FEB 23, 2017 12:00 PM PST

Rapid Learning for Precision Oncology

Speaker

Abstract

In this era of precision molecular medicine, knowledge changes rapidly and is highly dispersed.  Physicians and patients are faced with conflicting expert opinions and a shortage of actionable data, buried within a tsunami of literature.  Patient outcomes and quality of life vary widely across physicians and institutions, often falling off dramatically from elite institutions to rural and disadvantaged communities, as well as in developing countries.

Not only do individual physicians not know the optimal way to treat any complex case; they don’t even how to find this out.  Advanced cancers are characterized by thousands of molecular aberrations, potentially creating tens of thousands of clinically distinct subtypes. Moreover, there are hundreds of approved therapies, which have never been tested head to head, or in combinations. Current clinical trial designs, including contemporary “adaptive” Bayesian designs, cannot efficiently search this huge combinatorial space, especially given the limited number of cancer patients.

In the absence of definitive clinical studies, the best way to help current patients achieve better outcomes is by aggregating and validating the insights, intuitions, and experiences of our best clinicians.  Every day, thousands of patients who have exhausted the standard of care are treated with off-label drugs and cocktails. These treatment decisions are based largely on the judgments and experience of individual physicians, but the results are seldom reported, so nothing is learned. Cancer Commons is building a platform that will coordinate these thousands of ad hoc “N of 1” experiments, capture their results, and rapidly share them.  Our goal is to transform the everyday practice of oncology into a global adaptive search for better treatments and cures.  Given the wide variation in treatment and outcomes, we are convinced that getting the right knowledge to the right patient and the right physician at the right time will save many lives.

 

Learning Objective 1: How to capture, validate and share the knowledge generated from the thousands of N-of-1 experiments that take place daily in oncology practices, far faster than journals and conferences.

Learning Objectives 2: How to coordinate these experiments across all patients and treatments to search for optimal combination therapies and regimens far more efficiently than clinical trials.  


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FEB 23, 2017 12:00 PM PST

Rapid Learning for Precision Oncology


Specialty

Oncology

University

T-Cells

Antibodies

Research

Immunology

Immunity

Research And Development

Gene Expression

Bacteria

Transfusion Medicine

Dna

Big Data

Molecular Biology

Cancer Research

Geography

Asia50%

North America25%

Europe25%

Registration Source

Website Visitors100%

Job Title

Student25%

Clinical Laboratory Scientist25%

Educator/Faculty25%

Medical Laboratory Technician25%

Organization

Manufacturer - Other25%

Academic Institution25%

Medical School25%

Hospital25%


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