Developing Targeted Therapies for T-cell Acute Lymphoblastic Leukemia

<strong>Introduction</strong> Acute lymphoblastic leukemia (ALL) is the most common cancer in children, and risk-adapted chemotherapies have dramatically improved the outcomes of this disease. Compared to B-cell ALL, T-cell ALL (T-ALL) is more aggressive and has worse outcomes from chemotherapy. There is a great unmet need to develop biomarkers and novel targeted therapies for this type of cancer. Working together with internal and external collaborators, we performed a large-scale pharmacotyping assay in over 300 primary ALL samples. By combining pharmacotypying and genomic profiling of these samples, we identified a substantial T-ALL population that showed strong response to dasatinib, a known ABL1 inhibitor. Importantly, none of the T-ALL responders harbored BCR-ABL1 or fusions predicted to respond to ABL1 inhibitors. We identified differences in somatic genetic alterations between dasatinib responders and non-responders. For example, as the prevalence of NOTCH1 mutations and TCF7-SPI1 fusion is higher in dasatinib responders than non-responders, both of which are known to be associated with T-cell differentiation. Because clonal expansion often occurs during leukemogenesis, resulting in heterogeneity in the leukemia cell population and affecting dasatinib response. Therefore, we tested these hypotheses: 1) a subset of T-ALL cases responds to dasatinib in an ABL1-independent manner; 2) dasatinib sensitivity in T-ALL is associated with T-cell differentiation stage; 3) intra-tumoral heterogeneity in T-ALL affects dasatinib response; 4) dasatinib at the dosages used in pre-clinical models can reach desired drug exposure and effect in patients predicted by pharmacokinetic & pharmacodynamic (PK & PD) modeling; and 5) an LCK degradation approach can achieve better therapeutic efficacy than dasatinib in T-ALL. <strong>Methods</strong> We applied a network-based Bayesian Inference of Drivers (NetBID) algorithm using RNA-seq data to infer activity of each gene and identified drivers for dasatinib sensitivity. Genome-wide CRISPR/Cas9 screening, phosphorylation flow cytometry, and phosphorylation proteomics assays were employed to confirm the dependency and inhibitory effect of dasatinib on its therapeutic targets (dasatinib sensitivity drivers). In vitro differentiation assay using mouse hematopoietic stem cells validated the effect of T cell differentiation stages on dasatinib response. Single cell RNA sequencing was performed to determine the intra-tumor heterogeneity in T ALL. We performed pharmacokinetic and pharmacodynamic (PK & PD) studies using a patient-derived xenograft (PDX) model to predict the achievable drug exposure and lymphocyte-specific protein tyrosine kinase (LCK) inhibition in human. Mouse dasatinib exposure was measured by liquid chromatography–mass spectrometry (LC-MS), and LCK inhibition was quantified by western blotting, which was further used to predict the exposure-response relationship in patients. To develop novel T-ALL targeting approaches, we designed and synthesized a set of proteolysis targeting chimeras (PROTACs) to degrade LCK in T-ALL. Western blotting was used to measure the extent of degradation of LCK and other target proteins by PROTACs. Cell viability assays were performed in leukemia cell lines and PDX cells to determine cytotoxicity of PROTACs. We also performed kinome scan assay to determine the targeting spectrum of the lead PROTAC. Lastly, T-ALL PDX mouse models were used for PK & PD profiling and evaluation of in vivo drug efficacy.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Biomedical Sciences

Year dc:date.available

2022

Author dc:creator

• Hu, Jianzhong

Contributors dc:contributor

• Jun J. Yang, PhD

• Acute lymphoblastic leukemia
• Pharmacodynamics
• Pharmacogenomics
• Pharmacokinetics
• PROTAC
• Targeted therapy
• Medical Cell Biology
• Medical Sciences
• Medicine and Health Sciences
• Oncology

Repository record dc:identifier

https://dc.uthsc.edu/dissertations/590

OAI identifier oai:identifier

oai:dc.uthsc.edu:dissertations-1585