Engineering topics

Tyner, Jeffrey W.

Publications and source records attributed to Tyner, Jeffrey W..

5 records

DOE OSTI2024

Targeting CCL2/CCR2 Signaling Overcomes MEK Inhibitor Resistance in Acute Myeloid Leukemia

Emerging evidence underscores the critical role of extrinsic factors within the microenvironment in protecting leukemia cells from therapeutic interventions, driving disease progression, and promoting drug resistance in acute myeloid leukemia (AML). This finding emphasizes the need for the identification of targeted therapies that inhibit intrinsic and extrinsic signaling to overcome drug resistance in AML. We performed a comprehensive analysis utilizing a cohort of ~300 AML patient samples. This analysis encompassed the evaluation of secreted cytokines/growth factors, gene expression, and ex vivo drug sensitivity to small molecules. Our investigation pinpointed a notable association between elevated levels of CCL2 and diminished sensitivity to the MEK inhibitors (MEKi). We validated this association through loss-of-function and pharmacologic inhibition studies. Further, we deployed global phosphoproteomics and CRISPR/Cas9 screening to identify the mechanism of CCR2-mediated MEKi resistance in AML. Our multifaceted analysis unveiled that CCL2 activates multiple prosurvival pathways, including MAPK and cell-cycle regulation in MEKi-resistant cells. Employing combination strategies to simultaneously target these pathways heightened growth inhibition in AML cells. Both genetic and pharmacologic inhibition of CCR2 sensitized AML cells to trametinib, suppressing proliferation while enhancing apoptosis. These findings underscore a new role for CCL2 in MEKi resistance, offering combination therapies as an avenue to circumvent this resistance. Our study demonstrates a compelling rationale for translating CCL2/CCR2 axis inhibitors in combination with MEK pathway-targeting therapies, as a potent strategy for combating drug resistance in AML. This approach has the potential to enhance the efficacy of treatments to improve AML patient outcomes.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Mapping the proteogenomic landscape enables prediction of drug response in acute myeloid leukemia

Acute myeloid leukemia is a poor prognosis cancer commonly stratified by genetic aberrations, but these mutations are often heterogeneous and don’t always predict therapeutic response. Here we combine transcriptomic, proteomic, and phosphoproteomic datasets with ex vivo drug sensitivity data to help understand the underlying pathophysiology of AML beyond mutations. We measured the proteome and phosphoproteome of 210 patients and combined them with genomics and transcriptomic measurements to identify four proteogenomic subtypes that complemented existing genetic subtypes. We then built a predictor to classify samples into subtypes based on 147 molecular features and mapped them to a ‘landscape’. Each region of this landscape corresponded to specific drug response patterns. We then built a drug response prediction model to identify drugs that target distinct subtypes. We can ultimately use these models to predict drug treatment response and prioritize treatments. Finally, we extended our models and mapped a series of cell lines representing various stages of quizartinib resistance into our subtype landscape, predicting and experimentally validating a switch in sensitivity to venetoclax to panobinostat, two drugs with very different mechanisms than quizartinib. Our results show how multi-omics data together with drug sensitivity data can inform therapy stratification and drug combinations in AML.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Mass Spectrometry–Based Proteogenomics: New Therapeutic Opportunities for Precision Medicine

Proteogenomics refers to the integration of comprehensive genomic, transcriptomic, and proteomic measurements from the same samples with the goal of fully understanding the regulatory processes converting genotypes to phenotypes, often with an emphasis on gaining a deeper understanding of disease processes. Although specific genetic mutations have long been known to drive the development of multiple cancers, gene mutations alone do not always predict prognosis or response to targeted therapy. The benefit of proteogenomics research is that information obtained from proteins and their corresponding pathways provides insight into therapeutic targets that can complement genomic information by providing an additional dimension regarding the underlying mechanisms and pathophysiology of tumors. This review describes the novel insights into tumor biology and drug resistance derived from proteogenomic analysis while highlighting the clinical potential of proteogenomic observations and advances in technique and analysis tools.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Proteomic and phosphoproteomic measurements enhance ability to predict ex vivo drug response in AML

Acute Myeloid Leukemia (AML) affects 20,000 patients in the US annually with a five-year survival rate of approximately 25%. One reason for the low survival rate is the high prevalence of clonal evolution that gives rise to heterogeneous sub-populations of leukemic cells with diverse mutation spectra, which eventually leads to disease relapse. This genetic heterogeneity drives the activation of complex signaling pathways that is reflected at the protein level. This diversity makes it difficult to treat AML with targeted therapy, requiring custom patient treatment protocols tailored to each individual’s leukemia. Toward this end, the Beat AML research program prospectively collected genomic and transcriptomic data from over 1000 AML patients and carried out ex vivo drug sensitivity assays to identify genomic signatures that could predict patient-specific drug responses. However, there are inherent weaknesses in using only genetic and transcriptomic measurements as surrogates of drug response, particularly the absence of direct information about phosphorylation-mediated signal transduction. As a member of the Clinical Proteomic Tumor Analysis Consortium, we have extended the molecular characterization of this cohort by collecting proteomic and phosphoproteomic measurements from a subset of these patient samples (38 in total) to evaluate the hypothesis that proteomic signatures can improve the ability to predict response to 26 drugs in AML ex vivo samples. In this work we describe our systematic, multi-omic approach to evaluate proteomic signatures of drug response and compare protein levels to other markers of drug response such as mutational patterns. We explore the nuances of this approach using two drugs that target key pathways activated in AML: quizartinib (FLT3) and trametinib (Ras/MEK), and show how patient-derived signatures can be interpreted biologically and validated in cell lines. In conclusion, this pilot study demonstrates strong promise for proteomics-based patient stratification to assess drug sensitivity in AML.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

The AML microenvironment catalyzes a step-wise evolution to gilteritinib resistance

This study reports a stepwise evolution of gilteritinib resistance in acute myeloid leukemia (AML) relapse, and identifies therapeutic vulnerabilities that could be exploited. Our in vitro model demonstrates that extrinsic microenvironmental factors within the bone marrow nurture the growth of residual cells that underlie early gilteritinib resistance. These early resistant cells exhibit a dependency upon Aurora B (AURKB) and undergo dynamic metabolic adaptations that fostered their transition to late resistance. In line with clinical observations of gilteritinib resistance, late resistant cells displayed an expansion of NRAS mutant subclones and a unique metabolic profile that originated during early resistance. Pharmacological inhibition of AURKB resensitized early resistant cells to gilteritinib. Early resistant cells from AML patients were found to display exquisite sensitivitye to AURKB inhibition. Together, our findings support that eradication of early resistant AML cells may increase the efficacy and durability of gilteritinib.

AML, Proteomics↗