# Measuring treatment response and residual disease in leukemia with personalized, sensitive, and quantitative genomic methods > **NIH NIH K99** · COLD SPRING HARBOR LABORATORY · 2021 · $124,738 ## Abstract Project Summary To improve patient outcome in cancer, better methods are urgently needed to measure therapeutic response and detect early relapse. In acute myeloid leukemia (AML), 50% of patients in remission will relapse within 2 years. Current methods lack the sensitivity and generality to detect minimal residual disease (MRD) in all of those patients. Multiplex Accurate Sensitive Quantitation (MASQ), is both sensitive and general. It can target up to 50 patient-specific mutations, with sequence error rates reduced to 1 in 1 million, and count mutant DNA molecules with molecular tags. In a pilot study of AML, MASQ detected somatic variants at levels ranging from 1 in 100 to nearly 1 in 1 million, with higher mutation frequencies in patients who relapsed. There is also a critical need to interpret minimal residual disease in the context of pre-leukemic clonal hematopoiesis and the evolution of leukemic cells. Relapse may arise from drug-resistant leukemic cells, a genetically diverged subclone, or a reservoir of pre-leukemic stem cells. In this proposal, I apply and improve innovative genomic tools for measuring treatment response, predicting clinical outcome, and investigating the nature of residual cells in AML. This project utilizes a large observational clinical study of AML to track patient-specific leukemia-associated variants in blood samples taken over the course of the disease. Aim 1 will analyze subclonal treatment response and the dynamics of relapse by tracking leukemia-associated variant allele frequencies across time. Aim 2 will establish the prognostic value of a personalized, highly sensitive, and quantitative test for residual disease in AML. Aim 3 proposes to isolate the rare residual cells harboring leukemia-associated variants from a remission blood sample to determine the genomic and transcriptomic profiles that may provide further biological and clinical insight into the disease. I have proposed a tailored career development plan that will prepare me for my transition to independence. Following my postdoctoral fellowship training, I aim to be an independent tenure-track professor at a major research university. The training environment at Cold Spring Harbor Laboratory (CSHL) provides access to world-renowned meetings and courses, and a plethora of investigators with expertise in cancer and quantitative biology. My professional development activities center around mentorship, communication, teaching, lab management, and preparing for the academic job search. My training will also include coursework in clinical translation and single cell analysis; presentations at conferences in genome informatics, cancer biology, and liquid biopsy; and mentored research goals under the guidance of my mentor Dr. Michael Wigler and my co- mentor Dr. Dan Levy. I have assembled a team of additional scientific advisors and collaborators including Dr. David Tuveson and Dr. Christopher Vakoc from CSHL and Dr. Steven Allen from Northwell Health. ## Key facts - **NIH application ID:** 10197045 - **Project number:** 5K99CA252616-02 - **Recipient organization:** COLD SPRING HARBOR LABORATORY - **Principal Investigator:** Andrea Moffitt - **Activity code:** K99 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2021 - **Award amount:** $124,738 - **Award type:** 5 - **Project period:** 2020-07-01 → 2022-10-21 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10197045 ## Citation > US National Institutes of Health, RePORTER application 10197045, Measuring treatment response and residual disease in leukemia with personalized, sensitive, and quantitative genomic methods (5K99CA252616-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10197045. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*