# Joint differentiation-state plasticity and genetic diversity modeling to predict response and improve efficacy of drug combination therapy

> **NIH NIH K22** · OREGON HEALTH & SCIENCE UNIVERSITY · 2024 · $188,919

## Abstract

PROJECT SUMMARY
Each tumor faces a common set of obstacles arising from tumor-intrinsic dynamics, microenvironment signals
and therapeutic interventions. The “canonical” view of tumorigenesis is a landscape within which each tumor
navigates a unique path by combining various tactics to overcome these obstacles. A number of the early
breakthroughs in cancer treatment directly resulted from coarse demarcations of these paths into distinct
subtypes based on “genomic landmarks” such as the BCR-ABL fusion in chronic myeloid leukemia. However,
in acute myeloid leukemia (AML), the majority of patients lack actionable mutations and nearly half of AML
patients present with normal karyotype. For certain AML driver mutations, molecularly targeted drugs have
shown initial clinical promise but not long-term durability; resistance remains a key challenge for targeted
therapies, prompting a shift to combination strategies. Intratumor heterogeneity and plasticity as well as the
tumor microenvironment play a fundamental role in shaping response to therapy and development of
resistance. Recent findings indicate that targeted agents have different efficacies on AML cells at distinct
stages of myeloid differentiation and lead to different resistance mechanisms. This observation opens the
potential to improve clinical utility by matching targeted drugs to cell phenotypes. A specific, yet clinically
important case of this observation is the different efficacies of targeted BCL2 inhibitor venetoclax and MEK
inhibitors in AML cells at distinct stages of myeloid differentiation. In our preliminary analysis of the Beat AML
cohort, we observe a concordance between this phenotype and the downstream transcriptional impact of BCL2
and MCL1 proteins suggesting a molecular link to a previously described venetoclax resistance mechanism.
We hypothesize that differentiation-state plasticity is a general modulator of short-term resistance to a wide
range of targeted compounds. A mechanistic understanding of cell differentiation-state plasticity in conjunction
with genome-driven cellular processes can improve stratification of patients to therapies and suggest more
tailored combinatorial treatments. To that end, I will use the largest assembled dataset of matched multi-omic
assembled profiles, drug screens, and clinical information for primary leukemia samples from the Beat AML
project at the Knight Cancer Institute. My objective is to perform systematic analysis of bulk genomic,
functional ex-vivo assays (drug and CRISPR), conjointly with single-cell transcriptomic, ex-vivo CyTOF and
single-cell drug response data in AML primary samples to understand the effects of tumor heterogeneity and
plasticity on drug response at an unprecedented detail. The anticipated outcomes are 1) improved patient
outcomes achieved by accurately matching therapies to individual patients and 2) nomination of new
approaches to mitigate plasticity-based drug resistance.

## Key facts

- **NIH application ID:** 10821393
- **Project number:** 5K22CA258799-03
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** Olga H Nikolova
- **Activity code:** K22 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $188,919
- **Award type:** 5
- **Project period:** 2022-05-10 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10821393

## Citation

> US National Institutes of Health, RePORTER application 10821393, Joint differentiation-state plasticity and genetic diversity modeling to predict response and improve efficacy of drug combination therapy (5K22CA258799-03). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10821393. Licensed CC0.

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