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Refining Molecular Risk Prediction & Individualized Lymphoma Therapy Using Circulating Tumor DNA

Arash Alizadeh

Arash Alizadeh


Board of Trustees of the Leland Stanford Junior University

Project Term: July 1, 2019 - June 30, 2024

My group studies variation in clinical outcomes of patients with aggressive lymphomas and tries to capture the underlying basis for this variation. We then integrate insights from our studies into molecular prediction tools that inform the probable outcomes of individual patients when treated with therapeutic regimens that are currently available. We hope to build precise risk models that have high predictive value for clinical outcomes of patients with lymphoma. Our goal is to use these models to inform therapeutic trials of novel strategies to improve the outcomes of blood cancer patients.

Lay Abstract

For patients with the most common lymphoma subtype, diffuse large B-cell lymphoma (DLBCL), curative outcomes are common. Still, nearly a third of DLBCL patients succumb to their disease. Survival has not significantly improved over the last 15 years despite many clinical trials during this period. Effective strategies to predict early treatment failures have largely been elusive. 

Our overall goal is to study the relations among baseline and dynamic risk factors including genetic mutations and circulating tumor DNA (ctDNA) in DLBCL patients. Circulating tumor DNA is DNA that originates from tumor cells and is shed into the bloodstream. Our central hypothesis is that novel biomarkers of cancer risk such as detection of ctDNA, and detailed genetic profiling can be used for early detection of residual disease, to identify for the identification of dynamic changes that anticipate treatment failure, and for the provision of early surrogate endpoints for future clinical trials. 

We will examine the relations among clinical variables, molecular risk factors, very early treatment responses (measured using blood samples and imaging), and clinical outcomes (including survival) in a large cohort of patients with DLBCL. This is relevant to patients with blood cancers because new strategies for cancer risk stratification and early detection of adverse outcomes have the potential to improve clinical outcomes in patients with these common and often deadly tumors.

This contribution is significant since knowledge of the molecular features associated with cancer outcomes and early detection of treatment failure may lead to novel ways to select better therapies for patients at highest risk of failures, by applying blood-based assays over the disease course for both tumor genotyping and disease monitoring. Our innovative approach, in which we will employ novel methods developed by our group, will lay the foundation for studies aimed at reducing risk of treatment failure as a means of improving clinical outcomes. Demonstrating that this approach can serve as a robust, early biomarker for patients with DLBCL would be transformative for future trial design and for rapid evaluation of novel, personalized treatment approaches in patients at highest risk for recurrence. Our study will also serve as a proof-of-principle that is applicable to other tumor types.

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