Personalized risk assessment and disease monitoring in non-Hodgkin lymphoma from circulating tumor DNA
Abstract/Contents
- Abstract
- Predicting an individual's response to treatment remains a major challenge in clinical oncology. Current methods rely on clinical and biological risk factors identified prior to therapy, such as tumor stage, histological grade, or tumor genotype. These factors are associated with differences in response and survival in large populations of patients; however, their ability to predict outcomes for individual patients is limited. Methods to robustly predict outcomes to therapy for individual patients remain elusive. Emerging blood-based biomarkers, such as circulating tumor-derived DNA (ctDNA), offer a potential path forward. Furthermore, ctDNA measurement has unique possibilities for serial assessments over time. Unfortunately, approaches to incorporate serial measurements over time in current risk stratification tools are lacking. We have previously developed Cancer Personalized Profiling by Deep Sequencing, or CAPP-Seq, a targeted next-generation sequencing platform for detection of ctDNA in multiple cancer subtypes. We extended CAPP-Seq for disease detection in diffuse large B cell lymphoma (DLBCL), the most common blood cancer in adults. Using CAPP-Seq, we demonstrated robust disease detection and noninvasive discovery of clinically relevant mutations in DLBCL. We explored the clinical utility of ctDNA by CAPP-Seq in DLBCL for early diagnosis of disease relapse and detection of molecular disease subtypes. Next, we used CAPP-Seq to monitor tumor dynamics during therapy in a cohort of over 200 DLBCL patients, demonstrating the prognostic significance of changes in ctDNA levels as quickly as three weeks into treatment. Moreover, we utilized serial ctDNA measurements to develop a dynamic model to predict an individual's disease risk over time. This model -- the Continuous Individualized Risk Index (CIRI) - provides a personalized estimate of disease risk over time. As more information becomes available during a patient's course, CIRI updates the disease risk, integrating the new information. CIRI outperformed the International Prognostic Index (IPI), the current gold standard for risk stratification, for identification of both event-free and overall survival in a training and validation context. Finally, we are exploring alternatives to probabilistic frameworks of disease risk. As ctDNA is fundamentally related to underlying cancer biology, we created an ordinary differential equation (ODE) based mathematical model relating ctDNA to tumor growth. By applying this model to ctDNA time series data, we can create a continuous view of tumor dynamics over time. Such mechanistic modeling allows prediction of not only patient-specific disease risk, but quantitative estimates of tumor volume and growth dynamics. As we enter the era of precision therapeutics, methods to determine a quantitative measure of personalized risk -- rather than finding populations of at-risk patients -- are increasingly important. Dynamic risk models such as CIRI can identify an individual's risk from disease throughout their course of therapy. Alternative approaches for disease monitoring grounded in the mechanism of tumor dynamics provide further opportunities for studying disease response and resistance to therapy. Tools for dynamic risk assessment and disease modeling are potentially applicable outside of lymphoma and ctDNA, and could guide future personalized therapeutic approaches.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Kurtz, David M |
|---|---|
| Associated with | Stanford University, Department of Bioengineering. |
| Primary advisor | Alizadeh, Ash |
| Thesis advisor | Alizadeh, Ash |
| Thesis advisor | Covert, Markus |
| Thesis advisor | Gambhir, Sanjiv Sam |
| Thesis advisor | Tibshirani, Robert |
| Advisor | Covert, Markus |
| Advisor | Gambhir, Sanjiv Sam |
| Advisor | Tibshirani, Robert |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | David M. Kurtz. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by David Matthew Kurtz
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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