traverse innovative technologies, thriving initiatives, and clinical case studies that enable the translation of precision medicine into direct improvements in health care. Conference attendees will have an opportunity to learn first-hand about the latest developments and advancements in precision medicine and cutting-edge new strategies and solutions that are changing how patients are treated.

See 2019 Agenda highlights:

  • Five tracks will showcase sessions on the latest advancements in precision medicine which include, but are not limited to:
    • AI & Data Science Showcase
    • Clinical & Research Tools Showcase
    • Clinical Dx Showcase
    • Creating Clinical Value with Liquid Biopsy ctDNA, etc.
    • Digital Health/Health and Wellness
    • Digital Phenotyping
    • Diversity in Precision Medicine
    • Drug Development (PPPs)
    • Early Days of Life Sequencing
    • Emerging Technologies in PM
    • Emerging Therapeutic Showcase
    • FDA Efforts to Accelerate PM
    • Gene Editing
    • Genomic Profiling Showcase
    • Immunotherapy Sessions & Showcase
    • Implementation into Health Care Delivery
    • Large Scale Bio-data Resources to Support Drug Development (PPPs)
    • Microbial Profiling Showcase
    • Microbiome
    • Neoantigens
    • Next-Gen. Workforce of PM
    • Non-Clinical Services Showcase
    • Pharmacogenomics
    • Point-of Care Dx Platform
    • Precision Public Health
    • Rare Disease Diagnosis
    • Resilience
    • Robust Clinical Decision Support Tools
    • Wellness and Aging Showcase

See 2019 Agenda highlights:

    • Five tracks will showcase sessions on the latest advancements in precision medicine which include, but are not limited to:
      • AI & Data Science Showcase
      • Clinical & Research Tools Showcase
      • Clinical Dx Showcase
      • Creating Clinical Value with Liquid Biopsy ctDNA, etc.
      • Digital Health/Health and Wellness
      • Digital Phenotyping
      • Diversity in Precision Medicine
      • Drug Development (PPPs)
      • Early Days of Life Sequencing
      • Emerging Technologies in PM
      • Emerging Therapeutic Showcase
      • FDA Efforts to Accelerate PM
      • Gene Editing / CRISPR
      • Genomic Profiling Showcase
      • Immunotherapy Sessions & Showcase
      • Implementation into Health Care Delivery
      • Large Scale Bio-data Resources to Support Drug Development (PPPs)
      • Microbial Profiling Showcase
      • Microbiome
      • Neoantigens
      • Next-Gen. Workforce of PM
      • Non-Clinical Services Showcase
      • Pharmacogenomics
      • Point-of Care Dx Platform
      • Precision Public Health
      • Rare Disease Diagnosis
      • Resilience
      • Robust Clinical Decision Support Tools
      • Wellness and Aging Showcase
  • Luminary and Pioneer Awards, honoring individuals who contributed, and continue to contribute, to the field of Precision Medicine
  • 2000+ multidisciplinary attendees, from across the entire spectrum of healthcare, representing different types of companies, technologies, and medical centers with leadership roles in precision medicine
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