Molecular characterization and personalized approaches to non-Hodgkin lymphoma from circulating
tumor DNA
Biological and clinical heterogeneity between patients remain inherent barriers to improving cancer
outcomes. This heterogeneity makes prediction of therapeutic outcomes and personalized treatment approaches
a major challenge in clinical oncology. This is exemplified by diffuse large B-cell lymphoma (DLBCL), the most
common non-Hodgkin lymphoma in adults. Over the last 30 years, significant strides have been made in
unraveling the inter-patient heterogeneity of this disease, leading to identification of molecular subtypes based
on the cell-of-origin and risk stratification tools based on clinical and radiographic factors. Recently, the field of
genomics has made additional strides in identifying the specific genes that drive lymphomas in individual
patients, which has in turn led to a revolution in personalized medicine. Nevertheless and despite these
advances, current personalized approaches to therapy have failed to improve outcomes for patients with DLBCL.
During the last decade, novel methods to detect cell-free tumor-derived DNA, or circulating tumor DNA
(ctDNA), have emerged. We previously applied Cancer Personalized Profiling by Deep Sequencing (CAPP-
Seq), a targeted sequencing approach for ctDNA detection, to patients with DLBCL. Additionally, we recently
explored the response dynamics of ctDNA in DLBCL patients receiving standard therapy, defining robust
molecular response criteria after as few as 21 days. Detection of ctDNA therefore provides an opportunity for
both improved understanding of tumor genotype and phenotype, including response to therapy, opening the door
to personalized approaches to diagnosis and disease management. In this proposal, I will further extend ctDNA
detection by CAPP-Seq to develop methods to comprehensively molecularly characterize DLBCL directly from
the blood plasma, including genome-wide copy-number profiling and detection of phased haplotypes, a unique
entity in B-cell malignancies (Aim 1). I will then refine and validate a novel framework, called the Continuous
Individualized Risk Index (CIRI), to integrate genomic information with ctDNA molecular response criteria to build
a clinically useful personalized model of risk for DLBCL patients (Aim 2). Finally, I will apply these tools to study
the genetics and molecular response dynamics of DLBCL patients receiving chimeric antigen receptor (CAR) T-
cells, an emerging therapy for relapsed and refractory DLBCL, which remains an area of clinical need (Aim 3).
This proposal will be carried out at the Stanford University School of Medicine, under the mentorship of
Ash Alizadeh, MD/PhD. Through completion of this proposal, I will gain the relevant experience in computational
biology and biomedical data science to successfully launch a career as an independent investigator focused on
developing and translating new technologies for patients with lymphoma.