Big Data

The ACCC IO Institute recognizes the need for industry and academic leaders to collaborate and share information to improve outcomes for patients, particularly during the design of clinical trials. Collaboration among thought leaders exploring the potential applications of Big Data to benefit patients being treated with immunotherapy to better answer questions about safety and efficacy of treatment options.

Meet the members of the Big Data Working Group.


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Nikesh Kotecha, PhD
VP Informatics
Parker Institute for Cancer Immunotherapy
San Francisco, CA

Nikesh Kotecha, PhD, currently serves as Vice President of Informatics at the Parker Institute for Cancer Immunotherapy (PICI). He leads informatics efforts to realize the institute’s mission to accelerate the development of breakthrough immune therapies and turn cancer into a curable disease. Dr. Kotecha’s focus on delivering computational and molecular technologies to the healthcare community drives him to work at the intersection of medicine, informatics, entrepreneurship, and education.

Prior to joining PICI, Dr. Kotecha was the CEO & Co-founder of Cytobank Inc. (part of Danaher Life Sciences), an analytics company that builds solutions around single-cell proteomics and cytometry, two technologies that are revolutionizing how we understand disease mechanisms. Dr. Kotecha is also a consulting faculty member in the Computational and Systems Immunology Program at Stanford University and an advisor at StartXMed, a Stanford startup accelerator.

Dr. Kotech earned a PhD in biomedical informatics from Stanford University and a BS in biomedical engineering from Boston University. He has more than 15 years of experience building analytic applications to address scientific and informatics problems in healthcare.

Insights from this working group

  • Predictive Modeling: What, Why, How
    By Ivo Abraham, PhD, RN

    Envision a day that cancer clinicians can ask an app to advise on immuno-oncology (IO) treatment options for a patient. That day may not be far off. Big data, deep analytics, and predictive modeling methods are transforming how cancer clinicians weigh treatment options.

  • Predictive Modeling to Inform IO Regimen Choice
    By Ari VanderWalde, MD, MPH, MBioeth

    Immune-related adverse events (irAEs) are extremely common in patients being treated with checkpoint inhibitors for advanced melanoma. The type, quality, and severity of these adverse events, however, varies by treatment regimen and by patient.

  • Big Data, Deep Analytics, Better Outcomes
    By Ivo Abraham, PhD, RN
    The promises of Big Data are intuitively appealing: (virtually) unlimited data that will enable us to answer (virtually) any questions that we may have. Unfortunately, by and of themselves, Big Data are rather useless. They require Deep Analytics: inquiring people equipped with engines of analysis to explore, discover, and invent.
  • Powering Personalized Immuno-Oncology: Big Data’s Role
    By Nikesh Kotecha, PhD
    Determining the best personalized treatment for a patient will require input from a team of physicians, ideally with access to a patient’s information over time and across multiple modalities. Collecting data in a consistent, secure, and scalable manner with the ability to share across disciplines will be vital to furthering personalized medicine. 
  • Harnessing Real-Time, Real-World Data to Improve Care
    By Matthew R. Zibelman, MD

    As a genitourinary medical oncologist specializing in immunotherapy for kidney and bladder cancers, I am continually striving for more ways to connect with and learn from my patients. The emerging availability of immuno-oncology (IO) drugs for the conditions I treat, as well as many other cancer types, has generated tremendous excitement amongst patients and oncologists, but there still is so much we don’t know.